bluuebunny/crossref_metadata_2025 huggingface dataset:
Energy Dependence of Solar Energetic Protons and Their Origin in Solar Cycles 23 and 24
Authors: Miteva Rositsa , Samwel Susan W. , Dechev Momchil
Published: August 2024

The study presents the compilation of a comprehensive catalog of solar energetic protons (SEPs) in solar cycles (SCs) 23 and 24 (1996–2019) in 10 energy channels from about 20 to 100 MeV based on data from the Energetic and Relativistic Nuclei and Electron (ERNE) instrument aboard Solar and Heliospheric Observatory (SOHO). For comparison, we added previously reported SEP fluxes by a number of different sources. We identified the SEP-solar origin in terms of solar flares and coronal mass ejections and calculated the statistical correlations (Pearson and partial) as a function of the SEP energy.
An Analysis of Extreme Rainfall Events in Cambodia
Authors: Pen Sytharith , Rad Saeed , Ban Liheang , Brang Sokhorng , Nuth Panha , Liao Lin
Published: August 2024

Extreme rainfall, also known as heavy rainfall or intense precipitation, is a weather event characterized by a significant amount of rainfall within a short period. This study analyzes the trends in extreme precipitation indices at 17 stations in four main regions in Cambodia—the Tonle Sap, coastal, Mekong Delta, and Upper Mekong regions—between 1991 and 2021. Analyzing the data with RClimDex v1.9 reveals diverse spatial and temporal variations. The statistical analysis of the extreme rainfall indices in Cambodia from 1991 to 2021 reveals significant trends. In the Tonle Sap region, consecutive dry days (CDDs) increased at most stations, except Battabang, Kampong Thmar, and Pursat, while consecutive wet days (CWDs) increased at most stations. These trends align with rising temperatures and reduced forest cover. In the coastal region, particularly at the Krong Khemarak Phummin station, most rainfall indices increased, with a slope value of 89.94 mm/year. The extreme rainfall indices max. 1-day precipitation (RX1day) and max. 5-day precipitation (RX5day) also increased, suggesting higher precipitation on days exceeding the 95th (R95p) and 99th percentiles (R99p). The Kampot station showed a significant increase in CDDs, indicating a heightened drought risk. In the Mekong Delta, the Prey Veng station recorded a decrease in the CDDs slope value by −4.892 days/year, indicating potential drought risks. The Stung Treng station, which is the only station in Upper Mekong, showed a decreasing trend in CDDs with a slope value of −1.183 days/year, indicating a risk of extreme events. These findings underscore the complex interplay between climate change, land use, and rainfall patterns in Cambodia.
Interpolation of Temperature in a Mountainous Region Using Heterogeneous Observation Networks
Authors: Ryu Soorok , Song Joon Jin , Lee GyuWon
Published: August 2024

Accurately generating high-resolution surface grid datasets often involves merging multiple weather observation networks and addressing the challenge of network heterogeneity. This study aims to tackle the problem of accurately interpolating temperature data in regions with a complex topography. To achieve this, we introduce a deterministic interpolation method that incorporates elevation to enhance the accuracy of temperature datasets. This method is particularly valuable for areas with intricate terrains. Our robust methodology integrates a network harmonization method with radial basis function (RBF) interpolation for complex topographical regions. The method was tested on 10 min average temperature data from Jeju Island, South Korea, over 2 years that had a spatial resolution of 100 m. The results show a significant reduction of 5.5% in error rates, from an average of 0.73 °C to 0.69 °C, by incorporating all adjusted data. Integrating a parameterized nonlinear temperature profile further enhances accuracy, yielding an average reduction of 4.4% in error compared to the linear model. The spatial interpolation method, based on regression-based radial basis functions, demonstrates a 6.7% improvement over regression-based kriging for the same temperature profile. This research offers a valuable approach for precise temperature interpolation, especially in regions with a complex topography.
Life Cycle Assessment and Costing of Large-Scale Battery Energy Storage Integration in Lombok’s Power Grid
Authors: Hemmati Mohammad , Bayati Navid , Ebel Thomas
Published: August 2024

One of the main challenges of Lombok Island, Indonesia, is the significant disparity between peak load and base load, reaching 100 MW during peak hours, which is substantial considering the island’s specific energy dynamics. Battery energy storage systems provide power during peak times, alleviating grid stress and reducing the necessity for grid upgrades. By 2030, one of the proposed capacity development scenarios on the island involves deploying large-scale lithium-ion batteries to better manage the integration of solar generation. This paper focuses on the life cycle assessment and life cycle costing of a lithium iron phosphate large-scale battery energy storage system in Lombok to evaluate the environmental and economic impacts of this battery development scenario. This analysis considers a cradle-to-grave model and defines 10 environmental and 4 economic midpoint indicators to assess the impact of battery energy storage system integration with Lombok’s grid across manufacturing, operation, and recycling processes. From a life cycle assessment perspective, the operation subsystem contributes most significantly to global warming, while battery manufacturing is responsible for acidification, photochemical ozone formation, human toxicity, and impacts on marine and terrestrial ecosystems. Recycling processes notably affect freshwater due to their release of 4.69 × 10−4 kg of lithium. The life cycle costing results indicate that over 85% of total costs are associated with annualized capital costs at a 5% discount rate. The levelized cost of lithium iron phosphate batteries for Lombok is approximately 0.0066, demonstrating that lithium-ion batteries are an economically viable option for Lombok’s 2030 capacity development scenario. A sensitivity analysis of input data and electricity price fluctuations confirms the reliability of our results within a 20% margin of error. Moreover, increasing electricity prices for battery energy storage systems in Lombok can reduce the payback period to 3.5 years.
Optimizing Cardiomyocyte Differentiation: Comparative Analysis of Bone Marrow and Adipose-Derived Mesenchymal Stem Cells in Rats Using 5-Azacytidine and Low-Dose FGF and IGF Treatment
Authors: Farag Ahmed , Koung Ngeun Sai , Kaneda Masahiro , Aboubakr Mohamed , Tanaka Ryou
Published: August 2024

Mesenchymal stem cells (MSCs) exhibit multipotency, self-renewal, and immune-modulatory properties, making them promising in regenerative medicine, particularly in cardiovascular treatments. However, optimizing the MSC source and induction method of cardiac differentiation is challenging. This study compares the cardiomyogenic potential of bone marrow (BM)-MSCs and adipose-derived (AD)-MSCs using 5-Azacytidine (5-Aza) alone or combined with low doses of Fibroblast Growth Factor (FGF) and Insulin-like Growth Factor (IGF). BM-MSCs and AD-MSCs were differentiated using two protocols: 10 μmol 5-Aza alone and 10 μmol 5-Aza with 1 ng/mL FGF and 10 ng/mL IGF. Morphological, transcriptional, and translational analyses, along with cell viability assessments, were performed. Both the MSC types exhibited similar morphological changes; however, AD-MSCs achieved 70–80% confluence faster than BM-MSCs. Surface marker profiling confirmed CD29 and CD90 positivity and CD45 negativity. The differentiation protocols led to cell flattening and myotube formation, with earlier differentiation in AD-MSCs. The combined protocol reduced cell mortality in BM-MSCs and enhanced the expression of cardiac markers (MEF2c, Troponin I, GSK-3β), particularly in BM-MSCs. Immunofluorescence confirmed cardiac-specific protein expression in all the treated groups. Both MSC types exhibited the expression of cardiac-specific markers indicative of cardiomyogenic differentiation, with the combined treatment showing superior efficiency for BM-MSCs.
Auxin-Producing Bacteria Used as Microbial Biostimulants Improve the Growth of Tomato (Solanum lycopersicum L.) Seedlings in Hydroponic Systems
Authors: Pappalettere Livia , Bartolini Susanna , Toffanin Annita
Published: August 2024

Seven auxin-producing endophytic bacterial strains (Azospirillum spp., Methylobacterium symbioticum, Bacillus spp.), and two different combinations of these strains were used to verify their influence on tomato during germination and development in hydroponic conditions where, as a novelty for Canestrino di Lucca cultivar, endophytic bacteria were inoculated. To emphasize the presence of bacterial auxins in roots and stems of seedlings, both in situ staining qualitative assessment and quantitative analysis were carried out. Moreover, hypogeal and epigeal growth of the plantlets were measured, and correlation analyses were conducted to examine the relationship between the amount of indolacetic acid (IAA) produced by the bacterial strains and root and stem parameters. Plantlets treated with microbial inoculants showed a significant increase in the survival rate compared to the control treatment. The best results as IAA producers were from Azospirillum baldaniorum Sp245 and A. brasilense Cd, which also induced significant root growth. On the other hand, Bacillus amyloliquefaciens and B. licheniformis induced the best rates in stem growth. These findings highlight the potential for using endophytic bacterial strains in a hydroponic co-cultivation system that enables inoculating plantlets, at an early stage of growth (5 days old).
Experimental Study of the Mechanical Behavior of a Steel Arch Structure Used in the Main Lining of a Highway Tunnel
Authors: Li Changjun , Zhuang Yizhou , Lu Yuquan , Zheng Guoping , Zheng Yunhui , Li Wenhao , Xue Chenbo , Guo Hongyu , Fang Yuchao
Published: August 2024

The steel rib and the lattice girder are two typical steel arch frames used in the primary lining of the New Austrian Tunnelling Method (NATM) highway tunnel. In the process of tunnel construction, it is necessary to choose the support method according to the mechanical properties, durability, construction difficulty, and economic benefits. In order to analyze the mechanical characteristics of steel arch frames, a loading and measurement test system for steel arch was designed, and destructive tests were carried out on a three-bar lattice girder, four-bar lattice girder, and I-beam steel rib. The steel utilization coefficient was introduced to compare the mechanical properties of the different types of steel frames. The results show that the I-beam steel rib has the largest steel utilization coefficient, but it is not well combined with concrete. Among the lattice girders, the three-bar W-shaped lattice girder uses the least amount of steel under the same load, which reduces the construction cost and is worth applying and popularizing. Additionally, the steel utilization coefficient of different types of lattice girders can be optimized through the ratio of steel bar diameters. It is proved that the steel utilization coefficient can provide a reference for the evaluation and selection of steel frames for the primary lining of highway tunnel engineering.
A Dynamic Iwan Model to Describe the Impact Failure of Bolted Joints
Authors: Chen Hao , Hao Zhiming , Kuang Jinxin , Li Jicheng
Published: August 2024

Due to the nonlinearity of the contact interface, as well as the material, jointed structures exhibit complex mechanical behaviors under impact loading. In order to accurately characterize the dynamic response of a joint, this work presents a nonlinear dynamic model (DICF model). First, the effects of loading velocity, preload and friction coefficient on the displacement–load curve are discussed based on a validated finite element model. Numerical simulation results show that the critical load and critical displacement are linearly related to the normalized logarithmic velocity and linearly related to the normalized preload and friction coefficient. Subsequently, a DICF model that consists of sliding, collision and failure is proposed. The constitutive relations of the model are derived, and dynamic correction functions are introduced to characterize the effects of velocity, preload and friction coefficient. A parameter identification method for the model is also provided. Finally, the DICF model is compared with the finite element simulation results, with an error of 0.43% for quasi-static conditions, a minimum error of 0.17% and a maximum error of −1.41% for impact conditions, in addition to significantly improved accuracy compared to the EC3 model, which indicates that it can effectively capture the behavior of bolted joints under impact loading conditions.
Correction: Defalla Abdel Hafez et al. Recycled Chicken Feather Sand as a Partial Replacement for Natural Sand for Producing Eco-Friendly Mortar. Buildings 2023, 13, 421
Authors: Defalla Abdel Hafez Radwa , Hadzima-Nyarko Marijana , Ahmed Samir M. , Tayeh Bassam A.
Published: August 2024

Missing Citation [...]
Rationale for the Initiation, Outcomes, and Characteristics of Chemotherapy Following CDK4/6 Inhibitors in Breast Cancer: A Real-World Cohort Study
Authors: Püsküllüoğlu Miroslawa , Ziobro Marek , Lompart Joanna , Rudzińska Agnieszka , Zemełka Tomasz , Jaworska Justyna , Ochenduszko Sebastian , Grela-Wojewoda Aleksandra
Published: August 2024

The standard therapy for hormone-receptor-positive, human epidermal growth factor receptor 2-negative advanced breast cancer includes the use of cyclin-dependent kinase 4/6 inhibitors (CDK4/6i) with endocrine therapy. The optimal post-CDK4/6i treatment sequence is unclear. This cohort study evaluated the initiation, characteristics, and outcomes of chemotherapy following CDK4/6i-based treatment. Among the 227 patients who began CDK4/6i therapy, 114 completed it. Seventy-nine female patients received further treatment, including 55 receiving chemotherapy. The average age was 60.1 years. Post-CDK4/6i chemotherapy was typically (69.1%) first-line due to an impending visceral crisis. The median progression-free survival (mPFS) was 3.0 months (range 0.5–18.9), and the median overall survival (mOS) was 8.3 months (0.5–26.1). The median OS from the end of CDK4/6i treatment was 12.4 months (1.5–26.8). In univariate analysis, neither mPFS nor mOS was associated with age, tumor grade, receptor status, Ki67 status, time from diagnosis to CDK4/6i cessation, therapy line, or CDK4/6i type. Dose reduction occurred in 12 patients (21.8%), and chemotherapy was ceased due to adverse events in 8 patients (14.6%). Chemotherapy showed limited benefit regardless of the regimen. The role of chemotherapy may evolve with broader CDK4/6i use in adjuvant treatment.
Radioimmunotheragnosis in Cancer Research
Authors: Garaulet Guillermo , Báez Bárbara Beatriz , Medrano Guillermo , Rivas-Sánchez María , Sánchez-Alonso David , Martinez-Torrecuadrada Jorge L. , Mulero Francisca
Published: August 2024

The combination of immunoPET—where an antibody (Ab) is labeled with an isotope for PET imaging—and radioimmunotherapy (RIT), using the same antibody with a therapeutic isotope, offers significant advantages in cancer management. ImmunoPET allows non-invasive imaging of antigen expression, which aids in patient selection for subsequent radioimmunotherapy. It also facilitates the assessment of tumor response to therapy, allowing for treatment adjustments if necessary. In addition, immunoPET provides critical pharmacokinetic data, including antibody biodistribution and clearance rates, which are essential for dosimetry calculations and treatment protocol optimization. There are still challenges to overcome. Identifying appropriate target antigens that are selectively expressed on cancer cells while minimally expressed on normal tissues remains a major hurdle to reduce off-target toxicity. In addition, it is critical to optimize the pharmacokinetics of radiolabeled antibodies to maximize tumor uptake and minimize normal tissue uptake, particularly in vital organs such as the liver and kidney. This approach offers the potential for targeted and personalized cancer therapy with reduced systemic toxicity by exploiting the specificity of monoclonal antibodies and the cytotoxic effects of radiation. However, further research is needed to address remaining challenges and to optimize these technologies for clinical use.
The Role of Astrocytes in CNS Disorders: Historic and Contemporary Views
Authors: Brenner Michael , Parpura Vladimir
Published: August 2024

This Special Issue of Cells presents a collection of 22 published, peer-reviewed articles on the theme of “Astrocytes in CNS Disorders,” including 9 reviews of the evidence implicating astrocytes in the etiology of specific disorders, and 13 original research papers providing such evidence [...]
High-Temperature Hot Corrosion Resistance of CrAl/NiCoCrAlY/AlSiY Gradient Composite Coating on TiAl Alloy
Authors: Sun Yuanyuan , Miao Qiang , Sun Shijie , Liang Wenping , Ding Zheng , Niu Jiangqi , Jia Feilong , Xu Jianyan , Gao Jiumei
Published: August 2024

TiAl alloys are used in high-temperature components such as the turbine blades of aeroengines because of their excellent properties. However, TiAl alloys are prone to thermal corrosion when in near-ocean service. In order to solve this problem, a hot-corrosion-resistant CrAl/NiCoCrAlY/AlSiY gradient composite coating was prepared on the surface of the TiAl alloy. The phase composition and morphology of the coating were analyzed. Hot corrosion tests of the traditional NiCoCrAlY coating and CrAl/NiCoCrAlY/AlSiY gradient composite coating on a TiAl substrate were performed. The samples were coated with 75%Na2SO4 + 25%NaCl salt film and treated at 950 °C for 100 h, and the corrosion products were analyzed. The results indicate that compared with the TiAl substrate and traditional NiCoCrAlY-coated samples, the composite coating showed better hot corrosion resistance, only slightly cracking, and no corrosion loss occurred. This is mainly because the continuous Al2O3 layer can effectively resist the damage caused by the melting reaction in salt, and the Cr-rich layer can not only slow the mutual diffusion of elements but also generate a good corrosion resistance chromium oxide protective layer under serious corrosion. Moreover, the corrosion mechanism of the TiAl substrate, traditional NiCoCrAlY coating, and experimental composite coating was analyzed in detail.
Nanoindentation Study on the Local Evaluation of Hydrogen-Induced Hardening Performance of Ferrite and Austenite in 2205 Duplex Stainless Steel: Experiment and Finite Element Modeling
Authors: Tao Ping , Zhou Wei , Liu Hongmei , Liu Xuedong
Published: August 2024

In this study, a combined experimental and finite element modeling methodology (FEM) for a nanoindentation study is presented to quantitatively investigate the influence of hydrogen on the mechanical properties of ferrite and austenite in 2205 duplex stainless steel. The experimental results showed that, during hydrogen charging, the nano-hardness of ferrite and austenite gradually increased with time, showing a hydrogen-induced hardening phenomenon. After 3 h of hydrogen charging, the nano-hardness of both ferrite and austenite reached a saturation state, and the values of the nano-hardness of ferrite and austenite increased by 17.5% and 46.1%, respectively. FEM is employed by using a dual-phase microstructure-based model to reproduce nanoindentation load–displacement curves. To minimize the indentation size effect, an analytical correction model considering geometrically necessary dislocations (GNDs) was proposed. By considering GNDs, the errors between numerical predictions and experimental data reduced from about 50% to less than 5%.
The Evolution of Surfaces on Medium-Carbon Steel for Fatigue Life Estimations
Authors: Seensattayawong Phanuphak , Kerscher Eberhard
Published: August 2024

Early in fatigue life, fatigue cracks are often initiated at persistent slip bands (PSBs), which play the main role in surface evolution when the components are subjected to cyclic loading. Therefore, this paper aims to study the behavior of the surface development of medium-carbon steel, specifically 42CrMo4 (SAE 4140). Tests were conducted using tension–compression fatigue testing with stress amplitudes set at 30%, 40%, and 50% of the ultimate tensile strength (UTS); a load ratio of R = −1; and a frequency of f = 10 Hz. The ultimate number of test cycles was 2 × 105. The fatigue test specimens with as-machined surface quality (Ra < 100 nm) were tested on a servo-hydraulic push–pull testing machine, and the tests were interrupted a few times to bring the specimens out for surface measuring with a confocal microscope. The linear roughness values of the arithmetic mean deviation (Ra), maximum height (Rz), maximum profile peak height (Rp), and maximum profile valley depth (Rv) were investigated and further used to determine the roughness evolution during cyclic loading (REC) by analyzing the inclinations of the fitting curves of roughness and number-of-cycles diagrams. REC could then be used to estimate and classify the fatigue lifetime.
Revolutionizing Bladder Health: Artificial-Intelligence-Powered Automatic Measurement of Bladder Volume Using Two-Dimensional Ultrasound
Authors: Alpert Evan Avraham , Gold Daniel David , Kobliner-Friedman Deganit , Wagner Michael , Dadon Ziv
Published: August 2024

Introduction: Measuring elevated post-void residual volume is important for diagnosing urinary outflow tract obstruction and cauda equina syndrome. Catheter placement is exact but painful, invasive, and may cause infection, whereas an ultrasound is accurate, painless, and safe. Aim: The purpose of this single-center study is to evaluate the accuracy of a module for artificial-intelligence (AI)-based fully automated bladder volume (BV) prospective measurement using two-dimensional ultrasound images, as compared with manual measurement by expert sonographers. Methods: Pairs of transverse and longitudinal bladder images were obtained from patients evaluated in an urgent care clinic. The scans were prospectively analyzed by the automated module using the prolate ellipsoid method. The same examinations were manually measured by a blinded expert sonographer. The two methods were compared using the Pearson correlation, kappa coefficients, and the Bland–Altman method. Results: A total of 111 pairs of transverse and longitudinal views were included. A very strong correlation was found between the manual BV measurements and the AI-based module with r = 0.97 [95% CI: 0.96–0.98]. The specificity and sensitivity for the diagnosis of an elevated post-void residual volume using a threshold ≥200 mL were 1.00 and 0.82, respectively. An almost-perfect agreement between manual and automated methods was obtained (kappa = 0.85). Perfect reproducibility was found for both inter- and intra-observer agreements. Conclusion: This AI-based module provides an accurate automated measurement of the BV based on ultrasound images. This novel method demonstrates a very strong correlation with the gold standard, making it a potentially valuable decision-support tool for non-experts in acute settings.
Perivascular Fat: A Novel Risk Factor for Coronary Artery Disease
Authors: Simantiris Spyridon , Pappa Aikaterini , Papastamos Charalampos , Korkonikitas Panagiotis , Antoniades Charalambos , Tsioufis Constantinos , Tousoulis Dimitris
Published: August 2024

Perivascular adipose tissue (PVAT) interacts with the vascular wall and secretes bioactive factors which regulate vascular wall physiology. Vice versa, vascular wall inflammation affects the adjacent PVAT via paracrine signals, which induce cachexia-type morphological changes in perivascular fat. These changes can be quantified in pericoronary adipose tissue (PCAT), as an increase in PCAT attenuation in coronary computed tomography angiography images. Fat attenuation index (FAI), a novel imaging biomarker, measures PCAT attenuation around coronary artery segments and is associated with coronary artery disease presence, progression, and plaque instability. Beyond its diagnostic capacity, PCAT attenuation can also ameliorate cardiac risk stratification, thus representing an innovative prognostic biomarker of cardiovascular disease (CVD). However, technical, biological, and anatomical factors are weakly related to PCAT attenuation and cause variation in its measurement. Thus, to integrate FAI, a research tool, into clinical practice, a medical device has been designed to provide FAI values standardized for these factors. In this review, we discuss the interplay of PVAT with the vascular wall, the diagnostic and prognostic value of PCAT attenuation, and its integration as a CVD risk marker in clinical practice.
COVID-19, Nutrients and Lifestyle Eating Behaviors: A Narrative Review
Authors: Cangelosi Giovanni , Palomares Sara Morales , Pantanetti Paola , De Luca Alessia , Biondini Federico , Nguyen Cuc Thi Thu , Mancin Stefano , Sguanci Marco , Petrelli Fabio
Published: August 2024

Background: COVID-19 infection, caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), quickly emerged as the most significant event of the new millennium. A balanced diet seems to ensure the proper functioning of the immune system and plays a fundamental role in the prevention of viral disease, inflammation, or thrombosis. The principal aim of this secondary study was to investigate the relationship between nutrients, lifestyle eating behaviors, and SARS-CoV-2 infection. Methods: A narrative review was conducted in the PubMed-Medline database, analyzing primary studies. Results: Our review identified 21 relevant studies: 13 focused on vitamins, 1 on omega-3 supplementation, 1 on probiotics, and 6 on lifestyle and dietary behaviors. Vitamin supplementation has shown promise in attenuating COVID-19 symptoms and reducing mortality risk. Specifically, vitamin D has demonstrated efficacy in enhancing immune responses among patients with the disease. While preliminary evidence suggests the potential benefits of omega-3 and probiotic supplementation in improving health outcomes for COVID-19 outpatients, further research is needed to solidify these findings. Conclusions: The lifestyle changes imposed by lockdown measures have adversely affected psychological well-being and exacerbated health issues associated with reduced physical activity and poor dietary habits.
Efficiency Analysis of Human Capital Investments at Micro and Large-Sized Enterprises in the Manufacturing Sector Using Data Envelopment Analysis
Authors: Carmona-Benítez Rafael Bernardo , Rosales-Córdova Aldebarán
Published: August 2024

Micro and large-sized enterprises are important elements to enhance the economic growth of any country, and even more so for developing countries such as Mexico. These enterprises highly contribute to job generation, competitiveness, and gross domestic product, factors that are important for the developing of a nation. The aim of this paper is to study the impact of human capital investments in the efficiency of the 21 economic activity subsectors for micro and large-sized enterprises in the Mexican manufacturing industry between 2009–2021. The database come from Mexico Annual Manufacturing Industry Survey. Four Data Envelopment Analysis models are developed to study the relationship between annual average working days, annual average wages, and annual average investment in training with average sales per year. Data indicate that, most of the micro-sized enterprises of the Mexican manufacturing sector do not invest in human capital training, contrary to their large-sized enterprises. The results show that investing in human capital training increase sales and wages in micro-sized enterprises of the Mexican manufacturing industry, but it is not evident in large-size enterprises of the Mexican manufacturing industry. The calculation of the economic activity subsectors efficiencies using the developed Data Envelopment Analysis models indicate that all the economic activity subsectors with scale efficiency equal to one optimally invest, and the average amount of investments in human capital training needed to increase the global and pure technical efficiencies of the others are calculated with the developed Data Envelopment Analysis models. In the three main economic activity subsectors of the Mexican manufacturing industry, a significant increase—in 83.33% of cases—in wages and salaries is seen in both micro and large-sized enterprises. Particularly, the results indicate that the Chemical industry economic activity subsectors show the highest efficiency in both micro and large-sized enterprises when the human capital training variable is present. This paper demonstrates the importance of investing in human capital to enhance the efficiency of micro and large-sized enterprises.
The Sustainability of Hospital Care in The Netherlands from a Labour Market Perspective: A Time Series Analysis of the Baumol Effect between 2000 and 2021
Authors: Blank Jos , Van Heezik Alex
Published: August 2024

In this paper, we focus on the sustainability of hospital care in The Netherlands from a labour market perspective. The continued increase in the demand for hospital care and the subsequent growth in the demand for hospital labour can cause permanent shortages of medical and nursing personnel, but can also affect labour conditions in other sectors of the economy. We analyse how the hospital sector put pressure on the labour market between 2000 and 2021 by applying a regression analysis to the share of hospital labour costs in the total economy on hospital production, wages and technical change. From the raw data, we observe a modest growth in hospital production over the whole research period. Strikingly, hospital wages underwent explosive growth compared to wages in other sectors of the economy in the same timeframe, seemingly disproving the hypothesis that hospital wages follow wages in other sectors. A worrisome result comes from the estimated Baumol parameter, which indicates that productivity growth in the hospital sector lagged significantly behind other sectors. This implies that the hospital sector, aside from developments in hospital care demand and hospital wages, is also causing a serious problem because of a low or negative productivity change. At this rate, we will stumble into the situation that the pressure of hospitals on the Dutch labour force will be doubled in the next decades. To avoid this situation, severe measures need to be taken, such as implementing incentives and sanctions to improve productivity.
Public R&D and Growth: A dynamic Panel Vector-Error-Correction Model Analysis for 14 OECD Countries
Authors: Ziesemer Thomas H. W.
Published: August 2024

This paper addresses the controversial issue of the direct and indirect effects of public R&D on growth. We look at six variables of R&D-driven growth jointly for 14 OECD countries using methods of dynamic systems for panel data analysis: GDP, technical change, domestic and foreign businesses and public R&D. Cointegration tests suggest four long-run relations for the six variables. We estimate these relations using group mean versions of fully modified and dynamic OLS. Domestic public R&D has positive long-run regression coefficients for direct effects on productivity and indirect ones via private R&D. Here, we build a panel vector-error-correction model with these long-term relations. Shocks to domestic public R&D enhance domestic private R&D, technical change and the GDP. Permanent changes in foreign public and private R&D have positive growth effects, which are transitional for foreign public R&D.
Unraveling the Nexus between Road Transport Infrastructures and Economic Growth: Empirical Insights from Nepal’s Case
Authors: Sharma Keshab Kumar , Bhandary Netra Prakash , Subedi Mandip , Pradhananga Rojee
Published: August 2024

Sustained efforts and investments in different sectors are essential for the overall development of a region. Various studies around the globe underscore the importance of investment in road transport infrastructure in many developing countries to achieve their development targets. The relationship between infrastructure investment and economic growth is often found to be inconsistent. This ambiguity leads to a lack of consensus on an appropriate scale of investment required among the policy makers. So, it is often necessary to depend on empirical evidence by developing causality direction, which significantly contributes to policy implications in developing countries. The objective of this analytical study is developing a relationship between the road transport infrastructure and economic growth of Nepal. For this, relevant data from 1998 to 2022 were used to perform a unit root test and determine the order of integration, followed by cointegration analysis to determine the long-run relationship between the variables. In addition, the vector error correction model (VECM) was employed to find the direction of causality. The findings indicate unidirectional long-run causality from gross capital formation, exports of goods and services, expenditure on road transport infrastructures, and road length to the GDP of Nepal. Furthermore, the expenditure on road transport infrastructures is observed to have a short-run impact on economic growth. This study recommends that a suitable transportation policy should be implemented to boost investment on road transport infrastructures to achieve sustainable economic growth in Nepal-like developing nations.
United in Green: A Bibliometric Exploration of Renewable Energy Communities
Authors: Domenteanu Adrian , Delcea Camelia , Florescu Margareta-Stela , Gherai Dana Simona , Bugnar Nicoleta , Cotfas Liviu-Adrian
Published: August 2024

In recent years, the domain of renewable energy communities has experienced dynamic growth, spurred by European Union (EU) legislation that became law for all 27 Member States in June 2021. This legislative framework intensified research efforts aimed at discovering new methods for sustainable energy sources through the development of individual and collective energy communities. Each EU country has implemented distinct frameworks for renewable energy communities, leading to varied legislative approaches. This has spurred exponential investment, facilitating the deployment of photovoltaic and battery energy storage systems, offering significant economic and environmental benefits to community members. Against this backdrop, the purpose of this analysis is to investigate academic publications related to renewable energy communities. Using a dataset extracted from the ISI Web of Science database, this study employs a bibliometric approach to identify the main authors, affiliations, and journals and analyze collaboration networks, as well as discern key topics and the countries involved. The analysis reveals an annual growth rate of 42.82%. Through thematic maps, WordClouds, three-field plots, and a review of the top 10 globally cited documents, this study provides a comprehensive perspective on the evolving domain of renewable energy communities.
Network Attack Classification with a Shallow Neural Network for Internet and Internet of Things (IoT) Traffic
Authors: Ehmer Jörg , Savaria Yvon , Granado Bertrand , David Jean-Pierre , Denoulet Julien
Published: August 2024

In recent years, there has been a tremendous increase in the use of connected devices as part of the so-called Internet of Things (IoT), both in private spaces and the industry. Integrated distributed systems have shown many benefits compared to isolated devices. However, exposing industrial infrastructure to the global Internet also generates security challenges that need to be addressed to benefit from tighter systems integration and reduced reaction times. Machine learning algorithms have demonstrated their capacity to detect sophisticated cyber attack patterns. However, they often consume significant amounts of memory, computing resources, and scarce energy. Furthermore, their training relies on the availability of datasets that accurately represent real-world data traffic subject to cyber attacks. Network attacks are relatively rare events, as is reflected in the distribution of typical training datasets. Such imbalanced datasets can bias the training of a neural network and prevent it from successfully detecting underrepresented attack samples, generally known as the problem of imbalanced learning. This paper presents a shallow neural network comprising only 110 ReLU-activated artificial neurons capable of detecting representative attacks observed on a communication network. To enable the training of such small neural networks, we propose an improved attack-sharing loss function to cope with imbalanced learning. We demonstrate that our proposed solution can detect network attacks with an F1 score above 99% for various attacks found in current intrusion detection system datasets, focusing on IoT device communication. We further show that our solution can reduce the false negative detection rate of our proposed shallow network and thus further improve network security while enabling processing at line rate in low-complexity network intrusion systems.
Trajectory Control of Quadrotors via Spiking Neural Networks
Authors: Oniz Yesim
Published: August 2024

In this study, a novel control scheme based on spiking neural networks (SNNs) has been proposed to accomplish the trajectory tracking of quadrotor unmanned aerial vehicles (UAVs). The update rules for the network parameters have been derived using the Lyapunov stability theorem. Three different trajectories have been utilized in the simulated and experimental studies to verify the efficacy of the proposed control scheme. The acquired results have been compared with the responses obtained for proportional–integral–derivative (PID) and traditional neural network controllers. Simulated and experimental studies demonstrate that the proposed SNN-based controller is capable of providing better tracking accuracy and robust system response in the presence of disturbing factors.
A Linear Regression-Based Methodology to Improve the Stability of a Low-Cost GPS Receiver Using the Precision Timing Signals from an Atomic Clock
Authors: Manandhar Shilpa , Saravanan Sneha , Meng Yu Song , Tan Yung Chuen
Published: August 2024

The global positioning system (GPS) is widely known for its applications in navigation, timing, and positioning. However, its accuracy can be greatly impacted by the performance of its receiver clocks, especially for a low-cost receiver equipped with lower-grade clocks like crystal oscillators. The objective of this study is to develop a model to improve the stability of a low-cost receiver. To achieve this, a machine-learning-based linear regression algorithm is proposed to predict the differences of the low-cost GPS receiver compared to the precision timing source. Experiments were conducted using low-cost receivers like Ublox and expensive receivers like Septentrio. The model was implemented and the clocks of low-cost receivers were steered. The outcomes demonstrate a notable enhancement in the stability of low-cost receivers after the corrections were applied. This improvement underscores the efficacy of the proposed model in enhancing the performance of low-cost GPS receivers. Consequently, these low-cost receivers can be cost-effectively utilized for various purposes, particularly in applications requiring the deployment of numerous GPS receivers to achieve extensive spatial coverage.
Improved African Vulture Optimization Algorithm Based on Random Opposition-Based Learning Strategy
Authors: Kuang Xingsheng , Hou Junfa , Liu Xiaotong , Lin Chengming , Wang Zhu , Wang Tianlei
Published: August 2024

This paper proposes an improved African vulture optimization algorithm (IROAVOA), which integrates the random opposition-based learning strategy and disturbance factor to solve problems such as the relatively weak global search capability and the poor ability to balance exploration and exploitation stages. IROAVOA is divided into two parts. Firstly, the random opposition-based learning strategy is introduced in the population initialization stage to improve the diversity of the population, enabling the algorithm to more comprehensively explore the potential solution space and improve the convergence speed of the algorithm. Secondly, the disturbance factor is introduced at the exploration stage to increase the randomness of the algorithm, effectively avoiding falling into the local optimal solution and allowing a better balance of the exploration and exploitation stages. To verify the effectiveness of the proposed algorithm, comprehensive testing was conducted using the 23 benchmark test functions, the CEC2019 test suite, and two engineering optimization problems. The algorithm was compared with seven state-of-the-art metaheuristic algorithms in benchmark test experiments and compared with five algorithms in engineering optimization experiments. The experimental results indicate that IROAVOA achieved better mean and optimal values in all test functions and achieved significant improvement in convergence speed. It can also solve engineering optimization problems better than the other five algorithms.
Outlier Detection in Streaming Data for Telecommunications and Industrial Applications: A Survey
Authors: Mfondoum Roland N. , Ivanov Antoni , Koleva Pavlina , Poulkov Vladimir , Manolova Agata
Published: August 2024

Streaming data are present all around us. From traditional radio systems streaming audio to today’s connected end-user devices constantly sending information or accessing services, data are flowing constantly between nodes across various networks. The demand for appropriate outlier detection (OD) methods in the fields of fault detection, special events detection, and malicious activities detection and prevention is not only persistent over time but increasing, especially with the recent developments in Telecommunication systems such as Fifth Generation (5G) networks facilitating the expansion of the Internet of Things (IoT). The process of selecting a computationally efficient OD method, adapted for a specific field and accounting for the existence of empirical data, or lack thereof, is non-trivial. This paper presents a thorough survey of OD methods, categorized by the applications they are implemented in, the basic assumptions that they use according to the characteristics of the streaming data, and a summary of the emerging challenges, such as the evolving structure and nature of the data and their dimensionality and temporality. A categorization of commonly used datasets in the context of streaming data is produced to aid data source identification for researchers in this field. Based on this, guidelines for OD method selection are defined, which consider flexibility and sample size requirements and facilitate the design of such algorithms in Telecommunications and other industries.
Dynamic Edge-Based High-Dimensional Data Aggregation with Differential Privacy
Authors: Chen Qian , Ni Zhiwei , Zhu Xuhui , Lyu Moli , Liu Wentao , Xia Pingfan
Published: August 2024

Edge computing enables efficient data aggregation for services like data sharing and analysis in distributed IoT applications. However, uploading dynamic high-dimensional data to an edge server for efficient aggregation is challenging. Additionally, there is the significant risk of privacy leakage associated with direct such data uploading. Therefore, we propose an edge-based differential privacy data aggregation method leveraging progressive UMAP with a dynamic time window based on LSTM (EDP-PUDL). Firstly, a model of the dynamic time window based on a long short-term memory (LSTM) network was developed to divide dynamic data. Then, progressive uniform manifold approximation and projection (UMAP) with differential privacy was performed to reduce the dimension of the window data while preserving privacy. The privacy budget was determined by the data volume and the attribute’s Shapley value, adding DP noise. Finally, the privacy analysis and experimental comparisons demonstrated that EDP-PUDL ensures user privacy while achieving superior aggregation efficiency and availability compared to other algorithms used for dynamic high-dimensional data aggregation.
Increased Soil Moisture in the Wet Season Alleviates the Negative Effects of Nitrogen Deposition on Soil Microbial Communities in Subtropical Evergreen Broad-Leaved Forest
Authors: Chen Wen , Hou Zheng , Zhang Donghui , Chen Leixi , Wang Keqin , Song Yali
Published: August 2024

The rapid increase in reactive nitrogen (N) released into the environment by human activities has notably altered the structure and composition of forest soil microbial communities (SMCs), profoundly impacting the N cycle in terrestrial ecosystems. However, the response of soil microorganisms to nitrogen addition in different seasons is not clear. This study delved into how SMCs in a subtropical region of central Yunnan, China, specifically in an evergreen broad-leaved forest (EBLF), respond to N deposition during both the dry and wet seasons. Through high-throughput sequencing, we assessed the composition and structure of SMCs under varying N addition treatments across seasons, examining their interplay with soil chemical properties, enzyme activities, and community responses. The findings revealed significant outcomes following four years of N addition in the subtropical EBLF: (1) Significant changes were observed due to the interaction between N addition and seasonal changes. Soil pH significantly decreased, indicating increased soil acidification, particularly in the dry season. Increased moisture in the wet season mitigated soil acidification. (2) In the dry season, N addition led to a decrease in microbial richness and diversity. In the wet season, N addition increased microbial richness and diversity, alleviating the downward trend observed in the dry season. (3) N addition significantly impacted the composition of soil bacterial and fungal communities. Dominant fungal genera in the wet season were particularly sensitive to N addition. (4) Seasonal changes and N addition altered soil microbial community structures. Soil chemical properties and enzyme activities significantly influenced the microbial community structure. However, due to differences in soil moisture, the key environmental factors that regulate microbial communities have changed significantly during the dry and wet seasons. This study serves as a foundation for understanding how N deposition impacts SMCs in EBLF ecosystems in subtropical regions, offering valuable insights for the scientific management of forest ecological resources amidst global change trends.