Larger Ones Research Articles

FEATURES OF THE ARCHITECTURAL AND PLANNING STRUCTURE OF THE GOLDEN HORDE CITIES OF THE LOWER VOLGA REGION

The Lower Volga region was the center of all trade routes, both economically and politically, until the collapse of the Golden Horde state. Starting from the 40-50s of the 13th century, in the Lower Volga region began the town-planning activities of the Golden Horde khans. Since the cities of the Golden Horde arose not as a result of the long-term development of the settlement network, but with the support of the rule of the Golden Horde khans, the historical features of the cities include the absence of a traceable transition process from settlements to cities. In addition, some “typical” criteria for determining the urban character of a monument, for example the presence of fortifications, do not apply to the cities of the Golden Horde. In many cities of the Golden Horde, even very large ones, fortifications were not found until the civil strife of the 60-70s of the 14th century. The spread of Islam in the Golden Horde Volga region from the beginning of the 14th century influenced the development of Golden Horde architecture and decoration. The ornamental style of the architectural and planning structure of the Golden Horde cities of the lower Volga region was primarily under the strong influence of the masters of Central Asia, Transcaucasia and Iran.

Ensemble pretrained language models to extract biomedical knowledge from literature.

The rapid expansion of biomedical literature necessitates automated techniques to discern relationships between biomedical concepts from extensive free text. Such techniques facilitate the development of detailed knowledge bases and highlight research deficiencies. The LitCoin Natural Language Processing (NLP) challenge, organized by the National Center for Advancing Translational Science, aims to evaluate such potential and provides a manually annotated corpus for methodology development and benchmarking. For the named entity recognition (NER) task, we utilized ensemble learning to merge predictions from three domain-specific models, namely BioBERT, PubMedBERT, and BioM-ELECTRA, devised a rule-driven detection method for cell line and taxonomy names and annotated 70 more abstracts as additional corpus. We further finetuned the T0pp model, with 11 billion parameters, to boost the performance on relation extraction and leveraged entites' location information (eg, title, background) to enhance novelty prediction performance in relation extraction (RE). Our pioneering NLP system designed for this challenge secured first place in Phase I-NER and second place in Phase II-relation extraction and novelty prediction, outpacing over 200 teams. We tested OpenAI ChatGPT 3.5 and ChatGPT 4 in a Zero-Shot setting using the same test set, revealing that our finetuned model considerably surpasses these broad-spectrum large language models. Our outcomes depict a robust NLP system excelling in NER and RE across various biomedical entities, emphasizing that task-specific models remain superior to generic large ones. Such insights are valuable for endeavors like knowledge graph development and hypothesis formulation in biomedical research.

The organisational context of middle management role behaviour in Hungarian schools

ABSTRACT Growing evidence suggests that the effectiveness of a school’s operations is determined not by a single leader, but by a community of leaders. Middle managers can coordinate processes in formal positions and lead informal groups, facilitating the realisation of leadership goals. This study utilises a large-scale survey to analyse middle managers’ perceptions of organisational learning and discuss their additional supporting roles and responsibilities considering organisational-level contingency variables in a centralised education system (Hungary). How can middle management function in a centralised educational system? How can it act as a ‘bridge’ and support organisational and individual learning? Our findings reveal that middle managers perceive leadership similarly to school staff (i.e. in terms of learning organisational behaviour), while principals’ perceptions significantly differ. The study examined teachers’ perceptions of organisational learning in various positions according to school size and type. It was found that middle managers play a more prominent role in smaller schools compared to larger ones, as they are more likely to utilise their strategic role in contexts with a more cohesive community. These findings suggest that the function of middle management should be better exploited in larger organisations.

Frequent winners explain apparent skewness preferences in experience-based decisions

Do people's attitudes toward the (a)symmetry of an outcome distribution affect their choices? Financial investors seek return distributions with frequent small returns but few large ones, consistent with leading models of choice in economics and finance that assume right-skewed preferences. In contrast, many experiments in which decision-makers learn about choice options through experience find the opposite choice tendency, in favor of left-skewed options. To reconcile these seemingly contradicting findings, the present work investigates the effect of skewness on choices in experience-based decisions. Across seven studies, we show that apparent preferences for left-skewed outcome distributions are a consequence of those distributions having a higher value in most direct outcome comparisons, a "frequent-winner effect." By manipulating which option is the frequent winner, we show that choice tendencies for frequent winners can be obtained even with identical outcome distributions. Moreover, systematic choice tendencies in favor of right- or left-skewed options can be obtained by manipulating which option is experienced as the frequent winner. We also find evidence for an intrinsic preference for right-skewed outcome distributions. The frequent-winner phenomenon is robust to variations in outcome distributions and experimental paradigms. These findings are confirmed by computational analyses in which a reinforcement-learning model capturing frequent winning and intrinsic skewness preferences provides the best account of the data. Our work reconciles conflicting findings of aggregated behavior in financial markets and experiments and highlights the need for theories of decision-making sensitive to joint outcome distributions of the available options.

Designing an energy storage system based on water tower pumping to store the energy generated by the turbo-expander implemented in a gas pressure reduction station

In the present research, an energy recovery and storage strategy for a natural gas pressure reduction station with a capacity of 50,000 Nm3/h has been proposed. Currently, the gas pressure reduction station does not have an energy recovery system; hence, energy of high-pressure natural gas is wasted in it. For this reason, first, the energy recovery system was thermodynamically designed based on the use of a turbo-expander instead of the regulator, and the extracted energy was calculated. The results showed that 1.376 GWh electrical energy can be generated annually using the recovered energy. Then the economic analysis of the use of the proposed plan was discussed. The results of the economic analysis showed that the investment payback period for installing the turbo-expander in the considered station is 2.4 years. In the last part of the research, an energy storage system was designed to store the generated electrical energy. For this purpose, an energy storage system based on water pumping in water towers was designed. Water towers with different classes were investigated. The obtained results showed that the average energy conversion efficiency in the energy storage system varies from about 70 % for small water towers to about 74 % for large ones.

Machine Learning Classifiers for Credit Risk Analysis

The modern world is a place of global commerce. Since globalization became popular, entrepreneurs of small and medium enterprises to large ones have looked up to banks, which have existed in various forms since antiquity, as their pillars of support. The risk of granting loans in various forms has significantly increased as a consequence of this, the businesses face financing difficulties. Credit Risk Analysis is a major aspect of approving the loan application that is done by analyzing different types of data. The goal is to minimize the risk of approving the loan for the Individuals or businesses who might not pay back on time. This research paper addresses this challenge by applying various machine learning classifiers to the German credit risk dataset. By evaluating and comparing the accuracy of these models to identify the most effective classifier for credit risk analysis. Furthermore, it proposes a contributory approach that combines the strengths of multiple classifiers to enhance the decision-making process for loan approvals. By leveraging ensemble learning techniques, such as the Voting Ensemble model, the aim is to improve the accuracy and reliability of credit risk analysis. Additionally, it explores tailored feature engineering techniques that focus on selecting and engineering informative features specific to credit risk analysis.

Atherosclerosis and wine

One of the most important risk factors for the origin and development of atherosclerosis is certainly excessive intake of fat in everyday nutrition. This risk factor, and the way it affects the origin and development of atherosclerosis, is described in the article “Atherosclerosis and lymph/risk factors”. In this article, based on verified and proven facts, it is suggested that the primary causes and initiators of atherosclerotic changes in the arterial system of the circulatory system are chylomicrons, especially large ones, which occur under the influence of several risk factors.

Long-term response of plankton assemblage to differentiated nutrient reductions in Laizhou Bay, China

Nutrient reductions are considered effective methods to control eutrophication; however, our understanding of how plankton assemblages respond to nutrient reductions is limited. In the present study, we used a long-term dataset between 2004 and 2018 from Laizhou Bay, China, to describe the main characteristics of plankton assemblage response to nutrient reduction, and to compare the differences in plankton response between the different nutrient reduction regions. The results showed that plankton abundance showed obvious decreasing trends in response to nutrient reductions. The phytoplankton abundance decline rate was directly proportional to the magnitude of nutrient reductions. The large-scale shellfish culture may disrupt the response of mesozooplankton to nutrient reductions. Positive recovery signs in the plankton community were observed with species dominance decreasing for small individuals and increasing for large ones. The results suggested that the reduction in nitrogen and phosphorus positively affected plankton abundance and community recovery in Laizhou Bay.

Governance and efficiency in Chinese foundations

AbstractThis study examines the impact of governance on the efficiency of nonprofit organizations and the relationship among different governance mechanisms. Using a dataset of 3496 Chinese foundations from 2011 to 2015, we find that internal governance, external donor governance, and external governance environment are significantly and positively associated with foundations’ efficiency. Importantly, substitutional relationships exist among these governance mechanisms. Financial reporting quality is a major channel through which governance influences efficiency. Furthermore, the positive association between governance and efficiency is more pronounced for small foundations than for large ones and for noneducation‐focused foundations than for education‐focused ones. This study offers practical guidance for foundations to improve their efficiency by enhancing both internal and external governance and will be of interest to foundation leaders, donors, and policymakers.

An online low-dimension fuzzy modeling method for time-varying processes

Many industrial processes are full of nonlinearity and noise. Fuzzy modeling often leads to issues with dimension disaster and poor robustness for such processes. To address these issues, an online low-dimension fuzzy method (OLDF) is proposed here. First, an explicit mapping-based low-dimension fuzzy method (EMLF) is proposed to handle intricate nonlinear and noisy processes. In this method, similar fuzzy sets are combined into larger ones using a novel similarity criterion to reduce the fuzzy rules. Then, an explicit mapping strategy is developed to represent the local nonlinearities in each enlarged fuzzy set through an explicit mapping function. Subsequently, a robust objective function is constructed to improve the robustness of EMLF model. This objective function minimizes both the mean and variance of the modeling error, ensuring robust performance even in scenarios featuring non-Gaussian noise or outliers. After that, an online update strategy is designed to account for time-varying dynamics. It designs an adaptive detection mechanism to identify changes in dynamic behaviors. Following detection, global and local updating strategies are developed to dynamically update model parameters. Further analysis and proof highlight the effectiveness of the proposed method. Experiments with time-varying processes also confirm its success.

The Energy Transition in SMEs: The Italian Experience

Encouraging energy transition (ET) has become a global imperative for nations and companies, and not just large ones. Not all economic organizations pursue this process with the same intensity. Particularly, the factors driving Small and Medium Enterprises (SMEs) towards ET are not yet clearly defined, despite SMEs representing most companies and bearing a significant responsibility for pollution. To shed light on this matter, this study presents a framework of the main drivers that stimulate SMEs placed in Italy to pursue energy transition. We conducted a Partial Least Squares (PLS) analysis on data collected from innovation-oriented SMEs enlisted in a specific register of the Italian Ministry of Economic Development. The research findings show a weak interest among SMEs towards ET. While country-specific conditions and social and psychological traits affect the decision to invest in ET, firm-specific factors and ‘stakeholder’ pressures seem to be almost irrelevant. Policy measures with a different focus are necessary. Specifically, the provision of facilitation emerges as the most effective tool for encouraging ET among SMEs.

Experimental Study of Wake Evolution under Vertical Staggered Arrangement of Wind Turbines of Different Sizes

During the expansion of a wind farm, the strategic placement of wind turbines can significantly improve wind energy utilization. This study investigates the evolution of wake turbulence in a wind farm after introducing smaller wind turbines within the gaps between larger ones, focusing on aspects such as wind speed, turbulence intensity, and turbulence integral length scale. The flow field conditions are described using parameters like turbulence critical length and power spectral density, as determined through wind tunnel experiments. In these experiments, a single large wind turbine model and nine smaller wind turbine models were used to create a small wind farm unit, and pressure distribution behind the wind turbines was measured under various operating conditions. The results indicate that downstream wind speed deficits intensify as the number of small wind turbines in operation increases. The impact of these smaller turbines varies with height, with a relatively minor effect on the upper blade tip and increasingly adverse effects as you move from the upper blade tip to the lower blade tip. Through an analysis of power spectral density, the contribution of vortex motion to wake turbulence kinetic energy is further quantified. In the far wake region, the number of small wind turbines has a relatively small impact on wind speed fluctuations.

Exploring the value of ecosystem services at health clinic gardens in a South African context

Ecosystem services (ESs) valuation is important for advancing biodiversity objectives and investing in green infrastructure (GI) establishment. Little is known about the potential of health clinic gardens (HCGs) to provide ESs and their potential values. This study sought to determine the plant and bird species composition and potential value of ESs provided by 32 HCGs in two South African district municipalities. HCG sizes range from 513.0m2 to 13531.0m2 and each had at least one micro-garden. Among the 312 plant species observed across all HCGs, 60% were exotic. The small HCGs had 31 ± 3.6 different plant species per garden, compared to the medium-sized (32 ± 3.7) and large ones (49 ± 5.2). Provisioning ESs from the observed species were prominent with 17.4 ± 1.16 species potentially used for general well-being, 13.66 ± 1.08 as potential food plants, and 11.91 ± 0.93 potentially raw materials. Trees in HCGs were estimated to sequester up to 163481.87 kg of carbon from trees covering an area of up to 9620.80m2. Approximately 1390.70 ± 427.50m2 of HCG space had the potential to provide a habitat for plants and birds, and 11 bird species were observed across all HCGs. Sixty-three bird nests encountered in 18 HCGs are believed to belong to the Passer domesticus. HCGs resemble other types of gardens like home and domestic gardens, and their rich plant biodiversity positions them as ideal ecosystems to benefit even the most vulnerable members of society. They provide an opportune platform for knowledge upscaling in the cultivation and use of plant species as a supplement of primary healthcare and food security issues in the summer months to the rural, urban, and peri-urban poor in the Global South.

Utilizing Differences in Mercury Injection Capillary Pressure and Nuclear Magnetic Resonance Pore Size Distributions for Enhanced Rock Quality Evaluation: A Winland-Style Approach with Physical Meaning

Pore structure is a fundamental parameter in determining the hydrocarbon storage capacity and flow characteristics of a reservoir. Mercury injection capillary pressure (MICP) and nuclear magnetic resonance (NMR) are two commonly utilized techniques for characterizing rock pore structures. However, current studies indicate that disparities in testing methodologies due to distinct physical characteristics lead to a partial misalignment in pore size distributions. We conducted MICP (dynamic) and NMR (static) experiments on eight tight sandstone and eight shale samples and proposed a method to utilize information from the differences in MICP and NMR pore size distributions, aiming to enhance the accuracy of rock quality analysis. We observed that in rock cores where large pores are interconnected with smaller pore throats, MICP tends to overestimate the proportion of these smaller pores and underestimate the larger ones. Furthermore, we integrated information from both dynamic and static experimental processes based on physical significance and found that the fitting accuracy of the newly proposed method is superior to the Winland r35 equation. Compared to the Winland r35 equation, our new method significantly improves fitting accuracy, increasing the R-squared value from 0.46 to 0.93 in sandstones and from 0.80 to 0.87 in shales. This represents a potential high-precision, comprehensive tool for rock quality analysis, offering a new perspective for an in-depth understanding of rock properties.

Limiting coarsening of a two-bubble foam with viscosity

Foams age as gas diffuses from smaller bubbles to larger ones. This results in an increase of the average bubble size, which leads to a modification of the foam rheology and overall stability. This is why understanding coarsening is crucial for the creation of aerated materials. The coarsening rate is affected by various mechanisms, which are impacted by geometry (bubble size and bubble size distribution), liquid fraction, air solubility in the liquid phase, permeability of the interfaces, mechanical properties of the liquid phase and of the interfaces. In many real systems changing a single parameter will impact several mechanisms. In this work, we use a two-bubble experiment to isolate and explore the impact of bulk and surface viscosity on the rate of bubble coarsening. The experimental set-up allows us to decouple the impact of viscous dissipation from the rate of gas diffusion. We build a model in which the gas transfer is driven by Laplace pressure differences and hindered by viscous stresses. We need to take into account gravitational stresses, and dissipation in the connections, with which our model is fully predictive of the experimental data.

Closing the Gap between Experiment and Simulation─A Holistic Study on the Complexation of Small Interfering RNAs with Polyethylenimine.

Rational design is pivotal in the modern development of nucleic acid nanocarrier systems. With the rising prominence of polymeric materials as alternatives to lipid-based carriers, understanding their structure-function relationships becomes paramount. Here, we introduce a newly developed coarse-grained model of polyethylenimine (PEI) based on the Martini 3 force field. This model facilitates molecular dynamics simulations of true-sized PEI molecules, exemplified by molecules with molecular weights of 1.3, 5, 10, and 25 kDa, with degrees of branching between 50.0 and 61.5%. We employed this model to investigate the thermodynamics of small interfering RNA (siRNA) complexation with PEI. Our simulations underscore the pivotal role of electrostatic interactions in the complexation process. Thermodynamic analyses revealed a stronger binding affinity with increased protonation, notably in acidic (endosomal) pH, compared to neutral conditions. Furthermore, the molecular weight of PEI was found to be a critical determinant of binding dynamics: smaller PEI molecules closely enveloped the siRNA, whereas larger ones extended outward, facilitating the formation of complexes with multiple RNA molecules. Experimental validations, encompassing isothermal titration calorimetry and single-molecule fluorescence spectroscopy, aligned well with our computational predictions. Our findings not only validate the fidelity of our PEI model but also accentuate the importance of in silico data in the rational design of polymeric drug carriers. The synergy between computational predictions and experimental validations, as showcased here, signals a refined and precise approach to drug carrier design.

An investigation of cracks caused by concrete shrinkage and temperature difference in common reinforced concrete bridge structures.

Cracking phenomena commonly occur in reinforced concrete structures of bridge and culvert components. While some bridge design standards, such as the AASHTO LRFD standard from America, allow for the presence of cracks, they do not provide direct quantitative limits on permissible crack widths. This research analyzes the crack formation mechanism of reinforced concrete structures under the influence of shrinkage and temperature loading, using the "tension-chord" model from the fib MODEL CODE. The research findings demonstrate that despite the steel reinforcement adequately arranged to resist shrinkage and temperature, as per American bridge design standards, cracks frequently occur with widths that may exceed the allowable limits specified in the fib MODEL CODE. The main reason is the inadequate control of concrete construction temperature, especially for large-sized concrete structures. Other findings are: with the same steel ratio, using small diameter reinforcement can significantly reduce the crack width compared to larger ones; Increasing the steel ratio can reduce the crack width but increases the susceptibility to cracking; Crack does not occur immediately after maintenance completion but after a period ranging from several days to several months; Crack width due to concrete shrinkage and temperature changes is typically 0.3mm but may reaches to 0.7mm due to high hydration heat of large sizes structures.

Unravelling spatiotemporal patterns of solar photovoltaic plants development in China in the 21st century

Solar energy, as an environmentally sustainable power source, is gaining increasing popularity worldwide, driving a surge in the number of solar photovoltaic (PV) plants. China, which has a prominent role in this domain, requires continuous updates to its PV plant data for spatiotemporal analyses. However, there remains an absence of a comprehensive and timely dataset of PV plants across China, leaving PV installation dates and other crucial attributes for comprehensive analyses underexplored. This study leverages Sentinel-2 data as a primary source to propose an optimized deep learning approach for PV plant extraction in China. Statistical analyses of PV plant attributes, including its installation date, size, site slope, and site land cover, were implemented from multiple data sources. Comprehensive analyses were conducted to unravel their spatiotemporal development patterns in the 21st century. The results indicate that as of 2023, China boasts 4347 PV plants, collectively spanning 4146 km2, which are predominantly concentrated in Northwest and North China. 2016 and 2017 marked substantial growth in China’s PV plants, while other years exhibited stability. These plants exhibit the distinct spatial characteristics of installing smaller PV plants on flat terrain covered by vegetation or barren land. Over time, a notable trend in the installation of China’s PV plants has been the increasing preference for establishing larger ones in smooth terrain, with a focus on preventing damage to natural resources. The results reveal China’s optimization of PV plant site selection and construction strategies, aligning with global environmental goals and sustainable energy practices.

Condyloma acuminata - the review of the treatment methods

Introduction and purpose:
 Condyloma acuminata, a benign manifestation of HPV infection, is a common sexually transmitted disease. Condyloma acuminata most commonly affects the anogenital area, but can also occur in the oral cavity. Macroscopically the lesions present as raised, fleshy papules, sometimes flat and broad, pedicled or cauliflower-like. Generally, the lesions are multiple, and as they grow, tend to coalesce into larger ones. Due to the lack of symptoms, condyloma acuminata are often found incidentally during routine medical appointments, e.g. in gynecologist’s offices. Additional symptoms and signs are pain, minor bleeding and pruritus. Patients suffering from condyloma acuminata are often in psychological and psychosexual distress. 
 This work aims to provide a summary of the currently available treatment options based on scientific reports to date.
 A brief description of the state of knowledge:
 Among the factors that increase the risk of the HPV infection are age, lifestyle, promiscuity, smoking, HIV infection, a history of chlamydia or gonorrhea infections. To date, no treatment algorithm has been developed for condyloma acuminata. Two vaccines are on offer as primary prevention, Gardasil and Cervarix. The available treatment options are divided into two main groups: patient-administered and physician administered. Surgical methods have the highest therapeutic efficacy. Condyloma acuminata has a high recurrence rate. Usually, a multi-session combination treatment is needed to reach total remission.
 Summary (conclusions):
 The treatment of condyloma acuminata includes personalized targets due to their diverse clinical picture and the limitations of certain patient groups. The available treatment options do not exhaust the needs of patients suffering from condyloma acuminata. Further research into the effectiveness of specific treatments is advisable.

Remote Sensing Micro-Object Detection under Global and Local Attention Mechanism

With the rapid advancement of technology, satellite and drone technologies have had significant impacts on various fields, creating both opportunities and challenges. In areas like the military, urban planning, and environmental monitoring, the application of remote sensing technology is paramount. However, due to the unique characteristics of remote sensing images, such as high resolution, large-scale scenes, and small, densely packed targets, remote sensing object detection faces numerous technical challenges. Traditional detection methods are inadequate for effectively detecting small targets, rendering the accurate and efficient detection of objects in complex remote sensing images a pressing issue. Current detection techniques fall short in accurately detecting small targets compared to medium and large ones, primarily due to limited feature information, insufficient contextual data, and poor localization capabilities for small targets. In response, we propose an innovative detection method. Unlike previous approaches that often focused solely on either local or contextual information, we introduce a novel Global and Local Attention Mechanism (GAL), providing an in-depth modeling method for input images. Our method integrates fine-grained local feature analysis with global contextual information processing. The local attention concentrates on details and spatial relationships within local windows, enabling the model to recognize intricate details in complex images. Meanwhile, the global attention addresses the entire image’s global information, capturing overarching patterns and structures, thus enhancing the model’s high-level semantic understanding. Ultimately, a specific mechanism fuses local details with global context, allowing the model to consider both aspects for a more precise and comprehensive interpretation of images. Furthermore, we have developed a multi-head prediction module that leverages semantic information at various scales to capture the multi-scale characteristics of remote sensing targets. Adding decoupled prediction heads aims to improve the accuracy and robustness of target detection. Additionally, we have innovatively designed the Ziou loss function, an advanced loss calculation, to enhance the model’s precision in small target localization, thereby boosting its overall performance in small target detection. Experimental results on the Visdrone2019 and DOTA datasets demonstrate that our method significantly surpasses traditional methods in detecting small targets in remote sensing imagery.