Variety Of Patterns Research Articles

Probabilistic Forecasting of Regional Solar Power Incorporating Weather Pattern Diversity

Power grid stability depends on the ability to forecast solar power generation at the regional level. Most previous research on probabilistic forecasting has focused on the use of machine learning to predict the output of individual solar power plants rather than regional solar power generation, and few studies have considered the effects of seasonal weather patterns at the regional level. In this study, climate and geographic data were collected from 83 weather stations between 2019 and 2021 for use in developing a probabilistic model by which to predict regional solar power generation. The results of weather pattern analysis based on unsupervised machine learning and ensemble voting were used to build a probabilistic quantile regression model for the short-term prediction of regional solar power generation capacity. The efficacy of the model was assessed using data from 48 solar PV power plants, which included four sub-datasets pertaining to four target regions. Highly accurate results were consistently obtained across all regions in both the winter and summer seasons. The proposed probabilistic model outperformed conventional deterministic model by 6.55% and conventional probabilistic model by 4.03% in terms of total normalized mean absolute error (NMAE). Prediction intervals generated by the proposed model could be used as input parameters for regional power dispatch decisions.

Emergence of Diverse Epidermal Patterns via the Integration of the Turing Pattern Model with the Majority Voting Model

Animal skin patterns are increasingly explained using the Turing pattern model proposed by Alan Turing. The Turing model, a self-organizing model, can produce spotted or striped patterns. However, several animal patterns exist that do not correspond to these patterns. For example, the body patterns of the ornamental carp Nishiki goi produced in Japan vary randomly among individuals. Therefore, predicting the pattern of offspring is difficult based on the parent fish. Such a randomly formed pattern could be explained using a majority voting model. This model is a type of cellular automaton model that counts the surrounding states and transitions to high-number states. Nevertheless, the utility of these two models in explaining fish patterns remains unclear. Interestingly, the patterns generated by these two models can be detected among very closely related species. It is difficult to think that completely different epidermal formation mechanisms are used among species of the same family. Therefore, there may be a basic model that can produce both patterns. Herein, the Turing pattern and majority voting method are represented using cellular automata, and the possibility of integrating these two methods is examined. This integrated model is equivalent to both models when the parameters are adjusted. Although this integrated model is extremely simple, it can produce more varied patterns than either one of the individual models. However, further research is warranted to determine whether this model is consistent with the mechanisms involved in the formation of animal fish patterns from a biological perspective.

Mapping the terrain of hate: identifying and analyzing online communities and political parties engaged in hate speech against Muslims and LGBTQ+ communities

AbstractThis study investigates the impact of X on political discourse and hate speech in Finland, focusing on Muslim and LGBTQ+ communities from 2018 to 2023. During this period, these groups have experienced increased hate speech and a concerning surge in hate crimes. Utilizing network analysis methods, we identified online communities and examined the interactions between Finnish MPs and these communities. Our investigation centered on uncovering the emergence of networks propagating hate speech, assessing the involvement of political figures, and exploring the formation dynamics of digital communities. Employing agenda-setting theory and methodologies including text classification, topic modeling, network analysis, and correspondence analysis, the research uncovers varied communication patterns in retweet and mention networks. Retweet networks tend to be more fragmented and smaller, with participation primarily from far-right Finns Party MPs, whereas mention networks exhibit wider political representation, including members from all parties. Findings highlight the Finns Party MPs' significant role in fostering divisive, emotionally charged communications within politically segregated retweet communities, contrasting with their broader engagement in mention networks. The study underscores the necessity for cross-party efforts to combat hate speech, promote inclusive dialogue, and mitigate political polarization.

Multi-scale population analysis unit construction method considering scene feature variability and long/short-term patterns in spatiotemporal population activities

ABSTRACT Differences in the spatial scale and component elements of urban scenes affect the analysis of spatiotemporal changes in population distribution (PD) patterns. Fixed scales and geometric forms constrained previous analyses of PD and spatiotemporal changes, which neglected the realistic characteristics of urban scenarios. These limitations hinder the ability to capture the diversity and spatiotemporal representation of PD patterns across multiple spatial scales. This study developed a multi-spatial scale population analysis unit (PAU) construction method considering the scenario heterogeneity and long/short-term patterns of spatiotemporal population changes (PC). Initially, we decompose the multi-scale changes in population time series patterns and their relationships with scene features along the temporal dimension to analyze the primary and secondary factors. Starting from the temporal characteristics of the PD and PC, we established a validation and correction method for the factors. Finally, combined with a multi-feature clustering method, a multi-scale PAU construction method driven by scene feature factors is proposed. Experiments were conducted at different spatial scales in both routine and emergent scenarios. The results indicated that this method can help to obtain more homogeneous analysis regions and enhance the stability and phase pattern representations of changes in PDs, thereby improving the interpretability of analytical results.

Integrating multiple vehicle drivetrains into an energy system simulation model for Japan

To reduce the impact of climate change, the Japanese economy has set mitigation goals that include the decarbonisation of the energy sector and the electrification of transport. As a result, zero-emission vehicles could change the electricity demand curve and need to be integrated into energy system models to estimate their impact and any opportunities or challenges they represent to grid stability. While previous studies have integrated single vehicle technologies in the simulation of country-level energy grids, the present study improves on available models by integrating a country-level energy system model with a transmission grid, while considering two different drivetrains and improving on the diversity of the vehicle movement patterns considered. The simulation model results highlight that the electricity demand of each drivetrain is distinct, with a midday peak for battery electric vehicles and less pronounced morning and afternoon peaks for fuel cell electric vehicles. An important conclusion is that the infrastructure setup and associated use rules can be expected to significantly impact transport demand curves, indicating the need to further investigate how policy changes can impact on the overall configuration of the energy mix.

A Robotic Solution for Precision Smoothing and Roughening of Precast Concrete Surfaces: Design and Experimental Validation

Prefabricated construction has pioneered a new model in the construction industry, where prefabricated component modules are produced in factories and assembled on-site by construction workers, resulting in a highly efficient and convenient production process. Within the construction industry value chain, the smoothing and roughening of precast concrete components are critical processes. Currently, these tasks are predominantly performed manually, often failing to achieve the desired level of precision. This paper designs and develops a robotic system for smoothing and roughening precast concrete surfaces, along with a multi-degree-of-freedom integrated intelligent end-effector for smoothing and roughening. Point-to-point path planning methods are employed to achieve comprehensive path planning for both smoothing and roughening, enhancing the diversity of textural patterns using B-spline curves. In the presence of embedded obstacles, a biologically inspired neural network method is introduced for precise smoothing operation planning, and the A* algorithm is incorporated to enable the robot’s escape from dead zones. Experimental validation further confirms the feasibility of the entire system and the accuracy of the machining path planning methods. The experimental results demonstrate that the proposed system meets the precision requirements for smoothing and offers diversity in roughening, affirming its practicality in the precast concrete process and expanding the automation level and application scenarios of robots in the field of prefabricated construction.

Metasurface Loaded Ceramic based MIMO Antenna with Improved Gain and Circular Polarization Characteristics

Abstract In this communication, the conceptualization involves the design of a multiple input multiple output (MIMO) (dual-port) ring dielectric resonator antenna and the integration of a meta-surface onto this configuration. It introduces three distinctive attributes, which characterize the novelty of the proposed design: (a) facilitates the conversion of linearly polarized waves into circularly polarized counterparts; (b) contributes to a substantial enhancement in gain in between the working band; and (c) there is notable improvement in the isolation between the antenna ports to more than 25 dB. The experimental measurements validate the simulated results of proposed design and confirms its applicability in between 2.45–3.0 GHz. Notably, a 3-dB axial ratio is created in the frequency span of 2.56-2.99 GHz, along with an impressive gain of approximately 8.3 dBi. Its consistent radiation pattern and excellent diversity performance make it suitable for WiMAX (2.6 GHz) use.

Vascular Risk Factors and Brain Health in Aging: Insights from a Community-Based Cohort Study.

The aging population and high rates of Alzheimer's disease (AD) create significant medical burdens, prompting a need for early prevention. Targeting modifiable risk factors like vascular risk factors (VRFs), closely linked to AD, may provide a promising strategy for intervention. This study investigates how VRFs influence cognitive performance and brain structures in a community-based cohort. In this cross-sectional study, 4,667 participants over 50 years old, drawn from the Beijing Ageing Brain Rejuvenation Initiative project, were meticulously examined. Cognitive function and VRFs (diabetes mellitus, hypertension, hyperlipidemia, obesity, and smoking), were comprehensively assessed through one-to-one interviews. Additionally, a subset of participants (n = 719) underwent MRI, encompassing T1-weighted and diffusion-weighted scans, to elucidate gray matter volume and white matter structural network organization. The findings unveil diabetes as a potent detriment to memory, manifesting in atrophy within the right supramarginal gyrus and diminished nodal efficiency and degree centrality in the right inferior parietal lobe. Hypertension solely impaired memory without significant structural changes. Intriguingly, individuals with comorbid diabetes and hypertension exhibited the most pronounced deficits in both brain structure and cognitive performance. Remarkably, hyperlipidemia emerged as a factor associated with enhanced cognition, and preservation of brain structure. This study illuminates the intricate associations between VRFs and the varied patterns of cognitive and brain structural damage. Notably, the synergistic effect of diabetes and hypertension emerges as particularly deleterious. These findings underscore the imperative to tailor interventions for patients with distinct VRF comorbidities, especially when addressing cognitive decline and structural brain changes.

The relationship between landscape pattern and plant species diversity in east Dongting lake wetland based on different Eco-environment

Exploring the impact factors associated with biodiversity and the relationship between them has always been a concerned issue in recent years. However, the previous research mostly focus on theoretical layer. Accordingly, the relationship between landscape pattern and biodiversity is to be analyzed in this research. The landscape pattern determines the function and ecological process of the landscape, and affects the species flow, information flow and energy flow in the landscape. Land use patterns has inevitably left an impact on the landscape pattern. Landscape pattern determines the function and ecological process of landscape and thus plays a significant role in biodiversity. East Dongting Lake National Nature Reserve is taken as the research object of the paper, and the remote sensing image data of three different time periods are collected, including 2000, 2010 and 2020. With an interpretation of the vegetation landscape pattern changes inside the protected area to collect and analyze the vegetation coverage. By comparing landscape patterns and the dynamic changes of land use in different periods of time, the correlation between landscape pattern characteristics and regional biodiversity is to be analyzed. Research shows: (1) From 2000 to 2020, the vegetation coverage of East Dongting Lake increased, but the landscape shape, scale, diversity and uniformity index decreased to varying degrees. (2) At the class level of landscape type, the relationship between landscape index and biodiversity is different. A complex relationship between farmland landscape and biodiversity. There is a significant positive correlation between the index of grassland landscape type and the index of regional biodiversity. (3) The correlation analysis results at the landscape level show that the landscape characteristic index is positively correlated with the regional biodiversity index. The grassland landscape in the area is the main habitat of biological species. At the same time, as the main grain producing area, the impact of farmland landscape cannot be ignored. This study has certain theoretical guiding significance for the protection and management of biodiversity in the region in terms of maintaining landscape pattern in particular the grassland landscape area and increasing vegetation coverage in the process of land use.

Bioaccumulation and translocation of radionuclides heavy metals in Cynodon dactylon: A phytoremediation approach in Al-Dora refinery

Oil extraction methods such as fracking and drilling can disperse radioactive elements into the environment, posing potential hazards. Certain plants like Bermuda grass can combat this through rhizodegradation, a process that breaks down pollutants into harmless substances. The efficiency of this process varies by location and is influenced by factors such as soil compatibility and the presence of beneficial microbes. A study showed that Bermuda grass roots absorbed high levels of radioactive elements, especially Uranium in the soil. The distribution and concentration of these isotopes in Bermuda grass varied across different sites. For instance, Cesium-137 showed higher activity in leaves than in soil. These findings highlight the varied accumulation patterns of elements in Bermuda grass and the influence of natural radionuclide distribution on the environment. Approaches like soil amendments, microbial assistance, and genetically modified Bermuda grass can enhance rhizoremediation efficiency. Strategic planning and species selection are key to overcoming rhizoremediation challenges and enhancing its effectiveness. In addition, soil samples from the AL-Dora Refinery showed a high degree of contamination by heavy metals, with the emission from burning fuel and crude oil being the main sources. This indicates the high contamination of soil by these heavy metals. Overall, the text discusses the potential of Bermuda grass in mitigating the environmental impacts of oil extraction through rhizoremediation, and the challenges and strategies in enhancing its effectiveness. It also highlights the issue of soil contamination by heavy metals due to oil refinery emissions. This research examines the levels of heavy metal contamination in the soil of the Al-Dora refinery area, situated in central Iraq. The contamination factor (Cf) was employed to ascertain the contamination status, revealing a significant to extremely high degree of contamination by various heavy metals. The study further investigates the bioaccumulation and translocation of these heavy metals from the soil to the roots and leaves of Cynodon dactylon plants. Two methodologies were utilized to assess the role of Cynodon dactylon in soil phytoremediation: a comparison of heavy metal concentrations in plant parts with global standards, and the application of the Bioconcentration factor (BCF). The findings suggest a high potential for Cynodon dactylon to be employed in the clean-up and phytoremediation process of the contaminated soil. This presents a promising strategy towards environmental remediation in areas contaminated with heavy metals.

Dynamic Gait Analysis in Paediatric Flatfeet: Unveiling Biomechanical Insights for Diagnosis and Treatment.

Flatfeet in children are common, causing concern for parents due to potential symptoms. Technological advances, like 3D foot kinematic analysis, have revolutionized assessment. This review examined 3D assessments in paediatric idiopathic flexible flat feet (FFF). Searches focused on paediatric idiopathic FFF in PubMed, Web of Science, and SCOPUS. Inclusion criteria required 3D kinematic and/or kinetic analysis during posture or locomotion, excluding non-idiopathic cases, adult feet, and studies solely on pedobarography or radiographs. Twenty-four studies met the criteria. Kinematic and kinetic differences between FFF and typical feet during gait were outlined, with frontal plane deviations like hindfoot eversion and forefoot supination, alongside decreased second peak vertical GRF. Dynamic foot classification surpassed static assessments, revealing varied movement patterns within FFF. Associations between gait characteristics and clinical measures like pain symptoms and quality of life were explored. Interventions varied, with orthoses reducing ankle eversion and knee and hip abductor moments during gait, while arthroereisis normalized calcaneal alignment and hindfoot eversion. This review synthesises research on 3D kinematics and kinetics in paediatric idiopathic FFF, offering insights for intervention strategies and further research.

Imaging-Based Deep Learning for Predicting Desmoid Tumor Progression.

Desmoid tumors (DTs) are non-metastasizing and locally aggressive soft-tissue mesenchymal neoplasms. Those that become enlarged often become locally invasive and cause significant morbidity. DTs have a varied pattern of clinical presentation, with up to 50-60% not growing after diagnosis and 20-30% shrinking or even disappearing after initial progression. Enlarging tumors are considered unstable and progressive. The management of symptomatic and enlarging DTs is challenging, and primarily consists of chemotherapy. Despite wide surgical resection, DTs carry a rate of local recurrence as high as 50%. There is a consensus that contrast-enhanced magnetic resonance imaging (MRI) or, alternatively, computerized tomography (CT) is the preferred modality for monitoring DTs. Each uses Response Evaluation Criteria in Solid Tumors version 1.1 (RECIST 1.1), which measures the largest diameter on axial, sagittal, or coronal series. This approach, however, reportedly lacks accuracy in detecting response to therapy and fails to detect tumor progression, thus calling for more sophisticated methods. The objective of this study was to detect unique features identified by deep learning that correlate with the future clinical course of the disease. Between 2006 and 2019, 51 patients (mean age 41.22 ± 15.5 years) who had a tissue diagnosis of DT were included in this retrospective single-center study. Each had undergone at least three MRI examinations (including a pretreatment baseline study), and each was followed by orthopedic oncology specialists for a median of 38.83 months (IQR 44.38). Tumor segmentations were performed on a T2 fat-suppressed treatment-naive MRI sequence, after which the segmented lesion was extracted to a three-dimensional file together with its DICOM file and run through deep learning software. The results of the algorithm were then compared to clinical data collected from the patients' medical files. There were 28 males (13 stable) and 23 females (15 stable) whose ages ranged from 19.07 to 83.33 years. The model was able to independently predict clinical progression as measured from the baseline MRI with an overall accuracy of 93% (93 ± 0.04) and ROC of 0.89 ± 0.08. Artificial intelligence may contribute to risk stratification and clinical decision-making in patients with DT by predicting which patients are likely to progress.

A compact double‐sided four‐port printed antenna with pattern and polarization diversity for super‐wideband applications

SummaryA compact, double‐sided four‐port printed antenna with pattern and polarization diversity performance is proposed for super‐wideband (SWB) applications. The proposed printed antenna consists of microstrip line‐fed corner truncated four identical square patches. One pair of patches is placed strategically on the top side of the substrate, and another pair is on the bottom side with rotational symmetry to realize a compact four‐port antenna with higher port isolation (>25 dB) throughout the operating band along with pattern diversity. A half‐circular slit is etched in the partial ground plane of all four antennas to achieve SWB (1–19.81 GHz) response. A meander‐shaped decoupling structure is incorporated on both sides of the substrate to enhance the port isolation. T‐shaped slots on the patch and corner truncations are conceived to achieve circular polarization (CP) across 5.15–5.83 GHz. The beauty of this structure lies in the easy switching between LHCP and RHCP by just controlling the port excitation. Moreover, the strategic placement of antenna elements produces bidirectional patterns with proper choice of port excitation. A good agreement is observed between the measured and simulation results.

Characterizing dysregulations via cell-cell communications in Alzheimer’s brains using single-cell transcriptomes

BackgroundAlzheimer’s disease (AD) is a devastating neurodegenerative disorder affecting 44 million people worldwide, leading to cognitive decline, memory loss, and significant impairment in daily functioning. The recent single-cell sequencing technology has revolutionized genetic and genomic resolution by enabling scientists to explore the diversity of gene expression patterns at the finest resolution. Most existing studies have solely focused on molecular perturbations within each cell, but cells live in microenvironments rather than in isolated entities. Here, we leveraged the large-scale and publicly available single-nucleus RNA sequencing in the human prefrontal cortex to investigate cell-to-cell communication in healthy brains and their perturbations in AD. We uniformly processed the snRNA-seq with strict QCs and labeled canonical cell types consistent with the definitions from the BRAIN Initiative Cell Census Network. From ligand and receptor gene expression, we built a high-confidence cell-to-cell communication network to investigate signaling differences between AD and healthy brains.ResultsSpecifically, we first performed broad communication pattern analyses to highlight that biologically related cell types in normal brains rely on largely overlapping signaling networks and that the AD brain exhibits the irregular inter-mixing of cell types and signaling pathways. Secondly, we performed a more focused cell-type-centric analysis and found that excitatory neurons in AD have significantly increased their communications to inhibitory neurons, while inhibitory neurons and other non-neuronal cells globally decreased theirs to all cells. Then, we delved deeper with a signaling-centric view, showing that canonical signaling pathways CSF, TGFβ, and CX3C are significantly dysregulated in their signaling to the cell type microglia/PVM and from endothelial to neuronal cells for the WNT pathway. Finally, after extracting 23 known AD risk genes, our intracellular communication analysis revealed a strong connection of extracellular ligand genes APP, APOE, and PSEN1 to intracellular AD risk genes TREM2, ABCA1, and APP in the communication from astrocytes and microglia to neurons.ConclusionsIn summary, with the novel advances in single-cell sequencing technologies, we show that cellular signaling is regulated in a cell-type-specific manner and that improper regulation of extracellular signaling genes is linked to intracellular risk genes, giving the mechanistic intra- and inter-cellular picture of AD.

Symmetry Breaking of Three Self-Organization Rules: A General Theory for the Origin of Complexity

Complex spatiotemporal patterns in nature significantly challenge reductionism-based modern science. The lack of a paradigm beyond reductionism hinders our understanding of the emergence of complexity. The diversity of countless patterns undermines any notion of universal mechanisms. Here, however, we show that breaking the symmetry of three simple and self-organization rules gives rise to nearly all patterns in nature, such as a wide variety of Turing patterns, fractals, spiral, target and plane waves, as well as chaotic patterns. The symmetry breaking is rooted in the basic physical quantities, such as positive and negative forces, space, time and bounds. Besides reproducing the hallmarks of complexity, we discover some novel phenomena, such as abrupt percolation of Turing patterns, phase transition between fractals and chaos, chaotic edge in traveling waves, etc. Our asymmetric self-organization theory established a simple and unified framework for the origin of complexity in all fields, and unveiled a deep relationship between the first principles of physics and the complex world.

The intrinsic substrate specificity of the human tyrosine kinome.

Phosphorylation of proteins on tyrosine (Tyr) residues evolved in metazoan organisms as a mechanism of coordinating tissue growth1. Multicellular eukaryotes typically have more than 50 distinct protein Tyr kinases that catalyse the phosphorylation of thousands of Tyr residues throughout the proteome1-3. How a given Tyr kinase can phosphorylate a specific subset of proteins at unique Tyr sites is only partially understood4-7. Here we used combinatorial peptide arrays to profile the substrate sequence specificity of all human Tyr kinases. Globally, the Tyr kinases demonstrate considerable diversity in optimal patterns of residues surrounding the site of phosphorylation, revealing the functional organization of the human Tyr kinome by substrate motif preference. Using this information, Tyr kinases that are most compatible with phosphorylating any Tyr site can be identified. Analysis of mass spectrometry phosphoproteomic datasets using this compendium of kinase specificities accurately identifies specific Tyr kinases that are dysregulated in cells after stimulation with growth factors, treatment with anti-cancer drugs or expression of oncogenic variants. Furthermore, the topology of known Tyr signalling networks naturally emerged from a comparison of the sequence specificities of the Tyr kinases and the SH2 phosphotyrosine(pTyr)-binding domains. Finally we show that the intrinsic substrate specificity of Tyr kinases has remained fundamentally unchanged from worms to humans, suggesting that the fidelity between Tyr kinases and their protein substrate sequences has been maintained across hundreds of millions of years of evolution.

VARIABLE ANGLE LOCKING PLATE VERSUS MULTIPLE K WIRE FIXATION FOR INTRA-ARTICULAR DISTAL RADIUS FRACTURE: A PROSPECTIVE COMPARATIVE STUDY OF FUNCTIONAL OUTCOME IN ELDERLY

Distal radius fracture (DRF) is a common wrist injury and it accounts for about one-sixth of emergency department visits. In spite of availability of various treatment options, none is universally effective for comminuted intraarticular fracture, because of varied fractures patterns. Two commonly used surgical methods are multiple K-wire fixation and open reduction and internal fixation by variable angle locking plates. Objective of the study was to compare functional outcomes of variable angle locking plate and multiple K wire fixation for intra-articular distal radius fracture in elderly. Prospective observational study was conducted among the patients of distal radius fractures with age more than 50 years. Patients were divided in to two groups with 24 cases in each group. Group one included patients treated with variable angle locking plate and group two included patients treated with multiple K-wires. Post surgery patients were assessed at 6 weeks, 3, 6 and 12 months. Evaluation was done clinically, and the outcomes were assessed by using the Disabilities Arm, Shoulder and Hand (quickDASH) score (primary outcome), Patient-Rated Wrist Evaluation (PRWE) score, range of motion, grip strength and were compared between the groups. Patients treated with variable angle locking plate was found to have better functional outcome than those treated with multiple K-wire initially during the study, but later by the end of the study at 12 months, both groups were found to be having similar outcomes, with no significant difference between the groups. This signifies that among variable angle locking plate and multiple K-wire fixation, no single procedure is superior to the other in long term.

Romanian Bee Product Analysis: Chemical Composition, Antimicrobial Activity, and Molecular Docking Insights.

Bee products are considered true wonders of nature, used since ancient times, and studied even today for their various biological activities. In this study, we hypothesise that Romanian bee products from different origins (micro apiary products, lyophilised forms, commercial) exhibit distinct chemical compositions, influencing their biological activities. An LC-MS analysis revealed varied polyphenolic content patterns, with cumaric acid, ferulic acid, rosmarinic acid, and quercitine identified in significant amounts across all samples. Primary anti-inflammatory evaluation phases, including the inhibition of haemolysis values and protein denaturation, unveiled a range of protective effects on red blood cells (RBC) and blood proteins, contingent upon the sample concentration. Antimicrobial activity assessments against 12 ATCC strains and 6 pathogenic isolates demonstrated varying efficacy, with propolis samples showing low efficacy, royal jelly forms displaying moderate effectiveness, and apilarnin forms exhibiting good inhibitory activity, mostly against Gram-positive bacteria. Notably, the lyophilised form emerged as the most promising sample, yielding the best results across the biological activities assessed. Furthermore, molecular docking was employed to elucidate the inhibitory potential of compounds identified from these bee products by targeting putative bacterial and fungal proteins. Results from the docking analysis showed rosmarinic and rutin exhibited strong binding energies and interactions with the putative antimicrobial proteins of bacteria (-9.7 kcal/mol to -7.6 kcal/mol) and fungi (-9.5 kcal/mol to -8.1 kcal/mol). The findings in this study support the use of bee products for antimicrobial purposes in a biologically active and eco-friendly proportion while providing valuable insights into their mechanism of action.

Pattern of cooling benefits from ecospaces during urbanization: A case study of the Yangtze River Economic Belt

Urban ecological spaces are effective thermoregulators under global warming. However, the cooling efficiency of urban ecological spaces during the urbanization has not been studied comprehensively. Here, we investigate the spatio-temporal dynamics of Urban Cold Island (UCI) intensity in 11 typical cities of the Yangtze River Economic Belt (YREB). We determined the impact of ecological landscape trends on these dynamics by using GlobalLand and MODIS 8 d mean land surface temperature (LST) data for three periods (2000, 2010, and 2020), and the landscape pattern index and diversity index. We found that in the past 20 years, the built-up area has increased by sixfold; 62.53 % and 37.47 % of YREB were warming or cooling, with 71.22 % of the daytime cooling and 93 % of the nighttime warming. The average UCI intensity of YREB has increased from 0.518 to 0.847 and is negatively correlated with LST with a decreasing slope. As the UCI intensity of green spaces increased, that of blue spaces decreased. Surface area and landscape pattern are the key determinants of UCI intensity in blue and green spaces, respectively, especially the landscape shape index (LSI). Therefore, maintaining ecological spaces, enriching the structural integrity of green spaces, and improving blue space connectivity can help cities at different development levels cope with heat stress during regional urbanization.

INFLUENCE OF SILK ROAD CULTURE ON MODERN DESIGN: ARTISTIC FEATURES OF CHINESE BROCADE PATTERNS

Goal: this study aims to analyze the influence of Silk Road culture on the style of brocade patterns in the Tang and Ming Dynasties of China, as well as to consider the practices of using brocade motifs of ancient dynasties in modern design. Methodology: based on the research of scientific literature, this article compares and analyses the stylistic characteristics of brocade patterns: connected beads, curly grass, treasure and cloud; the analysis makes it possible to trace the process of formation and evolution of brocade patterns in the Tang Dynasty. The methods of figurative and stylistic, formal analysis, methods of systematisation, and processing of results are also involved. Results. The study pointed out that the opening of the Silk Road promoted cultural exchanges between East and West, and the prosperity of politics, economy, and culture in the Tang Dynasty promoted the diversity and development of patterns. The above-mentioned motives, through integration with foreign cultures, formed symmetrical beauty, curvilinear beauty, and symbolic characteristics of Tang Dynasty patterns. In the example of the creativity of the modern Chinese designer Chen Liwen, the transformation of the motif «Tiger Sniffs the Rose» and the image of the Chinese tiger of the Ming Dynasty period in the development of modern design products is shown. Scientific novelty. This article reveals the historical evolution of the popular brocade patterns in the Tang and Ming Dynasties of China, summarizes his unique artistic style, and provides inspiration for contemporary clothing innovation design. Practical significance. The significance of this study lies in providing historical data for the integration of ethnic cultures and demonstrating the overall characteristics and evolution process of the Tang and Ming Dynasties patterns.