In the practical application of hydraulic rotating machinery, it is essential to thoroughly explore drag reduction and rheological characteristics of drag-reducing additives to optimize machinery efficiency and reduce equipment consumption. This paper combines simulation and experimental approaches to investigate the drag-reduction performance and rheological properties of drag-reducing additives. Numerical simulations are initially conducted to investigate the shear-thinning properties of drag-reducing fluid and explore variations in drag-reduction rate. Turbulent phenomena characteristics are described by analyzing turbulent statistical quantities. Subsequently, the rheological behaviors of polyethylene oxide (PEO), cetyltrimethyl ammonium chloride (CTAC), and their mixed solutions under different conditions are scrutinized using a rotational rheometer. The findings indicate that the drag reduction effect amplifies as the rheological index n and characteristic time λ decrease. The numerical simulations show a maximum drag reduction rate of 20.18%. In rheological experiments, a three-stage viscosity variation is observed in single drag-reducing additives: shear thickening, shear thinning, and eventual stabilization. Composite drag-reducing additives significantly reduce the apparent viscosity at low shear rates, thereby strengthening the shear resistance of the system. Full article

Clarifying the spatiotemporal distribution and influencing factors of mosque architecture in China’s Hehuang region has significant positive implications for the overall protection and development of the region’s architectural cultural heritage. This study utilizes field surveys and acquires POI data of traditional mosques built before 1993 in the region to analyze the distribution characteristics of mosques, aiming to explore future development trends of these religious structures. It also investigates the influencing factors, with the goal of emphasizing the primary and secondary factors affecting mosque distribution. The study finds the following: (1) Mosques are generally centered around the Huangshui Valley, displaying a “central clustering, peripheral dispersal” distribution pattern, forming a spatial structure of “two cores, one belt, multiple points”, with distinct differentiation and overall uneven distribution. (2) Mosques are primarily situated at elevations between 2147 and 2764 m; on slopes less than 15°, in sunny and gentle slopes; within 20 km from rivers; within 14 km from roads; in areas receiving 400–500 mm annual rainfall; and within temperature ranges of 5.54–10.22°C. (3) The study also finds that the spatial distribution of mosques is profoundly influenced by both natural geographical factors and human environmental factors. The better the natural location, the larger and denser the population, the richer the cultural resources, the higher the level of economic development, and the greater the concentration of Hui people, the more numerous and concentrated the mosques. (4) Population factors are the dominant factors for the clustered distribution of traditional mosques in the Hehuang area. Since the construction of mosques in the region is closely related to the number of Hui people and the proportion of Muslim adherents, areas with a high concentration of mosques also have relatively larger populations of Hui people. Temperature, precipitation, altitude, rivers, and roads are foundational factors for traditional mosques in the Hehuang area, influencing mosque distribution as external factors. Full article

Background/Objectives: Sleep and mental health are closely linked, with sleep deprivation increasing the risk of mental health problems in college students. This study aimed to analyze the role of sleep in the mental health status of a sample of Italian freshmen, considering various mental health outcomes and potential interactions between sleep and other relevant factors, such as sociodemographic characteristics, academic experiences, and mental health history. Methods: All freshmen from a medium-sized Italian university were invited to participate in a multidimensional online survey (n = 3756). Sleep quality was assessed through questions on average hours of sleep per night and on satisfaction of perceived sleep quality. Mental health outcomes included psychophysical well-being, psychological distress, substance use, and problematic internet use. Statistical analysis involved multivariate analysis of variance, followed by pairwise comparisons. Results: The sample (n = 721) exhibited low levels of well-being and a high prevalence of psychological distress (52.1%). Approximately one-third of students (n = 258) were dissatisfied with their sleep quality, and one-fourth (n = 186) reported inadequate sleep (less than 7 hours per night). More specifically, 24.4% of students slept on average six hours per night, and 1.4% slept five hours or less. Satisfaction with perceived sleep quality significantly influenced well-being, psychological distress, and cannabis use (η_p² = 0.02). Interaction effects were observed between satisfaction with sleep quality and drop-out intentions (η_p² = 0.01), as well as between satisfaction with sleep quality and history of mental health diagnosis (η_p² = 0.02), both of which were significant for psychological distress and cannabis use. Conclusions: This study highlights the influence of perceived sleep quality on academic distress among college freshmen, particularly those with higher intentions of leaving university and with a history of mental health diagnosis. Full article

Phase synchronization serves as an effective method for analyzing the synchronization of electroencephalogram (EEG) signals among brain regions and the dynamic changes of the brain. The purpose of this paper is to study the construction of the functional brain network (FBN) based on phase synchronization, with a special focus on neural processes related to human balance regulation. This paper designed four balance paradigms of different difficulty by blocking vision or proprioception and collected 19-channel EEG signals. Firstly, the EEG sequences are segmented by sliding windows. The phase-locking value (PLV) of core node pairs serves as the phase-screening index to extract the valid data segments, which are recombined into new EEG sequences. Subsequently, the multichannel weighted phase lag index (wPLI) is calculated based on the new EEG sequences to construct the FBN. The experimental results show that due to the randomness of the time points of body balance adjustment, the degree of phase synchronization of the datasets screened by PLV is more obvious, improving the effective information expression of the subsequent EEG data segments. The FBN topological structures of the wPLI show that the connectivity of various brain regions changes structurally as the difficulty of human balance tasks increases. The frontal lobe area is the core brain region for information integration. When vision or proprioception is obstructed, the EEG synchronization level of the corresponding occipital lobe area or central area decreases. The synchronization level of the frontal lobe area increases, which strengthens the synergistic effect among the brain regions and compensates for the imbalanced response caused by the lack of sensory information. These results show the brain regional characteristics of the process of human balance regulation under different balance paradigms, providing new insights into endogenous neural mechanisms of standing balance and methods of constructing brain networks. Full article

When we take into account how the boundaries between human, animal, and environmental health are inextricably linked and increasingly intertwined, it comes as no surprise that the One Health approach has assumed an unprecedented level of importance over the past decade [1]. [...] Full article

The release of cyanide from cyanogenic precursors is the central core of the plant defences based on the cyanogenesis process. Although cyanide is formed as a coproduct of some metabolic routes, its production is mostly due to the degradation of cyanohydrins originating from cyanogenic glycosides in cyanogenic plants and the 4-OH-ICN route in Brassicaceae. Cyanohydrins are then hydrolysed in a reversible reaction generating cyanide, being both, cyanohydrins and cyanide, toxic compounds with potential defensive properties against pests and pathogens. Based on the production of cyanogenic-derived molecules in response to the damage caused by herbivore infestation, in this review, we compile the actual knowledge of plant cyanogenic events in the plant–pest context. Besides the defensive potential, the mode of action, and the targets of the cyanogenic compounds to combat phytophagous insects and acari, special attention has been paid to arthropod responses and the strategies to overcome the impact of cyanogenesis. Physiological and behavioural adaptations, as well as cyanide detoxification by β-cyanoalanine synthases, rhodaneses, and cyanases are common ways of phytophagous arthropods defences against the cyanide produced by plants. Much experimental work is needed to further understand the complexities and specificities of the defence–counter-defence system to be applied in breeding programs. Full article

In this paper, we consider the time-fractional Black–Scholes model with deterministic, time-varying coefficients. These time parametric constituents produce a model with greater flexibility that may capture empirical results from financial markets and their time-series datasets. We make use of transformations to reduce the underlying model to the classical heat transfer equation. We show that this transformation procedure is possible for a specific risk-free interest rate and volatility of stock function. Furthermore, we reverse these transformations and apply one-dimensional optimal subalgebras of the infinitesimal symmetry generators to establish invariant solutions. Full article

Fractional integro-differential equations (FIDEs) of both Volterra and Fredholm types present considerable challenges in numerical analysis and scientific computing due to their complex structures. This paper introduces a novel approach to address such equations by employing a Cubic B-spline collocation method. This method offers a robust and systematic framework for approximating solutions to the FIDEs, facilitating precise representations of complex phenomena. Within this research, we establish the mathematical foundations of the proposed scheme, elucidate its advantages over existing methods, and demonstrate its practical utility through numerical examples. We adopt the Caputo definition for fractional derivatives and conduct a stability analysis to validate the accuracy of the method. The findings showcase the precision and efficiency of the scheme in solving FIDEs, highlighting its potential as a valuable tool for addressing a wide array of practical problems. Full article

Orthogonal chirp division multiplexing (OCDM) offers a promising modulation technology for shallow water underwater acoustic (UWA) communication systems due to multipath fading resistance and Doppler resistance. To handle the various channel distortions and interferences, obtaining accurate channel state information is vital for robust and efficient shallow water UWA communication. In recent years, deep learning has attracted widespread attention in the communication field, providing a new way to improve the performance of physical layer communication systems. In this paper, the pilot-based channel estimation is transformed into a matrix completion problem, which is mathematically equivalent to the image super-resolution problem arising in the field of image processing. Simulation results show that the deep learning-based method can improve the channel distortion, outperforming the equalization performed by traditional estimator, the performance of Bit Error Rate is improved by 2.5 dB compared to the MMSE method in OCDM system. At the 7.5 to 20 dB region, it achieves better bit error rate performance than OFDM systems, and the bit error rate is reduced by approximately 53% compared to OFDM when the SNR value is 20, which is very useful in shallow water UWA channels with multipath extension and severe time-varying characteristics. Full article

Rotary kiln has been widely used in hazardous waste treatment because of its strong adaptability to raw materials, high productivity, and simple processing technology. However, the formation of deposits reduces its performance period and profitability. This study characterized the deposit mineralogy and thermodynamically and experimentally investigated its formation mechanism. The results show that the main phases of the deposit are magnetite, monolithic iron, olivine, and yellow feldspar. They indicate that the deposit formation process was accompanied by the participation of alkaline and iron oxides. The intermediate product Ca₂SiO₄ can promote the generation of low melting point phases, such as CaFeSiO₄ and Ca₂Al₂SiO₇, which are the main phases of deposit materials. Additionally, the reduction intermediate product FeO facilitated the generation of a liquid ferrous mixture (Fe₃O₄-FeO and Fe₃O₄-FeO-Fe mixture), which in turn further promoted the growth of the initial deposit phase. The solid deposit formed and attached to the kiln inner wall, along with a decrease in temperature. These results are expected to provide an idea or approach for fundamentally solving the problem of deposits in the rotary kiln. Full article

To inhibit the deep conversion of partial oxidation products (POX-products) in C-H bonds’ functionalization utilizing O₂, 5-(4-(chloromethyl)phenyl)-10,15,20-tris(perfluorophenyl)porphyrin cobalt(II) and 5-(4-(chloromethyl)phenyl)-10,15,20-tris(perfluorophenyl)porphyrin copper(II) were immobilized on the surface of hybrid silica to conduct relay catalysis on the surface. Fluorocarbons with low polarity and heterogeneous catalysis were devised to decrease the convenient accessibility of polar POX-products to catalytic centers on the lower polar surface. Relay catalysis between Co and Cu was designed to utilize the oxidation intermediates alkyl hydroperoxides to transform more C-H bonds. Systematic characterizations were conducted to investigate the structure of catalytic materials and confirm their successful syntheses. Applied to C-H bond oxidation, not only deep conversion of POX-products was inhibited but also substrate conversion and POX-product selectivity were improved simultaneously. For cyclohexane oxidation, conversion was improved from 3.87% to 5.27% with selectivity from 84.8% to 92.3%, which was mainly attributed to the relay catalysis on the surface excluding products. The effects of the catalytic materials, product exclusion, relay catalysis, kinetic study, substrate scope, and reaction mechanism were also investigated. To our knowledge, a practical and novel strategy was presented to inhibit the deep conversion of POX-products and to achieve efficient and accurate oxidative functionalization of hydrocarbons. Also, a valuable protocol was provided to avoid over-reaction in other chemical transformations requiring high selectivity. Full article

The Dung beetle optimization (DBO) algorithm, devised by Jiankai Xue in 2022, is known for its strong optimization capabilities and fast convergence. However, it does have certain limitations, including insufficiently random population initialization, slow search speed, and inadequate global search capabilities. Drawing inspiration from the mathematical properties of the Sinh and Cosh functions, we proposed a new metaheuristic algorithm, Sinh–Cosh Dung Beetle Optimization (SCDBO). By leveraging the Sinh and Cosh functions to disrupt the initial distribution of DBO and balance the development of rollerball dung beetles, SCDBO enhances the search efficiency and global exploration capabilities of DBO through nonlinear enhancements. These improvements collectively enhance the performance of the dung beetle optimization algorithm, making it more adept at solving complex real-world problems. To evaluate the performance of the SCDBO algorithm, we compared it with seven typical algorithms using the CEC2017 test functions. Additionally, by successfully applying it to three engineering problems, robot arm design, pressure vessel problem, and unmanned aerial vehicle (UAV) path planning, we further demonstrate the superiority of the SCDBO algorithm. Full article

Proteins are an abundant biopolymer in organic waste feedstocks for biorefining. When degraded, amino acids are released, but their fate in non-methanogenic microbiomes is not well understood. The ability of a microbiome obtained from an anaerobic digester to produce volatile fatty acids from the twenty proteinogenic amino acids was tested using batch experiments. Batch tests were conducted using an initial concentration of each amino acid of 9000 mg COD L⁻¹ along with 9000 mg COD L⁻¹ acetate. Butyrate production was observed from lysine, glutamate, and serine fermentation. Lesser amounts of propionate, iso-butyrate, and iso-valerate were also observed from individual amino acids. Based on 16S rRNA gene amplicon sequencing, Anaerostignum, Intestimonas, Aminipila, and Oscillibacter all likely play a role in the conversion of amino acids to butyrate. The specific roles of other abundant taxa, including Coprothermobacter, Fervidobacterium, Desulfovibrio, and Wolinella, remain unknown, but these genera should be studied for their role in fermentation of amino acids and proteins to VFAs. Full article

The dried Mume fructus (MF) is a special food and herbal medicine with a long history of processing and application. The browning index (BI) of Prunus mume (PM) is pivotal in determining the medicinal value and edible quality of MF. Exploring the BI of PM holds significant importance for both the selection of PM varieties and understanding the formation mechanism of high-quality MF. This study systematically analyzed the physicochemical properties, BI, and quality indicators of four PM varieties (Qingzhu Mei, Yesheng Mei, Nangao Mei, and Zhaoshui Mei) after processing into MF. The results showed significant differences in eight physicochemical indicators among the four PM varieties (p < 0.05). Notably, Qingzhu Mei exhibited the highest titratable acid content, Nangao Mei had the most prominent soluble solid and soluble sugar content, and Zhaoshui Mei showed outstanding performance in reducing sugar, soluble protein, and free amino acids. Regarding drying characteristics, Yesheng Mei and Nangao Mei required a shorter drying time. In terms of BI, Nangao Mei exhibited the greatest degree of browning and its color appearance was darker. When considering quality evaluation, Nangao Mei excelled in rehydration ability and extract content, while Zhaoshui Mei demonstrated outstanding levels of total phenols, total flavonoids, and total antioxidant capacity. Overall, the evaluation suggested that the Nangao Mei variety was more suitable for MF processing. These research results provide a valuable theoretical foundation for understanding the BI of PM varieties and serve as a reference for the selection of PM varieties as raw materials for processing MF. Full article

Doubly coupled designs (DCDs) have better space-filling properties between the qualitative and quantitative factors than marginally coupled designs (MCDs) which are suitable for computer experiments with both qualitative and quantitative factors. In this paper, we propose a new class of DCDs, called group doubly coupled designs (GDCDs), and provide methods for constructing two forms of GDCDs, within-group doubly coupled designs and between-group doubly coupled designs. The proposed GDCDs can accommodate more qualitative factors than DCDs, when the subdesigns for the qualitative factors are symmetric. The subdesigns of qualitative factors are not asymmetric in the existing results on DCDs, and in this paper, we construct GDCDs with symmetric and asymmetric designs for the qualitative factors, respectively. Moreover, detailed comparisons with existing MCDs show that GDCDs have better space-filling properties between qualitative and quantitative factors. Finally, the methods are particularly easy to implement. Full article

This study explores cryptocurrency investment strategies by adapting the robust framework of factor investing, traditionally applied in equity markets, to the distinctive landscape of cryptocurrency assets. It conducts an in-depth examination of 31 prominent cryptocurrencies from December 2017 to December 2023, employing the Fama–MacBeth regression method and portfolio regressions to assess the predictive capabilities of market, size, value, and momentum factors, adjusted for the unique characteristics of the cryptocurrency market. These characteristics include high volatility and continuous trading, which differ markedly from those of traditional financial markets. To address the challenges posed by the perpetual operation of cryptocurrency trading, this study introduces an innovative rebalancing strategy that involves weekly adjustments to accommodate the market’s constant fluctuations. Additionally, to mitigate issues like autocorrelation and heteroskedasticity in financial time series data, this research applies the Newey–West standard error approach, enhancing the robustness of regression analyses. The empirical results highlight the significant predictive power of momentum and value factors in forecasting cryptocurrency returns, underscoring the importance of tailoring conventional investment frameworks to the cryptocurrency context. This study not only investigates the applicability of factor investing in the rapidly evolving cryptocurrency market, but also enriches the financial literature by demonstrating the effectiveness of combining Fama–MacBeth cross-sectional analysis with portfolio regressions, supported by Newey–West standard errors, in mastering the complexities of digital asset investments. Full article

The energy revolution in Saudi Arabia has accelerated significantly since 2016, driven by the National Vision 2030. Significant changes to energy subsidies took place, and the renewable energy sector has seen rapid growth. The paper presents an empirical analysis of the Saudi energy transition by emphasizing the drivers of fuel consumption in KSA. It primarily attempts to explore the long-run (LR) connection between oil consumption and several economic variables such as economic growth, crude oil prices, investment, and the labor force in Saudi Arabia (KSA) from 1991 up to 2021. The paper implemented the vector error correction model (VECM) and performed different diagnostic tests to provide more evidence about the validity and robustness of the tests. The empirical findings highlighted how important the labor force, savings, GDP, and crude oil price are in determining oil consumption for KSA. The law of demand is significantly present, which negatively affects oil consumption for KSA as an oil exporting country. The results also supported the existence of a long-term direct correlation between the variables and oil consumption. Furthermore, the short-term estimation highlighted that only saving has a negative impact on oil consumption for a single lagged period. Our findings provide governments and regulators with further incentive to slow the expansion in oil consumption, as a larger labor force is demanding more oil to attain the target, faster economic growth, and increased savings are all contributing factors. Our findings are significant because they can assist policymakers, investors, and regulators in generating more efficient oil substitutes and making them affordable for the economy. Full article

The planar cell polarity (PCP) system is essential for positioning cells in 3D networks to establish the proper morphogenesis, structure, and function of organs during embryonic development. The PCP system uses inter- and intracellular feedback interactions between components of the core PCP, characterized by coordinated planar polarization and asymmetric distribution of cell populations inside the cells. PCP signaling connects the anterior–posterior to left–right embryonic plane polarity through the polarization of cilia in the Kupffer’s vesicle/node in vertebrates. Experimental investigations on various genetic ablation-based models demonstrated the functions of PCP in planar polarization and associated genetic disorders. This review paper aims to provide a comprehensive overview of PCP signaling history, core components of the PCP signaling pathway, molecular mechanisms underlying PCP signaling, interactions with other signaling pathways, and the role of PCP in organ and embryonic development. Moreover, we will delve into the negative feedback regulation of PCP to maintain polarity, human genetic disorders associated with PCP defects, as well as challenges associated with PCP. Full article

Category level 6D object pose estimation aims to predict the rotation, translation and size of object instances in any scene. In current research methods, global average pooling (first-order) is usually used to explore geometric features, which can only capture the first-order statistical information of the features and do not fully utilize the potential of the network. In this work, we propose a new high-order pose estimation network (HoPENet), which enhances feature representation by collecting high-order statistics to model high-order geometric features at each stage of the network. HoPENet introduces a global high-order enhancement module and utilizes global high-order pooling operations to capture the correlation between features and fuse global information. In addition, this module can capture long-term statistical correlations and make full use of contextual information. The entire network finally obtains a more discriminative feature representation. Experiments on two benchmarks, the virtual dataset CAMERA25 and the real dataset REAL275, demonstrate the effectiveness of HoPENet, achieving state-of-the-art (SOTA) pose estimation performance. Full article

During the 2008 financial crisis, central banks (such as the Fed) adopted a quantitative easing (QE) policy to stimulate their countries’ economies and overcome severe economic and financial recessions. However, apart from stimulating the economy by issuing a substantial amount of currency to purchase long-term bonds and suppress interest rates, QE policy also contributed to a boom in the real estate and construction sectors. Therefore, this study employs data envelopment analysis to measure the business performance (BP) of construction companies, and explore the impact of QE policy on the BP of construction companies, between 2004 and 2015, using hierarchical regression. We also examine the moderating role of the debt ratio on the relationship. Focused on publicly listed construction companies in Taiwan, this research reveals three encouraging findings. Firstly, QE policy indeed enhanced the BP of Taiwanese construction companies. Secondly, performance improvements in construction companies due to QE policy show a time-diminishing trend, suggesting the importance of seizing the initial policy benefits of QE implementation. Lastly, construction companies with appropriate financial leverage may exhibit better BP. These findings can provide valuable insights for relevant government entities and decision-makers in the industry for policy and investment decisions. Full article

The burgeoning significance of environmental, social, and governance (ESG) metrics in realms such as investment decision making, corporate reporting, and risk management underscores the imperative for a robust, comprehensive solution capable of effectively capturing, representing, and analysing the multifaceted and intricate ESG data landscape. Facing the challenge of aligning with diverse standards and utilising complex datasets, organisations require robust systems for the integration of ESG metrics with traditional financial reporting. Amidst this, the evolving regulatory landscape and the demand for transparency and stakeholder engagement present significant challenges, given the lack of standardized ESG metrics in certain areas. Recently, the use of ontology-driven architectures has gained attention for their ability to encapsulate domain knowledge and facilitate integration with decision-support systems. This paper proposes a knowledge graph in the ESG metric domain to assist corporations in cataloguing and navigating ESG reporting requirements, standards, and associated data. Employing a design science methodology, we developed an ontology that serves as both a conceptual foundation and a semantic layer, fostering the creation of an interoperable ESG Metrics Knowledge Graph (ESGMKG) and its integration within operational layers. This ontology-driven approach promises seamless integration with diverse ESG data sources and reporting frameworks, while addressing the critical challenges of metric selection, alignment, and data verification, supporting the dynamic nature of ESG metrics. The utility and effectiveness of the proposed ontology were demonstrated through a case study centred on the International Financial Reporting Standards (IFRS) framework that is widely used within the banking industry. Full article

An in-band low radar cross section (RCS) microstrip patch antenna based on a phase control metasurface is proposed. As the size of the phase control metasurface changes, it will have different phase adjustments to the incident electromagnetic wave. Two kinds of phase control metasurfaces with a 90° reflection phase difference are arranged in a checkerboard configuration and loaded above a microstrip array antenna. The metal of the microstrip array antenna can fully reflect the electromagnetic wave, so the incident wave passes through the metasurface again and forms a reflected wave with a phase difference of 180° ± 37° when passing through the phase control metasurfaces of different sizes. Thus, the microstrip array antenna can achieve in-band RCS reduction. The metamaterial forms a transmission window in the microstrip patch array antenna band to maintain the radiation performance. Finally, a reasonable agreement is obtained between the measured and simulated results. Full article