bluuebunny/crossref_metadata_2025 huggingface dataset:
A Global Multi-Source Tropical Cyclone Precipitation (MSTCP) Dataset
Authors: Morin Gabriel , Boudreault Mathieu , García-Franco Jorge L.
Published: June 2024

A global tropical cyclone precipitation dataset covering the period from January 1979 to February 2023 is presented. Global precipitation estimates were taken from the newly developed high-resolution Multi-Source Weighted-Ensemble Precipitation, version 2 (MSWEP V2) and TC tracks were obtained from the International Best Track Archive for Climate Stewardship (IBTrACS) dataset. This Global Multi-Source Tropical Cyclone Precipitation (MSTCP) dataset is comprised of two main products and files in the format of tables: the main and profile datasets. The main file provides various TCP statistics per TC track, including mean and maximum precipitation rates over a fixed and symmetrical radius of 500 km. The profile dataset comprises the azimuthally averaged precipitation every 10-km away from the center of each storm (until 500 km). The case study of Hurricane Harvey is used to show that MSWEP estimates agree well with another commonly used satellite product. The main statistics of the dataset are analyzed as well, including the differences in the dataset metrics for each of the six TC basins and for each Saffir-Simpson category for storm intensity.
Deep learning with mixup augmentation for improved pore detection during additive manufacturing
Authors: Ahmmed Bulbul , Rau Elisabeth G. , Mudunuru Maruti K. , Karra Satish , Tempelman Joshua R. , Wachtor Adam J. , Forien Jean-Baptiste , Guss Gabe M. , Calta Nicholas P. , DePond Phillip J. , Matthews Manyalibo J.
Published: June 2024

In additive manufacturing (AM), process defects such as keyhole pores are difficult to anticipate, affecting the quality and integrity of the AM-produced materials. Hence, considerable efforts have aimed to predict these process defects by training machine learning (ML) models using passive measurements such as acoustic emissions. This work considered a dataset in which keyhole pores of a laser powder bed fusion (LPBF) experiment were identified using X-ray radiography and then registered both in space and time to acoustic measurements recorded during the LPBF experiment. Due to AM’s intrinsic process controls, where a pore-forming event is relatively rare, the acoustic datasets collected during monitoring include more non-pores than pores. In other words, the dataset for ML model development is imbalanced. Moreover, this imbalanced and sparse data phenomenon remains ubiquitous across many AM monitoring schemes since training data is nontrivial to collect. Hence, we propose a machine learning approach to improve this dataset imbalance and enhance the prediction accuracy of pore-labeled data. Specifically, we investigate how data augmentation helps predict pores and non-pores better. This imbalance is improved using recent advances in data augmentation called Mixup, a weak-supervised learning method. Convolutional neural networks (CNNs) are trained on original and augmented datasets, and an appreciable increase in performance is reported when testing on five different experimental trials. When ML models are trained on original and augmented datasets, they achieve an accuracy of 95% and 99% on test datasets, respectively. We also provide information on how dataset size affects model performance. Lastly, we investigate the optimal Mixup parameters for augmentation in the context of CNN performance.
Purification, characterization and three-dimensional structure prediction of multicopper oxidase Laccases from Trichoderma lixii FLU1 and Talaromyces pinophilus FLU12
Authors: Egbewale Samson O. , Kumar Ajit , Mokoena Mduduzi P. , Olaniran Ademola O.
Published: June 2024

Broad-spectrum biocatalysts enzymes, Laccases, have been implicated in the complete degradation of harmful pollutants into less-toxic compounds. In this study, two extracellularly produced Laccases were purified to homogeneity from two different Ascomycetes spp. Trichoderma lixii FLU1 (TlFLU1) and Talaromyces pinophilus FLU12 (TpFLU12). The purified enzymes are monomeric units, with a molecular mass of 44 kDa and 68.7 kDa for TlFLU1 and TpFLU12, respectively, on SDS-PAGE and zymogram. It reveals distinct properties beyond classic protein absorption at 270–280 nm, with TlFLU1's peak at 270 nm aligning with this typical range of type II Cu site (white Laccase), while TpFLU12's unique 600 nm peak signifies a type I Cu2+ site (blue Laccase), highlighting the diverse spectral fingerprints within the Laccase family. The Km and kcat values revealed that ABTS is the most suitable substrate as compared to 2,6-dimethoxyphenol, caffeic acid and guaiacol for both Laccases. The bioinformatics analysis revealed critical His, Ile, and Arg residues for copper binding at active sites, deviating from the traditional two His and a Cys motif in some Laccases. The predicted biological functions of the Laccases include oxidation–reduction, lignin metabolism, cellular metal ion homeostasis, phenylpropanoid catabolism, aromatic compound metabolism, cellulose metabolism, and biological adhesion. Additionally, investigation of degradation of polycyclic aromatic hydrocarbons (PAHs) by purified Laccases show significant reductions in residual concentrations of fluoranthene and anthracene after a 96-h incubation period. TlFLU1 Laccase achieved 39.0% and 44.9% transformation of fluoranthene and anthracene, respectively, while TpFLU12 Laccase achieved 47.2% and 50.0% transformation, respectively. The enzyme structure–function relationship study provided insights into the catalytic mechanism of these Laccases for possible biotechnological and industrial applications.
Multistage feature fusion knowledge distillation
Authors: Li Gang , Wang Kun , Lv Pengfei , He Pan , Zhou Zheng , Xu Chuanyun
Published: June 2024

Generally, the recognition performance of lightweight models is often lower than that of large models. Knowledge distillation, by teaching a student model using a teacher model, can further enhance the recognition accuracy of lightweight models. In this paper, we approach knowledge distillation from the perspective of intermediate feature-level knowledge distillation. We combine a cross-stage feature fusion symmetric framework, an attention mechanism to enhance the fused features, and a contrastive loss function for teacher and student models at the same stage to comprehensively implement a multistage feature fusion knowledge distillation method. This approach addresses the problem of significant differences in the intermediate feature distributions between teacher and student models, making it difficult to effectively learn implicit knowledge and thus improving the recognition accuracy of the student model. Compared to existing knowledge distillation methods, our method performs at a superior level. On the CIFAR100 dataset, it boosts the recognition accuracy of ResNet20 from 69.06% to 71.34%, and on the TinyImagenet dataset, it increases the recognition accuracy of ResNet18 from 66.54% to 68.03%, demonstrating the effectiveness and generalizability of our approach. Furthermore, there is room for further optimization of the overall distillation structure and feature extraction methods in this approach, which requires further research and exploration.
Comparative study of MAFLD as a predictor of metabolic disease treatment for NAFLD
Authors: Imai Jin , Takashimizu Shinji , Suzuki Nana , Ohshinden Kana , Sawamoto Kana , Mishima Yusuke , Tsuruya Kota , Arase Yoshitaka , Yamano Mitsuhiko , Kishimoto Noriaki , Yamada Chizumi , Inoue Nagamu , Moriyama Kengo , Baba Akiyasu , Suzuki Hidekazu , Kagawa Tatehiro , Nishizaki Yasuhiro
Published: June 2024

A novel concept of Metabolic Associated Fatty Liver Disease (MAFLD) was proposed, incorporating metabolic abnormalities such as obesity and diabetes, which are risk factors that affect the prognosis. Non-Alcoholic Fatty Liver Disease (NAFLD), entails fat accumulation in the liver without alcohol consumption and is often linked to obesity, insulin resistance, and metabolic syndrome. However, the broad nature of the disease concept has hindered prognosis accuracy. In this study, we assess the contribution of the impact of diagnostic criteria for MAFLD on metabolic disease progression compared to conventional diagnostic criteria for NAFLD. A total of 7159 patient who were presented to the health screening center in Tokai University Hospital both in 2015 and 2020 were included in the study. Fatty liver was diagnosed using abdominal ultrasonography. The diagnostic criteria for NAFLD were consistent with the global guidelines based on alcohol consumption. The diagnostic criteria for MAFLD were based on the International Consensus Panel. Medications (anti-hypertensive, diabetic, and dyslipidemia medications) were evaluated by self-administration in the submitted medical questionnaire. A total of 2500 (34.9%) participants were diagnosed with fatty liver (FL +), 1811 (72.4%) fit both NAFLD and MAFLD diagnostic criteria (overlap), 230 (9.2%) fit only the NAFLD diagnostic criteria (NAFLD group) and 404 (16.1%) fit the MAFLD diagnostic criteria (MAFLD group) at 2015. Over the next 5 years, medication rates increased in the NAFLD group for anti-hypertensive, + 17 (7.4%); diabetes, + 3 (1.3%); and dyslipidemia, + 32 (13.9%). In contrast, the only-MAFLD group showed a more significant increase with + 49 (12.1%), + 21 (5.2%), and + 49 (12.1%), for the respective medications, indicating a substantial rise in patients starting new medications. Our analysis of repeated health check-ups on participants revealed that the diagnostic criteria for MAFLD are more predictive of future treatment for metabolic disease than conventional diagnostic criteria for NAFLD.
Predicting ineffective thrombolysis in acute ischemic stroke with clinical and biochemical markers
Authors: Li Yinglei , Li Ning , Zhou Yuanyuan , Li Litao
Published: June 2024

**Ischemic stroke remains a leading cause of morbidity and mortality globally. Despite the advances in thrombolytic therapy, notably recombinant tissue plasminogen activator (rtPA), patient outcomes are highly variable. This study aims to introduce a novel predictive model, the Acute Stroke Thrombolysis Non-Responder Prediction Model (ASTN-RPM), to identify patients unlikely to benefit from rtPA within the critical early recovery window. We conducted a retrospective cohort study at Baoding No.1 Central Hospital including 709 adult patients diagnosed with acute ischemic stroke and treated with intravenous alteplase within the therapeutic time window. The ASTN-RPM was developed using Least Absolute Shrinkage and Selection Operator (LASSO) regression technique, incorporating a wide range of biomarkers and clinical parameters. Model performance was evaluated using Receiver Operating Characteristic (ROC) curves, calibration plots, and Decision Curve Analysis (DCA). ASTN-RPM effectively identified patients at high risk of poor response to thrombolysis, with an AUC of 0.909 in the training set and 0.872 in the validation set, indicating high sensitivity and specificity. Key predictors included posterior circulation stroke, high admission NIHSS scores, extended door to needle time, and certain laboratory parameters like homocysteine levels. The ASTN-RPM stands as a potential tool for refining clinical decision-making in ischemic stroke management. By anticipating thrombolytic non-response, clinicians can personalize treatment strategies, possibly improving patient outcomes and reducing the burden of ineffective interventions. Future studies are needed for external validation and to explore the incorporation of emerging biomarkers and imaging data.
Cumulative incidence and risk factors for medication-related osteonecrosis of the jaw during long-term prostate cancer management
Authors: Tani Masaru , Hatano Koji , Yoshimura Akihiro , Horibe Yuki , Liu Yutong , Sassi Nesrine , Oka Toshiki , Okuda Yohei , Yamamoto Akinaru , Uemura Toshihiro , Yamamichi Gaku , Ishizuya Yu , Yamamoto Yoshiyuki , Kato Taigo , Kawashima Atsunari , Nonomura Norio
Published: June 2024

Bone-modifying agents (BMA) are extensively used in treating patients with prostate cancer with bone metastases. However, this increases the risk of medication-related osteonecrosis of the jaw (MRONJ). The safety of long-term BMA administration in clinical practice remains unclear. We aimed to determine the cumulative incidence and risk factors of MRONJ. One hundred and seventy-nine patients with prostate cancer with bone metastases treated with BMA at our institution since 2008 were included in this study. Twenty-seven patients (15%) had MRONJ during the follow-up period (median, 19 months; interquartile range, 9–43 months). The 2-year, 5-year, and 10-year cumulative MRONJ incidence rates were 18%, 27%, and 61%, respectively. Multivariate analysis identified denosumab use as a risk factor for MRONJ, compared with zoledronic acid use (HR 4.64, 95% CI 1.93–11.1). Additionally, BMA use at longer than one-month intervals was associated with a lower risk of MRONJ (HR 0.08, 95% CI 0.01–0.64). Furthermore, six or more bone metastases (HR 3.65, 95% CI 1.13–11.7) and diabetes mellitus (HR 5.07, 95% CI 1.68–15.2) were risk factors for stage 2 or more severe MRONJ. MRONJ should be considered during long-term BMA administration in prostate cancer patients with bone metastases.
Differences between pathologic and non-pathologic high myopia in 4-year outcomes of anti-VEGF therapy for macular neovascularization
Authors: Honda Yuki , Miyata Manabu , Miyake Masahiro , Hata Masayuki , Numa Shogo , Mori Yuki , Ooto Sotaro , Tamura Hiroshi , Ueda-Arakawa Naoko , Muraoka Yuki , Takahashi Ayako , Sado Keina , Kido Ai , Tsujikawa Akitaka
Published: June 2024

This retrospective observational study aimed to investigate the difference in 4-year outcomes of ranibizumab or aflibercept therapy for macular neovascularization (MNV) with high myopia between pathologic myopia (PM) and non-PM. This study was conducted at Kyoto University Hospital and included consecutive treatment-naïve eyes with active myopic MNV, in which a single intravitreal ranibizumab or aflibercept injection was administered, followed by a pro re nata (PRN) regimen for 4 years. Based on the META-PM study classification, eyes were assigned to the non-PM and PM groups. This study analyzed 118 eyes of 118 patients (non-PM group, 19 eyes; PM group, 99 eyes). Baseline, 1-year, and 2-year best-corrected visual acuity (BCVA) were significantly better in the non-PM group (P = 0.02, 0.01, and 0.02, respectively); however, the 3-year and 4-year BCVA were not. The 4-year BCVA course was similar in both groups. However, the total number of injections over 4 years was significantly higher in the non-PM than in the PM group (4.6 ± 2.6 vs. 2.9 ± 2.6, P = 0.001). Four-year BCVA significantly correlated only with baseline BCVA in both non-PM (P = 0.047, β = 0.46) and PM groups (P < 0.001, β = 0.59). In conclusion, over the 4-year observation period, the BCVA course after anti-VEGF therapy for myopic MNV was similar in the eyes with non-PM and those with PM; however, more additional injections in a PRN regimen were required in the eyes with non-PM compared to those with PM. Thus, more frequent and careful follow-up is required for the eyes with non-PM compared with those with PM to maintain long-term BCVA.
Nutritional upgrade of olive mill stone waste, walnut shell and their mixtures by applying solid state fermentation initiated by Pleurotus ostreatus
Authors: Arapoglou Dimitrios , Eliopoulos Christos , Markou Giorgos , Langousi Ioanna , Saxami Georgia , Haroutounian Serkos A.
Published: June 2024

Present study concerns the transformation of the agro-industrial by-products olive mill stone waste (OMSW) and walnut shell (WS) to a protein-enriched animal feedstuff utilizing the solid state fermentation (SSF) technique. For this purpose, various mixtures of these by-products were exploited as substrates of the SSF process which was initiated by the P. ostreatus fungus. The respective results indicated that the substrate consisted of 80% WS and 20% OMSW afforded the product with the highest increase in protein content, which accounted the 7.57% of its mass (69.35% increase). In addition, a 26.13% reduction of lignin content was observed, while the most profound effect was observed for their 1,3–1,6 β-glucans profile, which was increased by 3-folds reaching the 6.94% of substrate’s mass. These results are indicative of the OMSW and WS mixtures potential to act as efficient substrate for the development of novel proteinaceous animal feed supplements using the SSF procedure. Study herein contributes to the reintegration of the agro-industrial by-products aiming to confront the problem of proteinaceous animal feed scarcity and reduce in parallel the environmental footprint of the agro-industrial processes within the context of circular economy.
Optimization of antimicrobial nanocomposite films based on carboxymethyl cellulose incorporating chitosan nanofibers and Guggul gum polysaccharide
Authors: Mohammadi Hamed , Rezaeigolestani Mohammadreza , Mohsenzadeh Mohammad
Published: June 2024

The present study utilized response surface methodology (RSM) to investigate the impact of varying concentrations of carboxymethyl cellulose (CMC: 0.75–1.75 wt%), Commiphora mukul polysaccharide (CMP: 0–1 wt%), and Chitosan Nanofiber (CHNF: 0–1 wt%) on the physical and antimicrobial characteristics of nanocomposite films based on CMC. The optimization process aimed to enhance ultimate tensile strength (UTS), strain at break (SAB), and antibacterial activity, while minimizing water vapor permeability (WVP), solubility, swelling, moisture content, opacity, and total color difference (ΔE). The results revealed that both CMP and CHNF had a positive influence on reducing moisture content, WVP, and increasing UTS. However, higher concentrations of CMP and CHNF had a divergent effect on SAB, ΔE, and swelling. The incorporation of CMP led to increased opacity and solubility, while the inclusion of CHNF resulted in decreased opacity and solubility. Notably, only CHNF addition significantly improved the antibacterial properties of the films. By applying the optimization procedure utilizing RSM, the formulation containing CMC (1.5 wt%), CMP (0.25 wt%), and CHNF (0.75 wt%) demonstrated superior physical, mechanical, and antibacterial properties in the biodegradable film matrix. These findings highlight the potential of utilizing these components to enhance the performance of CMC-based nanocomposite films.
Amino acid-stabilized luminescent gold clusters for sensing pterin and its analogues
Authors: Sych Tomash S. , Shekhovtsov Nikolai V. , Buglak Andrey A. , Kononov Alexei I.
Published: June 2024

New fluorescent amino acids-based gold nanoclusters (AA-AuNCs) with a quantum yield of up to 26% have been synthesized. Luminescent cost-effective sensors based on AA-AuNCs have been developed for detection of pterins.
Preparation, activity and mechanism of a metallic Cu/TiO2 nanotube array catalyst by a fast solar drying method for photothermal CO2 reduction under concentrating light
Authors: Zhang Zekai , Yan Wei , Wang Ying , Cui Guokai , Lu Hanfeng
Published: June 2024

Metallic Cu/TiO2 nanotube arrays have been prepared by a fast solar drying method, which shows extremely high activity in CO2 photoreduction under concentrating light.
Origin of discrete resistive switching in chemically heterogeneous vanadium oxide crystals
Authors: Naik B. Raju , Chandran Yadu , Rohini Kakunuri , Verma Divya , Ramanathan Shriram , Balakrishnan Viswanath
Published: June 2024

Vacancy-induced filament formation and rupturing across Magnéli phases of VO2. The resistive switching shows the variations in threshold voltage with discreteness as a function of number of cycles.
Shape-tunable two-dimensional assemblies from chromophore-conjugated crystallizable poly(l-lactides) with chain-length-dependent photophysical properties
Authors: Chakraborty Chhandita , Rajak Aritra , Das Anindita
Published: June 2024

This study unveils shape-tunable precision two-dimensional structures from chromophore-conjugated poly(l-lactides) by crystallization-driven self-assembly and the polymer chain length-dependent optical properties of the surface-occupied π-systems.
Dynamic mechanical equilibrium of silicon anodes for lithium-ion batteries enabled by surface hydroxyl-rich bonding
Authors: Zhao Fangfang , Tao Bowen , Yu Liming , Pan Cong , Ma Lei , Wei Liangming , Tang Gen , Wang Yue , Guo Xiang
Published: June 2024

The dynamic mechanical equilibrium strategy effectively suppresses the severe volume expansion of silicon anodes, achieving excellent cycle stability.
From cubane-assembled Mn-oxo clusters to monodispersed manganese oxide colloidal nanocrystals
Authors: He Yan , Liu Yang , Zheng Huijuan , Xiang Zhen , Zhou Zheng , Geng Fengting , Geng Longlong , Dikarev Evgeny V. , Han Haixiang
Published: June 2024

The high nuclearity cubane-assembled Mn-oxo clusters serve as effective precursors for the preparation of monodispersed Mn3O4 nanocrystals through the colloidal chemistry approach.
Revealing local oxygen transport in ionomer films on multidimensional nanoscale catalysts in fuel cells
Authors: Zhou Yumeng , Fan Linhao , Wang Jiaqi , Du Qing , Jiao Kui
Published: June 2024

The one-dimensional nanowire Pt-based catalysts achieves a much lower local oxygen transport resistance than three-dimensional catalysts owing to the broader and shorter transport paths in well-layered ionomer films on one-dimensional catalysts.
Enabling stable aqueous Zn metal anodes using scandium acetate electrolyte additives
Authors: Chen Chun , Li Liansheng , Long Zuxin , Ang Edison Huixiang , Liang Qinghua
Published: June 2024

A scandium acetate electrolyte additive enables the improved stability of Zn metal anodes in an aqueous ZnSO4 electrolyte with suppressed side reactions and Zn dendrite formation due to the buffered pH value and homogenized Zn2+ distribution.
Unravelling the origin of capacity fade in Prussian white hard carbon full cells through operando X-ray diffraction
Authors: Nielsen Ida , Hall Charles Aram , Mattsson Agnes-Matilda , Younesi Reza , Buckel Alexander , Ek Gustav , Brant William R.
Published: June 2024

The capacity loss during long-term cycling of Prussian white hard carbon full cells is due to sodium inventory loss in combination with polarization and not because of structural degradation.
Growth regulation of an easily crystallized organic long-persistent luminescence system with in situ anti-counterfeiting applications
Authors: Liu Duo , Song Yakun , Wang Hao , Wang Tiantian , Zhou Zhiyong , Liu Zhen , Lu Weiwei , Wang Tianyang
Published: June 2024

A new method to regulate the growth of OLPL-doped materials, focusing on easily crystallizable doping systems for in situ anti-counterfeiting ‘paper-ink’ strategy applications.
Solving freshwater conservation challenges through next-generation sequencing approaches
Authors: Esser Milena , Brinkmann Markus , Hecker Markus
Published: June 2024

Traditional aquatic biomonitoring has limitations in resolution and understanding ecosystem dynamics. Next-generation sequencing methods offer a more comprehensive assessment of biodiversity and ecosystem functioning, overcoming these constraints.
A systematic mapping to investigate the application of machine learning techniques in requirement engineering activities
Authors: Hassan Shoaib , Li Qianmu , Aurangzeb Khursheed , Yasin Affan , Khan Javed Ali , Anwar Muhammad Shahid
Published: June 2024

Over the past few years, the application and usage of Machine Learning (ML) techniques have increased exponentially due to continuously increasing the size of data and computing capacity. Despite the popularity of ML techniques, only a few research studies have focused on the application of ML especially supervised learning techniques in Requirement Engineering (RE) activities to solve the problems that occur in RE activities. The authors focus on the systematic mapping of past work to investigate those studies that focused on the application of supervised learning techniques in RE activities between the period of 2002–2023. The authors aim to investigate the research trends, main RE activities, ML algorithms, and data sources that were studied during this period. Forty‐five research studies were selected based on our exclusion and inclusion criteria. The results show that the scientific community used 57 algorithms. Among those algorithms, researchers mostly used the five following ML algorithms in RE activities: Decision Tree, Support Vector Machine, Naïve Bayes, K‐nearest neighbour Classifier, and Random Forest. The results show that researchers used these algorithms in eight major RE activities. Those activities are requirements analysis, failure prediction, effort estimation, quality, traceability, business rules identification, content classification, and detection of problems in requirements written in natural language. Our selected research studies used 32 private and 41 public data sources. The most popular data sources that were detected in selected studies are the Metric Data Programme from NASA, Predictor Models in Software Engineering, and iTrust Electronic Health Care System.
A novel weighting factor determined method of model predictive torque control for permanent magnet synchronous motor
Authors: Yang Tao , Wang Yiming , Miao Yiru , Xu Qiwei , Guo Qiang
Published: June 2024

In the cost function of model predictive torque control (MPTC) of permanent magnet synchronous motor (PMSM), torque and flux linkage have different dimensions, and the weighting factor needs to be properly adjusted to optimise the control performance. Therefore, based on the discrete mathematical model of PMSM, the torque predictive error and flux predictive error expressions are mapped to the two‐phase static coordinate system, which is equivalent to the distance expressions from the endpoint of the voltage vector to the straight line and to the circle respectively. Because they have the same dimension, they can be directly superimposed to form the cost function proposed, which eliminates the selection process of the weighting factor. Then, the candidate voltage vector is substituted into the cost function in turn, and the optimal voltage vector with the minimum cost function is selected as the voltage input of the next control cycle. Finally, the simulation model and an experimental platform are established to compare the steady‐state and dynamic performance of the geometric solution‐based weighting factor determined method proposed with other MPTC methods.
Robust LFC design using adaptive neuro‐fuzzy inference‐aided optimal fractional‐order PIDA control for perturbed power systems with solar and wind power sources
Authors: Roy Tushar Kanti , Yu Samson S. , Mahmud Md. Apel , Trinh Hieu
Published: June 2024

Maintaining stability in modern power systems is challenging due to complex structures, rising power demand, and load disturbances. The integration of renewable energy sources further threatens stability by causing imbalances between generation and demand. Conventional load frequency stabilization methods fall short in such scenarios. This paper proposes an optimal fractional‐order proportional‐integral‐derivative‐acceleration (FOPIDA) controller, enhanced by a robust adaptive neuro‐fuzzy inference system (ANFIS), to improve load frequency control and reliability in power systems with wind and solar generators. First, the dynamical model of a multi‐area interconnected power system, including a thermal power plant, wind turbine, and solar photovoltaic generators, is developed. A decentralized ANFIS‐FOPIDA controller is then designed for load frequency control objectives. The gains of this controller are optimized using the whale optimization algorithm (WOA), focusing on frequency deviation and tie‐line power exchange. Simulations on a New England IEEE 10‐generator 39‐bus power system demonstrate the approach's effectiveness under various disturbances, including random load‐generation disturbances and nonlinear generation behaviors. Comparisons with other strategies, such as fractional order (FO) beetle swarm optimization algorithm (FOBSOA)‐FOPIDA, WOA‐PIDA, and WOA‐ANFIS‐PIDA, and recent control approaches highlight the superior performance of the WOA‐ANFIS‐FOPIDA method in enhancing power system stability.
An overview of riverbank erosion prediction techniques applied to the Mekong Delta, Vietnam
Authors: Pham Ngoc Thinh
Published: June 2024

In recent times, the Mekong Delta has been experiencing severe riverbank erosion, causing significant damage to property, homes, land, and riverside infrastructure. The region is facing increasing intensity of erosion events, attributed to the impact of climate change and the effects of hydropower dams and water control systems upstream on the Mekong River. This paper discusses five key methodologies currently applied for riverbank erosion prediction in the Mekong Delta: digital map overlay, historical topographic data analysis, geophysical and terrain change analysis, physical phenomenon simulation using analogous conditions, and empirical formula-based prediction. Each method's theoretical foundation, procedural steps, and potential limitations are explored in depth.
Life cycle of the facility and its cost at the construction stage
Authors: Kruglyakova Victoria Markovna
Published: June 2024

The article presents a study of the main aspects of managing the value of real estate in the process of implementing investment and construction activities. The study was conducted in the context of the concept of the "life cycle of a capital construction facility" with the decomposition of its stages into regulatory, financial, economic and engineering. The life cycle of an object creation project is defined as a subsystem in the structure of the overall life cycle of an object, combining a set of stages implemented in the process of creating an object. The author has formulated the main factors affecting the value of real estate during the construction at various stages of the project life cycle. The paper defines a mechanism for managing the cost of an object as interrelated stages of the project life cycle, price-forming factors and methodological support for the process of its economic assessment with the definition of types of value, taking into account the objectives of determining the value of the object in the process of its creation. The use of the tools and methods presented by the author is designed to ensure an increase in the efficiency of managerial decision-making during the construction.
High Quality Universal Education Create High Quality Human Resources for The Construction Industry, Case In HCMC
Authors: Tran Thi Loi , Vo Anh Tuan , Le Thi Thanh Binh
Published: June 2024

In the era of the Fourth Industrial Revolution and the rapid development of Artificial Intelligence, the construction industry is facing great challenges in providing quality education that effectively meets the needs increasing number of highly skilled human resources, especially in the fields of construction and architecture. This study aims to address the question of how universities specializing in civil engineering and architecture in Ho Chi Minh City (HCMC) and Vietnam can train and produce qualified students to meet the increasing demands of the labor market. The research uses qualitative methods, conducting case studies at Ho Chi Minh City University of Architecture. Data will be collected through conducting in-depth interviews with stakeholders and decision makers at different management levels. The study aims to identify strategies and directions that can lead to the development of high-quality human resources in the field of construction and architecture. The results of this study will provide valuable insights to local and central government agencies as well as universities and businesses in need of high-quality human resources, helping them strengthen their training process, building and applying policies to support and improve overall quality. Higher education in the construction industry.
Simulation modeling as a means of integrating infrastructure for individual mobility in urban environments
Authors: Krasnikova Irina , Dyakonova Irina , Evdoshenko Oleg
Published: June 2024

The article presents the concept of sustainable development of the territory of the Moscow city district by introducing paths for individual mobility into the transport network. A simulation model of the street road network is created to substantiate the efficiency of implementation, traffic safety, and connectivity of street routes. The accuracy and validity of the obtained model is determined by the use of research methods, which provide a detailed description of the problem area, structured processes and objects, and also establish features of their interaction. To visualize the obtained results, a graph of conflicting and connecting flows in the transport network of the district and graphical models of their behavior are developed.
Simulation modeling as a means of forecasting passenger traffic at Moscow Metro transfer stations
Authors: Boikova Galina , Mironov Pavel
Published: June 2024

The article substantiates the necessity of simulation modeling for passenger traffic processes at Moscow Metro stations. The study aims to develop a simulation model based on pedestrian flows to predict changes in their density and directions in the organization of the station space. The use of the method of structural analysis allowed to establish all the key objects of the studied processes and the links between them. Input parameters and process interaction rules are obtained using abstraction, synthesis, grouping, and statistical analysis techniques. Based on the results obtained, formal models of the behavior of each group of objects have been developed. A 3D twin model corresponding to the real research object was created to visualize the simulation model results and demonstrate the results of the scenario implementation. Moscow Metropolitan can use the results obtained for the operational management of flows at the stations and the modernization of their infrastructure.
Environmental aspect in traffic flow simulation modeling
Authors: Krasnikov Aleksandr , Mironov Pavel
Published: June 2024

The article deals with the problem of air pollution by automobile transportation. Such emissions are a problem in modern cities worldwide, and they harm numerous aspects of human life and the environment. Changing traffic flows and modernization of road infrastructure are ways to reduce the load on the ecological state of urban areas. Developing concepts and assessing their impact on the transportation system and the environment is possible through simulation models. The study establishes all the objects and their qualitative and quantitative characteristics necessary to create a digital twin of traffic flows. Using parametric modeling techniques allowed us to create a simulation model reflecting all aspects of the subject area. The developed model allows conducting experiments on changing the parameters of objects to predict changes in the system’s behavior in terms of its ecological state.
Logistics processes in optimizing the financial results of transport companies
Authors: Tukhkanen Tatiana , Korenyakina Natalia , Malkhasyan Anastasia
Published: June 2024

The article presents a method of evaluating the effectiveness of interaction between business entities, built on the analysis of the procurement and distribution logistics system stage, which allows to reasonably approach the choice of contractors to ensure optimal profit enterprise through the optimization of logistics processes and logistics costs. A model for improving the management of logistics processes at the enterprise in the interaction with counterparties is proposed. Practical value is manifested in the use of developed tools for managing logistics processes to optimize the financial results of transport companies.
Methodological approaches to the CO2 emissions estimation in rail transport
Authors: Bezdudnaya Anna , Kol Olga , Smirnov Roman , Treyman Marina
Published: June 2024

Today there is a growing interest in the problems of assessing the impact of greenhouse gases on the environment. Modern methods for estimating the volume of CO2 emissions from cargo transportation processes are inaccurate. The study aims to develop a methodology for calculating and estimating the volume of CO2 emissions generated by rail transport during the transportation of goods and its subsequent validation. Research methods – analysis of Russian and international regulatory documents, scientific and practical methodological approaches to determine the volume of CO2 emissions for rail transport. It is substantiated that the actual methodological approach should consider direct emissions, indirect emissions and specific parameters: carbon intensity and cargo intensity, mass of transported cargo. The result of study is a methodology for calculating the volume of CO2 emissions, considering the specifics of freight transportation on railway transport and accounting for direct and indirect CO2 emissions. An example of calculating the volume of CO2 emissions during the transportation of building materials by rail between St. Petersburg and Murmansk is given. The proposed methodology makes it possible to include the amount of estimated CO2 emissions in the service, which will facilitate the introduction of "green" certificates for customers.
Promising directions for the artificial intelligence development in the housing and utilities sector
Authors: Telichenko Valeriy , Rimshin Vladimir , Ketsko Ekaterina
Published: June 2024

Modern technologies require the improvement of automation and labour savings. Therefore, successful construction companies are everywhere introducing artificial intelligence into their business, which actually optimizes any processes without human intervention. At the same time, the final product quality increases. Investing in high technology may be a daunting task for many businesses, but in the long run, reducing waste and material consumption will have a positive impact on profitability. Investments in the technologies development in the housing and communal services are increasing around the world, and households are increasingly switching to smart metering devices. Artificial intelligence technologies allow organizations in housing and communal services to reduce the operators cost and automate the most frequent communications with residents. The innovative technologies introduction for the development of housing and communal services is aimed primarily at optimizing the services range in accordance with the population needs and rationalizing their use in the context of sustainable territories development. The authors analyse the prospects for the housing and communal complex development in the digitalization period, explores the possibilities and directions for the introduction and application of artificial intelligence in the field of housing and communal services in Russia and abroad.
The Impact of Artificial Intelligence on Sustainable Building Technologies
Authors: Lin Ji , Abdrasulov Sovetbek Misirkulovich , Yuhao Zhao , Shaiyldaeva Asel
Published: June 2024

With the rapid development of artificial intelligence, the demand of urban development for talents with relevant technical and theoretical knowledge is constantly increasing, which is of great significance for the promotion of scientific and technological progress and social development. This study through the construction of China city talent vitality evaluation index system, analysis of Luoyang, Ningbo, Wuhu city talent vitality level, summarizes the Chinese city talent development and deficiencies, from the talent growth, talent competitiveness, talent entrepreneurship, talent mobility and talent innovation in the practice of Chinese city talent strategy logic thinking.
Devising modern “green tools” in the context of sustainable socio-economic development
Authors: Stozhko Dmitrii , Stozhko Natalia
Published: June 2024

The purpose of the article is to study and evaluate the possibilities for the practical development of the principles and norms of the “green economy” in the production and trade of food products in Russian markets. The objectives of the study are to identify and evaluate specific “green tools” in the field of ensuring (environmental) food security and health of food consumers; determining the direction of the emerging Russian model of environmental policy in relation to food markets in modern conditions; formulating recommendations for ensuring sustainable development in the food industry. Based on a comparative analysis of existing scientific developments in the field of posing and solving the above issues, a conclusion was made about the need for a serious scientific rethinking of the hitherto existing concepts of the “green economy”, taking into account the steadily increasing socio-economic and geopolitical turbulence and risks that negatively affect both food policy in general, and the practical state of specific norms and principles of the green economy in the food sector. Based on factual analysis, an assessment of the environmental, economic and crime situations in the sphere of food consumption in the Russian Federation in recent years is given.
Analysis of the medicinal flora of the Kurshab River basin in Eastern Alay to preserve biodiversity
Authors: Karimov Bolotbek , Dursunbaeva Aida , Babekov Anarbai , Umetaliev Myrzaali , Abzhaparova Ainura
Published: June 2024

The object of the study is the Kurshab River basin of Eastern Alay, which is characterized by unexplored medicinal potential. The article analyzes the species composition of the medicinal flora of the Eastern Alay and their distribution according to life forms, ecological groups, as well as the peculiarities of vertical distribution. It is indicated that the medicinal plants of the Kurshab River basin number about 140 species belonging to 118 genera and 53 families, of which 63 are used in scientific and folk medicine. In terms of taxonomic groups, the leading place belongs to angiosperms - 132 species (96.3%), among medicinal gymnosperms - 5 species (3.6%), horsetails and pteridophytes - one species each (0.71%); In terms of life forms, the first place is occupied by perennial plants - 86 species (61.9%), then shrubs and annuals - 15 species each (10.8%), trees -13 (9.1%), and then biennials - 8 (5.7%) and subshrubs – 2 species (1.5%). Medicinal plants are found in all identified plant zones of the Kurshab river basin. More than half of them are found in the forest-shrub zone (53.5-56.1%), and there are fewer of them in the other zones. The article notes that of the 204 species studied, 104 species are alkaloid-bearing. In terms of the number of alkaloid-bearing plants, the leading place belongs to the families Ranunculaceae, Asteraceae, Chenopodiaceae, Boraginaceae, Lamiaceae, Scrophulariaceae. It has been noted that the flora of the Kurshab River basin is one of the promising areas of the Eastern Alay; if used skillfully and rationally, it can serve as an additional source for the pharmaceutical industry.
Industrial site rehabilitation: Design and challenges
Authors: Pestryakov Alexey Nikolaevich , Sbrodova Nadezhda Vasilyevna , Belyusova Elena Vasilyevna
Published: June 2024

The article is devoted to the design of reclamation works after completion of construction works on the construction/reconstruction of the railway line of the enterprise. The authors described the current regulatory framework, as well as types of reclamation works and stages of project implementation. Within the premises of the experimental area after the construction and installation works were completed the authors conducted the survey and design of reclamation procedures, which included planning, construction of a drainage ditch (as part of the technical stage), landscaping (as part of the biological stage). The authors, after analyzing the survey materials and comparing design solutions, believe that the designers superficially reacted to the task at hand, they did not sufficiently take into account the impact of atmospheric and surface water on the site under consideration. Since the site is located in an urban area, the identified problem will definitely require a solution in the future.
Biological purification of polluted waters as a factor in sustainable environmental management
Authors: Karimov Bolotbek , Abdyrakhmanova Zhazgul , Emilbekova Dinara , Kholmuradova Totigul , Israilova Gulbarchin
Published: June 2024

This article is about accelerating wastewater treatment through a biological method - creating thickets of aquatic and water-coastal plants. Many species of higher aquatic plants grow quite well in various polluted waters and produce large amounts of biomass. At the same time, they absorb mineral and organic components from polluted waters, necessary for the microbiological oxidation of organic substances. In order to determine the purification effect of Lemna minor L., Wolffia arrhiza and Azolla caroliniana, the authors describe experiments conducted on municipal water in Osh and wastewater from a poultry farm. Planting material of plants grown in the laboratory was added to pools with wastewater at the rate of 400 g of duckweed, 500 g of Wolffia and 400 g of Azolla of fresh biomass per 1 m2 . The temperature was monitored and the pH was measured daily. The chemical composition and physical properties of the test waters were determined every 3 days. When growing the above plants, municipal and wastewater from poultry farms is enriched with oxygen, and the degree of purification from organic and mineral substances is intensified. As a result, the authors come to the conclusion that Lemna minor, Wolffia arrhiza and Azolla caroliniana can be used for wastewater treatment, since they are capable of detoxifying it. They extract nitrogen, phosphorus and potassium from water and store nitrogen, phosphorus and potassium in their shoots, and also absorb carbon dioxide and enrich the water with oxygen.
Dependence of seasonal changes in the taxonomic composition and numerical indicators of phytoplankton in the Naiman reservoir on environmental factors
Authors: Abdymomunova Bunisa , Abdymomunov Islam , Turdubaeva Gulsara , Zhakypbekova Atyrgul , Sarieva Yrysbu
Published: June 2024

The article describes the planktonic algae seasonality dependence on environmental factors in the Naiman reservoir, located in southern Kyrgyzstan. According to the biological seasons, the predominance of one or another type of algae is determined by various factors. The object of study, materials and research methods are described. Phytoplankton not only shows a productivity index, but is also an indicator for determining water quality in aquatic ecosystems. Phytoplankton accumulates nutrients, enriches water with oxygen and plays a vital role in the process of water self-purification. The phytoplankton community is the most important element of a reservoir's biota, trophic chain's initial link, the main element of biotic circulation and water self-purification processes, as a widely used biological monitoring system component, its structural and functional characteristics allow to determine the trophic and ecological state of various reservoirs types.