bluuebunny/crossref_metadata_2025 huggingface dataset:
Antibody-Drug Conjugates and Their Potential in the Treatment of Patients with Biliary Tract Cancer
Authors: Alexander Shaun , Aleem Umair , Jacobs Timothy , Frizziero Melissa , Foy Victoria , Hubner Richard A. , McNamara Mairéad G.
Published: September 2024

Background: Biliary tract cancers (BTCs) are aggressive in nature, often presenting asymptomatically until they are diagnosed at an advanced stage. Surgical resection or liver transplantation are potential curative options. However, a large proportion of patients present with incurable locally advanced or metastatic disease and most of these patients are only eligible for palliative chemotherapy or best supportive care. More recently, targeted therapies have proven beneficial in a molecularly selected subgroup of patients with cholangiocarcinoma who have progressed on previous lines of systemic treatment. However, only a minority of patients with BTCs whose tumours harbour specific molecular alterations can access these therapies. Methods: In relation to ADCs, studies regarding use of antibody–drug conjugates in cancer, particularly in BTCs, were searched in Embase (1974 to 2024) and Ovid MEDLINE(R) (1946 to 2024) to obtain relevant articles. Examples of current clinical trials utilising ADC treatment in BTCs were extracted from the ClinicalTrials.gov trial registry. Conclusions: Overall, this review has highlighted that ADCs have shown encouraging outcomes in cancer therapy, and this should lead to further research including in BTCs, where treatment options are often limited. The promising results observed with ADCs in various cancers underscore their potential as a transformative approach in oncology, warranting continued exploration and development and the need for education on the management of their specific toxicities. By addressing current challenges and optimising ADC design and application, future studies could potentially improve treatment outcomes for patients with BTCs and beyond, potentially in both early and advanced stage settings.
Prevalence of Sarcopenia Determined by Computed Tomography in Pancreatic Cancer: A Systematic Review and Meta-Analysis of Observational Studies
Authors: Láinez Ramos-Bossini Antonio Jesús , Gámez Martínez Antonio , Luengo Gómez David , Valverde-López Francisco , Melguizo Consolación , Prados José
Published: September 2024

Introduction: Sarcopenia, a condition characterized by a loss of skeletal muscle mass, is increasingly recognized as a significant factor influencing patient outcomes in pancreatic cancer (PC). This systematic review and meta-analysis aimed to estimate the prevalence of sarcopenia in patients with PC using computed tomography and to explore how different measurement methods and cut-off values impact such prevalence. Materials and Methods: Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines, a comprehensive search of PubMed, Web of Science, and EMBASE databases was performed, identifying 48 observational studies involving 9063 patients. Results: The overall pooled prevalence of sarcopenia was 45% (95% CI, 40–50%), but varied significantly by the method used: 47% when measured with the skeletal muscle index and 33% when assessed with the total psoas area. In addition, in studies using SMI, sarcopenia prevalence was 19%, 45%, and 57% for cutoff values <40 cm2/m2, 40–50 cm2/m2, and >50 cm2/m2, respectively. Moreover, the prevalence was higher in patients receiving palliative care (50%) compared to those treated with curative intent (41%). High heterogeneity was observed across all analyses, underscoring the need for standardized criteria in sarcopenia assessment. Conclusions: Our findings highlight the substantial variability in sarcopenia prevalence, which could influence patient outcomes, and stress the importance of consensus in measurement techniques to improve clinical decision making and research comparability.
Comparative Analysis of Optimal Reaction Conditions for Hydrothermal Carbonization and Liquid Hot-Water Processes in the Valorization of Peapods and Coffee Cherry Waste into Platform Chemicals
Authors: Lozano Pérez Alejandra Sophia , Romero Mahecha Valentina , Guerrero Fajardo Carlos Alberto
Published: September 2024

The management of coffee and peapod waste presents significant environmental challenges, with millions of tons generated annually, leading to disposal issues and resource inefficiencies. Hydrothermal processes offer a promising valorization method, though biomass characteristics significantly influence the resulting products. Biomass characterization revealed distinct profiles for coffee cherry waste (moisture: 10.94%, ashes: 7.79%, volatile matter: 79.91%, fixed carbon: 1.36%, cellulose: 27.6%, hemicellulose: 12.5%, and lignin: 13.7%) and peapods (moisture: 7.77%, ashes: 4.22%, volatile matter: 74.18%, fixed carbon: 13.0%, cellulose: 20.2%, hemicellulose: 17.4%, and lignin: 5.0%). Experiments were conducted in 100 mL and 500 mL hydrothermal reactors with varying conditions for temperature (120–260 °C), time (1–4 h), stirring (none and at 5000 and 8000 rpm), biomass/water ratio (1:5, 1:10, 1:20, and 1:40), particle size (0.5–5 mm), and catalysts (acids and bases). The results showed that peapods produced over 30 times more platform chemicals than coffee. High temperatures (over 180 °C) degraded peapods, whereas coffee yields increased. Both biomasses were influenced similarly by reaction conditions: lower biomass/water ratios, smaller particle sizes, acid catalysts, and no stirring increased yields. Peapods consistently had higher yields than coffee in all conditions. Biochar analysis revealed anthracite from coffee and coal from peapods.
Bacteriological Profile and Antibiotic Susceptibility of Neonatal Sepsis Cases in the Neonatal Intensive Care Unit of a Tertiary Hospital in Türkiye
Authors: Dorum Bayram Ali , Elmas Bozdemir Şefika , Kral Bensu Zadeoğlu , Erdoğan Ayten , Çakır Salih Çağrı
Published: September 2024

Objective: We aimed to determine the demographic data, mortality, and morbidity of early- and late-neonatal sepsis cases, the etiologic agents in these cases, and the antibiotic susceptibility of these agents. Methods: This study was conducted retrospectively in a tertiary neonatal intensive care unit (NICU). The demographic, clinical, and laboratory data of newborns diagnosed with culture-proven sepsis within 24 months were evaluated. Results: Two hundred and eleven culture data points belonging to 197 infants were evaluated. Forty percent of the infants had a history of premature birth. The most common clinical findings were respiratory distress and feeding intolerance. Coagulase-negative staphylococci (CoNS) were detected most frequently as early- and late-sepsis agents. The most common Gram-negative bacteria detected as late-sepsis agents were Klebsiella spp. and Escherichia coli (E. coli). The overall mortality rate was 10%. Conclusions: Neonatal sepsis continues to have high mortality rates in tertiary NICUs. CoNS was the most common agent, highlighting the importance of developing and maintaining personnel training and handwashing practices. It will be important to consider the resistance rates of Klebsiella spp., the most common Gram-negative agent in late-onset sepsis (LOS) cases, to commonly used antibiotics in empirical treatments.
Model Predictive Control of Spatially Distributed Systems with Spatio-Temporal Logic Specifications
Authors: Komizu Ikkei , Kobayashi Koichi , Yamashita Yuh
Published: September 2024

In this paper, for spatially distributed systems, we propose a new method of model predictive control with spatio-temporal logic specifications. We formulate the finite-time control problem with specifications described by SSTLf (signal spatio-temporal logic over finite traces) formulas. In the problem formulation, the feasibility is guaranteed by representing control specifications as a penalty in the cost function. Time-varying weights in the cost function are introduced to satisfy control specifications as well as possible. The finite-time control problem can be written as a mixed integer programming (MIP) problem. According to the policy of model predictive control (MPC), the control input can be generated by solving the finite-time control problem at each discrete time. The effectiveness of the proposed method is presented through a numerical example.
Harmonizing In Vitro Techniques for Anti-Aging Cosmetic Ingredient Assessment: A Comprehensive Review
Authors: Vinardell Maria Pilar , Maddaleno Adriana Solange , Mitjans Montserrat
Published: September 2024

Skin grows old due to intrinsic factors, such as age and associated hormonal changes, and external factors, like solar radiation and chemical substances to which we are exposed. With age, skin shows thinning, laxity, pallor, increased dryness, and the appearance of wrinkles, attributed to a decrease in collagen and elastin produced by fibroblasts. Several theories explain skin aging, including the free radical formation, neuroendocrine, and mitochondrial decline theories. Based on a scientific understanding of skin behavior, different in vitro methods are used to evaluate the effects of new ingredients in cosmetics. Commonly used methods include anti-collagenase, anti-elastase, and anti-hyaluronidase activity alongside techniques utilizing skin cells or 3D models. Although these methods are recognized and widely used, they lack standardization. In this review, a literature search has been conducted to examine the characteristics and variations of these methods across the laboratories. A key issue identified in this review is that many papers provide insufficient detail regarding their protocols. Moreover, the number of studies using cells is less significant than the ones determining enzyme inhibition. Our findings revealed that, in many cases, there is limited information available, underscoring the urgent need to initiate a comprehensive standardization process for the methodologies used to demonstrate anti-aging activity.
Crystallization Kinetics of Tacrolimus Monohydrate in an Ethanol–Water System
Authors: Zhang Suoqing , Zhao Jixiang , Kong Ming , Li Jiahui , Li Mingxuan , Ma Miao , Tong Li , Li Tao , Chen Mingyang
Published: September 2024

Nucleation and growth during the crystallization process are crucial steps that determine the crystal structure, size, morphology, and purity. A thorough understanding of these mechanisms is essential for producing crystalline products with consistent properties. This study investigates the solubility of tacrolimus (FK506) in an ethanol–water system (1:1, v/v) and examines its crystallization kinetics using batch crystallization experiments. Initially, the solubility of FK506 was measured, and classical nucleation theory was employed to analyze the induction period to determine interfacial free energy (γ) and other nucleation parameters, including the critical nucleus radius (r*), critical free energy (∆G*), and the molecular count of the critical nucleus (i*). Crystallization kinetics under seeded conditions were also measured, and the parameters of the kinetic model were analyzed to understand the effects of process states such as temperature on the crystallization process. The results suggested that increasing temperature and supersaturation promotes nucleation. The surface entropy factor (f) indicates that the tacrolimus crystal growth mechanism is a two-dimensional nucleation growth. The growth process follows the particle size-independent growth law proposed by McCabe. The estimated kinetic parameters reveal the effects of supersaturation, temperature, and suspension density on the nucleation and growth rates.
Personalized Immunotherapy Achieves Complete Response in Metastatic Adenoid Cystic Carcinoma Despite Lack of Conventional Biomarkers
Authors: Tokat Ünal Metin , Adibi Ashkan , Aydın Esranur , Özgü Eylül , Bilgiç Şevval Nur , Tutar Onur , Özbek Doğançay Merve , Demiray İrem , Demiray Mutlu
Published: September 2024

There is currently no effective treatment strategy for recurrent/metastatic adenoid cystic carcinoma (R/M ACC). Furthermore, recent single-agent and combination immunotherapy trials have failed in unselected ACC cohorts, unlike non-ACC salivary gland cancers. Genomic profiling revealed no actionable targets but NOTCH1 and KDM6A frameshift and CTCF splice site mutations (no MYB/L fusion) with a low tumor mutational burden (TMB), microsatellite stable (MSS) and negative programmed death ligand 1 (PD-L1) were observed. We recommended an anti-programmed cell death protein 1 (anti-PD-1) plus anti-Cytotoxic T-lymphocyte-associated protein 4 (anti-CTLA-4) combination based on TMB 2-fold greater-than-median TMB in ACC, tumor harboring multiple immunogenic frameshift or splice site mutations, and PD-L1 negativity. Accordingly, we achieved a complete response in a radiotherapy (RT) and chemotherapy (CT)-refractory patient with locally recurrent lacrimal gland (LG) ACC and lung metastasis following personalized immunotherapy in combination with integrative therapeutics. Therefore, it is crucial to assess not only conventional immune biomarkers but also patient-specific parameters, especially in “immune-cold” cancer types.
A Long-Term Target Search Method for Unmanned Aerial Vehicles Based on Reinforcement Learning
Authors: Wei Dexing , Zhang Lun , Yang Mei , Deng Hanqiang , Huang Jian
Published: September 2024

Unmanned aerial vehicles (UAVs) are increasingly being employed in search operations. Deep reinforcement learning (DRL), owing to its robust self-learning and adaptive capabilities, has been extensively applied to drone search tasks. However, traditional DRL approaches often suffer from long training times, especially in long-term search missions for UAVs, where the interaction cycles between the agent and the environment are extended. This paper addresses this critical issue by introducing a novel method—temporally asynchronous grouped environment reinforcement learning (TAGRL). Our key innovation lies in recognizing that as the number of training environments increases, agents can learn knowledge from discontinuous trajectories. This insight leads to the design of grouped environments, allowing agents to explore only a limited number of steps within each interaction cycle rather than completing full sequences. Consequently, TAGRL demonstrates faster learning speeds and lower memory consumption compared to existing parallel environment learning methods. The results indicate that this framework enhances the efficiency of UAV search tasks, paving the way for more scalable and effective applications of RL in complex scenarios.
Neural Dynamics Associated with Biological Variation in Normal Human Brain Regions
Authors: Guisande Natalí , Rosso Osvaldo A. , Montani Fernando
Published: September 2024

The processes involved in encoding and decoding signals in the human brain are a continually studied topic, as neuronal information flow involves complex nonlinear dynamics. This study examines awake human intracranial electroencephalography (iEEG) data from normal brain regions to explore how biological sex influences these dynamics. The iEEG data were analyzed using permutation entropy and statistical complexity in the time domain and power spectrum calculations in the frequency domain. The Bandt and Pompe method was used to assess time series causality by associating probability distributions based on ordinal patterns with the signals. Due to the invasive nature of data acquisition, the study encountered limitations such as small sample sizes and potential sources of error. Nevertheless, the high spatial resolution of iEEG allows detailed analysis and comparison of specific brain regions. The results reveal differences between sexes in brain regions, observed through power spectrum, entropy, and complexity analyses. Significant differences were found in the left supramarginal gyrus, posterior cingulate, supplementary motor cortex, middle temporal gyrus, and right superior temporal gyrus. This study emphasizes the importance of considering sex as a biological variable in brain dynamics research, which is essential for improving the diagnosis and treatment of neurological and psychiatric disorders.
Assessing Credibility in Bayesian Networks Structure Learning
Authors: Barth Vitor , Serrão Fábio , Maciel Carlos
Published: September 2024

Learning Bayesian networks from data aims to create a Directed Acyclic Graph that encodes significant statistical relationships between variables and their joint probability distributions. However, when using real-world data with limited knowledge of the original dynamical system, it is challenging to determine if the learned DAG accurately reflects the underlying relationships, especially when the data come from multiple independent sources. This paper describes a methodology capable of assessing the credible interval for the existence and direction of each edge within Bayesian networks learned from data, without previous knowledge of the underlying dynamical system. It offers several advantages over classical methods, such as data fusion from multiple sources, identification of latent variables, and extraction of the most prominent edges with their respective credible interval. The method is evaluated using simulated datasets of various sizes and a real use case. Our approach was verified to achieve results comparable to the most recent studies in the field, while providing more information on the model’s credibility.
Distributed Adaptive Optimization Algorithm for High-Order Nonlinear Multi-Agent Stochastic Systems with Lévy Noise
Authors: Yang Hui , Sun Qing , Yuan Jiaxin
Published: September 2024

An adaptive neural network output-feedback control strategy is proposed in this paper for the distributed optimization problem (DOP) of high-order nonlinear stochastic multi-agent systems (MASs) driven by Lévy noise. On the basis of the penalty-function method, the consensus constraint is removed and the global objective function (GOF) is reconstructed. The stability of the system is analyzed by combining the generalized Itô’s formula with the Lyapunov function method. Moreover, the command filtering mechanism is introduced to solve the “complexity explosion” problem in the process of designing virtual controller, and the filter errors are compensated by introducing compensating signals. The proposed algorithm has been proved that the outputs of all agents converge to the optimal solution of the DOP with bounded errors. The simulation results demonstrate the effectiveness of the proposed approach.
Prediction of Heat Transfer during Condensation of Ammonia Inside Tubes and Annuli
Authors: Shah Mirza M.
Published: September 2024

Ammonia has been used as a refrigerant since the beginning of the refrigeration industry and it continues to be an important industrial refrigerant. However, there is no well-verified method for predicting heat transfer during condensation in tubes and annuli. Available information is often contradictory, especially about the effect of oil. In this paper, available test data and predictive methods are reviewed. Reliable test data are identified and compared to well-known general correlations. The effect of oil on heat transfer is investigated. The results of this research are presented, and recommendations are made for design calculations.
Investigating Changes in Natural Gas Demand across Great Britain for Domestic Heating Using Daily Data: 2018 to 2024
Authors: Phillips Geraint , Wilson Grant
Published: September 2024

This study analyses data from natural gas combination boilers across a 6-year timeframe, exploring how demand for space heating and hot water have both changed over time, and highlights the impact of factors such as external temperature and the UK energy price cap. The results show that there has been a significant decrease in annual space heating and hot water demand since 2021. Space heating typically contributes 88% of the total annual gas demand of the boilers, with hot water contributing the other 12%. For the same mean temperature across the fourth quarter (8.5 °C), 2018 had a daily mean energy demand of 50.4 kWh, whereas the 2022 value was 41.4 kWh. This 9.0 kWh (18%) difference of the daily mean for Q4 suggests a shift in consumer demand influenced by other factors such as the energy price cap. This analysis provides additional understanding of how consumer energy demand for heating continues to evolve and invites further studies to be completed on future trends of energy demand for both space heating and hot water. Here, we also highlight the benefit of considering space heating and hot water as separate demands, as this provides additional insights and is something the paper helps to advocate for.
Novel Custom Loss Functions and Metrics for Reinforced Forecasting of High and Low Day-Ahead Electricity Prices Using Convolutional Neural Network–Long Short-Term Memory (CNN-LSTM) and Ensemble Learning
Authors: Wang Ziyang , Mae Masahiro , Yamane Takeshi , Ajisaka Masato , Nakata Tatsuya , Matsuhashi Ryuji
Published: September 2024

Day-ahead electricity price forecasting (DAEPF) is vital for participants in energy markets, particularly in regions with high integration of renewable energy sources (RESs), where price volatility poses significant challenges. The accurate forecasting of high and low electricity prices is particularly essential, as market participants seek to optimize their strategies by selling electricity when prices are high and purchasing when prices are low to maximize profits and minimize costs. In Japan, the increasing integration of RES has caused day-ahead electricity prices to frequently fall to almost zero JPY/kWh during periods of high RES output, creating significant profitability challenges for electricity retailers. This paper introduces novel custom loss functions and metrics specifically designed to improve the forecasting accuracy of extreme prices (high and low prices) in DAEPF, with a focus on the Japanese wholesale electricity market, addressing the unique challenges posed by the volatility of RES. To implement this, we integrate these custom loss functions into a Convolutional Neural Network–Long Short-Term Memory (CNN-LSTM) model, augmented by an ensemble learning approach and multimodal features. The proposed custom loss functions and metrics were rigorously validated, demonstrating their effectiveness in accurately predicting high and low electricity prices, thereby indicating their practical application in enhancing the economic strategies of market participants.
Real-Time Power Regulation of Flexible User-Side Resources in Distribution Networks via Dual Ascent Method
Authors: Yang Yu , Wen Fushuan , Yang Jiajia , Liu Hangyue , Liu Dazheng , Xin Shujun , Fan Hao , Wu Cong
Published: September 2024

Flexible user-side resources are of great potential in providing power regulation so as to effectively address the challenges of reverse power flow and overvoltage issues in distribution networks characterized by high photovoltaic (PV) penetration. However, existing distributed algorithms typically implement control signals after the convergence of the algorithms, making it difficult to track frequent and rapid fluctuations in PV power outputs in real time. Given this background, an online-distributed control algorithm for the real-time power regulation of flexible user-side resources is proposed in this paper. The objective of the established control model is to minimize network losses by dynamically adjusting active power outputs of flexible user-side resources and reactive power outputs of PV inverters while respecting branch power flow and voltage magnitude constraints. Furthermore, by deconstructing the centralized problem into a primal–dual one, a distributed control strategy based on the dual ascent method is implemented. With the proposed method, agents can achieve global optimality by exchanging limited information with their neighbors. The simulation results verify the good balance between economic efficiency and voltage control performance of the proposed method.
Sliding Mode Observer-Based Phase-Locking Strategy for Current Source Inverter in Weak Grids
Authors: Zheng Yubin , Deng Hui , Liu Xiong , Guan Yuanpeng
Published: September 2024

The current source inverter (CSI) has become the main grid-connected interface of distributed generation systems due to its advantages, such as boost capability, current controllability, and short-circuit protection capability. However, in weak grids, the grid-connected CSI that uses a phase-locked loop to achieve grid voltage synchronization has problems, such as instability in the fundamental positive-sequence voltage phase detection at the point of common coupling and instability in the current loop control, which seriously hamper the promotion and application of the CSI and its interconnected systems. For this reason, this paper proposes a sliding mode observer-based phase-locking strategy for the CSI. The strategy proposes a sliding mode observer for grid voltage phase detection, so that the grid current can directly follow the grid voltage, solving the problem of inconsistency or distortion between the voltage phase of the point of common coupling and the grid voltage phase in weak grids. On this basis, the grid impedance is regarded as part of the CL filter, and a robust parameter design method is proposed for the grid current closed-loop control in weak grids, which achieves robust operation of a CSI in weak grids. Finally, an experimental platform for a single-phase grid-connected CSI is built to verify the effectiveness and feasibility of the proposed scheme.
Unlocking Nature’s Potential: Modelling Acacia melanoxylon as a Renewable Resource for Bio-Oil Production through Thermochemical Liquefaction
Authors: Ozkan Sila , Sousa Henrique , Gonçalves Diogo , Puna Jaime , Carvalho Ana , Bordado João , dos Santos Rui Galhano , Gomes João
Published: September 2024

This study is focused on the modelling of the production of bio-oil by thermochemical liquefaction. Species Acacia melanoxylon was used as the source of biomass, the standard chemical 2-Ethylhexanol (2-EHEX) was used as solvent, p-Toluenesulfonic acid (pTSA) was used as the catalyst, and acetone was used for the washing process. This procedure consisted of a moderate acid-catalysed liquefaction process and was applied at 3 different temperatures to determine the proper model: 100, 135, and 170 °C, and at 30-, 115-, and 200-min periods with 0.5%, 5.25%, and 10% (m/m) catalyst concentrations of overall mass. Optimized results showed a bio-oil yield of 83.29% and an HHV of 34.31 MJ/kg. A central composite face-centred (CCF) design was applied to the liquefaction reaction optimization. Reaction time, reaction temperature, as well as catalyst concentration, were chosen as independent variables. The resulting model exhibited very good results, with a highly adjusted R-squared (1.000). The liquefied products and biochar samples were characterized by Fourier-transformed infrared (FTIR) and thermogravimetric analysis (TGA); scanning electron microscopy (SEM) was also performed. The results show that invasive species such as acacia may have very good potential to generate biofuels and utilize lignocellulosic biomass in different ways. Additionally, using acacia as feedstock for bio-oil liquefaction will allow the valorisation of woody biomass and prevent forest fires as well. Besides, this process may provide a chance to control the invasive species in the forests, reduce the effect of forest fires, and produce bio-oil as a renewable energy.
Flexibility-Oriented AC/DC Hybrid Grid Optimization Using Distributionally Robust Chance-Constrained Method
Authors: Chen Yue , Lu Qiuyu , Zeng Kaiyue , Yang Yinguo , Xie Pingping
Published: September 2024

With the increasing integration of stochastic sources and loads, ensuring the flexibility of AC/DC hybrid distribution networks has become a pressing challenge. This paper aims to enhance the operational flexibility of AC/DC hybrid distribution networks by proposing a flexibility-oriented optimization framework that addresses the growing uncertainties. Notably, a comprehensive evaluation method for operational flexibility assessment is first established. Based on this, this paper further proposes a flexibility-oriented operation optimization model using the distributionally robust chance-constrained (DRCC) method. A customized solution method utilizing second-order cone relaxation and sample average approximation (SAA) is also introduced. The results of case studies indicate that the flexibility of AC/DC hybrid distribution networks is enhanced through sharing energy storage among multiple feeders, adaptive reactive power regulation using soft open points (SOPs) and static var compensators (SVCs), and power transfer between feeders via SOPs.
A Survey of Quantitative Techniques in Electricity Consumption—A Global Perspective
Authors: Khan Atif Maqbool , Wyrwa Artur
Published: September 2024

This study uses the Scopus and Web of Science databases to review quantitative methods to forecast electricity consumption from 2015 to 2024. Using the PRISMA approach, 175 relevant publications were identified from an initial set of 821 documents and subsequently subjected to bibliometric analysis. This analysis examined publication trends, citation metrics, and collaboration patterns across various countries and institutions. Over the period analyzed, the number of articles has steadily increased, with a more rapid rise observed after 2020. Although China dominates this research field, strong bibliographic coupling worldwide indicates significant international collaboration. The study suggests that no single method consistently outperforms others across all contexts and that forecasting methods should be adapted to regional contexts, considering specific economic, social, and environmental factors. Furthermore, we emphasize that review papers should compare methods and results regarding both time horizon and temporal resolution, as these aspects are crucial for the accuracy and applicability of the forecasts.
Exploring the Coordination of Park Green Spaces and Urban Functional Areas through Multi-Source Data: A Spatial Analysis in Fuzhou, China
Authors: Xu Han , Zheng Guorui , Lin Xinya , Jin Yunfeng
Published: September 2024

The coordinated development of park green spaces (PGS)with urban functional areas (UFA) has a direct impact on the operational efficiency of cities and the quality of life of residents. Therefore, an in-depth exploration of the coupling patterns and influencing factors between PGS and UFA is fundamental for efficient collaboration and the creation of high-quality living environments. This study focuses on the street units of Fuzhou’s central urban area, utilizing multi-source data such as land use, points of interest (POI), and OpenStreetMap (OSM) methods, including kernel density analysis, standard deviational ellipse, coupling coordination degree model, and geographical detectors, are employed to systematically analyze the spatial distribution patterns of PGS and UFA, as well as their coupling coordination relationships. The findings reveal that (1) both PGS and various UFA have higher densities in the city center, with a concentric decrease towards the periphery. PGS are primarily concentrated in the city center, exhibiting a monocentric distribution, while UFA display planar, polycentric, or axial distribution patterns. (2) The spatial distribution centers of both PGS and UFA are skewed towards the southwest of the city center, with PGS being relatively evenly distributed and showing minimal deviation from UFA. (3) The dominant type of coupling coordination between PGS and various UFA is “Close to dissonance”, displaying a spatial pattern of “high in the center, low on the east-west and north-south wings”. Socioeconomic factors are the primary driving force influencing the coupling coordination degree, while population and transportation conditions are secondary factors. This research provides a scientific basis for urban planning and assists planners in more precisely coordinating the development of parks, green spaces, and various functional spaces in urban spatial layouts, thereby promoting sustainable urban development.
Does Urban Green Space Pattern Affect Green Space Noise Reduction?
Authors: Feng Liyi , Wang Jiabing , Liu Binyan , Hu Fangbing , Hong Xinchen , Wang Wenkui
Published: September 2024

The effect of urban green spaces on traffic noise reduction has been extensively studied at the level of single vegetation, hedges, etc., but there is a lack of corresponding studies at the scale of spatial patterns of urban green spaces. Therefore, this study aims to analyze the relationship between the spatial pattern of urban green space and the change in green space’s noise reduction capacity. Through the morphology spatial pattern analysis method, this analysis divides the urban green space in the Fuzhou high-tech zone into seven types of elements with different ecological definitions and simulates the noise condition of the urban environment with the presence of green space as well as without the presence of green space by computer simulation, calculates the distribution map of the noise reduction produced by the urban green space, and analyzes the correlation between the seven types of green space elements and the noise reduction with the geographically weighted regression modeling analysis. The study finds that (1) Urban green space patterns can significantly affect the net noise reduction of green space. Areas with high green coverage can produce a stronger green space noise reduction effect. (2) More complex green space shapes and more fragmented urban green space can produce higher noise reduction. (3) The green space close to the source of noise can exert a stronger noise reduction effect. Therefore, in the process of planning and design, from the perspective of improving the urban acoustic environment, the configuration of high-quality green spaces in areas with higher levels of noise pollution should be given priority, which may have better noise reduction effects.
Assessment of High-Severity Post-Fire Soil Quality and Its Recovery in Dry/Warm Valley Forestlands in Southwest China through Selecting the Minimum Data Set and Soil Quality Index
Authors: Qin Xiaosong , Wang Yi , Hou Dongdong , Li Yongkang
Published: September 2024

Recurrent wildfires can negatively affect soil quality, and post-fire soil quality recovery is critical for maintaining sustainable ecosystem development. The objective of this study was to evaluate the changes and recovery of soil properties and soil quality in the forests of dry/warm river valleys in southwest China after disturbance by high-severity fires. In this study, the impact of fire on soil properties and soil quality was investigated for three years post-fire. Unburned forest land with a similar natural environment compared to the fire area was used as a control. Soil samples were collected from three different depths of 0–10 cm, 10–20 cm, and 20–30 cm, respectively. Principal component analysis (PCA) combined with the Norm value was used to select the minimum data set (MDS), thus calculating the soil quality index (SQI). The results showed that the soil properties changed significantly after high-severity fires. On average, soil bulk density (0.91 g/cm3, p = 0.001), total nitrogen (0.12 g/kg, p = 0.000), total phosphorus (0.10 g/kg, p = 0.000), and total potassium (5.55 g/kg, p = 0.000) were significantly lower in the burned areas than in the unburned areas at the first sampling. These indicators increased in the following three years but still did not recover to unburned levels. Compared with the above indicators, soil porosity and organic matter increased post-fire, but gradually decreased over time. Soil clay, geometric mean diameter, and total potassium were included in the MDS. The SQI was ranked as unburned > 3 years > 2 years > 1 year > 6 months. The SQI was significantly (p = 0.001) reduced six months post-fire by an average of 36%, and, after three years of recovery, the soil quality of the post-fire areas could be restored to 81% of soil in unburned areas. Apparently, high-severity fires caused changes in soil properties, thereby significantly decreasing soil quality. Soil quality gradually improved with increasing restoration time. However, the complete recovery of soil quality post-fire in forest land in the dry/warm river valley will take a longer time.
The Role of Climate Change and Human Intervention in Shaping Vegetation Patterns in the Fen River Basin of China: Implications of the Grain for Green Program
Authors: Niu Kaijie , Liu Geng , Zhan Cun , Kang Aiqing
Published: September 2024

The Fen River Basin (FRB), an ecologically fragile region in China, exemplifies the intricate interplay between vegetation dynamics and both climatic and human-driven factors. This study leverages a 40-year (1982–2022) dataset, utilizing the kernel-based normalized difference vegetation index (kNDVI) alongside key climatic variables—rainfall (PRE), temperature (TMP), and solar radiation (SRAD)—to investigate vegetation variations and their drivers in the FRB, particularly in relation to the Grain for Green Program (GGP). Our analysis highlights significant greening across the FRB, with the kNDVI slope increasing by 0.0028 yr−1 and green-covered areas expanding by 92.8% over the study period. The GGP facilitated the greening process, resulting in a notable increase in the kNDVI slope from 0.0005 yr−1 to 0.0052 yr−1 and a marked expansion in the area of significant greening from 24.6% to 95.8%. Regional climate shifts, characterized by increased warming, heightened humidity, and a slight rise in SRAD, have further driven vegetation growth, contributing 75%, 58.7%, and 23.6% to vegetation dynamics, respectively. Notably, the GGP has amplified vegetation’s sensitivity to climatic variables, with areas significantly impacted by multiple climate factors expanding from 4.8% to 37.5%. Specially, PRE is the primary climatic influence, impacting 71.3% of the pertinent regions, followed by TMP (60.1%) and SRAD (30%). The integrated effects of climatic and anthropogenic factors, accounting for 47.8% and 52.2% of kNDVI variations, respectively, collectively influence 96% of the region’s vegetation dynamics. These findings underscore the critical role of climate change and human interventions in shaping vegetation patterns and provide a robust foundation for refining ecological conservation strategies, particularly in the context of global warming and land-use policies.
The Combined Effect of Four Nutraceutical-Based Feed Additives on the Rumen Microbiome, Methane Gas Emission, Volatile Fatty Acids, and Dry Matter Disappearance Using an In Vitro Batch Culture Technique
Authors: Ike Kelechi A. , Okedoyin Deborah O. , Alabi Joel O. , Adelusi Oludotun O. , Wuaku Michael , Olagunju Lydia K. , Anotaenwere Chika C. , Gray DeAndrea , Dele Peter A. , Kholif Ahmed E. , Thomas Misty D. , Anele Uchenna Y.
Published: September 2024

This study aimed to investigate the effect of an essential oil/fumaric combination, mannan-oligosaccharide, galactooligosaccharide, and a mannan-oligosaccharide/galactooligosaccharide combination on the dry matter disappearance (DMD), gas production, greenhouse gasses, volatile fatty acid, and microbial community of a total mixed ration using a 24 h in vitro batch culture technique. The study design was a completely randomized design with four treatments as follows: a control treatment without any additives, the control treatment supplemented with galactooligosaccharide at 3% (Gos treatment), a galactooligosaccharide and mannan-oligosaccharide mixture at 1:1 at 3% (Gosmos treatment), or an essential oil blend (200 μL/g feed) and fumaric acid at 3% combination (Eofumaric treatment). The Gosmos treatment had the highest (p < 0.05) DMD (63.8%) and the numerical lowest acetate–propionate ratio (p = 0.207), which was 36.9% higher compared to the control. The lowest Shannon index, Simpson’s index, and all the diversity indices were recorded for the Eofumaric treatment, while the other treatments had similar Shannon index, Simpson’s index, and diversity index. The Z-score differential abundance between the Eofumaric and the control indicated that the inclusion of the Eofumaric treatment differentially increased the abundance of Patescibacteria, Synergistota, Chloroflexi, Actinobacteriota, Firmicutes, and Euryarchaeota while Verrucomicrobiota, WPS-2, Fibrobacterota, and Spirochaetota were decreased. The Random Forest Classification showed that the lower relative abundance of Fibrobacterota, Spirochaetota, and Elusimicrobiota and the higher relative abundance of Firmicutes and Chloroflexi were most impactful in explaining the microbial community data. Overall, the essential oil blend showed great potential as a methane gas mitigation strategy by modifying rumen fermentation through changes in the microbial community dynamics.
Rapid Real-Time Prediction Techniques for Ammonia and Nitrite in High-Density Shrimp Farming in Recirculating Aquaculture Systems
Authors: Chen Fudi , Qiu Tianlong , Xu Jianping , Zhang Jiawei , Du Yishuai , Duan Yan , Zeng Yihao , Zhou Li , Sun Jianming , Sun Ming
Published: September 2024

Water quality early warning is a key aspect in industrial recirculating aquaculture systems for high-density shrimp farming. The concentrations of ammonia nitrogen and nitrite in the water significantly impact the cultured animals and are challenging to measure in real-time, posing a substantial challenge to water quality early warning technology. This study aims to collect data samples using low-cost water quality sensors during the industrial recirculating aquaculture process and to construct predictive values for ammonia nitrogen and nitrite, which are difficult to obtain through sensors in the aquaculture environment, using data prediction techniques. This study employs various machine learning algorithms, including General Regression Neural Network (GRNN), Deep Belief Network (DBN), Long Short-Term Memory (LSTM), and Support Vector Machine (SVM), to build predictive models for ammonia nitrogen and nitrite. The accuracy of the models is determined by comparing the predicted values with the actual values, and the performance of the models is evaluated using Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), and Root Mean Square Error (RMSE) metrics. Ultimately, the optimized GRNN-based predictive model for ammonia nitrogen concentration (MAE = 0.5915, MAPE = 28.95%, RMSE = 0.7765) and the nitrite concentration predictive model (MAE = 0.1191, MAPE = 29.65%, RMSE = 0.1904) were selected. The models can be integrated into an Internet of Things system to analyze the changes in ammonia nitrogen and nitrite concentrations over time through aquaculture management and routine water quality conditions, thereby achieving the application of recirculating aquaculture system water environment early warning technology.
Chromatographic Comparison of Commercially Available Columns for Liquid Chromatography in Polar Pesticide Detection and Quantification Using a Score-Based Methodology
Authors: Verdini Emanuela , Pacini Tommaso , Orsini Serenella , Sdogati Stefano , Pecorelli Ivan
Published: September 2024

The detection and quantification of polar pesticides in liquid chromatography coupled with mass spectrometry present significant analytical challenges. This study compares the performance of three LC columns (Hypercarb™, Raptor Polar X™, and Anionic Polar Pesticide™) in separating and quantifying eleven polar pesticides in chicken eggs using a score-based methodology. Analytes include glyphosate, its metabolites, and other high-polarity pesticides like Ethephon, Glufosinate, and Fosetyl aluminum, included in the EU’s official control plan. Polar pesticides, characterized by high polarity and hydrophilicity, lead to analytical issues such as poor retention and unconventional peak shapes with traditional reversed-phase methods. Their weak interaction with hydrophobic stationary phases complicates separation, necessitating specific stationary phases to enhance retention and selectivity. This study evaluates these columns’ efficacy in complex matrices like chicken eggs and other food samples. Chromatographic separation was performed using a UPLC system coupled with a Q-TOF mass spectrometer; extraction and purification involved freeze-out, centrifugation, and filtration steps. The study highlights the critical role of column selection in achieving accurate and reliable separation and quantification of highly polar analytes in matrices of animal origin, offering in the meantime an easy-to-apply methodology of selection for the right determination of the best chromatographic column for different purposes.
Hydration and Hardening Properties of High Fly-Ash Content Gel Material for Cemented Paste Backfill Utilization
Authors: Xiao Bolin , Wang Jiandong , Wu Aixiang , Guo Ruiming
Published: September 2024

As more and more mines utilize the cemented paste backfill (CPB) mining method, the demand for reducing backfill cost and carbon footprint is increasing and becoming more critical. In this work, a new backfill gel binder made with 40 wt.% of low-quality Class F fly ash (FCM) is proposed to replace ordinary Portland cement (OPC). The binder hydration and gel hardening properties were experimentally investigated through X-ray diffraction, Mercury intrusion porosimetry, uniaxial compression, and thermogravimetric analysis. Three different mine tailings were used to verify the FCM’s applicability. Results show that the strength performance of FCM-CPB is 72% of that of OPC-CPB, while FCM production cost is almost less than half of OPC. The hydration process of the FCM-CPB can be divided into five stages, and the main hydration products are ettringite and gel-like hydrates. The 31.2% porosity of FCM-CPB at 28-day curing is higher than that of 7-day curing, while the average pore size is lower, and the structure is denser. The FCM can meet the strength requirement of three different mine tailings regarding different subsequent filling and cut-and-fill mining methods. The proposed FCM provides a feasible alternative with economic and environmental benefits.
Composite Hydrogel of Polyacrylamide/Starch/Gelatin as a Novel Amoxicillin Delivery System
Authors: Poyraz Yağmur , Baltacı Nisa , Hassan Gana , Alayoubi Oubadah , Uysal Bengü Özuğur , Pekcan Önder
Published: September 2024

This study investigates the development and characterization of a novel composite hydrogel composed of polyacrylamide (PAAm), starch, and gelatin for use as an amoxicillin delivery system. The optical properties, swelling behavior, and drug release profile of the composite hydrogel’s were studied to evaluate its efficacy and potential applications. UV-visible spectroscopy was employed to determine the optical properties, revealing significant transparency in the visible range, which is essential for biomedical applications. The incorporation of starch and gelatin into the polyacrylamide matrix significantly enhanced the hydrogel’s swelling capacity and biocompatibility. Studies on drug delivery demonstrated a sustained release profile of amoxicillin in simulated gastrointestinal fluids, which is essential for maintaining therapeutic levels for a prolonged amount of time. The results indicate that the composite hydrogel of PAAm/starch/gelatin has good swelling behavior, appealing optical characteristics, and a promising controlled drug release mechanism. These results point to this hydrogel’s considerable potential as a drug delivery method, providing a viable path toward enhancing the medicinal effectiveness of amoxicillin and maybe other medications.