This study introduces a new discrete distribution which is a weighted version of Poisson-Lindley distribution. The weighted distribution is obtained using the negative binomial weight function and can be fitted to count data with over-dispersion. The p.m.f., p.g.f. and simulation procedure of the new weighted distribution, namely weighted negative binomial Poisson-Lindley (WNBPL), are provided. The maximum likelihood method for parameter estimation is also presented. The WNBPL distribution is fitted to several insurance datasets, and is compared to the Poisson and negative binomial distributions in terms of several statistical tests.

The negative binomial regression model (NBRM) is a generalized linear model which relaxes the restrictive assumption by the Poisson regression model when the variance is equal to the mean. The estimation of the parameters of the NBRM is obtained using the maximum likelihood (ML) method. Maximum likelihood estimator becomes unstable when the explanatory variables are linearly dependent, a situation known as multicollinearity. Based on this, we developed a new estimator called modified jackknifed Negative Binomial Kibria-Lukman (MJNBKL) estimator for the radiation of multicollinearity in NBRM using four different biasing (shrinkage) parameters. We establish superiority condition for MJNBKL estimator over the ones. The performance MJNBKL estimator was ascertained by comparing it with the existing ones through a Monte Carlo simulation study and two real life application datasets. The results of the simulation and real life application show that MJNBKL estimator outperformed the other estimators compared with by having the smallest MSE across all sample sizes and for different levels of correlation for the four biasing parameters used and the third biasing parameter is the optimal shrinkage parameter with the lowest MSE.

In this note we study the binomial model applied to European, American and Bermudan type of derivatives. Our aim is to give the necessary and sufficient conditions under which we can define a fair value via replicating portfolios for any derivative using simple mathematical arguments and without using no arbitrage techniques. Giving suitable definitions we are able to define rigorously the fair value of any derivative without using concepts from probability theory or stochastic analysis therefore is suitable for students or young researchers. It will be clear in our analysis that if $e^{r \delta} \notin [d,u]$ then we can not define a fair value by any means for any derivative while if $d \leq e^{r \delta} \leq u$ we can. Therefore the definition of the fair value of a derivative is not so closely related with the absence of arbitrage. In the usual probabilistic point of view we assume that $d < e^{r \delta} < u$ in order to define the fair value but it is not clear what we can (or we can not) do in the cases where $e^{r \delta} \leq d$ or $e^{r \delta} \geq u$.

The lithium-ion batteries are widely used as a power source for portable devices, including cell phones. The useful life is about 2 years or 500 cycles, contributing to the generation of waste from electrical and electronic equipment (WEEE). Mining of lithium and cobalt damages the environment and is onerous; therefore, sustainable alternatives, such as obtaining these elements from secondary sources as recycling of lithium-ion batteries, are essential to provide the inputs used in the sector. However, the metallurgical route which will used to recovering them must be considered, due to this work aims for a more environmentally favorable process using DL-malic acid 1.5 M and instead of compared with sulfuric acid 2 M, heat pretreatment of 1 h and 3 h, and for all conditions, experiments were carried out with and without adding the oxidizing agent hydrogen peroxide. The best yields occurred in presence of H2O2 10 % v/v, and heat pretreatment of 1 h: 33.49 % Co and 4.63 % Li, and 29.78 % Co e 3.44 % Li were recovered by sulfuric acid and DL-malic acid, respectively.

In the dynamic landscape of modern education, characterized by an increasingly active involvement of IT technologies in learning, the imperative to transfer to students the skills necessary to integrate Artificial Intelligence (AI) into the process represents an important goal. This paper presents a novel framework for knowledge transfer, diverging from traditional programming language-centric approaches by integrating PSoC 6 microcontroller technology. This framework establishes a cyclical learning cycle encompassing theoretical fundamentals and practical experimentation, fostering AI literacy at the edge. Through a structured combination of theoretical instruction and hands-on experimentation, students develop proficiency in understanding and harnessing AI capabilities. Emphasizing critical thinking, problem-solving, and creativity, this approach equips students with the tools to navigate the complexities of real-world AI applications effectively. By leveraging PSoC 6 as an educational tool, a new generation of individuals is efficiently cultivated with essential AI skills. These individuals are adept at leveraging AI technologies to address societal challenges and drive innovation, thereby contributing to long-term sustainability initiatives. Specific strategies for experiential learning, curriculum recommendations, and results of knowledge application are presented, aimed at preparing students to excel in a future where AI will be omnipresent and indispensable.

Epithelial–mesenchymal transition (EMT) is defined as a cellular process during which epithelial cells acquire mesenchymal phenotypes and behavior following the downregulation of epithelial features. EMT and its reversed process, the mesenchymal-epithelial transition (MET), and the special form of EMT, the endothelial-mesenchymal transition (EndMT), have been thought as a universal dogma controlling developmental and pathological processes. More than half a century of EMT study has made it a large research field and a mainstream concept. However, discrepancies and disputes over EMT and EMT research have also grown over time. Particularly, neither the epithelial and mesenchymal states/properties nor their regulatory networks nor specific markers have been defined, rendering the EMT, MET and EndMT concepts groundless. Moreover, EMT and MET effects should not be a cause, but rather a consequence of or an accompanying effect during developmental and pathological processes. EMT and MET represented by the change in cell shapes or adhesiveness or symbolized by EMT factors are biased interpretation of the overall change in cellular property and regulatory networks during development and cancer progression. The true meaning of EMT effects in some developmental and pathological processes, such as fibrosis, needs re-evaluation. But the core EMT factors are actually a few components of the regulatory networks of neural stemness, which determines tumorigenicity and pluripotency. The EMT effects in cancer progression and neural crest formation are wrong attribution of the role of neural stemness during cancer progression and the cell-intrinsic property of neural crest cells to the unknown mesenchymal state. It is time to reassess the significance of EMT and its related concepts in scientific research.

Recently, the focus of entity linking research has centered on the zero-shot scenario, where the entity mention to be labeled at the time of testing was never observed during the training phase or may belong to a different domain than the source domain. Current studies have used BERT as the base encoder as it effectively establishes distributional links between source and target domains. The currently available negative sampling methods all use an extractive approach, which makes it difficult for the models to learn diverse and more challenging negative samples. To address this problem, we propose a generative negative sampling method, Adaptive_mixup_hard, which generates more difficult negative entities by fusing the features of both positive and negative samples on top of hard negative sampling and introduces a transformable adaptive parameter W to increase the diversity of negative samples. Next, we fuse our method with the Biencoder architecture and evaluate its performance under three different score functions. Ultimately, experimental results on the standard benchmark dataset Zeshel demonstrate the effectiveness of our method.

Transportation authorities aim to boost road safety by identifying risky locations and applying suitable safety measures. The Highway Safety Manual (HSM) is a vital resource for US transportation professionals, aiding in the creation of Safety Performance Functions (SPFs), which are predictive models for crashes. These models rely on Negative Binomial distribution-based regression and misinterpreting them due to unmet statistical assumptions can lead to erroneous conclusions, including inaccurately assessing crash rates or missing high-risk sites. The Florida Department of Transportation (FDOT) has introduced context classifications to HSM SPFs, complicating assumption violation identification. This study, part of an FDOT-sponsored project, investigates established statistical diagnostic tests to identify model violations and proposes a novel approach to determine optimal spatial regions for Empirical Bayes adjustment. This adjustment aligns HSM-SPFs with regression assumptions. The study employs a case study involving Florida roads. Results indicate that a 20-mile radius offers an optimal spatial sample size for modeling crashes of all injury levels, ensuring accurate assumptions. For severe injury crashes, which are less frequent and harder to predict, a 60-mile radius is suggested to fulfill statistical modeling assumptions. This methodology guides FDOT practitioners in assessing the conformity of HSM-SPFs with intended assumptions and determining appropriate region sizes.

We evaluate some binomial cubic Fibonacci sums.

We evaluate various binomial sums involving the powers of Fibonacci and Lucas numbers.

In Sub-Saharan African Countries such as Nigeria with high prevalence rate, Child HIV/AIDs acquired through Mother-to-Child transmission (MTCT) can be largely prevented by using a well-established prevention programme and scheme. This study examined factors that can enhanced Prevention of Mother-to-Child transmission (PMTCT) in Nasarawa State. To achieve this, structured questionnaire were used to collect data from one hundred and sixteen (116) women attending two (2) primary facilities and two (2) secondary facilities in the State. This study utilized methods of Poisson Regression, Negative Binomial Regression and Logistic Regression Analyses. Results revealed that women with at least a secondary school education, women with husband in military and women with perceived confidentiality of their HIV status significantly enhanced PMTCT of HIV in Nasarawa State while significant proportion of the women attest to the fact that drugs are available in the facilities (p-value=0.0000<0.05) . Other factors include mother income level, willingness to continue with PMTCT programme and women in support group can also enhanced PMTCT though they are not significant. This study recommends that the factors identified should be explored by NGOs, Ministry of Health and, Support groups and other relevant agencies since they have the capacity to enhanced PMTCT of HIV in Nasarawa State, Nigeria.

Negative temperature coefficient (NTC) materials are usually based on ceramic semiconductors and electrons are involved in their transport mechanism. A new type of NTC material, adequate for alternating current (AC) applications, is represented by zeolites. Indeed, zeolites are single-charge carrier ionic conductors with a temperature-dependent electrical conductivity. In particular, electrical transport in zeolites is due to the monovalent charge-balancing cations, like K⁺, capable of hopping between negatively charged sites in the aluminosilicate framework. Owing to the highly non-linear electrical behavior of the traditional electronic NTC materials, the possibility to have alternative types of materials, showing linearity in the electrical behavior, is very desirable. Among different zeolites, the natural clinoptilolite has been selected for investigating the NTC behavior since it is characterized by high zeolite content, convenient Si/Al atomic ratio, good mechanical strength, due to its compact microstructure, and low toxicity. Clinoptilolite has shown a rapid and quite reversible impedance change under heating, characterized by a linear dependence on temperature. X-ray diffraction (XRD) has been used to identify the natural zeolite, to establish all types of crystalline phases present in the mineral, and to investigate the thermal stability of these phases up to 150°C. X-ray photoelectron spectroscopy (XPS) analysis has been used for the chemical characterization of the natural clinoptilolite sample, providing important information on the cationic content and framework composition. In addition, since electrical transport takes place in the zeolite free-volume, a Brunauer–Emmett–Teller (BET) analysis of the mineral has been also performed.

The modern supply chain (SC) is growing in terms of data, devices, users, and stakeholders, which introduced new security challenges and threats, especially with the reliance on centralized servers or cloud platforms. In addition, increased trust among system participants exposes the SC to a higher risk of vulnerabilities which require strong security measures. This article proposes a hybrid security framework for SC systems, BC-DRLzSC, that integrates Blockchain (BC) and Deep Reinforcement Learning (DRL) designed to operate in a zero trust (ZT) environment. In particular, we propose a decentralized BC-based approach integrated with smart contracts to manage system participant registration and authentication and to control access to system resources. BC-DRLzSC adopts a ZT architecture to reinforce SC security, which can be achieved with an advocate to verify each entity’s trustworthiness before granting or retaining access to system resources. Incorporating the ZT architecture, with BC and DRL, can potentially and significantly bolster SC system security. DRL is employed to develop a proactive attack detection model that continuously monitors the incoming traffic from authenticated nodes within the network and predicts any malicious actions. Finally, we evaluate the performance of our proposed DRL solution using the NSL-KDD dataset.

Tuberculosis cause of death worldwide and the leading cause from a single infectious agent, ranking above Human immunodeficiency virus (HIV) and Acquired Immune Deficiency Syndrome (AIDS). The aim of this study is to ascertain the trend of tuberculosis prevalence and the effect of HIV prevalence onl Tuberculosis case in some West African countries from 2000 to 2016 using count panel data regression models. The data used annual HIV and Tuberculosis cases spanning from 2000 to 2016 extracted from online publication of World health Organization (WHO). Panel Poisson regression model and Negative binomial regression model for fixed and random effects were used to analyzed the count data, the result revealed a positive trend in TB cases while increased in HIV cases leads to increase in TB cases in West African countries. Among the competing models used in this study, Panel Negative Binomial Regression Model with fixed effect emerged the best model with log likelihood value of -1336.554. This study recommended that Government and NGOs need more strategies to fight against HIV menace in West Africa as this will in turn reduced TB cases in West Africa.

The global agricultural sector is increasingly pressured to adopt sustainable practices and reduce its environmental impact. In this context, greenhouses play a crucial role in enabling year-round crop production, ensuring food security, and minimizing reliance on traditional open-field farming. However, the energy consumption associated with greenhouse operations poses a significant challenge to achieving sustainability goals. As a result, there is a growing emphasis on transitioning greenhouses towards near-zero energy consumption. Near-zero energy consumption in greenhouses refers to the ambitious objective of minimizing energy usage to the greatest extent possible while maintaining optimal growing conditions for crops. This goal encompasses reducing energy consumption for heating, cooling, lighting, and other operational needs, as well as exploring renewable energy sources to power greenhouse operations. This review article offers a comprehensive overview of greenhouse energy consumption, with the main goal of analyzing the present situation, identifying key challenges, exploring potential opportunities, and proposing future perspectives for decreasing energy usage in greenhouse environments. As the focus on sustainable agricultural practices grows, the need to reduce energy consumption in greenhouses becomes increasingly important. The review critically examines current technological models and strategies applied in smart greenhouse applications, as well as the monitoring of microclimatic conditions inside the greenhouse, encompassing factors such as temperature, humidity, CO2 levels, soil quality, and crop cultivation. Moreover, it aims to present existing literature that investigates the advancement of greenhouses toward achieving significant reductions in energy consumption.

The decarbonization of the built environment, both in new construction and renovation, is crucial to mitigate its relevant impact on climate change and achieve the Paris agreement goals. This study presents a systematic LCA-based methodology to assess the whole life carbon emissions of buildings, applied to a proposal for the regeneration of one of Milan’s disused railway yards. As an entry for the 2020 Reinventing Cities competition, Scalo Lambrate is a project for a mainly residential neighborhood with a public park. Strategies to reduce carbon emissions deriving both from the operational energy and construction and maintenance were evaluated and their effects compared to a reference scenario over a time horizon of 100 years. The results show that, while the opportunities to reduce carbon emissions during the use phase are somehow limited due to the already stringent performance requirements for new builds, the use of fast-growing biogenic materials for construction materials, even if mixed with more traditional ones, can provide a significant reduction of the global warming potential over the whole life cycle, with a reduction of 70% over the baseline. The remaining emissions can be offset with afforestation initiatives that, however, must be assessed against land use issues.

The pursuit of Artificial Intelligence (AI) that emulates human cognitive processes is a cornerstone of ethical AI development, ensuring that emerging technologies can integrate seamlessly into societal frameworks requiring nuanced understanding and decision-making. Zero-Shot Instance Retrieval (ZSIR) stands at the forefront of this endeavour, potentially providing a robust platform for AI systems, particularly large visual language models, to demonstrate and refine cognition-aligned learning without the need for direct experience. In this paper, we critically evaluate current cognition alignment methodologies within traditional zero-shot learning paradigms, using visual attributes and word embedding generated by large AI models. We propose a unified similarity function that quantifies the cognitive alignment level, bridging the gap between AI processes and human-like understanding. Through extensive experimentation, our findings illustrate that this similarity function can effectively mirror the visual-semantic gap, steering the model towards enhanced performance in zero-shot instance retrieval. This work not only benchmarks the cognition alignment of AI, but also sets a new precedent for the development of visual language models attuned to the complexities of human cognition.}

In this paper, we derive and prove, by means of Binomial theorem and Faulhaber's formula, the following identity between $m$-order polynomials in $T$ $$\sum_{k=1}^{\ell}\sum_{j=0}^m A_{m,j}k^j(T-k)^j=\sum_{k=0}^{m}(-1)^{m-k}U_m(\ell,k)\cdot T^k=T^{2m+1}, \ \ell=T\in\mathbb{N}.$$

Over the years, the need for autonomous systems such as drones, also known as unmanned aerial vehicles (UAVs), has significantly increased in popularity across several industries, including the military. The Internet of Battlefield Things (IoBT) represents a modern technological advancement that significantly enhances the operational efficacy of defense systems. It establishes an integrated military force by connecting individuals to intelligent technology in drones, radios, equipment, antennas and ground stations. This is achieved through the utilization of cloud and edge computing, sensors, mobile devices, embedded systems, and IoT-based systems. However, IoBT security is significantly compromised by enemies that exploit the vulnerabilities and malicious intrusions that specifically target the external environment or to obtain sensitive data. In addition, a comprehensive understanding of cyberattacks and countermeasures is essential for assuring the security of IoBT. On the other hand, as cyber threats get more complicated, trust becomes increasingly important for the security of digital systems. Within this context, the zero trust architecture contends that the security of a complex network is inherently vulnerable to internal and external threats. In defense, UAV security system applications, for example, the primary purpose of the zero trust architecture is to limit the vulnerability of military UAV systems to cyberattacks, hence reducing the possibility of data breaches and illegal entry. This study conducts a systematic review aimed at identifying different aspects of cybersecurity strategies for protecting UAV systems within the IoBT domain. Furthermore, zero trust architecture is presented in the study as an effective prospective solution to the security issues that develop in defense UAV systems.

Leveraging the open-world understanding capacity of large-scale visual-language pre-trained models has become a hot-spot in point cloud classification. Recent approaches rely on transferable visual-language pre-trained models, classifying point clouds by projecting them into 2D images and evaluating consistency with textual prompts. These methods benefit from the robust open-world understanding capabilities of visual-language pre-trained models and require no additional training. However, they face several challenges summarized as prompt ambiguity, image domain gap, view weights confusion, and feature deviation. In response to these challenges, we propose PointBLIP, a zero-training point cloud classification network based on the recently introduced BLIP-2 visual-language model. PointBLIP is adept at processing similarities between multi-images and multi-prompts. We separately introduce a novel method for point cloud zero-shot and few-shot classification, which involves comparing multiple features to achieve effective classification. Simultaneously, we enhance the input data quality for both the image and text sides of PointBLIP. In point cloud zero-shot classification tasks, we outperform state-of-the-art methods on three benchmark datasets. For few-shot classification tasks, to the best of our knowledge, we present the first zero-training few-shot point cloud method, surpassing previous works under the same conditions and showcasing comparable performance to full-training methods.

Cosmic inflation has presented solutions for a number of important cosmological problems, yet left some unanswered. In this paper, we present a cosmological model based on the quantization of the zero-point field which is consistent with empirical data, requires fewer assumptions, and presents answers to some of those unanswered questions. A comparison between standard cosmology and the theory presented in this paper is given below. Vacuum energy density: Standard inflationary model needs both Hubble’s constant and Matter density to estimate it. But, new cosmological model needs only Hubble’s constant. Non-vacuum energy density: Standard model can’t predict it. But, the new model can predict using only Hubble’s constant. Ratio of vacuum energy to total energy: Standard model can’t predict it. But, new model can predict it, that too without using Hubble’s constant. Energy conservation: In standard model, total energy is not conserved before inflation. But, in the new model, energy is conserved right from beginning of the universe whose net energy (including gravitational potential energy) is always zero. Flatness and homogeneity: Standard model needs Inflaton field with a specific potential energy distribution to explain it. But, new model doesn’t need any such hypothetical field, just the zero-point field is sufficient. Based on the new cosmological model, in the conclusion, realistic possibility for existence of multiverse and a mechanism for end of universe are discussed.

We generate numerically on a lattice an ensemble of stationary metrics, with spherical symmetry, which have Einstein action S_E « ħ. This is obtained through a Metropolis algorithm with weight exp(-β²S_E²) and β » ħ^-1. The squared action in the exponential allows to circumvene the problem of the non-positivity of S_E. The discretized metrics obtained exhibit a spontaneous polarization in regions of positive and negative scalar curvature. We compare this ensemble with a class of continuous metrics previously found, which satisfy the condition S_E=0 exactly, or in certain cases even the stronger condition R(x)=0 for any x. All these gravitational field configurations are of considerable interest in quantum gravity, because they represent possible vacuum fluctuations and are markedly different from Wheeler's ''spacetime foam''.

In this paper, we mainly show that generalized hyperharmonic number sums with reciprocal binomial coefficients can be expressed in terms of classical (alternating) Euler sums, zeta values and generalized (alternating) harmonic numbers.

The Energy Performance of Buildings Directive 2010 calls for the Nearly Zero Energy Standard for new buildings from 2021 onwards: Buildings using “almost no energy” are powered by renewable sources or energy produced by the building itself. For residential buildings, this ambitious new standard has already been reached. But for other building types this goal is still far away. The potential of these buildings to meet a Nearly Zero Energy Standard was investigated by analyzing ten case studies representing non-residential buildings with different uses. The analysis shows that the primary characteristics common to critical building types are a dense building context with a very high degree of technical installation (such as hospital, research and laboratory buildings). The large primary energy demand of these types of buildings cannot be compensated by building and property-related energy generation including off-site renewables. If the future Nearly Zero Energy Standard were to be defined with lower requirements because of this, the state related properties of Bavaria suggest that the real potential energy savings available in at least 85% of all new buildings would be insufficiently exploited. Therefore, it would be useful to instead individualize the legal energy verification process for new buildings to distinguish critical building types such as laboratories and hospitals.

This study has developed a Matlab application for simulating statistical models project (SMp) probabilistic distributions that are similar to binomial and Poisson, which were created by mathematical procedures. The simulated distributions are graphically compared with these popular distributions. The application allows to obtain many probabilistic distributions, and shows the trend (τ ) for n trials with success probability p, i.e. the maximum probability as τ=np. While the Poisson distribution PD(x;µ) is a unique probabilistic distribution, where PD=0 in x=+∞, the application simulates many SMp(x;µ,Xmax) distributions, where µ is the Poisson parameter and value of x with generally the maximum probability, and Xmax is the upper limit of x with SMp(x;µ,Xmax) ≥ 0 and limit of the stochastic region of a random discrete variable. It is shown that by simulation via, one can get many and better probabilistic distributions than by mathematical one.

This study has developed a Matlab application for simulating statistical models project (SMp) probabilistic distributions that are similar to binomial and Poisson, which were created by mathematical procedures. The simulated distributions are graphically compared with these legendary distributions. The application allows to obtain many probabilistic distributions, and shows the trend (τ ) for n trials with success probability p, i.e. the maximum probability as τ=np. While the Poisson distribution PD(x;µ) is a unique probabilistic distribution, where PD=0 in x=+∞, the application simulates many SMp(x;µ,Xmax) distributions, where µ is the Poisson parameter and value of x with generally the maximum probability, and Xmax is upper limit of x with SMp(x;µ,Xmax) ≥ 0 and limit of the stochastic region of the random discrete variable X. It is shown that by simulation via, one can get many and better probabilistic distributions than by mathematical one.

This paper is motivated by spatio-temporal pattern in the occurrence of Leishmaniasis in Afghanistan and the relatively high number of zero counts. We hold the view that correlations that arise from spatial and temporal sources are inherently distinct. Our method decouples these two sources of correlations, there are at least two advantages in taking this approach. First, it circumvents the need to inverting a large correlation matrix, which is a commonly encountered problem in spatio-temporal analyses. Second, it simplifies the modelling of complex relationships such as anisotropy, which would have been extremely difficult or impossible if spatio-temporal correlations were simultaneously considered. We identify three challenges in the modelling of a spatio-temporal process: (1) accommodation of covariances that arise from spatial and temporal sources; (2) choosing the correct covariance structure and (3) extending to situations where a covariance is not the natural measure of association. Moreover, because the data covers a period that overlaps with the US invasion of Afghanistan, the high number of zero counts may be the result of no disease incidence or lapse of data collection. To resolve this issue, a model truncated at zero built on a foundation of the generalized estimating equations was proposed.

Medical data flows in streams as the data related to clinical tests and administered drugs by the doctors flows continuously. The doctors must be immediately alerted if negative associations are found among the drugs they prescribe. Data streams are to be processed in single scans as it is not possible to re-scan the data for any iterative processing. To detect negative drug connections, regular and frequent drug patterns must be processed. Negative correlations between disease-curing medications might create adverse responses that kill patients. This paper proposes an algorithm that finds the negative associations among regular and frequent patterns mined from medical data streams. The negative associations mined are the most effective and reduce the negative associations, which are more critical by 50%.

In this article, we assume that the beginning of the universe was before the Big Bang. In the beginning, all matter in the universe was combined in an infinitesimal spherical shape. This sphere was compressed to an incomprehensible value for a period, and then exploded and expanded time and space. We are referring to the negative time before the Big Bang. The evolution of the universe before the Big Bang, passing through the moment of the explosion to the end of the universe at the Big Rip, has been studied. In this article, we try to answer the questions; did the universe exist before the Big Bang? What is the origin of the universe and how did it arise? What are the stages of the evolution of the universe until the moment of the Big Rip? What is the length of time for the stages of this development?

The antimicrobial quantitative structure–activity relationship of plant flavonoids against Gram-positive bacteria was established in our previous works, and the cell membrane was confirmed as a major site of action. To investigate whether plant flavonoids have similar antibacterial effects and mechanisms against both Gram-negative and Gram-positive bacteria, here, the minimum inhibitory concentrations (MICs) of 37 plant flavonoids against Escherichia coli were determined using the microdilution broth method, and then the correlation between their lipophilic parameter ACD/LogP or LogD_7.40 value and their MIC was analyzed. Simultaneously, the correlation between the ACD/LogP or LogD_7.40 value and the MIC of 46 plant flavonoids reported in literature against E. coli was also analyzed. Both sets of results showed that there is a significant correlation between the LogP value and the MIC of plant flavonoids against Gram-negative bacteria. However, it is difficult to effectively predict the MIC of plant flavonoids against Gram-negative bacteria from their lipophilic parameters. By comparing two regression curves derived from plant flavonoids against Gram-negative and Gram-positive bacteria, it was further discovered that the antibacterial activities of most plant flavonoids against Gram-negative bacteria are stronger than those against Gram-positive bacteria when their LogP values are less than about 3.0, but the opposite is true when their LogP values are more than about 3.6. Moreover, this comparison also suggests that unlike mainly acting on the cell membrane of Gram-positive bacteria, plant flavonoids have multiple mechanisms against Gram-negative species, while the cell membrane is also an important action site among them. Combined with the correlation analyses between the enzyme inhibitory activity and the LogP value of the reported flavonoids, it was further suggested that DNA gyrase is another important target of plant flavonoids against Gram-negative bacteria.

The energy efficiency of existing buildings is a crucial factor in addressing Europe’s energy con-sumption challenges, accounting for nearly 40% of the total energy usage. The ongoing energy crisis, driven by the reliance on fossil fuels and increasing prices, has underscored the urgency of adopting sustainable and energy efficient practices especially to the existing building stock. Cyprus is par-ticularly vulnerable due to its heavy dependence on imported fuels for electricity generation and faces significant challenges in transforming its existing building stock into energy-efficient and sustainable structures. Extensive focus has been made by the government on the energy-efficient retrofit of non-residential public buildings. This study examines an existing cultural hall. The case study of the Cultural Hall represents the building stock before 2010 which is lacking thermal in-sulation, double glazing and any energy efficient element. It then offers an ideal case study to ex-plore sustainable solutions which can be easily adopted from the construction industry and from the building owners. The primary objective of this research is twofold: first, to develop a comprehensive retrofit plan to transform the cultural hall into a net-zero energy building; and second, to explore how this transformation can give value back to the local community. After running two simulation scenarios and providing two energy performance certificates, one with at the existing state and one with the intended retrofit measures, it is clear that the building can be a net zero energy consump-tion building. Some interesting results derived, present also how this building can provide value back to the community i.e. through solar carports for EV charging.

While the knowledge about the development of understanding positive integers is rapidly growing, the development of understanding zero is not well-known. Here we tested several components of preschoolers’ understanding zero: whether they can use empty sets in numerical tasks, whether they can use empty sets soon after they understand the cardinality principle, whether they know what the word “zero” refers to, and whether they categorize zero as a number. The results show that preschoolers can handle empty sets in numerical tasks as soon as they understand the cardinality principle or even earlier, and some of them know that these sets are labeled as “zero.” However, they are unsure whether zero is a number. These results identify three components of knowledge about zero: operational knowledge, linguistic knowledge, and meta knowledge. To account for these results we propose that preschoolers might understand numbers as the properties of items or objects in a set. In this view, zero cannot be a number, because an empty set does not include any items, and the missing items cannot have any property, excluding also the number property. This model might explain why zero is handled correctly in numerical tasks, while it is not regarded to be a number.

The fear of missing out (FoMO) is characterized in the literature as a fear that others are having rewarding experiences while one is missing out, and a constant need to keep connected with one’s social network. Driven by Social Determination Theory (SDT) FoMO has been linked with Problematic Social Networking Sites use (PSNSU), negative affectivity (NA), self-esteem (SE) and sleep disturbances. The present study reports findings from 512 individuals (79.1% women, mean age 30.5 years, SD= 8.61). Structural equation modelling (SEM) suggests that the duration of SNSs use and the numbers of SNSs platforms actively used partially mediated the relationship between FoMO and PSNSU. In turns, PSNSU partially mediated the relationship between FoMO and NA. Furthermore, the present study has extended the literature by incorporating the Vulnerability Model in the FoMO concept, identifying that SE partially mediated the relationship between FoMO and NA, while NA fully mediated the relationship between FoMO and sleeping disturbances. Accordingly, the present has extended previous research findings in showing exercise as a potential protective factor to prevent against FoMO. Practical and theoretical implications are discussed.

Let $a, b$ and $n > 1$ be three positive integers such that $a$ and $\sum_{j=0}^{n-1} b^j$ are relatively prime. In this paper, we prove that the toric ideal $I$ associated to the submonoid of $\mathbb{N}$ generated by $\{\sum_{j=0}^{n-1} b^j\} \cup \{\sum_{j=0}^{n-1} b^j + a\, \sum_{j=0}^{i-2} b^j \mid i = 2, \ldots, n\}$ is determinantal. Moreover, we prove that for $n > 3$, the ideal $I$ has a unique minimal system of generators if and only if $a < b-1$.

The purpose of this paper is to evaluate the timing of innovative investment in technology product life cycles using a compound binomial option with management flexibility. Considering the business cycles changes in the macroeconomic will affect consumer purchasing power. The focus is how to evaluate the optimal investment strategy and the project value. It was applied to different product stages (three stages including production innovation, manufacture innovation, and operation innovation) and factored to different risks to build a technology innovation strategy model. An aim of this study is the options premium of the best strategy timing for each innovation stage. Its application of the compound binomial options for the manufacture innovation will only be considered after the execution of the production innovation. The same condition is applied to the operation innovation, which will only be considered after the execution of the manufacture innovation. Then, this paper constructs the dynamic investment sequential decision model, assesses the feasibility of an investment strategy, and makes a decision on the appropriate project value and options premium for each stage under the possible change of Gross Domestic Product (GDP). This paper investigates the product life cycle innovation investment topic by using the compound binomial options method and will provide a more flexible strategy decision compared with other trend forecast criteria.

The paper combines two new original concepts about eco-energetic systems. The first one is related to the M.E.N. (Mega-Eco-Nega-Watt) paradigm, which is based on three different but complementary ecological economic spaces: MEGAWATT, as needed energy, ECOWATT, as ecological energy, and NEGAWATT, as preserved energy, even the renewable energies and technologies, in the context of electrical energy production. The second concept presented in this paper is the eco-energetic efficiency, introduced in order to facilitate a correlation between the energetic efficiency of the system and a necessary, new defined ecological coefficient. The proposed formula for eco-energetic efficiency enables an interesting form of reporting to the different situations in which the input energy, output energy, lost energy and externalities, involved in an energetic process interact to produce energy in a specific energetic system, in connection with the circular economy model. Finally, is presented an original diagram of the energetic chains to produce electricity in a resilience regim, with high eco-energetic efficiency from originating in different primary energetic sources as external (gravitation & solar sources), fuels (classical & radioactive), internal sources and others. Even what kind of energetic sources are used to obtain electricity, as coals, gas, wood, hydropower, nuclear power, wind power, biomass, solar systems, and others, the entire process should be sustainable in what is the transdisciplinary integration of the different representative spheres as energy, socio-economy, ecology (environment), with the main core, sustainable education, inclueding the law and administrative aspects, as necessary fields of the knowledge based society/economy.

The zero-catch problem is a key issue in CPUE(Catch Per Unit Effort) standardization, and previous studies have treated all zero-catch data uniformly, but this actually loses some correctly-labeled samples. On the other hand, for the main catches with few zero-catch samples, the problem of low performance of fisheries forecasting remains unsolved even though the forecasting model structure is updating constantly, since we cannot know whether the samples are correctly recorded. In this paper, we propose a method based on confident learning theory to detect anomalous samples in the datasets and unify zero-catch and non-zero samples as noise through an overarching framework of learning with noisy labels, which reveals the heterogeneity among zero-catch samples (as well as among non-zero samples) and the homogeneity between zero-catch samples and non-zero samples. Using three species of tuna in the tropical Atlantic Ocean with the spatial resolution of 0.5 ◦ × 0.5 ◦ and time resolution of days from 2016 to 2019 as experimental material, performance on all three classical machine learning models(Random forest, Support Vector Machine and XGBoost) is significantly improved compared to each baseline.This confirms that we propose a self-adaptive, effective method for detecting and repairing anomalous samples in the fishery dataset.

An interesting anomaly of the diffusion process with an apparently negative diffusion coefficient defined through the mean-square displacement in a one-dimensional quantum molecular chain model is shown. Nevertheless, the system satisfies the H-theorem, so that the second law of thermodynamics is satisfied. The reason why the “diffusion constant” becomes negative is due to the effect of the phase mixing process, which is a characteristic result of the one-dimensionality of the system. We illustrate the situation where this negative “diffusion constant” appears.

1) Introduction: Negative pressure wound therapy (NPWT) is a frequently applied open abdomen (OA) treatment. There are only a few experimental data supporting this method and describing the optimal settings and pressure distribution in the abdominal cavity during this procedure. The aim of our study was to evaluate pressure values at different points of the abdominal cavity during NPWT in experimental abdominal compartment syndrome (ACS) animal model. 2) Methods: In this study (permission Nr. 13/2014/UDCAR) 27 Hungahib pigs (15.4- 20.2 kg) were operated. ACS was generated by implanting a plastic bag in the abdomen through mini-laparotomy and filled with 2100- 3300 ml saline solution (37 C°) to an intraabdominal pressure (IAP) of 30 mmHg. After 3 hours, NPWT (Vivano Med ® Abdominal Kit, Paul Hartmann AG, Germany) or Bogota bag was applied. NPWT group was divided into -50, -100 and 150 mmHg suction group. Pressure distribution to the abdominal cavity was monitored at 6 different points of the abdomen via a multichannel pressure monitoring system. 3) Results: The absolute pressure levels were significantly higher above than below the layer. The values of the pressure were similar in the midline than laterally. Amongst the bowels, the pressure values changed periodically between 0 and -12 mmHg which might be caused by the peristaltic movements. 4) Conclusions: The porcine model of the present study seems to be well applicable for investigating ACS and NPWT. It was possible to provide valuable for clinicians. The pressure was well distributed by the protective layer to the lateral parts of the abdomen and this phenomenon did not change considerably during the therapy.

Background: Metabolic flux analysis (MFA) is an indispensable tool in metabolic engineering. The simplest variant of MFA relies on an overdetermined stoichiometric model of the cell’s metabolism under the pseudo-steady state assumption, to evaluate the intracellular flux distribution. Despite its long history, the issue of model error in the overdetermined MFA, particularly misspecifications of the stoichiometric matrix, has not received much attention. Method: We evaluated the performance of statistical tests from linear least square regressions, namely Ramsey RESET test, F-test and Lagrange multiplier test, in detecting model misspecifications in the overdetermined MFA, particularly missing reactions. We further proposed an iterative procedure using the F-test to correct such an issue. Result: Using Chinese hamster ovary and random metabolic networks, we demonstrated that: (1) a statistically significant regression does not guarantee high accuracy of the flux estimates, (2) the removal of a reaction with a low flux magnitude can cause disproportionately large biases in the flux estimates, (3) the F-test could efficiently detect missing reactions, and (4) the proposed iterative procedure could robustly resolve the omission of reactions. Conclusion: Our work demonstrated that statistical analysis and tests could be used to systematically assess, detect and resolve model misspecifications in the overdetermined MFA.

We first explore negative-ion formation in fullerenes C44, C60, C70, C98, C112, C120, C132 and C136 through low-energy electron elastic scattering total cross sections calculations using our Regge-pole methodology. Water oxidation to peroxide and water synthesis from H₂ and O₂ are then investigated using the anionic catalysts C44ˉ to C136ˉ. The fundamental mechanism underlying negative-ion catalysis involves hydrogen bond strength-weakening in the transition state. DFT transition state calculations found C60ˉ numerically stable for both water and peroxide synthesis, C100ˉ increases the energy barrier the most and C136ˉ the most effective catalyst in both water synthesis and oxidation to H2O2.

Day-ahead forecasting of electricity prices is important in deregulated electricity markets for all the stakeholders: energy wholesalers, traders, retailers, and consumers. Electricity price forecasting is an inherently difficult problem due to its special characteristic of dynamicity and non-stationarity. In this paper, we present a robust price forecasting mechanism that shows resilience towards aggregate demand response effect and provides highly accurate forecasted electricity prices to the stakeholders in a dynamic environment. We employ an ensemble prediction model in which a group of different algorithms participate in predicting the price for each hour of a day. We propose two different strategies, namely, Fixed Weight Method (FWM) and Varying Weight Method (VWM), for selecting each hour's expert algorithm from the set of participating algorithms. In addition, we utilize a carefully engineered set of features selected from a pool of features derived from information such as past electricity price data, weather data, and calendar data. The proposed ensemble model offers better results than both the Pattern Sequence-based Forecasting (PSF) method and our own previous work using Artificial Neural Networks (ANN) alone do on the datasets for New York, Australian, and Spanish electricity markets.

The SK model proposed by Sherrington and Kirkpatrick is an important mean field model for spin glasses. The dependence structure of Hamiltonians of the SK model is considered in this paper. We have investigated the covariance matrix of Hamiltonians of the SK model, and establish a relationship between the rank of the corresponding covariance matrix and the dimension of the space spanned by the Hamiltonians. Finally, we also give an upper bound for the rank of the covariance matrix, which might be the best.

This study addresses the implementation of the International Financial Reporting Standard 9 (IFRS 9) in the European Union as of January 1, 2018, replacing the International Accounting Standard 39 (IAS 39) to introduce a new model for recognizing Loan Loss Provisions (LLP) based on Expected Credit Loss (ECL). This model responds to criticisms of the former Incurred Credit Loss (ICL) system for its inability to reflect credit losses in a timely manner, potentially exacerbating the effects of financial crises (Bushman &amp; Williams, 2015; Gebhardt &amp; Novotny-Farkas, 2011). The study focuses on the effects of adopting the ECL model on the level of Loan Loss Allowances (LLA) in loans, own equity, and the Common Equity Tier 1 (CET1) ratio across 13 Portuguese commercial banks. A mean comparison test is used to evaluate scenarios before and after the application of the ECL model, highlighting the importance of regulator actions and the adequacy of loss recognition policies, including the effects of Regulation (EU) 2017/2395 of December 12. The results obtained demonstrate significant negative impacts on the net values of loans, own equity, and the CET1 ratio upon adopting the IFRS 9 ECL model, due to the widespread increase in LLAs.

The assortment of low-cost 3D printers for “in-practice” use, e.g., for clear aligner therapies, is ever increasing. To address concerns about the accuracy of orthodontic models produced on such printers when cost efficient modes of 3D printing are employed, this study examined the effect of print model body type and print angulation on accuracy. Six printing-configuration groups were included: 2 model types (solid or hollow shell) combined with 3 print angles (0°, 70°, or 90°) with 10 models/group; all models were printed with 100-µm layer thickness using a digital light processing-based 3-dimensional printer. Eleven selected structures and distances were measured on the printed models with a digital microscope and compared to the same measures on a digitized master model. The clinically-acceptability range was set at ±0.25 mm difference from the master model for single tooth measurements (intra-tooth) and ±0.5 mm for cross-arch measurements (inter-tooth). For individual measurements across all models, 98% fell within clinical acceptability. For mean measurements within each model group, only canine height for the shell-0° model had a mean difference (0.26 mm ± 0.03) outside the clinically acceptable range for intra-tooth measurements. Standard deviations for all intra-tooth measurements were within 0.07 mm. While none of the mean inter-tooth measurements exceeded the acceptability range, the standard deviations were larger (0.04 to 0.30 mm). Accuracy of orthodontic models for clear aligner therapies was not impacted beyond the clinically acceptable range when altering model body type and print angulation to improve efficiency of 3D printing. These findings suggest greater flexibility of the practitioner to alter print settings to address time and cost efficiency in various clinical scenarios and still maintain clinically acceptable model accuracy.

By introducing environmental mediator, for avoiding randomness in selecting indicators and subjectivity in deciding interaction path coefficients to reveal interaction mechanism among forest industry, ecology and environmental capacity, structural Pressure–State–Impact–Response (PSIR) and quantitative Structural Equation Modeling (SEM) are utilized with data on thirty–one provinces of China. We can find that: (i) forest industry has a negative influence on ecology, whereas ecology has a positive influence on industry; (ii) the destructive influence that industry has on ecology is much greater than the positive effect ecology has on industry; and (iii) environmental capacity plays a partial mediating role between industry and ecology. According to these results we can conclude that: (i) forest industry and ecology are not in a symbiotic relationship and have not reached a level of ecological security; (ii) the interaction between industry and ecology is in a period of transition from antagonistic to beneficial; and (iii) industry dominates ecology. However, we prospect the following advice: (i) methods of forest industry should be changed; (ii) new modes of industrial integration and circular economy should be developed; and (iii) the ability of the environment to sustain itself should be enhanced. The level of forest ecological security to discuss the symbiotic mechanism of industry and ecology will propose to be measured in future.

Classifying network flow subsequences in an Industrial IoT gateway is an effective method for identifying Industrial IoT faults. This paper proposes a subsequence classification algorithm SSGBUL-IKNN based on unsupervised learning encoding to address the problem of low classification accuracy. Firstly, we built a network module based on unsupervised learning to encode the flow sequence of the Industrial IoT gateway. In the encoding module, a subsequence calibration algorithm controls the increasing error during the coding process. Then, the specific fault type is obtained through an improved KNN classification algorithm based on the indication distance source from the integrated coding sequences converted by multi-dimensional network flow sequences. The test result on three Industrial IoT datasets of a sewage treatment plant shows that the accuracy of SSGBUL-IKNN in this paper exceeds 92%, significantly higher than the algorithm DTW and TSF. It reaches the classification requirements of Industrial IoT fault diagnosis.

The system of non-interacting electrically charged core-massless spring-shell mechanical units, demonstrating negative effective mass, is considered, seen as a Drude-Lorentz gas. When such an ideal gas is exposed to the external harmonic field, it demonstrates as the certain conditions the negative frequency-dependent electrical conductivity. The negative value of the electrical conductivity implies that the electrical current will flow against the direction of the electric field and correspondingly the direction of the electrical force. Low- and high-frequency asymptotic behavior of the electrical conductivity is addressed. The same system demonstrates at low frequencies the negative asymptotic refraction. Experimental realization of the introduced model system, based on the exploitation of plasma oscillations of the free electron gas is suggested.

Considering characteristics of high river density, special underlying surface in plain river network region, and municipal drainage system function, river network drainage unit model is proposed, which is defined as a region surrounded by main river or embankment. Flood storage variety and control projects regulation of small rivers in each unit is simulated. With drainage catchment as an object, according to its drainage capacity, simplified municipal drainage model was developed. Coupling river network drainage unit model, simplified municipal drainage model and 2D flood routing model, urban flood analysis model for plain tidal river network region was developed, which could be applied to analyzing flood from upstream river, storm surge and local rainfall. Demonstration research was carried out in Puxi flood protected area in Shanghai.

Attribute reduction is a core technique in the rough set domain and an important step in data preprocessing. Researchers have proposed numerous innovative methods to enhance the capability of attribute reduction, such as the emergence of multi-granularity rough set models, which can effectively process distributed and multi-granularity data. However, these innovative methods still have many shortcomings, such as how to deal with complex constraints and how to perform multi-angle effectiveness evaluations. Based on the multi-granularity model, this study proposes a new method of attribute reduction, namely using multi-granularity neighborhood information gain ratio as the measurement criterion. This method combines both supervised and unsupervised perspectives, and by integrating multi-granularity technology with neighborhood rough set theory, constructs a model that can adapt to multi-level data features. This model can select the optimal granularity level or attribute set according to the requirements. Finally, the proposed method is experimented with on 15 UCI datasets against six other attribute reduction algorithms. The experimental outcomes, upon analysis and comparative review, substantiate the reliability and consistency of the attribute reduction approach introduced in this study.

By repeated unloading and loading of the excavation dynamic and static loads, repeated train dynamic and static loads of multi-factors, the underpass tunnel, cross tunnel engineering influence area rock layer, the existing tunnel structure of the three constitutes a multifaceted structure, multi-factor interaction system. Existing tunnels into the prediction theory model of the new tunnel excavation process of dynamic and static loads, dynamic and static loads of existing tunnels are mostly analyzed individually, the results of synergistic analysis needs to be improved. Based on the theory of the current layer method, the paper comprehensively considers the stiffness of the existing tunnel, the stiffness of the new tunnel, the excavation method load and the existing tunnel load, coupled with Peck's empirical formula, the theoretical model of deformation prediction for excavation of a two-lane highway tunnel under the tunnel through the existing railroad is proposed, and verified and optimized by numerical calculations and monitoring data, which proves the applicability and reliability of the theoretical model in the similar strata and the project promotion.

Zero-emission trucks for regional and long haul missions are an option for fossil-free freight. The viability of such powertrains and system solutions was studied conceptually in project ESCALATE for trucks with GVW of 40 tonnes and beyond through various prime mover combinations. The study covers battery and fuel cell power sources with different degrees of battery electric as well as H2 and fuel cell operation. As a design basis, two different missions with a single-charge/H2 refill were analysed. The first mission was the VECTO long haul profile repeated up to 750 km, whereas the second was a real 520 km on-road mission in Finland. Based on the simulated energy consumption on the driving cycle, on-board energy demand was estimated, and the initial single-charge operational scenarios were analysed with five different power source topologies. The traction motors of the tractor were dimensioned so that a secondary mission of GVW up to 76 tons on a shorter route can be operated. Based on the powertrain and vehicle model, various infrastructure options for charging and H2 refuelling strategy as well as various operative scenarios with indicative total cost of ownership (TCO) were analysed.

Currently, in some domestic and foreign mines, the backfill drilling pipeline experience a rupture phenomenon even when the wear degree is low. This results in a delay in production due to the filling becoming 'sick'. This paper presents, for the first time, the damage mechanism from a mechanical perspective and re-derives the anti-extrusion strength model of the backfill drilling pipeline. We investigate the influence of law on the anti-extrusion strength of pipelines from the perspective of stratum and cement ring. We then verify the theoretical and simulation results through engineering examples. The results demonstrate that the Mises stress criterion is a suitable modification principle for the anti-extrusion strength model of the backfill drilling pipeline. The anti-extrusion strength of the pipeline is related to the elastic modulus and Poisson's ratio of the stratum, and the thickness of the cement ring. It is negatively affected by the depth of the stratum. For hard strata, a cement ring with a smaller elastic modulus is suitable, while for soft stratum, a cement ring with a larger elastic modulus is recommended. When the missing angle of the cement ring is less than 60°, the stress concentration factor increases up to 2.2. The stress unloading capacity of the cement ring ranges from 32.7% to 37.8%, and optimal performance of the cement ring is achieved when it has high strength and low rigidity. The backfill filling pipeline of a copper mine abroad was destroyed due to external extrusion force exceeding its anti-extrusion strength value. The modified pipeline anti-extrusion strength model is 18.2% higher than the casing API strength value. This finding can inform the design of the backfill filling pipeline for China's kilometer-deep wells in the future.

Background: Extracellular Vesicles are pivotal mediators in intercellular communication, facilitating the exchange of biological information among healthy, pathological and tumor cells. Between the diverse subtypes of extracellular vesicles, exosomes have unique properties, clinical and therapeutical applications, even towards cancer. Breast cancer ranks as one of the most prevalent malignancies across the globe. Both the tumor core and its surrounding microenvironment engage in a complex, orchestrated interaction that facilitates the cancer’s growth and spread. Methods: The most valuable and significant PubMed Literature about extracellular vesicles and adipose–derived stem cells exosomes was selected and analyzed in order to report their biological properties and potential applications, in particular on the Triple–Negative Breast Cancer. Results: Adipose–derived stem cells exosomes represent a potential tool to target the tumor core of the Triple–Negative Breast Cancer, its microenvironment and surrounding tissues including metastasis. The authors are developing a supportive Triple–Adipose–derived stem cells Exosomes [T.A.E.] Technology against breast cancer in order to perform a selective preconditioning of the cancer before or while the traditional oncological treatments are running. Conclusion: The possibility to exploit the impact of Adipose–derived stem cells exosomes within the T.A.E. Technology against the Triple–Negative breast cancer is real, highlighting a new era for supportive measures against breast cancer.Keywords: Extracellular vesicles; Exosomes; Adipose–derived stem cells, Tumor microenvironment, Triple–Negative Breast Cancer, T.A.E. Technology.

Concrete is more stable than filler as a false roof in a downward approach type infill quarry, but the mechanical model of a concrete false roof is not clear. As a result, there has been limited application. Therefore, it is necessary to establish the mechanical model of a concrete artificial false roof to enhance the stability of the downward approach infill quarry. This paper combines theoretical analysis, model derivation, finite element analysis and field application methods. Firstly, the basic assumptions of thin plate theory and elasticity mechanics are used as the premise to establish the differential equations of the deflection curve of the concrete thin plate false roof. Then, the three types of bending moment generation of the concrete thin plate false roof are explored. Secondly, the relationship between the location of dangerous surface and dangerous point in hard-supported weak-slab structure and soft-supported weak-slab structure is analyzed, and the type of uniform load and calculation model of concrete thin-slab false roof are improved. Finally, according to the established mechanical model of thin slab false roof, finite element analysis and field application were carried out. The results show that there are three types of deflection curve differential equations in the concrete thin slab false roof perpendicular to the strike, and three extreme points exist in the middle section of the thin slab and on both sides of the surrounding rock. The hazardous surface of the hard-supported weak-slab structure is present at the intersection of the encircling rock and the thin-slab fake ceiling, with the perilous point situated on the top of this surface. The hazardous surface of the soft-supported weak-slab structure can be found in the centre section of the thin-slab fake ceiling, with the perilous point being located at the bottom of this surface. Moreover, the damage mode for both structures is that of maximum tensile stress damage. Upon conducting a finite element analysis, the results indicate that the concrete's maximum tensile stress is significantly high.

Background Homologous recombination deficiency (HRD) evolved into a major diagnostic marker in high grade ovarian cancer (HGOC) predicting the response to poly (Adenosine diphosphate-ribose) polymerase inhibitors (PARPi) and also platinum-based therapy. In addition to HRD, the type of peritoneal tumor spread influences treatment response and patient survival; miliary type tumor spread has poorer predicted outcome than non-miliary type tumor spread. Methods Known methods for HRD assessment were adapted for our technical requirements and the Predictive-value Integrated Genomic Instability Score (PIGIS) for HRD assessment evolved as an outcome. PIGIS was validated in HGOC samples from 122 patients. We used PIGIS to analyze whether the type of tumor spread correlated with HRD status and whether this had an impact on survival. Results We demonstrated that PIGIS can discriminate HRD positive from HRD negative samples. Tumors with miliary tumor spread are HRD negative and have a very bad prognosis with a progression free survival (PFS) of 15.6 months and an overall survival (OS) of 3.9 years. However, HRD negative non-miliary spreading tumors in our cohort had a much better prognosis (PFS 35.4 months, OS 8.9 years), similar to HRD positive tumors (PFS 34.7 months, OS 8.9 years). Conclusions Our results indicate that in a predominantly PARPi naïve cohort the type of tumor spread might be a better predictor of response to platinum sensitivity than HRD. It remains to be determined, whether this also applies for sensitivity to PARPi.

Periodontitis is a chronic inflammatory disease which results in the destruction of the tooth’ s supporting tissues and the alveolar bone resorption. The complement becomes a major link between infection and inflammatory pathology including periodontitis. Gingipians as important virulence factors of P. gingivalis have the activity of C5 convertase, could cleave C5 into fully functional C5a to activate C5aR. The above process could be blocked by the C5aR antagonist (PMX-53) to suppress local periodontal inflammation, and then achieves the purpose of treatment of periodontitis. Nanoparticles incorporated within gelatin are promising carrier system for drug delivery in recent years. This study aimed to investigate whether gelatin nanoparticles loaded with PMX-53 prevents alveolar bone resorption in miniature swines model of periodontitis. Four miniature swines were placed ligatures around the maxillary and mandibular fourth premolar and first molar on both sides for seven weeks to induce periodontitis. Then, animals were assigned randomly to four groups: minocycline-treated group, gelatin with PMX53-treated group, gelatin-treated group and a sham control group. They were treated with 1ml related drugs respectively, into gingival sulcusl for 4 times at one-week intervals. We showed that local treatments with gelatin nanoparticles loaded with PMX-53 could inhibit alveolar bone loss of periodontitis. Our study revealed that gelatin nanoparticles loaded with PMX-53 prevented alveolar bone resorption miniature swines model of periodontitis. In addition, provided proof-of-concept for local targeting of gelatin nanoparticles loaded with PMX-53 as a powerful candidate for the treatment of periodontitis.

Zero-shot learning ability robotics enables robots to generalize their knowledge and perform tasks for which they have not been explicitly trained. This ability is extremely helpful because in classic learning approaches, robots are trained on specific tasks and require extensive data and labeled examples to perform accurately. However, in real-world scenarios, robots often encounter new or unseen tasks that were not part of their training data. We briefly survey works in this area to give the reader a perspective about progresses in this area.

New setting is introduced to study failed zero-forcing number and failed zero-forcing neutrosophic-number. Leaf-like is a key term to have these notions. Forcing a vertex to change its color is a type of approach to force that vertex to be zero-like. Forcing a vertex which is only neighbor for zero-like vertex to be zero-like vertex but now reverse approach is on demand which is finding biggest set which doesn’t force. LetNTG : (V,E,σ,μ) be a neutrosophic graph. Then failed zero-forcing number Z(NTG) for a neutrosophic graph NTG : (V,E,σ,μ) is maximal cardinality of a set S of black vertices (whereas vertices in V (G) \ S are colored white) such that V (G) isn’t turned black after finitely many applications of “the color-change rule”: a white vertex is converted to a black vertex if it is the only white neighbor of a black vertex. Failed zero-forcing neutrosophic-number Zn(NTG) for a neutrosophic graphNTG : (V,E,σ,μ) is maximal neutrosophic cardinality of a set S of black vertices (whereas vertices in V (G) \ S are colored white) such that V (G) isn’t turned black afterfinitely many applications of “the color-change rule”: a white vertex is converted to a black vertex if it is the only white neighbor of a black vertex. Failed zero-forcing number and failed zero-forcing neutrosophic-number are about a set of vertices which are applied into the setting of neutrosophic graphs. The structure of set is studied and general results are obtained. Also, some classes of neutrosophic graphs namely path-neutrosophic graphs, cycle-neutrosophic graphs, complete-neutrosophic graphs, star-neutrosophic graphs, bipartite-neutrosophic graphs, and t-partite-neutrosophic graphs are investigated in the terms of maximal set which forms both of failed zero-forcing number and failed zero-forcing neutrosophic-number. Neutrosophic number is reused in this way. It’s applied to use the type of neutrosophic number in the way that, three values of a vertex are used and they’ve same share to construct this number to compare with other vertices. Summation of three values of vertex makes one number and applying it to a comparison. This approach facilitates identifying vertices which form failed zero-forcing number and failed zero-forcing neutrosophic-number. In path-neutrosophic graphs, the set of vertices such that every given two vertices in the set, have distance at least two, forms maximal set but with slightly differences, in cycle-neutrosophic graphs, the set of vertices such that every given two vertices in the set, have distance at least two, forms maximal set. Other classes have same approaches. In complete-neutrosophic graphs, a set of vertices excluding two vertices leads us to failed zero-forcing number and failed zero-forcing neutrosophic-number. In star-neutrosophic graphs, a set of vertices excluding only two vertices and containing center, makes maximal set. In complete-bipartite-neutrosophic graphs, a set of vertices excluding two vertices from same parts makes intended set but with slightly differences, in complete-t-partite-neutrosophic graphs, a set of vertices excluding two vertices from same parts makes intended set. In both settings, some classes of well-known neutrosophic graphs are studied. Some clarifications for each result and each definition are provided. Using basic set not to extend this set to set of all vertices has key role to have these notions in the form of failed zero-forcing number and failed zero-forcing neutrosophic-number. The cardinality of a set has eligibility to form failed zero-forcing number but the neutrosophic cardinality of a set has eligibility to call failed zero-forcing neutrosophic-number. Some results get more frameworks and perspective about these definitions. The way in that, two vertices don’t have unique connection together, opens the way to do some approaches. A vertex could affect on other vertex but there’s no usage of edges. These notions are applied into neutrosophic graphs as individuals but not family of them as drawbacks for these notions. Finding special neutrosophic graphs which are well-known, is an open way to pursue this study. Some problems are proposed to pursue this study. Basic familiarities with graph theory and neutrosophic graph theory are proposed for this article.

Myelin protein zero (P0), a type I transmembrane protein, is the most abundant protein in peripheral nervous system (PNS) myelin – the lipid-rich, periodic structure that concentrically encloses long axonal segments. Schwann cells, the myelinating glia of the PNS, express P0 throughout their development until the formation of mature myelin. In the intramyelinic compartment, the immunoglobulin-like domain of P0 bridges apposing membranes together via homophilic adhesion, forming a dense, macroscopic ultrastructure known as the intraperiod line. The C-terminal tail of P0 adheres apposing membranes together in the narrow cytoplasmic compartment of compact myelin, much like myelin basic protein (MBP). In mouse models, the absence of P0, unlike that of MBP or P2, severely disturbs the formation of myelin. Therefore, P0 is the executive molecule of PNS myelin maturation. How and when is P0 trafficked and modified to enable myelin compaction, and how disease mutations that give rise to incurable peripheral neuropathies alter the function of P0, are currently open questions. The potential mechanisms of P0 function in myelination are discussed, providing a foundation for the understanding of mature myelin development and how it derails in peripheral neuropathies.

In this work model of mirror elements equilibrium of the micromechanical components is developed, the behavior analysis of the mirror element of micromechanical mirrors in case changing of control voltages of electrostatic actuators is carried out, an expression for determining the maximum value of deflection voltage at which the snap-down effect will take the following form is obtained in case of the influence of the coefficient of the electrostatic rigidity of electrostatic actuators. The developed equilibrium model of mirror elements and the obtained results of modeling can be used at design of micromechanical mirrors with internal suspensions.

In this paper, a semi-empirical model of air waves induced by falling rock is described. The model is composed of a uniform motion phase (velocity close to 0 m·s^-1) and an acceleration movement phase. The uniform motion phase was determined based on experimentally and the acceleration movement phase was derived by theoretical analysis. A series of experiments were performed to verify the semi-empirical model and elucidated the law of the uniform motion phase. The acceleration movement phase accounted for a larger portion with a greater height of the falling rock. Experimental results of different falling heights of the goaf showed close agreement with theoretical analysis values. The semi-empirical model could accurately and conveniently estimate the velocity of air wave induced by falling rock. Thus, the semi-empirical model can provide a reference and basis for estimating the speed of air waves and designing protective measures in mines.

The main objective of this paper is to find a close link between the adaptive level of significance, presented here, and the sample size. We, statisticians, know of the inconsistency, or paradox, in the current classical tests of significance that are based on p-value statistics that is compared to the canonical significance levels (10%, 5% and 1%): "Raise the sample to reject the null hypothesis" is the recommendation of some ill-advised scientists! This paper will show that it is possible to eliminate this problem of significance tests. The Bayesian Lindley's paradox – "increase the sample to accept the hypothesis" – also disappears. Obviously, we present here only the beginning of a possible prominent research. The intention is to extend its use to more complex applications such as survival analysis, reliability tests and other areas. The main tools used here are the Bayes Factor and the extended Neyman-Pearson Lemma.

This paper addresses the input coupling problem for the shape memory alloy (SMA) actuated parallel platform with completely unknown nonlinear dynamics. The problem means that in a parallel platform with multiple SMA actuators acting together, the displacement of one of the actuators will simultaneously affect the remaining actuators to change. Due to the coupling problem and nonlinear characteristics of the SMA actuators, it is not easy to control the platform to achieve bi-directional swing with completely unknown nonlinear dynamics. Therefore, a model-free adaptive sliding mode control method is proposed to improve the control accuracy of the complex motion of the platform, avoiding the complex modeling process of the SMA actuated parallel platform. Based on the model-free adaptive control (MFAC) theory, a multiple input and multiple output (MIMO) dynamic linearized mathematical model of the parallel platform is established using the online input and output data of the system. Then, a sliding mode controller is designed based on the dynamic model. A rigorous theoretical analysis proves that the proposed model-free adaptive sliding mode controller is able to stably control the SMA actuated parallel platform, and its tracking error is uniformly ultimately bounded (UUB). Finally, experiments are conducted on the established SMA actuated parallel platform. The results show that the proposed model-free controller is feasible and effective, and can control the system well to achieve the complex motion of bi-directional swing.

Triple-negative breast cancer (TNBC) is an aggressive and highly metastatic type of tumor. TNBC is often enriched in tumor-infiltrating neutrophils (TINs), which support cancer growth in part by counteracting tumor-infiltrating lymphocytes (TILs). Prior studies identified the enhancer of zeste homolog 2 (EZH2) as a pro-tumor methyltransferase in primary and metastatic TNBC. We hypothesized that EZH2 inhibition in TNBC cells per se would exert anti-tumor activity by altering the tumor immune microenvironment. To test this hypothesis, we used CRISPR to generate EZH2-knockout (KO) and overexpressing lines from parent (wild-type -WT) 4T1 cells, an established murine TNBC model. In vitro, EZH2 KO and OE cells showed little change in replicative capacity and invasiveness, but marked changes in surface marker profile and cytokine/chemokine secretion compared to WT cells. On in vivo injection, EZH2 KO cells showed significantly reduced primary tumor growth and a 10-fold decrease in lung metastasis compared to WT cells, while EZH2 OE cells were unchanged. Compared to WT tumors, TIN:TIL ratios were greatly reduced in EZH2 KO tumors but unchanged in EZH2 OE tumors. Our findings show a tumor-intrinsic role of EZH2 in the 4T1 TNBC model in regulating TIN/TIL poise, primary tumor progression and lung metastasis in vivo.

In this article, the research team has systematically developed a method to model the kinematics and dynamics of a 3-wheeled robot subjected to external disturbances and sideways wheel sliding. These models will be used to design control laws that compensate for wheel slippage, model uncertainties, and external disturbances. These are control algorithms developed based on Dynamic Surface Control (DSC). Adaptive trajectory tracking DSC algorithm using fuzzy logic system (AFDSC) and radial neural network (RBFNN) with fuzzy logic system to overcome the disadvantages of DSC and expand the application domain for wheeled Mobile Robots (WMR). However, this Adaptive Fuzzy Neural Network Dynamic Surface Control (AFNNDSC) adaptive controller ensures the closed system is stable and follows the preset trajectory in the presence of wheel slippage model uncertainty and is affected by significant amplitude disturbances. The stability and convergence of the closed-loop system are guaranteed based on the Lyapunov analysis. The AFNNDSC adaptive controller is evaluated by simulation on MATLAB/Simulink software and in a steady state. The maximum position error on the right wheel and left wheel is 0.000572 (m) and 0.000523 (m), and the angular velocity tracking error in the right and left wheels of the control method is 0.000394 (rad/s). The experimental results show the correctness of the theoretical analysis, the effectiveness of the proposed controller, and the possibility of practical application.

One-dimensional chain of core-shell pairs connected by ideal springs enables design of the metamaterial demonstrating the negative effective density and negative specific thermal capacity. We assume that the molar thermal capacity of the reported metamaterial is governed by the Dulong-Petit law in its high temperature limit. The specific thermal capacity depends of the density of the metamaterial; thus, it is expected to be negative, when the effective density of the chain is negative. The range of the frequencies enabling the effect of the negative thermal capacity is established. Dependence of the effective thermal capacity on the exciting frequency for various core/shell mass ratios is elucidated. The effective thermal capacity becomes negative in the vicinity of the local resonance frequency ω0 in the situation when the frequency ω approaches ω0 from above. The effect of the negative effective thermal capacity is expected in metals in the vicinity of the plasma frequency.

The paper presents an error model of a measurement chain containing link performing discrete wavelet transform algorithm which is most often the last stage of measurement signal processing. The goal is to determine the uncertainty budget of the input quantities of the wavelet transform algorithm. The error model takes into account parts of analog, analog-to-digital and digital processing, describing the properties of subsequent fragments of the chain using their transmittance and processing functions. The proposed model enables the description of both deterministic and non-deterministic properties of signal errors. The proposed model was validated using an example measurement chain created for this purpose.

Although, promotion of urbanization culture in recent decades has made inevitable development of cities in the world, however, the development can be guided in a direction that leave, to the extent possible, minimum socioeconomic and environmental impacts. For this, it is required to first forecast auto-spreading orientation of cities and suburbs in rural areas over time and then avoid shapeless growth of cities. This paper is an attempt to develop a dynamic hybrid model based on logistic regression (LR), Markov chain (MC), and cellular automata (CA) for prediction of future urban sprawl in fast-growing cities. The model was developed using 12 widely-used urban development criteria, whose significant coefficient was determined by logistic regression, and validated by relative operating characteristic (ROC) analysis. The validated model was run in Guilan, a tourist province in northern Iran with a very high rate of urban development. For this, changes in the area of urban land use were detected over the period of 1989 to 2013 and then, future sprawl of the province was forecasted by the years 2025 and 2037. The analysis results revealed that the area of urban land use was increased by more than 1.7 % from 36012.5 ha in 1989 to 59754.8 ha in 2013, and the area of Caspian Hyrcanian forestland was reduced by 31628 ha. The results also predicted an alarming increase in the rate of urban development in the province by the years 2025 and 2037, during which urban land use is predicted to develop 0.9 % and 1.38 %, respectively. The development pattern is expected to be uneven and scattered, without following any particular direction. The development will occur close to the existing or newly-formed urban basements as well as around major roads and commercial areas. This development, if not controlled, will lead to the loss of 13863 ha of Hyrcanian forests and if the trend continues, 21013 ha of Hyrcanian forests and 20208 ha of Barren/open lands are expected to be destroyed by the year 2037. In general, the proposed model is an efficient tool for the support of urban planning decisions and facilitates the process of sustainable development of cities by providing decision-makers with an overview on future development of cities where the growth rate is very fast.

In this research, catalytic steam reforming acetic acid derived from the aqueous portion of bio-oil for hydrogen production was investigated by using different Ni/ATC (Attapulgite Clay) catalysts prepared by precipitation, impregnation and mechanical blending methods. The fresh and reduced catalysts were characterized by XRD, N₂ adsorption-desorption, TEM and H₂-TPR. The comprehensive results demonstrated that the interaction between active metallic Ni and ATC carrier was significantly improved in Ni/ATC catalyst prepared by precipitation method, and in which the mean Ni particle size was the smallest (~13 nm) resulted in the highest metal dispersion (7.5%). The catalytic performance of the three catalysts was evaluated through the process of steam reforming of acetic acid in a fixed-bed reactor under atmospheric pressure at two different temperatures, such as 550 ℃ and 650 ℃. Results showed that the Ni/ATC (PM-N/ATC) prepared by precipitation method, achieved the highest H₂ yield of ~82% and little lower acetic acid conversion efficiency of ~85% than that (~95%) of Ni/ATC (IM-NATC) prepared by impregnation method. In addition, the deactivation catalysts after reaction for 4 h were analyzed by XRD, TGA-DTG and TEM, which demonstrated that the catalyst deactivation was not caused by the amount of carbon deposition, but owed to the significant agglomeration and sintering of Ni particles in the carrier.

The reflectance $R$ of monolayer graphene for the normal incidence of electromagnetic radiation is known to be remarkably defined only by $\pi$ and the fine-structure constant $\alpha$. It is shown in this paper that the reflectance (or the sum of transmittance and absorptance) of monolayer graphene, expressed as a quadratic equation with respect to the fine-structure constant $\alpha$ must unsurprisingly introduce the 2$^\text{nd}$ fine-structure constant $\alpha_2$, as the 2$^\text{nd}$ root of this equation, as well as two $\pi$-like constants for a convex and a saddle surface. It turns out that this 2$^\text{nd}$ fine-structure constant is negative, and the sum of its reciprocal with the reciprocal of the fine-structure constant $\alpha$ is independent of the reflectance value $R$ and remarkably equals $-\pi$. Particular algebraic definition of the fine-structure constant $\alpha^{-1} = 4\pi^3 + \pi^2 + \pi \approx 137.036$, containing the free $\pi$ term and agreeing with the physical definition of this dimensionless constant to the 5$^\text{th}$ significant digit, when introduced to this sum, yields $\alpha_2^{-1} = -4\pi^3 - \pi^2 - 2\pi \approx -140.178$. Assuming universal validity of the physical definition of $\alpha$, $\alpha_2$ defines the negative speed of light in vacuum $c_n$. The average of this speed $c_n$ and the speed of light in vacuum $c$ is in the range of the Fermi velocity ($10^6$ m/s). Furthermore, the 2$^\text{nd}$ negative fine-structure constant $\alpha_2$ alone introduces the imaginary set of base Planck units.

Zero-dose children, or children who have not received any routine vaccination, are a priority population for global health policy makers as these children are at high risk of mortality from vaccine-preventable illnesses. We conducted a narrative review to identify potential interventions, both within and outside of the health sector, to reach zero-dose children. We reviewed peer-reviewed and grey literature and identified 59 relevant resources. Additionally, we interviewed six key informants to enhance synthesis of our findings. Data were organized into three priority settings: (1) urban slums, (2) remote or rural communities, and (3) conflict settings. We found that zero-dose children in the three-priority settings face differing barriers to vaccina-tion and, therefore, require context-specific interventions, such as leveraging slum health commit-tees for urban slums or integrating with existing humanitarian response services for conflict set-tings. Three predominant themes emerged for grouping the various interventions: (1) community engagement, (2) health systems strengthening and integration, and (3) technological innovations. The barriers to reaching zero-dose children are multifaceted and nuanced to each setting, there-fore, no one intervention is enough. Technological interventions especially must be coupled with community engagement and health systems strengthening efforts. Evaluations of the suggested interventions are needed to guide scale up, as the evidence-base around these interventions is rel-atively small.

The purpose of this project was to demonstrate the potential of converting an existing internal combustion engine for hydrogen operation at relatively low cost and to optimize the combustion process for ultra-low NOx emissions. The conversion has been carried out on a 450cc naturally aspirated single cylinder engine. The first baseline measurements and setup have shown lower NOx emissions than the baseline gasoline engine (without catalytic converter) for similar power output and engine efficiency. Further optimization of the combustion process allowed to achieve Euro VI and (latest European Commission proposal) Euro VII heavy-duty NOx limits without exhaust gas aftertreatment system (EATS) in steady state conditions. The concept is very promising and the knowledge gained should allow for further optimization to achieve near zero NOx emissions at high engine efficiency and power output with supercharging. It should also allow an efficient conversion of other engine types for different applications. In particular, a project with the aim to convert a heavy duty 6 cylinder engine intended for public transport buses has started. It follows a feasibility study which has shown the competitiveness of the hydrogen engine for this application.

Smart manufacturing encompasses a category of manufacturing that employs computer-integrated capabilities and technologies to enhance supply chain optimization, production efficiency, and recyclability. Yet limited studies have been conducted to optimize these prospects from the perspective of low resource countries where such innovative studies have not been adequately explored. This paper aims to examine the implications of smart manufacturing practices (Smart Procurement, Smart Supply Chain, Smart Production Planning and Control, Automation and Industrial Robot, and Supply Chain Configuration) on pro-environmental behaviour and develop a baseline moderated mediation model to explain the relationship between smart manufacturing and pro-environmental behaviour with a role of green dynamic capability, and environmental orientation. This study is anchored on quantitative research approach, deductive reasoning, and explanatory design. Cross – sectional survey data have been obtained from 382 manufacturing companies in Ghana. Our hypotheses have been tested using Structural Equation Modelling and SMART-PLS version 3.8.9. The study has revealed that smart manufacturing practices significantly and positively relate to pro-environmental behaviour. Moreover, green dynamic capability and environmental orientation moderate the relationship between smart manufacturing and pro-environmental behaviour. The implications of the study include the emergence of a baseline model to guide policymakers, practitioners, and academicians to explain the relationship between smart manufacturing and sustainable manufacturing in the context of developing countries. Again, the social implication of the study includes the realisation of decent job and economic growth, responsible consumption and production as well as actions taken to combat climate change.

A comprehensive kinetic model of the Fischer-Tropsch synthesis (FTS) is developed in a fixed bed reactor under operating conditions (temperature, 230–235°C, pressure, 20–25 bar, gas hourly space velocity, 4000–5000 cm³(STP)/h/g_catalyst ,H₂/CO feed molar ratio, 2.1) over a Co based catalyst. Reaction rate equations based on Eley-Rideal (ER) type model for initiation step and Langmuir-Hinshelwood-Hougen-Watson (LHHW) type model for propagation and termination steps of the FTS reactions have been considered and the readsorption of olefins were taken into account. The model that was reported in the literature was modified in order to explain many significant deviations from the ASF distribution. Optimum parameters have been obtained by Genetic Algorithms (GA). The calculated activation energies to produce n-paraffins and 1-olefins were in the range of 82.24 to 90.68 kJ/mol and 100.66 to 105.24 kJ/mol, respectively. The hydrocarbon distribution in FTS reactions was satisfactorily predicted particularly for paraffins.

This paper explores the potential of using the SAM segmentator to enhance the segmentation capability of known methods. SAM is a promptable segmentation system that offers zero-shot generalization to unfamiliar objects and images, eliminating the need for additional training. The open-source nature of SAM on GitHub allows for easy access and implementation. In our experiments, we aim to improve the segmentation performance by providing SAM with checkpoints extracted from the masks produced by DeepLabv3+, then merging the segmentation masks provided by these two networks. Additionally, we examine the \enquote{oracle} method (as upper bound baseline performance), where segmentation masks are inferred only by SAM with checkpoints extracted from ground truth. In addition, we tested in the CAMO datasets an ensemble of PVTv2 transformers; combining the ensemble and SAM yields state-of-the-art performance in that dataset. The results of our study provide valuable insights into the potential of incorporating the SAM segmentator into existing segmentation techniques. We release with this paper the open-source implementation of our method.

This paper addresses a new micromechanical model to predict biaxial tensile moduli of plain weave fabric (PWF) composites by considering the interaction between the orthogonal interlacing strands. The two orthogonal yarns in micromechanical unit cell (UC) were idealized as the curved beams with a path depicted by using sinusoidal shape functions. The biaxial tensile moduli of PWF composites were derived by means of the minimum total complementary potential energy principle founded on micromechanics. The biaxial tensile tests were respectively conducted on the RTM-made EW220/5284 PWF composites at five biaxial loading ratios of 0, 1, 2, 3 and ∞ to validate the new model. The predictions from the new model were compared with experimental data and good correlation was achieved between the predictions and actual experiments, demonstrating the practical and effective use of the proposed model. Using the new model, the biaxial tensile moduli of plain weave fabric (PWF) composites could be predicted based only on the properties of basic woven fabric.

Canine mammary tumors (CMT) are the most common cancer in noncastrated female dogs. Interestingly, triple-negative tumors are the most common molecular subtype in female dogs. In this study, we proposed to evaluate the expression of VEGFR-2, PDGFR and microvascular density (MVD) in a group of metastatic and nonmetastatic triple-negative CMT and compare the expression based on clinical parameters. Twenty-six female dogs with triple-negative mammary tumors were divided into three groups: nonmetastatic tumors (NMT) (N=11), tumors with lymph node metastasis (LNM) (N=10) and tumors with lung metastasis (LM) (N=5). We observed increased VEGFR-2 expression in LNM compared with NMT and a positive correlation between tumor grade and VEGFR-2 expression. A positive correlation was noted between VEGFR-2 and PDGFR expression. Regarding microvascular density (MVD), we identified a higher number of vessels in primary tumors with lymph node metastasis and lung metastasis compared with tumors with no metastasis. The primary tumors with lung metastasis exhibited an increased MVD compared with carcinoma with lymph node metastasis. Overall, our results suggest a deregulation of VEGFR-2 and PDGFR and high MVD in metastatic tumors, indicating a role for angiogenesis in tumor progression.

Background: This study aimed to identify the association between occupational stress and depression-well-being by proposing a comprehensive and flexible job burden-capital model with its corresponding hypotheses. Methods: For this research, 1618 valid samples were gathered from the electronic manufacturing service industry in Hunan Province, China; self-rated questionnaires were administered to participants for data collection after obtaining their written consent. The proposed model was fitted and tested through structural equation model analysis. Results: Single-factor correlation analysis results indicated that coefficients between all items and dimensions had statistical significance. The final model demonstrated satisfactory global goodness of fit (CMIN/DF=5.37, AGFI=0.915, NNFI=0.945, IFI=0.952, RMSEA=0.052). Both the measurement and structural models showed acceptable path loadings. Job burden and capital were directly associated with depression and well-being or indirectly related to them through personality. Multi-group structural equation model analyses indicated general applicability of the proposed model to basic features of such a population. Gender, marriage and education led to differences in the relation between occupational stress and health outcomes. Conclusions: The job burden-capital model of occupational stress-depression and well-being was found to be more systematic and comprehensive than previous models.

The paper continues the study of efficient algorithms for the computation of zeta functions over the complex plane. We aim to apply the modifications of algorithms to the investigation of underlying fractal structures associated with the Riemann zeta function. We discuss the computational complexity and numerical aspects of the implemented algorithms based on series with binomial-like coefficients.

Assessment of extreme events and climate change on reservoir inflow is important for water and power stressed countries. Projected climate is subject to uncertainties related to climate change scenarios and Global Circulation Models (GCMs’). Extreme climatic events will increase with the rise in temperature as mentioned in the AR5 of the IPCC. This paper discusses the consequences of climate change that include extreme events on discharge. Historical climatic and gauging data were collected from different stations within a watershed. The observed flow data was used for calibration and validation of SWAT model. Downscaling was performed on future GCMs’ temperature and precipitation data, and plausible extreme events were generated. Corrected climatic data was applied to project the influence of climate change. Results showed a large uncertainty in discharge using different GCMs’ and different emissions scenarios. The annual tendency of the GCMs’ is bi-vocal: six GCMs’ projected a rise in annual flow, while one GCM projected a decrease in flow. The change in average seasonal flow is more as compared to annual variations. Changes in winter and spring discharge are mostly positive, even with the decrease in precipitation. The changes in flows are generally negative for summer and autumn due to early snowmelt from an increase in temperature. The change in average seasonal flows under RCPs’ 4.5 and 8.5 are projected to vary from -29.1 to 130.7% and -49.4 to 171%, respectively. In the medium range (RCP 4.5) impact scenario, the uncertainty range of average runoff is relatively low. While in the high range (RCP 8.5) impact scenario, this range is significantly larger. RCP 8.5 covered a wide range of uncertainties, while RCP 4.5 covered a short range of possibilities. These outcomes suggest that it is important to consider the influence of climate change on water resources to frame appropriate guidelines for planning and management.

This paper aims to develop a hierarchical risk assessment model using the newly-developed evidential reasoning (ER) rule, which constitutes a generic conjunctive probabilistic reasoning process. In this paper, we first provide a brief introduction to the basics of the ER rule and emphasize the strengths for representing and aggregating uncertain information from multiple experts and sources. Further, we discuss the key steps of developing the hierarchical risk assessment framework systematically, including (1) formulation of risk assessment hierarchy, (2) representation of both qualitative and quantitative information, (3) elicitation of attribute weights and information reliabilities, (4) aggregation of assessment information using the ER rule and (5) quantification and ranking of risks using utility-based transformation. The proposed hierarchical risk assessment framework can potentially be implemented to various complex and uncertain systems. A case study on the fire/explosion risk assessment of marine vessels demonstrates the applicability of the proposed risk assessment model.

We provide a detailed analysis of the electronic properties of graphene-like materials with charge carriers living on a curved substrate, focusing in particular on constant negative-curvature spacetime. An explicit parametrization is also worked out in the remarkable case of Beltrami geometry, with an analytic solution for the pseudoparticles modes living on the curved bidimensional surface. We will then exploit the correspondent massless Dirac description, to determine how it affects the sample local density of states.

We report the negative effective mass metamaterials based on the electro-mechanical coupling exploiting plasma oscillations of a free electron gas. The negative mass appears as a result of vibration of a metallic particle with a frequency of ω which is close the frequency of the plasma oscillations of the electron gas m_2 relatively to the ionic lattice m_1. The plasma oscillations are represented with the elastic spring k_2=ω_p^2 m_2, where ω_p is the plasma frequency. Thus, the metallic particle vibrated with the external frequency ω is described by the effective mass m_eff=m_1+(m_2 ω_p^2)/(ω_p^2-ω^2 ) , which is negative when the frequency ω approaches ω_p from above. The idea is exemplified with two conducting metals, namely Au and Li.

In current transportation hubs, it is a common phenomenon that passengers travel with necessary wheeled-luggage or suitcases. Due to the fact that most luggage occupies certain space within dense passenger crowds with giant mass inertia, its abnormal motions, such as drifting, frequently trig unavoidable local disturbances and turbulence in surrounding pedestrian flows, further increasing security risk. While, current researches primarily concentrate on examining the impact of luggage on crowd evacuation efficiency through scenario-based experiments and analyzing the spatial characteristics of crowd distribution in surveillance videos. There still lacks of the dynamic disturbance mechanisms and crowd stability associated with the combined motion of passengers and luggage. Therefore, this study considers the luggage-laden passenger as a deformable particle, the resulting disturbance on surrounding non-luggage passengers is analyzed and quantified into a nonlinear pressure term. Subsequently, the disturbance propagation model of passenger-luggage is developed by adapting the classical Aw-Rascle traffic flow model with incorporation of the pressure term. Simulation experiments of disturbances caused by luggage drifting and retrograding validate that the proposed propagation model aligns with real-world situations in transportation hubs. Quantitively, when luggage-laden passenger drifted left, the disturbing force of left side crowd can reach a peak of 238N in the passenger density of 3.0 p/m2, and the maximum difference between the left and right sides disturbing force can reach 153N. Furthermore, it is recommended that the proposed model can be applied to crowd flow analysis and risk assessment.

Antioxidant, anti-inflammatory and chemo-preventive features have been reported for the carotenoid diatoxanthin. Diatoxanthin is only produced by a few groups of microalgae where it functions in photoprotection. Its large-scale production in microalgae is currently not feasible. In fact, rapid conversion into the inactive pigment diadinoxanthin is triggered when the cells are removed from the high-intensity light source, which will be the case during large-scale harvesting of microalgae biomass. Zeaxanthin epoxidase (ZEP) 2 and/or ZEP3 have been suggested to be responsible for the back-conversion of high-light accumulated diatoxanthin to diadinoxanthin in low light in diatoms. Using the CRISPR/Cas9 gene editing technology we knocked out the ZEP2 and ZEP3 genes in the already commercially used marine diatom Phaeodactylum tricornutum to investigate their role in the diadinoxanthin-diatoxanthin cycle, and to determine if one of the mutant strains could function as a diatoxanthin production line. Light shift experiments proved that ZEP3 encodes the enzyme converting diatoxanthin to diadinoxanthin in low light. Loss of ZEP3 caused the high-light accumulated diatoxanthin to be stable for several hours after the cultures had been returned to low light, suggesting that zep3 mutant strains could be suitable as commercial production lines of diatoxanthin.

The organic fraction of municipal solid waste (OFMSW) is a significant environmental threat, and an economic and social challenge to manage. As such, the efficient treatment of OFMSW is a significant key factor in achieving sustainable waste management. Decentralized Composting (DC) develops a new framework of waste management. The DC Analysis Model (DCAM), proposed in our previous study, provides a powerful tool for decision makers, based on the quantification of the DC project characteristics. In this paper, we focus on the qualitative analysis as a complementary tool to support decision making, in cases where quantitative analysis is unequivocal. The qualitative analysis identifies the main players in the field, the critical stakeholders and the potential conflicts between them. It also reveals the root problems and the core competencies for the project's implementation. DCAM qualitative analysis in Shefa-Amr (case study) indicates that unresolved root problems, such as the “lack of national regulation”, “clear ownership of the project” and “lack of ongoing budget” can result in an unsustainable composting system. Countering that, “the commitment of the municipality”, together with “economic viability” and securing “suitable areas for placing composters” are among the most important core competencies for the effective implementation of DC projects.

Gestational diabetes mellitus (GDM) pathophysiology comprises clinical and genetic factors. In fact, GDM has been associated with several single nucleotide polymorphisms (SNPs). This study aimed to build a prediction model of GDM combining clinical and genetic risk factors. A total of 1588 pregnant women from the San Carlos Cohort participated in the present study, 1069 (67.3%) Caucasian (CAU) and 519 (32.7%) Latin American (LAT), 255 (16.1%) with GDM. The incidence of GDM was similar in both groups (16.1% CAU and 16.0% LAT). Genotyping was performed by IPLEX Mass ARRAY PCR selecting 110 SNPs based on literature references. The SNPs showing the strongest likelihood of GDM development were the rs10830963, rs7651090 and rs1371614 in CAU and rs1387153 and the rs9368222 in LAT. Clinical variables including age, pre-pregnancy body mass index and fasting plasma glucose (FPG) at 12 gestational weeks predicted the risk of GDM (AUC 0.648, 95%CI 0.601-0.695 in CAU; AUC 0.688, 95%CI 0.628-9.748 in LAT) and adding the SNPs modestly improved prediction (AUC 0.722, 95%CI 0.680-0.764 in CAU; AUC 0.769, 95%CI 0.711-0.826 in LAT). In conclusion, adding genetic variants enhanced the prediction model of GDM risk in CAU and LAT pregnant women. An RCT is underway to demonstrate its usefulness

Analysing the human body by direct observation can lead to misinterpretation of underlying trends, whereas in vitro organ models facilitate their understanding. Likewise, in vitro organ models serve as valuable tools for assessing the toxicity of compounds. However, none of the existing models had the features we considered critical for toxicity assessment, so we developed an in vitro liver model for toxicity assessment. We used four different types of primary liver cells: Hepatic sinusoidal endothelial cells, hepatic stellate cells, Kupffer cells and hepatocytes. We cultured them in different combinations of composition and volume of cell medium, hepatocyte proportion of total cells, and addition of extracellular matrix. We added rifampicin, ibuprofen and 5-fluorouracil to this model and observed the microanatomy and physiology changes for a week with preclinical and clinical instruments. Among the different model configurations, we selected the feature combination of the in vitro model that had similar biomarker values to those measured by the clinical instruments used in clinical diagnostics. When we exposed the selected model configuration to rifampicin, ibuprofen and 5-fluorouracil, the viability of Kupffer cells and liver sinusoidal endothelial cells was significantly lower than in the untreated samples. In contrast, we observed negligible viability of hepatocytes compared to the untreated samples. We have built an in vitro liver model that resembles the liver microenvironment and we have analysed it with clinical instrumentation to facilitate the data translation. In this sense, we have established that Kupffer and LSEC cells are suitable candidates for the search for clinical diagnostic markers of liver function.

Distributed/semi-distributed models are considered to be sensitive to the spatial resolution of the data input. In this paper, we take a small catchment in high urbanized Yangtze River Delta, Qinhuai catchment as study area, to analyze the impact of spatial resolution of precipitation and the potential evapotranspiration (PET) on the long-term runoff and flood runoff process. The data source includes the TRMM precipitation data, FEWS download PET data, and the interpolated metrological station data. GIS/RS technique was used to collect and pre-process the geographical, precipitation and PET series, which were then served as the input of CREST (Coupled Routing and Excess Storage) model to simulate the runoff process. The results clearly showed that, the CREST model is applicable to the Qinhuai catchment; the spatial resolution of precipitation had strong influence on the modelled runoff results and the metrological precipitation data cannot be substituted by the TRMM data in small catchment; the CREST model was not sensitive to the spatial resolution of the PET data, while the estimation fourmula of the PET data was correlated with the model quality. This paper focused on the small urbanized catchment, suggesting the influential explanatory variables for the model performance, and providing reliable reference for the study in similar area.

Many corporations aspire to become Net Zero Carbon Dioxide by 2030-2050. This paper examines what it will take. It requires understanding where energy is produced and consumed, the magnitude of CO2 generation, and the Carbon Cycle. Reviews are provided for prior technologies for reducing CO2 emissions from fossil to focus on their limitations and to show that none offer a complete solution. Both biofuels and CO2 sequestration reduce future CO2 emissions from fossil fuels. They will not remove CO2 already in the atmosphere. Planting trees has been proposed as one solution. Trees are a temporary solution. When they die, they decompose and release their carbon as CO2 to the atmosphere. The only way to permanently remove CO2 already in the atmosphere is to break the Carbon Cycle by growing biomass from atmospheric CO2 and sequestering biomass carbon. Permanent sequestration of leaves is proposed as a solution. Leaves have a short Carbon Cycle time constant. They renew and decompose every year. Theoretically, sequestrating a fraction of the world’s tree leaves can get the world to Net Zero without disturbing the underlying forests. This would be CO2 capture in its simplest and most natural form. Permanent sequestration may be achieved by redesigning landfills to discourage decomposition. In traditional landfills, waste undergoes several stages of decomposition, including rapid initial aerobic decomposition to CO2, followed by slow anaerobic decomposition to methane and CO2. The latter can take hundreds to thousands of years. Understanding landfill chemistry provides clues to disrupting decomposition at each phase.

Triple negative breast cancer (TNBC) has a negative expression of estrogen receptors (ER), progesterone receptors (PR), or human epidermal growth factor receptors (HER2). The survival rate for TNBC is generally worse than other breast cancer subtypes. TNBC treatment has made significant advances, but certain limitations remain. Treatment for TNBC can be challenging since the disease has various subtypes. Individualized therapy is recommended rather than generalized therapy, given the genetic variations among these TNBC subtypes. A precise and effective treatment for TNBC requires identifying prognostic markers. Depending on the type and stage of TNBC, patients are treated with various therapeutic interventions. A variety of treatment options are available, such as chemotherapy, immunotherapy, radiotherapy, and surgery. Chemotherapy is the most common of these options. TNBC is generally treated with systemic chemotherapy using drugs such as anthracyclines and taxanes in the neoadjuvant or adjuvant settings. Developing resistance to anticancer drugs and off target toxicity are the primary hindrances to chemotherapeutic solutions for cancer. It is imperative that researchers, clinicians, and pharmaceutical companies work together to develop effective treatment options for TNBC. Several studies have suggested nanotechnology as a potential solution to the problem of suboptimal TNBC treatment.

The implementation of green logistics can reduce the increasingly serious environmental damage and improve the operating efficiency of logistics enterprises. In this paper, through quantitative calculation of logistics negative external costs, including traffic accidents, atmospheric pollution, noise pollution, and pollution caused by packaging, we build accounting standards to calculate the negative external cost of logistics enterprises that mainly generate pollution from transportation and packaging. Then, assuming the impetus provided by government implementing incentive mechanisms in a free market economy, we establish a ‘Logistics Negative External Cost Internalizing’ model, which quantitatively analyzes the relationships among freight rate, freight volume, investment of enterprise for implementing green logistics, and the subsidy given by government, under the comparison of the two modes ‘enterprise operates first’ and ‘government subsidizes first’. The results show that the introduction of ‘government subsidizes first’ incentive mechanism and increasing freight rate are helpful in promoting the process of logistics market greening.

The ongoing conflict in Syria and Iraq has caused many residents of these countries to flee. This study explored how these refugees cope with traumatic experiences from pre-migration to post-migration phases and how they develop resilience experiences at the individual, familial, and community levels. Data were collected through individual interviews and analyzed using Interpretative Phenomenological Analysis. A discussion of the findings revealed (1) a cumulative effect of prolonged adversity from pre-migration to resettlement phases; (2) the impacts of post-migration context as it facilitates refugees’ adjustment or leads to re-traumatization through increased marginalization; and (3) finally, an overview of refugees’ ongoing survival. Clinical implications are presented along with policy implications and future directions for research.

Accurate diagnosis for the 400 million people with rare diseases is critical for healthcare decisions, prognosis, understanding disease mechanisms, and identification of treatments. Despite advances in genome sequencing, barriers such as high interpretation costs, diagnostic expertise, throughput associated delays, and uncertain variant classifications persist, with demand exceeding capacity. Many variant classification methods focus narrowly on specific consequences, leading to the use of complex integrative pipelines that often overlook transcript variability and lack prediction transparency. To overcome these limitations, we introduce DITTO. This transparent, transcript-aware machine-learning method demonstrates superior overall performance in accuracy, recall, and precision when compared to existing tools. DITTO is publicly available at https://github.com/uab-cgds-worthey/DITTO

The Imbalance Cost Pass-Through (ICPT) is a flexible component of the Incentive Regulation (IBR) that empowers power producers to adjust tariffs in response to variable fuel prices, thereby enhancing the economic resilience of electricity generation. However, the cost of electricity generation is significantly influenced by the volatility of commodity prices, including coal, gas and LNG. In Malaysia, the Energy Commission has conducted biannual reviews of fuel and other generation costs. Any cost savings or increases identified during these reviews will be passed on to customers in the form of rebates or surcharges. Due to that reason, this study proposes ICPT price forecasting for the electricity market in Peninsular Malaysia. The study aims to construct an ICPT-related baseline model for the peninsular generation data by employing three forecasting methods. The forecasting performance is analysed by a Mean Absolute Percentage Error (MAPE). In findings, the ARIMA method is one of the most accurate forecasting methods for fuel prices compared to the Moving Average (MA) and LSSVM methods. The observed price differences between the ARIMA and LSSVM models for ICPT are minimal. The ICPT price for July-December 2022 and January-June 2023 is MYR 0.21/ kWh for ARIMA and MYR 0.18/kWh for LSSVM, which are close to the actual TNB’s ICPT tariff. As for the forecasting, the ICPT price is expected to drop for the next announcement. The findings of this study may contribute to a positive impact on the sustainability of the energy sector in Malaysia.

Hydrogen plays an important role in the implementation of Australia’s Net-Zero Strategy. It competes with lithium-ion rechargeable batteries in the electrification of Australian land transportation sector, and can provide an alternative energy storage medium for the greening of the country’s electrical grid system. This paper reviews the role hydrogen plays in the current government’s Net-Zero strategy, and investigates the potential role of other bioenergy sources. It is found that hydrogen and batteries play essentially complementary roles in reducing transport emissions, and that the former energy source is more suited to the operation of long-distance commercial service vehicles than batteries. It is suggested that bioenergy fuels such as biomethane, second generation biofuels and HVO can provide suitable bridging alternatives in the event that Australia’s EV strategy is unable to meet the 2030 target.

Despite the increase in popularity of Zero Waste (ZW) concept, the successful implementation of this concept in waste management is still facing many challenges. The plastic recycling rate in Australia is currently only about 9.4%, which could leave up to 90.6% of plastic consumption being sent to landfills. The state of Victoria (in Australia) has proposed an ambitious plan to upgrade its waste and recycling system and to divert about 80% of waste from landfills by 2030. The aim of this research is to study Victoria’s current waste management plan and to develop a simulation model to assess the feasibility of it achieving zero plastic waste by 2035. In this direction, a fundamental knowledge of global ZW implementation needs to be acquired in order to understand the challenges, obstacles, and uncertainties in achieving ZW target. A simulation model is established using a method called double baselines. This method was developed as an improvisation to address the limitation of data availability for the model development. The model will run on 4 scenarios including one from Victoria’s current plan. Outcomes from the model are produced in comparative charts covering 6 key considerations including the rates of plastic consumption, waste to landfill, diversion, recycling, relative accumulative cost and effort. The findings of this study pointed out that Victoria’s current plan are feasible for its goal and presented with opportunities for improvement especially towards zero plastic waste. Besides, study results also reveal that the Victoria’s current plan to achieve 80% diversion rate by 2030 is possible but the zero plastic waste target by 2035 is less likely to happen.

Semi-continuous data are very common in the social science and economics. In this paper, a Bayesian variable selection procedure is developed to assess the influence of exogenous factors including observed and unobserved on the semi-continuous data. Our formulation is based on the two-part latent variable model with polytomous response. We consider two schemes for the penalties of regression coefficients and factor loadings: the Bayesian spike and slab bimodal prior and the Bayesian lasso prior. Within the Bayesian framework, we implement Markov Chains Monte Carlo sampling method to conduct posterior inference. To facilitate posterior sampling, we recast the logistic model in part one as the norm-like mixture model. Gibbs sampler is designed to draw observations from the posterior. Our empirical results show that with suitable hyper-parameters, the spike and slab bimodal method slightly outperforms the Bayesian lasso in the current analysis. Finally, a real example related to the China household finance survey is analyzed to illustrate the application of the methodology.. .

By partly generalizing the Lipschitz condition of existing results to the generalized Lipschitz one, the author utilizes a fixed point theorem, variational method and Lyapunov function method to derive the unique existence of globally asymptotical input-to-state stability of positive stationary solution for Gilpin-Ayala competition model with diffusion and delayed feedback under Dirichlet zero boundary value. Remarkably, it is the first paper to derive the unique existence of the stationary solution of reaction-diffusion Gilpin-Ayala competition model, which is globally asymptotical input-to-state stability. And numerical examples illuminate the effectiveness and feasibility of the proposed methods.