Continuing the investigation already started (See Farcaş, A.-A.; Bende, A. PCCP 2024, 26, 14937), the light absorption and charge-transfer properties of the dopamine (DA), dopamine zwitterion (called as dopamine-semi-quinone or DsQ) and dopamine-o-quinone (DoQ) adsorbed on the graphene nanoparticle surface have been investigated using the ground state and linear-response time-dependent density functional theories, considering the B97X-D3BJ/def2-TZVPP level of theory. In terms of the strength of molecular adsorption on the surface, the DsQ form has 50 % higher binding energy than that for the DA or DoQ cases (-20.24 kcal/mol vs. -30.41 kcal/mol). The results obtained for electronically excited states and UV-Vis absorption spectra show that the photochemical behavior of DsQ is more similar to DA than that observed for DoQ. Of the three systems analyzed, the DsQ-based complex showed the most active CT phenomenon, both in terms of the number of CT-like states and the amount of charge transferred. Of the first thirty electronically excited states computed for the DsQ case, eleven are purely CT-type and nine are mixed CT and localized (or Frenkel) excitations. By varying the adsorption distance between the molecule and the surface vertically, the amount of charge transfer obtained for DA decreases significantly as the distance increases, for DoQ it remains stable, while for DsQ there are states for which little change is observed, while for others there is a significant change. Furthermore, the mechanistic compilation of the electron orbital diagrams of the individual components cannot describe in detail the nature of the excitations inside the complex.

Despite the growing capabilities of large language models, concerns exist about the biases they develop. In this paper, we propose a novel, automated mechanism for debiasing through specified dataset augmentation in the lens of bias producers that can be useful in a variety of industries, especially ones that are “restricted” and have limited data. We consider that bias can occur due to intrinsic model architecture and dataset quality. The two aspects are evaluated using two different metrics we created. We show that our dataset augmentation algorithm reduces bias as measured by our metrics.

The design of more efficient photosensitizers is a matter of great importance in the field of cancer treatment by means of photodynamic therapy. One of the main processes involved in the activation of apoptosis in cancer cells is the oxidative stress on DNA once a photosensitizer is excited by light. As a consequence, it is a matter of great relevance to investigate in detail the binding modes of the chromophore with DNA, and the nature of the electronically excited states that participate in the induction of DNA damage, for example, charge-transfer states. In this work, we investigate the electronic structure of the anthraquinone photosensitizer intercalated into a double-stranded poly(dG-dC) decamer model of DNA. First, the different geometric configurations are analyzed by means of classical molecular dynamics simulations. Then, the excited states for the most relevant poses of anthraquinone inside the binding pocket are computed by an electrostatic-embedding quantum mechanics/molecular mechanics approach, where anthraquinone and one of the nearby guanine residues are described quantum mechanically to take into account intermolecular charge-transfer states. The excited states are characterized as monomer, exciton, excimer and charge-transfer states based on the analysis of the transition density matrix, and each of these contributions to the total density of states and absorption spectrum is discussed in terms of the stacking interactions. These results are relevant as they represent the footing for future studies on the reactivity of anthraquinone derivatives with DNA and give insights on possible geometrical configurations that potentially favor the oxidative stress of DNA.

The AUC-ROC is a widespread measure to assess the performance of a binary classification model across various discrimination thresholds. Yet, it is not apt for unbalanced class problems and is misleading for early retrieval. For instance, link prediction in complex networks is an unbalanced early retrieval problem, whose goal is to offer a ranking of the nonobserved network links, prioritizing a small cohort of positive links (for which we are sure the labels are reliable) on top of a list largely populated by unlabeled links (for which the labels are uncertain). Differently from binary classification, here the evaluation focuses on how the predictor prioritizes the positive class, because the negative class does not exist and is replaced by an unlabeled class. For decades, scholars have been investigating the necessity to correct and to generalize the AUC-ROC to work for both unbalanced classification and early retrieval evaluation, this is requested in many applied domains of computational science. Here we propose the area under the magnified ROC (AUC-mROC), a new measure that adjusts the standard AUC-ROC to work for unbalanced early retrieval problems such as link prediction. Finally, we introduce the area under the generalized ROC (AUC-gROC), which unifies both AUC-mROC and AUC-ROC to provide a universal and reliable evaluation measure that can work for both classification and early retrieval problems, regardless of class proportion.This discovery represents a critical achievement, culminating in success after decades of contentious research. Its solution promises to significantly impact various domains within applied computational science.

Sparse training (ST) aims to ameliorate deep learning by replacing fully connected artificial neural networks (ANNs) with sparse or ultra-sparse ones, such as brain networks are, therefore it might benefit to borrow brain-inspired learning paradigms from complex network intelligence theory. Here, we launch the ultra-sparse advantage challenge, whose goal is to offer evidence on the extent to which ultra-sparse (around 1% connection retained) topologies can achieve any leaning advantage against fully connected. Epitopological learning is a field of network science and complex network intelligence that studies how to implement learning on complex networks by changing the shape of their connectivity structure (epitopological plasticity). One way to implement Epitopological (epi- means new) Learning is via link prediction: predicting the likelihood of non-observed links to appear in the network. Cannistraci-Hebb learning theory inspired the CH3-L3 network automata rule for link prediction which is effective for general-purpose link prediction. Here, starting from CH3-L3 we propose Epitopological Sparse Meta-deep Learning (ESML) to apply Epitopological Learning to sparse training. In empirical experiments, we find that ESML learns ANNs with ultra-sparse hyperbolic (epi-)topology in which emerges a community layer organization that is meta-deep (meaning that each layer also has an internal depth due to power-law node hierarchy). Furthermore, we discover that ESML can in many cases automatically sparse the neurons during training (arriving even to 30% neurons left in hidden layers), this process of node dynamic removal is called percolation. Starting from this network science evidence, we design Cannistraci-Hebb training (CHT), a 4-step training methodology that puts ESML at its heart. We conduct experiments on 7 datasets and 5 network structures comparing CHT to dynamic sparse training SOTA algorithms and the fully connected counterparts. The results indicate that, with a mere 1% of links retained during training, CHT surpasses fully connected networks on VGG16, GoogLeNet, ResNet50, and ResNet152. This key finding is an evidence for ultra-sparse advantage and signs a milestone in deep learning. CHT acts akin to a gradient-free oracle that adopts CH3-L3-based epitopological learning to guide the placement of new links in the ultra-sparse network topology to facilitate sparse-weight gradient learning, and this in turn reduces the convergence time of ultra-sparse training. Finally, CHT offers the first examples of parsimony dynamic sparse training because, in many datasets, it can retain network performance by percolating and significantly reducing the node network size. Our code is available at: https://github.com/biomedical-cybernetics/Cannistraci-Hebb-training.

Dynamic sparse training is an effective strategy to alleviate the training and inference demands of artificial neural networks. However, current sparse training methods face a challenge in achieving high levels of sparsity while maintaining performance comparable to that of their fully connected counterparts. The Cannistraci-Hebb training (CHT) method produces an ultra-sparse advantage compared to fully connected training in various tasks by using a gradient-free link regrowth method, which relies solely on the network topology. However, its rigid selection based on link prediction scores may lead to epitopological local minima, especially at the beginning of the training process when the network topology might be noisy and unreliable. In this article, we introduce the Cannistraci-Hebb training soft rule (CHTs), which applies a flexible approach to both the removal and regrowth of links during training, fostering a balance between exploring and exploiting network topology. Additionally, we investigate the network topology initialization using several approaches, including the bipartite scale-free and bipartite small-world network models. Empirical results show that CHTs can surpass the performance of fully connected networks with MLP architecture by using only 1% of the connections (99% sparsity) on the MNIST, EMNIST, and Fashion MNIST datasets and can provide remarkable results with only 0.1% of the links (99.9% sparsity). In some MLPs for image classification tasks, CHTs can reduce the active neuron network size to 20% of the original nodes (neurons), demonstrating a remarkable ability to generalize better than fully connected architectures, reducing the entire model size. This represents a relevant result for dynamic sparse training. Finally, we present evidence from larger network models such as Transformers, with 10% of the connections (90% sparsity), where CHTs outperform other prevalent dynamic sparse training methods in machine translation tasks.

The conformational dependence of the matrix element for spin-orbit coupling and of the electronic coupling for charge separation are determined for an electron donor-acceptor system containing a pyrene acceptor and a dimethylaniline donor. Different kinetic and energetic aspects that play a role in the spin-orbit charge transfer intersystem crossing (SOCT-ISC) mechanism are discussed. This includes parameters related to initial charge separation and the charge recombination pathways using the (Semi-Classical) Marcus Theory for electron transfer. The spin-orbit coupling, which plays a significant role in charge recombination to the triplet state can be probed by (TD-)DFT, using the latter as a tool to understand and predict the SOCT-ISC mechanism. The matrix elements for spin-orbit coupling for acetone and 4-thio-thymine are used for benchmarking. (Time Dependent-) Density Functional Theory (DFT and TD-DFT) calculations are applied using the quantum chemical program Amsterdam Density Functional (ADF).

Chronic pancreatitis is a progressive fibroinflammatory disorder with no currently satisfactory treatment. Emerging evidence suggests an association between gut microbial dysbiosis and chronic pancreatitis. Although direct causative evidence is lacking, it is hypothesized that the gut microbiota may play a pivotal role in modulating pancreatic function via the gut-pancreas axis. Thus, modulating the gut microbiota through the administration of probiotics or prebiotics may alleviate pancreatic disorders. In this review, we first propose the potential mechanisms by which specific probiotics or prebiotics may ameliorate chronic pancreatitis, including the alleviation of small intestinal bacterial overgrowth (SIBO), facilitation of short-chain fatty acids (SCFAs) production, activation of the glucagon-like peptide 1 receptors (GLP-1Rs) in the pancreas. Since there are currently no probiotics or prebiotics used for the treatment of chronic pancreatitis, we have discussed research in other disease models that use probiotics or prebiotics to modulate pancreatic endocrine and exocrine functions and prevent pancreatic fibrosis. This provides indirect evidence for their potential application in the treatment of chronic pancreatitis. We anticipate that this research will stimulate further investigation into the gut-pancreas axis and the potential therapeutic value of probiotics and prebiotics in chronic pancreatitis.

Biohydrogen is a versatile energy carrier for the generation of electric energy from renewable sources. Hydrogenases can be used in enzymatic fuel cells to oxidize dihydrogen. The rate of electron transfer (ET) at the anodic side between the [NiFe]-hydrogenase enzyme distal iron-sulfur cluster and the electrode surface can be described by the Marcus equation. All parameters for the Marcus equation are accessible from DFT calculations. The distal cubane FeS-cluster has a three cysteine and one histidine coordination [Fe₄S₄](His)(Cys)₃ first ligation sphere. The reorganization energy (inner- and outer-sphere) is almost unchanged upon a histidine-to-cysteine substitution. Differences in rates of electron transfer between the wild-type enzyme and the all-cysteine mutant can be rationalized by a diminished electronic coupling between the donor and acceptor molecules in the [Fe₄S₄](Cys)₄ case. The fast and efficient electron transfer from the distal iron-sulfur cluster is realized by a fine-tuned protein environment which facilitates the flow of electrons. This study enables the design and control of electron transfer rates and pathways by protein engineering.

The structure and phenomena arising from charge transfer in cold field emission modus in a single closed carbon nanotube under cold field emission conditions were studied. By studying microphotographs in various microscopes - AFM, SEM, TEM it was found inhomogeneities in the form of two types of superlattices. The conductivity of the emitter section of a nanodiode circuit with an emitter made of an individual closed carbon nanotube (CNT) and arrays of closed CNTs was studied. High values of cold field emission were observed, as well as nonlinearity and anomalies of the current-voltage characteristic (I-V characteristics), which manifested themselves in the form of peaks at low and high current values. Peaks in the CVC have distinct areas of negative differential conductivity. It is argued, that the anomalously high currents in the I–V characteristics of cold field emission of electrons from a nanoemitter made of closed CNTs can be associated with a sharp increase in the density of electron states at energies near the van Hove singularity.

By introducing a suitable renormalization process the charge carrier and phonon dynamics of a double-stranded helical DNA molecule is expressed in terms of an effective Hamitonian describing a linear chain, where the renormalized transfer integrals explicitly depend on the relative orientations of the Watson-Crick base pairs, and the renormalized on-site energies are related to the electronic parameters of consecutive base pairs along the helix axis, as well as to the low-frequency phonons dispersion relation. The existence of synchronized collective oscillations enhancing the π-π orbital overlapping among different base pairs is disclosed from the study of the obtained analytical dynamical equations. The role of these phonon-correlated, long-range oscillation effects on the charge transfer properties of double standed DNA homopolymers is discussed in terms of the resulting band structure.

Iterated games, in which the same economic interaction is repeatedly played between the same agents, are an important framework for understanding the effectiveness of strategic choices over time. To date very little work has applied information theory to the information sets used by agents in order to decide what action to take next in such strategic situations. This article looks at the mutual information between previous game states and an agent's next action by introducing two new classes of games: ’invertible games’ and ‘cyclical games'. By explicitly expanding out the mutual information between past states and the next action we show under what circumstances these expressions can be simplified. These information measures are then applied to the Traveler's Dilemma game and the Prisoner's Dilemma game, the Prisoner's Dilemma being invertible, to illustrate their use. In the Prisoner's Dilemma a novel connection is made between the computational principles of logic gates and both the structure of games and the agents' decision strategies. This approach is applied to the cyclical game Matching Pennies to analyse the foundations of a behavioural ambiguity between two well studied strategies: ‘Tit-for-Tat' and ’Win-Stay, Lose-Switch'.

Keywords: olfactory rehabilitation; chronic pain; fMRI;

We generalize the point information gain (PIG) and derived quantities, i.e., point information gain entropy (PIE) and point information gain entropy density (PIED), for the case of the Rényi entropy and simulate the behavior of PIG for typical distributions. We also use these methods for the analysis of multidimensional datasets. We demonstrate the main properties of PIE/PIED spectra for the real data on the example of several images, and discuss further possible utilizations in other fields of data processing.

RNA sequencing techniques, like bulk RNA-seq and Single Cell (sc) RNA-seq, are critical tools for the biologist looking to analyze the genetic activity/transcriptome of a tissue or cell during an experimental procedure. Platforms like Illumina's next-generation sequencing (NGS) are used to produce the raw data for this experimental procedure. This raw FASTQ data must then be prepared via a complex series of data manipulations by bioinformaticians. This process currently takes place on an unwieldy textual user interface like a terminal/command line that requires the user to install and import multiple program packages, preventing the untrained biologist from initiating data analysis. Open-source platforms like Galaxy have produced a more user-friendly pipeline, yet the visual interface remains cluttered and highly technical, remaining uninviting for the natural scientist. To address this, SeqMate is a user-friendly tool that allows for one-click analytics by utilizing the power of a large language model (LLM) to automate both data preparation and analysis (differential expression, trajectory analysis, etc). Furthermore, by utilizing the power of generative AI, SeqMate is also capable of analyzing such findings and producing written reports of upregulated/downregulated/user-prompted genes with sources cited from known repositories like PubMed, PDB, and Uniprot.

Abstract: In order to explore new options for colon targeting, this study designs new polyelectrolyte complex (PEC) coacervate xerogels of chitosan (Ch) with Albizia procera (AP). It was determined by potentiometric titration tests that a 1:5 Ch/AP weight ratio was necessary for 1:1 charge stoichiometry. Rheological characterizations experiments were carried out for the pH independent coacervates and 1:5 coacervate was found to be having the highest G^/ value in the strain sweep experiment. In the frequency sweep experiments the coacervate (1:5) with the highest storage modulus (G^/) values was produced at pH 4.5.

This paper introduces a new adaptation of the enthalpic lattice Boltzmann method (LBM) tailored to simulate the intricate dynamics of conjugate heat transfer in non-homogeneous media, showcasing considerable potential for enhancing heat exchanger designs. This approach expands upon the conventional enthalpic LBM framework by seamlessly incorporating tailored source terms, enabling precise representation of local and transient variations in the medium’s thermophysical properties. The resulting LBM model comprehensively captures the energy conservation equation, with exceptions made only for flow compressibility and viscous dissipation, thereby significantly augmenting its versatility in analyzing heat exchanger configurations. Verification tests, comprising assessments of both transient and steady-state solutions, unequivocally validate the accuracy of the proposed model, further bolstering its reliability for analyzing heat exchange processes within intricate heat exchanger geometries. To demonstrate its efficacy, simulations of conjugate heat transfer processes involving fluid natural convection within structured cavities were performed, imposing both Dirichlet and Neumann boundary conditions to emulate scenarios encountered in practical heat exchanger operations. The findings yield compelling insights, particularly in the transient regime, revealing the beneficial impact of structured cavities on enhancing heat transfer processes. These insights inform potential design optimizations for heat exchangers. The results emphasize the potential of the modified enthalpic LBM approach to elucidate complex heat transfer phenomena in non-homogeneous media and structured geometries, offering valuable implications for heat exchanger engineering and optimization.

The integration of bacteriophages, a particular class of viruses that specifically infect bacteria and archaea, in biosensors for the monitoring of pathogens in foods and beverages is highly desirable. To this end, an increasing focus has been set on the exploration of covalent and physical methods for the immobilization of phages on solid surfaces. This work investigates the electrostatic assembly of tailed phages, specifically anti-Listeria monocytogenes P100 phages, on an ultrathin self-assembled monolayer (SAM) of 11-amino-1-undecanethiol (AUT). The cationic properties of AUT may allow for the electrostatic capture of P100 in a capsid-down fashion, thus, exposing the specific receptor-binding proteins on their tails to the corresponding pathogens in analytical samples. The physical properties of immobilized phages have been studied using AFM, SEM, and electrochemical techniques, providing insight into the orientation of the phages and revealing that the pH plays a remarkable role in the morphology and charge transfer behavior of the adsorbed films. Overall, this research portrays SAMs of amino-akylthiols as a valid platform for the oriented immobilization of bacteriophages on surfaces for electroanalytical purposes.

The aim of the study was to examine the long-term prognostic value of changes in cardio-ankle vascular index (CAVI) within a year after coronary artery bypass grafting (CABG). Methods. Patients with coronary artery disease (n = 251) in whom cardio-ankle vascular index (CAVI) was assessed using the VaSera VS-1000 device before and in a year after CABG. Groups with improved CAVI (CAVI decreased from pathological value to normal or the index remained within normal values) or worsened CAVI (persistent pathological index value or an increase in CAVI to pathological values or an increase of 1 unit or more) were identified. The groups were compared for the following events at 10-year follow-up: death from all causes, myocardial infarction, stroke/transient ischemic attack, and the combined endpoint. Results. A year after CABG surgery, 45% of patients showed positive dynamics or stable normal CAVI values. Death from all causes was significantly more common in the group with CAVI progression - 32 (27.6%) than in the group with CAVI improvement - 14 (14.8%; p = 0.029). Patients with CAVI progression were more likely to have MACE (death, MI, stroke/TIA) in 49 (42.2%) cases, compared with patients with CAVI improvement - in 23 (24.5%; p=0.008). Negative dynamics of CAVI (p = 0.024), number of shunts (p = 0.006), and the presence of carotid stenosis more than 30% (p = 0.051) were independent predictors of death from all causes at 10-year follow-up after CABG. The presence of carotid stenosis more than 30% (p = 0.002) and the group with negative dynamics of CAVI after a year (p = 0.008) were independent predictors of the development of the combined endpoint during long-term follow-up. Conclusions. Patients with negative dynamics of CAVI one year after CABG were more likely to experience death from all causes and combined endpoint in the long-term follow-up than patients with normal CAVI. In further studies, it is advisable to evaluate the dynamics of CAVI and, based on this, identify the most effective interventions to improve the prognosis of patients after CABG.

Several problems of radiative transfer are yet unsolved because of the difficulties of the calculations involved in them, especially if the intervening shapes are geometrically complex. The main goal of our investigation in this domain is to convert the formulas that were previously derived, into a graphical interface based on the projected solid-angle principle. Such procedure is now feasible by virtue of several widely diffused programs for Algorithms Aided Design (AAD). Accuracy and reliability of the process is controlled by means of the analytical software DianaX developed at an earlier stage by the authors. With this new approach the often cumbersome procedure of lighting and thermal exchange calculations can be simplified and made available for the neophyte, with the undeniable advantage of reduced computer time.

With the rapid growth of large language models (LLMs), there is increasing demand for memory and computation in LLMs. Recent efforts on post-training pruning of LLMs aim to reduce the model size and computation requirements, yet the performance is still sub-optimal. In this paper, we present a plug-and-play solution for post-training pruning of LLMs. The proposed solution has two innovative components: 1) Relative Importance and Activations (RIA), a new pruning metric that jointly considers the weight and activations efficiently on LLMs; and 2) Channel Permutation, a new approach to maximally preserve important weights under N:M sparsity. The two proposed components can be readily combined to further enhance the N:M semi-structured pruning of LLMs. Our empirical experiments show that RIA alone can already surpass all existing post-training pruning methods on prevalent LLMs, e.g., LLaMA ranging from 7B to 65B. Furthermore, N:M semi-structured pruning with channel permutation can even outperform the original LLaMA2-70B on zero-shot tasks, together with practical speed-up on specific hardware. Our code is available at: https://github.com/biomedical-cybernetics/Relative-importance-and-activation-pruning

Hydroiodic acid (HI) treated - reduced graphene oxide (rGO) ink/conductive polymeric composites are considered as promising cold cathodes in terms of high geometrical aspect ratio and low field emission (FE) threshold devices. In this study, four simple, cost-effective, solution-processed approaches for rGO-based field effect emitters were developed, optimized and compared; rGO layers were coated on a) n+ doped Si substrate, b) n⁺-Si/P3HT:rGO, c) n⁺-Si/PCDTBT:rGO and d) n⁺-Si/PCDTBT:PC₇₁BM:rGO composites, respectively. The fabricated emitters were optimized by tailoring the concentration ratios of their preparation and field emission characteristics. In a critical composite ratio, FE performance was remarkably improved compared to the pristine Si, as well as n⁺-Si/rGO field emitter. In this context, the impact of various materials, such as polymers, fullerene derivatives, as well as different solvents on rGO function reinforcement and consequently on FE performance upon rGO-based composites preparation was investigated. The field emitter consisted of n⁺-Si/PCDTBT:PC₇₁BM(80%):rGO(20%)/rGO displayed a field enhancement factor of ∼2850, with remarkable stability over 20h and low turn-on field in 0.6V/μm. High-efficiency graphene-based FE devices realization paves the way towards low-cost, large-scale electron sources development. Finally, the contribution of this hierarchical, composite film morphology was evaluated and discussed.

Subway construction is often in a complex natural and human-machine operating environment, and that complicated setting leads to subway construction more prone to safety accidents, which can cause substantial casualties and monetary losses. Thus, it is necessary to investigate the safety risks of subway construction. The existing literature on the identification and assessment of subway construction safety risks(SCSR) is susceptible to the influence of subjective factors. Moreover, although existing studies have explored the interrelationships between different risks, these studies usually analyze the interrelationships of single risks, lack the study of risk chain transfer relationships, and fail to find out the key path of risk transfer. Therefore, this paper innovatively combines text mining, association rules and complex networks to deep mine subway construction safety incident reports and explore risk transfer process. Firstly, it uses text mining technology to identify subway construction safety risk; Then, association rules are introduced to explore the causal relationships among safety risk; Finally, the key safety risk and important transfer paths of subway construction safety accidents (SCSA) are obtained based on the complex network model. Research results show that (a) improper safety management, unimplemented safety subject responsibilities, violation of operation rules, non-perfect safety responsibilities system and insufficient safety education and training are the key safety risk in SCSA; (b) two shorter key risk transfer paths in the subway construction safety network can be obtained: insufficient safety education and training→lower safety awareness→violation of operation rules→safety accidents; insufficient safety checks or hidden trouble investigations→violation of operation rules→safety accidents; (c) in the process of risk transfer, the risk can be controlled by controlling the key nodes or cutting off the transfer path. The results of the study provide new ideas and methods for SCSR identification and influence element mining, which help safety managers propose accurate subway construction safety risk control measures.

Background and Objectives: Knee osteoarthritis (KOA) is a degenerative disease that is targeting continuously people of different ages, but especially the elderly population, the number of which tends to increase continuously at the global level. Apart from age, excess weight can influence the evolution of the disease, with obesity being associated with a weak inflammation stage, and an imbalance between pro-inflammatory and anti-inflammatory cytokines. The present work aimed to identify specific biomarkers alike ACRP-30, IL-10, TNF-α, and IL-6 in synovial fluid and correlate them with patients’ clinical data. Materials and Methods: 24 subjects with KOA and over 50 years of age are involved in the present study. All patients performed a supine X-ray at the target knee. Ultrasound was performed using an ESAOTE MyLab Omega (ESAOTE Spa, Italy) ultrasound device and a 6-19 MHz linear array transducer. Results: The obtained results highlighted a significant correlation between age and adiponectin level (p=0.0451, r=-0.412). Also, the IL-10 values are lower in cases where the intensity of the pain is more pronounced (p=0.0405, r=-0.421). In addition, analyzing the data by gender, it was observed that in the case of males, stiffness is more related to age (p=0.0079, r=0.7993), compared to women (p=0.0203, r=0.6223). In the case of women, the progression of the disease tends to increase more intensively total WOMAC score’s values (p=0.00031, r=0.8342), compared with men (p=0.0289, r=7013). Regarding interleukins and BMI, significant correlations were observed only in the case of men. Conclusions: A significant correlation between age and adiponectin, and adiponectin and IL-6, suggests that advanced age may contribute to adiponectin reduction. Comparing men with women, it was observed that men's age is more related to rigidity, and IL-6 and IL-10 are directly correlated to BMI and women seem to be more sensitive to pain and stiffness.

Graves' disease (GD) is an autoimmune disorder in which Thyrotropin Receptor Antibodies (TRAb) continuously stimulate thyroid receptors, leading to excessive production of thyroid hormones. TRAb also affect the orbital tissues, causing Graves' ophthalmopathy. Treatment to control Graves' disease consists of antithyroid drugs, radioiodine and thyroidectomy, and through such treatments, TRAb is reduced and the disease is alleviated. The mechanism by which TRAb, which play a critical role in the pathogenesis of Graves’ disease, decreases after GD treatment is still unknown. This paper presents a theoretical mechanism of how GD patients maintain a vicious cycle and how GD treatment can reduce the TRAb levels and breaks the cycle using stress-based ideas.

Complexity science studies physical phenomena that cannot be explained by the mere analysis of the single units of a system but requires to account for their interactions. A feature of complexity in connected systems is the emergence of mesoscale patterns in a geometric space, such as groupings in bird flocks. These patterns are formed by groups of points that tend to separate from each other, creating mesoscale structures. When multidimensional data or complex networks are embedded in a geometric space, some mesoscale patterns can appear respectively as clusters or communities, and their geometric separability is a feature according to which the performance of an algorithm for network embedding can be evaluated. Here, we introduce a framework for the definition and measure of the geometric separability (linear and nonlinear) of mesoscale patterns by solving the travelling salesman problem (TSP), and we offer experimental evidence on embedding and visualization of multidimensional data or complex networks, which are generated artificially or are derived from real complex systems. For the first time in literature the TSP’s solution is used to define a criterion of nonlinear separability of points in a geometric space, hence redefining the separability problem in terms of the travelling salesman problem is an innovation which impacts both computer science and complexity theory.

Background: Pediatric mallet finger injuries lack specific treatment guidelines, necessitating an exploration of effective techniques. Our aim was to evaluate the outcomes of the extension block technique in adolescent mallet finger management. Methods: A comprehensive retrospective analysis was conducted involving 36 adolescents who underwent surgery between 2014 and 2022. Ishiguro’s extension block pinning technique was the primary method employed. Treatment outcomes were evaluated using Crawford's criteria, and complications were also assessed. Results: Bone union was achieved in all cases with a mean union time of 42 days (range: 30-92 days). According to Crawford's criteria, 27 cases (75%) were classified as excellent, 5 cases (14%) as good, and 4 cases (11%) as fair. Minimal complications were observed, including three cases (8%) of superficial infections, one case (3%) of dermal necrosis, and another rare instance (3%) of osteomyelitis. No cases of poor outcomes or significant long-term complications were noted. The average follow-up period was 8.6 months (range: 2-42 months). Conclusions: Ishiguro’s extension block technique is a promising surgical option for adolescent mallet fingers. Its adoption could enhance pediatric orthopedic care, offering a valuable child-friendly treatment alternative.

Four models of Ce(III) complexes with four ligands based on a 1,2,3-triazole derivatives were studied at four DFT levels. The effect of different ligands on the atomic charge value on the cerium ion was analyzed. Several relationships were clearly established between this atomic charge and that of the surrounding atoms, as well as with several geometric parameters of the ligand and with the molecular properties of the Ce(III) complex. The experimental IR and Raman spectra of the newly obtained Ce(III) complex with the (pyrrolidin-1-yl)-2H-1,2,3-triazole-4-carboxylate ligand were interpreted based on their comparison to the theoretical scaled ones using the scaling equations determined by two procedures and four DFT levels. Therefore, the structure predicted for the synthesized Ce(III) complex was clearly characterized and confirmed. The potential antioxidant action of Ce(III) complex was elucidated and compared with other Ln(III) complexes in order to find out the differences in their biological activity profiles.

It has been known since the 1930’s that all immunoglobulins carry a weak negative charge in physiological solvents. However, there has been no systematic exploration of this fundamental property. Accurate charge measurements have been made using membrane confined electrophoresis in two solvents (pH 5.0 and pH 7.4) on a panel of twelve mAb IgGs, as well as their F(ab’)₂ and Fc fragments. The following observations were made at pH 5.0: 1) the measured charge differs from the calculated charge by ~40 for the intact IgGs, and by ~20 for the Fcs; 2) the intact IgG charge depends on both Fv and Fc sequences, but does not equal the sum of the F(ab)’₂ and Fc charge; 3) the Fc charge is consistent within a class. In phosphate buffered saline, pH 7.4: 1) the intact IgG charges ranged from 0 to -13; 2) the F(ab’)₂ fragments are nearly neutral for IgG1s and IgG2s, and about -5 for some of the IgG4s; 3) all Fc fragments are weakly anionic, with IgG1 < IgG2 < IgG4; 4) the charge on the intact IgGs does not equal the sum of the F(ab’)₂ and Fc charge. In no case is the calculated charge, based on H⁺ binding, remotely close to the measured charge. The charge on IgGs in physiological solvent is sufficiently small to minimize its contribution to thermodynamic nonideality. Some of the mAbs carried a charge in physiological salt that was outside the range observed for serum-purified human poly IgG. To best match physiological properties, a therapeutic mAb should have a measured charge that falls within the range observed for serum-derived human IgGs.

A major problem of the technological production process of the metallurgical-grade silicon is formation of a large amount of microsilica. However, this material can be a significant source of raw materials for its own production. Hence, this study is aimed to solve the problem of micro-silica utilization, i.e. the technogenic dust-like waste generated during the melting of the technical-grade silicon. Within this study, the technology of the joint briquetting of microsilica was investigated with the carbonaceous reducing agents, i.e. by obtaining of mono-charge and by melting of the technical-grade silicon from the obtained mono-charge in the 200kVA ore-thermal furnace. Results of the study show that the briquettes in the melting process of the technical-grade silicon can be used in the range from 0 to 50%. The using of briquettes may significantly improve the technological parameters. When briquetted charge is applied only, the technological parameters will deteriorate significantly. The material balance of silicon melting showed the recovery rates of the main elements (Si, Al, Ca and Fe). It recorded a decrease in content of aluminum from 1.9-2.1% to 0.5-0.7%, calcium from 0.2-0.3% to 0.07-0.1% and titanium from 0.2-0.23% to 0.11-0.13% during the transition to briquetted monocharge. Phosphorus content increased from 0.004-0.005 to 0.011-0.013%.

As the demand of heating and air conditioning is increasing day by day due to getting a comfortable life, for that reason, the market for heat pump and air conditioning systems or both, is seeing a steady increase in low global warming potential. All researchers on heat pump and air conditioning systems are searching for environmentally friendly refrigerants. However, a significant number of contemporary refrigerants (HCFCs) have the potential to cause global warming and ozone depletion, such as those found in heat pump air conditioning systems. Using more environmentally friendly refrigerants, including hydrocarbon and blend refrigerant mixtures, is one way to lower GWP and Ozone depletion potential. This study examines the performance of several alternative refrigerants, including R32, R134A, R410A, R22, and R290. In order to examine the performance of R290, theoretical and simulation results are compared on various refrigerants while taking into account a number of performance parameters, including the refrigerant energy efficiency ratio, mass flow rate, coefficient of performance, cooling capacity, and compressor work for one ton of refrigeration (1TR) air conditioners. One player with a very low GWP is R290. However, the only drawback is that it can catch fire and is hazardous to use in split-type commercial heat pump air conditioning applications. The environmental and thermo physical properties of HC-290 are significantly better than those of other materials, according to theoretical and simulation studies utilizing Cool Pack software, hence it is a viable replacement.

Microwave technology has a potential application in the extraction of zinc from sulphide ores, knowledge of the dielectric properties of these ores plays a major role in the microwave design and simulation for any process. The dielectric properties of zinc sulfide concentrate for two different apparent densities—1.54 and 1.63 g/cm³—have been measured by using the resonance cavity perturbation technique at 915 and 2450 MHz during the roasting process for the temperature ranging from room temperature to 850 °C. The variations of dielectric constant, the dielectric loss factor, the dielectric loss tangent and the penetration depth with the temperature, frequency and apparent density have been investigated numerically. The results indicate that the dielectric constant increases as the temperature increases and temperature has a pivotal effect on the dielectric constant, while the dielectric loss factor has a complicated change and all of the temperature, frequency and apparent density have a significant impact to dielectric loss factor. Zinc sulfide concentrate is high loss material from 450 to 800 °C on the basis of theoretical analyses of dielectric loss tangent and penetration depth, its ability of absorbing microwave energy would be enhanced by increasing the apparent density as well. The experimental results also have proved that zinc sulfide concentrate is easy to be heated by microwave energy from 450 to 800 °C. In addition, the experimental date of dielectric constant and loss factor can be fitted perfectly by Boltzmann model and Gauss model, respectively.

Transient conjugate heat transfer measurements under varying temperature and velocity inlet boundary conditions at incompressible flow conditions were performed for flat plate and ribbed channel geometries. Therefrom local adiabatic wall temperatures and heat transfer coefficients are determined. Those data are analyzed using typical heat transfer correlations, e.g. Nu=CRe^mPr^n determining the local distribution of C and m. It is shown that they are closely linked. A relationship lnC=A−mB is observed with A and B as modeling parameters. They could be related to parameters in log law or power law representations for turbulent boundary layer flows. The parameter m is shown to have a close link to local pressure gradients and therewith near wall streamlines as well as friction factor distributions. A normalization of the C parameter allows to derive a Reynolds analogy factor and therefrom local wall shear stresses.

Abstract: Oxidative metabolism is crucial for leukemic stem cell (LSC) function and drug resistance in acute myeloid leukemia (AML). Mitochondrial metabolism also affects the immune system and therefore the antitumor response. Modulation of oxidative phosphorylation (OxPHOS) has emerged as a promising approach to improve therapy outcome for AML patients. However, the effect of mitochondrial inhibitors on the immune compartment in the context of AML is yet to be explored. Immune checkpoints such as the ecto-nucleotidase CD39 and programmed dead ligand 1 (PD-L1) have been reported to be expressed in AML and linked to chemoresistance and poor prognosis. In the present study, we first demonstrated that a novel selective electron transfer chain complex (ETC) I inhibitor, EVT-701, decreased OxPHOS metabolism of murine and human cytarabine (AraC)-resistant leukemic cell lines. Furthermore, we showed that, while AraC induced immune response regulation by increasing CD39 expression and by reinforcing interferon-γ/PD-L1 axis, EVT-701 reduced CD39 and PD-L1 expression in vitro in a panel of both murine and human AML cell lines, especially upon AraC treatment. Altogether, this work uncovers a non-canonical function of ETCI in controlling CD39 and PD-L1 immune checkpoints, thereby improving the anti-tumor response in AML.

Primary biliary cholangitis (PBC) is a cholestatic liver disease characterized by immune-mediated injury to small bile ducts. Although PBC is an autoimmune disease, the results of traditional immunosuppressive therapy are disappointing. Nearly 40% of PBC patients do not respond to the first-line drug UDCA. Without appropriate intervention, PBC patients would eventually progress to liver cirrhosis and even death. There is an urgent need to develop new therapies. The gut-liver axis emphasizes the interconnection between the gut and the liver, and there is increasing evidence that gut microbiota and bile acids play an important role in cholestatic diseases. Dysbiosis of gut microbiota, imbalance of bile acids, and immune-mediated bile duct injury constitute the triad of pathophysiology in PBC. Understanding the gut microbiota-bile acid network and corresponding immune functions in PBC provides a new perspective for its treatment strategies. Therefore, we summarize the latest advancements in research of gut microbiota and bile acids in PBC and, for the first time, explore the potential of immune factors related to them as novel immunotherapy targets. This article discusses potential therapeutic approaches focusing on regulating gut microbiota, maintaining bile acid homeostasis, their interactions, and related immune factors.

In order to solve the problems of waveform distortion and signal delay by many physical and electrical systems with linear low-pass transfer characteristics with multiple complex poles, a general digital-signal-processing (DSP)-based method of real-time recovery of the original source waveform from the distorted output waveform is proposed. From the convolution kernel representation of a multiple-pole low-pass transfer function with an arbitrary denominator polynomial with real valued coefficients, it is shown that the source waveform can be accurately recovered in real time using a particular moving average algorithm with real-valued DSP computations only, even though some or all of the poles are complex. The proposed digital signal recovery method is DC-accurate and unaffected by initial conditions, transient signals, and resonant amplitude enhancement. This method can be applied to most sensors and amplifiers operating close to their frequency response limits or around their resonance frequencies to accurately deconvolute the multiple-pole characteristics and to improve the overall performances of data acquisition systems and digital feedback control systems.

The spectra and global distributions of the X-ray emissions generated by the solar wind charge-exchange (SWCX) process in the terrestrial magnetosheath are investigated based on a global hybrid model and a global geocoronal hydrogen model. The solar wind O6+ ions which are the primary charge state for oxygen ions in solar wind are considered. The line emissivity of the charge-exchange born O5+ ions are calculated by Spectral Analysis System for Astrophysical and Lab oratory (SASAL). It is found that the emission lines from the O5+ ranges from 105.607 to 118.291 eV with the strong line at 107.047 eV. We then simulated the magnetosheath X-ray emission intensity distributions with a virtual camera at two positions of north pole and dusk at six stages during the passing of a perpendicular interplanetary shock combined with a tangential discontinuity structure through the Earth’s magnetosphere. During this process, the X-ray emission intensity increases with time, and the maximum value is 27.11 keV cm-2 s-1 sr-1 on the dayside, which is 4.5 times that before the solar wind structure reaches the Earth. A clear shock structure can be seen in the magnetosheath and moves earthward. The maximum emission intensity seen at dusk is always higher than that seen at north pole.

In this paper, we propose an assumption of which the relation between electron and phonon is discussed and investigated based on the experiment of γ photon and electric interconverting with each other. We calculated the charge of a particular standing wave topology with Maxwell’s equation rotated in accordance space with the SO3 transformation. The derived charge value is 1.6035 × 10E−19C around 0.0826% different from the experimental data, indicating the proposal is a promising theory to explain the origin of charge and spin of electron particle. Electron spin angular momentum comes from the orbital angular momentum of photon rotation (electromagnetic field rotation), and electron charge comes from photon, that is, the space-time rotation effect of electromagnetic wave.

The real systems are often complex, nonlinear, and noisy in various areas including mathematics, natural science, and social science. We present the symplectic entropy (SymEn) measure as well as an analysis method based on SymEn to estimate the nonlinearity of the complex system by analyzing the given time series. The SymEn estimation is a kind of entropy based on symplectic principal component analysis (SPCA) which represent organized but unpredictable behaviors of systems. The key to SPCA is to preserve the global submanifold geometrical properties of the systems through symplectic transform in the phase space, which is a kind of the measure-preserving transforms. The capability of preserving the global geometrical characteristics makes the SymEn a test statistic to detect the nonlinear characteristics in several typical chaotic time series and the stochastic characteristic in the Gaussian white noise. The results are in agreement with findings in the approximate entropy (ApEn), the sample entropy (SampEn) and the fuzzy entropy (FuzzyEn). Moreover, the SymEn method is also used to analyze the nonlinearities of the real signals (including the EEG signals for ASD and healthy subjects, and the sound and vibration signals for the mechanical systems). The results indicate that the SymEn estimation can be taken as a measure for describing the nonlinear characteristics in the data collected from the natural complex systems.

Background: Clinical trails have revealed that patients with depression generally accompanied with learning and memory impairment, which critically impact on individual’s health and development. Danggui-Shaoyao-San (DSS), a famous Chinese complex prescription, significantly overcame depression and relieved cognitive disorder based on previous research and publication. However, its effectsand potential mechanism against chronic restraint stress (CRS) remained unknown. Methods: CRS animal model was established and mice were divided to six groups while they were oral administrated with Danggui-Shaoyao-San at doses of 1.25, 2.5 and 5 g/kg for 14 days. Emotional and cognitive performances were detected by behavior tests, meanwhile neural plasticity and its molecular mechanism were examined by Dil staining, western blot and immunofluorescence. Results: DSS treatment dose-dependently improved locomotion ability in open filed test, overcame depression behavior in forced swimming test and elevated plus maze test, enhanced learning and memory ability in Morris water maze test. CRS decreased number of total spines and mushroom spines, while DSS treatment dose-dependently restored these by Dil staining. Expression of BDNF and GluR1 were significantly down-regulated in CRS group, which were significantly normalized by DSS. Conclusions: DSS treatment dose-dependently reversed CRS-induced cognitive impairments by inducing structural remodeling of neurons.

Almost two decades ago, Ernesto P. Borges and Bruce M. Boghosian embarked on the intricate task of composing a manuscript to honor the profound contributions of Constantino Tsallis to the realm of statistical physics, coupled with a concise exploration of q-Statistics. Fast forward to Constantino Tsallis’ illustrious 80th birthday celebration in 2023, where Deniz Eroglu and Ugur Tirnakli delved into Constantino’s collaborative network, injecting renewed vitality into the project. With hearts brimming with appreciation for Tsallis’ enduring inspiration, Eroglu, Boghosian, Borges and Tirnakli proudly present this meticulously crafted manuscript as a token of their gratitude.

A grand unification of electroweak and strong forces is presented based on a new idea called Unified Charged Vectors. Using this new concept, it is shown that: The electroweak and strong forces can be unified into a single electro-weak-strong force. The various charges and coupling constants of fermions (like the electroweak mixing angle) can be predicted, rather than presupposed. The Lagrangian symmetries can be predicted rather than presupposed. The standard model Lagrangian can be cast into a simple unified form.

The next-generation communication systems demand integration of sensing, communication, and power transfer (PT) capabilities, requiring high spectral efficiency, energy efficiency, and low cost, while also necessitating robustness in high-speed scenarios. Integrated sensing and communication systems (ISACS) exhibit the ability to simultaneously perform communication and sensing tasks using a single RF signal, while simultaneous wireless information and power transfer (SWIPT) systems can handle simultaneous information and energy transmission, and orthogonal time frequency space (OTFS) signals are adept at handling high Doppler scenarios. Combining the advantages of these three technologies, a novel cyclic prefix (CP) OTFS based integrated simultaneous wireless sensing, communication, and power transfer system (ISWSCPTS) framework is proposed in this work. Within the ISWSCPTS, the CP-OTFS matched filter (MF) based target detection and parameters estimation (MF-TDaPE) algorithm is proposed to endow the system with sensing capabilities. To enhance the system’s sensing capability, a waveform design algorithm based on CP-OTFS ambiguity function shaping (AFS) is proposed, which is solved by iteratively method. Furthermore, to maximize the system’s sensing performance under communication and PT Quality of Service (QoS) constraints, a semidefinite relaxation (SDR) beamforming design (SDR-BD) algorithm is proposed, solved using through SDR technique. Simulation results demonstrate that the ISWSCPTS exhibits stronger parameter estimation performance in high-speed scenarios compared to orthogonal frequency division multiplexing (OFDM), waveform designed by CP-OTFS AFS demonstrates superior interference resilience, and beamforming designed by SDR-BD strikes a balance in the overall performance of the ISWSCPTS.

An information-theoretic data mining method is employed to analyze categorical spatiotemporal Geographic Information System land use data. Reconstructability Analysis (RA) is a maximum-entropy-based data modeling methodology that works exclusively with discrete data such as those in the National Land Cover Database (NLCD). The NLCD are organized into a spatial (raster) grid, and are available in consistent format for every five years from 2001 to 2021. An NLCD tool reports how much change occurred for each category of land use; for the study area examined the most dynamic class is Evergreen Forest (EFO), so the presence or absence of EFO in 2021 was chosen as the dependent variable that our data modeling attempts to predict. RA predicts the outcome with approximately 80% accuracy using a sparse set of cells from a spacetime data cube consisting of neighboring lagged-time cells. When the predicting cells are all Shrubs &amp; Grasses there is a high probability for a 2021 state of EFO, while when the predicting cells are all EFO, there is a high probability that the 2021 state will be not-EFO. These findings are interpreted as detecting forest clear-cut cycles that show up in the data, and explain why this class is so dynamic. This study introduces a new approach to analyzing GIS categorical data and expands the range of applications that this entropy-based methodology can successfully model.

This article will continue ansatz gage matrix of Iyer Markoulakis Helmholtz Hamiltonian mechanics points’ fields gage to Pauli Dirac monopole particle fields ansatz gage general formalism at Planck level, by constructing a Pauli Dirac Planck circuit matrix field gradient of particle monopole flow loop. This circuit assembly gage (PDPcag) that maybe operating at the quantum level, demonstrates the power of point fields matrix theoretical quantum general formalism of Iyer Markoulakis Helmholtz Hamiltonian mechanics transformed to Coulomb gage metrics, to form eigenvector fields of magnetic monopoles as well as electron positron particle gage metrics fields. Eigenvector calculations performed based on Iyer Markoulakis quantum general formalism are substituted for gage values of typical eigenvectors of dipolar magnetically biased monopoles with their conjugate eigenvectors, as well as eigenvector fields that are of the electron and positron particles. Then they are compiled to form combinatorial eigenvector matrix bundle of the monopole particle circuit field constructs assembly. Evaluation of this monopole particle fields matrix provided eigenvector fields results like SUSY, having Hermitian quantum matrix with electron-positron annihilation alongside north and south monopoles collapsing to dipolar “stable” magnetism, representing electromagnetic gaging typical metrics fields. Applying experimental observations on magnetic poles with measuring magnetic forces John Hodge’s results were showing asymmetrical pole forces; author has mathematically constructed asymmetric\strings\gage\metrics to characterize electromagnetic gravity, putting together while integrating with stringmetrics gravity that author has been reporting in earlier published articles. Physical Analysis with generalization of mass-charge and charge-fields gage metrics to quantum relativity gage metrics fields are proposed based on author’s proof formalism paper providing derivational algorithmic steps, to determine gage parametric values within the equation of Coulomb gage. Vortex fields’ wavefunctions and the scalar potential characterized by a function and a coupling constant having quantum density matrix together define the gage metrics quantifiable observable measurement physics of electron-positron cross-diagonal fields; contrastingly, diagonal terms of PDPcag matrix characterizes electron-positron particle eigenvector fields, while Hilbert Higgs mass metrics characterizes eigen-matter. Author is already working with Christopher O’Neill about magic square symmetry configurations to quantitatively understand symmetry, structure, and the real space geometry that are expected to form out of vacuum quanta point fields’ quantitative quantum general formalism theory of Iyer Markoulakis. In addition, author is currently collaborating with Manuel Malaver’s astrophysical Einstein Minkowski modified space time metrics evaluations of the sense-time-space relativistic general metrics to have means to account for curving or shaping of spacetime topology of a five-dimensional sense-time-space. Manuel Malaver’s specialization with modified Einstein Maxwell equations for modeling galaxies and stars cosmological physics, utilizing Einstein-Maxwell-Tolman- Schwarzschild and Reissner-Nordström spacetime and black holes theoretical formalisms have author of this paper collaboratively model quantum astrophysics of dark energy Star’s theory with Einstein-Gauss-Bonnet gravity equations.

Our knowledge of electricity is based on two parallel lines of research: one focusing on ‘charge’, the other on ‘electron’. The distinction is blurred. The difficulty is that the fundamental nature of elementary charge is unknown. Here I present evidence that charge is a resting electron and electron is a moving charge – one physical entity two behavioural identities. That is, charge is nonrelativistic electron and electron is relativistic charge. Charges obey the laws of static electricity (electrostatics); electrons obey the laws of moving electricity (electrodynamics). Whether positive or negative, a charge is ejected from the atom at tremendous speed, losing the characteristics of ‘a charge’ and gaining those of ‘an electron’. I conclude that an atom, hence nature in general, contains an equal number of positive and negative electrons, recognised at rest as opposite charges. This solves the mystery of the electron-positron numerical asymmetry. It means that static positions and negatrons (charges) play equivalent roles as subatomic building blocks of matter, revealing a new atomic structure.

It is shown that if there exists in nature a pair of heavy elementary particles each with a charge equal to that of the proton and with mass energy , then their direct interaction gives rise to a spontaneous fractionation of the two charges into three pairs of elementary particles according to two equiprobable channels, one hadronic and one leptonic. The leptons and quarks produced are in agreement with the first generation of the Standard Model of matter. Fractionation suggests the relative abundances of the elementary particles, introducing some questions about the possibility of the real existence of a currently unknown particle and its role in the formation of an apparent matter-antimatter asymmetry in the universe.

We describe a family of paraxial and quasi-monochromatic optical wave packets with finite energy and smoothly shaped amplitude in space and time that develops a singularity in the intensity when spatiotemporally focused by imparting a converging spherical wave front and a negative temporal chirp. This singular behavior upon ideal focusing is manifested in actual focusing with finite apertures and in media with high-order dispersion in an "exploding" behavior featuring indefinitely increasing concentration of the energy when opening the aperture radius, thus exercising a continuous control on the focal intensity and spatial and temporal resolution. These wave packets offer a new way of focusing that outperforms what can be achieved with standard Gaussian wave packets in terms of focal intensity and resolution, and new possibilities in applications where energy concentration and its control are crucial.

Electricity load forecasting is a crucial undertaking within all the deregulated markets globally. In contemporary times, the transition from conventional electricity grids to Smart Grids constitutes an area where extensive research is conducted on a global scale. Among the research challenges, the investigation of Deep Transfer Learning (DTL) in the field of electricity load forecasting represents a fundamental effort that imparts generality to Artificial Intelligence applications, due to new capabilities, such as knowledge transfer and computational power reduction. In this paper a comprehensive study is conducted for day-ahead electricity load forecasting. For this purpose, three Sequence-to-Sequence (Seq2seq) Deep Learning (DL) models are used, namely the Multilayer Perceptron (MLP), the Convolutional Neural Network (CNN) and the Ensemble Learning Model (ELM), which is consisted of the weighted combination of the outputs of MLP and CNN models. Also, the study focuses on the development of different forecasting strategies based on DTL and emphasizing the way the datasets are trained and fine-tuning for higher forecasting accuracy. In order to implement the forecasting strategies using Deep Learning models, load datasets from three Greek islands, Rhodes, Lesvos, and Chios, are used. The main purpose is to apply DTL for day-ahead predictions (1-24 hours) for each month of the year for Chios dataset after training and fine-tuning the models using the datasets of the three islands in various combinations. After several trials, four DTL strategies are illustrated. In the first strategy (DTL Case 1), each of the three DL models is trained using only Lesvos dataset, while fine-tuning is performed on the dataset of Chios island, in order to create day-ahead predictions for Chios load. In the second strategy (DTL Case 2), data from both Lesvos and Rhodes concurrently are used for the DL model training period, and fine-tuning is performed on the data from Chios. The third DTL strategy (DTL Case 3) contains the training of the DL models using Lesvos dataset, and the testing period performed directly on the Chios dataset without fine-tuning. The fourth strategy is a Multi-task Deep Learning (MTDL) approach, which has been extensively studied in recent years. In MTDL, the three DL models are trained simultaneously on all three datasets and the final predictions are made on the unknown part of the dataset of Chios. In this paper, we explore the performance of DTL and compare the results with those produced with MTDL. The results demonstrated that DTL can be applied with high efficiency for day-ahead load forecasting. Specifically, the two cases with fine-tuning (DTL Case 1 and 2) outperformed MTDL in terms of load prediction accuracy. Regarding the DL models, all three exhibit very high prediction accuracy, especially in the two cases with fine-tuning. The ELM excels compared to the single models. More specifically, for conducting day-ahead predictions, it has been concluded that the MLP model presents best monthly forecasts with a MAPE of 6.24% and 6.01% for the first two cases, the CNN model presents best monthly forecasts with a MAPE of 5.57% and 5.60% respectively and the ELM model achieves best monthly forecasts with a MAPE of 5.29% and 5.31%, respectively, indicating the very high accuracy it can achieve.

In addition to their importance in statistical thermodynamics, probabilistic entropy measurements are crucial for understanding and analyzing complex systems, with diverse applications in time series and one-dimensional profiles. However, extending these methods to two- and three-dimensional data still requires further development. In this study, we present a new method to classify spatiotemporal processes based on entropy measurements. To test and validate the method, we selected four classes of similar processes related to the evolution of random patterns: dynamic colored noises ((i)~white and (ii)~red); (iii)~weak turbulence from reaction-diffusion; (iv)~hydrodynamic fully developed turbulence, and (v)~plasma turbulence from MHD. Considering seven possible ways to measure entropy from a matrix, we present the method as a parameter space composed of the two best separating measures of the five selected classes. The results highlight better combined performance of Shannon Permutation Entropy ($S^p_H$) and a new approach based on Tsallis Spectral Permutation Entropy ($S^s_q$). Notably, our observations reveal the segregation of reaction terms in this $S^p_H \times S^s_q$ space, a result that identifies specific sectors for each class of dynamic process, and can be used to train machine learning models for automatic classification of complex spatiotemporal patterns.

Chemical vapor deposition (CVD) is a vital process for depositing thin films of various materials with precise control over thickness, composition, and properties. Understanding the heat and mass transfer mechanisms during CVD is essential for optimizing process parameters and ensuring high-quality film deposition. This review provides an overview of recent advancements in the heat and mass transfer modeling for chemical vapor deposition processes. It explores innovative modeling techniques, recent research findings, emerging applications, and challenges in the field. Additionally, it discusses future directions and potential areas for further advancement in CVD modeling.

Apple replant disease (ARD) is a complex issue caused by various fungal, oomycetes pathogens and parasites which can differ in their abundance between sites within a geographic region. Knowledge of the specific etiology of ARD at a particular site is required in order to develop commercially viable soil management strategies to combat specific/individual components of the disease. In this study we analyzed the soil from six ARD sites for the presence of fungal, bacterial and oomycetes communities and plant parasitic nematodes. Five fungal, and 17 bacterial classes were differentially represented in the microbiomes from different locations. Mortierellomycetes was the most abundant fungal taxa represented followed by Sordariomycetes. Mortierella exigua a fungal endophyte was the most abundant fungal amplicon sequence variant (ASV) in the core microbiome. Proteobacteria was the most prevalent phylum identified in orchard soils. Several potential phytopathogenic fungi involved in ARD and endophytes including Fusarium oxysporum, F. solani, Nectria ramulariae, Ilyonectria robusta and Nectriaceae were identfied in the orchard soil. Pythium attrantheridium (Globisporangium attrantheridium), P. monospermum and P. ultimum (Globisporangium ultimum) were the most abundant oomycete taxa identified in these soil samples. Six different groups of plant-parasitic nematodes were found across the six orchards soil. Root-lesion nematodes, Pratylenchus spp., which are commonly associated with ARD, were identified in all orchards soil at population densities ranging from 12 to 33/100 cm3 soil. This research contributes valuable understanding of the ARD complex as the apple industry needs alternative approaches to combat the disease.

Currently there are fewer depth models applied to pepper picking detection, while the existing generalized neural networks have problems such as large model parameters, long training time, and low model accuracy.In order to solve the above problems, this paper proposes a Yolo-chili target detection algorithm for chili pepper detection. First, the classical target detection algorithm yolov5 is used as a benchmark model, and an adaptive spatial feature pyramid structure combining the attention mechanism and the idea of multi-scale prediction is introduced to improve the model's detection effect on occluded peppers and small target peppers. Secondly, a three-channel attention mechanism module is introduced to improve the algorithm's long-distance recognition ability and reduce the interference of redundant testers. Finally, the quantized pruning method is used to reduce the model parameters and realize the lightweight processing of the model. Applying the method to the homemade chili pepper dataset, the AP value of chili pepper reaches 93.11%; the accuracy rate is 93.51% and the recall rate is 92.55%.The experimental results show that yolo-chili is able to achieve accurate and real-time pepper detection under complex orchards.

As a result of decades of austerity, the economies of the Eurozone are experiencing under-investment and crumbling infrastructures in an era of extremely severe emergencies as the cli-mate change and aggressive global competition. The private savings glut of the rich and the strict fiscal rules suggest a crowding-in approach in partnership with big finance in a new age of in-fra-structures. If governments mint public infrastructures into investable assets, they can gen-erate steady returns for investors and asset managers who look after pensions and insurances schemes and unlock trillions now devoted to welfare state. For the big finance to accept to be partner, however, governments must take the task of de-risking returns. Innovation technology and macro-financial literacy can synergically act to enhance this new de-risking role to address in a financially sustainable way the intense phenomena of Natural Hazards (NHs) that can severely compromise economic growth. This is precisely the aim of our Public-Private Partnership (PPP) among the Department of Economics, Management and Institutions of the University of Naples and a Knowledge Transfert Management, the Department of Engineering and the Aca-demic Spin-Off “STRAIN”, of the University of Campania. Our PPP has been contributing to the eval-uation and mitigation of NHs such as floods, landslides and earthquakes since 2012. The ex-tremely advanced results of this multi-stakeholder partnership ensure a better knowledge of NHs which, as evidenced in the paper, is the fundamental input for the desired Public-Private In-surance Programmes (PIPPs).

Complex atypical endometrial hyperplasia (CAH) carries a high probability of cancer. Intraoperative evaluation of endometrial cancer in cases of CAH has not been reliable. The safety and sensitivity of sentinel lymph node (SLN) sampling has been validated. We aim to evaluate the efficacy and safety of SLN sampling in CAH managed by the da Vinci robotic platform. A total of 113 patients with a preoperative diagnosis of CAH were included in this retrospective cohort study. All underwent a robot assisted total laparoscopic hysterectomy and bilateral salpingo-oophorectomy with 69 patients undergoing SLN sampling. Statistical analysis calculated the probability of cancer, SLN map rate, and surgical complications. Predictors of cancer were evaluated. Forty-seven percent of the entire cohort were diagnosed with endometrial cancer. Median age was 63 years in the SLN cohort (N = 69) and 61 in the No SLN cohort (N = 44) (p= 0.363).  Median BMI was 34 Kg/m2 in the SLN cohort and 40 in the No SLN cohort (p= 0.004). Bilateral SLN map was 92.8% and unilateral SLN map rate was 7.2%. There were no grade 3-4 complications in the SLN cohort, and 4 grade 3-4 complications in the No SLN group (p=0.021). A preoperative diagnosis of CAH bordering on or cannot rule out cancer was the only predictor of cancer.

The most important plastic resins used for wire coating are Polyvinyl Chloride (PVC), Nylon, Polysulfone and Low-high density polyethylene (LDPE / HDPE). In this article,the coating process is performed using elastic-viscous fluid as a coating material for wire coating in a pressure type coating die. The elastic-viscous fluid is electrically conducted in the presence of an applied magnetic field. The governing non-linear equations are modeled and then solved analytically by utilizing an Adomian decomposition method (ADM). The convergence of the series solution is established. The results are also verified by Optimal Homotopy Asymptotic Method (OHAM). The effect of different emerging parameters such as non-Newtonian parameters α and β, magnetic parameter M and the Brinkman number Br on solutions (velocity and temperature profiles) are discussed through several graphs. Additionally, the current result also compares with the published work already available in the literature.

The objective of this work is to analyze the improvement of heat transfer in trapezoidal fin heat exchangers using numerical simulation. Heat exchangers are essential components frequently used in contemporary electronic gadgets to effectively disperse heat produced by high-power components. Nevertheless, as electronic devices continue to advance and become more powerful, the heat transfer capacity of heat exchangers is facing more requirements, therefore requiring enhanced heat dissipation efficiency. Afterwards, various fin angles were adjusted, and the heat transmission efficiency was compared. Modifying the angles can significantly improve the effi-ciency of convective heat dissipation. Moreover, it has been found that employing an optimized trapezoidal fin structure can further enhance the efficiency of heat transfer. The results indicate that trapezoidal fin heat exchangers provide superior heat dissipation efficiency compared to conventional heat exchangers. This is important for enhancing the thermal dissipation capabili-ties of electrical gadgets, hence guaranteeing steady functionality and extending their longevity. Subsequent study might explore supplementary methods to boost heat transmission in different heat exchanger configurations in order to meet the growing requirements for heat dissipation.

This paper describes the first results of an Image Recognition Based Location (IRBL) for mobile application focusing on the procedure to generate a Database of range images (RGB-D). In an indoor environment, to estimate the camera position and orientation, a prior spatial knowledge of the surrounding is needed. In order to achieve this objective a complete 3D survey of two different environment (Bangbae metro station of Seoul and E.T.R.I. building in Daejeon – Republic of Korea) was performed using LiDAR (Light Detection And Ranging) instrument and the obtained scans were processed in order to obtain a spatial model of the environments. From this, two databases of reference images were generated using a specific software realized by the Geomatics group of Politecnico di Torino (ScanToRGBDImage). This tool allow to generate synthetically different RGB-D images) centered in the each scan position in the environment. Later, the external parameters (X, Y, Z, ω, φ, κ) and the range information extracted from the DB images retrieved, are used as reference information for pose estimation of a set of acquired mobile pictures in the IRBL procedure. In this paper the survey operations, the approach for generating the RGB-D images and the IRB strategy are reported. Finally the analysis of the results and the validation test are described.

Huperzine A is an herbal reversible inhibitor of Acetylcholinesterase (AChE). A molecular docking analysis on Huperzine A molecule has been carried out to understand its structure, conformational flexibility, intermolecular interaction and the binding affinity in the active site of AChE enzyme. Further, the charge density distribution of huperzine A molecule (lifted from the active site of AChE) was determined from the high level quantum chemical calculations coupled with charge density analysis. The binding affinity of Huperzine A towards AChE was calculated from the molecular docking; the lowest docked energy is -8.46 kcal/mol. In the active site, huperzine A molecule interacts with acyl binding pocket-Phe330 of AChE, that is, the bicyclo ring group of huperzine A forms an intermolecular interaction with the oxygen atom of main chain of the amino acid residue Phe330 at the distances 3.02 and 3.25 Å respectively. On the other hand, a gas phase study on huperzine A molecule also performed using HF and DFT (B3LYP) methods with the basis set 6-311G**. The molecular structure, conformation, and the charge density distribution of huperzine A molecule in the gas phase have determined using quantum chemical calculations and the charge density analysis. The comparative studies between the gas phase and the active site forms of huperzine A molecule, explicitly reveals the degree of conformational modification and the charge density redistribution of huperzine A when present in the active site. The dipole moment of the molecule in the active site is 6.85 D, which is slightly higher than its gas phase value (5.91 D). The electrostatic potential (ESP) surface of active site molecule clearly shows the strong electronegative and positive ESP regions of the molecule, which are the expected strong reactive locations of the molecule.

The article presents a technology for producing nitrogen-containing nanoporous biocarbon with high degree of elemental composition homogeneity, confirmed by electron spectroscopy. The research explores the relationship among the electronic characteristics of supramolecular nanoporous carbon, its porous structure, and the capacitance at its interface with the electrolyte. The obtained biocarbon is characterized by micro and mesopores, as evidenced by the results of studies by the method of nitrogen adsorption/desorption. The specific area of the available porous surface was found to be 1247 m2/g. Comparing this result with the data of small-angle X-ray scattering, we conclude that micropores are open while mesopores remain closed. This material was found to have a saturation magnetization σs of 1.4 A∙m2∙kg-1 and a coercive force Hc of 10 kA/m. The impedance studies of electrochemical cells of supercapacitors made of the experimental biocarbon were carried out. Equivalent electrical circuits of the studied electrochemical processes were constructed and characterized.

A careful examination of the fundamentals of electromagnetic theory shows that due to the underlying mathematical assumptions required for Stokes' Theorem, global charge conservation cannot be guaranteed in topologically non-trivial spacetimes. However, in order to break the charge conservation mechanism we must also allow the electromagnetic excitation fields D, H to possess a gauge freedom, just as the electromagnetic scalar and vector potentials $\varphi$ and A do. This has implications for the treatment of electromagnetism in spacetimes where black holes both form and then evaporate, as well as extending the possibilities for treating vacuum polarisation. Using this gauge freedom of D, H we also propose an alternative to the accepted notion that a charge passing through a wormhole necessarily leads to an additional (effective) charge on the wormhole's mouth.

A space charge density wireless measurement system based on the idea of distributed measurement is proposed for collecting and monitoring the space charge density in an ultra-high-voltage direct-current (UHVDC) environment. The proposed system architecture is composed of a number of wireless nodes connected with space charge density sensors and a base station. The space charge density sensor based on atmospheric ion counter method is elaborated and developed, and the ARM microprocessor and Zigbee radio frequency module are applied. The wireless network communication quality and the relationship between energy consumption and transmission distance in the complicated electromagnetic environment is tested. Based on the experimental results, the proposed measurement system demonstrates that it can adapt to the complex electromagnetic environment under the UHVDC transmission lines and can accurately measure the space charge density.

This article discusses the application of thermal quality control methods for plant tissues. The purpose of the work is to determine the optimal parameters of thermal impact on a spherical-shaped plant control object, ensuring the detection of surface and subsurface defects. The authors of the work proposed mathematical models of the temperature field for a spherical body with defects and a flat sample when exposed to the thermal influence of a pulsed source. As a result of the use of mathematical models, the thermophysical characteristics of plant tissues of varying degrees of disease damage were obtained, which made it possible to simulate the temperature field of the control object and obtain an image of classified tissues. The developed recommendations made it possible to select the optimal parameters of the thermal effect on the test object.

Due to environmental concerns, natural refrigerants and their use in refrigeration and air conditioning systems are receiving more attention from manufacturers, end users and the scientific community. The study of heat transfer and pressure drop is essential for accurate design and more energy efficient cycles using natural refrigerants. The aim of this work is to provide an overview of the latest outcomes related to heat transfer and pressure drop correlations for ammonia, propane, isobutane and propylene and to investigate the current state of the art in terms of operating conditions. Available data on the existing correlations of heat transfer coefficient and pressure drop for natural refrigerants have been collected through a systematic search. Whenever possible, validity intervals are given for each correlation and the error is quantified. It is the intention of the authors that this could be a valuable support for researchers and an aid to design, with particular reference to heat pumps. A procedure based on the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement was adopted, and the Scopus database was used to query the relevant literature. A total of 135 publications qualified for inclusion in the survey; 34 articles report experimental investigations for not usual geometric conditions. Of the 101 selected papers related to usual geometric conditions, N = 50 deal only with HTC, N = 16 deal only with pressure drop and the remainder (N = 35) analyse both HTC and pressure drop. Among the 85 HTC papers (N = 53) deal with the evaporating condition, N = 30 with condensation and only N = 2 with the heat transfer correlations under both conditions. Most of the 101 articles concern propane and isobutane. The high temperatures are less widely investigated.

The backwater lowland habitats of large rivers, like the Mississippi River in North America, present complex often inaccessible environments for traditional capture-based fish biodiversity sampling. Our knowledge of the assemblages of fishes that occupy such habitats are often incomplete, and this can compromise management efforts. We employed environmental DNA (eDNA) metabarcoding methods to sample a Mississippi River bottom wetland system to assess ichthyofaunal diversity, and assemblage structure across habitat types, and we compared our results to capture-based survey records for the same habitats. We collected water samples in spring and fall of 2022 from slough, ditch, shallow lake and bayou habitats that varied in depth, vegetation, seasonal variability, and connectivity to the Mississippi River channel. We detected a diverse array of fish species that included 51 taxa. Nearly all species previously documented in the habitats were detected by eDNA metabarcoding, and we increased the number of documented species by more than a third. Most of the species were ubiquitous across the range of habitats, but there was also substantial assemblage structure, with some species exhibiting clear habitat specificity. Fall sampling was limited to the deeper bayou habitats where seasonal variation between spring and fall was minimal. eDNA metabarcode sampling was demonstrated to be effective at detecting invasive species as well as uncommon species, which included several species of conservation concern.

Background: Atrial fibrillation (AF) is increasingly recognized in pediatric patients, presenting unique challenges in management due to its association with various underlying heart conditions. This study aimed to evaluate the prevalence, management strategies, and outcomes of AF in this population. Methods: A retrospective analysis was conducted at a tertiary pediatric cardiology center, including patients aged ≤18 years diagnosed with AF between January 2015 and December 2023. The study focused on demographic details, clinical presentations, treatments, and outcomes. Descriptive statistics were employed to assess treatment efficacy, recurrence rates, and complications. Results: The study included 36 pediatric patients (median age: 15 years, IQR: 13-17; 58% male). Of these, 52.8% had acquired heart disease, 16.7% had congenital heart anomalies, and 16.7% presented with lone AF. The initial management strategies involved electrical cardioversion in 53.3% of patients and pharmacological conversion with amiodarone in 46.7%. Rhythm control therapy was administered to over 80% of the cohort, and 63.9% were placed on oral anticoagulation, predominantly for rheumatic and congenital heart diseases. The overall success rate of rhythm control was 96.2%, with an AF recurrence rate of 3.8%. Ischemic stroke was the most common complication, occurring in three patients, all with underlying rheumatic heart disease. Conclusion: AF in pediatric patients is predominantly associated with rheumatic and congenital heart diseases, though a significant proportion of patients present with lone AF. Despite effective rhythm control in most cases, neurological complications, particularly ischemic strokes in patients with underlying heart disease, remain a critical concern. These findings underscore the need for more comprehensive studies to better understand the etiology, risk factors, and optimal management strategies for pediatric AF.

This paper presents an innovative multi-agent, data-driven reinforcement learning (RL) approach to develop and utilize the thermal equivalent network model that represents the motor's thermal dynamics. A multi-agent reinforcement learning is designed and trained to adjust the model parameters using data from several motor driving cycles. To ensure the incoming driving cycle matches the historical data before employing the pre-trained RL agents, offline statistical analysis and clustering techniques are developed and used. Numerical simulations highlighted the RL agent's ability to develop strategies that effectively address the variability of driving cycles. The proposed RL framework showed its capability to accurately reflect the motor's thermal behavior under various driving conditions.

The oscillating heat pipe (OHP) is a new member in the family of heat pipes, and it has great potential applications in energy conservation. However, the fluid flow and heat transfer in the OHP as well as the fundamental effects of inner diameter on them have not been fully understood, which are essential to the design and optimization of the OHP in real applications. Therefore, by combining the high-speed visualization method and infrared thermal imaging technique, the fluid flow and thermal performance in the OHPs with inner diameters of 1, 2 and 3 mm are presented and analyzed. The results indicate that three fluid flow motions, including small oscillation, bulk oscillation and circulation, coexist or, respectively, exist alone with the increasing heating load under different inner diameters, with three flow patterns occurring in the OHPs, viz. bubbly flow, slug flow and annular flow. These fluid flow motions are closely correlated with the heat and mass transfer performance in the OHPs, which can be reflected by the characteristics of infrared thermal images of condensers. The decrease in the inner diameter increases the frictional flow resistance and capillary instability while restricting the nucleate boiling in OHPs, which leads to a smaller proportion of bubbly flow, a larger proportion of short slug flow, a poorer thermal performance, and easier dry-out of working fluid. In addition, when compared with the 2 mm OHP, the increasing role of gravity induces the thermosyphon effect and weakens the 'bubble pumping' action, which results in a little smaller and bigger thermal resistances of 3 mm OHP under small and bulk oscillation of working fluid, respectively.

The main purpose of this study is the determination of the optimal data acquisition and processing parameters of electric and seismic tomographic techniques, for the investigation of complex geological environments. Two different study areas, in central-east Peloponnese and SW Attica were selected, where a detailed geological mapping and surface geophysical survey were carried out. The applied geophysical survey included the application of electrical resistivity tomography (ERT) and seismic refraction tomography (SRT). The geoelectrical measurements were acquired with different arrays and electrode configurations. Moreover, various types of seismic sources were used at seventeen shot locations along the seismic arrays. For the processing of geoelectrical data, clustered datasets were created increasing the depth of investigation and the discriminatory capability. The seismic data processing included: (a) creation of synthetic models and seismic records to determine the effectiveness and capabilities of the technique, (b) spectral analysis of the seismic records to determine the optimal seismic source type and (c) inversion of the field data to create representative subsurface velocity models. The results of the two techniques successfully delineated the complex subsurface structure that characterizes these two geological environments. The application of the ERT combined with the SRT, are the two dominant, high-resolution techniques for the elucidation of complex subsurface structures.

Surface initiated atom transfer radical polymerization (SI-ATRP) is one of the most versatile technique to modify the surface properties of material. Recent developed metal free SI-ATRP makes such technique more widely applicable. Herein photo-induced metal-free SI-ATRP of methacrylates, such as methyl methacrylate, N-isopropanyl acrylamide, and N,N- dimethylaminoethyl methacrylate, on the surface of SBA-15 was reported to fabricate organic-inorganic hybrid materials. SBA-15 based polymeric composite with adjustable graft ratio was obtained. The structure evolution during the SI-ATRP modification of SBA-15 was monitored and verified by FT-IR, XPS, TGA, BET, and TEM. The obtained polymeric composite showed enhanced adsorption ability for the model compound toluene in aqueous. This procedure provides a low cost, ready availability, and facile modification way to synthesize the polymeric composites without the contamination of metal.

Some peptide-based drugs including oxytocin, vasopressin, ziconotide, pramlintide, nesiritide, and octreotide, contain one intramolecular disulfide bonds. A novel and reusable monodispersed silica nanosphere-supported Pt(IV) complex (SiO2@TPEA@Pt(IV)) was synthesized via a four-step procedure and used for formation of intramolecular disulfide bonds in peptides. TEM and chemical mapping results for the Pt(II) intermediates and for SiO2@TPEA@Pt(IV) show that the silica nanospheres possess a monodisperse spherical structure and contain uniformly distributed Si, O, C, N, Cl, and Pt. The valence state of Pt on the silica nanospheres was characterized by XPS. The Pt(IV) loaded on SiO2@TPEA@Pt(IV) was 0.15 mmol/g, as determined by UV-vis spectrometry. Formation of intramolecular disulfides in six dithiol-containing peptides of variable lengths by use of SiO2@TPEA@Pt(IV) was investigated and the relative oxidation yields were determined by HPLC. In addition, peptide 1 (Ac-CPFC-NH2) was utilized to study the reusability of SiO2@TPEA@Pt(IV). No significant decrease in the relative oxidation yield was observed after ten reaction cycles. Moreover, the structure of SiO2@TPEA@Pt(IV) after being used ten cycles was determined to be similar to its initial one, demonstrating the cycling stability of the complex.

The conventional well placement was done manually by using a numerical reservoir simulator, and it required a lengthy trial-and-error process. It required great experience and expertise to manipulate the variables and uncertainties that lie on the reservoir to determine the best placement of the well. In addition, the traditional gradient-based methods such as Line-search and Trust-region were not viable in terms of maximum results obtained. Gradient-based methods were too dependent on the surface gradient of the solution and may only converge to local optima instead of global optima. Complex reservoirs have rough surfaces with high uncertainties, which hinders the traditional gradient-based method from converging to global optima. Thus, genetic algorithms were utilized to automate the manual trial-and-error process and to overcome the limitations of the traditional gradient-based method. The objectives of this study were to analyse the effect of different initial well placement distributions, the number of random solution sizes, and the crossover rate on cumulative oil production. A synthetic reservoir model built using CMG Builder was used as the testing platform for the optimization problems. Well-placement parameterization and optimization set-up were carried out using the CMG CMOST optimization tool. The integration of CMG IMEX and CMOST optimized cumulative oil production based on the objectives of the study. The results obtained showed that the higher number of random solutions used resulted in higher cumulative oil production, with more generations needed to reach the optimum solution. It can be concluded that the larger number of random solutions used increased the probability of reaching the optimum solution, but will take more generations.

Rising organic charge in northern freshwaters is attributed to increasing levels of Dissolved Natural Organic Matter (DNOM) and shifts in water chemistry. Organic charge concentration may be determined by charge balance calculations (Org.-) or modelled (OAN-) using the conceptual Oliver and Hruška models based on the density of weak acid functional sites (SD) present in DNOM. The charge density (CD) is governed by the SD and protonation and complexation reactions on the functional groups. The models use this SD as a key parameter in empirically fitting the model to Org.-. Utilizing extensive datasets of water chemistry, this study shows that spatial and temporal differences in SD and CD are influenced by variations in the humic-to-fulvic ratio of DNOM and the organic aluminum (Al) complexation, as well as the mole fraction of CD to SD governed by the acidity. Site median SD obtained for 44 long-term monitored acid-sensitive lakes was 11.1 and 13.9 µEq/mg C for the Oliver and the Hruška model, respectively. During the 34 years of monitoring the CD increased by 70%, likely due to rising pH and declining Al complexation with DNOM. Present-day median SD values for the Oliver and Hruška models in 16 low-order streams are 13.8 and 15.8 µEq/mg C, respectively, and 10.8 and 12.5 µEq/mg C, respectively, in 10 high order rivers.

Hepatocellular carcinoma (HCC) development is associated with altered modifications in DNA methylation, changing transcriptional regulation. Emerging evidence indicates that DNA methyltransferase 1 (DNMT1) plays a key role in carcinogenesis process. This study aimed to investigate how pirfenidone (PFD) modifies this pathway and the effect generated by the association between c-Myc expression and DNMT1 activation. Rats F344 were used for HCC development using 50 mg/kg of diethylnitrosamine (DEN) and 25 mg/kg of 2-aminofluorene (2-AAF). HCC/PFD group received simultaneous doses of 300 mg/kg of PFD. All treatments lasted 12 weeks. On the other hand, HepG2 cells were used to evaluate the effects of PFD in restoring DNA methylation in the presence of the inhibitor 5-Aza. Histopathological, biochemical, immunohistochemical, and western blot analysis were carried out and our findings showed that PFD treatment reduced the amount and size of tumors along with decreased Glipican-3, β-catenin, and c-Myc expression in nuclear fractions. Also, this treatment improved lipid metabolism by modulating PPARγ and SREBP1 signaling. Interestingly, PFD augmented DNMT1 and DNMT3a protein expression, which restores global methylation, both in our in vivo and in vitro models. In conclusion, our results suggest that PFD could slow down HCC development by controlling DNA methylation.

Charge-dipole interactions are very common interactions among atoms and molecules, especially materials that can emit light or contain free charges. The second-order charge-dipole interactions, proportional to $\frac{1}{R^4}$, are stronger than the second-order dipole-dipole interactions or van der Waals interactions, proportional to $\frac{1}{R^6}$, at longer distances. In reality, there is more than one atom or charge; therefore, we focus on few-body charge-dipole interactions, such as charge-dipole-dipole interactions. Laser cooling and trapping allow us to study such interactions with much higher precision. In this article, charge-dipole interactions will be investigated in ultracold gases. To increase the interaction strength, we excite the ultracold atoms to highly excited states, Rydberg states. Here, we treat one Rydberg atom as a dipole, the excited electron and the ion core are the two poles of an electric dipole. Specifically, we study charge-atom interactions in ultracold Rydberg gases.

The gravitational charge should be the energy instead of mass. This modification will lead to some different results and the experiments to test are also presented. Especially, we propose a scheme to achieve the negative energy and gravitational repulsive force in the lab.

. Protein cysteine S-glycosylation is a relatively rare and less well characterized post-translational modification (PTM). Creating reliable model proteins that carry this modification is challenging. The lack of available model or natural S-glycosylated proteins significantly hampers the development of MS-based methodologies for detecting protein cysteine S-glycosylation in real-world proteomic studies. There is also limited MS-sequencing data describing easier to create synthetic S-glycopeptides. Here, we present the results of an in-depth manual analysis of automatically annotated CID/HCD spectra for model S-glucopeptides. The CID spectra show a long series of y/b-fragment ions with retained S-glucosylation, regardless of the dominant m/z signals corresponding to neutral loss of 1,2-anhydroglucose from the precursor ions. In addition, the spectra show signals manifesting glucosyl transfer from the cysteine position onto Lys, Arg side chains, and a peptide N-terminus. Other spectral evidence indicates that the N-glucosylated initial products of transfer are converted into N-furanosylated (i.e., glycated) structures due to Amadori rearrangement. We discuss the peculiar transfer of the glucose oxocarbenium ion (Glc+) to positively charged guanidinium residue (ArgH+) and propose a mechanism for the gas-phase Amadori rearrangement involving a hydride ion shift.

Modern automotive systems should must satisfy strict reliability requirements. Most of the real vehicle systems and safety-critical networks have complex interconnections. Sensitivities and probabilistic uncertainties of reliability of System with Complex Interconnections (SwCI) can be investigated by the Monte-Carlo Simulation (MCS). The paper focuses on sensitivities and parametrical uncertainties of SwCIs reliability. The proposed method can be used forimplemented in the investigation of uncertainties of SwCIs reliability – in other wordsi.e., for in the determination of critical system elements and to estimateion of the Required Number of Spare Parts (RNSP) of the system, which depends on the probability of allowable spare equipment shortage.

Furthermore, the nasal congestion typical of rhinitis can contribute to upper airway obstruction and sleep-disordered breathing. A vicious circle is created between asthma and sleep-disordered breathing: uncontrolled asthma and sleep-disordered breathing can worsen each other. In children with genetic obesity, despite alterations in the immune system, fewer allergies are found compared to the general population. The causes of this reduced allergenicity are unclear but probably involve genetic, immunological, and environmental factors. Further studies are needed to understand the mechanisms. The study emphasizes the importance of jointly evaluating and managing allergies, obesity, and sleep-disordered breathing in children, considering their close interconnection.

Efficient gene transfer and expression tool is lacking for shrimps and shrimp cells. To solve this, this study has developed a shrimp DNA virus-mediated gene transfer and expression system, consisting of insect Sf9 cells for viral packaging, shrimp viral vector of pUC19-IHHNV-PH-GUS and baculoviral vector of Bacmid or Bacmid-VP28 encoding shrimp WSSV envelope protein VP28. The pUC19-IHHNV-PH-GUS vector was constructed by assembling the genomic DNA of shrimp infectious hypodermal and hematopoietic necrosis virus (IHHNV), which had shortened inverted terminal repeats, into pUC19 backbone, and then an expression cassette of baculoviral polyhedron (PH) promoter-driven GUS reporter gene was inserted immediately downstream of IHHNV for proof-of-concept. It was found that the viral vector of pUC19-IHHNV-PH-GUS could be successfully packaged into IHHNV-like infective virions in the Sf9 cells, and gene transfer efficiency of this system had been evaluated and verified in three systems of Sf9 cells, shrimp hemolymph cells and tissues of infected shrimps, but GUS expression could be detected only in the cases when the viral vector was co-transfected or co-infected with a baculovirus of Bacmid or Bacmid-VP28 due to the Bacmid-dependence of PH promoter. Moreover, the packaging and infection efficiencies could be significantly improved when Bacmid-VP28 was used instead of Bacmid.

A dioxomolybdenum catalyst [(MoO2Cl2-O-MoO2Cl2)(L)] (1) (with L (1S,2S)-N,N´-bis(2-pyridinecarboxamide)-1,2-cyclohexane) was prepared and used as catalyst for the desulfurization of a multicomponent model fuel containing the most refractory sulfur compounds in real fuels. This complex showed to have a high efficiency to oxidize the aromatic benzothiophene derivative compounds present in fuels, mainly using a biphasic 1:1 model fuel/MeOH system. This process conciliates catalytic oxidative and extractive desulfurization, occurring the oxidation of the sulfur compounds in the polar organic solvent. The oxidative catalytic performance of (1) showed to be influenced by the presence of water in the system. Using 50% aq. H2O2 it was possible to reuse the catalyst and the extraction solvent MeOH during ten consecutive cycles without loss of desulfurization efficiency.

An electrochemical bioassay based on the rotating droplet electrochemistry by using electron transfer mediator was developed for the evaluation of a wide variety of pollutants such as antibiotics, heavy metals and pesticides in the water environment. Ferricyanide was used as electron transfer mediator for obtaining the catalytic response of Escherichia coli. The electrochemical response of E.coli was measured via hydrodynamic chronoamperometry in a microdroplet on a screen-printed carbon electrode (SPCE). The constructed electrode system successfully evaluate the catalytic response of E.coli solution in presence of ferricyanide. The assay for antibiotic toxicity on E.coli was carried out. The EC50 for ampicillin, sulfamonomethoxine, chlorotetracycline, tetracycline and oxytetracycline valuated by the pre-incubation method were 0.26, 0.77, 5.25, 18.5, 19.0 µM, respectively. The toxicity order was ampicillin &gt; sulfamonomethoxine &gt; chlorotetracycline &gt; tetracycline&gt;oxytetracycline. The proposed method can be used to evaluate the antibiotic toxicities in different real sample, such as pond water, powder and raw milk. Recoveries were found in the range 90 and 99%. The developed methods do not require additional incubation time to evaluate toxicity.

The two combined technologies WPT and AI still have huge prospects for several industries such as consumer electronics and industry. This paper looks at the recent developments in WPT, particularly concerning AI-improved efficiency, range, and reliability. AI-driven also works to control power, manage device alignment, and test when maintenance is necessary, this makes powering smarter and more efficient. Accuracy is given by deploying machine learning and computer vision, and on the other hand, the maintenance procedure is done through predictions. Furthermore, the ability of the management system to include the use of AI in power regulation enables the control of consumption based on the load. In this study, the positive future effect of AI is discussed concerning improvements in WPT technologies, which is paramount in subsequent applications. Based on the results, it is essential to continue the investigation of the topic within this interdisciplinary science to advance existing problems and discover novel possibilities for evolution. Data based on experiments shows a significant improvement in the efficiency of information transfer where AI is used with rates up to 25% higher than those using the traditional ways. Additionally, advanced maintenance forecasting algorithms improve system availability by cutting down on 30% of the downtimes when applied in real-life applications of AI technology. The study also looks at the possibilities of the outlook to the future including emergence of the distributed artificial intelligence for autonomous WPT network, and additional potential of quantum computing for the use to improve the algorithm. This indicates that there is a new era for the convergence of WPT and AI to enhance the progress of power transfer system in conditions of elegant, efficient, and robust.

In the current era of emerging technologies and cutting-edge inventions, it is equally important to consider the ethical aspects of research and innovations to have an ethical space for the sustainable progress and safety of humanity. The field of nanotechnology research and innovation often faces criticism due to its potential adverse impacts on health, the environment, limited accessibility, high costs, and concerns about data privacy. Therefore, it is crucial to address and resolve these ethical issues systematically. In this regard, we present our findings and methods for exploring the application of the ethics technology assessment method by considering its relevance at different stages of innovation development. Specifically, the present work provides a conceptual framework for ethics technology assessment focused on evaluating a significant innovation in nanotechnology within the domain of food safety with specific reference to mycotoxin remediation. By leveraging the value-sensitive design approach, we described various strategies for analyzing the ethical implications of nanotechnology-based innovations in mycotoxin remediation. Through comprehensive ethics technology assessment, nanotechnology-based innovations can be guided toward becoming more value-oriented, thereby making substantial contributions to social progress.

Lotions with or without 28% ascorbyl glucoside arginine complex (AGAC) were applied twice daily for 24 weeks in a double‐blind half‐face study of 27 Japanese females with solar lentigos (SLs). Pigmentation scores were evaluated using a photo‐scale and skin colors were assessed using a color difference meter and a Mexameter. Treatment with the test lotion elicited a significant decrease in pigment scores at 24 weeks compared to week 0; with a significant decrease in pigment scores at 24 weeks compared to the placebo lotion‐treated SLs. In the test lotion‐treated SLs; the L values and melanin index (MI) significantly (p<0.0001) increased and decreased; respectively; at 12 and 24 weeks of treatment compared to week 0. Comparisons of increased L (△L) values or decreased MI (△MI) values between test and placebo lotion‐treated SLs demonstrated that the test lotion‐treated SLs had significantly higher △L or △MI values than the placebo lotion‐treated SLs both at 12 and 24 weeks of treatment. The sum of our results strongly indicates that AGAC is distinctly effective in ameliorating the hyperpigmentation levels of SLs at a visibly recognizable level by the subjects without any hypo‐pigmenting effects or skin irritation.

Low temperature dielectric property of charge/orbital ordered manganite, Pr1-xCaxMnO3 for 0.40≤x≤0.50 is investigated systematically as a function of Ca-content, x. The Ca-content dependence of dielectric permittivity and dissipation factor exhibit distinct maxima around x=0.45. The overall dielectric response of charge ordered Pr1-xCaxMnO3 is dominated by polarization induced by polaron hopping and exhibits thermally activated relaxation behaviour. The dielectric relaxation behaviour over the investigated temperature range is analysed with the help of small polaron hopping model and as well as variable range hopping model. The estimated polaron parameters also display non-monotonic variation with x and exhibit broad minima between x=0.425-0.45. The observed results suggest that a modulation of checker board type charge ordering pattern in Pr1-xCaxMnO3 is possibly taking place in the Ca-content range of investigation 0.40≤x≤0.50.

The present study explored whether exposure to complex childhood trauma (CCT) is reflected in the resting-state electroencephalography (EEG) temporal alpha power of complex trauma-exposed adolescents, given the association of temporal alpha with fear circuitry (learning and conditioning) in a sample of 25 adolescents and similar controls, ages 12-17 years. Both trauma and psychopathology were screened or assessed, and resting-state EEG was recorded, following a preregistered protocol for data collection. Temporal alpha power, corresponding to T5 and T6 electrode location (10-20 international system), was extracted from resting-state EEG in both eyes-open and eyes-closed conditions. We found that in the eyes-open condition, right, but not left, temporal alpha was significantly higher in adolescents exposed to CCT, relative to healthy controls, suggesting that childhood trauma exposure may have a measurable impact on alpha oscillatory patterns, specifically in the right temporal region. Our study highlights the importance of considering neural markers, such as temporal alpha power, in understanding the long-term consequences of CCT exposure in developmental samples, with possible important clinical implications in guiding neuroregulation interventions.

This study addresses the critical need for standardizing Building Information Modeling (BIM) Execution Plans (BEPs) in the Architecture, Engineering, Construction, and Operations (AECO) sector. By analyzing 36 BEP documents from international organizations, we identify key elements and propose a comprehensive framework aimed at enhancing digital transformation and collaboration in complex construction projects. This research employs scientometric analysis to map the evolution of BEP standards and integrates empirical data from industry surveys to validate the proposed framework. Our findings highlight the benefits of standardized BEPs in reducing inefficiencies and improving project outcomes, thereby providing a significant contribution to the field of BIM implementation.

Based on the dynamic strength variation mechanism of concrete under complex loading conditions, two approaches were devised: numerical testing and CT scanning, guided by the principle of minimizing energy dissipation. A methodology for calculating concrete's failure area and measuring its strength was developed. The analysis delves into the mechanisms behind failure patterns and variations in concrete specimen strength, focusing on crack initiation, development, and the resultant failure surface formed by crack propagation, along with the stress distribution in concrete specimens subjected to diverse loading conditions. Through evaluating the strength and computed failure area of concrete specimens, it is evident that the loading method influences both the strength and failure area of concrete specimens. In general, a larger failure area correlates with a higher measured strength.

This article presents a novel approach to understanding the influence of electric Energy on space-time curvature. EFE actual calculations refer solely to gravitational charges. In this article, I expand them to include the electric charge with calculations carried specifically for electric charges, with exciting results. The article leverages the universal consistency of physical laws, applying the known effects of gravitational charges on space-time curvature to electric charges. The research focuses on calculating the specific impact of electric charges on space-time curvature, rooted in the principle that space-time curvature is omnipresent. The findings reveal that space-time curvature's alteration due to an electric charge is influenced by various factors, including the charge's location, magnitude, mass, density, associated electric field, and its motion through space-time. Notably, the conversion factor for electric charges differs from the conversion factor for gravitational charges. These insights significantly enhance our understanding of electromagnetism and general relativity, paving the way for a more profound comprehension of electric charges.

Almost all modern inorganic light-emitting diode (LED) designs are based on double heterojunctions (DHJs) whose structure and current injection principle have remained essentially unchanged for decades. Although highly efficient devices based on the DHJ design have been developed and commercialized for energy-efficient general lighting, the conventional DHJ design requires burying the active region (AR) inside a pn-junction. This has hindered the development of emitters utilizing nanostructured ARs located close to device surfaces such as nanowires or surface quantum wells. Modern DHJ III-N LEDs also exhibit resistive losses which arise from the DHJ device geometry. The recently introduced diffusion-driven charge transport (DDCT) emitter design offers a novel way to transport charge carriers to unconventionally placed ARs. In a DDCT device, the AR is located apart from the pn-junction and the charge carriers are injected into the AR by bipolar diffusion. This device design allows the integration of surface ARs to semiconductor LEDs and offers a promising method to reduce resistive losses in high power devices. In this work, we present a review of the recent progress in gallium nitride (GaN) based DDCT devices, and an outlook of potential DDCT has for opto- and microelectronics.

With metal oxide semiconductors being a promising candidate for accurate indoor air quality assessments, multiple drawbacks of the gas sensors prevent their widespread use. Examples include poor selectivity, instability over time, and sensor poisoning. Complex calibration methods and advanced operation modes can solve some of those drawbacks. However, this leads to long calibration times, which are unsuitable for mass production. In recent years, multiple attempts to solve calibration transfer have been made with the help of direct standardization, orthogonal signal correction, and many more methods. Besides those, a new promising approach is transfer learning from deep learning. This article will compare different calibration transfer methods, including direct standardization, piecewise direct standardization, transfer learning for deep learning models, and global model building. The machine learning methods to calibrate the initial models for calibration transfer are feature extraction, selection, and regression (established methods) and a custom convolutional neural network TCOCNN. It is shown that transfer learning can outperform the other calibration transfer methods regarding the root mean squared error, especially if the initial model is built with multiple sensors. It was possible to reduce the number of calibration samples by up to 99.3 % (from 10 days to approximately 2 hours) and still achieve an RMSE for acetone of around 18 ppb (15 ppb with extended individual calibration) if six different sensors were used for building the initial model. Furthermore, it was shown that the other calibration transfer methods (direct standardization and piecewise direct standardization) also work reasonably well for both machine learning approaches, primarily when multiple sensors are used for the initial model.

Human papillomavirus (HPV) uses the cellular trafficking machinery to reach the cell nucleus and initiate a successful infection. The HPV capsid consists of two proteins: L1 (the major capsid protein) and L2 (minor capsid protein), and L2 is known to be responsible for trafficking the viral genome to the nucleus. From previous studies, we know that many cellular proteins interact with L2 and are involved in the trafficking process at different stages of the infectious entry process. To further investigate how HPV-16 achieves successful viral entry, we have performed a mass spectrometry analysis to identify novel interacting proteins that associate with incoming HPV-16 virions. We show that HPV-16 pseudovirions interact with a group of cellular cargo proteins (COPI), and here we confirm that this is mediated by the direct binding of L2, but not L1, to the COPB1 subunit. We also show that the ability of L2 to interact with COPB1 is conserved in both low- and high-risk HPV types. Furthermore, we also show that the interaction is essential for successful infection entry of multiple HPV types, thereby identifying a novel constituent of the infectious entry pathway of the HPV.

Alzheimer’s is a degenerative brain condition brought on by intricate brain changes after cell injury. The prevalence of Alzheimer’s disease is highest in those over 65. Presently, AD is detected by manual examination of a patient’s Magnetic Resonance Imaging (MRI) scan and neuropsychological assessments. Due to its subpar accuracy and performance, solutions based on artificial intelligence (AI) have not been made more widely available. This paper proposes a deep Convolutional Neural Network (CNN) with the pre-trained MobileNet-V2 architecture as the model’s base for feature extraction and reducing the parameter size and training time. The proposed model accurately classifies various early stages of AD. The proposed approach visualizes the class activation maps in the form of a heat map on brain MRI scans via the Grad-CAM algorithm that lifts the black-box nature of the deep model. To handle the imbalance problem of the AD data set acquired from Kaggle, the oversampling algorithm SMOTE interpolates new images to even the class samples. The proposed multi-class classification deep model is named Bio-AD and evaluated using different quality metrics. The Bio-AD model has the following values for evaluation metrics: 97.92%, 99.87%, 97.87%, 98%, 98%, and 0.0646 for accuracy, Area Under the Curve (AUC), F1-score, Precision, Recall, and loss respectively. The proposed Bio-AD model is compared with DenseNet-169, VGG-19, and InceptionResNet-V2 using different quality metrics and outperformed in comparison with all other models using the AD data set.

In this paper, the ratio of gross agricultural production (GAP) and agricultural carbon emission is defined as agricultural carbon emission efficiency (ACEE). Considering rural labor transfer (RLT) increases the agricultural machinery intensity (AMI), the two are substitutes for each other and may interact with agricultural carbon emission efficiency (ACEE), we constructed a Simultaneous Equations Model (SEM) of ACEE, RLT and AMI, and analyzed the interaction mechanism of these three variables by using the Three-Stage Least Squares (3SLS) method to solve the endogeneity of the model. The following conclusions are drawn. First, RLT and AMI significantly promote the improvement of ACEE, while the improvement of ACEE and AMI further promotes RLT. Secondly, the causal relationship and influence mechanism of ACEE, RLT and AMI are interactive and multi-directional. An increase in AMI promotes ACEE, but an increase in ACEE inhibits an increase in AMI, suggesting that the marginal rate of substitution of machinery for labor is decreasing. Finally, China has significant regional heterogeneity. Labor transfer and machinery are only direct factors in increasing ACEE, while factors such as economic base, technology level and farmers' attitudes provide environmental support for the effective improvement of ACEE.

Fibrodysplasia Ossificans Progressiva (FOP) is an enigmatic, ultra-rare genetic disorder characterized by progressive heterotopic ossification, wherein soft connective tissues undergo pathological transformation into bone structures. This incapacitating process severely limits patient mobility and poses formidable challenges for therapeutic intervention. Predominantly caused by missense mutations in the ACVR1 gene, the disorder has hitherto defied comprehensive mechanistic understanding and effective treatment paradigms. This write-up offers a comprehensive overview of the contemporary understanding of FOP's complex pathobiology, underscored by advances in molecular genetics and proteomic studies. We shed light on targeted therapy, spanning genetic therapeutics, enzymatic and transcriptional modulation, stem cell therapies, and innovative immunotherapies. We also focused on the intricate complexities surrounding clinical trial design for ultra-rare disorders like FOP, addressing fundamental statistical limitations, ethical conundrums, and methodological advancements essential for the success of interventional studies. We advocate for the adoption of a multi-disciplinary approach that converges bench-to-bedside research, clinical expertise, and ethical considerations to tackle the challenges of ultra-rare diseases like FOP and comparable ultra-rare diseases. Overall, this article serves a dual role: as a definitive scientific resource for ongoing and future FOP research and as a call to action for innovative solutions to address methodological and ethical challenges that impede progress in the broader field of medical research for ultra-rare conditions.

The Marun-Keu complex plays a significant role in understanding the geological evolution of the Ural orogen; however, it remains poorly understood. This study aims to provide insights into the complex's age, protolith composition, rock formation conditions, and its position in the region's history. The zircons from the host granitic gneisses are characterized by magmatic cores with an age of 470 Ma and metamorphic rims with an age of approximately 370 Ma. We suggest that the metamorphic rims were formed during eclogite metamorphism and that the metagranitoids hosting the eclogites experienced eclogite metamorphism simultaneously with the basic and ultrabasic rocks that are common in this area. Heterogeneous zircons were also isolated from the selvage of a pegmatite vein, in which four domains are distinguished, two to three of which can be identified within single grains, as follows: 1) igneous cores with an age of approximately 470 Ma and the geochemical characteristics of zircon crystallized in basic rocks; 2) zircons recrystallized during eclogite metamorphism with geochemical characteristics intermediate between those of the magmatic cores and true eclogitic zircon; 3) pegmatitic zircon, exhibiting the most sharply differentiated REE spectra of all four domains, characterized by a prominent positive Ce anomaly and a weakly expressed negative Eu anomaly; and 4) eclogitic zircon, observed in the form of veins and rims, superimposed in relation to the other three domains. The age of the latter three domains is within the error range and is estimated to be approximately 370 Ma. This indicates that the processes of eclogite metamorphism and the formation of pegmatites occurred at approximately the same time in the studied area.

Extracellular electron transfer (EET) is key to the success of microbial fuel cells (MFCs). Clostridium sp. often occurs in MFC anode communities, but its ability to perform EET remains controversial. We have employed Clostridium pasteurianum DSM 525 as biocatalyst in a glycerol-fed MFC, designated MFC_DSM. We have also followed the EET of this biocatalyst in the presence of a mediator, namely soluble neutral red (NR), soluble methyl viologen (MV), neutral red film (FNR), or methyl viologen film (FMV). MFC_DSM provided power and current densities (j) of 0.39 mW.cm^-2 and 2.47 mA.cm^-2, respectively, which evidenced that the biocatalyst performs direct electron transfer (DET). Introducing 150.0 µM NR or MV into the MFC_DSM improved the current density by 7.0- and 3.7-fold (17.05 and 8.45 mA.cm^-2), respectively. After 20 cyclic voltammetry (CV) cycles, the presence of FNR in the MFC_DSM anodic chamber provided almost twofold higher current density (30.76 µA.cm^-2) compared the presence of NR in the MFC_DSM. Introducing MV or FMV into the MFC_DSM anodic chamber gave practically the same current density after 10 CV cycles. The MFC_DSM anodic electrode might interact with FMV weakly than with FNR, so FNR is more promising to enhance C. pasteurianum DSM 525 EET within MFC_DSM.

In this study, we present comprehensive climatologies of effective ultraviolet (UV) quantities and photosynthetically active radiation (PAR) over Cyprus for the period 2004 to 2023, leveraging the synergy of earth observation (EO) data and radiative transfer model simulations. The EO dataset, encompassing satellite and re-analysis data for aerosols, total ozone column, and water vapor, alongside cloud modification factors, to capture the nuanced dynamics of Cyprus's atmospheric conditions. With a temporal resolution of 15 minutes and a spatial of 0.05°x0.05°, these climatologies undergo rigorous validation against established satellite datasets and are further evaluated through comparisons with ground-based global horizontal irradiance measurements provided by the Meteorological Office of Cyprus. This dual-method validation approach not only underscores the models' accuracy but also highlights their proficiency in capturing intra-daily cloud coverage variations. Our analysis extends to investigating the long-term trends of these solar radiation quantities, examining their interplay with changes in cloud attenuation, aerosol optical depth (AOD), and total ozone column (TOC). Significant decreasing trends in the noon ultraviolet index (UVI), of -2 to -4% per decade have been found in Autumn, especially marked on the island’s Northeastern part, mainly originating from the (significant) positive trends in TOC. The significant decreasing trends in TOC, of -2 to -3% per decade that were found in Spring do not result in correspondingly significant positive trends in the noon UVI since variations in cloudiness and aerosols also have a strong impact on the UVI in this season. The seasonal trends in the Daylight Integral (DLI) were generally not significant. These insights provide a valuable foundation for further studies aimed at developing public health strategies and enhancing agricultural productivity, highlighting the critical importance of accurate and high-resolution climatological data.

Based on some experiments, the paper describes with the help of polarization the physic-chemical processes that take place in the contact area of friction pairs, during the selective transfer as a result of the change in the input speed of the lubricant (glycerin), as well as how it influences their wear. Through polarization, the quantitative and qualitative transformations of glycerin are studied during frictional stress with selective transfer, the determination of the properties of the selective solution, and the damage to the metals and alloys of the friction pairs, but also after frictional stress.