bluuebunny/crossref_metadata_2025 huggingface dataset:
Chromosome Rearrangement in Elymus dahuricus Revealed by ND-FISH and Oligo-FISH Painting
Authors: Jiang Chengzhi , Liu Xiaodan , Yang Zujun , Li Guangrong
Published: September 2023

As a perennial herb in Triticeae, Elymus dahuricus is widely distributed in Qinghai–Tibetan Plateau and Central Asia. It has been used as high-quality fodders for improving degraded grassland. The genomic constitution of E. dahuricus (2n = 6x = 42) has been revealed as StStHHYY by cytological approaches. However, the universal karyotyping nomenclature system of E. dahuricus is not fully established by traditional fluorescent in situ hybridization (FISH) and genomic in situ hybridization (GISH). In this study, the non-denaturing fluorescent in situ hybridization (ND-FISH) using 14 tandem-repeat oligos could effectively distinguish the entire E. dahuricus chromosomes pairs, while Oligo-FISH painting by bulked oligo pools based on wheat-barley collinear regions combined with GISH analysis, is able to precisely determine the linkage group and sub-genomes of the individual E. dahuricus chromosomes. We subsequently established the 42-chromosome karyotype of E. dahuricus with distinctive chromosomal FISH signals, and characterized a new type of intergenomic rearrangement between 2H and 5Y. Furthermore, the comparative chromosomal localization of the centromeric tandem repeats and immunostaining by anti-CENH3 between cultivated barley (Hordeum vulgare L.) and E. dahuricus suggests that centromere-associated sequences in H subgenomes were continuously changing during the process of polyploidization. The precise karyotyping system based on ND-FISH and Oligo-FISH painting methods will be efficient for describing chromosomal rearrangements and evolutionary networks for polyploid Elymus and their related species.
Temporal and Spatial Dynamics of Organ Water Content in Maize with Different Senescence Types
Authors: Gao Shang , Ming Bo , Li Lu-Lu , Wang Yi-Zhou , Xue Jun , Hou Peng , Wang Ke-Ru , Zhou Shun-Li , Li Shao-Kun , Xie Rui-Zhi
Published: September 2023

Understanding the water status of specific organs can be helpful in evaluating the life activities and growth conditions of maize. To accurately judge organ growth conditions and thus design appropriate interventions, it is necessary to clarify the true water dynamics of each maize organ. Using multiple maize cultivars with different growth periods, spatio-temporal water dynamics were analyzed here in the leaves, stalks, and ear components. Leaf water content was found to gradually decrease from both the bottom and top of the plant to the middle, whereas stalk water content decreased sequentially from the top to the bottom. Each successively higher node from the bottom of the plant was associated with decreases of 0.99% and 1.27% water content in the leaves and stalks, respectively. The water dynamics in leaves and internodes showed three clear stages: the slow loss, rapid loss, and balance stage. A water content of 60% appeared to be an irreversible turning point for initiation of senescence. Using normalized growth period as a measure, each of the tested cultivars could be assigned into one of two types based on their water dynamics: stay-water or general type. General-type cultivars had a shorter duration with a high water content and a water loss rate approximately twice as high as that of the stay-water type. This may have been related to the leaf senescence characteristics. However, the stay-water trait did not interfere with water dynamics of the ear components. Therefore, it may not be robust to evaluate the kernel dehydration of maize according to leaf senescence conditions due to the weak correlation between kernel water content and leaf senescence characteristics.
Evaluation of Anticancer Activity of 76 Plant Species Collected in Andalusia (Spain) against Lung Cancer Cells
Authors: Jiménez-González Víctor , Benítez Guillermo , Pastor Julio Enrique , López-Lázaro Miguel , Calderón-Montaño José Manuel
Published: September 2023

Every year, cancer kills millions of people around the world. Finding more selective anticancer agents is essential to improve the low survival rates of patients with metastatic cancers. Since the research of natural products is a valuable approach to the discovery of new compounds and the Iberian flora offers a rich source of unstudied plants, we have carried out a random screening of 76 plant species from 43 families collected in Andalusia (South of Spain). Using non-malignant cells (HaCaT) and lung cancer cells (A549), we found that the extract from Arum italicum Mill. subsp. italicum (Araceae), Mandragora autumnalis Bertol. (Solanaceae), Rhamnus alaternus L. (Rhamnaceae), and Lomelosia simplex (Desf.) Raf. subsp. dentata (Jord. & Fourr.) Greuter & Burdet (Dipsacaceae) showed selective cytotoxicity against lung cancer cells. Extracts of plant species belonging to the Iridaceae family showed high selective activity against cancer cells, highlighting that the Xiphion xiphium (L.) M.B. Crespo, Mart.-Azorín & Mavrodiev flower extract was more selective against lung cancer cells than the standard anticancer drugs, cisplatin and 5-fluorouracil. This extract also showed modest selective cytotoxicity against bladder carcinoma cells (T24). The number of cells in the G1 phase increased after treatment with the extract from Xiphion xiphium. Our research indicates that various plants are potential sources for the isolation and development of new anticancer drugs.
Mechanical Properties and Non-Isothermal Crystallization Kinetics of Polylactic Acid Modified by Polyacrylic Elastomers and Cellulose Nanocrystals
Authors: Meng Weixiao , Zhang Xiaojie , Hu Xiuli , Liu Yingchun , Zhang Jimin , Qu Xiongwei , Abdel-Magid Beckry
Published: September 2023

In this paper, a polyacrylic elastomer latex with butyl acrylate (BA) as the core and methyl methacrylate (MMA) copolymerized with glycidyl methacrylate (GMA) as the shell, named poly(BA-MMA-GMA) (PBMG), was synthesized by seeded emulsion polymerization. Cellulose nanocrystal (CNC) was dispersed in the polyacrylic latex to prepare PBMG/CNC dispersions with different CNC contents. The dried product was mixed with polylactic acid (PLA) to fabricate PLA/PBMG/CNC blends. The addition of PBMG and PBMG/CNC improved the mechanical properties of the PLA matrix. Differential scanning calorimetry (DSC) was used to investigate the non-isothermal crystallization kinetics. The Avrami equation modified by the Jeziorny, Ozawa and Mo equations was used to analyze the non-isothermal crystallization kinetics of PLA and its blends. Analysis of the crystallization halftime of non-isothermal conditions indicated that the overall rate of crystallization increased significantly at 1 wt% content of CNC. This seemed to result from the increase of nucleation density and the acceleration of segment movement in the presence of the CNC component. This phenomenon was verified by polarizing microscope observation.
Chitosan-Based Self-Healing Hydrogel: From Fabrication to Biomedical Application
Authors: Pan Siyu , Zhu Chongyu , Wu Yuwei , Tao Lei
Published: September 2023

Biocompatible self-healing hydrogels are new-generation smart soft materials that hold great promise in biomedical fields. Chitosan-based self-healing hydrogels, mainly prepared via dynamic imine bonds, have attracted broad attention due to their mild preparation conditions, excellent biocompatibility, and self-recovery ability under a physiological environment. In this review, we present a comprehensive overview of the design and fabrication of chitosan-based self-healing hydrogels, and summarize their biomedical applications in tissue regeneration, customized drug delivery, smart biosensors, and three/four dimensional (3D/4D) printing. Finally, we will discuss the challenges and future perspectives for the development of chitosan-based self-healing hydrogels in the biomedical field.
Mechanical and Surface Properties of Edible Coatings Elaborated with Nanoliposomes Encapsulating Grape Seed Tannins and Polysaccharides
Authors: Monasterio Angela , Núñez Emerson , Brossard Natalia , Vega Ricardo , Osorio Fernando A.
Published: September 2023

Edible composite coatings (ECC) formulated from biopolymers that incorporate antioxidant molecules represent an innovative alternative to improve food texture and provide health benefits. Tannins have aroused great interest due to their ability to stabilize suspensions and counteract the effects of free radicals. The mechanical and surface properties are crucial to establishing its quality and applicability. In this study, the objective was to analyze the mechanical and surface properties of ECC made with nanoliposomes that encapsulate grape seed tannins (TLS) and polysaccharides such as hydroxypropylmethylcellulose (HPMC) and kappa carrageenan (KCG) for their future direct application in foods susceptible to oxidation. The inclusion of HPMC or KCG affected the density, showing values in the range of 1010 to 1050 [kg/m3], evidencing significant changes (p < 0.05) in the surface tension in the TLS/FS-HPMC and TLS/FS mixtures. KCG and in the dispersion coefficients, with values in the range of −2.9 to −17.6 [mN/m] in HPS (S1) and −17.6 to −40.9 [mN/m] in PDMS (S2). The TLS/FS-HPMC coating showed higher stiffness and elastic recovery capacity than the TLS/FS-KCG coating, suggesting that the presence of TLS influenced the stiffness of the polymer. HPMC is recommended as a suitable polymer for coating solids, while KCG is more appropriate for suspensions. These findings provide valuable information for directly applying these ECC compounds to food products, potentially offering better preservation and health benefits.
Challenging the Integration of Youth, Faith, and Sports: Alternative Religious Beliefs and Assumptions
Authors: White John B. , Parker Andrew , Meyer Andrew R.
Published: September 2023

The sport/faith interface has long been a site of contention for religious youth who routinely experience two significant obstacles to living out their faith amidst the complexities of sporting locales. The first is a general problem that pertains to the character of adolescent spirituality and is typified by a subscription to a compromised, diluted religious belief system known as Moralistic Therapeutic Deism (MTD). The second concerns the way in which the Christian religion functions as a servant to sport and when the reality of the gospel becomes subordinate to the identity, power, and cultural meaning and norms of the institution of sports and is commonly termed ‘sportianity’. This paper maps the contours of a high school retreat for Christian student-athletes—Baylor University’s Faith and Sports Institute (FSI) retreat- that intentionally seeks to address these problems. The paper is theological in that we interpret MTD as a religious belief system and how it structures and orients reality and the lived experiences of Christians in general and religious youth in particular. As contributors to the original design of the retreat, we unpack some of the relevant working cultural and religious presuppositions that have the potential to dominate how Christians think about and practice the integration of faith and sports. In turn, we explain key aspects of the baseline narrative for the design and development of the retreat and tease out how these presuppositions are antithetical to orthodox Christianity. In conclusion, we suggest a number of immediate implications that frame how the FSI retreat has moved forward in relation to the integration of Christian faith and sports.
Estimating Monthly River Discharges from GRACE/GRACE-FO Terrestrial Water Storage Anomalies
Authors: Duvvuri Bhavya , Beighley Edward
Published: September 2023

Simulating river discharge is a complex convolution depending on precipitation, runoff generation and transformation, and network attenuation. Terrestrial water storage anomalies (TWSA) from NASA’s Gravity Recovery and Climate Experiment (GRACE) and its follow-on mission can be used to estimate monthly river discharge (Q). Monthly discharges for the period April 2002–January 2022 are estimated at 2870 U.S. Geological Survey gauge locations (draining 1K to 3M km2) throughout the continental U.S. (CONUS) using two-parameter exponential relationships between TWSA and Q. Roughly 70% of the study sites have a model performance exceeding the expected performance of other satellite-derived discharge products. The results show how the two model parameters vary based on hydrologic characteristics (annual precipitation and range in TWSA) and that model performance can be affected by snow accumulation/melt, water regulation (dams/reservoirs) or GRACE signal leakage. The generally favorable model performance and our understanding of variability in model applicability and associated parameters suggest that this concept can be expanded to other regions and ungauged locations.
Evaluation of Simulated CO2 Point Source Plumes from High-Resolution Atmospheric Transport Model
Authors: Li Chao , Wang Xianhua , Ye Hanhan , Wu Shichao , Shi Hailiang , Luo Haiyan , Li Zhiwei , Xiong Wei , Li Dacheng , Sun Erchang , An Yuan
Published: September 2023

Coal-fired power plants, as major anthropogenic CO2 emission sources, constitute one of the largest contributors to global greenhouse gas emissions. Accurately calculating the dispersion process of CO2 emissions from these point sources is crucial, as it will aid in quantifying CO2 emissions using remote sensing measurements. Employing the Lagrangian Particle Dispersion Theory Model (LPDTM), our study involves modeling CO2 diffusion from point sources. Firstly, we incorporated high-resolution DEM (Digital Elevation Model) and artificial building elements obtained through the Adaptive Deep Learning Location Matching Method, which is involved in CO2 simulation. The accuracy of the results was verified using meteorological stations and aircraft measurements. Additionally, we quantitatively analyzed the influence of terrain and artificial building characteristics on high spatial resolution atmospheric CO2 diffusion simulations, revealing the significance of surface characteristics in dispersion modeling. To validate the accuracy of the LPDTM in high-resolution CO2 diffusion simulation, a comparative experiment was conducted at a power plant in Yangzhou, Jiangsu Province, China. The simulated result was compared with observation from aerial flights, yielding the R2 (Correlation Coefficient) of 0.76, the RMSE (Root Mean Square Error) of 0.267 ppm, and the MAE (Mean Absolute Error) of 0.2315 ppm for the comparison of 73 pixels where the plume intersected with flight trajectories. The findings demonstrate a high level of consistency between the modeled CO2 point source plume morphology and concentration quantification and the actual observed outcomes. This study carried out a quantitative assessment of the influence of surface features on high-resolution atmospheric CO2 point source diffusion simulations, resulting in an enhanced accuracy of the simulated CO2 concentration field. It offers essential technological and theoretical foundations for the accurate quantification of anthropogenic CO2 emissions using top-down approaches.
LiDAR Data and HRSI to Evaluate the Mitigating Effect of Forests into Rockfall Risk Analysis Using SOM: Mt San Liberatore Case Study
Authors: Ambrosino Antonella , Di Benedetto Alessandro , Fiani Margherita
Published: September 2023

The aim of our work is to develop a methodology to identify the areas most prone to natural rockfall retention through the integrated use of remote sensing data. The area chosen as a case study is located in Campania (Italy) nearby Mount San Liberatore. In this area, which is itself geomorphologically predisposed to landslide risk, there are several rockfall risk hotspots, so defined because of the high exposed value constituted by an articulated infrastructure network located along the northwest slope of the mountain. The area is largely covered by dense vegetation, of which holm oak is the most representative type, characterized by a taproot apparatus that, giving it strength and stability, makes it an ideal tree for slope protection. Based on high-resolution multispectral satellite images, vegetation indices (VIs) were calculated to estimate health status, approximate age, average height, robustness, and vigor. Morphometric parameters suitable for describing slope dynamics were also calculated, derived from LiDAR data. The classification of areas with similar characteristics was carried out using Self-Organizing Maps. The results made it possible to identify all those areas where there is a greater contribution of protective forests in the mitigation of rockfall risk and, consequently, to identify areas to carry out a combined strengthening of protective actions.
Using Wavelet Coherence to Aid the Retrieval of Volcanic SO2 from UV Spectra
Authors: Barrington Charlotte , Taisne Benoit , Costa Fidel
Published: September 2023

Changes in the emission rate of volcanic sulphur dioxide (SO2) are crucial parameters for identifying volcanic unrest and forecasting the eruptive activity. Ground-based ultraviolet (UV) remote sensing provides a near continuous record of the SO2 emission rate, with Differential Optical Absorption Spectroscopy (DOAS) being the preferred method for quantifying SO2 absorption from recorded spectra. However, retrieving accurate column amounts of SO2 using DOAS requires a complex fitting procedure that relies on user expertise for selecting suitable fit parameters and visually inspecting the fit results. We explore an alternative approach that exploits the well-defined spatial frequencies present in sky-scattered UV spectra. We use wavelet coherence to compare UV spectra recorded with calibration cells of known SO2 concentration in the wavelength–spatial frequency plane. Our findings reveal that the Magnitude-Squared Wavelet Coherence (MSWC) is inversely proportional to the SO2 concentration, suggesting that this relationship could be used to quantify volcanic SO2 in natural spectra. To validate this approach, we analyze UV spectra recorded by scanning-DOAS instruments from the Network of Volcanic and Atmospheric Change (NOVAC) at Masaya volcano, Nicaragua, and Soufrière Hills volcano, Montserrat. We observe a favourable comparison between the MSWC values we calculate and the slant column densities (SCDs) of SO2 obtained using the DOAS and iFit algorithms, respectively. We demonstrate the MSWC to be a robust indicator of SO2 which may potentially serve as a proxy for differential SCDs of volcanic SO2. The straightforward computation of the wavelet coherence between spectra offers an efficient means to identify spectra which contain the signature of the volcanic plume and an objective approach to validate results obtained using traditional fitting routines.
Energy-Efficient Unmanned Aerial Vehicle-Aided Visible Light Communication with an Angle Diversity Transmitter for Joint Emergency Illumination and Communication
Authors: Huang Qiang , Wen Wanli , Liu Min , Du Pengfei , Chen Chen
Published: September 2023

Unmanned aerial vehicle-aided visible light communication (UAV-VLC) can be used to realize joint emergency illumination and communication, but the endurance of UAV is a key limiting factor. In order to overcome this limitation, this paper proposes the use of an angle diversity transmitter (ADT) to enhance the energy efficiency of the UAV-VLC system. The ADT is designed with one bottom LED and several evenly distributed inclined side LEDs. By jointly optimizing the inclination angle of the side LEDs in the ADT and the height of the hovering UAV, the study aims to minimize the power consumption of the UAV-VLC system while satisfying the requirements of both illumination and communication. Simulation results show that the energy efficiency of the UAV-VLC system can be greatly enhanced by applying the optimized ADT. Moreover, the energy efficiency enhancement is much more significant when the LEDs in the ADT have a smaller divergence angle, or more side LEDs are configured in the ADT. More specifically, a 50.9% energy efficiency improvement can be achieved by using the optimized ADT in comparison to the conventional non-angle diversity transmitter (NADT).
Quasi Real-Time Apple Defect Segmentation Using Deep Learning
Authors: Agarla Mirko , Napoletano Paolo , Schettini Raimondo
Published: September 2023

Defect segmentation of apples is an important task in the agriculture industry for quality control and food safety. In this paper, we propose a deep learning approach for the automated segmentation of apple defects using convolutional neural networks (CNNs) based on a U-shaped architecture with skip-connections only within the noise reduction block. An ad-hoc data synthesis technique has been designed to increase the number of samples and at the same time to reduce neural network overfitting. We evaluate our model on a dataset of multi-spectral apple images with pixel-wise annotations for several types of defects. In this paper, we show that our proposal outperforms in terms of segmentation accuracy general-purpose deep learning architectures commonly used for segmentation tasks. From the application point of view, we improve the previous methods for apple defect segmentation. A measure of the computational cost shows that our proposal can be employed in real-time (about 100 frame-per-second on GPU) and in quasi-real-time (about 7/8 frame-per-second on CPU) visual-based apple inspection. To further improve the applicability of the method, we investigate the potential of using only RGB images instead of multi-spectral images as input images. The results prove that the accuracy in this case is almost comparable with the multi-spectral case.
Multiport Converter Utility Interface with a High-Frequency Link for Interfacing Clean Energy Sources (PV\Wind\Fuel Cell) and Battery to the Power System: Application of the HHA Algorithm
Authors: Ibrahim Nagwa F. , Ardjoun Sid Ahmed El Mehdi , Alharbi Mohammed , Alkuhayli Abdulaziz , Abuagreb Mohamed , Khaled Usama , Mahmoud Mohamed Metwally
Published: September 2023

The integration of clean energy sources (CESs) into modern power systems has been studied using various power converter topologies. The challenges of integrating various CESs are facilitated by the proper design of multi-port power converter (MPPC) architecture. In this study, a brand-new two-stage MPPC is suggested as a solution to the intermittent nature and slow response (SR) of CESs. The suggested system combines a DC\DC and a DC\AC converter and storage unit, and the suggested circuit additionally incorporates a number of CESs (PV\wind\fuel cell (FC)). This article discusses the power management and control technique for an integrated four-port MPPC that links three input ports (PV, wind, and FC), a bidirectional battery port, and an isolated output port. One of the recent optimization techniques (Harris Hawk’s algorithm) is applied to optimize the system’s controller gains. By intelligently combining CESs with complementary characteristics, the adverse effects of intermittency are significantly mitigated, leading to an overall enhancement in system resilience and efficiency. Furthermore, integrating CESs with storage units not only addresses SR challenges but also effectively combats intermittent energy supply. The proposed system exhibits improved dynamic capabilities, allowing it to efficiently distribute excess energy to the load or absorb surplus energy from external sources. This dual functionality not only optimizes system operation but also contributes to a reduction in system size and cost, concurrently enhancing reliability. A comprehensive investigation into operational principles and meticulous design considerations are provided, elucidating the intricate mechanics of the suggested MPPC system. Employing MATLAB/Simulink, the proposed architecture and its control mechanisms undergo rigorous evaluation, affirming the feasibility and efficacy of this innovative system.
Fast Pyrolysis of Tea Bush, Walnut Shell, and Pine Cone Mixture: Effect of Pyrolysis Parameters on Pyrolysis Crop Yields
Authors: Kar Turgay , Kaygusuz Ömer , Güney Mükrimin Şevket , Cuce Erdem , Keleş Sedat , Shaik Saboor , Owolabi Abdulhameed Babatunde , Nsafon Benyoh Emmanuel Kigha , Ogunsua Johnson Makinwa , Huh Jeung-Soo
Published: September 2023

Liquid products obtained by the fast pyrolysis process applied to biomass can be used as chemical raw materials and liquid fuels. In this study, tea bush, walnut shell, and pine cone samples selected as biomass samples were obtained from Trabzon and Rize provinces in the Eastern Black Sea Region and used. When considered in terms of our region, the available biomass waste samples are easy to access and have a high potential in quantity. To employ them in the experimental investigation, these biomass samples were first ground, sieved to a particle size of 1.0 mm, and mixed. A fast pyrolysis process was applied to this obtained biomass mixture in a fixed-bed pyrolysis reactor. The effects of temperature, heating rate, and nitrogen flow rate on the product yields of the fast pyrolysis technique used on the biomass mixture are examined. A constant particle size of 1.0 mm, temperatures of 300, 400, 500, 600, and 750 °C, heating rates of 100, 250, 400, and 600 °C.min−1, and flow rates of 50, 100, 200, and 300 cm3.min−1 were used in tests on fast pyrolysis. The studies showed the 500 °C pyrolysis temperature, 100 °C min−1 heating rate, and 50 cm3.min−1 nitrogen flow rate gave the maximum liquid product yield. The liquid product generated under the most compelling circumstances is analyzed to determine moisture, calorific value, fixed carbon, ash, raw coke, and volatile matter. Additionally, the crude bio-oil heating value, measured at 5900 cal/g and produced under the most favorable pyrolysis circumstances, rose by around 40% compared to its starting material. The liquid product obtained from rapid pyrolysis experiments can be used as liquid fuel. The evaluation of the potential of chemical raw materials can be a subject of research in a different discipline since there are many chemical raw materials (glycerine, furfurals, cellulose and derivatives, carbonaceous materials, and so forth) in fast pyrolysis liquids.
Anomaly Perception Method of Substation Scene Based on High-Resolution Network and Difficult Sample Mining
Authors: Song Yunhai , He Sen , Wang Liwei , Zhou Zhenzhen , He Yuhao , Xiao Yaohui , Zheng Yi , Yan Yunfeng
Published: September 2023

The perception of anomalies in power scenarios plays a crucial role in the safe operation and fault prediction of power systems. However, traditional anomaly detection methods face challenges in identifying difficult samples due to the complexity and uneven distribution of power scenarios. This paper proposes a power scene anomaly perception method based on high-resolution networks and difficult sample mining. Firstly, a high-resolution network is introduced as the backbone for feature extraction, enhancing the ability to express fine details in power scenarios and capturing information on small target anomaly regions. Secondly, a strategy for mining difficult samples is employed to focus on learning and handling challenging and hard-to-recognize anomaly samples, thereby improving the overall anomaly detection performance. Lastly, the method incorporates GIOU loss and a flexible non-maximum suppression strategy to better adapt to the varying sizes and dense characteristics of power anomaly targets. This improvement enables higher adaptability in detecting anomalies in power scenarios. Experimental results demonstrate significant improvements in power scene anomaly perception and superior performance in handling challenging samples. This study holds practical value for fault diagnosis and safe operation in power systems.
A Comparison of Machine Learning Models for Predicting Rainfall in Urban Metropolitan Cities
Authors: Kumar Vijendra , Kedam Naresh , Sharma Kul Vaibhav , Khedher Khaled Mohamed , Alluqmani Ayed Eid
Published: September 2023

Current research studies offer an investigation of machine learning methods used for forecasting rainfall in urban metropolitan cities. Time series data, distinguished by their temporal complexities, are exploited using a unique data segmentation approach, providing discrete training, validation, and testing sets. Two unique models are created: Model-1, which is based on daily data, and Model-2, which is based on weekly data. A variety of performance criteria are used to rigorously analyze these models. CatBoost, XGBoost, Lasso, Ridge, Linear Regression, and LGBM are among the algorithms under consideration. This research study provides insights into their predictive abilities, revealing significant trends across the training, validation, and testing phases. The results show that ensemble-based algorithms, particularly CatBoost and XGBoost, outperform in both models. CatBoost emerged as the model of choice throughout all assessment stages, including training, validation, and testing. The MAE was 0.00077, the RMSE was 0.0010, the RMSPE was 0.49, and the R2 was 0.99, confirming CatBoost’s unrivaled ability to identify deep temporal intricacies within daily rainfall patterns. Both models had an R2 of 0.99, indicating their remarkable ability to predict weekly rainfall trends. Significant results for XGBoost included an MAE of 0.02 and an RMSE of 0.10, indicating their ability to handle longer time intervals. The predictive performance of Lasso, Ridge, and Linear Regression varies. Scatter plots demonstrate the robustness of CatBoost and XGBoost by demonstrating their capacity to sustain consistently low prediction errors across the dataset. This study emphasizes the potential to transform urban meteorology and planning, improve decision-making through precise rainfall forecasts, and contribute to disaster preparedness measures.
The Artificial Intelligence Revolution in Digital Finance in Saudi Arabia: A Comprehensive Review and Proposed Framework
Authors: Al-Baity Heyam H.
Published: September 2023

Artificial Intelligence (AI) has proliferated in the last few years due to the vast data we pro-duce daily and available computing power. AI can be applied in many different sectors, such as transportation, education, healthcare, banking, and finance, among many others. The financial industry is rapidly embracing AI due to its potential for high-cost savings in financial services. AI could transform the financial sector by creating opportunities for tailored, faster, and more cost-effective services. Saudi Arabia is emerging as a fast-growing market in this industry with a strong commitment to technology-driven institutions. While AI is gaining prominence and receiving government support, it has not yet become a critical component for enhancing the efficiency of financial transactions. Limited published research on AI adoption in the Saudi Arabian financial industry calls for a comprehensive literature review to examine the current state of AI implementation in this sector. Therefore, this study explores the benefits, limitations, and challenges of leveraging AI in finance, highlighting the importance of ethical and regulatory considerations for successful AI adoption in the sector. This study’s findings reveal that research has been conducted on how AI improves processes in the financial sector by integrating critical components and efficient algorithms tailored to the industry’s needs. Based on these findings, this study proposes a sequential framework at the macro and micro levels of management to guide AI’s development and integration into the financial sector. Additionally, the framework draws insights from the existing literature to provide a detailed understanding of opportunities, challenges, and areas for improvement to maximize AI’s potential in the Saudi Arabian financial sector.
Directing Reused Industrial Heritage to Public Taste: The Case of 1933 Old Millfun, Shanghai
Authors: Zheng Xuesen , Guo Sifan , Heath Timothy
Published: September 2023

Industrial heritage carries the task of not only preserving and inheriting historical culture, but also solving the homogenization of the historical area. The culture cannot be passed on without public participation; people are not only creators but also transmitters of culture. Thus, it will be an important issue to direct industrial culture to public taste. This study is conducted with regard to 1933 Old Millfun, Shanghai, focusing on the visitor’s cultural experience, and exploring the public’s assessment of cultural expression in combination with the Fuzzy Comprehensive Evaluation Method. Through establishing an industrial culture quality evaluation system, the results suggest that the Fuzzy Comprehensive Evaluation Method is effective in clarifying the public needs for industrial culture from the tastes of most visitors, and is able to propose a clear development direction for the assessment subject. The research demonstrates a methodology for evaluating the compatibility between heritage culture and public acceptance, weakening the conflict between building designers and users in the future.
The Cultural Landscape of Rural Cemeteries on the Polish–Czech Borderlands: Multi-Faceted Visual Analysis as an Element of Tourism Potential Assessment
Authors: Dzikowska Anna , Zaręba Anna , Krzemińska Alicja , Pawłowski Kamil
Published: September 2023

This article presents the tourism potential of the Polish–Czech borderlands (Klodzko Land, Lower Silesia, Poland), in which historical sacred sites, namely cemeteries and churches, play a significant role. An analysis of the most important features of sacred sites was carried out to present the diversity of the architectural richness of the region. At the same time, attention was paid to additional elements, such as their visual aspects and their impact on shaping space. The research is based on the sacred sites located on the Polish–Czech borderland. As part of the work, an analysis of historical, natural, and landscape conditions was performed along with the spatial layout of 106 cemeteries. During the field research, inventories of landscape and natural values were carried out. The assessment of landscape value was carried out using an original concept to enable an indication of site’s tourism potential on the basis of visual aspects of its space. The article and the research methods contained within concern interdisciplinary research combining architecture, landscape architecture, socio-economic geography, and tourism.
A Model for an Order-Picking Problem with a One-Directional Conveyor and Buffer
Authors: Czerniachowska Kateryna , Wichniarek Radosław , Żywicki Krzysztof
Published: September 2023

Customers seek items at low prices, excellent quality, and customization in today’s market. Additionally, the need for quick product delivery is rising because of the expansion of e-commerce. Order picking is an essential element of order fulfillment and is regarded as one of the most costly and time-consuming tasks for warehouses. The study aims to create a model for the ordering in logistics that involves the group of racks with assigned one-way conveyor and buffers. The focus of this study is on improving the customer order completion time because of appropriate tasks assigned to pickers. The simulation was performed using the CPLEX solver. The findings highlight the need for picking operations to manage time resources, as well as the importance of sustainable work of order pickers in logistic centers in fostering sustainable performance outcomes.
Physical, Rheological, and Permanent Deformation Behaviors of WMA-RAP Asphalt Binders
Authors: Bohn Kátia Aline , Thives Liseane Padilha , Specht Luciano Pivoto
Published: September 2023

With the rapid global expansion of road networks, the asphalt industry faces several environmental challenges, such as material shortages, environmental concerns, escalating material costs, demand for eco-friendly materials, and the implementation of “Net Zero” policies. Given these challenges and recognizing the need to explore new solutions, this research evaluated asphalt binder samples incorporating Warm Mix Asphalt (WMA) and Reclaimed Asphalt Pavement (RAP), or WMA-RAP. The assessment focused on analyzing the physical, rheological, and permanent deformation characteristics of WMA-RAP samples containing 20%, 35%, and 50% recycled pavement. The study utilized a chemical surfactant-type WMA additive, Evotherm® P25. The findings showed that the WMA-RAP combination resulted in increased stiffness ranging from 247% to 380% and a reduced phase angle of 16% to 26% with an increasing RAP content from 20% to 50% at Tref 20 °C and 10 Hz. Furthermore, the penetration decreased from 20% to 47%, and the softening point increased from 7% to 17%. An improvement of 2 PGHs was observed by adding 35% and 50% RAP. Additionally, WMA samples containing up to 50% RAP presented more elevated permanent deformation resistance, supporting traffic levels of 64V or 70H. WMA-RAP binders allow mixture production at lower temperatures—an amount of 30 °C less—conserving energy and decreasing the need for new aggregate materials by incorporating recycled materials, thus minimizing the environmental impact.
Towards Trusted Data Sharing and Exchange in Agro-Food Supply Chains: Design Principles for Agricultural Data Spaces
Authors: Šestak Martina , Copot Daniel
Published: September 2023

In the modern agricultural landscape, realizing data’s full potential requires a unified infrastructure where stakeholders collaborate and share their data to gain insights and create business value. The agricultural data ecosystem (ADE) serves as a crucial socio-technical infrastructure, aggregating diverse data from various platforms and, thus, advertising sustainable agriculture and digitalization. Establishing trustworthy data sharing and exchange in agro-food value chains involves socioeconomic and technological elements addressed by the agricultural data space (ADS) and its trust principles. This paper outlines key challenges to data sharing in agro-food chains impeding ADE establishment based on the review of 27 studies in scientific literature. Challenges mainly arise from stakeholders’ mistrust in the data-sharing process, inadequate data access and use policies, and unclear data ownership agreements. In the ADE context, interoperability is a particularly challenging topic for ensuring the long-term sustainability of the system. Considering these challenges and data space principles and building blocks, we propose a set of design principles for ADS design and implementation that aim to mitigate the adverse impact of these challenges and facilitate agricultural data sharing and exchange.
The Routing Problem for Electric Truck with Partial Nonlinear Charging and Battery Swapping
Authors: Han Hongwen , Chen Luxian , Fang Sitong , Liu Yang
Published: September 2023

The maritime industry has introduced the concept of “green ports” as a means to achieve sustainable development by reducing carbon emissions. Within ports, trucks play a crucial role in transportation operations. However, there is limited comprehensive research on the electric truck routing problem containing practical constraints such as charging options and charging processes. This study presents a more realistic routing problem for electric trucks, with a specific focus on multiple charging options within green ports. To address this challenge, we formulate a mixed-integer programming model designed to minimize overall operational costs associated with the transportation of trucks over the planning horizon. In order to solve this problem effectively, we devise an Adaptive Large Neighborhood Search (ALNS) algorithm, embedded with several customized operators. Through a series of numerical experiments, the effectiveness of the proposed algorithm is verified. The experimental results provide compelling evidence of the superior performance of the proposed algorithm compared to the original ALNS algorithm. Furthermore, sensitivity analysis is conducted, leading to valuable managerial insights.
Efficient Production of Doughnut-Shaped Ce:Nd:YAG Solar Laser Beam
Authors: Garcia Dário , Liang Dawei , Almeida Joana , Catela Miguel , Costa Hugo , Tibúrcio Bruno D. , Guillot Emmanuel , Vistas Cláudia R.
Published: September 2023

Laser beams with a doughnut-shaped profile have garnered much attention for their contribution to trapping nanoparticles and improving the scanning speed during laser-based 3D metal printing. For this reason, the production of a doughnut-shaped solar laser beam by end-side pumping a Ce:Nd:YAG rod with a small reflective parabolic collector was investigated. The resultant beam profile shape depended on the absorbed solar power, displaying a TEM00-mode profile at elevated input power. This phenomenon was primarily attributed to the role of distributing energy around the central region of the crystal. In contrast, at lower input power, a doughnut-shaped beam emerged, characterized by minimal energy distribution at the center. Through experiments conducted with a collection area of 0.226 m2 and a nominal solar irradiance from 970 W/m2 to 1000 W/m2, it was demonstrated that sufficient energy was available to generate a doughnut-shaped beam with a solar laser collection efficiency of 5.96 W/m2, surpassing previous measurements by 1.32 times. Further research with a larger collection area of 0.332 m2 and a diverse solar irradiance range of 650 W/m2 to 800 W/m2 revealed that the presence of a thin layer of cloud caused a transition from a doughnut-shaped to a TEM10-mode and, eventually, a TEM00-mode as the absorbed input solar power increased. Notably, under heavier cloud cover, the laser beam exhibited deformation at low input power instead of maintaining a doughnut-shaped profile. This research significantly enhances our comprehension of doughnut-shaped solar laser beams and their reliance on solar energy. By harnessing the plentiful and readily accessible energy from the Sun, the incorporation of solar energy into the realm of solar-pumped lasers holds immense promise for promoting sustainability. This transformative utilization can progressively diminish the industry’s carbon footprint, yielding long-term environmental benefits.
A Novel Hybrid Method Based on the Marine Predators Algorithm and Adaptive Neuro-Fuzzy Inference System for the Identification of Nonlinear Systems
Authors: Baştemur Kaya Ceren
Published: September 2023

In this study, a hybrid method based on the marine predators algorithm (MPA) and adaptive neuro-fuzzy inference system (ANFIS) is presented to identify nonlinear systems exhibiting symmetrical or asymmetrical behavior. In other words, the antecedent and conclusion parameters of the ANFIS are adjusted by the MPA. The performance of the MPA is evaluated on eight nonlinear systems. The mean squared error is used as the error metric. Successful results were obtained on the eight systems. The best mean training error values belonging to the eight systems are 1.6 × 10−6, 3.2 × 10−3, 1.5 × 10−5, 9.2 × 10−6, 3.2 × 10−5, 2.3 × 10−3, 1.7 × 10−5, and 8.7 × 10−6. In the ANFIS training carried out to solve the related problems, the performance of the MPA was compared with the butterfly optimization algorithm, the flower pollination algorithm, moth–flame optimization, the multi-verse optimizer, the crystal structure algorithm, the dandelion optimizer, the RIME algorithm, and the salp swarm algorithm. The results have shown that the performance of the MPA mostly outperforms other algorithms in both training and testing processes.
Bridging the Gap: Can COVID-19 Research Help Combat African Swine Fever?
Authors: Pakotiprapha Danaya , Kuhaudomlarp Sakonwan , Tinikul Ruchanok , Chanarat Sittinan
Published: September 2023

African swine fever (ASF) is a highly contagious and economically devastating disease affecting domestic pigs and wild boar, caused by African swine fever virus (ASFV). Despite being harmless to humans, ASF poses significant challenges to the swine industry, due to sudden losses and trade restrictions. The ongoing COVID-19 pandemic has spurred an unparalleled global research effort, yielding remarkable advancements across scientific disciplines. In this review, we explore the potential technological spillover from COVID-19 research into ASF. Specifically, we assess the applicability of the diagnostic tools, vaccine development strategies, and biosecurity measures developed for COVID-19 for combating ASF. Additionally, we discuss the lessons learned from the pandemic in terms of surveillance systems and their implications for managing ASF. By bridging the gap between COVID-19 and ASF research, we highlight the potential for interdisciplinary collaboration and technological spillovers in the battle against ASF.
Combinatorial Effects of miRNAs in HSV-2 Infection of Macrophages: An In Silico and In Vitro Integration Approach
Authors: Banerjee Anwesha , Dass Debashree , Dhotre Kishore , Wakchoure Pooja , More Ashwini , Rana Santanu , Khan Abdul A. , Mukherjee Anupam
Published: September 2023

The rising issues of herpes simplex virus (HSV)-2 drug ramifications have encouraged the researchers to look for new and alternative approaches that pose minimum adversities in the host while efficiently reducing the HSV-2 infection. Although microRNAs (miRNAs), as unorthodox approaches, are gaining popularity due to eliciting highly reduced immunogenic reactions, their implications in HSV-2 research have been rarely explored. In this study, a pool of cellular miRNAs with significance in HSV-2-induced inflammatory and immune responses have been identified. Computationally recognizing the host targets of these miRNAs through network biology and machine learning, in vitro validation has been addressed along with the identification of their regulation in the HSV-2 infection. To signify the role of these identified miRNAs, they have been individually ectopically expressed in macrophages. The ectopic expression of the individual miRNAs was able to suppress HSV-2 viral gene expression. Taking a step forward, this study also highlights the Box–Behnken design-based combinatorial effect of ectopically expressed miRNAs on maximum suppression of HSV-2 infectivity. Therefore, the concentrations of each of the miRNAs optimized in a combination, predicted through expert systems biology tools were validated in vitro to not only recover the target expressions but also inhibit the HSV-2 infection in the macrophages. Overall, the study offers miRNAs as intriguing alternatives to commercially available medications against HSV-2. Moreover, the study illuminates the prophylactic potentiality of the miRNAs, which is significant since there are currently no vaccines available for HSV-2. Moving forward, the miRNAs are employed in an innovative strategy that incorporates intricate biological system models and in vitro confirmation methods to deliver a prospective combinatorial miRNA therapeutic against HSV-2 infection.