In this paper, aiming at the safety evaluation of ultimate bearing safety capacity of composite pile bridge foundation, the symmetrical reliability method with forward and inverse analysis con-sidering the randomness of parameters is proposed to evaluate the safety performance of com-posite pile foundation. The proposed method, considering the randomness of parameters, was used to calculate the safety factor of ultimate bearing capacity of composite pile foundation to meet the prescribed target reliability requirements. Through the example calculation of the safety coefficient of pile foundation, the results show that the statistical characteristics of random vari-ables that affect the safety coefficient of pile foundation have a significant impact. The degree of the safety coefficient depends on the probability distribution type and distribution parameters of the random parameters, among which the more important is the variation characteristics of the random variables. The stronger the parameter variability, the lower the safety level of the struc-ture. During design and evaluation, it is necessary to measure the safety reserve of pile founda-tions in accordance with engineering practice to ensure the safe construction and well operation of the pile.

Let $\mathcal{X}$ be a Banach space. Let $\{\tau_j\}_{j=1}^n, \{\omega_k\}_{k=1}^m\subseteq \mathcal{X}$ and $\{f_j\}_{j=1}^n$, $\{g_k\}_{k=1}^m\subseteq \mathcal{X}^*$ satisfy $ |f_j(\tau_j)|\geq 1$ for all $ 1\leq j \leq n$, $|g_k(\omega_k)|\geq 1 $ for all $1\leq k \leq m$. If $x \in \mathcal{X}\setminus \{0\}$ is such that $x=\theta_\tau\theta_f x=\theta_\omega\theta_g x$, then we show that \begin{align}\label{FKDB} \|\theta_fx\|_0\|\theta_gx\|_0\geq \frac{\bigg[1-(\|\theta_fx\|_0-1)\max\limits_{1\leq j,r \leq n,j\neq r}|f_j(\tau_r)|\bigg]^+\bigg[1-(\|\theta_g x\|_0-1)\max\limits_{1\leq k,s \leq m,k\neq s}|g_k(\omega_s)|\bigg]^+}{\left(\displaystyle\max_{1\leq j \leq n, 1\leq k \leq m}|f_j(\omega_k)|\right)\left(\displaystyle\max_{1\leq j \leq n, 1\leq k \leq m}|g_k(\tau_j)|\right)}. \end{align} We call Inequality (\ref{FKDB}) as \textbf{Functional Kuppinger-Durisi-B\"{o}lcskei Uncertainty Principle}. Inequality (\ref{FKDB}) improves the uncertainty principle obtained by Kuppinger, Durisi and B\"{o}lcskei \textit{[IEEE Trans. Inform. Theory (2012)]} (which improved the Donoho-Stark-Elad-Bruckstein uncertainty principle \textit{[SIAM J. Appl. Math. (1989), IEEE Trans. Inform. Theory (2002)]}). We also derive functional form of the uncertainity principle obtained by Studer, Kuppinger, Pope and B\"{o}lcskei \textit{[EEE Trans. Inform. Theory, (2012)]}.

We show that one of the two important uncertainty principles derived by Maccone and Pati \textit{[Phys. Rev. Lett., 2014]} can be derived for arbitrary maps defined on subsets of $\mathcal{L}^p$ spaces for $1< p<\infty$. Our main tool is the Clarkson inequalities. We also derive a nonlinear uncertainty principle for weak parallelogram spaces and Type-p Banach spaces.

The influence of the secondary process of evaporation of the reaction product on the kinetics of interaction of gases (O₂, Cl₂, NH₃, H₂O and N₂H₄ vapors) with the surface of some metals (Cr, Pb, Si, Ge) and compounds (BN, SiO₂, SiC) is discussed. Also is considered the case when the growth of the scale is preceded by the process of gas etching of the metal surface. A general equation is given that describes the scale growth-evaporation kinetic (sample mass change - time) curves during the interaction of gases with the surface of metals and compounds. Special cases of parabolic, cubic and fourth degree processes are discussed.

Green economy has emerged as a significant pillar of sustainable development in the current global context, and the unpredictability of economic policy has progressively garnered the interest of businesses and government officials as one of the obstacles they must confront. This paper examines how uncertainty in the green economy impacts the green economy's efficacy. The research concentrates on how changes in a particular region can result in geospatial diffusion (spatial spillover), which is a form of spatial spillover wherein the uncertainty of the green economy can impact its efficacy. This paper examines the policy formulation and current state of the green economy across various regions, as well as the diffusion of economic policy uncertainty across geographical areas and the subsequent impact on the green economy's status quo. There is a negative correlation between economic policy uncertainty and green economy efficacy, according to research. There is a spatial spillover effect between economic policy uncertainty and the efficacy of the green economy. Regional variations exist in the impact of economic policy uncertainty on the efficacy of the green economy. The findings indicate that the implementation of AI has a more substantial beneficial effect on carbon emissions. An increase in carbon dioxide emissions poses a growing challenge for the industrial sector in its pursuit of carbon neutrality.

Bio-absorbable stents are a novel tool which relieve stenosis and then resorb as the patient grows, avoiding obligatory re-intervention. This case describes the novel use of an iron bio-absorbable stent in a child with surgically repaired Tetralogy of Fallot who post-operatively developed a severely stenotic right ventricular outflow tract.

While the foundations of climate science and ethics are well established, fine-grained climate predictions, as well as policy-decisions, are beset with uncertainties. This chapter maps climate uncertainties and classifies them as to their ground, extent and location. A typology of uncertainty is presented, centered along the axes of scientific and moral uncertainty. This typology is illustrated with paradigmatic examples of uncertainty in climate science, climate ethics and climate economics. Subsequently, the chapter discusses the IPCC’s preferred way of representing uncertainties and evaluates its strengths and weaknesses from a risk management perspective. Three general strategies for decision-makers to cope with climate uncertainty are outlined, the usefulness of which largely depends on whether or not decision-makers find themselves in a context of deep uncertainty. The chapter concludes by offering two recommendations to ease the work of policymakers, faced with the various uncertainties engrained in climate discourse.

The ocean of the planet Earth, which is as thin as a cicada's wings, play a role of catalyst to the evolving of the ancient plates. The ocean and the ancient plates of the planet Earth formed a magic Whirlpool of the Cross “十” shape with double whirl arms. The central core of the Cross “十” shape Ocean-Plate Whirlpool is the Hint Black Hole (HBH) located at the Northwestern Pacific Ocean floor which is decorated with invisible geological age distribution of clam shell. The double whirl arms of ancient plates are distinguished to be left and right at Bering Strait and just formed the ring of fire of the Pacific Ocean.The Cross “十” shape Ocean-Plate Whirlpool of the planet Earth was born at the equatorial area about 66 Ma ago and is rotating anticlockwise and drifting northward by a mini footprint of spiral at s speed of as slow as a snail moves. Obviously, it would come to an end at the North Pole about 100 Ma in the future. So, the lifespan of the Cross “十” shape Ocean-Plate Whirlpool would be about 166 Ma, from birth to death on the planet Earth.What is hidden behind of the evolving of the oceans and the ancient plates is the golden law of geometrical progression of ladder pagoda of attributes evolving of them, which is “1➨2➨4➨2➨1”, including upgrading and downgrading process. It seems to hint that what is happening on the planet Earth, including the top-level species of human race, is actually unique in the Milky Way galaxy in essence.The property roles of expanding and shrinking are existing in the Cross “十” shape Ocean-Plate Whirlpool by face to face and the exchange of property roles took place tens of millions of years ago. And that situation would happen again tens of millions of years in the future. It seems a giant circle of evolving of the exchange of property roles would be drawn by the Cross “十” shape Ocean-Plate Whirlpool in her lifetime.

The boosted interest in the use of rare earth elements (REEs) in modern technologies had also increase the necessity of their recovery from various sources, including raw materials and wastes. Though Hydrometallurgy plays a key role in these recovery processes, some drawbacks (apparent or not) of these processes, had led to the development of a more environmental friendship subclass named Solvometallurgy, in which non-aqueous solvents substituted to the aqueous media of the hydrometallurgical processing. The non-aqueous solvents chosen for these usages are, besides ionic liquids (ILs), the so-named Deep Eutectic Solvents (DEEs). The utilization of DEEs included the leaching of REEs from the different sources containing them, and also in the separation-purification steps necessary for the yielding of these elements, normally as oxides or salts, in the most purified form. This work reviewed the most recent literature (2023 year) about the utilization of deep eutectic solvents in the recovery of REEs from various sources, and in the coupling of these two (DESs and REEs) to derived compounds to be used in other fields.

The freshwater bodies of Nepal host a great deal of fish diversity; however, the molecular study of Nepalese fish is still in its early stages. Conservation management tactics require knowledge of the real species composition, often impeded by descriptions of intermittent species based on traditional taxonomy. Catfish, one of the most overfished taxa on the planet are abundant and heavily exploited in the eastern region of Nepal. Hence, actual catfish diversity in this region needs to be examined using a combined morphological and molecular DNA barcoding approach. In this study, we sequenced the cytochrome oxidase subunit I (COI) gene in Nepalese fish species for the first time and used it for molecular identification. Altogether, 99 specimens representing 29 species belonging to 21 genera and 8 families were successfully barcoded. They were found to be 98.5–100% identical to the known sequences deposited in the GenBank and BOLD databases. Of these, two species, Amblyceps arunachalense and Erethistoides sicula were recorded for the first time in Nepal. Out of 29 species, one species was found to be Endangered, one species as Vulnerable, two species were classified as Near Threatened, one species as Data Deficient, and 24 species were categorized as Least Concern. The intraspecific genetic distances within species ranged from 0 to 5%. The highest intraspecific genetic distances (Kimura 2 parameter) were found among the individuals of two species, namely E. sicula (4%) and Glyptothorax telchitta (5%), indicating the presence of putative (sub) species. The interspecific genetic distances among the catfishes in eastern Nepal ranged between 8.47% (between Glyptothorax trilineatus and G. cavia) and 26.38% (between Amblyceps arunachalense and Chaca chaca). The GC content at the 1st, 2nd, and 3rd positions of the codons was found to be 42.4%, 49.3% and 43.8%, respectively. These findings will support future initiatives for fish resource inventory, monitoring and management. It is also an important input to the DNA barcode library of catfish in the eastern region of Nepal.

The Ali-Cesaro Stolz theorem, introduced in this paper, extends the classical Cesaro-Stolz theorem by addressing cases where the latter does not yield results or provides a "Does Not Existe (DNE) form. This novel theorem leverages the properties of limits and Z-transforms, offering a robust framework for analyzing sequences and series. The theorem is particularly effective in scenarios where traditional approaches fail, providing new insights and tools for mathematical analysis. This paper presents a detailed formulation of the Ali-Cesaro Stolz theorem, including proofs, applications, and a discussion on its implications and advantages over existing methods.

Let $(\Omega, \mu)$, $(\Delta, \nu)$ be measure spaces and $\{\tau_\alpha\}_{\alpha\in \Omega}$, $\{\omega_\beta\}_{\beta \in \Delta}$ be 1-bounded continuous Parseval frames for a Hilbert space $\mathcal{H}$. Then we show that \begin{align}\label{UE} \log (\mu(\Omega)\nu(\Delta))\geq S_\tau(h)+S_\omega (h)\geq -2 \log \left(\frac{1+\displaystyle \sup_{\alpha \in \Omega, \beta \in \Delta}|\langle\tau_\alpha , \omega_\beta\rangle|}{2}\right) , \quad \forall h \in \mathcal{H}_\tau \cap \mathcal{H}_\omega, \end{align} where \begin{align*} &\mathcal{H}_\tau := \{h_1 \in \mathcal{H}: \langle h_1 , \tau_\alpha \rangle \neq 0, \alpha \in \Omega\}, \quad \mathcal{H}_\omega := \{h_2 \in \mathcal{H}: \langle h_2, \omega_\beta \rangle \neq 0, \beta \in \Delta\},\\ &S_\tau(h):= -\displaystyle\int\limits_{\Omega}\left|\left \langle \frac{h}{\|h\|}, \tau_\alpha\right\rangle \right|^2\log \left|\left \langle \frac{h}{\|h\|}, \tau_\alpha\right\rangle \right|^2\,d\mu(\alpha), \quad \forall h \in \mathcal{H}_\tau, \\ & S_\omega (h):= -\displaystyle\int\limits_{\Delta}\left|\left \langle \frac{h}{\|h\|}, \omega_\beta\right\rangle \right|^2\log \left|\left \langle \frac{h}{\|h\|}, \omega_\beta\right\rangle \right|^2\,d\nu(\beta), \quad \forall h \in \mathcal{H}_\omega. \end{align*} We call Inequality (\ref{UE}) as \textbf{Continuous Deutsch Uncertainty Principle}. Inequality (\ref{UE}) improves the uncertainty principle obtained by Deutsch \textit{[Phys. Rev. Lett., 1983]}. We formulate Kraus conjecture for 1-bounded continuous Parseval frames. We also derive continuous Deutsch uncertainty principles for Banach spaces.

Abstract. The remarks by the Supreme Leader of the Islamic Revolution on the toll of the famines that afflicted occupied Iran in World War I and World War II indicate a desire to incorporate these calamities in Iran’s national narrative and collective memory. His criticism of famine historiography and stated toll numbers, and the unfolding catastrophic famine in Gaza, justify and necessitate a fresh look at each famine’s historiography and toll. This article re-estimates the toll of each famine by including many overlooked and neglected primary sources and in ways not previously done. It builds a trajectory of Iran’s demographic development from 1810 to 1944, and derives the toll of each famine. The World War II famine and typhus epidemics had claimed 5.5 million. World War I famine was also more deadly, though the 1869-73 famine was not far behind. At least 15.5 million Iranians died in the two world wars, a fact nowhere reflected in the history of the conflicts. Iran’s 1944 population of 12 million was unchanged from that of 1850 and a century of population growth obliterated

The Indorama Eleme Petrochemical Limited (IEPL) is found in the southern part of Nigeria more specifically on the Rivers State southern religion Nigeria. It consists of five distinct units, each of which generates its own unique trash at a rate of around 3115.98 tonnes per year. This study had the goal of concentrating on the management of the processing wastes in order to minimize the negative effects that they have on the environment. It is necessary to have an understanding of the number, nature, and make-up of IEPL's industrial wastes in order to effectively manage and regulate the development of waste there. As a result, we decided to collect data through the use of questionnaires. The classification of industrial wastes was accomplished by doing a comparative study and synthesis of research that were relevant to the management of petrochemical waste. Because an individual coding system is required for the integrated management of industrial waste, we gave each type of trash a code that consists of thirteen digits. The primary components of trash were catalysts (9.58 percent), metallic materials (7.62 percent), plastic barrels (35.77 percent), coke (12.66 percent), wood (4.47%), oil (4.15%), glass (0.028 percent), cooling tower packaging (6.7 percent), and other material (18.83 percent). According to the findings of the investigation into the physical qualities of the wastes, only 11.81% of these residues were liquid, while 88.19% were solid. The current handling of these wastes has been experiencing significant difficulties as of late. garbage management at IEPL was particularly challenging due to the wide variety of wastes produced there and the dangerous nature of the wastes themselves (88.19% of garbage was hazardous waste). Recycling and reuse of garbage may have been the greatest decision in some situations; yet, incineration and disposal of waste are also necessary options.

This article examines air sampling as an environmental surveillance strategy for early pathogen detection and offers a preliminary comparison of air and wastewater surveillance. Air sampling involves collecting bioaerosols to analyze the microbial composition of the air, utilizing techniques like PCR and metagenomic sequencing. Air sampling appears especially promising for detecting respiratory pathogens and highly transmissible pandemic agents, as these spread primarily via aerosols and droplets. The article details various air sampling techniques, their efficiency, and practical considerations, categorizing them into filtration, impaction, impingement, electrostatic precipitation, and condensation methods. It also explores the composition of airborne microbes, emphasizing the low concentration of viral nucleic acids and the challenges this poses for metagenomic sequencing. The selection of sampling locations, including indoor environments and airplanes, is discussed as critical for effective pathogen detection. Finally, the article contrasts air and wastewater sampling, concluding that while both methods should ideally be explored as layered defenses, air sampling does not collectively appear more promising than wastewater sampling.

Many probability-based uncertainty quantification (UQ) schemes require a large amount of sampled data to build credible probability density function (PDF) models for uncertain parameters. Unfortunately, the data collected in compressor blades of aero-engine are mostly limited due to expensive and time-consuming tests. In this paper, we develop a preconditioner-based data-driven polynomial chaos (PDDPC) method that can efficiently deal with uncertainty propagation of limited sampled data. The calculation accuracy of PDDPC method is closely related to the sample size of collected data. Therefore, the influence of sample size on PDDPC method is investigated using a nonlinear test function. Subsequently, we consider the real manufacturing errors of stagger angle for compressor blades. Under three different operating conditions, PDDPC method is applied to investigate the effect of stagger angle error on UQ results of multiple aerodynamic parameters of a two-dimensional compressor blade. The results show that as the sample size of measured data increases, UQ results of aerodynamic performance obtained by PDDPC method gradually converge. There exists a critical sample size that ensures accurate UQ analysis of compressor blades. The probability information contained in the machining error data is analyzed through Kullback-Leibler divergence, and the critical sample size is determined. The research results can make a valuable reference for the fast and cheap UQ analysis of compressor blades in practical engineering.

Measurement uncertainty is typically expressed in terms of a symmetric interval , where denotes the measurement result and the expanded uncertainty. However, in the case of heteroscedasticity, symmetric uncertainty intervals can be misleading. In this paper, a different approach for the calculation of uncertainty intervals is introduced. This approach is applicable when a validation study has been conducted with samples with known concentrations. It will be shown how, under certain circumstances, asymmetric uncertainty intervals arise quite naturally and lead to more reliable uncertainty intervals.

This article seeks to strengthen efforts in technological developments based on the classification of PET plastic that positively impacts sustainable development and contributes to suitable solutions in collection centers in Mexico. Three experiment designs and machine learning tools for data processing were developed. For the experimentation, three factors were considered: the bottle's size, the amount of liquids inside the bottle, and the label on the bottle. The first experiment was to identify the distance of the sensor from post-consumer PET bottles. The second experiment was to determine the detection ability of the sensor with different levels of liquids inside the bottles, and the third was to determine the detection ability on the bottle labels. A digital lux meter on a microcontroller was developed to monitor illuminance on post-consumer PET plastic when it contains liquid as it passes through a conveyor belt for processing at an average rate of three bottles per second. With the implemented methodology, liquids were satisfactorily detected inside the transparent PET bottles when they had beverages between 25% and 100% of their capacity. Finally, this paper highlights that it is possible to implement an affordable design to identify bottles with liquids for collection centers.

In general, during emergencies, healthcare is provided by tents and temporary structures that do not consider the environmental and social impact of the structure as a first term, in favor of swift response. The resultant construction intended as a temporary solution often persists long-term. This paper aims to analyze an alternative and innovative modular structure designed as a transitory solution in emergency and everyday life. In the first part of the paper, the conceptual framework, and the preliminary design for the new approach to basic non-conventional sanitary spaces are described, by investigating the benefits of a safe space as a generator space for care services and community. In the second part, the technological requirements of the system, its energy efficiency, and environmental impact are analyzed.

Ensuring the security of vertical applications in fifth-generation (5G) mobile communication systems and previous generations is crucial. These systems must prioritize maintaining the confidentiality, integrity, and availability of services and data. Examples of vertical applications include smart cities, smart transportation, public services, Industry 4.0, smart grids, smart health, and smart agriculture. Each vertical application has specific security requirements and faces unique threats within the mobile network environment. Thus, it is essential to implement comprehensive and robust security measures. This approach helps minimize the attack surface and effectively manage risks. This survey thoroughly examines mobile networks and their security challenges in vertical applications, shedding light on associated threats and potential solutions. Our study considers the interplay between security considerations in 5G, legacy networks, and vertical applications. We emphasize the challenges, opportunities, and promising directions for future research in this field and the importance of securing vertical applications in the evolving landscape of mobile technology.

This document serves as supplemental information to Shapero et al. (2023), which is itself a comment on Nowell et al. (2022). Environmental Health Perspective’s publishing standards for article comments have strict word counts and do not allow the addition of new primary data analyses; therefore, this document provides an additional level of detail and supporting analyses that serve as an important backup to and expansion of Shapero et al. (2023) and that also respond to some comments within the author’s reply to our comments (Holmes and Nowell, 2023). Nowell et al. (2022) evaluated potential PM_2.5 community impacts from sugarcane harvesting. However, their analysis is flawed by erroneous assumptions and misapplied technical approaches, as discussed in Shapero et al. (2023) and as detailed in this supplemental information document. Additionally, the authors ended their analysis with 2018 data, but later data shows no marginal increase in PM_2.5 during sugarcane harvest following 2019 even using the authors’ approach (Holmes and Nowell, 2023). Therefore, the authors’ evaluations have no relevance to current conditions. Nonetheless, even their evaluation and conclusions regarding PM_2.5 concentrations purportedly attributable to sugarcane harvesting prior to 2019 are unsupported and technically unsound as detailed below.

In this article, we investigate both the Schrödinger and continuity equations in the presence of nonlocal potential that arising from electron-electron interaction and local one in the context of minimal uncertainty in momentum within commutative and noncommutative frameworks. Furthermore, the Frahn–Lemmer potential type is used. Interestingly, the combined effects of both the phase-space noncommutativity and nonlocality on the current density while considering a minimal uncertainty in momentum are examined. We find that the current density, as conventionally defined, does not fulfil the condition of current conservation, thus, a new definition of the current density that encompasses the aforementioned contributions is given. Thereafter, it is shown that the computed current using the new definition of the current density satisfies the current conservation. Noting that using both the $★$product and linear Bopp-Shift, the noncommutativity is inserted.

Let $(\{f_n\}_{n=1}^\infty, \{\tau_n\}_{n=1}^\infty)$ and $(\{g_n\}_{n=1}^\infty, \{\omega_n\}_{n=1}^\infty)$ be unbounded continuous p-Schauder frames ($0<p<1$) for a disc Banach space $\mathcal{X}$. Then for every $x \in ( \mathcal{D}(\theta_f) \cap\mathcal{D}(\theta_g))\setminus\{0\}$, we show that \begin{align}\label{UB} \|\theta_f x\|_0\|\theta_g x\|_0 \geq \frac{1}{\left(\displaystyle\sup_{n,m \in \mathbb{N} }|f_n(\omega_m)|\right)^p\left(\displaystyle\sup_{n, m \in \mathbb{N}}|g_m(\tau_n)|\right)^p}, \end{align} where \begin{align*} & \theta_f: \mathcal{D}(\theta_f) \ni x \mapsto \theta_fx \coloneqq \{f_n(x)\}_{n=1}^\infty\in \ell^p(\mathbb{N}), \quad \theta_g: \mathcal{D}(\theta_g) \ni x \mapsto \theta_gx \coloneqq \{g_n(x)\}_{n=1}^\infty\in \ell^p(\mathbb{N}). \end{align*} Inequality (1) is unexpectedly different from both bounded uncertainty principle \textit{[arXiv:2308.00312v1]} and unbounded uncertainty principle \textit{[arXiv:2312.00366v1]} for Banach spaces.

Let $(\Omega, \mu)$, $(\Delta, \nu)$ be measure spaces. Let $(\{f_\alpha\}_{\alpha\in \Omega}, \{\tau_\alpha\}_{\alpha\in \Omega})$ and $(\{g_\beta\}_{\beta\in \Delta}, \{\omega_\beta\}_{\beta\in \Delta})$ be unbounded continuous 1-Schauder frames for a Banach space $\mathcal{X}$. Then for every $x \in ( \mathcal{D}(\theta_f) \cap\mathcal{D}(\theta_g))\setminus\{0\}$, we show that \begin{align}\label{UB} \mu(\operatorname{supp}(\theta_f x))\nu(\operatorname{supp}(\theta_g x)) \geq \frac{1}{\left(\displaystyle\sup_{\alpha \in \Omega, \beta \in \Delta}|f_\alpha(\omega_\beta)|\right)\left(\displaystyle\sup_{\alpha \in \Omega , \beta \in \Delta}|g_\beta(\tau_\alpha)|\right)}. \end{align} where \begin{align*} &\theta_f:\mathcal{D}(\theta_f) \ni x \mapsto \theta_fx \in \mathcal{L}^1(\Omega, \mu); \quad \theta_fx: \Omega \ni \alpha \mapsto (\theta_fx) (\alpha):= f_\alpha (x) \in \mathbb{K},\\ &\theta_g: \mathcal{D}(\theta_g) \ni x \mapsto \theta_gx \in \mathcal{L}^1(\Delta, \nu); \quad \theta_gx: \Delta \ni \beta \mapsto (\theta_gx) (\beta):= g_\beta (x) \in \mathbb{K}. \end{align*} We call Inequality (\ref{UB}) as \textbf{Unbounded Donoho-Stark-Elad-Bruckstein-Ricaud-Torr\'{e}sani Uncertainty Principle}. Along with recent \textbf{Functional Continuous Uncertainty Principle} (derived in [arXiv:2308.00312v1 [math.FA], 1 August 2023]), Inequality (\ref{UB}) also improves Ricaud-Torr\'{e}sani uncertainty principle \textit{[IEEE Trans. Inform. Theory, 2013]}. In particular, it improves Elad-Bruckstein uncertainty principle \textit{[IEEE Trans. Inform. Theory, 2002]} and Donoho-Stark uncertainty principle \textit{[SIAM J. Appl. Math., 1989]}.

The hot wire anemometer is a commonly used device for measuring air speed in laboratories. In this paper, the relationship between air speed and the hot wire temperature in different air speed ranges was analyzed based on the theory of thermal equilibrium. A measurement circuit and hot wire shape were designed based on the principle of the hot wire anemometer, and the linear relationship between current and temperature in the hot wire at different air speeds was verified in an adjustable air speed field. The measured current was used to represent air speed. The designed hot wire anemometer was experimentally validated to provide measurement results that match the theoretical values in different air speed ranges. Finally, the sensitivity of the anemometer in different measurement ranges was determined based on the uncertainty of the instrument's sensitivity and the measurement formula.

Bluecat is a recently proposed methodology to upgrade a deterministic model (D-model) into stochastic (S-model), based on the hypothesis that the information contained in a time series of observations and the concurrent predictions by the D-model is sufficient to support this upgrade. Prominent characteristics of the methodology are its simplicity and transparency, which allow easy use in practical applications, without sophisticated computational means. Here we utilize the Bluecat methodology and expand it in order to be combined with climatic model outputs, which often require extrapolation out of the range of values covered by observations. We apply the expanded methodology to the precipitation and temperature processes in a large area, namely the entire territory of Italy. The results showcase the appropriateness of the method for hydroclimatic studies, as regards the assessment of the performance of the climatic projections, as well as their stochastic conversion with simultaneous bias correction and uncertainty quantification.

Projects are rarely executed exactly as planned. Often, the actual durations of a project’s activities differ from the planned ones, resulting in costs stemming from the inaccurate estimation of the activities’ completion dates. While monitoring the project at various inspection points is pricy, it can lead to better estimation of the project completion time, hence saving on costs. Nonetheless, identifying the optimal inspection points is a difficult task, as it requires evaluating a large number of the project’s path options, even for small-scale projects. This paper proposes an analytical method for identifying the optimal project inspection points by using Information Theory measures. We search for monitoring (inspection) points that can maximize the information about the estimated project’s duration or completion time. The proposed methodology is based on a simulation-optimization scheme using a Monte Carlo engine that simulates potential activities’ durations. An exhaustive search is performed of all possible control points to find those with the highest expected information gain on the project duration. The proposed algorithm’s complexity is not affected the number of activities, and can address large projects with hundreds or thousands of activities. Numerical experimentation and analysis of various parameters are presented.

Starting from unidentified objects moving inside a two-dimensional Euclidean manifold, we propose a simple method to detect the topological changes that occur during their reciprocal interactions and shape morphing. This method, which allows the detection of topological holes development and disappearance, makes it possible to solve the uncertainty due to disconnectedness, lack of information and absence of objects’ sharp boundaries, i.e., the three troubling issues which prevent scientists to select the required proper sets/subsets during their experimental assessment of natural and artificial dynamical phenomena, such as fire propagation, wireless sensor networks, migration flows, neural networks’ and cosmic bodies’ analysis.

The relativistic effects of the dynamical properties of light at angular incidence were analyzed from the perspectives of Bohr indeterminacy and Heisenberg uncertainties and statistical dispersion. It was found that these effects report minimal uncertainties that agree with one or the other according to the angular range of incidence and that decrease with increasing refringence of the medium, constituting a specific relativistic uncertainty at angular incidence. An anomaly is indicated for the uncertainty principle in the Quantum Theory (QT) setting for small angles of incidence, where the accuracy of the angular position does not imply an increase in the uncertainty of the linear momentum. The anomalies arise because TQ does not predict the alternation between the classical and relativistic regimes of photon inertia at angular incidence. Specific relativistic uncertainty particularizes the uncertainty principle in the transmission of light between media pairs at angular incidence for the relativistic scenario, considering an observer that registers the relativistic effects of measurements that interfere with the observed system, in another inertial referential.

first_pagesettingsOrder Article ReprintsOpen AccessArticleThe Carbon Footprint of Spanish University Websitesby Sonia Sanchez-Cuadrado ¹ andJorge Morato ^2,*1Department of Library and Information Science, Complutense University of Madrid, 28010 Madrid, Spain²Department of Computer Science, Carlos III University of Madrid, Leganes, 28911 Madrid, Spain^*Author to whom correspondence should be addressed.Sustainability 2024, 16(13), 5670; https://doi.org/10.3390/su16135670 (registering DOI)Submission received: 31 May 2024 / Revised: 22 June 2024 / Accepted: 23 June 2024 / Published: 3 July 2024(This article belongs to the Special Issue Sustainable Education in the Digital Society: Applying Communication Technologies and Digital Tools to Education) Downloadkeyboard_arrow_down Browse Figures Versions NotesAbstractThe consumption of digital content is essential for economic and scientific development, leading to a high dependence on information and communication technologies, which significantly impacts the environment. Quantifying this ecological impact is challenging due to its intangible nature and the diversity of factors. This research focuses on assessing the energy consumption and CO₂ production of Spanish university websites to evaluate their levels of digital pollution and environmental impact. The calculation of the carbon footprint considers factors related to energy consumption and the use of green or renewable energies. The findings indicate that only 17% of university websites can be considered environmentally friendly. There are significant differences in CO₂ consumption and emissions depending on whether the websites implement environmentally sound practices. This study highlights the lack of initiatives aimed at reducing energy consumption and greenhouse gas emissions associated with web information management. The study shows that Spanish universities have the potential to contribute to the reduction in CO₂ emissions by implementing sustainable development practices on their websites. Furthermore, it fosters awareness about technology usage and its environmental impact, promoting the adoption of green and sustainable approaches.

Cities are complex systems requiring urban design models that balance order and disorder. Collective creativity initiatives engage citizens in these processes, empowering bottom-up approaches that prioritize people and social well-being within urban development. This paper investigates an 'Urban Laboratory' as a case study, examining the potential of collective creativity to address urban complexity. The successful and ongoing project 'El Campo de Cebada' in Madrid, Spain, demonstrates how a community transformed a vacant lot into a vibrant social hub. The phases of this study include case selection, data collection, data analysis, and presentation of the results. This study identifies key enabling factors, including agents, management, social dynamics, infrastructure, and actions. These insights offer a methodological framework for designing future collaborative, resilient, and inclusive urban spaces, addressing the complex needs of communities within our cities.

The aim of this study was to quantify methane (CH4) emissions from dairy cows during milking by maximum amplitude of eructation peaks and compare estimates with other metrics for CH4 emissions. Spot measurements of CH4 and carbon dioxide (CO2) emissions were recorded for at least 7 days of continuous sampling using an infrared gas analyser in a robotic milking station whilst cows were milked. Concentrations of CH4 and CO2 were measured at 1 second intervals. A total of 105,701 CH4 spot measurements were obtained from 2,206 dairy cows on 18 farms. Methane emissions per individual cow were quantified by maximum amplitude of eructation peaks, average area under eructation peaks (integral of eructation), average concentration during milking and ratio of CH4 to CO2 concentration. There was a high correlation (r = 0.71) between CH4 emissions quantified by maximum amplitude and integral of eructation peaks. The correlation between maximum amplitude and average CH4 concentration was low, and there was no correlation between maximum amplitude and ratio of CH4 to CO2. As both approaches identify and extract eructation peaks to quantify enteric CH4 emissions, the substantial association between maximum amplitude and eructation integral was expected.

The mechanistic Target of Rapamycin Complex 1 (mTORC1) regulates ribosome biogenesis and the transcription of ribosomal genes. Its inactivation impacts protein synthesis necessary for transport of cell adhesion molecule. In Dictyostelium discoideum, the activity of mechanistic Target of Rapamycin Complex 2 (mTORC2) is crucial for cAMP synthesis and relay, processes that depend on heterotrimeric G-proteins and Ras GTPases. Mutants lacking rip3 or lst-8 genes exhibit impaired adenyl cyclase activation and cAMP signaling, resulting in deficient expression of the cell adhesion protein CsaA. Despite this, rip3^- cells, which have impaired mTORC2, upregulate Cad-1 expression, a cell adhesion protein regulated by Ras-GTPases, to compensate for the loss of CsaA. lst-8^- cells, which have both mTORC1 and mTORC2 inactivated, show reduced nutrient sensing, premature CsaA expression and solitary cell behavior. These cells also show defective transport of adhesion proteins to the cell surface, impeding aggregation. Similar defects are observed with caffeine treatment, suggesting both mTORC1 and mTORC2 inhibition. This study highlights the interplay between mTORC1 and mTORC2 in regulating cell adhesion and aggregation through cAMP signaling and protein synthesis pathways in Dictyostelium.

Attention Deficit Hyperactivity Disorder (ADHD) diagnosis poses significant challenges due to its heterogeneous nature and reliance on subjective assessments. This study leverages deep learning to develop a robust diagnostic model by integrating a comprehensive set of EEG biomarkers. We incorporate parameters such as theta/beta ratio, coherency measures, delta power, event-related potentials (ERPs), power spectral density (PSD), microstates, entropy measures, fractal dimension, and source localization. Our convolutional neural network (CNN) model, designed to process these diverse features, demonstrates high accuracy and stability in distinguishing ADHD patients from controls, from all ages. The model's architecture includes convolutional layers for spatial feature extraction, followed by dense layers that integrate additional EEG parameters. The results indicate that deep learning, coupled with a rich feature set, can significantly enhance the predictive accuracy of ADHD diagnosis, offering a promising tool for clinical application.

Phytopathogenic fungi are living organisms that cause plant diseases and great damage to agri-cultural products. Despite the wide range of commercial fungicide products in use, there is a clear need for new and environmentally friendly fungicides. Here we propose a new ecological fun-gicide, copper alginate microspheres prepared by ionic gelation. The microspheres were charac-terized (morphology and topography, encapsulation efficiency, load capacity, swelling behavior, rheology, kinetics and mechanism of copper ions release) and their in vitro antifungal potential against selected genera of phytopathogenic fungi was evaluated. Novel copper alginate micro-spheres inhibited spore germination of Botrytis cinerea and mycelial growth of Cercospora beticola and Phytophthora ramorum. They offer effective disease control while minimizing environmental impact and promoting sustainable agriculture practices.

Raru’s plants bark has long been used as traditional medicine in Batak’s tribe. Raru’s bark is derived from several trees that are usually used in a mixture in Batak traditional beverages. This bark is generally used as antidiabetic remedies. This study aims to combine the rarus’s bark extract and mocaf activated-carbon (MAC) in order to study antidiabetics in rats. The combination of raru’s bark extract and MAC were tested on male rats as of Sprague Dawley strain for testing antidiabetic bioactivity. The result showed that antidiabetic activity of raru’s extract and MAC exposed the antidiabetic level declines in rat’s blood. The various treatments of different concentration ratios of raru’s extract and MAC useful in the experiments showed that the levels of blood level reduction were slightly different (14-21%). The raru’s extract pure was observed more effective in decreasing the blood glucose level in rat’s blood, in comparison the positive control. The mixture of raru’s extract and MAC with 75:25 ratio is better than 50:50 ratio. The involvement of MAC in the alleged antidiabetic agent did little in improving the blood glucose level decrease in rat’s blood. Raru’s extract and MAC indicate its potential utilization as an antidiabetic agent.

Objectives: Few studies have evaluated and compared artificial intelligence (AI) models for breast cancer detection on the same database. The purpose is to compare the performances of 3 artificial intelligence algorithms on the same dataset at a free operative threshold and to evaluate how the performance is impacted by the chosen threshold. Materials: In this retrospective single-center study, a dataset of 314 2D screening mammograms performed between 2012 and 2020 was established with a prevalence of 19.6% of histologically proven cancer. Three AI constructors using CE (European conformity) marking algorithms have agreed to take part in the study by submitting the cancer-enriched cohort to their algorithm. They chose a free operative threshold to distinguish benign and malignant mammograms. The most suspicious lesion was marked and correctly located by the AI algorithm. Statistical parameters were analyzed and compared between the algorithms. Results: Regarding both sensitivity and specificity, at the chosen threshold, AI 1 had the best compromise between sensitivity (74%) and specificity (79%). AI 2 had a statistically lower sensitivity (52%; p<0.05) with a higher specificity (98.4%) than the other AI algorithms. AI 3 had a nonsignificant sensitivity difference (69.9%) but a significantly lower specificity (45.3%; p<0.001) than the other two. The performances varied depending on the chosen threshold; when the AI 2 threshold was lowered, the sensitivity increased (69.9%), while a higher specificity (86.1%) was maintained. Conclusion: Two AI algorithms stood out in terms of performance when the threshold was optimized, resulting in an acceptable sensitivity and specificity.

The diagnosis of a chronic disease, such as multiple sclerosis, has both psychological and physical effects. Living with the disease and its uncertain consequences requires a great deal of psychological resilience in order to employ more comprehensive coping strategies in stressful situations. This study investigated the effect of a four-week online self-directed resilience training on the perception of psychological resilience among multiple sclerosis patients. A total of 64 MS patients were recruited for a randomized controlled trial. The experimental group underwent a 28-day online self-directed training program consisting of daily exercises aimed at strengthening a resilient mindset. Psychological resilience was measured through self-assessment immediately before, immediately after, and three months after the training. A repeated measures ANOVA revealed a statistically significant improvement in the perception of four factors related to stress: perceived worries, tension, joy and demands. Two resilience coping strategies were measured, of which one, a resilient orientation, improved significantly in the short and long term. The study suggests that online self-directed resilience training might provide an easily accessible, low-cost option for patients with MS to improve their psychological resilience. The transferability to other patient groups should be examined.

The removable partial denture, despite new technologies and new materials, continues to play a significant role in clinical dental practice. The purpose of the following study was to evaluate the design of new retention devices in Kennedy's second grade classrooms. For the study of "lamellar" retentive devices, 4 prosthetists were chosen and selected, according to inclusion and exclusion criteria, 16 patients, 8 women and 8 men, whose upper arch had to be rehabilitated with a removable partial prosthesis. The results showed that the parameters taken into consideration such as satisfaction, stability, aesthetics and retention had an average score higher than 3.30 (range 1-4), with an average standard deviation of 0.6225 in the 4 indicative parameters that the data they are relatively homogeneous and coherent. In conclusion we can state that the skeletonized prosthesis with lamellar retentions, designed in Kennedy's second grade classes, has shown good acceptance in selected patients

Actually, the study aimed to manage and to analyse the results of the laboratory methods, using liver samples, preparing and analizing in pathology service. This mentioned, are known as routinally practice for hepatitis C diagnosis, which conduct to hepatic cirrhosis. In this study direction, chronic liver diseases stimulate a degree of hepatocyte injury. So previously mentioned modifications, alters thet affect the liver architecture and finally ends in cirrhosis are well analyse on microscopic preparates. Folowing morphological steps, it is possible to analyse normal and alterate liver structure, on colored preparates.

This research addresses the strategic issue of why key decision-makers fail to foresee potential extreme ‘black swan’ events. Following a review of the literature, a conceptual framework is developed that identifies two types of organisational blindness that are reflected in Tetlock’s hedgehog cognitive thinking style, being the oversimplification of uncertainty (e.g., inductive biases) and an unquestioned, top-down, reference narrative. This framework is tested using a case study approach and qualitative analysis of secondary data sources available from the Royal Commission of Inquiry and other published reports following the 2010 methane explosion at the Pike River Coal Ltd’s mine (Pike) in New Zealand, that killed 29 miners and caused the loss of all funds invested. The results indicate that the combined effect of both blindness’s meant that Pike’s collective intelligence was limited, and for the three key decision-makers at the Pike River mine, some type of extreme ‘black swan’ event was apparently inevitable. This research provides theoretical and practical contributions to the analysis of business and public policy decision-making under uncertainty.

Theoretical physics addresses the fundamental principles of nature and the mysteries of the universe. In the last century, the theory of relativity challenged people's understanding of time and space, while quantum mechanics revealed various forms of energy. However, the concept of gravity remains elusive. All matter in the universe is composed of energy that can be converted into other forms, yet it is always conserved. However, achieving a state of equilibrium, represented by the equal sign, can be challenging to achieve on both macro and quantum scales. A phenomenon that can transition from one state to infinity is called collision and is essential for understanding natural phenomena and physical processes throughout the universe. In the study of general relativity cannot be three-dimensional problems to gain mathematical inspiration, understand the application of new dimensions. Here, we elucidate the nature of gravity, which inspires the solution of the Riemann hypothesis. Our results show that the conduction mode of dimensional energy is defined by Euler's formula. And a special mode of collision is derived which translates into vibration.

Background: Glutathione and its related enzymes constitute one of the most important antioxidant defense mechanisms against oxidative stress and cancer formation in the body. Bladder cancer is the second most common urological malignancy after prostate cancer. Oxidative stress plays a significant role in the development and prognosis of bladder cancer. The aim of this study was to examine the effect of glutathione on the prognosis of non-muscle-invasive bladder cancer. Methods: The 98 patients with bladder tumors were those with T1G3 pathology who had undergone intravesical Bacillus Calmette-Guérin therapy, while the 30 controls were healthy individuals with no history of transitional cell carcinoma of the bladder. The 128 participants were divided into four groups: Group 1 comprised 41 patients who did not experience recurrence during follow-up, Group 2 included 28 patients who had recurrent tumors, Group 3 consisted of 29 patients who progressed to muscle-invasive stages, and Group 4 was composed of 30 healthy volunteers. Blood samples were collected from all participants. Glutathione levels were measured, and statistical analysis were performed. Results: There was a statistically significant difference in reduced glutathione levels among the groups (p < 0.001), attributed to Group 4 exhibiting higher reduced glutathione levels compared to Groups 1, 2, and 3 (p < 0.001). No significant differences were observed in reduced glutathione levels between Groups 1 and 2, Groups 1 and 3, or Groups 2 and 3 (p > 0.999). Total glutathione levels varied significantly among the groups (p < 0.001), with Group 4 having higher levels than Groups 1, 2, and 3 (p < 0.001). However, there were no significant differences between Groups 1 and 2, Groups 1 and 3, or Groups 2 and 3 in terms of total glutathione levels (p = 0.473, p = 0.747, and p > 0.999, respectively). Regarding oxidized glutathione levels, a significant difference was observed (p < 0.001), with Group 4 showing lower levels than the remaining three groups (p < 0.001). No significant differences were found between Groups 1 and 2, Groups 1 and 3, or Groups 2 and 3 in relation to oxidized glutathione levels (p > 0.999, p = 0.171, and p > 0.999, respectively). Conclusions: The current study revealed that glutathione is a factor influencing the development of new bladder cancer but did not affect its prognosis. Nevertheless, we recommend that future studies with larger bladder cancer patient cohorts should be conducted to comprehensively determine the effect of glutathione on the prognosis of bladder cancer.

Melanoma, one of the most aggressive forms of skin cancer, is also one of the most diagnosed cancer types. While surgical excision of the lesions is the primary treatment for melanoma, not all cases are candidates for surgical procedures. New treatments and complementary options are necessary, given the increasing diagnosis rate. In the present study, a norcantharidin-containing nanoemulsion was developed and evaluated in vivo using a syngeneic graft murine model. Our model contemplates an amputation surgery simulating the standard treatment and the role of the nanoemulsion as a potential adjuvant therapy. We observed a significant decrease in the growth rate of the melanoma lesion in the treated groups compared to the control group, both at the 20th and 30th days of treatment. Moreover, we evaluated the drug bioavailability in serum samples, and the results showed that norcantharidin was detectable in a range of 0.1 to 0.18 mg per mL in the treated groups. Furthermore, histopathological analysis was performed on the amputated tumors, where significant differences were found regarding size, mitosis rate, lymphocytic infiltration, and multispectral quantitative image analysis compared to the control group. The norcantharidin-containing nanoemulsion could be a potential alternative or adjuvant therapy if more clinical studies are conducted. Topical nanosystems can become or complement standard therapies, which is needed as melanoma affects not only in terms of mortality but also the patient's morbidity and life quality.

Glycerol conversion to valuable biomolecules has been a research avenue that has attracted a significant interest in recent years due to its increasing over production, with possibilities for chemical transformation for eco-friendly processes and distinct compounds with biological activities. Also, in the current climate change in which a major emphasis is being placed on the design of green processes, solvents are key factors from both an economic and environmental point of view. In this context, due to its abundance and low-price, glycerol represents an innovative solvent coming from biomass and produce in high amount. On the other hand, Epichlorydrin, Glycidol and Solketal have become useful intermediates in chemical transformations when using glycerol as a raw material. This review focuses on glycerol as a green solvent in the synthesis of complex molecules, Epichlorydrin, Glycidol, Solketal and Tosyl solketal as an efficient recycling process for glycerol, as well as building blocks in the synthesis of glycerol derivatives, which are undeniable new source of potentials drugs.

Background: Gender differences characterize the prevalence and attitudes toward weight management. Despite limited evidence suggesting greater weight loss in women with anti-obesity pharmacotherapy, gender-specific analysis remains underexplored. This retrospective study aimed to evaluate the gender-specific response to liraglutide 3.0 mg treatment in people with obesity without type 2 diabetes (T2D). Methods: Data were collected from 47 patients (31 women, 16 men), with age > 18 years, BMI ≥ 30 kg/m^2, absence of T2D, and with exclusion of prior anti-obesity treatment, comorbidities, or bariatric surgery. Only patients who maintained the liraglutide 3.0 mg dose for at least 6 months were included. Results: Both sexes showed significant reductions in weight and BMI at 3 and 6 months. Men achieved greater weight loss (WL), BMI reduction, % WL, WL >5% and >10% than women, and they also showed more significant improvements in metabolic parameters (total and LDL cholesterol, Fibrosis-4 Index FIB-4). No significant gender differences were observed in glucose metabolism or renal function. Conclusion: This study showed a greater therapeutic effect of liraglutide 3.0 mg in men. Given men's higher risk of cardiovascular disease (CVD), and underrepresentation in clinical weight loss programs, these findings may increase male engagement and improve their CVD risk.

Per- and polyfluoroalkyl substances (PFAS) are arguably the most common water contaminants in the world today. While several research experiments have been done to understand and remove PFAS from the environment, there is still a lot of unknown. Little has been known about the use of Machine learning (ML) to understand PFAS. This work hence reviews some leading ML approaches and applications in PFAS studies in the distribution, transport, removal, and occurrence predictions of PFAS. Several evaluation matrices were examined and used to perform this function. There are still a lot of areas whereby ML can be used to improve our PFAS knowledge base, some of these were briefly stated in this review.

Antiphospholipid antibody syndrome (APS) is an autoimmune disorder characterised by thrombosis and the presence of antiphospholipid antibodies (aPL): lupus anticoagulant and/or IgG/IgM anti-β2-glycoprotein I and anticardiolipin antibodies. APS carries significant morbidity for a relatively young patient population from recurrent thrombosis in any vascular bed (arterial, venous or microvascular), often despite current standard of care, which is anticoagulation with vitamin K antagonists (VKA). Platelets have established roles in thrombosis at any site, and platelet hyperreactivity is clearly demonstrated in the pathophysiology of APS. Together with excess thrombin generation, platelet activation and aggregation are the common end result of all the pathophysiological pathways leading to thrombosis in APS. However, antiplatelet therapies play little role in APS, reserved as a possible option of low dose aspirin in addition to VKA in arterial or refractory thrombosis. This review outlines the current evidence and mechanisms for excessive platelet activation in APS, how it plays a central role in APS-related thrombosis, what evidence for antiplatelets is available in clinical outcomes studies and potential future avenues to define how to target platelet hyperreactivity better with minimal impact on haemostasis.

Presenting a case report about a 14-year-old female patient presenting with a history of weakness in all four limbs since the age of 5. The patient has a history of difficulty in walking, wearing footwear, climbing stairs, and performing fine motor tasks. During the neurological examination showed mild spasticity, muscle weakness, areflexia, ill-sustained clonus, and sensory deficits. Radiological examination, including a contrast-enhanced MRI of the brain was done which showed findings that are consistent with Megalencephalic leukoencephalopathy with subcortical cysts, also known as Van der Knaap disease.

A high-resolution catalog for the 2014 Ms6.5 Ludian aftershocks was constructed based on the deep learning phase-picking model (CERP) and the seismic-phase association technology (PALM). A specific training strategy, which combines the advantages of the conventional short-long window average energy ratio algorithm (STA/LTA) and AI algorithm, is employed to retrain the CERP model. The P & S phases were accurately detected and picked on continuous seismic waveforms by the retained AI model. Hypoinverse and HypoDD were utilized for precise location of 3286 events. Compared to the previous results, our new catalog exhibits superior performances in terms of location accuracy and the number of aftershock events, thereby enabling a more de-tailed depiction of the deep-seated tectonic features. According to the distribution of aftershocks, it can be inferred that, (1) the seismogenic fault of Ludian earthquake is the NW-trending Baogunao-Xiaohe Fault. (2) the Ludian aftershocks interconnected the discontinuous NW-trending Baogunao-Xiaohe Fault, and intersected with the Zhaotong-Ludian Fault. (3) It suggests that the NE-trending Zhaotong-Ludian Fault may have been intersected by the NW-trending Baogunao -Xiaohe Fault, indicating that the Baogunao-Xiaohe Fault is likely a rela-tively young Neogene fault.

Global warming may increase the potential evapotranspiration (Etp), thereby affecting the amount of clean water resources available in a region. In this study, the systematic biases in temperature outputs of 24 global climate models (GCMs) under 3 emission scenarios are corrected by using 8 bias correction methods. The trend of Etp in Yangzi River Delta in the 21st century is projected by using 4 Etp calculation formulas. The uncertainty of the projections is estimated and decomposed by using multi-way analysis of variance. The influence of uncertainty on the projected change signal is quantified by using the signal-to-noise ratio. The results show that, relative to the reference period (1971~2000), the annual mean daily Etp in Yangzi River Delta will increase by 0.14~0.17mm/d during the period 2021~2050 and by 0.21~0.41mm/d during the period 2061~2090, respectively. During 2021~2050, the uncertainty of Etp increase projections is dominantly contributed by the main effects of GCM (63%) and Etp calculation formula (24%). During 2061~2090, it is mainly contributed by the main effect of GCM (36%), followed by the main effects of emission scenario (34%) and Etp calculation formula (18%). The increase projections of Etp are generally reliable and robust during the two projection periods.

We derive an uncertainty principle for Lipschitz maps acting on subsets of Banach spaces. We show that this nonlinear uncertainty principle reduces to the Heisenberg-Robertson-Schrodinger uncertainty principle for linear operators acting on Hilbert spaces.

Let $(\Omega, \mu)$, $(\Delta, \nu)$ be measure spaces. Let $(\{f_\alpha\}_{\alpha\in \Omega}, \{\tau_\alpha\}_{\alpha\in \Omega})$ and $(\{g_\beta\}_{\beta\in \Delta}, \{\omega_\beta\}_{\beta\in \Delta})$ be continuous 1-Schauder frames for a Banach space $\mathcal{X}$. Then for every $x \in \mathcal{X}\setminus\{0\}$, we show that \begin{align*} \mu(\operatorname{supp}(\theta_f x)) \geq \frac{1}{\displaystyle\sup_{\alpha \in \Omega, \beta \in \Delta}|f_\alpha(\omega_\beta)|}, \quad \nu(\operatorname{supp}(\theta_g x))\geq \frac{1}{\displaystyle\sup_{\alpha \in \Omega , \beta \in \Delta}|g_\beta(\tau_\alpha)|}. \end{align*} where \begin{align*} &\theta_f: \mathcal{X} \ni x \mapsto \theta_fx \in \mathcal{L}^1(\Omega, \mu); \quad \theta_fx: \Omega \ni \alpha \mapsto (\theta_fx) (\alpha):= f_\alpha (x) \in \mathbb{K},\\ &\theta_g: \mathcal{X} \ni x \mapsto \theta_gx \in \mathcal{L}^1(\Delta, \nu); \quad \theta_gx: \Delta \ni \beta \mapsto (\theta_gx) (\beta):= g_\beta (x) \in \mathbb{K}. \end{align*} This solves a problem asked by K. M. Krishna in the paper 'Functional Continuous Uncertainty Principle' \textit{[arXiv:2308.00312v1]}.

Let $(\Omega, \mu)$, $(\Delta, \nu)$ be measure spaces. Let $(\{f_\alpha\}_{\alpha\in \Omega}, \{\tau_\alpha\}_{\alpha\in \Omega})$ and $(\{g_\beta\}_{\beta\in \Delta}, \{\omega_\beta\}_{\beta\in \Delta})$ be continuous p-Schauder frames for a Banach space $\mathcal{X}$. Then for every $x \in \mathcal{X}\setminus\{0\}$, we show that \begin{align}\label{CUE} \mu(\operatorname{supp}(\theta_f x))^\frac{1}{p} \nu(\operatorname{supp}(\theta_g x))^\frac{1}{q} \geq \frac{1}{\displaystyle\sup_{\alpha \in \Omega, \beta \in \Delta}|f_\alpha(\omega_\beta)|}, \quad \nu(\operatorname{supp}(\theta_g x))^\frac{1}{p} \mu(\operatorname{supp}(\theta_f x))^\frac{1}{q}\geq \frac{1}{\displaystyle\sup_{\alpha \in \Omega , \beta \in \Delta}|g_\beta(\tau_\alpha)|}. \end{align} where \begin{align*} &\theta_f: \mathcal{X} \ni x \mapsto \theta_fx \in \mathcal{L}^p(\Omega, \mu); \quad \theta_fx: \Omega \ni \alpha \mapsto (\theta_fx) (\alpha):= f_\alpha (x) \in \mathbb{K},\\ &\theta_g: \mathcal{X} \ni x \mapsto \theta_gx \in \mathcal{L}^p(\Delta, \nu); \quad \theta_gx: \Delta \ni \beta \mapsto (\theta_gx) (\beta):= g_\beta (x) \in \mathbb{K} \end{align*} and $q$ is the conjugate index of $p$. We call Inequality (\ref{CUE}) as \textbf{Functional Continuous Uncertainty Principle}. It improves the Functional Donoho-Stark-Elad-Bruckstein-Ricaud-Torr\'{e}sani Uncertainty Principle obtained by M. Krishna in \textit{[arXiv:2304.03324v1, 2023]}. It also answers a question asked by Prof. Philip B. Stark to the author.

The aim of this article is to propose a core game theory model of transaction costs wherein it is indicated how direct costs determine the probability of loss and subsequent transaction costs. The existence of optimum is proven, and the way in which exposure influences the location of the optimum is demonstrated. The decisions are described as a two-player game and it is discussed how the transaction cost sharing rule determines whether the optimum point of transaction costs is the same as the equilibrium of the game. The dispute between actors regarding changing the share of transaction costs to be paid by each party is modeled by a non-cooperative bargaining game. Requirements of efficient transaction cost sharing rules are defined, and it is posited that solution exists which is not unique. Policy conclusions are also devised based on principles of design of institutions to influence the nature of transaction costs.

We define life as the amplification of quantum uncertainty up to macroscopic scales. A living being is any amplifier that achieves this goal. We argue that everything we know about life can be explained from this idea. We study a ladder mechanism to estimate the probability that the amplification occurs spontaneously in nature. The amplification mechanism is so sensitive to small variations of its own parameters that it acts as a bifurcation itself, i.e. it implies that the universe is either everywhere dead or alive wherever possible. Since the first option is excluded by the existence of life on earth, we infer that the universe hosts a huge number of inhabited planets (possibly one per star on average). We also investigate models of conscious and unconscious learning processes, as well as the structure of the brain and evolution. Finally, we address the problem of creating artificial life.

This paper examines a persuasion game between two agents with one-sided asymmetric information, where the informed agent can reveal her private information prior to playing a Battle-of-the-Sexes coordination game. We find that in the presence of strategic uncertainty in coordination there exists an equilibrium where there is no 'unraveling' of information. We provide a purification argument for this mixed strategy equilibrium to strengthen the central result, which is robust to several extensions, including both-sided asymmetric information and imprecise information revelation.

Energy Measurement and Verification (M&V) aims to make inferences about the savings achieved in energy projects, given the data and other information at hand. Traditionally, a frequentist approach has been used to quantify these savings and their associated uncertainties. We demonstrate that the Bayesian paradigm is an intuitive, coherent, and powerful alternative framework within which M&V can be done. Its advantages and limitations are discussed, and two examples from the industry-standard International Performance Measurement and Verification Protocol (IPMVP) are solved using the framework. Bayesian analysis is shown to describe the problem more thoroughly and yield richer information and uncertainty quantification than the standard methods while not sacrificing model simplicity. We also show that Bayesian methods can be more robust to outliers. Bayesian alternatives to standard M&V methods are listed, and examples from literature are cited.

The design flood concept (DF) provides for an essential tool in designing the hydraulic works, in defining the reservoir operation programs and for a reliable flood hazard identification. Under a simplified approach, the maximum discharge and the floods volume are statistically processed to reasonably define the DF. Yet, the integral hydrograph provides additional key temporal and quantitative details of important significance for flood management and particularly for the res-ervoirs operation and associated risks of failures. The procedure presented in this paper (as applied on a set of compatibly shaped hydrographs) involves the following key stages: (i) normalize the floods, (ii) define similar flood shape classes and (iii) evaluate the average dimensionless flood (ADF) for each class. The ADFs are finally transformed into a set of (DF)s. Many statistical distributions approximate acceptably the frequent values of the maximum discharges or the flood volumes, yet displaying a significant spread for medium or rare probabilities of exceedance (PE). This scattering, which can be explained by the epistemic uncertainty, defines an area of uncertainty both for measured and extrapolated values. In considering upper and lower values of the uncertainty in-tervals as limits for maximum discharges and flood volumes, then by combining them compatibly, a set of DFs - as completely defined hydrographs, with different shapes - results for each PE. The herein proposed procedure defines both one peak DF and multi-peaks DF. Subsequently, such DFs do assist water managers in examining and establishing tailored approaches for a variety of input hydrographs. Among the DFs that would correspond to a same PE, the most compact floods arise a special interest, for they are basic in defining the set of safe operation rules for hydraulic structures.

This article presents a comprehensive overview of recent discoveries and advancements in the field of steroid chemistry, highlighting the isolation and characterization of various steroidal compounds from natural sources. The paper discusses a wide range of steroids, including pregnane steroids, steroidal alkaloids, ketosteroids, and novel triterpenoids, derived from marine organisms, fungi, and plants. Significant findings include the isolation of bioactive compounds such as the cytotoxic erectsterates from microorganisms, soft corals, the unusual tetracyclic steroid penicillitone from a fungal culture, and innovative steroidal derivatives with potential anti-inflammatory and anticancer activities. The synthesis of steroids from microorganisms as a tool for pharmaceutical development is also explored, showcasing the role of microbial biotransformation in generating steroidal drugs. Additionally, the paper emphasizes the ecological and medicinal relevance of these compounds, which are often used in traditional medicine and have potential therapeutic applications in treating diseases like cancer and microbial infections. This article serves as a vital resource for researchers interested in the chemical diversity of steroids and their applications in drug discovery and development.

Background: Infective endocarditis (IE) is the result of infections by gram-positive bacteria and, in this condition, the redox homeostasis is lost by overproduction of H2O2 resulting in overstimulation of the immune system and up- regulation of the production of proinflammatory cytokines. Objectives: The aim of this preliminary study of three cases with IE, was to evaluate the levels of oxidative biomarkers and of the enzymatic and non-enzymatic antioxidant system, in subjects with IE. This is a preliminary retrospective analytical study of cases that had undergone aortic valve replacement surgery complicated by IE. Methods: We determined malondialdehyde (MDA), total antioxidant capacity (TAC), carbonyl groups, glutathione (GSH), thiols and the nitrate/nitrite ratio (NO3−/NO2−) in homogenized tissue of cardiac valves. We also measured the activities of GST, GPx, GR and TrxR. The super oxide dismutase (SOD) isoforms and peroxidase activities were determined using native gels. Results: There were increases in the activities of antioxidant enzymes such as GST, SOD isoforms and peroxidases (p=0.01) and decreases in OS markers such as GSH, TAC, (p=0.01); however, MDA was increased (p=0.01). Conclusions: The results suggest that there is loss of the intricate redox homeostasis system in the patients with EI in the aortic valve may be favors the bacterial process and damages the patient probably leading to a fatal outcome.

The collection of forty-nine 8-hydroxyquinoline derivatives, substituted at C5, C6 or C7, was designed and studied by computational methods of modified POM analysis, using SwissADME, Osiris and Molinspiration tool. POM analysis (Petra/Osiris/Molinspiration) and its modified version using SwissADME instead of Petra software are indisputably tools of modern chemoinformatics, often published in the professional literature as well as in the field of research of new antibacterial active compounds, developed to replace existing antibiotics that fail due to increased resistance of bacteria. NitroxolineTM and the group of 8-hydroxyquinoline derivatives with their broad antibacterial effect and the possibility of extensive derivatization of the skeleton, represent a promising direction in the research of new antibacterial active substances. Twenty 8-hydroxyquinoline derivatives were also evaluated on their antibacterial activity against eight selected G+ and G- bacterial strains with a defined resistance mechanism. All structures were in accordance with Lipinski's criteria, twenty-one structures were interesting from the point of view of individual or average value of the bioactivity prediction score and fourteen structures showed one or more mechanisms of toxicity prediction. Furthermore, eleven structures showed a low rate of GI absorption or the inability to cross the BBB barrier, fourteen substances showed a BRENK or PAIN warning, eleven substances are an undesirable substrate of P-glycoprotein and all structures predicted the same level of bioavailability and a relatively low rate of metabolism in the liver. Five structures were selected by the intersection of the POM criteria. The virtual structures 17, 25, 35 and 43 can be favored for the continuity of next study. The resulting scores of bioactivity can indicate the potential of studied structures to be used for the design and development of new active antibacterial agents.

Recent advancements in mass spectrometry have significantly enhanced our understanding of complex lipid profiles, opening new avenues for oncological diagnostics. This review highlights the importance of lipidomics in the comprehension of certain metabolic pathways and its potential for the detection and characterization of various cancers, in particular melanoma. Through detailed case studies, we demonstrate how lipidomic analysis has led to significant breakthroughs in the identification and understanding of cancer types and its potential for detecting unique biomarkers that are instrumental in its diagnosis. Additionally, the review addresses the technical challenges and future perspectives of these methodologies, including their potential expansion and refinement for clinical applications. The discussion underscores the critical role of lipidomic profiling in advancing cancer diagnostics, proposing a new paradigm in how we approach this devastating disease, with particular emphasis on its application in comparative oncology.

Background: Angiogenesis is essential for various physiological and pathological processes, such as embryonic development and cancer cell proliferation, migration, and invasion. Long noncoding RNAs (lncRNAs) play pivotal roles in normal homeostasis and disease processes by regulating gene expression through various mechanisms, including as competing endogenous RNAs (ceRNAs) of target microRNAs (miRNAs). The lncRNA MYU is known to promote prostate cancer proliferation via the miR-184/c-Myc regulatory axis, and to be upregulated in vascular endothelial cells under hypoxic conditions, which often occurs in solid tumors. In the present study, we investigated whether MYU might affect cancer growth by regulating angiogenesis in vascular endothelial cells under hypoxia. Methods: The expression of MYU-regulated miR-23a-3p and interleukin-8 (IL-8) in HUVEC cell lines was examined using qRT-PCR. CCK-8 assay, EdU assay, Wound-healing assay and Tube-formation assay were used to assess the effect of MYU on cell proliferation, migration and tube formation of HUVEC cells in vitro. Dual-luciferase reporter assay was performed to examine the effect of miR-23a-3p on MYU and IL-8 expression. Results: We found that overexpression of MYU and knockdown of miR-23a-3p in human umbilical vein endothelial cells (HUVECs) under hypoxia promoted cell proliferation, migration, and tube formation. Mechanistically, MYU was shown to bind competitively to miR-23a-3p, thereby preventing miR-23a-3p binding to the 3′ untranslated region of IL-8 mRNA. In turn, increased production of pro-angiogenic IL-8 promoted HUVEC proliferation, migration, and tube formation under hypoxia. Conclusion: This study identified a new role for lncRNA MYU as a ceRNA for miR-23a-3p, and uncovered a novel MYU–miR-23a-3p–IL-8 regulatory axis for angiogenesis. MYU and/or miR-23a-3p may thus represent new targets for the treatment of hypoxia-related diseases by promoting angiogenesis.

The role of the microbiome in cancer and its crosstalk with the tumor microenvironment (TME) has been extensively studied and characterized. An emerging field in the cancer microbiome research is the concept of the intratumoral microbiome, which refers to the microbiome residing within the tumor. This microbiome primarily originates from the local microbiome of the tu-mor-bearing tissue or from translocating microbiome from distant sites, such as the gut. Despite the increasing number of studies on intratumoral microbiome, it remains unclear whether it is a driver or a bystander of oncogenesis and tumor progression. This review aims to elucidate the intricate role of the intratumoral microbiome in tumor development by exploring its effects on reshaping the multi-leveled ecosystem in which tumors thrive, the TME. To dissect the complexity and the multitude of layers within the TME, we distinguish six specialized tumor microenvironments, namely the immune, metabolic, hypoxic, acidic, mechanical and innervated microenvironments. Accordingly, we attempt to decipher the effects of the intratumoral microbiome on each specialized microenvironment and ultimately decode its tumor-promoting or tumor-suppressive impact. Additionally, we portray the intratumoral microbiome as an orchestrator in the tumor milieu, fine-tuning the responses in distinct, specialized microenvironments and remodeling the TME in a multi-leveled and multi-faceted manner.

This research explores the application of Support Vector Machine (SVM) and Digital Surface Models (DSM) derived from Unmanned Aerial Vehicle (UAV) data for a vertical-horizontal classification of mangrove ecosystems in Tongke-tongke and Samataring Village. The study focuses on integrating high-resolution DSM subtracted from Digital Terrain Models (DTM) to classify mangrove vegetation into sixteen distinct vertical-horizontal classes based on vegetation type and elevation. The analysis revealed that older mangroves, particularly those within the 25-35 meters elevation range, dominate the landscape, accounting for 43.40% of the total classified area. This dominance suggests a mature and ecologically stable mangrove ecosystem, which is crucial for biodiversity conservation and carbon storage. The study demonstrates the effectiveness of SVM and UAV technologies in providing detailed and accurate ecological assessments of mangrove forests. The classification results contribute valuable insights into the structural diversity and health of mangrove ecosystems, facilitating targeted conservation and management strategies. Additionally, the research highlights the utility of advanced remote sensing and machine learning techniques in enhancing the precision of environmental monitoring and the assessment of carbon sequestration capabilities within mangrove ecosystems. The findings underscore the potential of UAV-based remote sensing for ecological studies, offering a replicable method for similar assessments in other regions, thereby supporting global environmental management and climate action initiatives.

Clinical and laboratory diagnostics are particularly important in monitoring the internal homeostasis of the body in high-yielding animals, especially in dairy cows, whose metabolism is under constant pressure to obtain high parameters of milk production and reproduction. Knowledge of laboratory test results and relationships based on significant correlations between various milk and blood parameters may be very useful in veterinary diagnostics and helpful in cattle practice in order to protect animal health and the quality of milk production. The aim of the study was to analyze the correlation between selected hematological biomarkers such as white blood corpuscles (WBC), red blood corpuscles (RBC), hemoglobin concentration (HGB), hematocrit (HCT), platelets (PLT), erythrocyte mean corpuscular volume (MCV), mean corpuscular hemoglobin content (MHC), mean corpuscular hemoglobin concentration (MCHC) and selected quantitative-qualitative parameters of cow’s milk such as current milk production, somatic cells count (SCC), colony of bacteria forming units (CFU), milk fat (MF), milk protein (MP), milk lactose (ML), milk solids (S), solids non-fat in milk (SNF), daily milk production. Samples were collected three times at daily intervals from 11 clinically healthy late-lactation dairy cows obtaining 33 sets of test results. This repeated study of cows allowed for obtaining a homogeneous group in order to determine statistical correlations between selected blood and milk parameters. Many significant correlations were found between the examined parameters. In cows, significant positive correlations were recorded between milk production and HGB, HCT, MCV or MHC. Significant negative correlations were also found between SCC and HGB, SCC and HCT, SNF and MCV, SNF and MHC, and between MF and RBC.

Cardiopulmonary resuscitation (CPR) is a widely practiced lifesaving skill. Conventional CPR is based on manual chest compressions and delivering mouth-to-mouth breaths. There has been advancement in biomedical technology over the last few decades which has led to the introduction of new techniques and devices in CPR as well. In light of these advancements, we propose SMEAR (Standard, Mechanical, ECMO Assisted, Augmented virtual and Remote) classification system to provide structured overview from traditional to novel methods and devices. The standard or Manual CPR, widely practiced by the general public during out-of-hospital cardiac arrests, combines active chest compressions with or without mouth breathing. However, to enhance CPR’s efficacy, various mechanical devices have been developed, encompassing Active Chest Compression, Passive Decompression, and those integrated with Automated External Defibrillators. Feedback sensor-equipped devices, include the Metronome, Accelerometer, Barometer, and Triaxial Field Induction. Mechanical devices, classified by their compression mechanisms, can be isolated – like the Piston-driven, Load-distributing, and Active Compression-Decompression (ACD) – or combined, as in ACD-ITV (Impedance Threshold Device), ACD-IAC (Interposed Abdominal Compression), and ACD-ITV-EECP (Enhanced External Counter Pulsation). Beyond mechanics, Extracorporeal CPR offers veno-arterial Extracorporeal Membrane Oxygenation in refractory cardiac arrest and can improve outcomes. Augmented Virtual Reality usage in training and provision of CPR is at the early stages. It has the potential of expanded usage in training public to provide high-quality CPR. A provider can be remotely guided step by step via telephone or smartphone applications, ensuring continuous support until professional assistance arrives.

The prevalence of patients on buprenorphine therapy presenting for elective surgery has increased. Buprenorphine is a widely used medication for management of patients with chronic pain. It is also used for maintenance therapy for patients with a history of opioid use disorder (OUD). Due to lack of standardized protocol for managing patients on buprenorphine peri-operatively, we performed a retrospective analysis to compare pain score outcomes and post-operative opiate requirements between patients who continued versus discontinued buprenorphine. We identified 35 patients, 11 continued buprenorphine and 24 discontinued buprenorphine. The average Post-Anesthesia Care Unit (PACU) pain score was 7.54 for those who discontinued pre-operative buprenorphine and 5.59 for those who continued (P value 0.0339). The average post-operative Morphine Milligram Equivalent (MME) use was 86.13 for those who continued pre-operative buprenorphine and was 107.70 for those who discontinued buprenorphine (P value 0.6439). The results from our study correlate with several previous studies which showed lower PACU pain scores in patients who continued buprenorphine. There is a benefit of decreased pain post-operatively when pre-operative buprenorphine is continued and having a decrease in relapse in those with a history of OUD.

Nystagmus induced by applying an intense vibratory stimulus to the skull (SVIN) indicates vestibular functional asymmetry. In unilateral vestibular loss, a 100-Hz bone-conducted vibration given to either mastoid immediately causes a primarily horizontal nystagmus. Test is performed in darkness to avoid visual fixation (VF) but there is no data about how much VF affects the often-intense SVIN. The aim is to analyze the amount of reduction of SVIN when VF is allowed during testing. Thus, all patients seen in a tertiary hospital for vertigo or dizziness with positive SVIN were included. SVIN was recorded for 10 seconds for each condition: without VF (aSVINwo) and VF (aSVINw). We obtained an aSVINwo and aSVINw as average slow-phase velocities (SPV) without and with VF. VF index (FISVIN) was calculated as the ratio of SPV. Among the 124 patients included, spontaneous nystagmus (SN) was found in 25% and the mSPV (without VF) of SN was 2.62.4º/s. Mean FISVIN was 0.270.29. FISVIN was 0 in 42 patients and &gt;0 FISVIN &lt;1 in 82 (mean FISVIN 0.390.02). Fixation suppression was found in all patients with SVIN in cases of peripheral vestibulopathy. FISVIN clearly delineates two populations of patients: with or without complete visual reduction of nystagmus.

The Shimensi super-large tungsten polymetallic deposit is located in Late Jurassic- Early Cretaceous Porphyry-Skarn tungsten ore belt in the south Yangtze metallogenic belt. There are 3 types of mineralization: veinlet-disseminated type, thick quartze vein type and hydrothermal crypto-explosive breccia type. Based on geological studies, this paper presents new petrographic, microthermometric, laser Raman spectroscopic and hydrogen and oxygen isotope research of the fluid inclusions from the deposit. The results show that there are five different types of fluid inclusions: liquid-rich inclusions, vapor-rich inclusions, pure liquid inclusions, pure vapor inclusions, and fluid inclusions containing a solid crystal. The homogenization temperatures of the fluid inclusion range from 140℃-270℃, the salinities are 3%-5% NaCleq and the densities of ore-forming fluid range from 0.64 g/cm3 to 0.99 g/cm3. For the analyses of Laser Raman spectroscopic, the ore-forming fluids can be approximated by a Ca2+-Na+-SO42--Cl- fluid system with small amount of CO2, CH4 and N2. Otherwise, the datas of the pressure, pH and Eh show a fluid metallogenic environment of low pressure, weak acid and weak reduction. The values of the homogenization temperature in 3 types of orebodies suggest that the mineralization is characterized by the decrease of the temperature under the condition of the fluid immiscibility. The H-O isotope values are interpreted to indicate that the ore-forming fluids are mainly composed of magmatic water, and also the meteoric water was added with the process of the magma rising.

Measurement of Globe temperature is essential for investigating thermal comfort in street canyon. Globe thermometer is the most common instrument used to measure globe temperature (GT), however its application in scale models is still uncertain and not thoroughly investigated as of today. Therefore, this study is explicitly conducted to investigate globe thermometer measurements and proposed an improvised customization of the globe thermometer size to enhance reliability in scale models. The experiments were carried out outdoors using a typical street canyon model (scale 1:100) with East-West street orientation. The experiment presented a comparison between a low solar reflective street canyon (albedo of 0.4) and a high solar reflective canyon (albedo of 0.6) in terms of surface temperatures, heat flux and globe temperature. Furthermore, this study also investigated the influence of solar orientation on the globe temperature using both experimental using scale models and microclimatic modelling using ENVI-met. It is observed that although the surface temperatures are lower with high solar reflective canyon compared to that with low solar reflective canyon, GT is higher in high reflective canyon during daytime, which could be due to combined effect of direct radiation and shortwave reflection. However, for hours after the sun set, GT becomes lower (up to 1°C) in the case of high reflective canyon compared to low reflective canyon.

This study used Dutch potatoes at the end of dormancy as the material to explore the impact of menthol (0.2 and 0.5 g/kg based on potato mass) treatment on sprouting inhibition and potato quality. The findings revealed that a menthol concentration of 0.5 g/kg effectively inhibited potato tuber sprouting and significantly reduced glucoside alkaloids production. After a storage period of 15 days, the sprouting percentage and glucoside alkaloids content of potatoes treated with 0.5 g/kg menthol were observed to be significantly lower at 4.17% and 68.63 mg/kg, respectively, compared to the control group which exhibited values of 100% and 282.01 mg/kg, respectively (P < 0.05). Throughout the storage period, 0.5 g/kg of menthol promoted respiration, reduced malondialdehyde production in potatoes, inhibited polyphenol oxidase activity, and slowed down tissue browning. Additionally, it mitigated the decline in starch and soluble protein content, inhibiting the accumulation of reducing sugars.

Breast cancer brain metastasis (BCBM) is a challenging condition with limited treatment options and poor prognosis. Understanding the interactions between tumor cells and the blood-brain barrier (BBB) is critical for developing novel therapeutic strategies. One promising target is estrogen receptor β (ERβ), which promotes the expression of key tight junction proteins, sealing the BBB and reducing its permeability. In this study, we investigated the effects of 17β-estradiol (E2) and the selective ERβ agonist diarylpropionitrile (DPN) on endothelial and cancer cells. Western blot analysis revealed the expression patterns of ERs in these cell lines, and estrogen treatment upregulated claudin-5 expression in brain endothelial cells. Using in vitro models of the BBB, we found that DPN treatment significantly increased BBB tightness about suppressed BBB transmigration activity of representative HER2-positive (BT-474) and triple-negative (MDA-MB-231) breast cancer cell lines. However, the efficacy of DPN treatment decreased when cancer cells were pre-differentiated in the presence of E2. Our results support ERβ as a potential target for the prevention and treatment of BCBM and suggest that targeted vector-based approaches may be effective for future preventive and therapeutic implications.

This study focused on evaluating the potential of natural fermentation of pea flour (F) to improve the release of antioxidant compounds. Two fermentation conditions of F were performed: 1: 36.4% w/w, 24 h, 30°C (FF1); 2: 14.3% w/w, 24 h, 37°C (FF2); pH values decreased to 4.4-4.7, with a predominance of lactic acid bacteria. An increment of the proteolysis degree (TNBS method) (greater for FF2), polypeptides aggregation and decrease of solubility, increase of &lt; 2 kDa peptides, and increase in the ORAC potency of PBS-soluble fractions after fermentation was demonstrated. After simulated gastrointestinal digestion (SGID, COST-INFOGEST), FF1D and FF2D showed greater proteolysis degrees (63 and 53%) than F1D and F2D (45%), with differences in the molecular composition of the different digests. ORAC and HORAC potencies increased in all cases. FF2D presented the greater ORAC value, with higher activities for &gt; 4 kDa, some in the range 2 to 0.3 kDa. and &lt; 0.10 kDa fractions. Fermentation also increased the 60 % ethanol extracted phenolic compounds, mainly flavonoids, and the ORAC activity. After SGID, the flavan-3-ols disappeared but some phenolic acids increased respect to F. Fermentation in condition 2 was considered the most appropriate to obtain a functional antioxidant ingredient.

Integrating large-scale wind energy in modern power systems is demanding more efficient mathematical models to properly address classical assumptions in power system problems. In particular, there are two main assumptions in power system problems with wind integration that have not been adequately studied yet; First, non-linear AC power flow equations have been linearized in most of the literature. Such simplifications can lead to inaccurate power flow calculations that may result in other technical issues. Second, wind power uncertainties are inevitable and have been mostly modelled using the traditional uncertainty modelling approaches, that may not be suitable for large-scale wind power integration. In this paper, we address both challenges: we present a tight second-order conic relaxation (SOCR) for optimal power flow (OPF) problem, and simultaneously, implement the new effective budget of uncertainty approach for uncertainty modelling that determines the maximum wind power admissibility first and then addresses the uncertainty in the model. To the best of our knowledge, this is the first study that proposes an effective robust second-order conic programming (ERSOCP) model that simultaneously addresses the issues of power flow linearization and wind power uncertainty with the new paradigm on the budget of uncertainty approach. Our numerical results show the merit of the proposed model against traditional linearized power flow equations as well as traditional uncertainty modelling approaches.

In this paper, we present a multi-task model that predicts disparities and confidence levels in deep stereo matching simultaneously. We do this by combining its successful model for each separate task and obtaining a multitask model that can be trained with a proposed loss function. We show the advantages of this model compared to training and predicting disparity and confidence sequentially. This method enables an improvement of 15% to 30% in the area under the curve (AUC) metric when trained parallel rather than sequential. In addition, the effect of weighting the components in the loss function on the stereo and confidence performance is investigated. By improving the confidence estimate, the practicality of stereo estimators for creating distance images is increased.

With advancements in digital technologies such as 5G communications, big data, and cloud computing, the components of network operation systems have become increasingly complex, significantly complicating system monitoring and maintenance. Correspondingly, automated log anomaly detection has become a crucial means to ensure stable network operation and protect them from malicious attacks or failures. Conventional machine learning and deep learning methods assume consistent distributions between the training and testing data, adhering to a closed-set recognition paradigm. Nevertheless, in realistic scenarios, systems may encounter new anomalies that were not present in the training data, especially in log anomaly detection. Inspired by evidential learning, we propose a novel anomaly detector called LogEDL, which supervises training of the model through an evidential loss function. Unlike traditional loss functions, the evidential loss function not only focuses on correct classification, but also quantifies the uncertainty of predictions. This enhances the robustness and accuracy of the model in handling anomaly detection tasks, while achieving functionality similar to open-set recognition. To evaluate the proposed LogEDL method, we conduct extensive experiments on three datasets, i.e., HDFS, BGL, and Thunderbird, to detect anomalous log sequences. Experimental results demonstrate that our proposed LogEDL achieves state-of-the-art performance in anomaly detection.

Khosravi, Drnov\v{s}ek and Moslehian [\textit{Filomat, 2012}] derived Buzano inequality for Hilbert C*-modules. Using this inequality we derive Deutsch entropic uncertainty principle for Hilbert C*-modules over commutative unital C*-algebras.

Let $\{\tau_n\}_{n=1}^\infty$ and $\{\omega_m\}_{m=1}^\infty$ be two modular Parseval frames for a Hilbert C*-module $\mathcal{E}$. Then for every $x \in \mathcal{E}\setminus\{0\}$, we show that \begin{align}\label{UE} \|\theta_\tau x \|_0 \|\theta_\omega x \|_0 \geq \frac{1}{\sup_{n, m \in \mathbb{N}} \|\langle \tau_n, \omega_m\rangle \|^2}. \end{align} We call Inequality (\ref{UE}) as \textbf{Noncommutative Donoho-Stark-Elad-Bruckstein-Ricaud-Torr\'{e}sani Uncertainty Principle}. Inequality (\ref{UE}) is the noncommutative analogue of breakthrough Ricaud-Torr\'{e}sani uncertainty principle \textit{[IEEE Trans. Inform. Theory, 2013]}. In particular, Inequality (\ref{UE}) extends Elad-Bruckstein uncertainty principle \textit{[IEEE Trans. Inform. Theory, 2002]} and Donoho-Stark uncertainty principle \textit{[SIAM J. Appl. Math., 1989]}.

In this study, we employ the GARCH-MIDAS (Generalised Autoregressive Conditional Heteroskedasticity variant of Mixed Data Sampling) model to explore the predictability of commodity return volatility in relation to US climate policy uncertainty (CPU). Our analysis utilizes the 20-day annualized realized commodity volatility returns of nine global commodities (Aluminium, Cocoa, Coffee, Copper, Cotton, Rice, Soybean, Sugar, and Wheat) to construct the GARCH-MIDAS model, with Climate Policy Uncertainty (CPU) serving as the predictive variable. The outcomes of our investigation reveal a consistently positive and statistically significant relationship between CPU and the selected commodities. This implies that CPU effectively serves as a robust predictor of volatility in commodity returns. The implications drawn from our results suggest that climate change policies play a pivotal role in influencing economic activities within the commodity market. This underscores the substantial impact of climate change considerations on investment decisions and further emphasizes the integral role of climate change in shaping economic choices.

To verify the reliability of the proposed stitching linear scan method for roundness measurement of small cylindrical workpieces, measurement uncertainties of the workpieces with various dimension are conducted. The cross-sectional circle of a cylindrical workpiece is divided into several equal arcs to be measured. Thus, the arc profile can be characterized according to the measured arcs. The roundness profile of the cylindrical part can be obtained by stitching these arc profiles together. Firstly, the measurement uncertainty of the workpiece with a diameter 1.5 mm and length 5.8 mm is evaluated to be 0.095 μm which can meet the uncertainty target of 0.1 μm. Considering the uncertainty of θx and θz which are the alignment around X-axis and Z-axis respectively changing with workpiece dimension changing, relevant uncertainties are carried out. As a result, there is no limitation with respect to workpiece diameter theoretically If the alignment can reach θz = θx = ±0.1° without considering the measurable range of the machine.

Properties of coherent states for the Heisenberg group over a field of $p$-adic numbers are investigated. It turns out that coherent states form an orthonormal basis. The family of all such bases is parametrized by a set of self-dual lattices in the phase space. The Wehrl entropy $S_W$ is considered and its properties are investigated. In particular, it is proved that $S_W\geq 0$ and vanishes only on coherent states. For a pair of different bases of coherent states, an entropic uncertainty relation is obtained. It is shown that the lower bound for the sum of the corresponding Wehrl entropies is given by the distance between the lattices. The proof uses the fact that the bases of coherent states corresponding to a pair of lattices are mutually unbiased.

The Ocean Color - Simultaneous Marine and Aerosol Retrieval Tool (OC-SMART) is a robust data processing platform that supports a large array of multi-spectral and hyper-spectral sensors. It provides accurate aerosol optical depths and remote sensing reflectances (Rrs estimates) that can be used to generate products such as absorption coefficients due to phytoplankton and detritus/Gelbstoff as well as backscattering coefficients due to particulate matter. The OC-SMART platform yields improved performance in complex environments by utilizing scientific machine learning (SciML) in conjunction with comprehensive radiative transfer computations. This paper expands the capability of OC-SMART by quantifying uncertainties in ocean color retrievals. Bayesian inversion is used to relate measured top of atmosphere radiances and a priori data to estimate posterior probability density functions and associated uncertainties. A framework of the methodology and implementation strategy is presented and uncertainty estimates for Rrs retrievals are provided to demonstrate the approach by applying it to MODIS, OLCI Sentinel-3, and VIIRS sensor data.

Land cover data are important basic data for earth system science and other fields. Multi-source remote sensing images have become the main data source for land cover classification. There are still many uncertainties in the scale effect of image spatial resolution on land cover classification. Since it is difficult to obtain multiple spatial resolution remote sensing images of the same area at the same time, the main current method to study the scale effect of land cover classification is to use the same image resampled to different resolutions, however errors in the resampling process lead to uncertainty in the accuracy of land cover classification. To study the land cover classification scale effect of different spatial resolutions of multi-source remote sensing data, we selected 1 m and 4 m of GF-2, 6 m of SPOT-6, 10 m of Sentinel-2 and 30 m of Landsat-8 multi-sensor data, and explored the scale effect of image spatial resolution on land cover classification from two aspects of mixed image element decomposition and spatial heterogeneity. For the study area, we compared the classification obtained from GF-2, SPOT-6, Sentinel-2, and Landsat-8 images at different spatial resolutions based on GBDT and RF. The results show that (1) GF-2 and SPOT-6 had the best classification results, and the optimal scale based on this classification accuracy was 4–6 m; (2) the optimal scale based on linear decomposition depended on the study area; (3) the optimal scale of land cover was related to spatial heterogeneity, i.e., the more fragmented and complex was the space, the smaller the scale needed; and (4) the resampled images were not sensitive to scale and increase the uncertainty of the classification. These findings have implications for land cover classification and optimal scale selection, scale effects and landscape ecology uncertainty studies.

Groundwater models serve as support tools to among others: assess water resources, evaluate management strategies, design remediation systems and optimize monitoring networks. Thus, the assimilation of information from observations into models is crucial to improve forecasts and reduce uncertainty of their results. As more information is collected routinely due to the use of automatic sensors, data loggers and real time transmission systems; groundwater modelers are becoming increasingly aware of the importance of using sophisticated tools to perform model calibration in combination with sensitivity and uncertainty analysis. Despite their usefulness, available approaches to perform this kind of analyses still present some challenges such as non-unique solution for the parameter estimation problem, high computational burden and a need of a deep understanding of the theoretical basis for the correct interpretation and use of their results, in particular the ones related to uncertainty analysis. We present a brief derivation of the main equations that serve as basis for this kind of analysis. We demonstrate how to use them to estimate parameters, assess the sensitivity and quantify the uncertainty of the model results using an example inspired by a real world setting. We analyze some of the main pitfalls that can occur when performing such kind of analyses and comment on practical approaches to overcome them. We also demonstrate that including groundwater flow estimations, although helpful in constraining the solution of the inverse problem as shown previously, may be difficult to apply in practice and, in some cases, may not provide enough information to significantly constrain the set of potential solutions.

: Mistrust, amplified by numerous artificial intelligence (AI) related incidents, has caused the energy and industrial sectors to be amongst the slowest adopter of AI methods. Central to this issue is the black-box problem of AI, which impedes investments and fast becoming a legal hazard for users. Explainable AI (XAI) is a recent paradigm to tackle this challenge. Being the backbone of the industry, the prognostic and health management (PHM) domain has recently been introduced to XAI. However, many deficiencies, particularly lack of explanation assessment methods and uncertainty quantification, plague this young field. In this paper, we elaborate a framework on explainable anomaly detection and failure prognostic employing a Bayesian deep learning model to generate local and global explanations from the PHM tasks. An uncertainty measure of the Bayesian model is utilized as marker for anomalies expanding the prognostic explanation scope to include model’s confidence. Also, the global explanation is used to improve prognostic performance, an aspect neglected from the handful of PHM-XAI publications. The quality of the explanation is finally examined employing local accuracy and consistency properties. The method is tested on real-world gas turbine anomalies and synthetic turbofan data failure prediction. Seven out of eight of the tested anomalies were successfully identified. Additionally, the prognostic outcome showed 19% improvement in statistical terms and achieved the highest prognostic score amongst best published results on the topic.

Based on a previous risk calculation study along a road corridor, risk is recalculated using stochastic simulation by introducing variability for most of the parameters in the risk equation. This leads to an exceedance curve comparable to that of catastrophe models. This approach introduces uncertainty into the risk calculation in a simple way, which can be used for poorly documented cases to fulfil lack of data. This approach seems to tend to minimize risk or to question risk calculations.

A Robust Optimization framework with original concepts and fundamentals also admitting a fusion of ideals from relative regret models and static robust optimization, containing conservatism concepts is disclosed. The algorithm uses a fine-tune strategy to tune the model so the robustness and a target ideality can be mutually achieved with a specified risk. The framework comprises original concepts, a mathematical approach and an algorithm. The statistical treatment of the data with the original concepts from the framework make it able to make short, middle or long-term decision-making setting. The framework has high tractability since the algorithm forces the creation of a setting that makes a robust optimization with the specified risk. The framework can be applied in linear and nonlinear mathematical models since that the objective function is monotonic in the domain of the active convex region. Several examples are solved to best understand the framework and all results demonstrated high tractability and performance. There is a wide range of applications. Along all the text, there is a profound discussion about its philosophy, objective, original concepts, fields of application, statistical and probabilistic fundamentals.

This paper introduces a surrogate model to reduce the huge computational load in the process of simulation-optimization and uncertainty analysis. First, the groundwater numerical simulation model was established, calibrated and verified in the northeast of Hetao Plain. Second, two surrogate models of simulation model were established using support vector regression (SVR) method, one (surrogate model A, SMA) was used to describe the corresponding relationship between the pumping rate and average groundwater table drawdown, and another (surrogate model B, SMB) was used to express the corresponding relationship between the hydrogeological parameter values and average groundwater table drawdown. Third, an optimization model was established to search an optimal groundwater exploitation scheme using the maximum total pumping rate as objective function and the limitative average groundwater table drawdown as constraint condition, the SMA was invoked by the optimization model for obtaining the optimal groundwater exploitation scheme. Finally, the SMB was invoked in the process of uncertainty analysis for assessing the reliability of optimal groundwater exploitation scheme. Results show that the relative error and root mean square error between simulation model and the two surrogate models are both less than 5%, which is a high approximation accuracy. The SVR surrogate model developed in this study could not only considerably reduce the computational load, but also maintain high computational accuracy. The optimal total pumping rate is 7947 m3/d and the reliability of optimal scheme is 40.21%. This can thus provide an effective method for identifying an optimal groundwater exploitation scheme and assessing the reliability of scheme quickly and accurately.

Recent researches proved that the underbridge geometry can be reconstructed by mounting a 3D laser scanner on a motorized cart travelling on a walkway located under the bridge. The walkway is moved by a truck and the accuracy of the bridge model depends on the accuracy of the trajectory of the scanning head with respect to a fixed reference system. In this paper, we describe the metrological characterization of a method that uses non-contact systems to identify the relative motion of the cart with respect to the walkway; the orientation of the walkway with respect to the bridge is determined using inclinometers and optical rails, while the position of the truck with respect to the bridge is measured using a conventional odometer. The measurement uncertainty of the proposed system was initially evaluated by numerical simulations and successively verified by experiments in laboratory conditions. The complete system has then been tested in operative conditions; the validity of the proposed approach has been demonstrated by comparing the geometry of buildings reconstructed with the proposed system with the geometry obtained with a static scan. Results evidenced that the errors are approximately 6 mm.

Air pollution has been a major environmental issue worldwide from the last few decades. It is a man-made issue that has a serious harmful impact on the environment and the health of humans. There are different sources of emission, but industrial processes and motor vehicles contribute to a chief part of air pollution. It has acute as well as chronic effects on the health of humans and affects different organs. It is thought the main environmental risk factor in the incidence and progression of several diseases such as lung cancer, asthma, Alzheimer's and Parkinson's diseases. Long-term exposure to air pollution has different toxicological impacts on humans such as respiratory and cardiovascular diseases, neuropsychiatric complications, eye irritation and skin diseases. As per the study by the World Health Organization (WHO), nitrogen oxides (NOx), carbon monoxide (CO), ozone (O₃), sulfur dioxide (SO₂), volatile organic compounds (VOCs) and heavy metals are the major air contaminants. These pollutants are different in their properties, composition and time of disintegration. The aim of this study is to review the different sources, types, impacts, challenges and mitigation of air pollution in detail.

Titanium alloys, notably Ti6Al4V, are frequently employed in biomedical applications due to their high mechanical strength and biocompatibility. But improving their surface qualities like biocompatibility and resistance to corrosion remains a crucial topic of study. This study investigates the effects of rhenium-carbon coatings deposited on Ti6Al4V substrates using magnetron sputtering technique with a molybdenum anchoring layer. Despite successful formation of rhenium carbides, elemental rhenium, and rhenium oxides confirmed by EDS and XRD analysis, the coatings exhibited poor adhesion, resulting in significant delamination as observed in SEM images. Corrosion resistance tests revealed that the coatings adversely affected the Ti6Al4V substrate, as indicated by increased corrosion current densities and more negative corrosion potentials in potentiodynamic polarization tests. Biocompatibility assessments using PK15 cells showed a notable reduction in cell viability and metabolic activity, particularly with coatings of higher surface roughness. These findings underscore the need for improved surface preparation and coating processes to enhance adhesion and biocompatibility of rhenium-carbon coatings on Ti6Al4V substrates. Future research should focus on optimizing coating adhesion, investigating cytotoxicity mechanisms related to surface roughness, and expanding biocompatibility evaluations to various cell types and biological environments.

Segregating one space from another is a rudimentary act of architecture and the basic principle of this act is creating differentiation between outside and inside. Thresholds, in their expanded or summed form, also produce spaces. They establish the staging for threshold areas in conjunction with space-defining elements. Threshold spaces are typically complicated spatial formations that serve as transition points. They pose a challenge to the interacting human senses in terms of perception. In this research threshold areas have been studied under a specific typology of architecture i.e the traditional housing in historic cities of India. The traditional dwelling settlements have evolved and grown over multiple layers of history, influenced by the culture, society, economy, climate and topography. The purpose of this research is to examine the characteristics of these transition spaces of the traditional dwelling units in relation to the symbolic reference and social meanings assigned to them that influence the architectural and urban spaces attached to them. It seeks to describe transition space in terms of its nature, character, morphology, and social significance. Transparency and material articulation, as well as hierarchy and enclosure, will be investigated. The importance and quality of the nature of "transition" will be examined from the inside to the outside. The research reveals that the space has great functional and socio-cultural importance. It allows multiple activities, provides protection from sun and rain, defines boundaries, connects people within the neighborhood, enhances security and gives an identity to the owner. The contributions made by this research have the potential to improve design considerations for transition areas between the dwelling units and the street.

In order to explore the effect of Wickerhamomyces on the production of flavor compounds in Xiaoqu Baijiu, this study examined the correlation between the fungal communities in Xiaoqu and ester compounds. It was hypothesized that Wickerhamomyces contributes to the aroma of Xiaoqu. Enhanced fermentation methods were used to validate the role of Wickerhamomyces in Xiaoqu Baijiu brewing. Compared to traditional Xiaoqu, enhanced fermentation significantly increased the acidity of fermented grains and starch utilization rate (P<0.05). The fungal communities in fermented grains were analyzed by high-throughput sequencing technology. The dominant fungi in both control and test groups were Saccharomyces, Cyberlindnera, Rhizopus, and Meyerozyma, with Wickerhamomyces replacing Saccharomymycopsis in the enhanced group. Flavor compounds in the fermentation experiments were analyzed, revealing that ethyl acetate content in Xiaoqu Baijiu increased by 949.94 mg/L in the test group, while isoamyl alcohol decreased by 23.62 mg/L and isobutanol decreased by 34.81 mg/L. Functional prediction analysis using PICRUSt2 confirmed a higher relative abundance of enzymes involved in ethyl acetate metabolism and a lower relative abundance of enzymes involved in higher alcohol metabolism in the test group. These findings demonstrate that Wickerhamomyces enhances ethyl acetate production and reduces higher alcohols during Xiaoqu Baijiu brewing, offering a theoretical foundation for enhancing the quality of Xiaoqu Baijiu.

Background: Symmetry is a special kind of balance. This study aims to systematically explore and apply the role of balanced composition in aesthetic judgments by focusing on balanced composition features and employing research methods from computational aesthetics and Neuroaesthetics. Methods: First, experimental materials were classified by quantifying balanced composition using several indices, including symmetry, center of gravity, and negative space. An EEG experiment was conducted with 18 participants, who were asked to respond dichotomously to the same stimuli under different judgment tasks (balance and aesthetics), with both behavioral and EEG data being recorded and analyzed. Subsequently, participants' data were combined with balanced composition indices to construct and analyze various SVM classification models. Results: Participants largely used balanced composition as a criterion for aesthetic evaluation. ERP data indicated that from 300-500ms post-stimulus, brain activation was more significant in the aesthetic task, with unbeautiful and imbalanced stimuli eliciting larger frontal negative waves and occipital positive waves. From 600-1000ms, beautiful stimuli caused smaller negative waves in the PZ channel. The results of the SVM models indicated that the model incorporating aesthetic subject data (ACC=0.9989) outperforms the model using only balanced composition parameters of the aesthetic object (ACC=0.7074). Conclusions: Balanced composition is a crucial indicator in aesthetics, with similar early processing stages in both balance and aesthetic judgments. Multimodal data models validated the advantage of including human factors in aesthetic evaluation systems. This interdisciplinary approach not only enhances our understanding of the cognitive and emotional processes involved in aesthetic judgments but also enables the construction of more reasonable machine learning models to simulate and predict human aesthetic preferences.

Introduction: Known for its persistent blood-feeding behavior from dawn to dusk, Aedes albopictus is recognized as one of the most dangerous mosquito species due to its role as a vector for diseases such as dengue. Studies have demonstrated that Aedes albopictus thrives in micro-breeding sites predominantly located on private properties, including abandoned containers and manholes used for rainwater and irrigation. This study investigates an innovative methodology, the TombiniNet, to mitigate the proliferation of Aedes albopictus by engaging citizens in the active management of micro-breeding sites on their properties. Materials and Methods: The study was conducted in Alessandria, a city with approximately 100,000 inhabitants, focusing on a sample of 200 houses and a population of about 5,000 people. Researchers conducted a comprehensive survey and analysis of micro-breeding sites for Aedes albopictus on both private and communal properties. The larval control measures were conducted from April to October, aligning with the peak mosquito breeding season. The study's sample included 51% of houses that allowed researchers access, and within these, 81% had Aedes albopictus larvae, often found in diverse water Containers holes of which basically manholes on villa garden floors. For monitoring the adult populations were used ovitraps that were not placed in private homes but in public areas to monitor and control the mosquito population. Results: The study identified that up to 70% of micro-breeding sites for Aedes albopictus in northern Alessandria were located on private properties. Citizen participation was critical to the success of the intervention, with a marked decrease in the number of adult tiger mosquitoes observed in areas where residents actively managed breeding sites using the TombiniNet. The TombiniNet was particularly effective in private areas, as it could be regularly maintained by residents, preventing debris accumulation that could hinder drainage. Discussion: The findings highlight the importance of engaging citizens in mosquito control programs to effectively manage Aedes albopictus populations in urban areas. The TombiniNet intervention demonstrated that a simple, cost-effective solution could significantly reduce mosquito breeding sites on private properties. By addressing both communal and private breeding sites, a comprehensive and sustainable approach to mosquito management can be achieved, thereby reducing the risk of disease transmission. Future studies should focus on long-term monitoring and the development of additional innovative solutions to enhance citizen participation and control measures. The study's success also pointed out the limitations of applying such interventions in public areas where debris could impede the net's effectiveness.

Pesticides are chemical compounds or mixtures of hazardous substances that are used by humans to increase and protect crops from pests and control disease-carrying insects. Because of its wide use, it may be poisonous to helpful and non-targeting plants and animals. Spraying or spreading pesticides over whole fields of crops can cause runoff to transfer the chemicals into aquatic ecosystems, and wind can carry the chemicals to nearby grazing regions, farms, and populated areas, where they can endanger other animals. This review summarises recent studies on pesticide combination toxicity to aquatic biota and approaches used to anticipate toxic effects. When toxicants harm economically significant organisms that humans consume, the negative effects become more severe and can cause stress conditions, vital organ damage, biochemical damage, or even the death of living things in both terrestrial and aquatic environments. Pesticides are highly toxic to aquatic organisms such as snails, fish, etc. Neurological dysfunction in freshwater organisms is also caused by pesticides. By producing excessive ROS, pesticides can cause severe biochemical and histological changes in freshwater organisms. So, it is therefore easy to understand the attention of the public on these substances. Because aquatic organisms play a major part in the food chain. However, through the biomagnification process, the quantity of pesticides increases in the higher organisms through the food chain. Which results play a terrific role in the higher organism’s body. So, It's essential to comprehend the way that insecticides work on aquatic organisms. In this review article, we are focusing on the way that pesticides work, which will help in the system of biomonitoring.

As the global population faces an increasing prevalence of physical disabilities, the challenge of motor rehabilitation has become increasingly urgent. Considering that approximately 15% of the global population lives with some form of disability, with a significant number having motor disa-bilities that greatly impact their quality of life, the integration of robotic technologies into rehabili-tation processes offers an opportunity for significant improvement in functional recovery and pa-tient autonomy. This analysis aims to evaluate the progress and challenges encountered in imple-menting robotic technology for the rehabilitation of patients with physical disabilities, highlighting the potential of emerging technologies such as exoskeletons, assisted training devices, and brain-computer interface systems in facilitating motor recovery. By examining relevant clinical studies, the analysis demonstrates the varied effectiveness of robotic therapies compared to traditional re-habilitation methods, while also highlighting the need for ongoing research to optimize care prac-tices and address challenges related to cost, accessibility, and treatment personalization. In con-clusion, robotic technologies represent a promising direction in motor rehabilitation, with the po-tential to revolutionize the care of patients with physical disabilities, significantly improving quali-ty of life and social integration.

In the main prickly pear (Opuntia ficus-indica) producing region in the State of Mexico, fruit production (tunas) occupies the largest cultivated area with 15,800 ha, while 900 ha are cultivated for fresh consumption (“nopal” or “nopalitos”). Two composite samples consisting of prickly pear cactus cladodes (n=6) and another of nopal vegetable (n=6) showing virus-like symptoms were collected. Both sample sets were subjected to high-throughput sequencing (HTS) to identify the viruses and viroids present. The HTS results were verified using RT-PCR and Sanger sequencing. Subsequently, 86 nopal samples (nopal vegetable, prickly pear cactus, xoconostle and wild) were analyzed via RT-PCR with specific primers for the viruses and viroids previously detected via HTS. Three viruses were discovered [Opuntia virus 2 (OV2), cactus carlavirus 1 (CCV-1), and Opuntia potexvirus A (OPV-A)], along with a previously reported viroid [Opuntia viroid 1 (OPVd-1)]. Additionally, two new viroid species were identified, provisionally named Mexican opuntia viroid (genus Pospiviroid) and Opuntia viroid 2 (OVd-2). Phylogenetic analysis, pairwise identity comparison, and conserved structural elements analysis confirmed the classification of these two viroids as new species within the Pospiviroidae family. This is the first report of a pospiviroid and two apscaviroids infecting prickly pear in the world. Overall, this study enhances our understanding of the virome associated with nopal in Mexico.