bluuebunny/crossref_metadata_2025 huggingface dataset:
Globalization and Informatization in the Digital Society and the Impact on Privacy
Authors: Romansky Radi
Published: July 2024

Globalization is a basic feature of contemporary digital society related to increasing level of informatization, electroniztion and communications through the global network and share different information. Regardless of the many advantages of the digital age, related to the intellectualization of various activities and objects (e-governance, e-health, social computing, smart home, smart city, etc.), the need for secure protection of users' privacy should not be overlooked. In this reason, the goal of the article is to determine the specific features of the main components of the globalization and the impact on the privacy and data protection. Based on the analysis of possible impacts the main recommendation for adequate data security and privacy protection are determined.
Comparative Analysis of Comprehensive Genomic Profile in Thymomas and Recurrent Thymomas Reveals Potentially Actionable Mutations for Target Therapies
Authors: Lococo Filippo , De Paolis Elisa , Evangelista Jessica , Dell’Amore Andrea , Giannarelli Diana , Chiappetta Marco , Campanella Annalisa , Sassorossi Carolina , Cancellieri Alessandra , Calabrese Fiorella , Vita Emanuele , Minucci Angelo , Bria Emilio , Castello Angelo , Urbani Andrea , Rea Federico , Margaritora Stefano , Scambia Giovanni
Published: July 2024

CGP do not substantially differ between initial tumor vs tumor recurrence and recurrent thymomas vs non-recurrent thymomas. Cell-cycle control genes alterations are associated with an early recurrence after thymectomy. Multiple target therapies are potentially available by performing a comprehensive CGP, suggesting that a precision medicine approach on these patients could be further explored.
Hyperspectral Imaging for Phenotyping Plant Drought Stress Using Univariate and Multivariate Modelling Techniques
Authors: Okyere Frank Gyan , Cudjoe Daniel Kingsley , Virlet Nicolas , Castle March , Riche Andrew Bernard , Greche Latifa , Simms Daniel M. , Mhada Manal , Mohareb Fady , Hawkesford Malcolm
Published: July 2024

Due to the adverse effect of prolonged drought stress on plants, accurate detection is essential for water use efficiency and maintaining productivity. Hyperspectral imaging is frequently used for non-invasive plant phenotyping, allowing for the long-term monitoring of crop health due to its sensitivity to subtle changes in leaf constituents. The broad spectrum of hyperspectral data enables the development of multiple vegetation indices (Vis) derived from the different spectral regions to estimate plant biophysical and biochemical traits. However, the known VIs often do not generalize well and perform poorly for multiple plant stresses. This study proposes new VIs combined with machine learning models to identify drought stress in wheat species under different nitrogen (N) levels. A wheat experiment was set up in the glasshouse with four treatments: well-watered high-N (WWHN), well-watered low-N (WWLN), drought-stress high N and drought-stress low-N. In addition to ensuring that plants were watered according to the experiment design, photosynthetic rate (Pn) and stomatal conductance (gs) were taken regularly, serving as the ground truth data for this study. Sensitive spectral features were selected using a custom-designed ensemble modelling technique. New drought VIs are proposed using different combinations of the selected features. Three classification models (support vector machines, random forest and deep neural network) were developed and trained using four sets of data: known VIs, proposed VIs, combined VIs (from the known and proposed VIs) and PCA-transformed features (over the whole spectral data). From the results, the proposed VIs outperformed the known VIs, yielding > 0.94 accuracies for all three models, and the performance improved when they were trained with the combined VIs. The combined VIs were used to train three regression models to predict the stomatal conductances and photosynthetic rates of plants. The random forest regression model performed best, suggesting that it could be used as a stand-alone tool to forecast gs and Pn and track drought stress in wheat.
MRI Based Monitoring of Accumulation of Polyethylene Terephthalate Nanoplastics
Authors: Bashirova Narmin , Butenschön Erik , Poppitz David , Gaß Henrik , Halik Marcus , Dentel Doreen , Tegenkamp Christoph , Matysik Joerg , Alia Alia
Published: July 2024

Polyethylene terephthalate (PET) is one of the most produced plastic materials in the world. The emer-gence of microplastics and nanoplastics (MPs/NPs) as a significant environmental contaminant has become a matter of increasing concern. While the toxicological effects of PET NPs have been widely re-searched, there is a lack of methodologies for studying their accumulation. The present study introduces a novel method to monitor the distribution of PET NPs in germinating wheat (Triticum aestivum L.) seeds. This involves the functionalization of superparamagnetic iron oxide nanoparticles (SPIONs) with PET NPs (PET-fSPION) coupled with magnetic resonance microimaging (µMRI) to provide insight into their distribution within the seed. The present study has demonstrated that PET-fSPION accumulates in specific regions of germinating wheat seeds, including the shoot apical meristem, the radicle, the cole-optile, the plumule, and the scutellum. Furthermore, the accumulation of PET-fSPION has been shown to exert a discernible effect on spin-spin relaxation, as observed via MRI and quantitative T2 relaxation time analysis. The accumulation of PET NPs in embryo regions was also confirmed by SEM. Diffu-sion-weighted magnetic resonance imaging (DW-MRI) and non-invasive chemical shift imaging analyses demonstrated that PET NPs resulted in restricted diffusion within the highlighted areas, as well as an impact on lipid content. Our study reveals that using µMRI with fSPION provides a non-invasive method to monitor the biodistribution of PET nanoparticles in wheat seeds. Additionally, it offers valuable in-sights into the microstructural interactions of PET.
Intrinsic Deformability of Human Acetylcholinesterase in Presence of Diverse Compounds: A Biophysical-Computational Study Based on Classical Dynamics, Elastic Network Models, Statistical Potentials, Energy Frustration, and Cavity Volumetry
Authors: González-Paz Lenin , Paz José Luis , Loroño-González Marcos A. , Contreras Julio Santiago , Lossada Carla , Vivas Alejandro , Marrero-Ponce Yovani , Martinez-Rios Felix , Rodríguez-Lugo Patricia , Balladores Yanpiero , Vera-Villalobos Joan , Alvarado Ysaias J.
Published: July 2024

The enzyme acetylcholinesterase (AChE) plays a crucial role in the termination of nerve impulses by hydrolyzing the neurotransmitter acetylcholine (ACh). The inhibition of AChE has emerged as a promising therapeutic approach for the management of neurological disorders such as Lewy body dementia and Alzheimer's disease. The potential of various compounds as AChE inhibitors has been investigated. In this study, we evaluated the impact of natural compounds of interest on the intrinsic deformability of human AChE using computational biophysical analysis. Our approach incorporates classical dynamics, elastic networks, statistical potentials, energy frustration, and volumetric cavity analyses. The results revealed that cyanidin induced significant changes in the flexibility and rigidity of AChE, especially in the distribution and volume of internal cavities, compared to model inhibitors such as TZ2PA6, and through a distinct biophysical-molecular mechanism from the other inhibitors considered. These findings suggest that cyanidin could offer potential mechanistic pathways for future research and applications in the development of new treatments for neurodegenerative diseases.
Effect of Biomass Burnings on Population Exposure and Health Impact at the End of 2019 Dry Season in Southeast Asia
Authors: Nguyen Hiep Duc , Ho Bang Quoc , Nguyen Quan Hong , Ngo Quang Xuan , Tran Duong Anh
Published: July 2024

At the end of the dry season in early March to early April each year, extensive agricultural biomass waste burnings occur throughout insular mainland Southeast Asia. During this biomass burnings period, smoke aerosols blanket the whole regions and were transported and dispersed by predominant westerly and south westerly winds to southern China, Taiwan and as far southern Japan and the Philippines. The extensive and intense burnings coincided with some wildfires in the forests due to high temperature making the region one of the global hot spots of biomass fires.

In this study, we focus on the effect of pollutants emitted from biomass burnings in March 2019 at the height of the burnings period to the exposed population and their health impact. The Weather Research Forecast- Chemistry (WRF-Chem) model was used to predict the PM2.5 concentration over the simulating domain and health impacts are then assessed on the exposed population in the four countries of Southeast Asia, namely Thailand, Laos, Cambodia and Vietnam. Using the health impact based on log-linear concentration response function and Integrated Exposure Response (IER), the results show that at the peak period of the burnings from 13 to 20 March 2019, Thailand experienced the highest impact, with an estimated 2170 premature deaths. Laos, Vietnam, and Cambodia followed, with estimated mortalities of 277, 565, and 314 deaths respectively. However, when considering the impact per head of population, Laos exhibited the highest impact, followed by Thailand, Cambodia, and Vietnam. The results highlight the significant health impact and associated economic cost of agricultural waste burnings in Southeast Asia at the end of the dry season. And hence policy makers should take these into account to design measures to reduce the negative impact of widespread burnings on exposed population in the region.
Optimization of Logistics Cargo Tracking and Transportation Efficiency based on Data Science Deep Learning Models
Authors: Li Ang , Zhuang Shikai , Yang Tianyi , Lu Wenran , Xu Jiahao
Published: July 2024

With the digital transformation of the logistics industry, smart logistics algorithms have become a core technology to improve efficiency and reduce costs. This paper reviews the development history of traditional logistics technology and discusses the key role of technologies such as the Internet of Things, big data analysis, artificial intelligence, and automation in logistics technology innovation. It focuses on the application of intelligent logistics algorithms in path optimization, intelligent scheduling, data mining and prediction, and intelligent warehousing. To solve the problem of inconsistency between training and testing objectives, this paper proposes DRL4Route, a deep reinforcement learning-based path optimization framework, and designs the DRL4Route-GAE model. Validated through extensive offline experiments and online deployments, the model significantly outperforms existing optimal benchmark methods on real datasets, improving the location deviation squared metrics and the top-three location prediction accuracy metrics. These research results provide important support to further promote the intelligent development of the logistics industry.
Idiopathic Pulmonary Fibrosis: Regulation, Cytokine Signaling, and Gaps in Our Knowledge
Authors: Jenkinson Faith , Rodgers Aoife M , Bell Rachael Lynda , Small Donna , Schock Bettina Claudia , Chaudhuri Nazia , Ingram Rebecca Jo
Published: July 2024

Idiopathic pulmonary fibrosis [IPF] is a chronic progressive disease characterised by the accumulation of scar tissue in the lung parenchyma. It primarily occurs in middle-aged and elderly adults and results in significant morbidity and mortality worldwide. The disease occurs due to repetitive lung epithelial injury, subsequent fibroblast activation and myofibroblast differentiation, resulting in excessive extracellular matrix deposition. This leads to scar formation and subsequent loss of lung function. Current treatment options for patients with IPF include the two anti-fibrotic drugs, pirfenidone and nintedanib, which can slow disease progression; however, there are currently no known cures for the disease. As such, novel methods and drug targets are warranted. In this review, we provide an up-to-date account of the importance of specific cytokines and the potential role of regulatory immune cells. We discuss their role in the pathogenesis of IPF and address some of the key gaps in knowledge.
(E)-2-Benzylidenecyclanones: Part XX. Reaction of Cyclic Chalcone Analogs with Cellular Thiols. Unexpected Increased Reactivity of 4-Chromanone- Compared to 1-Tetralone-Analogs in Thia-Michael Reactions [1]
Authors: Bognár Gábor , Kenari Fatemeh , Pintér Zoltán , Borges Igor D , Camargo Ademir J , De Oliveira Heibbe C. B. , Sanches-Neto Flávio O , Carvalho-Silva Valter H , Napolitano Hamilton B , Perjési Pál
Published: July 2024

In vitro cancer cell cytotoxicity (IC50 values) of (E)-3-(4’-methylbenzylidene)-4-chromanone (IIIb) showed >50.0 decreases in comparison to those of the respective tetralone derivative (IIIc). On the other hand, such a decrease was not observed in the analogous 4-OCH3 (IIc and IIIc) derivatives. Since the compounds can be considered cyclic chalcone analogs, kinetics and diastereoselectivity of non-enzyme catalyzed reactions with reduced glutathione (GSH) and N-acetylcysteine (NAC) of IIIb and IIIc were investigated to assess the possible role of thiol-reactivity in the increased cytotoxicity of IIIb. Reactivity of the compounds and stereochemical outcome of the reactions were evaluated using high-pressure liquid chromatography-mass spectrometry (HPLC-MS). Molecular modeling calculations were performed to rationalize the observed differences in the thiol-reactivities of the chromanones (III) and the previously investigated tetralones (II). The results indicated possible role of spontaneous thiol-reactivity of compounds III in their recorded biological effects.
Effect of Carbohydrases Treatment on Dietary Fibers and Bioactive Compounds of Cocoa Bean Shells (CBSs)
Authors: Disca Vincenzo , Jaouhari Yassine , Carrà Francesca , Martoccia Manuel , Travaglia Fabiano , Bordiga Matteo , Arlorio Marco
Published: July 2024

Cocoa bean shells (CBSs) represent the external layer of cocoa beans. The shells are removed during the winnowing after the pre-roasting, a processing step of chocolate production; in in the past they were considered a waste. CBSs possess many interesting nutritional characteristics in both macro and micronutrients. CBSs result very rich in dietary fiber and antioxidant compounds such as phenolic acids and flavan-3-ols. Soluble dietary fibers are potentially associated with fermentability and prebiotic activity on human gut microbiota instead antioxidants compounds have been linked with positive effects on human health. Based on these properties, the aim of this study was the valorization of CBSs thorough enzymatic treatments. The shells were treated both raw and defatted; three sets of carbohydrases have been used in order to destructure the fiber profile, potentially enhancing the potential prebiotic activity of CBSs. Cellulase, xylanase and pectinase were used to perform enzymatic treatments. After the enzymatic hydrolisis, both soluble and insoluble fibers fraction were quantified, in parallel with the total phenolic and the total flavonoid contents and the antioxidant activity. Moreover, the HPLC-PDA analysis was carried out for the main compounds of CBSs. The enzymatic treatmens allowed to obtain an enhancement in soluble fibers, for both defatted and raw CBSs, particularly using cellulase, xylanase and their combinations. Finally, a parallel preservation of the antioxidant capacity and the content of the main bioactive compounds after the treatments was observed. These results can be used to exploit the enzymatic treated CBSs to further industrial applications in the food and nutraceutical fields.
Structural Investigation of the Interaction Between a GC-376 Based Peptidomimetic PROTAC and the Viral Main Protease of Coxsackievirus B3 to Explore the Applicability of a Broad-Spectrum Antiviral PROTAC
Authors: De Santis Alessia , Grifagni Deborah , Orsetti Andrea , Lenci Elena , Rosato Antonio , D'Onofrio Mariapina , Trabocchi Andrea , Ciofi-Baffoni Simone , Cantini Francesca , Calderone Vito
Published: July 2024

The conservation of the main protease in viral genomes, combined with the absence of a homologous protease in humans, makes this enzyme family an ideal target for developing broad-spectrum antiviral drugs with minimal host toxicity. GC-376, a peptidomimetic 3CL protease inhibitor, has shown significant efficacy against coronaviruses. Recently, a GC-376-based PROTAC was developed to target and induce the proteosome-mediated degradation of the dimeric SARS-CoV-2 3CLPro protein. Extending this approach, the current study investigates the application of the GC-376 PROTAC to the 3CPro protease of Enteroviruses, specifically characterizing its interaction with CVB3 3CPro through X-ray crystallography, NMR and biophysical techniques. The present structural data show that there are some changes between the binding of CVB3 3CPro and SARS-CoV-2 3CLPro, but the overall similarity is strong. The most notable variation is the orientation of the phenylmethyl ester of GC-376 with the S4 subsite of the proteases. This is linked to the flexibility of the protein structure around the region of the catalytic triad shown by our NMR data. Other structural variations include distinct ligand interactions in subsites S1 and S2, influenced by the presence of Glu71 in CVB3 3CPro. Despite these differences, the GC-376 PROTAC fits well within the binding sites of both proteases, demonstrating its potential as a broad-spectrum antiviral agent.
Some Properties of Thermo-Treated Ash Wood
Authors: Lunguleasa Aurel , Spirchez Cosmin , Olarescu Alin
Published: July 2024

The purpose of this work is to analyze heat-treated ash wood at temperature of 185 ºC, in order to analyze its dimensional stability. For comparison, native ash/ not thermally treated wood, was be used. In order to analyze heat-treated ash wood, some main properties such as water absorption, swelling in thickness, roughness, Brinell hardness, modulus of rupture and elasticity, color change toward black with CIELab space, and many others properties were determined and processed. The results obtained after processing the experimental data have showed that heat-treated ash wood had good physical properties as absorption and swelling in thickness, and the slight increase in elasticity offers new fields of use. As a general conclusion of the work, it was established that the ash wood can be properly thermally treated, the dimensional stability characteristics being very good, and the decrease in bending resistance being almost imperceptible.
Analysis of Azure Zero Trust Architecture Implementation for Mid-size Organizations
Authors: Dakic Vedran , Moric Zlatan , Kapulica Ana , Regvart Damir
Published: July 2024

The Zero Trust Architecture (ZTA) security system follows the "never trust, always verify" principle. The process constantly verifies users and devices trying to access resources. This paper describes how Microsoft Azure uses ZTA to enforce strict identity verification and access rules across the cloud environment to improve security. Implementation is time-consuming and difficult. Azure's extensive services and customizations require careful design and implementation. Azure administrators struggle to navigate and change configurations due to its complex user interface (UI). Each Azure ecosystem component must meet ZTA criteria. ZTA's comprehensive policy definitions, multi-factor and pass-wordless authentication, and other advanced features are tested in a mid-size business scenario. These configuration changes require a solid grasp of Azure's architecture and Zero Trust. Implementing an Azure ZTA reduces vulnerabilities and restricts authorized users' access to critical resources. Implementation is time-consuming and expensive, requires a significant commitment to changing IT procedures, and can be confusing because the same features are available in multiple places. Azure ZTA has shown great potential for both hybrid and cloud-native companies.
Morphological and Molecular Identification of Bark and Ambrosia Beetles and Their Associated Predators in Honduran Coniferous Forests
Authors: Hernández Mauricio , Michel Mauricio , García Joel , Dueñas Geisy , Moncada Marcela , Amaya Kevin , Yánez Yensi , Pinto Alejandra , Matamoros Gabriela , Zamora Mario , Fontecha Gustavo
Published: July 2024

Bark beetles are one of the major causes of tree mortality in coniferous forests worldwide. The large-scale disturbances by beetle outbreaks have strongly increased in Honduras and other countries, leading to significant ecological, economic, and social consequences. However, the taxonomy, local distribution, and environmental aspects of most bark beetles and their biological predators remain poorly studied. Here, we applied a morphological approach to identify Scolytinae individuals across different forest areas in Honduras. Moreover, the barcoding approach was used by amplifying and sequencing the mitochondrial COI gene. The intraspecific genetic diversity of Ips apache was also analyzed. 1,131 individuals were examined, and 27 genera were identified. Most of the specimens were identified as belonging to the genus Ips, accounting for 53.2% of the total. Xyleborus accounted for 16.5% and Temnoscheila accounted for 10%. Less than four individuals were found for fifteen genera. Ips, Temnoscheila, Xyleborus, Hypothenemus and Pityophthorus exhibited the most extensive geographic distribution among the sampled sites. At the genus level, Olancho, El Paraíso, and Copán displayed the highest diversity. This study is the first report of two genera (Xylomeira and Stephanopachys) in Honduran pine forests. Among individuals of I. apache, there was evidence of intraspecific genetic diversity, but no population structure was detected. While our findings update the list of bark beetles and biological predators associated with Honduran coniferous forests, more taxonomic inventories and ecological studies are required to understand the spread of bark beetles in pine forests.
Energy-Optimized 3D Path Planning for Unmanned Aerial Vehicle
Authors: Nagy István , Laufer Edit
Published: July 2024

Drone technology is undoubtedly part of our everyday life these days. The business and industrial use of unmanned aerial vehicles (UAV) can provide favorable solutions in many areas of life, and they are also great for emergency situations and for reaching hard-to-reach places. However, there are many technological and regulatory challenges to overcome in their application. One of the weak links in the operation of UAVs is the limited availability of energy. In order to address this issue authors develop a novel trajectory planning method for UAVs to optimize the energy necessity during flight. For this, an “energy map”, which serves as the basis for trajectory planning, must first be prepared, i.e., the energy consumption of the individual components must be determined. This is followed by configuring the 3D environment including partitioning of the work space (WS), i.e. the definition of free spaces, occupied spaces (obstacles) and semi-occupied/free spaces. On the basis of the partitioned space, the corresponding graph-like path(s) has to be generated, where a graph search-based heuristic trajectory planning can be started, taking into account the most important wind conditions, e.g. velocity and direction. Finally, in order to test the theoretical results, some sample environment will be created, where the proposed path generations will be tested and analyzed. The suggested method is able to find the optimal path in terms of energy consumption.
ASD-GANNet: A GAN inspired Deep Learning Approach for the Classification of Human’s Autism Brain Disorder
Authors: Khan Naseer Ahmed , Shang Xuequn
Published: July 2024

Classification of pre-processed fMRI dataset using the functional connectivity(FC) based features is considered a challenging task due to high dimensional FC features set and smaller dataset size. To tackle this specific FC high dimensional features set and a smaller dataset size , We have proposed here a GAN based dataset augmenter to firstly train the GAN on the NYU Connectivity Features dataset and used the trained GAN to generate synthetic features per category and after getting sufficient number of features per category , A multi-head attention mechanism was used as a head for the classification . We name our proposed approach as "ASD-GANNet" which is End-To-End and does not require hand-crafted features as the multi-head attention mechanism focuses on the features that are more relevant . Moreover , We compared our results with the 06 available state-of-the-art techniques from the literature and Our proposed approach results using "NYU" site as a training set for generating GAN based synthetic dataset are promising. We achieved an overall 10-fold cross validation based accuracy of 82% ,sensitivity 82% and specificity 81% outperforming the available state-of-the art approaches. Sitewise comparison of our proposed approach also outperformed the the available state-of-the-art as out of the 17 sites our proposed approach has better results in the 10 sites.
An Innovative Honeypot Architecture for Detecting and Mitigating Hardware Trojans in IoT Devices
Authors: Houssam Amira , Soubra Hassan , Moulla Donatien Koulla , Abran Alain
Published: July 2024

The exponential growth and widespread adoption of Internet of Things (IoT) devices have introduced many vulnerabilities. Attackers frequently exploit these flaws, necessitating advanced technological approaches to protect against emerging cyber threats. This study introduces a novel approach utilizing hardware honeypots as an additional defensive layer against hardware vulnerabilities, particularly hardware trojans (HTs). HTs pose significant risks to the security of modern integrated circuits (ICs), potentially causing operational failures, denial of service, or data leakage through intentional modifications. The proposed system was implemented on a Raspberry Pi and tested on an emulated HT circuit using a Field-Programmable Gate Array (FPGA). This approach leverages hardware honeypots to detect and mitigate HTs in IoT devices. The results demonstrate that the system effectively detects and mitigates HTs without imposing additional complexity on the IoT devices. The trojan-agnostic solution offers full customization to meet specific security needs, providing a flexible and robust layer of security. The findings provide valuable insights into enhancing the security of IoT devices against hardware-based cyber threats, thereby contributing to the overall resilience of IoT networks. This innovative approach offers a promising solution to address the growing security challenges in IoT environments.
Preliminary Evaluation of Performance, Cost and Environmental Sustainability of a LH2‐Powered Narrow Body Aircraft
Authors: Ferretto¹ Davide , Camboni¹ Simone , Fusaro² Roberta
Published: July 2024

The commercial aviation industry is constantly evolving, driven by technological innovation and the growing demand for more efficient and environmental friendly transportation solutions. In this context, the design and development of next-generation aircraft is a crucial task, with the goal of reducing environmental impact and improving flight efficiency while continuing to ensure passenger comfort and safety. This paper aims at evaluating the technical feasibility as well as economic and environmental viability of a narrow body aircraft powered by hydrogen for direct combustion. Notably, the paper offers innovative insights regarding the update of literature models for conceptual design, in order to consider the impact of hydrogen fuel on the sizing loop at aircraft level. Moreover, it also focuses on the evaluation of operating costs for the aforementioned aircraft class, highlighting the required model updates and considerations to properly match the effect on costs of the introduction of the innovative fuel. Ultimately, a preliminary analysis of upstream sustainability of the concept, considering fuel life cycle, is provided, in order to understand the overall competitiveness, not only in terms of monetary resources but also of environmental impact, beyond the pure operating life of the airplane.
Evolution and Analysis of Water Yield under the Change of Land Use and Climate Change based on the PLUS-InVEST Model: A Case Study of the Yellow River Basin in Henan Province
Authors: Ma Xiaoyu , Liu Shasha , Guo Lin , Zhang Junzheng , Feng Chen , Feng Mengyuan , Li Yilun
Published: July 2024

Understanding the interrelationships between land use, climate change, and regional water yield is critical for effective water resource management and ecosystem protection. However, comprehensive insights into how water yield evolves under different land use scenarios and climate change remain elusive. This study employs the Integrated Valuation of Ecosystem Services and Trade-offs (InVEST) models, Patch-generating Land Use Simulation (PLUS) model, and Geodetector within a unified framework to evaluate the dynamics of land use, water yield, and their relationships with various factors (meteorological, social, economic, etc.). To forecast the Land Use/Cover Change (LUCC) pattern of the Yellow River Basin by 2030, three scenarios were considered: economic development priority (Scenario1), ecological development priority(Scenario2), and cropland development priority(Scenario3). Climate change scenarios were constructed using CMIP6 data, representing low stress (SSP119), medium stress (SSP245), and high stress (SSP585) conditions. The results show the following: (1) From 2000 to 2020, cropland was predominant in the Yellow River Basin, Henan Province, with significant land conversion to impervious land (construction land) and forest land; (2) Water yield changes during this period were primarily influenced by meteorological factors, with land use changes having negligible impact; (3) By 2030, the water yield of Scenario1 is highest among different land use scenarios, marginally surpassing ecological development priority by 0.5%; (4) Climate scenarios reveal significant disparities, with SSP126 yielding 67% higher water production than SSP245, driven predominantly by precipitation; (5) Geodetector analysis identifies precipitation as the most influential single factor, with significant interactions among meteorological and socio-economic factors. These findings offer valuable insights for policymakers and researchers in formulating land use and water resource management strategies.
Forecasting Temperature Trends Using SARIMAX: A Case Study in Ahmedabad City, India
Authors: Shah Vyom , Patel Nishil , Shah Dhruvin Apurva , Swain Debabrata , Mohanty Manorama , Acharya Biswaranjan , Gerogiannis Vassilis C. , Kanavos Andreas
Published: July 2024

Globalization and industrialization have significantly disturbed the environmental ecosystem, leading to critical challenges such as global warming, extreme weather events, and water scarcity. Forecasting temperature trends is crucial for enhancing the resilience and quality of life in smart sustainable cities, enabling informed decision-making and proactive urban planning. This research specifically targets Ahmedabad city in India and employs the Seasonal Auto Regressive Integrated Moving Average with eXogenous factors (SARIMAX) model to forecast temperatures over a ten-year horizon using two decades of real-time temperature data. The stationarity of the dataset was confirmed using the Augmented Dickey-Fuller test, and the Akaike Information Criterion (AIC) method helped identify the optimal seasonal parameters of the model, ensuring a balance between fidelity and predictive accuracy. The model achieved an RMSE of 1.0265, indicating high accuracy within the typical range for urban temperature forecasting. This robust measure of error underscores the modelβ€™s precision in predicting temperature deviations, which is particularly relevant for urban planning and environmental management. The findings provide city planners and policymakers with valuable insights and tools for preempting adverse environmental impacts, marking a significant step towards operational efficiency and enhanced governance in future smart urban ecosystems. Future work may extend the model's applicability to broader geographical areas and incorporate additional environmental variables to refine predictive accuracy further.
Daily and Weekly “High Doses” of Cholecalciferol for the Prevention and Treatment of Vitamin D Deficiency for Obese or Multi-morbidity and Multi-treatment Patients Requiring Multi-drugs – A Narrative Review
Authors: Pludowski Pawel , Marcinowska-Suchowierska Ewa , Togizbayev Galym , Belaya Zhanna , Grant William B. , Pilz Stefan , Holick Michael F.
Published: July 2024

Daily vitamin D supplementation using higher than normal dosing (up to the upper limit, UL) and intermittent (once or twice per week) dosing were studied in patients with increased risk of vitamin D deficiency. Using a PubMed database an adequate search for published randomized control trials and other studies was done and results were analyzed. This review provides overview of use of 7000 IU daily, 30,000 IU per week or twice weekly and 50,000 IU weekly of vitamin D on rapid restore of 25(OH)D concentrations to at least 30 ng/mL in different groups of patients at high risk of vitamin D deficiency. The above mention dosing should be considered in adults with obesity or multi-diseased patients on requiring multi-treatment with the use of several drugs, including drugs affecting vitamin D metabolism. The simple schedule for 7000 IU/day, 30,000 IU/week or twice weekly or 50,000 IU/week, all for 6-8 weeks of use by patients representing the increased risk of vitamin D deficiency were provided for consideration. It appeared that higher daily doses or intermittent doses suggested above are effective, safe and responsive for patient’s preferences.
Impact of Variability in the Delta Opioid Receptor (OPRD1) Gene on Body Mass Index and Psychometric Characteristics of Eating Disorders Patients
Authors: González-Rodríguez Laura , González Luz María , García-Herráiz Angustias , Mota-Zamorano Sonia , Flores Isalud , Gervasini Guillermo
Published: July 2024

Objectives: This study aimed to investigate whether genetic variations in the OPRD1 gene affect psychopathological symptoms and personality dimensions in eating disorders (ED) patients and/or contribute to ED risk.
Methods: The study involved 221 female patients with anorexia nervosa (AN), 88 with bulimia nervosa (BN) and 396 controls. Sixteen tag-SNPs in OPRD1 were identified. Psychometric evalu-ations were conducted using the Symptom Checklist 90 Revised (SCL-90R) and the Eating Dis-orders Inventory Test-2 (EDI-2). P-values obtained by regression models were corrected for mul-tiple testing by the FDR method.
Results: In AN patients, genotypes rs204077TT and rs169450TT were linked to lower BMI values (FDR-q=0.035 and 0.017, respectively), as was rs2234918 in a log-additive model (BMI: 18.0±0.28, 17.22±0.18 and 16.59±0.39 for TT, TC and CC carriers, FDR-q=0.012). Additionally, AN patients carrying the rs72665504AA genotype had higher scores in Interpersonal distrust (FDR-q=0.030), whilst BN carriers of rs513269TT and rs2873795TT showed lower scores in Ineffectiveness (FDR-q=0.041 and FDR-q=0.021). In the AN group, BMI correlated with variability in a distal haplotype (rs508448/rs204077/rs223491, FDR-q=0.028), which was also associated with the global PST index of SCL-90R (FDR-q=0.048). Associations were more noticeable in BN patients; again, the distal region of the gene was linked to EDI-2 total scores (FDR-q=0.004-0.048 for the four last haplotypes) and two global SCL-90R indices (GSI: FDR-q=0.011 and PSDI: FDR-q=0.003 for the last s204077/rs2234918/rs169450 combination). No associations with ED risk were observed.
Conclusions: Genetic variation in the OPRD1 gene, particularly in its distal region, is associated with BMI and psychopathological comorbidities in ED patients.
Speech Feature Extraction in Broadcast Hosting Based on Fluctuating Equation Inversion
Authors: Xu Chi
Published: July 2024

Speech is one of the most sophisticated human motor skills. Speaker identification is the ability of a software component or hardware to acquire a speech signal, recognize the speakers included in the signal, and identify the speaker after the audio signals have been received. This study proposes a fluctuating equation inversion method using feature extraction for broadcast hosting. Feature extraction aims to provide useful signal features from natural audio that can be applied to various downstream processes, including recitation, evaluation, and categorization. Initially, data were collected from the CASIA dataset. This study evaluated the experimental outcomes of the proposed approach using mel-frequency cepstral coefficients, gammatone frequency cepstral coefficients, and linear frequency cepstral coefficients. The proposed technique was tested on a publicly accessible dataset, and the findings showed that it performed better in terms of recognition accuracy (98%), precision (97%), recall (96.05%), sensitivity (92.56%), and F1-score (95.09%) than the conventional feature extraction methods. The proposed approach can be utilized to improve audio signal quality and user experience across broadcast-hosting applications.
Improved Pedestrian Detection Algorithm Based on YOLOv5s
Authors: Li Zhihua , Zhang Yuanbiao , Wang Chao , Tan Guopeng , Yan Yahui
Published: July 2024

In this study, we propose YOLOv5s-PGD algorithm for dense pedestrian detection, which can improve the recall and reduce the number of parameters compared with YOLOv5s. First, a minimum scale detection layer has been added to deepen the pyramid’s depth and enhance detection accuracy. Second, ghost convolution has been employed to replace standard convolution to increase real-time performance of the algorithm. Finally, depth separable convolution has been used to address issues of high parameters and large computational complexity that lower the efficiency of the algorithm. Experiment results demonstrate that the detection accuracy of the YOLOv5s-PGD algorithm on the CrowdHuman public dataset is up to 85.1%, which is 2.2% higher than that of YOLOv5s. Furthermore, the number of parameters has decreased by 19.7%, and the calculation burden has decreased by 2.5%. Consequently, the proposed YOLOv5s-PGD algorithm better satisfies the requirements of real-time detection, model optimization, and terminal deployment in dense pedestrian scenarios.
The Spatial Impact of Innovation Efficiency on Green Development
Authors: Chen Yanwu , Lin Jiamin , Cui Na , Zhu Yihe , Pan Jun
Published: July 2024

Innovation and green development are important aspects of a country’s economic and social development. Exploring the impact of innovation on green development from a spatial perspective can help local governments suggest measures to promote green development. The super-efficiency slack-based measure model and stochastic frontier analysis model were used separately to measure green development and innovation. The coupling coordination degree model was used to measure the interaction of “double efficiency.” Based on the spatial panel Durbin model, the spatial impact of innovation behavior on green development was studied, and regional heterogeneity was further studied. The study found that in the eastern region, provinces with a high degree of innovation and a developed financial industry have a negative effect on the green development of their neighboring provinces. In the central and western regions, the higher the innovation level of a region, the more negative the green development of neighboring regions is. A high-level financial industry promotes the green development of neighboring regions.
Income Inequality and Fertility Behavior: An Empirical Study on China
Authors: Wu Weicong , Zhao Xindong
Published: July 2024

The declining trend in China’s fertility rate is very pronounced, and since 2023, the population has entered a phase of negative growth, significantly constraining economic development. At the same time, income inequality, which creates many problems, remains a serious issue. Existing research does not discuss the impact of income inequality on fertility rates in China currently. This paper depends on both macro and micro perspectives to examine the relationship between income inequality and fertility. We used the macro perspective to study the impact of urban-rural income inequality on the birth rate. The results show that the widening income gap between urban and rural areas will reduce the birth rate, but this effect declines with increasing of the birth rate; this negative effect is the strongest in the eastern region and the weakest in the western region. We used micro perspective to study the effect of the Gini coefficient on fertility motivation. It was found that for every one percent increase of the Gini coefficient, the fertility motivation decreased by about 0.08%, indicating that income inequality also impacts fertility behavior negatively. Comparing different income groups, income inequality has no impact on the fertility motivation of low-income groups, but has a significant negative and positive impact, respectively, on the middle and high-income groups. The mediating effect model shows that income inequality can negatively affect fertility behavior by reducing social trust and subjective well-being. Therefore, the empirical results from China suggest that income inequality negatively affects fertility behavior.
Analysis of Capacity of the Improved Space-Time Block Code Based on MIMO System
Authors: Wang Zhongbao , Gu Jinliang , Wang Xingxing , Zhu Weihua , Teng Zhijun
Published: July 2024

The anti-intereference ability can be strengthened and the BER can be decreased when space-time block code (STBC) is applied to MIMO systems. The existing quasi-orthogonal code TBH can effectively improve the system capacity, but the effect is not good at high signal-to-noise ratio (SNR). In this paper, we introduce an improved quasi-orthogonal code, derive system capacity formula not with space-time coding but with TBH and improved quasi-orthogonal STBC, and simulate the relationship curve of the SNR and capacity in these systems based on MATLAB. The simulation shows that the system capacity with the improved code is bad in low SNR, but much better than that of the existed code with the increasing SNR, and the advantage on increasing capacity is outstanding, especially when the SNR is high.
Occlusion Handling Algorithm Based on Contour Detection
Authors: Dai Zhiheng , Hu Xiaojuan , Chen Chunyi , Yu Haiyang
Published: July 2024

Occlusion handling is a key technical issue in augmented reality research. This paper proposes a new occlusion algorithm based on object contour detection to address issues such as poor real-time occlusion processing, high computational complexity in comparing the depth values of virtual and real objects, and the presence of jagged, blurry, and hollow edges in occluded areas. First, based on the depth and color information, we obtained aligned images of real scenes. Second, we extracted the maximum closed contour of the real object in the scene and overlaid it with the aligned image. Subsequently, we generated a virtual object and obtained a depth map of the virtual object. Finally, by comparing the depth values of the stacked images with the virtual objects, masks are generated in real time and optimized to present the occlusion processing results. Experimental comparisons demonstrated that the algorithm presented in this study not only improves real-time performance but also enhances accuracy at the intersection edges of virtual and real images. Simultaneously, it is no longer limited by the size of real scene images and can achieve real-time virtual and real occlusion effects.
Effects of Digital Economy on Factor Market Distortion in China: A Spatial Econometric Approach
Authors: Xiang Yun , Lyu Yanfang , Wang Dong
Published: July 2024

Factor market distortion hinders economic growth, and digital economy may provide an impetus to help alleviate the misallocation. Based on China’s provincial panel data from 2013 to 2021, this study applies a dynamic evaluation method to measure the comprehensive level of digital economy, and constructs a spatial econometric model to investigate its impacts on factor market distortion, as well as the differences before and during the COVID-19 pandemic. The empirical findings indicate that China’s digital economy development shows a steady upward trend at the provincial level, has a spatial spillover effect on factor market distortion, and plays a dissipating role, which embodies the potential of digital economy in improving the optimal allocation of resources. At the same time, there is a phenomenon of unbalanced digital economy development in different areas, reflecting the “digital divide” problem. Moreover, during the COVID-19 pandemic, various mobility restrictions were launched, which promotes continuous penetration of the digital economy in various fields of the economy, and its effects on alleviating the factor market distortion are manifested. Overall, this study provides some enlightenments to better implement digitalization strategies.
Model Predictive Control for Discrete Time-Delay System Based on Equivalent-Input-Disturbance Method
Authors: Shi Zuguang , Gao Fang , Chen Wenbin
Published: July 2024

In this study, model predictive control (MPC) was considered for a class of discrete time-delay systems with external disturbances. Equivalent input disturbance (EID) is a commonly utilized active disturbance rejection technique that enhances the rejection of system disturbances. Any disturbance can be effectively suppressed using the EID approach, leaving no traces of the disturbance. This paper proposes a novel MPC control approach that combines the EID method with the MPC principle. An MPC law with disturbance-rejection performance was presented after obtaining and combining the EID estimates with performance indicators using the EID method. Optimizing the performance index is then converted into a semi-definite problem comprising of an objective function and linear matrix inequality through building a Lyapunov function. Based on this basis, an MPC design algorithm is proposed. A numerical simulation was conducted to confirm superiority and efficacy of the proposed method.