Search Results (49)

A parallel Fabry–Perot interferometer (FPI) optical fiber sensor, enhanced with UV glue, was proposed for environmental temperature detection. The UV glue is applied to the fiber’s sensing region using a coating method, forming an FP cavity through misalignment welding, allowing the FP to function as a temperature sensor. In parallel, a reference FPI with a similar free spectral range (FSR) is connected, generating a Vernier effect that amplifies small changes in the refractive index (RI) of the sensing region. The study demonstrates that UV glue enhances the temperature-sensing capabilities of the FP, and when combined with the Vernier effect, it significantly improves the sensitivity of a single interferometric sensor. The temperature sensitivity of the parallel-connected FPI is −2.80219 nm/°C, which is 7.768 times greater than that of a single FPI (−0.36075 nm/°C). The sensor shows high sensitivity, stability, and reversibility, making it promising for temperature-monitoring applications in various fields, including everyday use, industrial production, and the advancement of optical fiber temperature-sensing technologies. Full article

This study evaluates the potential impact of new energy communities (ECs) on the electric infrastructure within the Italian regulatory framework using publicly available information on reverse power flow metrics in high-voltage (HV)/medium-voltage (MV) interfaces and calculating the municipal energy balance. The current legislation is incentivizing EC configurations where members connected to the same HV/MV interface are sharing energy, predominantly produced by new-generation units. To identify critical territories, primary substation service areas are overlapped with reverse flow occurrences, focusing on cases that exceed 5% of the year. The output is utilized to indicate the municipalities that fall within these areas. The municipalities deemed critical are further evaluated, defining a Key Performance Index (KPI) as the ratio of local production capacity to consumption, with generation data procured by the national database on production units and load estimates derived from provincial cumulative data, adjusted using census information on population and employment with a municipal resolution. A piecewise linearization approach is employed to examine the cumulative distribution function (CDF) of the KPI, enabling a traffic light-like criticality classification. The results provide a relative assessment and highlight municipalities with a higher risk of detrimental impact of EC adoption within the current framework. The outcome is presented as a national georeferenced map illustrating the municipal criticality. This emphasizes the need for revising the regulative framework, potentially enabling the utilization of existing generators in critical areas and leveraging load flexibility and increased local energy sharing to procure benefits from EC adoption. Full article

The theory of semisimple rings plays a fundamental role in noncommutative algebra. We study the complexity of the problem of semisimple rings using the tools of computability theory. Following the general idea of computably enumerable (c.e. for short) universal algebras, we define a c.e. ring as the quotient ring of a computable ring modulo a c.e. congruence relation and view such rings as structures in the language of rings, together with a binary relation. We formalize the problem of being semisimple for a c.e. ring by the corresponding index set and prove that the index set of c.e. semisimple rings is ${\mathsf{\Sigma }}_3^0$-complete. This reveals that the complexity of the definability of c.e. semisimple rings lies exactly in the ${\mathsf{\Sigma }}_3^0$ of the arithmetic hierarchy. As applications of the complexity results on semisimple rings, we also obtain the optimal complexity results on other closely connected classes of rings, such as the small class of finite direct products of fields and the more general class of semiperfect rings. Full article

Industrial heritage, recognized as a significant aspect of historical and cultural heritage, has garnered considerable attention from scholars globally. To elucidate the spatial morphological characteristics and the underlying influencing factors of industrial heritage within karst regions, this study employs methods such as the interstice index, fractal dimension analysis, and spatial syntax. It conducts research on the spatial morphological characteristics of nine typical industrial heritages in Guizhou Province. The primary factors contributing to the variations in layout forms are the intricate karst topography and the functional requirements of production. The functional zoning of industrial heritage aligns with its layout, characterized by straightforward functional zones that have not developed into composite spaces. The overall connectivity of industrial heritage is relatively low, exhibiting weak integration, significant disparities in control values, low average depth values, and a deficiency in comprehensibility and diversity of options. This indicates that the internal connectivity of industrial heritage spaces is generally inadequate, with low accessibility, strong interrelations, average convenience, limited connectivity, and generally acceptable passage. The overall spatial, architectural, and roadway configurations of industrial heritage predominantly exhibit a uniform pattern. Importantly, industrial heritage reveals a highly variable overall spatial form, with an average fractal dimension of 1.57, complex architectural layouts (average fractal dimension of 1.50), and simplistic road network designs (average fractal dimension of 1.43), which collectively suggest high spatial complexity and irregular characteristics. This study can provide a reference for the analysis of spatial characteristics and influencing factors of other material cultural heritages, and it is of great significance for the systematic protection and revitalization of industrial heritage. Full article

The sustainable management of energy sources such as wind plays a crucial role in supplying electricity for both residential and industrial purposes. For this, accurate wind data are essential to bring sustainability in energy output estimations for wind stations. The choice of an appropriate distribution function significantly affects the actual wind data, directly influencing the estimated energy output. While the Weibull function is commonly used to describe wind speed at various locations worldwide, the variability of weather information across wind sites varies significantly. Probabilistic forecasting offers comprehensive probability information for renewable generation and load, assisting decision-making in power systems under uncertainty. Traditional probabilistic forecasting techniques based on machine learning (ML) rely on prediction uncertainty derived from previous distributional assumptions. This study utilized a Bayesian Recurrent Neural Network (BNN-RNN), incorporating prior distributions for weight variables in the RNN network layer and extending the Bayesian networks. Initially, a periodic RNN processes data for wind energy prediction, capturing trends and correlation characteristics in time-series data to enable more accurate and reliable energy production forecasts. Subsequently, the wind power meteorological dataset was analyzed using the reciprocal entropy approach to reduce dimensionality and eliminate variables with weak connections, thereby simplifying the structure of the prediction model. The BNN-RNN prediction model integrates inputs from RNN-transformed time-series data, dimensionality-reduced weather information, and time categorization feature data. The Winkler index is lower by 3.4%, 32.6%, and 7.2%, respectively, and the overall index of probability forecasting pinball loss is reduced by 51.2%, 22.3%, and 10.7%, respectively, compared with all three approaches. The implications of this study are significant, as they demonstrate the potential for more accurate wind energy forecasting through Bayesian optimization. These findings contribute to more precise decision-making and bring sustainability to the effective management of energy systems by proposing a Bayesian Recurrent Neural Network (BNN-RNN) to improve wind energy forecasts. The model further enhances future estimates of wind energy generation, considering the stochastic nature of meteorological data. The study is crucial in increasing the understanding and application of machine learning by establishing how Bayesian optimization significantly improves probabilistic forecasting models that would revolutionize sustainable energy management. Full article

The Western Taiwan Strait (WTS) Economic Zone connects the Yangtze River Delta and the Pearl River Delta, playing a significant role in China’s coastal economy and forming part of the East Asian economic structure. This study used panel data from 20 cities in the WTS Economic Zone, spanning 2011 to 2020, to investigate urban land use efficiency and its dynamic evolution characteristics. The study used a super-efficiency EBM model, which accounts for undesirable outputs, combined with kernel density estimation and Malmquist–Luenberger (ML) index analysis, to thoroughly examine the changes in total factor productivity (TFP) of urban land use and the factors driving these changes within the WTS Economic Zone. The findings are as follows: (1) From 2011 to 2020, the overall trend of urban land use efficiency in the WTS Economic Zone was upward, with coastal areas generally exhibiting higher urban land use efficiency compared to inland areas. (2) The urban land use efficiency of cities in the WTS Economic Zone displayed four types of changes: rising, stable, “U”-shaped, and inverted “U”-shaped. (3) The TEP index of the WTS Economic Zone exhibited a right-leaning “M” trend. Technological change was the primary driver of enhanced urban land use efficiency, although there is still room for improvement in technical efficiency. Based on these findings, this study proposes policy insights to foster high-quality development of urban land use efficiency in the WTS Economic Zone. Full article

With the improvement of social production efficiency and the enhancement of the supply chain system, the traditional linear supply chain model is gradually evolving into a more complex and dynamic industrial chain network. This article uses complex network theory combined with the basic attributes of the industrial chain and supply chain to conduct a comprehensive and in-depth analysis of the integrated circuit industry chain. Firstly, a cooperative network model of the integrated circuit industry chain in Shaanxi Province is established based on the supply chain relationships of enterprises. Secondly, the study analyzes the basic characteristics of the collaborative network model. Thirdly, this study explores the efficiency, resilience, and innovation capacity of industrial chains using a novel set of indexes: the industry chain efficiency index (ICEI), the industry chain resilience index (ICRI), and the industry chain innovation capability index (ICICI). By employing principal component analysis (PCA), the study provides a comprehensive evaluation of industrial chain performance. The findings reveal that the ICEI highlights the critical importance of average path length and network density, showing that shorter paths and higher density are associated with greater efficiency. The ICRI emphasizes the roles of average degree and standard deviation, indicating that higher connectivity and lower variability contribute to resilience. The ICICI identifies the clustering coefficient and network density as key factors, suggesting that tight-knit networks foster innovation. These results offer significant insights into the dynamics of industrial chain collaboration and provide practical recommendations for enhancing supply chain management. Finally, the effectiveness of the proposed method is demonstrated through a case study. The results of the case study indicate the following: (1) Key Enterprises’ Identification: The analysis identified key enterprises like Samsung Semiconductor and HT-tech with the highest betweenness centrality, highlighting their crucial intermediary roles within the network; (2) Efficiency and Innovation Assessment: Compared with foreign-owned and other immigrant businesses, local businesses generally perform below average in terms of efficiency and resilience, indicating that there is room for improvement in technology adoption and innovation capabilities. Full article

For a graph $G=(V_G,E_G)$, a degree-based graphical index $G{I}_d$ takes the general form $G{I}_d={\displaystyle \sum _{xy\in E_G}}\varphi (d_x,d_y)$, where $\varphi$ is a symmetric map and $d_i$ is the degree of $i\in V_G$. For $\alpha \in \mathbb{R}$, if $\varphi ={\left(d_x{d}_y\right)}^{\alpha }$ (resp. $\varphi ={(d_x+d_y)}^{\alpha }$), the index is called the general product-connectivity $R_{\alpha }$ (resp. general sum-connectivity $SC{I}_{\alpha }$) index. In this paper, by formulating an optimization problem, we determine the value(s) of $\alpha$, for which the linear/multiple correlation coefficient of $R_{\alpha }$ and $SC{I}_{\alpha }$ with physicochemical properties of benzenoid hydrocarbons is the strongest. This, in turn, fills some research gaps left by similar studies in this area. Full article

This work analyses the treatment of elements such as biases and their relationship with disinformation in international academic production. The first step in this process was to carry out a search for papers published in academic journals indexed in the main indexing platforms. This was followed by a bibliometric analysis involving an analysis of the production and impact of the selected publications, using social media techniques and a semantic content analysis based on abstracts. The data obtained from Web of Science, Scopus, and Dimensions, relating to health, biases, and fake news as well as post-truth, show how these works have multiplied in the last decade. The question relating to this research is as follows: How have cognitive biases been treated in national and international academic journals? This question is answered with respect to the scientific or research method. The results, which date from 2000 to 2024, show a considerable academic dedication to exploring the relationship between biases and health disinformation. In all these communities we have observed a relationship between production with the field of medicine as a general theme and social media. Furthermore, this connection is always tied to other subjects, such as an aversion to vaccines in Community 10; disinformation about COVID-19 on social media in Community 5; COVID-19 and conspiracy theories in Community 6; and content for the dissemination of health-related subjects on YouTube and the disinformation spread about them. The community analysis carried out shows a common factor in all the analysed communities—that of cognitive bias. Full article

This study examines the connections between various fracture indicators and production data with an example from one of the giant fields in the Middle East producing complex fractured carbonate lithologies. The field under study hosts two reservoirs with a long development and production history, including carbonates from the Asmari and Bangestan Formations. A fracture intensity map was generated based on the interpretation of image logs from 28 wells drilled within the field. Mud loss data were collected and mapped based on the geostatistical Gaussian Random Function Simulation (GRFS) algorithm. Maximum curvature maps were generated based on Asmari structural surface maps. Comparing the results shows a good agreement between the curvature map, fault distribution model, mud loss map, fracture intensity map, and productivity index. The results of image log interpretations led to the identification of four classes of open fractures, including major open fractures, medium open fractures, minor open fractures, and hairline fractures. Using the azimuth and dip data of the four fracture sets mentioned above, the fracture intensity log was generated as a continuous log for each well with available image log data. For this purpose, the fracture intensity log and a continuous fracture network (CFN) model were generated. The continuous fracture network model was used to generate a 3D discrete fracture network (DFN) for the Asmari Formation. Finally, a 3D upscaled model of fracture dip and azimuth, fracture porosity, fracture permeability, fracture length, fracture aperture, and the sigma parameter (the connectivity index between matrix and fracture) were obtained. The results of this study can illuminate the modeling of intricate reservoirs and the associated production challenges, providing insights not only during the initial production phase but also in the application of advanced oil recovery methods, such as thermal recovery. Full article

Background: Biolubricants represent a category of lubricating substances derived from sustainable sources such as vegetable oils, animal fats, and other bio-based materials. They are considered more environmentally friendly than mineral-based lubricants because they are biodegradable and nontoxic. Biolubricants derived from vegetable oils or animal fats were used as first-generation biolubricants. They have limited performance at extreme temperatures, both high and low, as well as low oxidative stability. Substitution of the double bonds by branching improves the performance and stability of the resulting second-generation biolubricants. Methods: In the past, the production of these compounds has relied on the chemical pathway. This method involves elevated temperatures and inorganic catalysts, leading to the necessity of additional purification steps, which decreases environmental sustainability and energy efficiency. A more environmentally friendly alternative, the enzymatic route, has been introduced, in accordance with the principles of “Green Chemistry”. Results: In this paper, the esterification of 2-methylhexanoic acid with 2-octyl-1-dodecanol and its optimization were developed for the first time. The synthesis was conducted within a jacketed batch reactor connected to a thermostatic bath in a solvent-free reaction medium and using Lipozyme^® 435 as biocatalyst. Conclusions: The high viscosity index value of this new hyperbranched ester (>200, ASTM D2270) suggests that it may be an excellent biolubricant to be used under extreme temperature conditions. Regarding sustainability, the main green metrics calculated point to an environmentally friendly process. Full article

Investigating the genetic diversity and population structure of wheat germplasm is crucial for understanding the underlying variability essential for breeding programs and germplasm preservation. This research aims to contribute novel insights with respect to the genetic makeup and relationships among these wheat genotypes, shedding light on the diversity present within the Turkish wheat germplasm. In this study, iPBS-retrotransposon markers were employed to analyze 58 wheat genotypes, encompassing 54 landraces and 4 cultivars sourced from Türkiye. These markers serve as genetic indicators that can be used to evaluate genetic variation, build genealogical trees, and comprehend evolutionary connections. The PCR products were visualized on agarose gel, and bands were scored as present/absent. The ten iPBS primers collectively yielded an average of 16.3 alleles, generating a total of 163 polymorphic bands. The number of alleles produced by individual markers ranged from 4 (iPBS-2386) to 29 (iPBS-2219). The genetic parameters were calculated using the popgen and powermarker programs. The genetic relationships and population structures were assessed using the ntsys and structure programs. Polymorphism information content (PIC) per marker varied from 0.13 (iPBS-2390) to 0.29 (iPBS-2386), with an average value of 0.22. Shannon’s information index (I) was calculated as 1.48, while the number of effective alleles (Ne) and Nei’s genetic diversity (H) were determined to be 0.26 and 0.31, respectively. Genotype numbers 3 (Triticum dicoccum) and 10 (Triticum monococcum) exhibited the maximum genetic distance of 0.1292, signifying the highest genetic disparity. Population structure analysis revealed the segregation of genotypes into three distinct subpopulations. Notably, a substantial portion of genotypes clustered within populations correlated with the wheat species. This population structure result was consistent with the categorization of genotypes based on wheat species. The comprehensive assessment revealed noteworthy insights with respect to allele distribution, polymorphism content, and population differentiation, offering valuable implications for wheat breeding strategies and germplasm conservation efforts. In addition, the iPBS markers and wheat genotypes employed in this study hold significant potential for applications in wheat breeding research and germplasm preservation. Full article

The growing interest in advancing microfluidic devices for manipulating fluids within micrometer-scale channels has prompted a shift in manufacturing practices, moving from single-component production to medium-size batches. This transition arises due to the impracticality of lab-scale manufacturing methods in accommodating the increased demand. This experimental study focuses on the design of master benchmarks 1–5, taking into consideration critical parameters such as rib width, height, and the relative width-to-height ratio. Notably, benchmarks 4 and 5 featured ribs that were strategically connected to the inlet, outlet, and reaction chamber of the master, enhancing their utility for subsequent replica production. Vat photopolymerization was employed for the fabrication of benchmarks 1–5, while replicas of benchmarks 4 and 5 were generated through polydimethylsiloxane casting. Dimensional investigations of the ribs and channels in both the master benchmarks and replicas were conducted using an optical technique validated through readability analysis based on the Michelson global contrast index. The primary goal was to evaluate the potential applicability of vat photopolymerization technology for efficiently producing microfluidic devices through a streamlined production process. Results indicate that the combination of vat photopolymerization followed by replication is well suited for achieving a minimum rib size of 25 µm in width and an aspect ratio of 1:12 for the master benchmark. Full article

The term “Fusarium Head Blight” (FHB) resistance supposedly covers common resistances to different Fusarium spp. without any generally accepted evidence. For food safety, all should be considered with their toxins, except for deoxynivalenol (DON). Disease index (DI), scabby kernels (FDK), and DON steadily result from FHB, and even the genetic regulation of Fusarium spp. may differ; therefore, multitoxin contamination is common. The resistance types of FHB form a rather complex syndrome that has been the subject of debate for decades. It seems that resistance types are not independent variables but rather a series of components that follow disease and epidemic development; their genetic regulation may differ. Spraying inoculation (Type 1 resistance) includes the phase where spores land on palea and lemma and spread to the ovarium and also includes the spread-inhibiting resistance factor; therefore, it provides the overall resistance that is needed. A significant part of Type 1-resistant QTLs could, therefore, be Type 2, requiring the retesting of the QTLs; this is, at least, the case for the most effective ones. The updated resistance components are as follows: Component 1 is overall resistance, as discussed above; Component 2 includes spreading from the ovarium through the head, which is a part of Component 1; Component 3 includes factors from grain development to ripening (FDK); Component 4 includes factors influencing DON contamination, decrease, overproduction, and relative toxin resistance; and for Component 5, the tolerance has a low significance without new results. Independent QTLs with different functions can be identified for one or more traits. Resistance to different Fusarium spp. seems to be connected; it is species non-specific, but further research is necessary. Their toxin relations are unknown. DI, FDK, and DON should be checked as they serve as the basic data for the risk analysis of cultivars. A better understanding of the multitoxin risk is needed regarding resistance to the main Fusarium spp.; therefore, an updated testing methodology is suggested. This will provide more precise data for research, genetics, and variety registration. In winter and spring wheat, the existing resistance level is very high, close to Sumai 3, and provides much greater food safety combined with sophisticated fungicide preventive control and other practices in commercial production. Full article

Satellite-based monitoring of cyanobacterial harmful algal blooms (CyanoHABs) heavily utilizes historical Envisat-MERIS and current Sentinel-OLCI observations due to the availability of the 620 nm and 709 nm bands. The permanent loss of communication with Envisat in April 2012 created an observational gap from 2012 until the operationalization of OLCI in 2016. Although MODIS-Terra has been used to bridge the gap from 2012 to 2015, differences in band architecture and the absence of the 709 nm band have complicated generating a consistent and continuous CyanoHAB monitoring product. Moreover, several Terra bands often saturate during extreme high-concentration CyanoHAB events. This study trained a fully connected deep network (CyanNet) to model MERIS-Cyanobacteria Index (CI)—a key satellite algorithm for detecting and quantifying cyanobacteria. The network was trained with Rayleigh-corrected surface reflectance at 12 Terra bands from 2002–2008, 2010–2012, and 2017–2021 and validated with data from 2009 and 2016 in Lake Okeechobee. Model performance was satisfactory, with a ~17% median difference in Lake Okeechobee annual bloom magnitude. The median difference was ~36% with 10-day Chlorophyll-a time series data, with differences often due to variations in data availability, clouds or glint. Without further regional training, the same network performed well in Lake Apopka, Lake George, and western Lake Erie. Validation success, especially in Lake Erie, shows the generalizability of CyanNet and transferability to other geographic regions. Full article