Search Results (297)

The capture of Jerusalem during the First Crusade, the extirpation of various heresies in the twelfth and thirteen centuries, the gradual expansion of Christian rule in the Iberian peninsula, and the mass conversion of Jews to Christianity there during the fourteenth century, all seemed to support a Christian triumphalism that imagined that as the End Time approached, Jews and other infidels would inevitably be absorbed into the Church. Nonetheless, an expanding medieval awareness of the many ‘Others’ beyond Christendom contributed to Christian anxieties that Jews (or Muslims) might expand their number through mass conversion, and not Christians. This paper will examine some sources of this anxiety. Full article

Enhancing farmers’ digital literacy is both an inevitable requirement for adapting to the digital age and an important measure for promoting the sustainable development of livestock and poultry manure resource utilization. This study surveyed and obtained data from 1047 farm households in Ningxia and Gansu, two provinces in China that have long implemented livestock manure resource utilization policies, from December 2023 to January 2024, and employed the binary probit model to analyze how digital literacy influences farmers’ willingness to adopt two livestock manure resource utilization modes, as well as to analyze the moderating role of three policy regulations. This paper also explores the heterogeneous results in different village forms and income groups. The results are as follows: (1) Digital literacy significantly and positively impacts farmers’ willingness to adopt both the “household collection” mode and the “livestock community” mode. For every one-unit increase in a farmer’s digital literacy, the probability of farmers’ willingness to adopt the “household collection” mode rises by 22 percentage points, and the probability of farmers’ willingness to adopt the “livestock community” mode rises by 19.8 percentage points. After endogeneity tests and robustness checks, the conclusion still holds. (2) Mechanism analysis results indicate that guiding policy and incentive policy have a positive moderation effect on the link between digital literacy and the willingness to adopt the “household collection” mode. Meanwhile, incentive policy also positively moderates the relationship between digital literacy and the willingness to adopt the “livestock community” mode. (3) Heterogeneity analysis results show that the positive effect of digital literacy on farmers’ willingness to adopt two livestock manure resource utilization modes is stronger in “tight-knit society” rural areas and in low-income households. (4) In further discussion, we find that digital literacy removes the information barriers for farmers, facilitating the conversion of willingness into behavior. The value of this study is as follows: this paper provides new insights for the promotion of livestock and poultry manure resource utilization policies in countries and regions similar to the development process of northwest China. Therefore, enhancing farmers’ digital literacy in a targeted way, strengthening the promotion of grassroots policies on livestock manure resource utilization, formulating diversified ecological compensation schemes, and establishing limited supervision and penalty rules can boost farmers’ willingness to adopt manure resource utilization models. Full article

Background: Polymerase chain reaction (PCR) is highly sensitive and specific for the rapid diagnosis of invasive fungal disease (IFD) but is not yet widely implemented due to concerns regarding limited standardisation between assays, the lack of commercial options and the absence of clear guidance on interpreting results. Objectives and Methods: This review provides an update on technical and clinical aspects of PCR for the diagnosis of the most pertinent fungal pathogens, including Aspergillus, Candida, Pneumocystis jirovecii, Mucorales spp., and endemic mycoses. Summary: Recent meta-analyses have demonstrated that quantitative PCR (qPCR) offers high sensitivity for diagnosing IFD, surpassing conventional microscopy, culture and most serological tests. The reported specificity of qPCR is likely underestimated due to comparison with imperfect reference standards with variable sensitivity. Although the very low limit of detection of qPCR can generate false positive results due to procedural contamination or patient colonisation (particularly in pulmonary specimens), the rates are comparable to those observed for biomarker testing. When interpreting qPCR results, it is essential to consider the pre-test probability, determined by the patient population, host factors, clinical presentation and risk factors. For patients with low to moderate pre-test probability, the use of sensitive molecular tests, often in conjunction with serological testing or biomarkers, can effectively exclude IFD when all tests return negative results, reducing the need for empirical antifungal therapy. Conversely, for patients with high pre-test probability and clinical features of IFD, qPCR testing on invasive specimens from the site of infection (such as tissue or bronchoalveolar lavage fluid) can confidently rule in the disease. The development of next-generation sequencing methods to detect fungal infection has the potential to enhance the diagnosis of IFD, but standardisation and optimisation are essential, with improved accessibility underpinning clinical utility. Full article

In this study, we conducted two steady-state visual evoked potential (SSVEP) studies to develop a practical brain–computer interface (BCI) system for communication and control applications. The first study introduces a novel visual stimulus paradigm that combines checkerboard patterns with flickering circles configured in single-, double-, and triple-layer forms. We tested three flickering frequency conditions: a single fundamental frequency, a combination of the fundamental frequency and its harmonics, and a combination of two fundamental frequencies. The second study utilizes personalized visual stimuli to enhance SSVEP responses. SSVEP detection was performed using power spectral density (PSD) analysis by employing Welch’s method and relative PSD to extract SSVEP features. Commands classification was carried out using a proposed decision rule–based algorithm. The results were compared with those of a conventional checkerboard pattern with flickering squares. The experimental findings indicate that single-layer flickering circle patterns exhibit comparable or improved performance when compared with the conventional stimuli, particularly when customized for individual users. Conversely, the multilayer patterns tended to increase visual fatigue. Furthermore, individualized stimuli achieved a classification accuracy of 90.2% in real-time SSVEP-based BCI systems for six-command generation tasks. The personalized visual stimuli can enhance user experience and system performance, thereby supporting the development of a practical SSVEP-based BCI system. Full article

The problem of the conversion of diesel buses to electric ones in connection with the inevitable introduction of the EURO 7 emission standards entails an automatic requirement to follow several additional United Nations Economic Commission for Europe rules, like R100 regulations. They regulate the preservation of battery units at longitudinal 12 g and transverse 10 g accelerations without penetrating into the elements of the bus body. Three models (12 modes in total) of battery units with frames made of S235 steel were analysed. The maximum stress value varies between 364.89 MPa and 439.08 MPa in 10 g and 12 g modes, respectively, which is beyond the tensile strength (360 MPa) and provokes plastic deformations. The max deformations were recorded in the models with the highest average stress: 63.04 mm in the 12 g mode with an average stress of 83.18 MPa. The minimum deformations of 6.95 and 7.95 mm were found in the 10 g modes (left and right acceleration direction, respectively), which meet the manufacturer’s requirements (45–50 mm maximum). The study’s primary contribution lies in developing a practical method for assessing battery unit integrity and structural behaviour during the conversion of diesel buses to electric propulsion, fully compliant with R100 regulations. By combining transient structural simulation, mathematical centre modelling of acceleration propagation, and centre of gravity prediction, the proposed approach enables engineers to evaluate electric conversions’ safety and certification feasibility without modifying the existing bus body. Full article

Background and Objectives: Adrenal insufficiency (AI) is an endocrine disorder characterized by inadequate cortisol production, leading to non-specific symptoms that delay diagnosis. The Low Dose Synacthen Test (LDST) is commonly used to evaluate adrenal function, but traditional cortisol cut-offs may not accurately reflect adrenal function in all patients. This study aims to identify baseline cortisol cut-offs to accurately rule in and out AI, reassess the value of cortisol increment during LDST, and evaluate the accuracy of 30 and 60 min cortisol measurements in diagnosing AI. Materials and Methods: We conducted a cross-sectional analysis of patients who underwent LDST at Farhat Hached University Hospital. Diagnostic accuracy of baseline cortisol levels and cortisol increment was assessed using ROC curve analysis to determine optimal cut-offs for predicting LDST outcomes. Results: Among 163 patients (mean age 42.9 years, 63% female), baseline cortisol ≤ 5.35 μg/dL had 100% specificity but 41.5% sensitivity for LDST failure. Conversely, baseline cortisol ≥ 12.4 μg/dL had 100% sensitivity with 45.9% specificity. Single measurements at 30 and 60 min correctly classified 92.64% and 93.87% of cases, respectively. ROC analysis of 30 and 60 min cortisol increments showed high diagnostic accuracy (AUC 0.923 and 0.914, respectively). The optimal cortisol increment cut-off was 6.35 μg/dL for ruling in AI (99% specificity). Conclusions: We propose a novel AI diagnostic algorithm based on a single 30 min cortisol measurement, complemented by revised baseline cortisol cut-offs and cortisol increment as additional criteria. This approach may enhance diagnostic accuracy and minimize unnecessary testing, warranting further clinical validation. Full article

The intentional yaw offset of wind turbines has shown potential to redirect wakes, enhancing overall plant power production, but it may increase fatigue loading on turbine components. This study analyzed fatigue loads on the NREL 5 MW reference wind turbine under varying yaw offsets using blade element momentum theory, dynamic blade element momentum, and the converging Lagrange filaments vortex method, all implemented in OpenFAST. Simulations employed yaw angles from −40° to 40°, with turbulent inflow generated by TurbSim, an OpenFAST tool for realistic wind conditions. Fatigue loads were calculated according to IEC 61400-1 design load case 1.2 standards, using thirty simulations per yaw angle across five wind speed bins. Damage equivalent load was evaluated via rainflow counting, Miner’s rule, and Goodman correction. Results showed that the free vortex method, by modeling unsteady aerodynamic forces, yielded distinct differences in damage equivalent load compared to the blade element method in yawed conditions. The free vortex method predicted lower damage equivalent load for the low-speed shaft bending moment at negative yaw offsets, attributed to its improved handling of unsteady effects that reduce load variations. Conversely, for yaw offsets above 20°, the free vortex method indicated higher damage equivalent for low-speed shaft torque, reflecting its accurate capture of dynamic inflow and unsteady loading. These findings highlight the critical role of unsteady aerodynamics in fatigue load predictions and demonstrate the free vortex method’s value within OpenFAST for realistic damage equivalent load estimates in yawed turbines. The results emphasize the need to incorporate unsteady aerodynamic models like the free vortex method to accurately assess yaw offset impacts on wind turbine component fatigue. Full article

Background: Heparan sulfate (HS) is widely implicated as a receptor for Chlamydia cell attachment and infectivity. However, the enzymatic modification of HS modified by the 3-O sulfotransferase-3 (3-OST-3) enzyme in chlamydial cell entry remains unknown. Methodology: To rule out the possibility that host cell 3-O sulfated heparan sulfate (3-OS HS) plays a significant role in C. muridarum entry, a Chinese hamster ovary (CHO-K1) cell model lacking endogenous 3-OST-3 was used. In addition, we further tested the efficacy of the phage-display-derived cationic peptides recognizing heparan sulfate (G1 peptide) and the moieties of 3-O sulfated heparan sulfate (G2 peptide) against C. muridarum entry using human cervical adenocarcinoma (HeLa 229) and human vaginal epithelial (VK2/E6E7) cell lines. Furthermore, molecular dynamics simulations were conducted to investigate the interactions of the Chlamydia lipid bilayer membrane with the G1 and G2 peptides, focusing on their binding modes and affinities. Results: The converse effect of 3-OST-3 expression in the CHO-K1 cells had no enhancing effect on C. muridarum entry. The G2 peptide significantly (>80%) affected the cell infectivity of the elementary bodies (EBs) at all the tested concentrations, as evident from the reduced fluorescent staining in the number of inclusion bodies. The observed neutralization effect of G2 peptide on C. muridarum entry suggests the possibility of sulfated-like domains being present on the EBs. In addition, data generated from our in silico computational structural modeling indicated that the G2 peptide ligand had significant affinity towards the C. muridarum lipid bilayer. Conclusions: Taken together, our findings show that the pretreatment of C. muridarum with 3-O sulfated heparan sulfate recognizing G2 peptide significantly prevents the entry of EBs into host cells. Full article

It has been proven that the performance of fluid catalytic cracking (FCC), as the most important oil refining process for converting low-value heavy oils into high-value transportation fuels, light olefins, and feedstocks for petrochemicals, depends strongly on the quality of the feedstock. For this reason, characterization of feedstocks and their relationships to FCC performance are issues deserving special attention. This study systematically reviews various publications dealing with the influence of feedstock characteristics on FCC performance, with the aim of identifying the best characteristic descriptors allowing prediction of FCC feedstock cracking capability. These characteristics were obtained by mass spectrometry, SARA analysis, elemental analysis, and various empirical methods. This study also reviews published research dedicated to the catalytic cracking of biomass and waste oils, as well as blends of petroleum-derived feedstocks with sustainable oils, with the aim of searching for quantitative relationships allowing prediction of FCC performance during co-processing. Correlation analysis of the various FCC feed characteristics was carried out, and regression techniques were used to develop correlations predicting the conversion at maximum gasoline yield and that obtained under constant operating conditions. Artificial neural network (ANN) analysis and nonlinear regression techniques were applied to predict FCC conversion from feed characteristics at maximum gasoline yield, with the aim of distinguishing which technique provided the more accurate model. It was found that the correlation developed in this work based on the empirically determined aromatic carbon content according to the n-d-M method and the hydrogen content calculated via the Dhulesia correlation demonstrated highly accurate calculation of conversion at maximum gasoline yield (standard error of 1.3%) compared with that based on the gasoline precursor content determined by mass spectrometry (standard error of 1.5%). Using other data from 88 FCC feedstocks characterized by hydrogen content, saturates, aromatics, and polars contents to develop the ANN model and the nonlinear regression model, it was found that the ANN model demonstrated more accurate prediction of conversion at maximum gasoline yield, with a standard error of 1.4% versus 2.3% for the nonlinear regression model. During the co-processing of petroleum-derived feedstocks with sustainable oils, it was observed that FCC conversion and yields may obey the linear mixing rule or synergism, leading to higher yields of desirable products than those calculated according to the linear mixing rule. The exact reason for this observation has not yet been thoroughly investigated. Full article

This report analyzes the different standard methods of quantity measurement, which, when applied in the processes of receiving and transferring fuel quantities, lead to discrepancies and accounting losses. Three main factors contribute to these discrepancies: unavoidable errors of measuring devices (calibration uncertainty ranging from 0.1 to 0.5% at best), systematic errors due to non-applied corrections during transactions, and systematic errors due to different regulations, which result in inconsistent conversion rules applied throughout the entire purchase-production-sales chain. Modeling of air buoyancy effects showed that neglecting buoyancy correction can lead to measurable and economically significant discrepancies, especially in large-scale operations. The mass of light petroleum products can be underestimated by up to 0.15%, potentially resulting in approximately $3 million in annual financial losses for a medium-sized refinery processing 10,000 tonnes per day. These findings underscore the necessity of applying buoyancy corrections for conventional weighing, especially for liquid petroleum products (LPP) measured in open systems. Conversely, for LPG weighed in closed, pressurized containers, a constant correction factor (0.99985) applies, but its economic impact is negligible. Therefore, the study recommends omitting this LPG correction unless contractually required, to streamline processes and reduce complexity. Achieving result comparability throughout the entire petroleum supply chain requires implementing uniform quantity calculation provisions using calibrated instruments and standardized methods under different conditions. This necessitates that all measurement results are traceable to reference conditions (mass in vacuum, volume at +15 °C). The proposed algorithms for oil mass and volume measurement and recalculation highlight the need for unified international regulations and a robust system. Full article

Carbon dioxide, the primary greenhouse gas responsible for global warming, represents today a critical environmental challenge for humans. Mitigating CO₂ emissions and other greenhouse gases is a pressing global concern. The primary goal of this study is to investigate the potential of particular ionic liquids (ILs) in capturing CO₂ for the sweetening of natural and other gases. The solubility of CO₂ was measured in three distinct ILs, which shared a common anion (triflate, TfO) but differed in their cations. The selected ionic liquids were {1-butyl-3-methylimidazolium triflate [BMIM][TfO], 1-butyl-1-methylpyrrolidinium triflate [BMP][TfO], and 1-butyl-4-methylpyridium triflate [MBPY][TfO]}. The solvents were screened based on results from a molecular computational study that predicted low CO₂ Henry’s Law constants. Solubility measurements were conducted at 303.15 K, 323.15 K, and 343.15 K and pressures up to 1.5 MPa using a gravimetric microbalance (IGA-003). The CO₂ experimental results were modeled using the Peng–Robinson Equation of state with three mixing rules: van der Waals one (vdWI), van der Waals two (vdWII), and the non-random two-liquid (NRTL) Wong–Sandler (WS) mixing rule. For the three ILs, the NRTL-WS mixing rule regressed the data with the lowest average deviation percentage of 1.24%. The three solvents had similar alkyl chains but slightly different polarities. [MBPY][TfO], with the largest size, exhibited the highest CO₂ solubility at all three temperatures. Calculation of its relative polarity descriptor (N) shows it was the least polar of the three ILs. Conversely, [BMP][TfO] showed the highest Henry’s Law constant (lowest solubility) across the studied temperature range. Comparing the results to published data, the study concludes that triflate-based ionic liquids with three fluorine atoms had lower capacity for CO₂ compared to bis(trifluoromethylsulfonyl) imide (Tf2N)-based ionic liquids with six fluorine atoms. Additionally, the study provided data on the enthalpy and entropy of absorption. A final comparison shows that the ILs had a lower CO₂ capacity than Selexol, a solvent widely used in commercial carbon capture operations. Compared to other ILs, the results confirm that the type of anion had a more significant impact on solubility than the cation. Full article

As electric vehicle (EV) adoption grows, efficient and accessible charging infrastructure is essential. This paper introduces a modular simulation environment to evaluate charging point configurations and operational strategies. The simulation incorporates detailed models of electrical consumers and user behaviour, leveraging real-world data to simulate charging scenarios. A rule-based control strategy is applied to assess six configurations for a supermarket parking lot charging point. Key findings include the highest profit being achieved with two fast chargers. In scenarios with a 50 kW grid connection limit, combining fast chargers with stationary battery storage proves effective. Conversely, mobile charging robots generate lower revenue, though grid peak limitations have minimal impact. The study highlights the potential of the simulation environment to optimise charging layouts, refine operational strategies, and develop energy management algorithms. This work demonstrates the utility of the simulation framework for analyzing diverse charging solutions, offering insights into cost efficiency and user satisfaction. The results emphasise the importance of tailored strategies to balance grid constraints, profitability, and user needs, paving the way for intelligent EV charging infrastructure development. Full article

This study examines the critical role of institutional quality in driving corporate adaptation to climate change within the EU-27 member states from 2006 to 2023. It aims to investigate how governance factors—control of corruption, government effectiveness, rule of law, and regulatory quality—influence business strategies for environmental resilience and sustainability, focusing on environmental investments and industrial production. Employing fixed and random effects regression models on a balanced panel dataset, we analyze two dependent variables: environmental protection investment corporations (EPIC), measuring investments in pollution prevention and environmental degradation reduction, and industrial production (IP), reflecting output in mining, manufacturing, and utilities. A composite institutional quality index, derived through principal component analysis (PCA) from the four governance indicators, captures their collective impact, reducing multicollinearity and enhancing analytical robustness. Control variables, including final energy consumption, environmental tax revenues, expenditure on environmental protection, and a Paris Agreement dummy, are incorporated to test the institutional quality effect. Results demonstrate that higher institutional quality significantly enhances EPIC, particularly in countries with greater environmental tax revenues, indicating that robust governance and fiscal policies incentivize sustainable corporate investments. Conversely, the effect on IP is less consistent, with higher fossil energy consumption and lower environmental tax revenues driving production, suggesting a reliance on high-polluting industries. The Paris Agreement positively influences IP, reflecting stronger climate-focused industrial strategies post-2015. These findings underscore the pivotal interplay between institutional quality and environmental fiscal policies in fostering corporate adaptation to climate change. Over the long term, strong governance is essential for aligning business practices with sustainability goals, reducing environmental degradation, and mitigating climate risks across the EU. This study highlights the need for cohesive policies to support green investments and transition industries toward renewable energy sources, addressing disparities in environmental performance among EU member states. Full article

The pursuit of Artificial General Intelligence (AGI) demands AI systems that not only perceive but also reason in a human-like manner. While symbolic systems pioneered early breakthroughs in logic-based reasoning, such as MYCIN and DENDRAL, they suffered from brittleness and poor scalability. Conversely, modern deep learning architectures have achieved remarkable success in perception tasks, yet continue to fall short in interpretable and structured reasoning. This dichotomy has motivated growing interest in Neural–Symbolic AI, a paradigm that integrates symbolic logic with neural computation to unify reasoning and learning. This survey provides a comprehensive and technically grounded overview of AI reasoning in the deep learning era, with a particular focus on Neural–Symbolic AI. Beyond a historical narrative, we introduce a formal definition of AI reasoning and propose a novel three-dimensional taxonomy that organizes reasoning paradigms by representation form, task structure, and application context. We then systematically review recent advances—including Differentiable Logic Programming, abductive learning, program induction, logic-aware Transformers, and LLM-based symbolic planning—highlighting their technical mechanisms, capabilities, and limitations. In contrast to prior surveys, this work bridges symbolic logic, neural computation, and emergent generative reasoning, offering a unified framework to understand and compare diverse approaches. We conclude by identifying key open challenges such as symbolic–continuous alignment, dynamic rule learning, and unified architectures, and we aim to provide a conceptual foundation for future developments in general-purpose reasoning systems. Full article

On 8 September 2022, Queen Elizabeth II, the United Kingdom’s longest-serving monarch, died at Balmoral, aged 96. She had reigned for 70 years. The death of Queen Elizabeth II was met with mixed reactions worldwide. On the one hand, some mourners wanted to pay their last respects to the longest-ruling monarch in the world. On the other hand, disgruntled people wanted to remember and narrate the Queen’s legacy, including her role in British colonialism. The debates opened up conversations, questioning the British Royal Family’s relevance in today’s world, particularly in light of its largely unrevised colonial history. On X, debates were rife and played out much more fiercely. In this paper, the author undertakes a digital ethnography analysis of how Black Twitter worldwide received and responded to the death of Queen Elizabeth. The study found that Black Twitter reacted to the Queen’s death by (1) resisting respectability politics; (2) resisting the erasure of Black history in Britain and beyond; (3) educating Black people about their history. The study argues that Black Twitter is an essential digital space for people worldwide to mobilize and form racial identity politics. Full article