Soil contamination with toxic heavy metals, such as lead (Pb), is a critical environmental issue with adverse effects on ecosystems. Cymbopogon citratus, known as lemongrass, is a medicinal plant with phytoremediation potential for Pb-contaminated soils, besides producing essential oil (EO) and bioactive compounds. Azospirillum brasilense is a plant growth promoting bacteria, and its inoculation into the soil can benefit plants in many ways, such as increased root growth and improved nutrient absorption. This study aimed to investigate the phytochemistry of lemongrass inoculated with A. brasilense and grown in Pb-contaminated soil to assess its responses to inoculation under different Pb levels. The experimental design was completely randomized in a 2 x 5 factorial scheme: two levels of A. brasilense (absence or presence) and five Pb levels (0, 50, 100, 300, and 500 mg Pb kg–1 of soil), totaling 60 experimental plots in ten treatments with six replicates. After four months of treatment, was analyzed total and reducing sugars, total phenolic content, flavonoids, antioxidant activity, antioxidant enzymes, proline, and EO content and composition. Soil Pb levels and A. brasilense inoculation affected phytochemical in lemongrass plants. Azospirillum inoculation reduced total sugars in the roots at all soil Pb levels, while increasing Pb levels favored a rise in sugar contents. There was an increase in flavonoid content in treatments associated with Pb and inoculated with A. brasilense. Antioxidant capacity was lower at lower Pb levels, regardless of bacterial inoculation. Enzymatic response was mainly affected by Pb concentrations between 50 and 100 mg kg–1 soil. EO content was influenced by soil Pb levels, with higher EO production at 500 mg Pb kg–1 soil and without A. brasilense inoculation. Overall, lemongrass cultivation in Pb-contaminated areas can be an alternative for phytoremediation and EO production for the industry. Moreover, A. brasilense inoculation proved to be beneficial in mitigating stress caused by high Pb concentrations in contaminated soils.

Nicotinamide adenine dinucleotide (NAD+) is an essential molecule involved in various metabolic reactions, acting as an electron donor in the electron transport chain and as a co-factor for NAD+-dependent enzymes. In the early 2000s, reports that NAD+ declines with aging introduced the notion that NAD+ metabolism is globally and progressively impaired with time. Since then, NAD+ became an attractive target for potential pharmacological therapies aiming to boost NAD+ levels to promote vitality and protect against age-related diseases. This review summarizes and discusses a collection of studies that report the levels of NAD+ with aging in different species (i.e., yeast, C. elegans, rat, mouse, monkey, and human) to determine whether the notion that overall NAD+ levels decrease with aging stands true. We find that despite systematic claims of overall changes in NAD+ levels with aging, the evidence to support it is very limited and often restricted to a single tissue or cell type. This is particularly true in humans, where the development of NAD+ levels during aging is still poorly characterized. There is a need for much larger, preferably longitudinal, studies aimed to assess how NAD+ levels develop with aging in various tissues. This will strengthen our conclusions on NAD+ during aging and should provide a foundation for better pharmacological targeting of relevant tissues.

Induction heating is a fast, reproducible, and efficient heating method used in various manufacturing processes. However, there is no established additive manufacturing (AM) process based on induction heating using wire as feedstock. This study investigates a novel approach to AM based on inductive heating, where a steel wire is melted and droplets are detached periodically using a two-winding induction coil. The process parameters and energy input into the droplets are characterized. The induction generator exhibits sluggish response to the excitation voltage, resulting in a lag in the coil current. The process is captured using a high-speed camera, revealing regular droplet formation and uniform size and shape when operated within an appropriate process window. Larger drops and increased spatter formation occur outside this window. The proposed method allows for the production of droplets with almost spherical shapes. Further analysis and characterization of droplet formation and energy input provide insights into process optimization and overall efficiency.

A review is given of the experimental values of the magnetic hyperfine structure and the Bohr-Weisskopf effect in muonic atoms. The need for more measurements and systematic calculations is discussed in the light of distinguishing between different models of the Bohr-Weisskopf effect in nuclei.

High throughput multiplex serological systems enable the small developer to set up tests at small cost, for microbes for which there are no commercial tests, and for aspects which have not been addressed by them. An example is testing for Zika and Tick Borne Encephalitis virus antibodies, where antigenic cross-reactions make diagnosis problematic. Our technique variant, Suspension Multiplex Immunoassay (SMIA) allows many samples to be tested for antibodies to many antigens in a short time. Computational compensation for cross-reactions is possible if a SMIA panel contains most of the potentially cross-reacting antigens. Using antibody avidity and pattern of reactivity to whole virus and nonstructural protein, antibodies due to vaccination and infection, respectively, as well as probable degree of protection, can be determined with high throughput. These multiplex techniques hold great promise for future diagnostic development. Theoretically, even large scale serological monitoring, like blood donor pathogen testing, could be done inexpensively and rationally with multiplex serology developed in house. However, the quality control demands are steep and in most cases out of scope for a single laboratory. There remain however a number of clinical applications where in house multiplex serology can be performed with adequate quality control under high throughput conditions.

Transcription factors (TFs) and microRNAs (miRNAs) are co-actors in genome-scale decoding and regulatory networks, often targeting common genes. In this paper, we describe the algebraic geometry of both TFs and miRNAs thanks to group theory. In TFs, the generator of the group is a DNA-binding domain while, in miRNAs, the generator is the seed of the sequence. For such a generated (infinite) group $\pi$, we compute the $SL(2,\mathbb{C})$ character variety, where $SL(2,\mathbb{C})$ is simultaneously a \lq space-time' (a Lorentz group) and a \lq quantum' (a spin) group. A noteworthy result of our approach is to recognize that optimal regulation occurs when $\pi$ looks like a free group $F_r$ ($r =1$ to $3$) in the cardinality sequence of its subgroups, a result obtained in our previous papers. A non free group structure features a potential disease. A second noteworthy result is about the structure of the Groebner basis $\mathcal{G}$ of the variety. A surface with simple singularities (like the well known Cayley cubic) within $\mathcal{G}$ is a signature of a potential disease even when $G$ looks like a free group $F_r$ in its structure of subgroups. Our methods apply to groups with a generating sequence made of two to four distinct DNA/RNA bases in $\{A,T/U,G,C\}$. Several human TFs and miRNAs are investigated in detail thanks to our approach.

Single-cell RNA sequencing (scRNA-seq) technology has significantly advanced our understanding of the diversity of cells and how this diversity is implicated in diseases. Yet, translating these findings across various scRNA-seq datasets poses challenges due to technical variability and dataset-specific biases. To overcome this, we present a novel approach that employs both an LLM-based framework and explainable machine learning to facilitate generalization across single-cell datasets and identify gene signatures to capture disease-driven transcriptional changes. Our approach uses scBERT, which harnesses shared transcriptomic features among cell types to establish consistent cell-type annotations across multiple scRNA-seq datasets. Additionally, we employ a symbolic regression algorithm to pinpoint highly relevant yet minimally redundant models and features for inferring a cell type’s disease state based on its transcriptomic profile. We ascertain the versatility of these cell-specific gene signatures across datasets, showcasing their resilience as molecular markers to pinpoint and characterize disease-associated cell types. Validation is carried out using four publicly available scRNA-seq datasets from both healthy individuals and those suffering from ulcerative colitis (UC). This demonstrates our approach’s efficacy in bridging disparities specific to different datasets, fostering comparative analyses. Notably, the simplicity and symbolic nature of the retrieved gene signatures facilitate their interpretability, allowing us to elucidate underlying molecular disease mechanisms using these models.

HLA class I alleles constitute established risk factors for non-infectious uveitis and preemptive genotyping of HLA class I alleles is standard practice in the diagnostic work-up. The HLA-A29 serotype is indispensable to Birdshot Uveitis (BU) and renders this enigmatic eye condition a unique model to better understand how the antigen processing and presentation machinery contributes to non-infectious uveitis or chronic inflammatory conditions in general. This review will discuss salient points regarding the protein structure of HLA-A29 using and how key amino acid positions impact the peptide binding preference and interaction with T cells. We discuss to what extent the risk genes ERAP1 and ERAP2 uniquely affect HLA-A29 and how the discovery of a HLA-A29-specific submotif may impact autoantigen discovery. We further provide a compelling argument to solve the long-standing question why BU only affects HLA-A29-positive individuals from Western-European ancestry by exploiting data from the 1000 Genomes Project. We combine novel insights from structural and immunopeptidomic studies and discuss the functional implications of genetic associations across the HLA class I antigen presentation pathway to refine the etiological basis of Birdshot Uveitis.

A single qubit may be represented on the Bloch sphere or similarly on the 3-sphere S3. Our goal is to dress this correspondence by converting the language of universal quantum computing (UQC) to that of 3-manifolds. A magic state and the Pauli group acting on it define a model of UQC as a positive operator-valued measure (POVM) that one recognizes to be a 3-manifold M3. More precisely, the d-dimensional POVMs defined from subgroups of finite index of the modular group PSL(2, Z) correspond to d-fold M3- coverings over the trefoil knot. In this paper, one also investigates quantum information on a few ‘universal’ knots and links such as the figure-of-eight knot, the Whitehead link and Borromean rings, making use of the catalog of platonic manifolds available on the software SnapPy. Further connections between POVMs based UQC and M3’s obtained from Dehn fillings are explored.

A single qubit may be represented on the Bloch sphere or similarly on the $3$-sphere $S^3$. Our goal is to dress this correspondence by converting the language of universal quantum computing (uqc) to that of $3$-manifolds. A magic state and the Pauli group acting on it define a model of uqc as a POVM that one recognizes to be a $3$-manifold $M^3$. E. g., the $d$-dimensional POVMs defined from subgroups of finite index of the modular group $PSL(2,\mathbb{Z})$ correspond to $d$-fold $M^3$- coverings over the trefoil knot. In this paper, one also investigates quantum information on a few \lq universal' knots and links such as the figure-of-eight knot, the Whitehead link and Borromean rings, making use of the catalog of platonic manifolds available on SnapPy. Further connections between POVMs based uqc and $M^3$'s obtained from Dehn fillings are explored.

Abstract: A fertilisation experiment with aim to determine the effect of different potassium fertiliser rates and their interaction with nitrogen and phosphorus on field rotation productivity, potassium balance, fertiliser utilization and changes in content of potassium in soil was carried out in Lithuania between 1971 and 2020. The multi-factorial scheme with 45-treatment plots, where seven (including zero) rates of nitrogen, phosphorus and potassium fertilisers were studied. The experimental treatments during the study period included winter wheat, spring wheat, spring barley, sugar beet, spring rapeseed, annual and perennial grasses. It was found that potassium fertilisers was the most effective on agricultural crops when used in combination with other major plant nutrients – nitrogen and phosphorus. The balance of potassium (K2O) in soil, when nitrogen and phosphorus fertilisers were applied together, to compensate for potassium removal, when applying low nitrogen (N) 72 kg ha-1 and phosphorus (P2O5) 64 kg ha-1 fertiliser rates, 128 kg ha-1 of potassium fertilisers is required. When using high nitrogen 180 kg ha-1 and phosphorus 160 kg ha-1 fertiliser rates, 160 kg ha-1 of potassium is needed. The highest potassium uptake, reaching 51.6%, was achieved when plants had been fertilised with nitrogen 108 kg ha-1, phosphorus 96 kg ha-1, and potassium 96 kg ha-1. When fertilising with potassium fertilisers alone, the content of plant available K2O content in the soil increased, while fertilisation with nitrogen and phosphorus combined it’s decreased, except in the plots where the plants had been fertilised with potassium fertiliser at rates of 128 kg ha-1 and higher. Due to the influence of fertilizers, the amount of non-exchangeable potassium in the soil also increased, but relatively less compared to the amount of available potassium content.

We consider partition functions, in the form of state sums, and associated probabilistic measures for aperiodic substrates described by model sets and their associated tiling spaces. We propose model set tiling spaces as microscopic models for small scales in the context of quantum gravity. Model sets possess special self-similarity properties that allow us to consider implications on large and observable scales from the underlying (non-ergodic) dynamics. In particular we consider the implication of the underlying aperiodic substrate for the well known problem of time in quantum gravity, and propose a correspondence between small and large scales, the so-called ergodic correspondence, that addresses the emergence of matter properties and spacetime structure. In the process we find a possible bound in the mass spectrum of fundamental particles.

This paper investigates if Corporate Social Responsibility (CSR) reports published by a selected group of Nordic companies are aligned with the Global Reporting Initiative (GRI) standards. To achieve this goal, several natural language processing, and text mining techniques were implemented and tested. We extracted strings, corpus, and hybrid semantic similarities from the reports and evaluated the models through the intrinsic assessment methodology. A quantitative ranking score based on index matching was developed to complement the semantic valuation. The final results show that Latent Semantic Analysis (LSA) and Global Vectors for Word Representation (GloVE) are the best methods for our study. Our findings will open the door to the automatic evaluation of sustainability reports which could have a strong impact on the environment.

Polymer-based (nano)composite foams, especially those having a microcellular structure, containing suitable conductive (nano)fillers, have been shown to act as good shielding materials, hence coming as promising to act as shields in electronic devices, limiting/avoiding electromagnetic interference (EMI) pollution. Nevertheless, due to their high (micro)structural complexity, related to their multiphase nature, there is still a lot of unawareness behind the shielding mechanisms acting in these materials, coming as a necessity to study their microstructure-cellular/porous structure-properties relations, especially in terms of their electrical conductivity and EMI shielding efficiency (EMI SE). To target and control the electrical conductivity through a controlled distribution/dispersion of conductive nanofillers, single or combined (nanohybrids), as there is a direct relation between electrical conductivity and EMI SE, as well as the main EMI shielding mechanisms working on the developed microcellular structure of the foams, which are expected to be absorption-dominated or absorption/multiple reflection-based, are two of the main objectives when combining polymer foaming with the addition of conductive nanofillers. The present review work considers the recent developments and trends on polymer-based foams containing conductive nanofillers, especially carbon-based such as carbon nanotubes or graphene-based materials, as well as similar porous structures created using trending technologies such as 3D printing, for EMI shielding applications. It is divided in different sections, starting with the strategy of microcellular foaming to develop polymer-based foams with enhanced EMI shielding characteristics, especially focusing on technologies and methods using supercritical CO2 (sCO2); continuing with the use of polymer foams as templates to synthesize carbon foams with improved EMI shielding performance for high use temperature applications; the recent strategy of combining different functional (nano)fillers to create hybrid nanofillers/nanohybrids to be used at lower amounts as conductive fillers in polymer foams; the control and selective distribution of the nanofillers in terms of favoring the formation of a more effective conductive network and hence promote the enhancement of the EMI SE; the very recent use of computational approaches to tailor the EMI shielding properties of composite foams; and the very trending possibility of creating cellular components with varied porous structures from these materials using 3D printing; to finish, some future perspectives are discussed.

The evaluation of research proposals and academic careers is subject to indicators of scientific productivity. Citations are critical signs of impact for researchers, and many indicators are based on these data. The literature shows that there are differences in citation patterns between areas. The scope and depth that these differences may have to motivate the extension of these studies considering types of articles and age groups of researchers. In this work, we conducted an exploratory study to elucidate what evidence there is about the existence of these differences in citation patterns. To perform this study, we collected historical data from Scopus. Analyzing these data, we evaluate if there are measurable differences in citation patterns. This study shows that there are evident differences in citation patterns between areas, types of publications, and age groups of researchers that may be relevant when carrying out researchers’ academic evaluation.

A popular account of the mixing patterns for the three generations of quarks and leptons is through the characters $\kappa$ of a finite group $G$. Here we introduce a $d$-dimensional Hilbert space with $d=cc(G)$, the number of conjugacy classes of $G$. Groups under consideration should follow two rules, (a) the character table contains both two- and three-dimensional representations with at least one of them faithful and (b) there are minimal informationally complete measurements under the action of a $d$-dimensional Pauli group over the characters of these representations. Groups with small $d$ that satisfy these rules coincide in a large part with viable ones derived so far for reproducing simultaneously the CKM (quark) and PNMS (lepton) mixing matrices. Groups leading to physical $CP$ violation are singled out.

The authors previously found a model of universal quantum computation by making use of the coset structure of subgroups of a free group $G$ with relations. A valid subgroup $H$ of index $d$ in $G$ leads to a \lq magic' state $\left|\psi\right\rangle$ in $d$-dimensional Hilbert space that encodes a minimal informationally complete quantum measurement (or MIC), possibly carrying a finite \lq contextual' geometry. In the present work, we choose $G$ as the fundamental group $\pi_1(V)$ of an exotic $4$-manifold $V$, more precisely a \lq small exotic' (space-time) $R^4$ (that is homeomorphic and isometric, but not diffeomorphic to the Euclidean $\mathbb{R}^4$). Our selected example, due to to S. Akbulut and R.~E. Gompf, has two remarkable properties: (a) it shows the occurence of standard contextual geometries such as the Fano plane (at index $7$), Mermin's pentagram (at index $10$), the two-qubit commutation picture $GQ(2,2)$ (at index $15$) as well as the combinatorial Grassmannian Gr$(2,8)$ (at index $28$) , (b) it allows the interpretation of MICs measurements as arising from such exotic (space-time) $R^4$'s. Our new picture relating a topological quantum computing and exotic space-time is also intended to become an approach of \lq quantum gravity'.

Revealing the time structure of physical or biological objects is usually performed thanks to the tools of signal processing like the fast Fourier transform, Ramanujan sum signal processing and many other techniques. For space-time topological objects in physics and biology, we propose a type of algebraic processing based on schemes in which the discrimination of singularities within objects is based on the space-time-spin group SL(2,C). Such topological objects possess an homotopy structure encoded in their fundamental group and the related SL(2,C) multivariate polynomial character variety contains a plethora of singularities somehow analogous to the frequency spectrum in time structures. Our approach is applied to an Akbulut cork in exotic R4, to an hyperbolic model of topological quantum computing based on algebraic surfaces and to microRNAs in genetics. Such diverse topics reveal the manifold of possibilities of using the concept of a scheme spectrum.

This paper aims to demonstrate how the detection of communities for epidemiological networks can be used to reduce the dimensionality of optimal control problems. A range of planted partition stochastic blockmodels are generated, where the underlying communities in the model vary in detectability. A high-performance computing workflow is then used to simulate and estimate reduced model dynamics for the networks, which in turn is used to find optimal control solutions.

The current work is dedicated to the search for new high-energy materials (HEMs) with improved characteristics which are gained through agglomeration with salts. The research was performed by Becke’s three-parameter hybrid functional approach with non-local correlation provided by Lee, Yang, and Parr and cc-pVTZ basis set. The geometry, total energy, and heat of formation of the most stable compounds forming due to 3-amino-5-[(2, 4, 6-trinitrophenyl) amino]-1H-1, 2, 4-triazole (APATO) within salts selected were obtained to foresee its influence on resistance to shock stimuli, detonation pressure, and velocity of the materials under study. Results obtained allow us to foresee that only agglomeration with precise salts could lead to significant improvement in the stability of the specific high-energy materials and the resistance to shock stimuli. We also show that the agglomeration leads to better energetic properties of the above compound, although the improvement could be insignificant in some cases.

When a suspension passes through a high-frequency standing sound wave, the particles it contains are manipulated by acoustic forces. In a one-dimensional sound field, these forces lead to a planar arrangement of the particles and the formation of agglomerates. It is known that the combination of these forces and depth filtration can be utilized to significantly increase the filter efficiency of coarse-pored media. So far, this concept has only been used in microfluidics. In this paper, we present the results of a scaled-up filtration channel to test the viability of the industrial application of acoustically assisted filtration systems for the removal of microparticles. The influences of acoustic power input, flow rate, and the porosity of the filter media are investigated. In addition to verifying the scalability, a significant decrease in the large particle fraction in the outflow of the channel was observed when a high-power sound field is applied. Furthermore, the formed agglomerates tend to rise to the fluid surface. The floating particles mostly consist of a large particle fraction.

Let {ξ1, ξ2. . . .} be a sequence of independent possibly differently distributed random variables defined on a probability space (Ω, F, P) with distribution functions {F_ξ1., F_ξ2 , . . .}. Let η be a counting random variable independent of sequence {ξ1, ξ2. . . .}. In this paper, we find conditions under which distribution function of randomly stopped sum Sη = ξ1 + ξ2 + . . . + ξη belongs to the class of generalized subexponential distributions.

Responding to changing value creation processes in the sheet metal working sec-tor, where the complexity and interchangeability of products challenge traditional dif-ferentiation strategies, this empirical analysis explores integrating service-oriented and data-driven business models as new paths to ensure competitiveness, especially for small and medium-sized enterprises (SMEs). This study aims to capture the current state and challenges associated with the implementation of these business models in this sector. The research was conducted through semi-structured interviews with SMEs in the industry. The findings indicate that service-oriented and data-driven business models are not yet widely adopted and that manufacturing companies re-quire support in their implementation. Fields of action were identified for the industry. These are "Creating awareness and understanding", "Recognizing added value", "In-creasing company maturity" and "Understanding the change process". Cooperation between science and industry is essential in tackling these fields of action to ensure the successful integration of such business models in manufacturing companies. This pa-per identifies challenges in the fields of action that companies must address through a structured approach promoting awareness, recognizing value, improving organiza-tional maturity, and understanding the change process to successfully implement ser-vice-oriented and data-driven business models.

It is shown how the secondary structure of proteins, musical forms and verses of poems are approximately ruled by universal laws relying on graph coverings. In this direction, one explores the group structure of a variant of the SARS-Cov-2 spike protein and the group structure of apolipoprotein-H, passing from the primary code with amino acids to the secondary structures organizing the foldings. Then one look at the musical forms employed in the classical and contemporary periods. Finally, one investigates in much detail the group structure of a small poem in prose by Charles Baudelaire and that of the Bateau Ivre by Arthur Rimbaud.

Every protein consists of a linear sequence over an alphabet of $20$ letters/amino acids. The sequence unfolds in the $3$-dimensional space through secondary (local foldings), tertiary (bonds) and quaternary (disjoint multiple) structures. The mere existence of the genetic code for the $20$ letters of the linear chain could be predicted with the (informationally complete) irreducible characters of the finite group $G_n:=\mathbb{Z}_n \rtimes 2O$ (with $n=5$ or $7$ and $2O$ the binary octahedral group) in our previous two papers. It turns out that some quaternary structures of protein complexes display $n$-fold symmetries. We propose an approach of secondary structures based on free group theory. Our results are compared to other approaches of predicting secondary structures of proteins in terms of $\alpha$ helices, $\beta$ sheets and coils, or more refined techniques. It is shown that the secondary structure of proteins shows similarities to the structure of some hyperbolic $3$-manifolds. The hyperbolic $3$-manifold of smallest volume --Gieseking manifold--, some other $3$ manifolds and the oriented hypercartographic group are singled out as tentative models of such secondary structures. For the quaternary structure, there are links to the Kummer surface.

The analysis of network structure is essential to many scientific areas, ranging from biology to sociology. As the computational task of clustering these networks into partitions, i.e., solving the community detection problem, is generally NP-hard, heuristic solutions are indispensable. The exploration of expedient heuristics has led to the development of particularly promising approaches in the emerging technology of quantum computing. Motivated by the substantial hardware demands for all established quantum community detection approaches, we introduce a novel QUBO based approach that only needs number-of-nodes many qubits and is represented by a QUBO-matrix as sparse as the input graph's adjacency matrix. The substantial improvement on the sparsity of the QUBO-matrix, which is typically very dense in related work, is achieved through the novel concept of separation-nodes. Instead of assigning every node to a community directly, this approach relies on the identification of a separation-node set, which - upon its removal from the graph - yields a set of connected components, representing the core components of the communities. Employing a greedy heuristic to assign the nodes from the separation-node sets to the identified community cores, subsequent experimental results yield a proof of concept. This work hence displays a promising approach to NISQ-ready quantum community detection, catalyzing the application of quantum computers for the network structure analysis of large scale, real world problem instances.}

Taking a DNA-sequence, a word with letters/bases A, T, G and C, as the relation between the generators of an infinite group $\pi$, one can discriminate two important families: (i) the cardinality structure for conjugacy classes of subgroups of $\pi$ is that of a free group on $1$ to $4$ bases and the DNA word, viewed as a substitution sequence, is aperiodic. (ii) The cardinality structure for conjugacy classes of subgroups of $\pi$ is not that of a free group, the sequence is generally not aperiodic and topological properties of $\pi$ have to be determined differently. The two cases rely on DNA conformations such as A-DNA, B-DNA, Z-DNA, G-quadruplexes, etc. We found a few salient results: Z-DNA, when involved in transcription, replication and regulation in a healthy situation implies (i). The sequence of telomeric repeats comprising $3$ distinct bases, most of the time, satisfies (i). For $2$-base sequences in the free case (i) or non free case (ii), the topology of $\pi$ may be found in terms of the $SL(2,\mathbb{C})$ character variety of $\pi$ and the attached algebraic surfaces. The linking of two unknotted curves --the Hopf link-- may occur in the topology of $\pi$ in cases of biological importance, in telomeres, G-quadruplexes, hairpins and junctions, a feature we already found in the context of models of topological quantum computing. For $3$- and $4$ base sequences, other knotting configurations are noticed and a building block of the topology is the $4$-punctured sphere. Our methods have the potential to discriminate potential diseases associated to the sequences.

With growing city tourism, there is an increasing need for urban travel demand models to consider traffic generated by visitors. Existing research has concentrated on socio-demographic and journey-related factors to determine what influences the mode choice of tourists. In contrast, revealed preference data, like travel time, is almost never considered. In this article, we present the results of discrete choice modeling of city tourists’ mode choice based on revealed preference data from a survey we conducted in Kassel, Germany. We used multinomial logit models and determined the model parameters using maximum likelihood estimations. Surprisingly, travel time played a smaller role in mode choice than understood from previously established knowledge about everyday mobility. In the final model, travel time was only significant for the alternative walking. Also, most other sociodemographic and journey-related variables showed no significant influence. The final model reproduced the mode choice, but the goodness of fit was lower than expected from other research. We conclude that modeling the travel behavior of tourists is more complex than everyday mobility. An alternative approach that we suggest would be to model trip chains rather than single trips.

Fused deposition modeling (FDM) is a rather new technology in the production of personalized dosage forms. Melting and printing of polymer–active pharmaceutical ingredient (API) – mixtures can be used to produce oral dosage forms with different dosage as well as release behavior. This process is utilized to increase the bioavailability of pharmaceutically relevant active ingredients that are poorly soluble in physiological medium by transforming them into solid amorphous dispersions (ASD). Release from such ASDs is expected to be faster and higher compared to the raw materials and thus enhance bioavailability. Printing directly from powder while forming ASDs was realized. Different techniques to change release patterns as well as a non-destructive way for determination of API distribution were shown. By measuring the melt viscosities of mixtures printed a rheological model for the printer used is proposed.

Our recent work has focused on the application of infinite group theory and related algebraic geometric tools in the context of transcription factors and microRNAs. We were able to differentiate between “healthy" nucleotide sequences and disrupted sequences that may be associated with various diseases. In this paper, we extend our efforts to the study of messenger RNA metabolism, showcasing the power of our approach. We investigate (i) mRNA translation in prokaryotes and eukaryotes, (ii) polyadenylation in eukaryotes, which is crucial for nuclear export, translation, stability, and splicing of mRNA, (iii) miRNAs involved in RNA silencing and post-transcriptional regulation of gene expression, and (iv) the identification of disrupted sequences that could lead to potential illnesses. To achieve this, we employ (a) infinite (finitely generated) groups fp, with generators representing the r+1 distinct nucleotides and a relation between them [e.g., the consensus sequence in the mRNA translation (i), the poly(A) tail in item (ii), and the miRNA seed in item (iii)], (b) aperiodicity theory, which connects “healthy groups" fp to free groups Fr of rank r and their profinite completion F^r, and (c) the representation theory of groups fp over the space-time-spin group SL2(C), highlighting the role of surfaces with isolated singularities in the character variety. Our approach could potentially contribute to the understanding of the molecular mechanisms underlying various diseases and help develop new diagnostic or therapeutic strategies.

The fast spread of COVID-19 constitutes a worldwide challenge to the public health, educational, and trade systems, affecting the overall wellbeing of human societies. The high transmission and mortality rates of this virus, and the unavailability of a vaccine or treatment, resulted in the decision of multiple governments to enact measures of social distancing. Thus, it is of general interest to consider the validity of the proposal for keeping a social distancing of at least 2 m from other persons to avoid the spread of COVID-19. The exposure to the bioaerosol can result in the deposition of the pathogen in the respiratory tract of the host causing disease and an immunological response. In the atmospheric context, the work evaluates the effect of aerodynamic diameter (size) of particles in carrying RNA copies of the novel coronavirus. A SARS-CoV-2 carrier person talking, sneezing, or coughing at distance of 2 m can still provide a pathogenic bioaerosol load with submicron particles that remain viable in air for up to 3 hours for exposure of healthy persons near and far from the source in a stagnant environment. The deposited bioaerosol creates contaminated surfaces, which if touched can act as a path to introduce the pathogen by mouth, nose, or eyes and cause disease.

In light of the self-simulation hypothesis, a simple form implementation of the principle of efficient language is discussed in a self-referential geometric quasicrystalline state sum model in three dimensions. Emergence is discussed in context of geometric state sum models.

This paper investigates the randomly stopped sums, minima and maxima of heavy- and light-tailed random variables. The conditions on the primary random variables, which are independent but generally not identically distributed, and counting random variable are given in order that the randomly stopped sum, random minimum and maximum is heavy/light tailed. The results generalize some existing ones in the literature. The examples illustrating the results are provided.

Transcription factors (TFs) are proteins that recognize specific DNA fragments in order to decode the genome and ensure its optimal functioning. TFs work at the local and global scales by specifying cell type, cell growth and death, cell migration, organization and timely tasks. We investigate the structure of DNA-binding motifs with the theory of finitely generated groups. The DNA ‘word’ in the binding domain -the motif- may be seen as the generator of a finitely generated group Fdna on four letters, the bases A, T, G and C. It is shown that, most of the time, the DNA-binding motifs have subgroup structure close to free groups of rank three or less, a property that we call ‘syntactical freedom’. Such a property is associated to the aperiodicity of the motif when it is seen as a substitution sequence. Examples are provided for the major families of TFs such as leucine zipper factors, zinc finger factors, homeo-domain factors, etc. We also discuss the exceptions to the existence of such a DNA syntactical rule and their functional role. This includes the TATA box in the promoter region of some genes, the single nucleotide markers (SNP) and the motifs of some genes of ubiquitous role in transcription and regulation.

It is shown that the representation theory of some finitely presented groups thanks to their $SL_2(\mathbb{C})$ character variety is related to algebraic surfaces. We make use of the Enriques-Kodaira classification of algebraic surfaces and the related topological tools to make such surfaces explicit. We study the connection of $SL_2(\mathbb{C})$ character varieties to topological quantum computing (TQC) as an alternative to the concept of anyons. The Hopf link $H$, whose character variety is a Del Pezzo surface $f_H$ (the trace of the commutator), is the kernel of our view of TQC. Qutrit and two-qubit magic state computing, derived from the trefoil knot in our previous work, may be seen as TQC from the Hopf link. The character variety of some two-generator Bianchi groups as well as that of the fundamental group for the singular fibers $\tilde{E}_6$ and $\tilde{D}_4$ contain $f_H$. A surface birationally equivalent to a $K_3$ surface is another compound of their character varieties.

The Kummer surface was constructed in 1864. It corresponds to the desingularisation of the quotient of a 4-torus by 16 complex double points. Kummer surface is kwown to play a role in some models of quantum gravity. Following our recent model of the DNA genetic code based on the irreducible characters of the finite group G5:=(240,105)≅Z5⋊2O (with 2O the binary octahedral group), we now find that groups G6:=(288,69)≅Z6⋊2O and G7:=(336,118)≅Z7⋊2O can be used as models of the symmetries in hexamer and heptamer proteins playing a vital role for some biological functions. Groups G6 and G7 are found to involve the Kummer surface in the structure of their character table. An analogy between quantum gravity and DNA/RNA packings is suggested.

We recently proposed that topological quantum computing might be based on $SL(2,\mathbb{C})$ representations of the fundamental group $\pi_1(S^3\setminus K)$ for the complement of a link $K$ in the $3$-sphere. The restriction to links whose associated $SL(2,\mathbb{C})$ character variety $\mathcal{V}$ contains a Fricke surface $\kappa_d=xyz -x^2-y^2-z^2+d$ is desirable due to the connection of Fricke spaces to elementary topology. Taking $K$ as the Hopf link $L2a1$, one of the three arithmetic two-bridge links [the Whitehead link $5_1^2$, the Berge link $6_2^2$, the double-eight link $6_3^2$] or the link $7_3^2$, the $\mathcal{V}$ for those links contains the reducible component $\kappa_4$, the so-called Cayley cubic. In addition, the $\mathcal{V}$ for the later two links contains the irreducible component $\kappa_3$, or $\kappa_2$, respectively. Taking $\rho$ to be a representation with character $\kappa_d$ ($d<4$), with $|x|,|y|,|z| \le 2$, then $\rho(\pi_1)$ fixes a unique point in the hyperbolic space $\mathcal{H}_3$ and is conjugate to a $SU(2)$ representation (a qubit). Even though details on the physical implementation remain open, more generally, we show that topological quantum computing may be developed from the point of view of three-bridge links, the topology of the $4$-punctured sphere and Painlev\'e VI equation. The $0$-surgery on the $3$ circles of the Borromean rings L6a4 is taken as an example.

Recent advancements in artificial intelligence (AI) and deep learning have revolutionized the field of protein engineering, particularly in the area of de novo protein design. This review article explores the impact of AI-driven approaches on protein design, with a specific focus on the role of structure prediction neural networks, such as AlphaFold and RoseTTAFold. The article discusses the paradigm shift brought about by these networks, which have enabled the design of proteins with unique structures and functions that are not found in nature. The review covers various aspects of AI-driven protein design, including the use of protein language models to harness evolutionary information, the development of de novo protein design workflows utilizing deep learning, and the application of generative models like RFdiffusion and RoseTTAFold All-Atom. The article also highlights the successes and applications of AI-driven protein design across diverse domains, such as enzyme engineering, antibody design, and vaccine development. Additionally, the review identifies current challenges and future directions in the field, emphasizing the need to address limitations in modeling conformational dynamics and designing proteins for in vivo functionality. The article concludes by underscoring the potential of AI-driven protein design to transform various aspects of science and technology, while also acknowledging the importance of interdisciplinary collaboration and the development of robust pipelines for the validation and optimization of designed proteins. Overall, this comprehensive review serves as a valuable resource for researchers and practitioners interested in understanding the current state and future prospects of AI-driven protein design.

This study is a synthesis of 159 articles that were selected for their relevance to comprehend key aspects of the Massive Open Online Courses (MOOCs) phenomenon, from a discourse analysis perspective. Since 2011, MOOCs are expanding worldwide so that the number of subscribers outpointed 101 million at the end of 2018. This paper explores the question whether the MOOCs are the embodiment of the global one-world classroom or whether, instead, they represent a low-cost alternative tailored to a segment that doesn´t have enough time or resources to attend a brick-and-mortar college. In addition, the review tackles the link between motivation and low completion rates. Finally, we discuss the need to devise better methods to assess the pedagogical value of MOOCs.

Since the 20th century, Japan has experienced a period of very rapid urbanization. Cities have experienced substantial densification and expansion, resulting in gradually elevated flood risk. Urban flooding has also occurred in most large cities in Japan, particularly in Tokyo. In response to this growing problem, much effort and resources have been spent on research and development aimed at understanding, simulating, and managing urban flood risk in Japan. The objective of this review is to summarize, discuss and share key outputs from some of the main research directions in this field, significant parts of which have been uniquely developed in Japan and only published in Japanese. After a general introduction to urban runoff modelling, in the next section, key historical works in Japan are summarized, followed by a description of the situation in Japan with respect to observations of precipitation and water level. Then, the storage function model approach is reviewed, including an extension to urban basins, as well as recent experiments with AI-based emulation in Japanese basins. Subsequently, we review the prospects of detailed hydrodynamic modelling involving high-resolution, vector-based Geographical Information System (GIS) data for the optimal description of the urban environment with applications in Tokyo. We conclude the paper with some future prospects related to urban flood risk modelling and assessment in Japan.

Cities are especially vulnerable to heavy rainfall events and the associated flooding. Urban drainage systems are not dimensioned to handle extreme heavy rainfall events, there is a need to enhance the resilience of those systems. Nature-based solutions (NBS) have the potential to retrofit existing urban drainage systems by providing, among other benefits, storage volume. This study presents an approach for including NBS with different degrees of implementation in a 1D/2D model and investigates their potential for flood mitigation in a study area in Berlin. The analyzed NBS are infiltration systems (e.g. infiltration swales) dimensioned to return periods of T = 5 and 100 years, various green roofs and tree locations (e.g. tree trenches). The NBS are represented by SWMM LID elements. The results show that overall the mitigation effect of NBS declines with increasing rainfall intensities. However, infiltration systems dimensioned to T = 100 years achieve almost three times the flood reduction compared to systems dimensioned to T = 5 years, even during extreme heavy rainfall events (100 mm), resulting in a reduced total flood volume of 15.1% to 25.8%. Overall, green roofs (excluding extensive green roofs) provide the greatest flood reduction (33.5%), while tree locations have the least effect.

The idea of replacing the broken linear economy with circular forms to help address the current sustainability crisis is gaining world-wide traction in policy, industry, and academia. This article presents results from an international interview study with 34 repair practitioners and experts in different fields. The article aims to improve understandings of the potential of repair so as to contribute to a more just, sustainable, and circular economy. Through a five-step qualitative method the results reveal and explore three tensions inherent in repair: first, repair activities constitute different forms of subjectivity; second, repair entails different and sometimes contested temporalities; and finally, even though repair is deeply political in practice, the politics of repair are not always explicit, and some repair activities are actively depoliticized. The opportunities and obstacles embodied in these tensions are generative in repair practices and debates, but poorly reflected in contemporary circular economy discourse. We conclude that a richer, more inclusive and politicized understanding of repair can support environmental justice in the implementation of CE and provide greater opportunities for just and transformational sustainability strategies and policies.

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.

Gobies and their ectoparasitic monogenean flatworms are promising models for species diversification because of their species richness. Recent decades have seen the discovery of several new species of Gyrodactylus (Monogenea: Gyrodactylidae) on European gobies, mostly in the sand goby lineage and especially in the Eastern Mediterranean. However, the monogenean fauna of other gobies is much less understood. Therefore, we scrutinized five gobiid species (34 specimens, vouchered, with some representatives sequenced), sampled in Greece, for monogenean ectoparasites. Only specimens of the giant goby Gobius cobitis were infected; they harbored Xenoligophoroides cobitis (Monogenea: Dactylogyridae) on their gills. Here we provide the first record from Greece, and the first ITS rDNA and COI sequences of the representative of this monotypic genus. Also 28S rDNA was sequenced and compared with published data from across its known distribution, suggesting clinal variation. Lack of sequence data of closely related dactylogyrid monogeneans prevents either proposing a sister-group for Xenoligophoroides, or inferring a scenario explaining the presence of a single known member of this genus on gobies. Possible hypotheses include either the ancestral long-term presence on gobiids but “missing the boat” of the diversification events in the “Gobius-lineage”, or a recent host switch from a non-gobiid host.

We find that the degeneracies and many peculiarities of the DNA genetic code may be described thanks to two closely related (fivefold symmetric) finite groups. The first group has signature $G=\mathbb{Z}_5 \rtimes H$ where $H=\mathbb{Z}_2 . S_4\cong 2O$ is isomorphic to the binary octahedral group $2O$ and $S_4$ is the symmetric group on four letters/bases. The second group has signature $G=\mathbb{Z}_5 \rtimes GL(2,3)$ and points out a threefold symmetry of base pairings. For those groups, the representations for the $22$ conjugacy classes of $G$ are in one-to-one correspondence with the multiplets encoding the proteinogenic amino acids. Additionally, most of the $22$ characters of $G$ attached to those representations are informationally complete. The biological meaning of these coincidences is discussed.

Abstract: : 2,3-dihydro-5,6,7,8-tetranitro-1,4-benzodioxine (TNBD), molecular formula =C8H4N4O10 is a completely nitrated in aromatic ring 1,4-benzodioxane derivative. The con-venient method of TNBD synthesis was developed (yield=81 %). The detailed structure of this compound has been investigated by X-ray crystallography. The results of differential thermal analysis (DTA) obtained with twice re-crystallized material showed onset at 247 and isoterm at 288 0C, with further total degradation at 300 0C. The experimental density 1,85 g/cm3 of TNBD was determined by X-ray crystallography. The spectral properties of TNBD (NMR, FT-IR, Ra-man) were explored. The detonation properties of TNBD calculated by EXPLO 5 code was slightly superior in comparison to standard high energy material - tetryl (detonation velocity of TNBD - 7727 m/s; detonation pressure - 278 kbar; tetryl – 7570 m/s and 226.4 kbar at 1.614 g/cm3, or 260 kbar at higher density - 1.71 g/cm3. The obtained preliminary results might suggest TNBD can be a potential thermostable high energy and density material (HEDM).

For technical or medical applications, the knowledge of the exact kinematics of the human hand is key to utilizing its capability to handle and manipulate objects and to communicate with other humans or machines. The optimal relationship between the number of measurement parameters, measurement accuracy as well as complexity, usability and cost of the measuring systems is hard to find. Biomechanic assumptions, the concepts of a biomechatronic system and the mechatronic design process as well as commercially available components are used to develop a sensorized glove. The proposed wearable can measure 14 of 15 angular values of a simplified hand model introduced in this paper. Additionally, five contact pressure values at the fingertips and inertial data of the whole hand with a degree of freedom of six are gathered. Due to the modular design and a hand size examination based on anthropometric parameters, the concept of the wearable is applicable for a large variety of hand sizes and adaptable to different use cases. Validations show a combined root-mean-square error of 0.99° to 2.38° for the measurement of all joint angles at one finger, surpassing the human perception threshold and the current state of the art in science and technology for comparable systems.

Vertical Indoor Farming (VIF) with hydroponics offers a promising perspective for sustainable food production. Intelligent control of VIF system components plays a key role in reducing operating costs and increasing crop yields. Modern machine vision (MV) systems use Deep Learning (DL) in combination with camera systems for various tasks in agriculture, such as disease and nutrient deficiency detection, and flower and fruit identification and classification for pollination and harvesting. This study presents the applicability of MV technology with DL modelling to detect the growth stages of chilli plants using YOLOv8 networks. The influence of different bird’s eye and side view datasets and different YOLOv8 architectures was analysed. To generate the image data for training and testing the YOLO models, chilli plants were grown in a hydroponic environment and imaged throughout their life cycle using four camera systems. The growth stages were divided into growing, flowering and fruiting classes. All trained YOLOv8 models showed reliable identification of growth stages with high accuracy. The results indicate that models trained with data from both views show better generalisation. YOLO’s middle architecture achieved the best performance.

This article addresses the problem of formation control of a quadrotor and one (or more) marine vehicles operating at the surface of the water with the end goal of encircling the boundary of a chemical spill, enabling such vehicles to carry and release chemical dispersants used during ocean cleanup missions to break-up oil molecules. Firstly, the mathematical models of the Medusa class of marine robots and quadrotor aircrafts are introduced, followed by the design of single vehicle motion controllers that allow these vehicles to follow a parameterized path individually using Lyapunov based techniques. At a second stage, a distributed controller using event triggered communications is introduced, enabling the vehicles to perform cooperative path following missions according to a pre-defined geometric formation. In the next step, a real time path planning algorithm is developed that makes use of a camera sensor, installed on-board the quadrotor. This sensor enables the detection in the image of which pixels encode parts of a chemical spill boundary and use them to generate and update in real time a set of smooth B-spline based paths for all the vehicles to follow cooperatively. The performance of the complete system is evaluated by resorting to 3-D simulation software, making it possible to simulate visually a chemical spill. Results from real water trials are also provided for parts of the system, where two Medusa vehicles are required to perform a static lawn-mowing path following mission cooperatively at the surface of the water.

The widely used logistic model for epidemic case reporting data may be either restrictive or unrealistic in presence of containment measures when implemented after an epidemic outbreak. For flexibility in epidemic case reporting data modelling, we combined an exponential growth curve for the early epidemic phase with a flexible growth curve to account for the potential change in growth pattern after implementation of containment measures. We also fitted logistic regression models to recoveries and deaths from the confirmed positive cases. In addition, the growth curves were integrated into a SIQR (Susceptible, Infective, Quarantined, Recovered) model framework to provide an overview on the modelled epidemic wave. We focused on the estimation of: (1) the delay between the appearance of the first infectious case in the population and the outbreak (“epidemic latency period"); (2) the duration of the exponential growth phase; (3) the basic and the time-varying reproduction numbers; and (4) the peaks (time and size) in confirmed positive cases, active cases and new infections. The application of this approach to COVID-19 data from West Africa allowed to discuss the effectiveness of some containment measures implemented across the region.

Classical k-ratio models, e.g. ZAF and phi(rho z), used in electron probe microanalysis (EPMA) assume a homogeneous or multi-layered material structure, which essentially limits the spatial resolution of EPMA to the size of the interaction volume where characteristic x-rays are produced. We present a new model for characteristic x-ray emission that avoids assumptions on the material structure to not restrict the resolution of EPMA a-priori. Our model bases on the spherical harmonic (PN) approximation of the Boltzmann equation for electron transport in continuous slowing down approximation. PN models have a simple structure, are hierarchical in accuracy and well-suited for efficient adjoint-based gradient computation, which makes our model a promising alternative to classical models in terms of improving the resolution of EPMA in the future. We present results of various test cases including a comparison of the PN model to a minimum entropy moment model as well as Monte-Carlo (MC) trajectory sampling, a comparison of PN-based k-ratios to k-ratios obtained with MC, a comparison with experimental data of electron backscattering yields as well as a comparison of PN and Monte-Carlo based on characteristic X-ray generation in a three-dimensional material probe with fine structures.

The main challenges in oil platforms are how to define optimal setpoints and decision-making for controlling variables due to many existing dynamical production uncertainties while conceding safe operation and minimizing discharge to sea and atmosphere without killing plantwide inventory. A novel scheduling model to optimize the whole essential units of an oil platform production gathering topside and subsea variables is disclosed. A proper topside and subsea scheduling model enables and creates harmony between optimal production, supply chain, costs, and market dynamics, satisfying demands, environmental and operations constraints leading to stability. The interest of the present research lies in determining the optimal topside and subsea operational settings and decision-making, establishing maximum E&P performance and production control setpoints while predicting and manipulating reservoir lifespan and its revitalization. This objective is attained through a novel multiperiod large-scale model for the planning and operational production scheduling and model predictive control (MPC) in oil platforms complying with sustainability, profitability, platform design capacities, and offloading of oil and gas to supply chains or to pipeline exportation. The present model details heat and mass transfer, PVT, flow profiles along a planning horizon and can be used to any oil platform since comprises all the essential unit operations for oil, gas, CO₂, H₂S and water. The model maximizes the total oil production over any planning horizon. The model is versatile, and decision-making can be either linear or nonlinear but rather a MILP or LP as best choice on optimizing large-scale systems. If the model is applied with auto rescheduling on site within variable hours or minutes, it becomes a real-time optimization schedule approach. Due to its high-velocity performance and robustness, a novel multi-objective function strategy, acting as an LP generic plantwide MPC with industrial scope to maximize production while controlling the process is presented, and as an example is here used to control slug flow to avoid equipment trips and inventory instability, at the same time production is maximized. The output from the decision-making was compared to actual plant data, and the results proved compliance to the design capacity with process safety and sustainability. Comparing to official Brazil's government data for oil, the case study showed superiority to the same size platforms, like FPSOs P-75 and MV32, e.g., more than 101.5% more capacity to produce oil in beginning of campaign after first oil, and 10.5% during campaign. The present work indicates that: 1) the production of millions of barrels is being delayed or left aside; 2) not scheduling production harms the environment and diminish process safety; 3) not scheduling production can create stochastic supply chain deliveries instead of organizing it by deterministic offloading days; 4) scheduling is essential to manipulate, revitalize and monitor reservoir's pressure and content under uncertainty; 5) the oil platform can be automatic optimized by a plantwide MPC reducing human activity/dependency; 6) scheduling gives more transparency to stakeholders and contractors by forecasting business data and capability, and can design, develop or enable new businesses.

Maritime ports represent an important ecosystem for pollutant emissions and, taking into account the ongoing energy transition, need to adopt new solutions to mitigate current emission levels. These emissions are partially avoidable if ships and vessels docked at the port use electric energy to feed their internal power needs instead of using their internal combustion engines. In Europe, there is an ongoing discussion on including such emissions in the European Emissions Trading System which will represent added costs for maritime operators. Onshore Power Supply systems can contribute to the ongoing energy transition by allowing the use of electric power to feed docked ships. As a first step to contribute to the development of Onshore Power Supply solutions, it is necessary to evaluate the added power needs that these systems would represent for the port. This paper presents a methodology that allows port operators to verify, straightforwardly and transparently, their power needs for Onshore Power Supply applications. The methodology is based on the historical data of docked ships at the port or quay level and provides an energy analysis of each type of vessel to determine the power to be installed at the port so that it is possible to supply energy to different types of ships and vessels simultaneously. Additionally, the implemented methodology provides economic and technical decision support factors by comparing the fuel costs with electric power costs, assessing the potential for this transition to Onshore Power Supply. The methodology is validated using a real case study for the port of Lisbon and obtained results demonstrate the potential for the installation of Onshore Power Supply in medium to large-dimension maritime ports.

This paper proposes the use of non-uniform extended surfaces installed externally to the tubes of the radiation section of fired heaters, in order to obtain a better heat flux distribution to the coils. To this end, the heat transfer mechanisms present in such equipment were studied through computational fluid dynamics (CFD), using simplified geometries that represent typical sizes of fired heaters. Also, a simplified model for the combustion was considered. Although this model oversimplifies the physics of the problem, it was able to give satisfactory results for the parameters being optimized, considering the main objective of this paper, that is to minimize the non-uniformity of heat flux in the tubes of the radiant section of fired heaters. It was possible to obtain optimized geometric parameters for different types of extended surfaces evaluated, coupling the results of these models with the Particle Swarm optimization method through the use of a response surface technique,. The results indicate a significant improvement in the uniformity of the heat flux distribution to the tubes through the use of the proposed extended surfaces. Thus, this solution reveals to be an interesting alternative to reduce the risks of fluid degradation and coking formation. Future studies must investigate the non-uniformity of the heat flux due to the presence of the flame and consider the interaction between the reactive flow and the participating medium. Nevertheless, this paper presents some results that justify the optimization of such extended surfaces taking into consideration thermal radiation.

Considering the predictions from the standard model of particle physics coupled with experimental results from particle accelerators, we discuss a scenario in which from the infinite possibilities in the Lie groups we use to describe particle physics, nature needs only the lower dimensional representations - an important phenomenology that we argue indicates nature is code theoretic. We show that the quantum deformation of the SU(2) Lie algebra at the fifth root of unity can be used to address the quantum Lorentz group representation theory through its universal covering group and gives the right low dimensional physical realistic spin quantum numbers confirmed by experiments. In this manner we can describe the spacetime symmetry content of relativistic quantum fields in accordance with the well known Wigner classification. Further connections of the fifth root of unity quantization with the mass quantum number associated with the Poincaré Group and the SU(N) charge quantum numbers are discussed as well as their implication for quantum gravity.

We present a discussion and analysis regarding the benefits and limitations of open and non-anonymized peer review based on literature results and responses to a survey on the reviewing process of alt.chi, a more or less open-review track within the CHI conference, the predominant conference in the field of human-computer interaction (HCI). This track currently is the only implementation of an open-peer-review process in the field of HCI while, with the recent increase in interest in open science practices, open review is now being considered and used in other fields. We collected 30 responses from alt.chi authors and reviewers and found that, while the benefits are quite clear and the system is generally well liked by alt.chi participants, they are reluctant to see it used in other venues. This concurs with a number of recent studies that suggest a divergence between support for a more open review process and its practical implementation. The data and scripts are available on https://osf.io/vuw7h/, and the figures and follow-up work on http://tiny.cc/OpenReviews.

Phenotypic plasticity in response to adverse conditions determines plant productivity and survival. The aim of this study was to test if two highly productive Populus genotypes, characterized by different in vitro etiolation patterns, differ also in their responses to hormones gibberellin (GA) and abscisic acid (ABA), and to a GA biosynthesis inhibitor paclobutrazol (PBZ). The experiments on shoot cultures of ‘Hybrida 275’ (abbr. H275; Populus maximowiczii × P. trichocarpa) and IBL 91/78 (Populus tremula × P. alba) were conducted either by modulating the physical in vitro environment or by adding specific chemicals to the nutrient medium. Our results show that there are significant differences between the studied genotypes in environmental and hormonal regulation of growth responses. The genotype H275, which responded to darkness with PBZ-inhibitable shoot elongation, was unable to recover its growth after treatment with ABA. In contrast, the genotype IBL 91/78, whose shoot elongation was not affected either by darkness or PBZ treatment, recovered so well after the ABA treatment that, when rooted subsequently, it developed longer shoots and roots than without ABA treatment. Our results indicate that GA catabolism and repressive signaling provide an important pathway to control growth and physiological adaptation in response to immediate or impending adverse conditions. These observations can help breeders define robust criteria for identifying genotypes with high resistance and productivity and highlight where genotypes exhibit susceptibility to stress.

Augmented and Virtual Reality have been experiencing a rapidly growth in recent years, but there is not still a deep knowledge on their capabilities and where they could be explored. In that sense, this paper presents a study on the accuracy and repeatability of the Microsoft's HoloLens 2 (Augmented Reality device) and HTC Vive (Virtual Reality device) using an OptiTrack system as ground truth. For the HoloLens 2, the method used was hand tracking, while in HTC Vive, the object tracked was the system's hand controller. A series of tests in different scenarios and situations were performed to explore what could influence the measures. The HTC Vive obtained results in the millimetre scale, while the HoloLens 2 revealed not so accurate measures (around 2 centimetres). Although the difference can seem to be considerable, the fact that HoloLens 2 was tracking the user's hand and not an inherit controller made a huge impact. The results were considered a significant step for the on going project of developing a human-robot interface to program by demonstration an industrial robot using Extended Reality, which shows great potential to succeed based on this data.

Breast cancer is the most common cause of mortality due to cancer for woman both in Lithuania and worldwide. Chances of survival after diagnosis differ significantly depending on the stage of disease at the time of diagnosis and other factors. One way to estimate survival is to construct a Kaplan-Meier estimate for each factor value separately. However, in cases when it is impossible to observe a large number of patients (for example, in case of countries with lower numbers of inhabitants) dividing data into subsets, say, by stage at diagnosis may lead to results where some subsets contain too little data so that results of Kaplan Meier (or any other) method will become statistically incredible. The problem may become even more acute if the researcher would like to use more risk factors, such as stage at diagnosis, sex, place of living, treatment method, etc. Alternatively, Cox models are used to analyse survival data with covariates, and they don’t require dividing the data into subsets according to chosen risks factors (hazards).

Cyanobacteria are promising organisms for the sustainable production of various biotechnologi-cal interesting products. Due to their energy production via photosynthesis, the cultivation of cyanobacteria expands the CO2 cycle. Most cyanobacteria form biofilms on surfaces in their natu-ral environment by surrounding the cells with a self-produced matrix of extracellular polymeric substances (EPS) that hold the cells together. These special growth properties need special reac-tors for cultivation. By immobilizing cyanobacteria on carriers, systems currently established in industry could also be used for biofilm formers. Various artificial carriers for immobilized growth of cyanobacteria and microalgae have already been described in the literature. However, the use of waste materials or natural biodegradable carriers would be more sustainable and is, therefore, the focus of this study. Dried Luffa cylindrica, zeolite, and corn stalks were investigated for their use as carriers for cyanobacteria. L. cylindrica was shown to be an excellent natural carrier for (i) Anabaena cylindrica (ii) Nostoc muscorum 1453-12a, and (III) Nostoc muscorum 1453-12b. Higher or at least similar growth rates were achieved when cyanobacteria were cultivated with L. cylindrica compared to submerged cultivation. Additionally, the production of EPS and C-phycocyanin was increased at least 1.4-fold in all strains by culturing on L. cylindrica. The improved growth could be explained on the one hand by the high surface area of L. cylindrica and its properties, and on the other hand by the release of growth-promoting nutrients from L. cylindrica to the medium.

Knee osteoarthritis (OA) is the most common joint disease worldwide. Exercise therapy has been identified as a first-line treatment option in patients suffering from knee OA. High-intensity interval training (HIIT) is an innovative exercise modality with potential in improving various disease-related outcomes. The purpose of this review is to explore the impact of HIIT on knee OA symptoms and physical functioning. A comprehensive search of scientific electronic databases was conducted to identify the articles on the effects of HIIT on knee OA. Thirteen studies were included in this review. Nine compared the effects of HIIT with those of low-intensity training, moderate-intensity continuous training, or a control group. Three evaluated the effects of HIIT alone. Eight reported a decrease in knee OA symptoms, and eight reported an increase in physical functioning. HIIT was shown to improve knee OA symptoms and physical functioning, but also aerobic capacity, muscle strength, and quality of life with minimal or no adverse events. However, compared to other exercise modalities, no clear superiority of HIIT was found. HIIT is a promising exercise strategy in patients with knee OA: anyway, the actual quality of evidence remains very low, and more high-quality studies are needed to confirm these promising outcomes.

As sessile organisms, plants face a wide range of abiotic stresses, with salinity being a prominent constraint affecting their growth, development, and productivity, particularly in arid and semi-arid regions. This study focused on understanding how salinity impacts Jatropha curcas, an important oilseed plant for biodiesel production. By examining the anatomy and ultrastructure of stomata and chloroplasts, we investigated the effects of prolonged salinity stress on J. curcas. This stress led to changes in stomatal density, stomatal index, and ostiole aperture, which case an imbalance of water conductivity in the xylem. Through transmission electron microscopy, we explored the subcellular organization of J. curcas chloroplasts and their contribution to plant photosynthetic efficiency, providing insights into their role in this process. Notably, salinity treatment resulted in a significant increase in starch granules accumulation, leading to impaired the granal and stromal grana lamellae, destroying this ultrastructure. Our findings indicate that the anatomy and ultrastructure of chloroplasts play a crucial role in influencing photosynthetic efficiency and hydraulic conductivity. This study offers new perspectives on the structure and function of chloroplasts in J. curcas, presenting innovative opportunities to develop strategies that enhance biofuel production in areas with high soil salinity.

The rationalization and modeling of an electric discharge in a fluid require the knowledge of electron swarm parameters. In this work, transport coefficients of alcohols, including methanol, ethanol, propanol, and butanol are reported. The electron energy distribution function, mean energy, reduced mobility, and diffusion coefficient, are calculated using the two-term expansion of the electron Boltzmann equation in the frame of BOLSIG+ and the Monte Carlo collision code METHES. For such, experimental and theoretical values of the electron-alcohol collision cross-section, covering a range of electron impact energy from 1 eV to 1000 eV, are employed. A temperature of 300K, reduced fields E/N between $1-10^4$ Td (1Td = $10^{-21}~\rm Vm^2$), and Plasma density of $10^{19}~\rm m^{-3}$ were used in simulations. The calculated coefficients are compared with data available from previous studies.

Enterococcus faecalis is a bacterium that can develop a multidrug resistance profile associated with the community as well as nosocomial-acquired infections. Among the treatment options for these infections are aminoglycosides combined with bacterial cell wall inhibitors such as beta-lactams, since E. faecalis is intrinsically resistant to aminoglycosides. One of its most representative resistance mechanisms is the expression of aminoglycoside-modifying enzymes, such as the aminoglycoside phosphotransferase type IIIa of E. faecalis (EfAPH(3')-IIIa). This enzyme acts by phosphorylating aminoglycosides in an ATP-dependent reaction, modifying the 3' position of hydroxyl groups of these antibiotics. Considering this scenario, 3,092 natural products obtained from the ZINC22 database were analyzed to select molecules with the highest affinity for the nucleotide-binding pocket of EfAPH(3')-IIIa, which could be potential aminoglycoside adjuvants. The molecules that showed the best-score results obtained from ensemble docking-based virtual screening were ZINC000000952700 (BS-1), ZINC000014793040 (BS-2) and ZINC000015498603 (BS-3). The most promising results were for BS-2, a flavone derivative, due to its improved stability profile in molecular dynamics simulation (average values of RMSD of 0.23 nm, and Rg of 1.94 nm), binding free energy calculations (average ΔG total of -35.3 nm), as well as better toxicological profile (lower probability of hepatotoxicity, carcinogenic, immunotoxicity, mutagenicity, and cytotoxicity effects), compared to BS-1 and BS-3. These results allow us to propose that a flavone derivative may act as an adjuvant to aminoglycosides in the treatment of E. faecalis infections, acting as an inhibitor in the nucleotide-binding pocket of EfAPH(3')-IIIa.

This paper presents a comprehensive cost-benefit analysis of producing alternative phosphorus (P) fertilizers from recycled water treatment residuals (Fe-WTR) and cowshed effluents, under the umbrella of circular economy approach. Fe-WTR is compared to a synthetic adsorbent, layered double hydroxide (LDH). Dairy wastewater is used as a P source for both adsorbents. The cost of 1 ton P-Fe-WTR sums up to 329 Euro. In contrast, the use of synthetic adsorbents (P-LDH) incurs significantly higher costs, amounting to 3329 Euros per ton, underscoring the economic advantage of utilizing waste-derived materials. Adopting Fe-WTR fertilizer not only offers substantial cost savings by avoiding landfill fees (40-150 Euros per ton) and wastewater discharge fines (500 Euros per ton) but also reduces environmental impacts. This sustainable method mitigates eutrophication and pollution, presenting a dual benefit of cost-effectiveness and environmental protection. Although our results are based on a specific case study, they reflect the potential value of extending the circular economic approach to P production worldwide: Recycling P from waste streams can be economically viable and environmentally preferable at large, aligning with global sustainability goals and demonstrating the practicality of circular economy principles in environmental management.

Model-based image reconstruction has brought improvements in terms of contrast and spatial resolution to imaging applications such as magnetic resonance imaging and emission computed tomography. However, their use for pulse-echo techniques like ultrasound imaging is limited by the fact that model-based algorithms assume a finite grid of possible locations of scatterers in a medium -- which does not reflect the continuous nature of real world objects and creates a problem known as off-grid deviation. To cope with this problem, we present a method of dictionary expansion and constrained reconstruction that approximates the continuous manifold of all possible scatterer locations within a region of interest. The expanded dictionary is created using a highly coherent sampling of the region of interest, followed by a rank reduction procedure based on a truncated singular value decomposition. We develop a greedy algorithm, based on the Orthogonal Matching Pursuit (OMP), that uses a correlation-based non-convex constraint set that allows for the division of the region of interest into cells of any size. To evaluate the performance of the method, we present results of 2-dimensional ultrasound image reconstructions with simulated data in a nondestructive testing application. Our method succeeds in the reconstructions of sparse images from noisy measurements, providing higher accuracy than previous approaches based on regular discrete models.

Buildings can play an important role in reducing GHG emissions through increased energy efficiency. The European Commission issued a proposal in 2021 suggesting that all new buildings should be “zero-emission Buildings” (ZEB), aiming at a zero GHG emission building stock by 2050.The extent to which ZEB can contribute to reduced GHG emissions, however, varies between countries, due to different energy systems. It is also important to consider other environmental effects to avoid that climate benefits come with unintended consequences.Here, we explore the life-cycle environmental performance for a ZEB in a case where electricity and heating is largely fossil-free. The assessment concentrates on i) environmental impact of the use stage in relation to the product stage, ii) the interrelation between different energy sources, with attention on household electricity, and iii) the performance for more impact categories than primary energy use and climate change. While our results generally support the use of ZEBs from an environmental perspective, they also show that the climate benefit in this setting is marginal. However, given that energy systems are connected and energy savings in one place can reduce the demand for fossil energy elsewhere, the climate benefit of ZEBs is likely underestimated. Besides methdological implications for future studies, this indicates that current EU policy is promising, as incentives for implementation of ZEBs is unaffected by domestic effects.

Snow is a vital environmental parameter and dynamically responsive to climate change, particularly in mountainous regions. Snow cover can be monitored at variable spatial scales using Earth Observation (EO) data. Long-lasting remote sensing missions enable the generation of multi-decadal time series and thus the detection of long-term trends. However, there have been few attempts to use these to model future snow cover dynamics. In this study, we therefore explore the potential of such time series to forecast the Snow Line Elevation (SLE) in the European Alps. We generate monthly SLE time series from the entire Landsat archive (1985-2021) in 43 Alpine catchments. Positive long-term SLE change rates are detected, with the highest rates (5-8 m/y) in the Western and Central Alps. We utilize this SLE dataset to implement and evaluate seven uni-variate time series modeling and forecasting approaches. The best results were achieved by Random Forests, with a Nash-Sutcliffe efficiency (NSE) of 0.79 and a Mean Absolut Error (MAE) of 258 m, Telescope (0.76, 268 m), and seasonal ARIMA (0.75, 270 m). Since the model performance varies strongly with the input data, we developed a Combined forecast based on the best performing methods in each catchment. This approach was then used to forecast the SLE for the years 2022-2029. In the majority of the catchments the shift of the forecast median SLE level retained the sign of the long-term trend. In cases where a deviating SLE dynamic is forecast a discussion based on the unique properties of the catchment and past SLE dynamics is required. In the future, we expect major improvements in our SLE forecasting efforts by including external predictor variables in a multi-variate modeling approach.

In the face of escalating climate concerns and the push for sustainable development, the global shift towards renewable and decentralized energy systems presents new challenges and opportunities. This study investigates integrating decentralized energy production, particularly photovoltaic (PV) systems, into national energy planning, aiming to optimize energy strategies that balance local production and consumption with national objectives. By analyzing the Swiss energy model, the research employs the EnergyScope and REHO models to assess the strategic implications of decentralized versus centralized energy systems. Results show that a decentralized approach can significantly reduce PV installation needs to 35 GW, about 23% of potential capacity, and decrease annual system costs by 10% to CHF 1230 per capita. This strategy emphasizes local consumption, minimizes grid reinforcement demands, and leverages economic advantages while addressing overproduction challenges through effective energy storage and grid management. Conclusions underline the strategic value of combining centralized and decentralized methods for resilient and sustainable energy planning. The study contributes to the discourse on energy policy and infrastructure planning, advocating for a hybrid model that accommodates both local conditions and broader energy objectives, urging further research into climate impacts and technology integration for a comprehensive energy future.

Exoskeletons designed to assist patients with activities of daily living are becoming increasingly popular, but still are subject to research. In order to gather requirements for the design of such systems, long-term gait observation of the patients over the course of multiple days in an environment of daily living are required. In this paper a wearable all-in-one data acquisition system for collecting and storing biomechanical data in everyday life is proposed. The system is designed to be cost efficient and easy to use, using off-the-shelf components and a cloud server system for centralized data storage. The measurement accuracy of the system was verified, by measuring the angle of the human knee joint at walking speeds between 3 and 12 km/h in reference to an optical motion analysis system. The acquired data was uploaded to a cloud database via a smartphone application. Verification results showed that the proposed toolchain works as desired. The system reached an RMSE from 2.9° to 8°, which is below most of the comparable system. The system provides a powerful, scalable platform for collecting and processing biomechanical data, which can help to automize the generation of an extensive database for human kinematics.

A model for enabling the production of lower carbon cement paste is proposed in this research. The model is based on predicting the strength of cement pastes with partial replacement of wheat straw ash (WSA) and silica fume (SF), two major wastes of agricultural and industrial production. The model is set up based on response surface methodology and Box-Behnken design, and included a suite of mechanical tests with distinct replacements following a micro an meso characterization. The model shows capability for predicting strength of any cement paste, including partial substitution of WSA and/or SF at any combination level with excellent accuracy, which is verified with three validation mixtures demonstrating maximum errors of less than 6% at the three ages. The analysis of the response surface evidences that any cement replacement in the range of 0-20% WSA and above 5% SF allows the reduction of the carbon footprint by maximizing the incorporation of both wastes. The proposed model can be used by setting the required strength of cement paste and calculating the maximum possible replacement, which should contribute to making the construction industry more sustainable by utilizing local waste.

In a bandsaw machine the blade guides provide additional stiffness and help to align the blade near the cutting region. Typically these are either in form of blocks made of carbide or ceramics or as sealed bearings. Abrasive particles, generated while cutting hard and brittle materials like natural stones, settle between the contact surfaces of the guides and the blade causing wear and premature failure. The hydrostatic guide system as presented in this work, is a contactless blade guiding method that uses force of several pressurized water jets to align the blade to the direction of the cut. For this investigation, cutting tests were performed on a marble block using a galvanic diamond coated bandsaw blade with the upper roller guides replaced by hydrostatic guides. The results show that the hydrostatic guides help to reduce the passive force while cutting to a constant near zero in contrast to the traditional guides. This also resulted in reduced surface roughness of the stone plates that were cut indicating a reduction in lateral vibration of the band. Additionally, it has also been shown that using hydrostatic guides the bandsaw blade can be tilted to counter the bandsaw drift opening opportunities for further research in active alignment control. This original research work has shown that the hydrostatic guide systems are capable of replacing and in fact perform better than state of the art bearing or block guides particularly for stone cutting applications.

Biofuels and biochemicals play significant roles in the transition towards a fossil-free society. However, large-scale biorefineries are not yet cost-competitive with their fossil-fuel counterparts, and it is important to identify biorefinery concepts with high economic performance. For evaluating early-stage biorefinery concepts, one needs to consider not only the technical performance and process costs but also the economic performance of the full supply chain and the impacts on feedstock and product markets. This article presents and demonstrates a conceptual interdisciplinary framework that can constitute the basis for evaluations of the full supply-chain performance of biorefinery concepts. This framework considers the competition for biomass across sectors, assumes exogenous end-use product demand, and incorporates various geographical and technical constraints. The framework is demonstrated empirically through a case study of a sawmill-integrated biorefinery producing liquefied biomethane from forestry and forest industry residues. The case study results illustrate that acknowledging biomass market effects in the supply chain evaluation implies changes in both biomass prices and the allocation of biomass across sectors. The proposed framework should facilitate the identification of biorefinery concepts with a high economic performance which are robust to feedstock price changes caused by the increase in biomass demand.

Most existing flexible count regression models allow only approximate inference. This work proposes a new framework to provide an exact and flexible alternative for modeling and simulating count data with various types of dispersion (equi-, under- and overdispersion). The new method, referred as “balanced discretization”, consists in discretizing continuous probability distributions while preserving expectations. It is easy to generate pseudo random variates from the resulting balanced discrete distribution since it has a simple stochastic representation in terms of the continuous distribution. For illustrative purposes, we have developed the family of balanced discrete gamma distributions which can model equi-, under- and overdispersed count data. This family of count distributions is appropriate for building flexible count regressionmodels because the expectation of the distribution has a simple expression in terms of the parameters of the distribution. Using the Jensen–Shannon divergence measure, we have shown that under equidispersion restriction, the family of balanced discrete gamma distributions is similar to the Poisson distribution. Based on this, we conjecture that while covering all types of dispersion, a count regression model based on the balanced discrete gamma distribution will allow recovering a near Poisson distribution model fit when the data is Poisson distributed.

Accurately glacier mapping is crucial for understanding climate change impacts, but existing efforts may be biased due to overlooking spatial autocorrelation during map validation. To address this, we compared several widely used machine learning algorithms as gradient boosting machines (GBM), k-nearest neighbor (KNN) and random forest (RF) with parametric logistic regression (GLM) and an unsupervised remote sensing-based method (NDSI) for mapping Peru's glacier regions in a thoughtful experimental setup. Spatial and non-spatial cross-validation methods were used to evaluate model’s performance and compared with a fully independent test set. Performance differences of up to 18% were found between bias-reduced (spatial) and overoptimistic (non-spatial) cross-validation results when compared to independent test set, emphasizing the need to consider spatial autocorrelation when using machine learning for glacier mapping. K-nearest neighbors (KNN) was the overall best model across regions consistently demonstrating the highest performance followed by logistic regression (LR) and gradient boosting machines (GBM). Our novel validation approach, accounting for spatial characteristics, provides valuable insights for glacier mapping studies and future efforts on glacier retreat monitoring. Incorporating this approach improves the reliability of glacier mapping, guiding future national-level initiatives.

In the age of social media, images come from unreliable sources that may change their content to fit a narrative. Automated detection of forged images is a complex task, especially considering recent technological advances in image manipulation software. However, in the existing literature, it is possible to identify two general approaches adopted by detection methods, active and passive. Active techniques preemptively act on an image by inserting structures before any manipulation is made that can verify its authenticity. In contrast, passive methods analyze the content of an image in search of traces of manipulation. In this way, this research proposes a novel approach to image manipulation detection, combining two passive methods through neural networks, creating a generalist approach capable of detecting with greater accuracy than the methods that compose it. Furthermore, this work used a combination of four datasets available in the literature for training and evaluation. After training, the merged approach obtained an accuracy of 89.59% in the set of validation images, significantly higher than the model trained with only unaltered images, which obtained 78.64%, and the two other models trained using images with a feature selection method applied to enhance inconsistencies that obtained 68.02% for Error-Level Analysis images and 50.70% for the method using Discrete Wavelet Transform. In addition to the performance improvement, the proposed approach’s accuracy variation was lower than the other models.

Anarchic urbanization and land artificialization expose urban ecosystems and Ecosystem Services (ES) to the threat. Urban ecosystems and trees play a crucial role in improving urban environments, and their management depends on the perceptions and preferences of urban residents. An assessment of the socio-ecological factors determining the perception of the actors allows for the proper design and planning of ecological urban policies and urban adaptation to climate change. In this study, we assessed the perception and discriminating variables of the different stakeholders of urban ES provided in the city of Cotonou. Thus, 381 city dwellers were individually interviewed after statistical sampling. Focus group discussions with the stakeholders also made it possible to highlight the ES provided in the different Land Use Units (LU). The results show that 73.23% of the city dwellers agreed that they were aware of ecosystem services. The hierarchical classification shows two homogeneous groups of perceivers with ethnicity, age and education as statistically discriminating sociological variables (pv˂0.001). Urban dwellers in the city of Cotonou perceived more SEs in the cultural and regulatory services category significantly (pv<0.001; v-test>3). The principal component analysis (PCA) reveals the varying availability of ES according to the different LU in the city. This study adds scientific value in the application of urban ecology to decision-making in climate and environment policy planning for sustainable cities in Africa and all over the world.

The forest regeneration phase in Sweden commonly involves mechanical soil preparation followed by planting of Scots pine or Norway spruce seedlings. The prepared soil offers planting positions with different properties, including decreased damage by pine weevil (Hylobius abietis L.). Nitrogen fertilization can be applied at the time of planting to aid establishment of the seedlings. In this study we compared the effect of organic nitrogen-fertilization for different planting positions, and seedling sizes on early survival and growth of Scots pine and Norway spruce seedlings. The main planting positions were capped mound, hinge, and mineral soil. Seedlings planted close to organic material were categorized as “low quality positions”, since proximity to organic material increase pine weevil attraction. Higher mortality rates related to pine weevil damage were recorded for seedlings planted in the low-quality positions, regardless of seedling size or N-fertilization. Pine weevil attack rates increased with increasing seedling size. Growth was in general lowest in mineral soil positions. The effect of organic N-fertilization on growth was positive for spruce regardless of planting positions and seedling size, while depending on planting position and seedling size for pine, indicating that effects of organic N-fertilization depend on seedling species, -size and planting position.

Glutamine is the most abundant and versatile amino acid in the body. In health and disease, the rate of glutamine consumption by immune cells is similar or greater than glucose. For instance, in vitro and in vivo studies have determined that glutamine is an essential nutrient for lymphocyte proliferation and cytokine production, macrophage phagocytic plus secretory activities and neutrophil bacterial killing. Glutamine release to the circulation and availability is mainly controlled by key metabolic organs, such as the gut, liver and skeletal muscles. During catabolic/hypercatabolic situations glutamine can become essential for metabolic function, but its availability may be compromised due to impairment of homeostasis in the inter-tissue metabolism of amino acids. For this reason, glutamine is currently part of clinical nutrition supplementation protocols and/or recommended for immune suppressed individuals. However, in a wide range of catabolic/hypercatabolic situations (e.g. ill/critically ill, post-trauma, sepsis, exhausted athletes) it is currently difficult to determine whether glutamine parenteral or enteral supplementation should be recommended based on the amino acid plasma concentration (glutaminemia). Although the beneficial immune based effects of glutamine supplementation is already established, many questions and evidence for positive in vivo outcomes still remain to be presented. Therefore, this paper provides an integrated review on how glutamine metabolism in key organs is important to cells of the immune system. We also discuss glutamine metabolism, action and important issues related to the effects of glutamine supplementation in catabolic situations.

The conversion of native habitats into anthropogenic ones compromises the original composition and configuration of the landscapes, influencing ecological dynamics and affecting biodiversity. Increasingly, landscape ecology has shown that these effects can only be understood if accessed at adequate spatial scales, as the scale which landscape structure is evaluated influences species responses. Here, we investigated how three variables of landscape composition (proportion of forest, coffee crop and pasture) and two of configuration (number of fragments and mean nearest neighbor distance) interfere in the richness and composition of medium and large-sized mammals considering a multiscale approach. We recorded medium and large-sized mammal species in 13 landscapes with predominantly matrices of coffee and pasture in Minas Gerais, Brazil. Then, we built distance-based linear models to identify the scale of effect of each landscape variable for both response variables considering eight scales (from 250 m to 2000 m). Finally, we verified the influence of the landscape on the richness and composition of mammals, considering the landscape variables in their respective scales of effect. We found 67% of the probable species occurring in the region. The scales of effect varied among landscape variables probably due to the different variables affect different aspects of organisms' ecological requirements. The proportion of pasture in the landscape explained the variation in species richness, while the proportion of forest explained the variation in species composition. In addition, the proportion of pasture in the landscape had a positive influence on species richness, indicating that this matrix may favor the presence of generalist species of habitat and/or this result is due to the higher concentration of species in the fragments immersed in this matrix. These results suggest that considering different responses to biodiversity is important to understand different aspects of the landscape's influence on biodiversity. In addition, the composition of the landscape is fundamental for the perpetuation of species and, therefore, both forest cover and types of matrices in the landscape must be considered to improve species conservation strategies. Finally, generalizing a spatial scale can lead to misinterpretations about the influence of the landscape on biodiversity.

Antisense technologies consist of the utilization of oligonucleotides or oligonucleotide analogs to interfere with undesirable biological processes, commonly through inhibition of expression of selected genes. This field holds a lot of promise for the treatment of a very diverse group of diseases including viral and bacterial infections, genetic disorders, and cancer. To date, drugs approved for utilization in clinics or in clinical trials target diseases other than bacterial infections. Although several groups and companies are working on different strategies, the application of antisense technologies to prokaryotes still lags with respect to those that target other human diseases. In those cases where the focus is on bacterial pathogens, a subset of the research is dedicated to produce antisense compounds that silence or reduce expression of antibiotic resistance genes. Therefore, these compounds will be adjuvants administered with the antibiotic to which they reduce resistance levels. A varied group of oligonucleotide analogs like phosphorothioate or phosphorodiamidate morpholino residues, as well as peptide nucleic acids, locked nucleic acids and bridge nucleic acids, the latter two in gapmer configuration, have been utilized to reduce resistance levels. The major mechanisms of inhibition include eliciting cleavage of the target mRNA by the host’s RNase H or RNase P, and steric hindrance. The different approaches targeted resistance to β-lactams including carbapenems, aminoglycosides, chloramphenicol, macrolides, and fluoroquinolones.

Feedback is a type of formative evaluation linked to student learning and academic achievement. However, it is not known how students perceive this process since there are no instruments to measure attitudes towards feedback. The objective of this work was to develop and validate an attitude scale towards academic feedback through an Exploratory Factor Analysis (EFA), using the principal components method and an internal consistency analysis using Cronbach's Alpha. The sample consisted of 274 students from 2015, 2016, and 2017 cohorts of pedagogy careers from five Chilean universities. The results showed the consolidation of the instrument and its final conformation in four components (Negative experience towards feedback, Effectiveness of feedback, Feedback as an opportunity, and Resistance towards feedback) with a total of 15 items, which allow measuring the students' attitude towards this written formative evaluation.

The broad global distribution of freshwater clams belonging to the genus Corbicula is driven by multiple hermaphroditic lineages. These lineages, characterized by shared morphological traits and phenotypic plasticity, pose challenges to morphological identification. Genetic markers, such as the mitochondrial COI gene, play a crucial role in delineating these lineages and their ranges. Morphotypes represent observed phenotypic variations, while lineages are defined based on genetic markers. Here, we comprehensively review Corbicula's distribution in Argentina, dis-criminate extant lineages based on both morphological and genetic (COI) data, and describe variation in internal and external morphology using 15 Argentine populations. Haplotype net-works identified two mitochondrial lineages: A/R morphotype (FW5 haplotype) and C/S mor-photype (FW17 haplotype). Strikingly, despite having similar vectors, origins, and invasive stages, Corbicula lineages exhibit virtually segregated distributions. However, intermediate morphotypes and both mitochondrial haplotypes are found in sympatry in northeastern Argen-tina, suggesting the presence of hybrids due to maternal genome retention. These findings con-tribute to the clarification of the identity and distribution of Corbicula lineages in Argentina, where the genus has been found for over half a century. Similar studies are needed in other areas to better understand the invasion patterns of a successful and plastic group.

This study investigates the application of Unmanned Aerial Vehicles (UAVs) equipped with a Micasense RedEdge-MX multispectral camera for the estimation of Secchi Depth (SD) in inland water bodies. The research analyzed and compared five sun-glint correction methodologies — Hedley, Goodman, Lyzenga, Joyce, and threshold removed glint to model the SD values derived from UAV multispectral imagery, highlighting the role of reflectance accuracy and algorithmic precision in SD modeling. While Goodman's method showed a higher correlation (0.92) with in situ SD measurements, Hedley's method exhibited the smallest average deviation (0.65 m), suggesting its potential in water resource management, environmental monitoring, and ecological modeling. The study also underscored the Quasi-Analytical Algorithm (QAA) potential in estimating SD due to its flexibility to process data from various sensors without requiring in situ measurements, offering scalability for large-scale water quality surveys. The accuracy of SD measures, calculated using QAA, was related to variability in water constituents of coloured dissolved organic matter and the solar zenith angle. A practical workflow for SD acquisition using UAVs and multispectral data was proposed for monitoring inland water bodies.

It has been known that the performance of the High-Pressure Grinding Rolls (HPGR) varies as a function of method used to confine laterally the rolls, their diameter/length (aspect) ratio as well as their condition, if new or worn. However, quantifying these effects through direct experimentation in machines with reasonably large dimensions is not straightforward given the challenge, among others, of guaranteeing that the feed material remains unchanged. The present work couples the discrete element method (DEM) to multibody dynamics (MBD) and a novel particle replacement model (PRM) to simulate the performance of pilot-scale HPGRs grinding pellet feed. It shows that rotating side plates, in particular when fitted with studs, allow reaching more uniform forces along the bed, which also translates in a more constant product size along the rolls as well as higher throughput. It also shows that the edge effect is relatively constant with roll length, leading to substantially larger proportional edge regions for high-aspect ratio rolls. On the other hand, the product from the center region of such rolls was found to be finer when pressed at identical specific forces. Finally, rolls were found to have higher throughput, but generate a coarser product when worn following the commonly observed trapezoidal profile. The approach used in industry to compensate for roller wear by increasing the specific force and roll speed has then been demonstrated to be effective to maintain and potentially even increase product fineness and throughput, as long as the minimum safety gap is not reached.

Portugal’s EPAC silo network, initially planned in the 1930s but constructed and utilized primarily in the 1970s, consisted of 31 silos with a total capacity of 841,100 t. The network’s usage declined, however, due to market liberalization and Portugal’s accession to the European Economic Community in 1985. This study focuses on adapting a methodology to inventory and analyse the 31 silos in the EPAC network considering their general features, construction, technological facilities, and socioeconomic aspects. The silos are situated in 30 cities and towns, predominantly in the country’s key grain-growing regions, particularly the Alentejo region. While there are variations in design and construction, most EPAC silos contain two or three rows of circular reinforced-concrete cells and use the spaces between cells for storage. Their capacities range from 6,000 to 35,000 t. Some are inland grain reception and storage silos, while others are larger-capacity port silos designed to unload grain rapidly onto ships using mechanical or pneumatic unloaders. These structures are a significant part of Portugal’s agro-industrial heritage and have in some cases been repurposed as museums or event venues. Compared to other agro-industrial buildings, silos pose unique conversion challenges due to their height and design complexities. Examples of successful reuse in countries like Spain and Italy may provide insights for potential silo projects in Portugal. However, analysis suggests that such proposals and similar initiatives may be viable only in the more highly populated towns.

In healthy adult brain, glial cell line-derived neurotrophic factor (GDNF) is exclusively expressed by neurons and in some instances, it has furthermore been shown to derive from a single neuronal subpopulation. Secreted GDNF acts in a paracrine fashion by forming a complex with GDNF family receptor α1 (GFRα1) which is mainly expressed by neurons and can act in cis as a membrane-bound or in trans as a soluble factor. The GDNF/GFRα1 complex signals through interaction with RET (“rearranged during transfection”) or with a lower affinity with neural cell adhesion molecule (NCAM). GDNF can also signal independently from GFRα1 via interaction with syndecan-3. RET being expressed by neurons involved in several pathways: nigro-striatal dopaminergic neurons, motor neurons, enteric neurons, sensory neurons, etc. could be the main determinant of the specificity of GDNF pro-survival effect. In injured brain, de novo expression of GDNF occurs in glial cells. Neuroinflammation has been reported to induce GDNF expression in activated astrocytes and microglia, infiltrating macrophages, nestin-positive neural stem cells and neuron/glia (NG2) progenitors. This disease-related GDNF overexpression can be either beneficial or detrimental depending on the localization in the brain and the level and duration of glial cells activation. Some reports also describe upregulation of RET and GFRα1 in glial cells, suggesting that GDNF could modulate neuroinflammation.

Oligonucleotides are key compounds widely used for research, diagnostics, and therapeutics. The rapid increase in oligonucleotide-based applications, together with the progress in nucleic acids research, led to the design of nucleotide analogs that when being part of these oligomers enhance their efficiency, bioavailability, or stability. One of the most useful nucleotide analogs are the first-generation bridge nucleic acids (BNA), also known as locked nucleic acids (LNA), which were used in combination with ribonucleotides, deoxyribonucleotides, or other analogs to construct oligomers with diverse applications. However, there is still room to improve their efficiency, bioavailability, stability, and, importantly, toxicity. A second generation BNA, BNANC (2'-O,4'-aminoethylene bridged nucleic acid), has been recently made available. Oligomers containing these analogs not only showed less toxicity when compared to LNA-containing compounds but in some cases also exhibited higher specificity. Although there are still few applications where BNANC-containing compounds were researched, the results are very promising warranting more efforts in incorporating these analogs for other applications. Furthermore, newer BNA compounds will be introduced in the near future offering great hope to oligonucleotide-based fields of research and applications.

Low-temperature processed ITO thin films offer the potential of overcoming the doping limit by suppressing the equilibrium of compensating oxygen interstitial defects. To elucidate this potential, electrical properties of Sn-doped In₂O₃ (ITO) thin films are studied in dependence on film thickness. In-operando conductivity and Hall effect measurements during annealing of room temperature deposited films with different film thickness in different environments allow to discriminate between the effects of crystallization, grain growth, donor activation and oxygen diffusion on carrier concentrations and mobilities. At 200 °C, a control of carrier concentration by oxygen incorporation or extraction is only possible for very thin films. The electrical properties of thicker films deposited at room temperature are mostly affected by grain size. The remaining diffusivity of compensating oxygen defects at 200 °C is sufficient to screen the high Fermi level induced by deposition of Al₂O₃ using atomic layer deposition (ALD), which disables the use of defect modulation doping at this temperature. The results indicate that achieving higher carrier concentrations requires a control of the oxygen pressure during deposition in combination with seed layers to enhance crystallinity or the use of near room temperature ALD.

Ferns and lycophytes, now known as monilophytes, have received scant molecular attention in comparison to angiosperms. The advent of high-throughput technologies allowed an advance towards a greater knowledge of their elusive genomes. In this work, proteomic analyses of heart-shaped gametophytes of two ferns were performed: the apomictic species Dryopteris affinis ssp. affinis and its sexual relative Dryopteris oreades. In total, a set of 218 proteins shared by these two gametophytes were analyzed using the STRING database, and the proteome associated with metabolism, genetic information processing, and responses to abiotic stress is discussed. Specifi-cally, we report proteins involved in the metabolism of carbohydrates, lipids, and nucleotides, the biosynthesis of amino acids, and secondary compounds, energy, oxido-reduction, transcrip-tion, translation, protein folding, sorting and degradation, and responses to abiotic stress. Look-ing at the interactome of this set of proteins, it represents a total network composed of 218 nodes and 1,792 interactions, obtained mostly from databases and textmining. The interactions among the identified proteins of the ferns D. affinis and D. oreades, together with the description of their biological functions, might contribute to a better understanding of the function and develop-ment of ferns as well as to fill knowledge gaps in plant evolution.

Energy conversion and distribution (heat and electricity) is characterized by long planning horizons, investment periods and depreciation times, and it is thus difficult to plan and tell the technology that optimally fits for decades. Uncertainties include future energy prices, applicable subsidies, regulation, and even the evolution of market designs. To achieve higher adaptability to arbitrary transition paths, a technical concept based on integrated energy systems is envisioned and described. The problem of intermediate steps of evolution is tackled by introducing a novel paradigm in urban infrastructure design.It builds on standardization, modularization and economies of scale for underlying conversion units. Building on conceptual arguments for such a platform, it is then argued how actors like (among others) municipalities and district heating system operators can use this as a practical starting point for a manageable and smooth transition towards more environmental friendly supply technologies, and to commit to their own pace of transition (bearable investment/risk). environmental friendly supply technologies. Merits are not only supported by technical arguments but also by strategical and societal prospects like technology neutrality and availability of real options.

The recently defined and yet rather new topic of Healthy aging is gathering more attention on the global stage. With world’s population getting older - it is rapidly becoming very important to develop and maintain functional abilities in older age and develop mechanisms to protect senior population from chronic diseases. One of the most effective components, as well as, one may call - process involved in, and strongly associated with aging is recently discovered and Nobel prize awarded - nitric oxide as a signaling molecule, which followed by later discoveries showed to have a positive metabolic, immune and anti-inflammatory effects. One of the most debated topics of the last decade in the scientific community is nitrates, one of the pathways involved in nitric oxide production. Thus, the objective of this study was to evaluate effect of different potassium nitrate concentrate supplementation on Drosophila melanogaster longevity. 0,5-3% potassium nitrate medium was analyzed on the life span and motor function in different groups consisting of 100 females fruit flies each. In this assay, female fly species supplemented with potassium nitrate diet showed life span increase by 18.6% and 5.1% with 1% and 2% KNO3- respectively with a positive impact on locomotor function. In conclusion, we found that low concentration of potassium nitrate medium increased lifespan and locomotor function in Drosophila melanogaster.

Steel structures, such as bridges, must be continuously maintained. Due to increasing trac loads, design and execution shortcomings, many steel bridges are subject to various types of damage, such as cracks. The bridges must be maintained by crack removal and re-welding the cracks. During this work, the bridge is closed for trac in order to suppress the crack ank movement during welding. As a result, trac loads on other routes are higher, which increases further damage on detour routes and subsequently additional welding work and costs on theses structures. Therefore, the aim is to provide a method that allows welding work on steel structures under running trac, respectively under cyclic loads. First, measurements at a representative steel bridge were conducted to derive gap opening parameters of amplitude and frequency for welding tests under cyclic loads. In a test setup for reproducible and comparable welding tests, specimen were welded under cyclic loads. Deep grinding of the root layer allowed defect-free welding within the range of 0.1 mm amplitude at a frequency of 2 Hz loadings. For such welds, the weld quality and the fatigue strength was comparable to the standard requirements.

Ferns and fern-allies, now known as Monylophytes, have received scant molecular attention, in relation to angiosperm group. The advent of high-throughput technologies allows to advance towards a greater knowledge of their elusive genome. In this work, samples of apogamous and sexual heart-shaped gametophytes from two ferns: the apomictic species Dryopteris affinis ssp. af-finisand its sexual relative Dryopteris oreades were extracted and identified. In total, a set of 218 proteins shared by these gametophytes were analysed by using the STRING database, and the proteome associated to metabolism, genetic information processing and responses to abitotic stress is discussed. Specifically, there are reported proteins involved in metabolism of carbohy-drates and lipids, biosynthesis of amino acids, metabolism of nucleotides, energy, and secondary compounds, oxido-reduction, transcription, translation, folding, sorting, and degradation, and response to abiotic stress. Some homologs of proteins found are MACCI-BOU (MAB1), MOSAIC DEATH 1 (MOD1), MAINTENANCE OF PHOTOSYSTEM II UNDER HIGH LIGHT 2 (MPH2), TRANSPARENT TESTA 5 (TT5), ALBINO OR GLASSY YELLOW 1 (AGY1), LEUCYL AMINOPEP-TIDASE 1 and 3 (LAP1 and LAP3), or LOW EXPRESSION OF OSMOTICALLY RESPONSIVE GENES 1 (LOS1). The interactome of the set of proteins was also studied, being the most common interac-tions database and textmining. All these data about the interactions that exist between the stud-ied proteins of the ferns D. affinis and D. oreades, together with the description of their biological function, might contribute to better understand the functioning and development of ferns as well as to fulfil gaps of knowledge in plant evolution.

This article introduces an innovative design method for public lighting systems that surpasses the limitations of conventional approaches, which rely on predefined lamp characteristics and spatial arrangement. By employing a multi-criteria decision algorithm, our study proposes an optimal design methodology considering several key aspects, including the distance between lamps, their type, power, and light distribution. The goal is to achieve optimal illumination that enhances visibility on public roads for drivers and pedestrians while simultaneously minimizing glare, installation costs and maximizing energy efficiency. The proposed methodology is implemented through an algorithm developed in Matlab, with results validated through simulations in Dialux. This information is used to construct a decision matrix, assessed using the CRITIC method across 180 different scenarios within a specific case study. The findings demonstrate the effectiveness of multi-criteria decision-making as a tool for significantly improving the planning and design of lighting in public illumination systems, allowing for selecting the optimal combination of parameters that ensure the best lighting conditions.

Like other organisms, fungi produce extracellular vesicles (EVs) that are involved in various biological processes, including intercellular communication and the transport of molecules between cells. These EVs can be applied in fungal pathogenesis, virulence, and interactions with other organisms, including host cells, in the case of fungal infections. While some types of mycoses are relatively common and easily treatable, certain neglected mycoses pose significant public health challenges, such as sporotrichosis, chromoblastomycosis, and paracoccidioidomycosis. These infectious diseases can cause significant morbidity and disability, leading to a reduced quality of life for the patients. So, research about the virulence factor is essential to understand how fungi escape the immune system. In this context, this manuscript reviews the study of fungi EVs, their cargo, their obtaining, and their role during the infectious process, which is extremely important for understanding this neglected mycosis.

Insecticidal toxins from Bacillus thuringiensis (Bt) are valuable tools for pest management worldwide, contributing to the management of human disease insect vectors and phytophagous insect pests of agriculture and forestry. Here, we report the effects of dual and triple Bt toxins expressed in transgenic cotton cultivars on the fitness and demographic performance of Helicoverpa zea (Boddie), a noctuid pest known as cotton bollworm and corn earworm. Life-history traits were determined for individuals of three field populations from a region where H. zea overwintering is likely. Triple-gene Bt cotton cultivars expressing Cry and Vip3Aa toxins killed 100% of the larvae in all populations tested. In contrast, dual-gene Bt cotton expressing Cry1Ac+Cry1F and Cry1Ac+Cry2Ab2 allowed population growth with the intrinsic rate of population growth (rm) 38% lower than on non-Bt cotton. The insects feeding on Bt cotton plants expressing Cry1Ac+Cry2Ab2, Cry1Ac+Cry1F, or Cry1Ab+Cry2Ae exhibited reduced larval weight, survival rate, and increased development time. Additionally, fitness parameters varied significantly among the insect populations, even on non-Bt cotton plants, likely because of their different genetic background and/or previous Bt toxin exposure. This is the first report of the comparative fitness of H. zea field populations on dual-gene Bt cotton after the recent reports of field resistance to certain Bt toxins. These results document the population growth rates of H. zea from an agricultural landscape with 100% Bt cotton cultivars. Our results will help to refine models designed to predict resistance evolution and improve insect resistance management for Bt crops.

The present work presents and discusses experimental performance and gaseous emission results of the use of ternary mixtures of diesel oil, biodiesel and ethanol in an ASTM-CFR engine. Eight different mixtures with biodiesel content equal to 8%, 20% and 60%, and ethanol content equal to 0%, 5% and 8% were analyzed. After the selection and preparation of the mixtures, the determination of the cetane number for each one was performed following the ASTM D613 standard. After cetane number evaluation, two off-standard operating conditions were selected, and performance and emissions were obtained. A thermodynamic analysis and a Bayesian inverse approach were applied and physical quantities of interest, such as indicated work, maximum pressure, rate of heat released by the fuel, ignition delay, and combustion duration, were obtained. In-cylinder pressure data were obtained through an AVL Indicating system installed in the ASTM-CFR engine. The heating values of each mixture were determined using a bomb calorimeter and thermal and indicated efficiencies were evaluated based on the total heat released by the fuel and the indicated work. Finally, a gaseous emissions analysis was conducted to evaluate the influence of the blend compositions, as well as of their cetane numbers, on the formation of NOx, CO and CO₂.

Zinc is a redox-inert trace element that is second only to iron in abundance in biological systems. In cells, zinc is typically buffered and bound to metalloproteins, but may also exist as a labile or chelatable (free ion) form. Zinc plays a critical role in prokaryotes and eukaryotes ranging from structural to catalytic to replication to demise. This review discusses the influential properties of zinc on various mechanisms of bacterial proliferation and synergistic action as anti-microbial element. We also touch upon the significance of zinc among eukaryotic cells and how it may modulate their survival and death through its inhibitory or modulatory effect on certain receptors, enzymes, and signaling proteins. A brief discussion on zinc chelators is also presented and chelating agents may be used with or against zinc to affect therapeutics against human diseases. Overall, the multidimensional effects of zinc in cells attest to the growing numbers of scientific research that reveal the consequential prominence of this remarkable transition metal in human health and disease.

In this paper we use the conformable fractional derivative to discuss some fractional linear differential equations with constant coefficients. By applying some similar arguments to the theory of ordinary differential equations, we establish a sufficient condition to guarantee the reliability of solving constant coefficient fractional differential equations by the conformable Laplace transform method. Finally, we analyze the analytical solution for a class of fractional models associated with Logistic model, Von Foerster model and Bertalanffy model is presented graphically for various fractional orders and solution of corresponding classical model is recovered as a particular case.