Found 46 results
Open Access
Article
18 June 2025Orthoptera Colonising the Urban Landscape and Urban Lawns: A Case of a Middle-Russian City
Orthoptera are often surveyed in research on urban environments, but results are ambiguous in different regions and cities. We studied the insects in a city located in the centre of the East-European plain, at the junction of the Continental and Boreal biogeoregions. We distinguished suburbs and the urban landscape and meadows and lawns within the urban landscape. To find orthopterans in grassland habitats, we used sweepnet, acoustic and visual observations, and pitfall traps. Urban habitats are colonised by 20 species of Orthoptera from 29 species observed in the suburbs. Only five species are as frequent in urban habitats as in suburban ones. The urban environment negatively affects both forest species, all three species of dry meadows and only one of ten grassland generalists. On lawns, we found 11 species. Total abundance and species numbers were lower in lawns than in meadows. Only three late-emerging and high-dispersing species were quite frequent in lawns. The occurrence of Conocephalus fuscus in lawns was positively influenced by the presence of uncut patches, Chorthippus dorsatus—by the density of the herb layer. Ch. mollis, which is native to dry meadows, preferred unshaded lawns. Chorthippus biguttulus is a single species inhabiting lawns of almost every quality.
Open Access
Article
06 June 2025Effects of Changing the Specific Surface Area in the Ceramic Matrix of CAC-Containing Refractory Castables on the Initial Stiffening and Setting Behaviour
Besides the coarse and medium grain size distribution, the matrix components play a central role in the performance of refractory castables. Practical experience shows that the particle size distribution (PSD) and the specific surface area of the ceramic matrix significantly influence processing, setting, and sintering behaviour. However, there is a lack of systematic studies on how PSD or specific surface area changes affect castable properties. This study aims to address this gap by varying ceramic matrices to create refractory model castables with different matrix surface areas. Three dispersing agents with different mechanisms (electrosteric and steric) were used at graded concentrations. Results show that castables with higher specific surface areas (using (very) finely ground and highly sintered alumina raw materials with high specific surface areas) and different dispersing agents and their concentrations show substantial differences in the initial stiffening and setting behaviour. Higher specific surface areas of the matrix result in an earlier first stiffening, while adding more dispersing agents leads to delayed stiffening. The refractory model castables’ first stiffening and hydration range (with a simultaneous temperature maximum) vary considerably depending on the dispersing agent used and its concentration, caused by completely different mechanisms.
Open Access
Article
04 June 2025Modular Transcriptional Regulation Using Switchable Transcription Terminators and Aptamers: Design and Optimization in Synthetic Biology
Transcriptional regulation is a key step in gene expression control. While transcription factor-based regulation has been widely used and offers robust control over gene expression, it can sometimes face challenges such as achieving high specificity, rapid dynamic responses, and fine-tuned regulatory precision, which have motivated the exploration of alternative regulatory strategies. With the development of synthetic biology, novel genetic elements such as Switchable Transcription Terminators (SWT) and aptamers provide more flexible and programmable strategies for transcriptional regulation. However, the independent regulatory capabilities of these two types of elements and their combined regulatory mechanisms still require further investigation. In this study, based on an in vitro transcription system, we systematically explored the transcriptional regulation potential of SWT and aptamers. We innovatively combined these two elements to construct a modular gene expression regulation system. First, we screened and optimized a series of SWTs, obtaining high-performance SWTs with low leakage expression and high ON/OFF ratios. These were further validated for reproducibility of their regulatory performance in E. coli. Next, we constructed multi-level cascading circuits using SWTs, successfully extending the system to six levels and building four types of biological logic gates based on SWT in vitro: AND gate, NOT gate, NAND gate, and NOR gate. Furthermore, based on a previously identified thrombin aptamer capable of transcriptional regulation, we confirmed that ligand binding significantly promoted gene transcription. Finally, we integrate switchable transcription terminators (SWTs) and aptamers to create a modular, ligand-responsive system. We combined aptamers with SWTs to construct heterologous input logic gates, successfully improving the precision and dynamic range of regulation. Compared to the individual regulation of SWT and aptamer, the Aptamer-SWT synergistic regulation enhanced transcription activation by up to 3.3-fold and 7.84-fold, respectively. Additionally, we co-utilized these two genetic elements to construct heterologous input AND and OR gates in vitro. This study expands the strategies for gene expression regulation and provides new elements and theoretical support for efficient, programmable transcriptional regulation in synthetic biology. This system holds potential for biosensing, gene circuit design, and nucleic acid therapy applications.
Open Access
Article
28 May 2025Mechanisms of Machine Vision Feature Recognition and Quality Prediction Models in Intelligent Production Line for Broiler Carcasses
With global broiler production reaching 103 million tons in 2024—a 1.5% increase over 2023—the poultry industry continues to grow rapidly. However, traditional broiler segmentation methods struggle to meet modern demands for speed, precision, and adaptability. First, this study proposes an improved lightweight image segmentation algorithm based on YOLOv8-seg and integrates the Segment Anything Model (SAM) for semi-automatic annotation, achieving precise mask segmentation of broiler parts. Subsequently, Key geometric features (e.g., area, perimeter, axes) were extracted using image processing techniques, with enhancements from HSV color transformation, convex hull optimization, and ellipse fitting. Furthermore, Image calibration was applied to convert pixel data to physical dimensions, enabling real-sample validation. Using these features, multiple regression models—including CNNs—were developed for carcass quality prediction. Finally, by analyzing the broiler segmentation process, machine vision techniques were effectively integrated with quality grading algorithms and applied to intelligent broiler segmentation production lines, providing technical support for the intelligent and efficient processing of poultry products. The improved YOLOv8-seg model achieved mAP@0.5:box scores of 99.2% and 99.4%, and the CNN model achieved R2 values of 0.974 (training) and 0.953 (validation). Compared to traditional systems, the intelligent broiler cutting line reduced failure rates by 11.38% and improved operational efficiency by over 3%, offering a reliable solution for automated poultry processing.
Open Access
Article
26 May 2025Electric Vehicles, Artificial Intelligence, and Climate Policy
This article explores the environmental implications of electrification and artificial intelligence (AI) infrastructure, emphasizing the importance of aligning technological development with climate goals. There is a lack of academic literature that explains and analyses such issues. Section 1 assesses the climate efficacy of promoting electric vehicles (EVs) and electric heating in regions where electricity is primarily coal-based. While electrification offers substantial climate benefits when powered by clean energy, lifecycle analyses reveal that EVs in coal-reliant grids may emit more greenhouse gases than internal combustion engine vehicles. Similarly, the climate performance of electric heat pumps depends on the carbon intensity of electricity sources. The section advocates for integrated policies that simultaneously promote electrification and grid decarbonization, enhancing emissions reductions and public health while mitigating the negative impacts of increased demand on polluting power plants. Section 2 uses Saudi Arabia as a case study and examines the environmental impact of AI data centers in the context of Saudi Arabia’s energy and climate policies. It highlights AI infrastructure’s energy and water intensity and its potential to strain environmental resources. To align AI development with national sustainability goals, the article recommends policies such as siting data centers near renewable energy sources, enforcing environmental efficiency standards, fostering R&D partnerships, mandating sustainability reporting, and expanding power purchase agreements and demand response participation. These measures aim to ensure responsible AI growth within climate-aligned frameworks. The implications of this study are that electrification and AI infrastructure can significantly reduce emissions and improve efficiency if powered by clean energy, but they also risk increasing environmental strain unless technological growth is carefully aligned with climate and sustainability goals.
Open Access
Review
22 May 2025NLRP3 Inflammasome and IL-11 in Systemic Sclerosis Pulmonary Fibroblasts
Systemic sclerosis (SSc) is an autoimmune disease characterized by widespread fibrosis affecting multiple organ systems. There is clinical heterogeneity among patients with SSc in terms of the organs affected. However, the pathophysiology of the disease remains elusive. The NLRP3 inflammasome is upregulated in SSc and exerts its fibrotic effects through activation of caspase-1, which in turn activates a fibrotic signaling cascade, resulting in increased collagen deposition and myofibroblast transition. Recently, IL-11 has been shown to be elevated in disease and has been shown to participate in downstream signaling via the NLRP3 inflammasome. A significant number of patients with SSc will develop pulmonary involvement, termed interstitial lung disease (SSc-ILD). Though this type of pulmonary involvement is distinct from other types of pulmonary fibrosis (such as idiopathic pulmonary fibrosis), it may be a valuable model to study mechanisms of fibrosis that could apply to other fibrotic diseases. Here, we discuss recent advances in understanding the mechanisms of the NLRP3 inflammasome and IL-11 in SSc pulmonary fibroblasts. We tie together some of the recent findings, such as senescence, the unfolded protein response, and reactive oxygen species, that contribute to fibrotic pathology via modulating NLRP3 activation, possibly leading to IL-11 expression.
Open Access
Article
15 April 2025Enhancing Streamflow Forecasting in Major West African Rivers by Utilizing Meta-Heuristic Algorithms and Climate Data Time Lag Analysis
Accurate streamflow prediction is essential for irrigation planning, water allocation, and flood risk management, particularly in water-scarce regions like the Niger River Basin. However, the complexity of hydrological processes and data limitations make reliable predictions challenging. This study optimizes Support Vector Machine (SVM) hyperparameters for daily streamflow prediction using time-lagged climate data and four metaheuristic algorithms—Binary Slime Mould Algorithm (BSMA), African Vulture Optimisation Algorithm (AVOA), Archery Algorithm (AA), and Intelligent Ice Fishing Algorithm (IIFA). Model performance was assessed using eight evaluation metrics, with results showing that AA and IIFA consistently outperform the others, achieving Nash-Sutcliffe Efficiency (NSE) values between 0.986–0.999 and 0.893–0.999, respectively. AVOA and BSMA show less consistent performance, with NSE ranges of 0.524–0.999 and 0.863–0.965, respectively. The study highlights the novel integration of multiple metaheuristic algorithms for optimizing machine learning models, offering insights into their effectiveness for hydrological prediction. By demonstrating the superior performance of AA and IIFA, this research provides a robust framework for enhancing long-term streamflow forecasting. These findings support improved water resource management in West Africa, helping policymakers address climate variability, water scarcity, and hydrological uncertainty.
Open Access
Article
08 April 2025Research on the Logic of Mobile Governance from the Perspective of Path Dependency: A Case Study of the Implementation of the “Coal-to-Gas” Policy in Rural Areas of Handan
Mobile governance, a commonly used governance approach in China, has always been controversial. Behind the persistence of mobile governance lies the underlying governance logic. This paper takes the implementation of the “coal-to-gas” policy in rural areas of Handan as a case study to analyze the path-dependent logic inherent in mobile governance. The paper argues that mobile governance’s selection path embodies path dependency characteristics, including three paths: conformist path dependency, policy-based path dependency, and demand-based path dependency. Mobile governance can be regulated through three paths: formulating a comprehensive list of rights and responsibilities for grassroots governance, the provincial government enacting relevant regulations to standardize the grassroots governance process, and vigorously developing e-government and digital government technologies to enhance the rule of law and standardization in grassroots governance.
Open Access
Case Report
01 April 2025Behcet’s Disease: An Uncommon Cause of Severe Tricuspid Stenosis
Behçet’s disease is a vasculitic condition of unknown etiology that is characterized by oral and genital ulcers as well as various skin and ocular lesions. Cardiovascular manifestations of Behçet’s disease are rare, with very few cases having been reported previously in literature. We report a case of severe tricuspid stenosis and pulmonary artery aneurysm in a 29-year-old man with Behçet’s disease, who demonstrated characteristic vascular findings on computed tomography angiography and diagnostic valvular findings on transthoracic echocardiogram and cardiac magnetic resonance imaging. The patient’s Behçet’s disease was treated initially with cyclophosphamide, azathioprine, and prednisone, which subsequently led to complete resolution of the pulmonary artery aneurysm. As for the tricuspid stenosis, though symptoms were managed with diuretic therapy, the severity of valvular dysfunction required consideration and an attempt at tricuspid valve replacement surgery, which unfortunately was met with complications and led to an unfavorable outcome of refractory cardiogenic shock and death. Given the rarity of cardiovascular involvement in patients with Behçet’s disease, along with the lack of clear treatment guidelines, management of findings of tricuspid stenosis and pulmonary artery aneurysm in these patients can be challenging.
Open Access
Article
10 March 2025Leveraging Drone Technology for Precision Agriculture: A Comprehensive Case Study in Sidi Bouzid, Tunisia
The integration of drone technology in precision agriculture offers promising solutions for enhancing crop monitoring, optimizing resource management, and improving sustainability. This study investigates the application of UAV-based remote sensing in Sidi Bouzid, Tunisia, focusing on olive tree cultivation in a semi-arid environment. REMO-M professional drones equipped with RGB and multispectral sensors were deployed to collect high-resolution imagery, enabling advanced geospatial analysis. A comprehensive methodology was implemented, including precise flight planning, image processing, GIS-based mapping, and NDVI assessments to evaluate vegetation health. The results demonstrate the significant contribution of UAV imagery in generating accurate land use classifications, detecting plant health variations, and optimizing water resource distribution. NDVI analysis revealed clear distinctions in vegetation vigor, highlighting areas affected by water stress and nutrient deficiencies. Compared to traditional monitoring methods, drone-based assessments provided high spatial resolution and real-time data, facilitating early detection of agronomic issues. These findings underscore the pivotal role of UAV technology in advancing precision agriculture, particularly in semi-arid regions where climate variability poses challenges to sustainable farming. The study provides a replicable framework for integrating drone-based monitoring into agricultural decision-making, offering strategies to improve productivity, water efficiency, and environmental resilience. The research contributes to the growing body of knowledge on agricultural technology adoption in Tunisia and similar contexts, supporting data-driven approaches to climate-smart agriculture.
