bluuebunny/crossref_metadata_2025 huggingface dataset:
Drought Sensitivity and Vulnerability of Rubber Plantation GPP—Insights from Flux Site-Based Simulation
Authors: Zhang Runqing , E Xiaoyu , Ma Zhencheng , An Yinghe , Bao Qinggele , Wu Zhixiang , Wu Lan , Sun Zhongyi
Published: May 2024

Drought, an intricate natural phenomenon globally, significantly influences the gross primary productivity (GPP) and carbon sink potential of tropical forests. Present research on the drought response primarily focuses on natural forests, such as the Amazon rainforest, with relatively limited studies on tropical plantations. Therefore, for a comprehensive understanding of global climate change, accurately evaluating and analyzing the sensitivity and vulnerability of rubber plantation GPP to various drought characteristics is crucial. The Standardized Precipitation Evapotranspiration Index (SPEI) was used in this research to quantify drought intensity. The Spatially Explicit Individual Based Dynamic Global Vegetation Model (SEIB-DGVM) was localized based on observation data from the Hainan Danzhou Tropical Agro-ecosystem National Observation and Research Station. Subsequently, the calibrated model was utilized to simulate the dynamic process of rubber plantation GPP under multi-gradient drought scenarios (2 extreme boundaries × 3 drought initiation seasons × 4 drought intensities × 12 drought durations × 12 SPEI time scales). The results show that the sensitivity and vulnerability of rubber plantation GPP exhibit significant differences under drought scenarios in different initiation seasons; GPP exhibits higher sensitivity to extreme, long-duration flash droughts in the early rainy season. Regarding vulnerability, the impact of extreme, long-duration flash droughts on GPP is most pronounced. This research lays the foundation for estimating the impact of droughts on the GPP of rubber plantations under future climate change scenarios, providing a scientific basis for enhancing regional ecological restoration and protection.
Analysis of the Spatiotemporal Evolution of Carbon Budget and Carbon Compensation Zoning in the Core Area of the Yangtze River Delta Urban Agglomeration
Authors: Tang Yuxin , Wang Ran , Ci Hui , Wei Jinyuan , Yang Hui , Teng Jiakun , Yan Zhaojin
Published: May 2024

As a world-class urban agglomeration, the Yangtze River Delta urban agglomeration is significant for China’s carbon neutrality strategy when calculating the carbon budget and dividing carbon compensation zones. This paper focused on 129 county-level cities in the core area of the Yangtze River Delta urban agglomeration, calculating the carbon budget for 2000–2020 using night-time light data and analyzing the evolution of spatiotemporal patterns. On this basis, a carbon compensation zoning model was constructed using the K-means algorithm; 129 cities were divided into different carbon compensation zones after combining this model with the main functional zones. The results showed that (1) the carbon emissions increased, with overall stabilization after 2012. The distribution of high carbon emission areas evolved from a “core-periphery” pattern into a “Z-shaped” pattern. Ecological carbon sinks showed a continuous decline, with the southern sinks performing better than the northern ones. (2) The carbon budget of the study area showed a deficit, gradually widening and exhibiting an unbalanced spatial distribution characterized by a “high in the south and low in the north” pattern. (3) Eleven types of carbon compensation zones were designated after overlaying the main functional zones. Low-carbon development suggestions were proposed for each zone type.
Evaluating the Cost-Effectiveness of Green Infrastructure for Mitigating Diffuse Agricultural Contaminant Losses
Authors: Matthews Yvonne S. , Holland Paula , Matheson Fleur E. , Craggs Rupert J. , Tanner Chris C.
Published: May 2024

New Zealand’s agricultural sector faces the challenge of maintaining productivity while minimizing impacts on freshwaters. This study evaluates the cost-effectiveness of various green infrastructure systems designed to reduce diffuse agricultural sediment and nutrient loads. Utilizing a quantitative economic and contaminant reduction modeling approach, we analyze the impacts of five interceptive mitigation systems: riparian grass filter strips, constructed wetlands, woodchip bioreactors, filamentous algal nutrient scrubbers, and detainment bunds. Our approach incorporates Monte Carlo simulations to address uncertainties in costs and performance, integrating hydrological flow paths and contaminant transport dynamics. Mitigation systems are assessed individually and in combination, using a greedy cyclical coordinate descent algorithm to find the optimal combination and scale of a system for a particular landscape. Applying the model to a typical flat pastoral dairy farming landscape, no single system can effectively address all contaminants. However, strategic combinations can align with specific freshwater management goals. In our illustrative catchment, the mean cost to remove the full anthropogenic load is NZD 1195/ha for total nitrogen, NZD 168 for total phosphorus, and NZD 134 for suspended solids, but results will vary considerably for other landscapes. This study underscores the importance of tailored deployment of green infrastructure to enhance water quality and support sustainable agricultural practices.
Mood Alternation with Adverbs of Uncertainty in Galician: A Multifactorial Analysis
Authors: Míguez Vítor
Published: May 2024

This study investigates the factors that significantly constrain mood selection in Galician within uncertainty adverb constructions, applying a logistic regression model. This analysis identified several significant factors affecting the choice between the indicative and subjunctive moods, including the temporal context of the clause, the preceding adverb, and the interaction of fictional and nonfictional registers with the verb type of the predicate and the gender of the speaker/writer. Time reference and the preceding adverb emerged as primary factors conditioning mood choice, with present and future time frames and adverbs encoding weaker epistemic values significantly predisposing toward the subjunctive mood. This study also highlighted the influence of the gender of the speaker/writer, demonstrating the preference of women toward the indicative mood in fictional texts. Verb type and register interact in complex ways that only partially align with previous findings. The results of the present study contribute to a deeper understanding of syntactic variation in Galician, adding to a growing body of quantitative research on Galician grammar.
Galician Perfective Periphrases among Complex Predicates: Degrees of Grammaticalization and the Possibility of a Perfect Tense
Authors: Jardón Natalia
Published: May 2024

The so-called perífrasis perfectivas in Galician present the action as concluded or realized. This particular aspectual feature constitutes the common ground for an otherwise heterogeneous set of constructions, ranging from rematar de ‘finish’+ infinitive (e.g., rematóu de beber ‘(s/he) finished drinking’) to ter ‘have’ + participle (e.g., teñen ido ‘(they) have gone (Rep.)’). This work provides a critical assessment of their syntactic and semantic properties in cases where the participle may not show agreement. This is the case for periphrases built on three auxiliaries: ter, levar, and dar, of which ter + participle stands out as the most grammaticalized one. The case of ter is further investigated in relation to European Portuguese (EP) and Brazilian Portuguese (BP), where ter + participle is considered a fully-fledged perfect tense. Additionally, the use of these periphrases in areas where Spanish is also present is evaluated from a contact perspective.
Correction: Foley et al. Emerging Technologies within Spine Surgery. Life 2023, 13, 2028
Authors: Foley David , Hardacker Pierce , McCarthy Michael
Published: May 2024

In the original publication [...]
The Impact of Metabolic Health and Obesity on Liver Transplant Candidates and Recipients
Authors: Vogel Alexander S. , Roediger Rebecca , von Ahrens Dagny , Fortune Brett E. , Schwartz Jonathan M. , Frager Shalom , Chacko Kristina R. , Tow Clara Y.
Published: May 2024

Poor metabolic health and obesity have significant impacts on the outcomes of patients suffering from chronic liver disease, particularly those with metabolic dysfunction-associated steatotic liver disease. Patients with such comorbidities who require liver transplant evaluation for advancing liver disease or liver failure require special consideration due to increased risk of cardiovascular disease, renal dysfunction, sarcopenic obesity, and cancer. Those who have had a history of prior bariatric surgery pose specific anatomical constraints and may also be at increased risk of alcohol use disorder. Pre-operative risk assessment as well as strict control of metabolic risk factors are essential to reduce intra-operative and post-liver transplant complications. As immunosuppressive therapy exacerbates metabolic dysfunction and risk for cancer, post-liver transplant care must focus on balancing the need to prevent rejection and the impact of progressive metabolic dysfunction in this unique, but growing, patient population.
Multiscale Texture Features to Enhance Lubricant Film Thickness for Prosthetic Hip Implant Bearing Surfaces
Authors: Tewelde Fitsum Berhe , Allen Quentin , Zhou Tianfeng
Published: May 2024

The longevity of prosthetic hip implants is significantly influenced by wear. Surface textures of various length scales can reduce the friction coefficient and wear of lubricated bearing surfaces. The optimization of multiscale texture parameters, aimed at maximizing lubricant film thickness, was achieved through hydrodynamic lubrication simulations that solve the Reynolds equation with a mass-conserving cavitation model under various operating conditions. The outcomes indicate that adding “interstitial” texture features to a pattern of microscale texture features can further increase the lubricant film thickness. Additionally, the lubricant film thickness increases as the interstitial texture feature aspect ratio and texture density decrease. Pin-on-disc experiments align with simulation findings, demonstrating that multiscale texturing with ultra-fast laser ablation on Ti6Al4V discs significantly improves wettability and reduces the friction coefficient of ultra-high molecular weight polyethylene pins when compared to untextured and microscale textured surfaces. The multiscale surface texturing also changes the evident wear mechanisms on the pins, reducing the incidence of abrasive scratches and adhesive wear compared to both untextured and just microscale textured surfaces.
Ethyl 4-((11-(Hexylamino)-11-oxoundecyl)oxy)benzoate
Authors: Montes-Patiño Jorge Jesús , Díaz-Zavala Nancy Patricia , Zapién-Castillo Samuel , Mésini Philippe J. , Lozano-Navarro Jessica Ismalé , Salas-Ordaz Lorena Margarita , Aguirre-Lam Marco Antonio
Published: May 2024

Ethyl 4-((11-(hexylamino)-11-oxoundecyl)oxy)benzoate was synthesized using 11-bromoundecanoic acid hexylamide and commercially available ethyl 4-hydroxybenzoate through a Williamson etherification synthesis. The structural characterization was performed using UV-Vis, FT-IR, 1H-NMR, 13C-NMR, and HRMS.
Effect of Partial Substitution of Zr for Ti Solvent on Young’s Modulus, Strength, and Biocompatibility in Beta Ti Alloy
Authors: Nomura Yusuke , Okada Mio , Manaka Tomoyo , Tsuchiya Taiki , Iwasaki Mami , Matsuda Kenji , Ishimoto Takuya
Published: May 2024

In orthopedics and dentistry, there is an urgent need to obtain low-stiffness implants that suppress the stress shielding caused by the use of metallic implants. In this study, we aimed to fabricate alloys that can reduce the stiffness by increasing the strength while maintaining a low Young’s modulus based on the metastable β-Ti alloy. We designed alloys in which Ti was partially replaced by Zr based on the ISO-approved metastable β-Ti alloy Ti-15Mo-5Zr-3Al. All alloys prepared by arc melting and subsequent solution treatment showed a single β-phase solid solution, with no formation of the ω-phase. The alloys exhibited a low Young’s modulus equivalent to that of Ti-15Mo-5Zr-3Al and a high strength superior to that of Ti-15Mo-5Zr-3Al and Ti-6Al-4V. This strengthening was presumed to be due to solid-solution strengthening. The biocompatibility of the alloys was as good as or better than that of Ti-6Al-4V. These alloys have potential as metallic materials suitable for biomedical applications.
Effects of Seawater on Mechanical Performance of Composite Sandwich Structures: A Machine Learning Framework
Authors: Osa-uwagboe Norman , Udu Amadi Gabriel , Silberschmidt Vadim V. , Baxevanakis Konstantinos P. , Demirci Emrah
Published: May 2024

Sandwich structures made with fibre-reinforced plastics are commonly used in maritime vessels thanks to their high strength-to-weight ratios, corrosion resistance, and buoyancy. Understanding their mechanical performance after moisture uptake and the implications of moisture uptake for their structural integrity and safety within out-of-plane loading regimes is vital for material optimisation. The use of modern methods such as acoustic emission (AE) and machine learning (ML) could provide effective techniques for the assessment of mechanical behaviour and structural health monitoring. In this study, the AE features obtained from quasi-static indentation tests on sandwich structures made from E-glass fibre face sheets with polyvinyl chloride foam cores were employed. Time- and frequency-domain features were then used to capture the relevant information and patterns within the AE data. A k-means++ algorithm was utilized for clustering analysis, providing insights into the principal damage modes of the studied structures. Three ensemble learning algorithms were employed to develop a damage-prediction model for samples exposed and unexposed to seawater and were loaded with indenters of different geometries. The developed models effectively identified all damage modes for the various indenter geometries under different loading conditions with accuracy scores between 86.4 and 95.9%. This illustrates the significant potential of ML for the prediction of damage evolution in composite structures for marine applications.
Non-Equilibrium Long-Wave Infrared HgCdTe Photodiodes: How the Exclusion and Extraction Junctions Work Separately
Authors: Kopytko Małgorzata , Majkowycz Kinga , Sobieski Jan , Manyk Tetiana , Gawron Waldemar
Published: May 2024

The cooling requirement for long-wave infrared detectors still creates significant limitations to their functionality. The phenomenon of minority-carrier exclusion and extraction in narrow-gap semiconductors has been intensively studied for over three decades and used to increase the operating temperatures of devices. Decreasing free carrier concentrations below equilibrium values by a stationary non-equilibrium depletion of the device absorber leads to a suppression of Auger generation. In this paper, we focus on analyzing exclusion and extraction effects separately, based on experimental and theoretical results for a HgCdTe photodiode. To carry out an experiment, the n+-P+-π-N+ heterostructure was grown by metal organic chemical vapor deposition on CdTe-buffered GaAs substrate. In order to separate the extraction and exclusive junctions, three different devices were evaluated: (1) a detector etched through the entire n+-P+-π-N+ heterostructure, (2) a detector made of the P+-π photoconductive junction and (3) a detector made of the π-N+ photodiode junction. For each device, the dark current density–voltage characteristics were measured at a high-temperature range, from 195 K to 300 K. Next, the carrier concentration distribution across the entire heterostructure and individual junctions was calculated using the APSYS simulation program. It was shown that when the n+-P+-π-N+ photodiode is reverse biased, the electron concentration in the π absorber drops below its thermal equilibrium value, due to the exclusion effect at the P+-π junction and the extraction effect at the π-N+ junction. To maintain the charge neutrality, the hole concentration is also reduced below the equilibrium value and reaches the absorber doping level (NA), leading to the Auger generation rate’s reduction by a factor of 2ni/NA, where ni is the intrinsic carrier concentration. Our experiment conducted for three separate detectors showed that the exclusion P+-π photoconductive junction has the most significant effect on the Auger suppression—the majority of the hole concentration drops to the doping level not only at the P+-π interface but also deep inside the π absorber.
Optimization of Chitosan Synthesis Process Parameters to Enhance PES/Chitosan Membrane Performance for the Treatment of Acid Mine Drainage (AMD)
Authors: Rasifudi Ndiwanga F. , Mekuto Lukhanyo , Mathaba Machodi J.
Published: May 2024

Acid mine drainage (AMD) is an environmental issue linked with mining activities, causing the release of toxic water from mining areas. Polyethersulphone (PES) membranes are explored for AMD treatment, but their limited hydrophilicity hinders their performance. Chitosan enhances hydrophilicity, addressing this issue. However, the effectiveness depends on chitosan’s degree of deacetylation (DD), determined during the deacetylation process for chitosan production. This study optimized the chitin deacetylation temperature, alkaline (NaOH) concentration, and reaction time, yielding the highest chitosan degree of deacetylation (DD) for PES/chitosan membrane applications. Prior research has shown that high DD chitosan enhances membrane antifouling and hydrophilicity, increasing contaminant rejection and permeate flux. Evaluation of the best deacetylation conditions in terms of temperature (80, 100, 120 °C), NaOH concentration (20, 40, 60 wt.%), and time (2, 4, 6 h) was performed. The highest chitosan DD obtained was 87.11% at 80 °C, 40 wt. %NaOH at 4 h of chitin deacetylation. The PES/0.75 chitosan membrane (87.11%DD) showed an increase in surface hydrophilicity (63.62° contact angle) as compared to the pristine PES membrane (72.83° contact angle). This was an indicated improvement in membrane performance. Thus, presumably leading to high contaminant rejection and permeate flux in the AMD treatment context, postulate to literature.
Model-Based Sensitivity Analysis of the Temperature in Laser Powder Bed Fusion
Authors: Yang Zhihao , Zhang Shiting , Ji Xia , Liang Steven Y.
Published: May 2024

To quantitatively evaluate the effect of the process parameters and the material properties on the temperature in laser powder bed fusion (LPBF), this paper proposed a sensitivity analysis of the temperature based on the validated prediction model. First, three different heat source modes—point heat source, Gaussian surface heat source, and Gaussian body heat source—were introduced. Then, a case study of Ti6Al4V is conducted to determine the suitable range of heat source density for the three different heat source models. Based on this, the effects of laser processing parameters and material thermophysical parameters on the temperature field and molten pool size are quantitatively discussed based on the Gaussian surface heat source. The results indicate that the Gaussian surface heat source and the Gaussian body heat source offer higher prediction accuracy for molten pool width compared to the point heat source under similar processing parameters. When the laser energy density is between 40 and 70 J/mm3, the prediction accuracy of the Gaussian surface heat source and the body heat source is similar, and the average prediction errors are 4.427% and 2.613%, respectively. When the laser energy density is between 70 and 90 J/mm3, the prediction accuracy of the Gaussian body heat source is superior to that of the Gaussian surface heat source. Among the influencing factors, laser power exerts the greatest influence on the temperature field and molten pool size, followed by scanning speed. In particular, laser power and scan speed contribute 38.9% and 23.5% to the width of the molten pool, 39.1% and 19.6% to the depth of the molten pool, and 38.9% and 21.5% to the maximum temperature, respectively.
Utilizing Industrial Sludge Ash in Brick Manufacturing and Quality Improvement
Authors: Huang Yu-Ming , Chen Chao-Shi , Chen Chen-Chung , Lai Jian-Wen
Published: May 2024

This research demonstrates changes in the behaviors and characteristics of sintered bricks while using industrial sludge ash (ISA) and waste glass (WG) as a replacement for clay in the brick manufacturing procedure. Owing to the limited amount of available land in Taiwan, it is becoming increasingly difficult to locate suitable sites for sanitary landfills, which is a common final disposal method for ash that is produced during thermal treatment in sludge factories. To meet the urgent need for land, the final waste disposal must convert this waste into a new resource. This research investigated the feasibility of using general industrial sludge ash waste, due to its abundance and high potential as a raw material in producing bricks. The result of this study shows that the bricks made from ISA and WG under a certain mixture proportion (ISA50%/WG40%/Clay10%) had excellent industrial potentials, such as compressive strength and water absorption rate. However, owing to the wide variety of components from different sources of ISA, the mixture proportion might vary accordingly. This study also analyzed the incineration index, proportion design, and process improvement, as well as investigating the possibility of increasing the total use of sludge ash as a resource. This study shows the potentials of utilizing wastes as raw materials in industrial manufacturing procedures. Therefore, more wastes can be tested and turned into resources in the future.
Influence of Welding Degree on the Meso-Mechanical Anisotropy, Fracture Propagation, and Fracture Surface Roughness of Welded Tuff
Authors: Huang Beixiu , Li Lihui , Li Chenglong , Qiao Sijia , Ranjith Pathegama Gamage
Published: May 2024

Welded tuffs have a wide range of welding degrees and show significant variability in mechanical behavior. However, the detailed influence of welding degree on the meso-mechanical behavior of welded tuffs remains unclear. Based on petrographic and pore-structure analysis, we conducted a series of meso-mechanical experiments on weakly to strongly welded tuffs by utilizing a mesoscale real-time loading-observation-acquisition system. The results indicated that the strongly and weakly welded tuffs showed a small range in mineralogical composition and porosity, while the meso-mechanical behavior exhibited significant variability. Strongly welded tuffs showed lower uniaxial compression strength, weaker mechanical anisotropy, and smaller fracture surface roughness. In contrast, weakly welded tuffs exhibited higher uniaxial compression strength, stronger mechanical anisotropy, and rougher fracture surface roughness. Welded tuffs with strong packing and welding of glass shards tended to have fractures propagating along the maximum principal direction, while those with weak packing and welding of glass shards may have had failure along the alignment of glass shards. The influence of welding degree on the meso-mechanical behavior of welded tuffs probably originates from their diagenesis environments, mainly depending on the combined effect of the pyroclastic properties and pseudo-rhyolitic structure. The findings reveal the meso-mechanical differences of welded tuffs and shed light on improving tuffs for stable and durable construction.
Solubility of Hydrogen in a WMoTaNbV High-Entropy Alloy
Authors: Liski Anna , Vuoriheimo Tomi , Byggmästar Jesper , Mizohata Kenichiro , Heinola Kalle , Ahlgren Tommy , Tseng Ko-Kai , Shen Ting-En , Tsai Che-Wei , Yeh Jien-Wei , Nordlund Kai , Djurabekova Flyura , Tuomisto Filip
Published: May 2024

The WMoTaNbV alloy has shown promise for applications as a solid state hydrogen storage material. It absorbs significant quantities of H directly from the atmosphere, trapping it with high energy. In this work, the dynamics of the absorption of hydrogen isotopes are studied by determining the activation energy for the solubility and the solution enthalpy of H in the WMoTaNbV alloy. The activation energy was studied by heating samples in a H atmosphere at temperatures ranging from 20 °C to 400 °C and comparing the amounts of absorbed H. The solution activation energy EA of H was determined to be EA=0.22±0.02 eV (21.2 ± 1.9 kJ/mol). The performed density functional theory calculations revealed that the neighbouring host atoms strongly influenced the solution enthalpy, leading to a range of theoretical values from −0.40 eV to 0.29 eV (−38.6 kJ/mol to 28.0 kJ/mol).
Optimizing the Utilization of Steel Slag in Cement-Stabilized Base Layers: Insights from Freeze–Thaw and Fatigue Testing
Authors: Song Peng-Cheng , Chen Guo-Xin , Chen Ying-Jie
Published: May 2024

This paper presents a study on the mechanical properties of cement-stabilized steel-slag-based materials under freeze–thaw cycles for a highway project in Xinjiang. Using 3D scanning technology the specimen model conforming to the real steel slag shape was established. The objectives of the study are as follows: to explore the sensitivity between the macro- and micro-parameters of the specimen and to establish a non-linear regression equation; and to study the changes in mechanical properties of materials under freeze–thaw cycles, fatigue loading, and coupled freeze–thaw cycle–fatigue loading. The results show that there are three stages of compression damage of the specimen, namely, linear elasticity, peak plasticity, and post-peak decline. Maximum contact forces between cracks and particles occur mainly in the shear zone region within the specimen. The compression damage of the specimen is a mixed tensile–shear damage dominated by shear damage. When freeze–thaw cycles or fatigue loads are applied alone, the flexural strength and fatigue life of the specimens show a linear relationship of decline. The decrease in flexural modulus at low stress is divided into the following: a period of rapid decline, a relatively smooth period, and a period of fracture, with a tendency to change towards linear decay with increasing stress. In the case of freeze–thaw–fatigue coupling, the flexural modulus of the specimen decreases drastically by about 50% in the first 2 years, and then enters a period of steady decrease in flexural modulus in the 3rd–5th years.
Catalyst-Enhancing Hydrothermal Carbonization of Biomass for Hydrochar and Liquid Fuel Production—A Review
Authors: Rasaq Waheed A. , Okpala Charles Odilichukwu R. , Igwegbe Chinenye Adaobi , Białowiec Andrzej
Published: May 2024

The research impact of catalysts on the hydrothermal carbonization (HTC) process remains an ongoing debate, especially regarding the quest to enhance biomass conversion into fuels and chemicals, which requires diverse catalysts to optimize bio-oil utilization. Comprehensive insights and standardized analytical methodologies are crucial for understanding HTC’s potential benefits in terms of biomass conversion stages. This review seeks to understand how catalysts enhance the HTC of biomass for liquid fuel and hydrochar production, drawing from the following key sections: (a) catalyst types applied in HTC processes; (b) biochar functionality as a potential catalyst; (c) catalysts increasing the success of HTC process; and (d) catalyst’s effect on the morphological and textural character of hydrochar. The performance of activated carbon would greatly increase via catalyst action, which would progress the degree of carbonization and surface modification, alongside key heteroatoms. As catalytic HTC technology advances, producing carbon materials for thermochemical activities will become more cost-effective, considering the ever-growing demands for high-performance thermochemical technologies.
Simulating and Modelling the Safety Impact of Connected and Autonomous Vehicles in Mixed Traffic: Platoon Size, Sensor Error, and Path Choice
Authors: Papadoulis Alkis , Imprialou Marianna , Feng Yuxiang , Quddus Mohammed
Published: May 2024

The lack of real-world data on Connected and Autonomous Vehicles (CAVs) has prompted researchers to rely on simulations to assess their societal impacts. However, few studies address the operational and technological challenges of integrating CAVs into existing transport systems. This paper introduces a new CAV driving model featuring a constant time gap longitudinal control algorithm that accounts for sensor errors and platoon formations of varying sizes. Additionally, it develops a high-level route-based decision-making algorithm for CAV path choice. These algorithms were tested in a calibrated motorway corridor simulation, examining different market penetration rates, platoon sizes, and sensor error scenarios. Traffic conflicts were used as a primary safety performance indicator. The findings indicate that CAV sensors are generally adequate, but optimal platoon sizes vary with market penetration rates. To further explore factors influencing traffic conflicts, a hierarchical Bayesian negative binomial regression model was used. This model revealed that in addition to unobserved heterogeneity and spatial autocorrelation, the standard deviation of speeds between lanes and the CAV market penetration rate significantly affect conflict occurrences. These results corroborate the simulation outcomes, enhancing our understanding of CAV deployment impacts on traffic safety.
Utilizing Reinforcement Learning to Drive Redundant Constrained Cable-Driven Robots with Unknown Parameters
Authors: Zhang Dianjin , Guo Bin
Published: May 2024

Cable-driven parallel robots (CDPRs) offer significant advantages, such as the lightweight design, large workspace, and easy reconfiguration, making them essential for various spatial applications and extreme environments. However, despite their benefits, CDPRs face challenges, notably the uncertainty in terms of the post-reconstruction parameters, complicating cable coordination and impeding mechanism parameter identification. This is especially notable in CDPRs with redundant constraints, leading to cable relaxation or breakage. To tackle this challenge, this paper introduces a novel approach using reinforcement learning to drive redundant constrained cable-driven robots with uncertain parameters. Kinematic and dynamic models are established and applied in simulations and practical experiments, creating a conducive training environment for reinforcement learning. With trained agents, the mechanism is driven across 100 randomly selected parameters, resulting in a distinct directional distribution of the trajectories. Notably, the rope tension corresponding to 98% of the trajectory points is within the specified tension range. Experiments are carried out on a physical cable-driven device utilizing trained intelligent agents. The results indicate that the rope tension remained within the specified range throughout the driving process, with the end platform successfully maneuvered in close proximity to the designated target point. The consistency between the simulation and experimental results validates the efficacy of reinforcement learning in driving unknown parameters in redundant constraint-driven robots. Furthermore, the method’s applicability extends to mechanisms with diverse configurations of redundant constraints, broadening its scope. Therefore, reinforcement learning emerges as a potent tool for acquiring motion data in cable-driven mechanisms with unknown parameters and redundant constraints, effectively aiding in the reconstruction process of such mechanisms.
On Summations of Generalized Hypergeometric Functions with Integral Parameter Differences
Authors: Bakhtin Kirill , Prilepkina Elena
Published: May 2024

In this paper, we present an extension of the Karlsson–Minton summation formula for a generalized hypergeometric function with integral parameter differences. Namely, we extend one single negative difference in Karlsson–Minton formula to a finite number of integral negative differences, some of which will be repeated. Next, we continue our study of the generalized hypergeometric function evaluated at unity and with integral positive differences (IPD hypergeometric function at the unit argument). We obtain a recurrence relation that reduces the IPD hypergeometric function at the unit argument to F34. Finally, we note that Euler–Pfaff-type transformations are always based on summation formulas for finite hypergeometric functions, and we give a number of examples.
Partition Entropy as a Measure of Regularity of Music Scales
Authors: Cubarsi Rafael
Published: May 2024

The entropy of the partition generated by an n-tone music scale is proposed to quantify its regularity. The normalized entropy relative to a regular partition and its complementary, here referred to as the bias, allow us to analyze various conditions of similarity between an arbitrary scale and a regular scale. Interesting particular cases are scales with limited bias because their tones are distributed along specific interval fractions of a regular partition. The most typical case in music concerns partitions associated with well-formed scales generated by a single tone h. These scales are maximal even sets that combine two elementary intervals. Then, the normalized entropy depends on each number of intervals as well as their relative size. When well-formed scales are refined, several nested families stand out with increasing regularity. It is proven that a scale of minimal bias, i.e., with less bias than those with fewer tones, is always a best rational approximation of log2h.
Convolutional Neural Networks for Local Component Number Estimation from Time–Frequency Distributions of Multicomponent Nonstationary Signals
Authors: Jurdana Vedran , Baressi Šegota Sandi
Published: May 2024

Frequency-modulated (FM) signals, prevalent across various applied disciplines, exhibit time-dependent frequencies and a multicomponent nature necessitating the utilization of time-frequency methods. Accurately determining the number of components in such signals is crucial for various applications reliant on this metric. However, this poses a challenge, particularly amidst interfering components of varying amplitudes in noisy environments. While the localized Rényi entropy (LRE) method is effective for component counting, its accuracy significantly diminishes when analyzing signals with intersecting components, components that deviate from the time axis, and components with different amplitudes. This paper addresses these limitations and proposes a convolutional neural network-based (CNN) approach for determining the local number of components using a time–frequency distribution of a signal as input. A comprehensive training set comprising single and multicomponent linear and quadratic FM components with diverse time and frequency supports has been constructed, emphasizing special cases of noisy signals with intersecting components and differing amplitudes. The results demonstrate that the estimated component numbers outperform those obtained using the LRE method for considered noisy multicomponent synthetic signals. Furthermore, we validate the efficacy of the proposed CNN approach on real-world gravitational and electroencephalogram signals, underscoring its robustness and applicability across different signal types and conditions.
Stagewise Accelerated Stochastic Gradient Methods for Nonconvex Optimization
Authors: Jia Cui , Cui Zhuoxu
Published: May 2024

For large-scale optimization that covers a wide range of optimization problems encountered frequently in machine learning and deep neural networks, stochastic optimization has become one of the most used methods thanks to its low computational complexity. In machine learning and deep learning problems, nonconvex problems are common, while convex problems are rare. How to find the global minimum for nonconvex optimization and reduce the computational complexity are challenges. Inspired by the phenomenon that the stagewise stepsize tuning strategy can empirically improve the convergence speed in deep neural networks, we incorporate the stagewise stepsize tuning strategy into the iterative framework of Nesterov’s acceleration- and variance reduction-based methods to reduce the computational complexity, i.e., the stagewise stepsize tuning strategy is incorporated into randomized stochastic accelerated gradient and stochastic variance-reduced gradient. The proposed methods are theoretically derived to reduce the complexity of the nonconvex and convex problems and improve the convergence rate of the frameworks, which have the complexity O(1/μϵ) and O(1/μϵ), respectively, where μ is the PL modulus and L is the Lipschitz constant. In the end, numerical experiments on large benchmark datasets validate well the competitiveness of the proposed methods.
Fault Distance Measurement in Distribution Networks Based on Markov Transition Field and Darknet-19
Authors: Wang Haozhi , Guo Wei , Shi Yuntao
Published: May 2024

The modern distribution network system is gradually becoming more complex and diverse, and traditional fault location methods have difficulty in quickly and accurately locating the fault location after a single-phase ground fault occurs. Therefore, this study proposes a new solution based on the Markov transfer field and deep learning to predict the fault location, which can accurately predict the location of a single-phase ground fault in the distribution network. First, a new phase-mode transformation matrix is used to take the fault current of the distribution network as the modulus 1 component, avoiding complex calculations in the complex field; then, the extracted modulus 1 component of the current is transformed into a Markov transfer field and converted into an image using pseudo-color coding, thereby fully exploiting the fault signal characteristics; finally, the Darknet-19 network is used to automatically extract fault features and predict the distance of the fault occurrence. Through simulations on existing models and training and testing with a large amount of data, the experimental results show that this method has good stability, high accuracy, and strong anti-interference ability. This solution can effectively predict the distance of ground faults in distribution networks.
DE-MKD: Decoupled Multi-Teacher Knowledge Distillation Based on Entropy
Authors: Cheng Xin , Zhang Zhiqiang , Weng Wei , Yu Wenxin , Zhou Jinjia
Published: May 2024

The complexity of deep neural network models (DNNs) severely limits their application on devices with limited computing and storage resources. Knowledge distillation (KD) is an attractive model compression technology that can effectively alleviate this problem. Multi-teacher knowledge distillation (MKD) aims to leverage the valuable and diverse knowledge distilled by multiple teacher networks to improve the performance of the student network. Existing approaches typically rely on simple methods such as averaging the prediction logits or using sub-optimal weighting strategies to fuse distilled knowledge from multiple teachers. However, employing these techniques cannot fully reflect the importance of teachers and may even mislead student’s learning. To address this issue, we propose a novel Decoupled Multi-Teacher Knowledge Distillation based on Entropy (DE-MKD). DE-MKD decouples the vanilla knowledge distillation loss and assigns adaptive weights to each teacher to reflect its importance based on the entropy of their predictions. Furthermore, we extend the proposed approach to distill the intermediate features from multiple powerful but cumbersome teachers to improve the performance of the lightweight student network. Extensive experiments on the publicly available CIFAR-100 image classification benchmark dataset with various teacher-student network pairs demonstrated the effectiveness and flexibility of our approach. For instance, the VGG8|ShuffleNetV2 model trained by DE-MKD reached 75.25%|78.86% top-one accuracy when choosing VGG13|WRN40-2 as the teacher, setting new performance records. In addition, surprisingly, the distilled student model outperformed the teacher in both teacher-student network pairs.
Development and Validation of a Liquid Chromatography–Tandem Mass Spectrometry Method for Screening Potential Citrate Lyase Inhibitors from a Library of Marine Compounds
Authors: Wang Jiahong , Guan Huashi , Xu Zhe
Published: May 2024

Tuberculosis, a persistent illness caused by Mycobacterium tuberculosis, remains a significant global public health challenge. The widespread use of anti-tuberculosis drugs has resulted in the emergence of drug-resistant strains, which complicates treatment efforts. Addressing this issue is crucial and hinges on the development of new drugs that can effectively target the disease. This involves identifying novel therapeutic targets that can disrupt the bacterium’s survival mechanisms in various environments such as granulomas and lesions. Citrate lyase, essential for the survival of Mycobacterium species at lesion sites and in granulomatous conditions, is a potential target for the treatment of tuberculosis. This manuscript aimed to construct an efficient enzyme inhibitor screening platform using ultra-high performance liquid chromatography-quadrupole-time-of-flight mass spectrometry (UHPLC-QTOF MS). This system can accurately identify compounds with enzyme inhibitory activity from a library of marine terpenoids and phenolic compounds. Utilizing the screened herbal enzyme inhibitors as a starting point, we analyzed their chemical structures and skillfully built a library of marine compounds based on these structures. The results showed that all of the tested compounds from the phenolics library inhibited citrate lyase by more than 50%, and a significant portion of terpenoids also demonstrated inhibition, with these active terpenoids comprising over half of the terpenoids tested. The study underscores the potential of marine-derived phenolic and terpenoid compounds as potent inhibitors of citrate lyase, indicating a promising direction for future investigations in treating tuberculosis and associated disorders.
Percutaneous Nephrostomy versus Ureteral Stent for Severe Urinary Tract Infection with Obstructive Urolithiasis: A Systematic Review and Meta-Analysis
Authors: Moon Young Joon , Jun Dae Young , Jeong Jae Yong , Cho Seok , Lee Joo Yong , Jung Hae Do
Published: May 2024

Background and Objectives: The European Association of Urology guidelines on urolithiasis highlight the limited evidence supporting the superiority of percutaneous nephrostomy (PCN) over retrograde ureteral stent placement for the primary treatment of infected hydronephrosis secondary to urolithiasis. We, therefore, conducted a systematic review and meta-analysis comparing the effects of PCN and retrograde ureteral stent in patients with severe urinary tract infections secondary to obstructive urolithiasis. Materials and Methods: Meta-analyses were performed to compare four outcomes: time for the temperature to return to normal; time for the white blood cell (WBC) count to return to normal; hospital length of stay; and procedure success rate. After a full-text review, eight studies were identified as relevant and included in our systematic review and meta-analysis. Results: No significant difference was detected between PCN and retrograde ureteral stenting for the time for the temperature to return to normal (p = 0.13; mean difference [MD] = −0.74; 95% confidence interval [CI] = −1.69, 0.21; I2 = 96%) or the time for the WBC count to return to normal (p = 0.24; MD = 0.46; 95% CI = −0.30, 1.21; I2 = 85%). There was also no significant difference between methods for hospital length of stay (p = 0.78; MD = 0.45; 95% CI = −2.78, 3.68; I2 = 96%) or procedure success rate (p = 0.76; odds ratio = 0.86; 95% CI = 0.34, 2.20; I2 = 47%). Conclusions: The clinical outcomes related to efficacy did not differ between PCN and retrograde ureteral stenting for severe urinary tract infection with obstructive urolithiasis. Thus, the choice between procedures depends mainly on the urologist’s or patient’s preferences.
Decreased Hepatic and Serum Levels of IL-10 Concur with Increased Lobular Inflammation in Morbidly Obese Patients
Authors: Solleiro-Villavicencio Helena , Méndez-García Lucía Angélica , Ocampo-Aguilera Nydia A. , Baltazar-Pérez Itzel , Arreola-Miranda José A. , Aguayo-Guerrero José A. , Alfaro-Cruz Ana , González-Chávez Antonio , Fonseca-Sánchez Miguel A. , Fragoso José Manuel , Escobedo Galileo
Published: May 2024

Background and Objectives: Non-alcoholic fatty liver disease (NAFLD) is associated with obesity and ranges from simple steatosis to non-alcoholic steatohepatitis (NASH), fibrosis, cirrhosis, and hepatocellular carcinoma. Accumulating evidence in animal models suggests that loss of interleukin-10 (IL-10) anti-inflammatory actions might contribute to lobular inflammation, considered one of the first steps toward NASH development. However, the role of IL-10 in lobular inflammation remains poorly explored in humans. We examined mRNA and protein levels of IL-10 in liver biopsies and serum samples from morbidly obese patients, investigating the relationship between IL-10 and lobular inflammation degree. Materials and Methods: We prospectively enrolled morbidly obese patients of both sexes, assessing the lobular inflammation grade by the Brunt scoring system to categorize participants into mild (n = 7), moderate (n = 19), or severe (n = 13) lobular inflammation groups. We quantified the hepatic mRNA expression of IL-10 by quantitative polymerase chain reaction and protein IL-10 levels in liver and serum samples by Luminex Assay. We estimated statistical differences by one-way analysis of variance (ANOVA) and Tukey’s multiple comparison test. Results: The hepatic expression of IL-10 significantly diminished in patients with severe lobular inflammation compared with the moderate lobular inflammation group (p = 0.01). The hepatic IL-10 protein levels decreased in patients with moderate or severe lobular inflammation compared with the mild lobular inflammation group (p = 0.008 and p = 0.0008, respectively). In circulation, IL-10 also significantly decreased in subjects with moderate or severe lobular inflammation compared with the mild lobular inflammation group (p = 0.005 and p < 0.0001, respectively). Conclusions: In liver biopsies and serum samples of morbidly obese patients, the protein levels of IL-10 progressively decrease as lobular inflammation increases, supporting the hypothesis that lobular inflammation develops because of the loss of the IL-10-mediated anti-inflammatory counterbalance.