Recent advances in nanomaterials have been heavily influenced by low-dimensional nanocarbon allotropes. In particular, carbyne has attracted attention for its potential as a true one-dimensional carbon chain with sp1 hybridization. To maximize the capabilities of this material, we employ a focused data-driven inverse design approach based on the carbon nanomaterials genome concept. This involves using deep learning neural network models to identify key descriptors tied to desired properties, enabling property prediction and reverse engineering of nanocarbons. Our iterative approach entails: (i) gathering growth/property data on nanostructures; (ii) identifying informative numerical/categorical predictors; (iii) developing deep learning models mapping descriptors to properties; (iv) refining models with new insights; (v) determining required descriptors/conditions for target properties via inverse mapping; (vi) validating models by synthesizing predicted nanostructures; and (vii) enhancing models with validation data. This allows uncovering hidden growth-property connections, precisely tuning nanocarbons for desired attributes. We introduce new methodologies including exciting synergistic effects, synchronizing atomic vibrations, active screen plasma, energy-driven transformations, surface acoustic micro/nano-manipulation, doping and directed self-assembly to expose relationships and integrate insights into the inverse design flow. This research promises to accelerate discovery of next-generation low-dimensional nanocarbons with exceptional properties and applications.

Radical reactions are powerful in the synthesis of diverse molecular scaffolds bearing functional groups. In pervious review articles, we have presented 1,2-difunctionalizations, remote 1,3-, 1,4-, 1,5-, 1,6- and 1,7-difunctionalizations, and addition followed by cyclization reactions. Presents in this paper are radical cyclization followed by the second functionalization reactions. The second functionalization could be realized by atom transfer reactions, radical or transition metal-assisted coupling reactions, and reactions with neutral molecules, cationic and anionic species.

Livestock farming plays a crucial role in agriculture and is important in meeting people's needs for a better life in the new era. However, due to China's "double carbon" target, the development of animal husbandry has been affected by the level of carbon emissions. To study the spatial dis-tribution and dynamic evolution of livestock carbon emissions in Shandong Province, we used panel data from 16 prefecture-level cities in Shandong Province from 2001 to 2022 to determine the spatial differentiation of livestock carbon emissions in Shandong Province and their dynamic evolution by combining spatial and non-parametric estimation methods based on livestock carbon emissions and livestock emission intensity measured by city. The results of the study show that the distribution of carbon emissions from animal husbandry in Shandong Province is in a "dis-persed-aggregated" pattern, and the intensity of carbon emissions shows a gradual downward trend, with a ladder-like distribution characterized by high in the west and low in the east. Through the analysis of the dynamic evolution of nuclear density, we observe that the regional differences of animal husbandry carbon emissions in Shandong Province are expanding, showing the characteristics of polarization. The difference of carbon emissions in the peninsula is gradually narrowing, and the phenomenon of polarization is weakening. The carbon emissions of animal husbandry in the central and western regions of Shandong are polarized.

Unsupervised metric learning has been generally studied as a byproduct of dimensionality reduction or manifold learning techniques. Manifold learning techniques like Diusion maps, Laplacian eigenmaps has a special property that embedded space is Euclidean. Although laplacian eigenmaps can provide us with some (dis)similarity information it does not provide with a metric which can further be used on out-of-sample data. On other hand supervised metric learning technique like ITML which can learn a metric needs labeled data for learning. In this work propose methods for incremental unsupervised metric learning. In rst approach Laplacian eigenmaps is used along with Information Theoretic Metric Learning(ITML) to form an unsupervised metric learning method. We rst project data into a low dimensional manifold using Laplacian eigenmaps, in embedded space we use euclidean distance to get an idea of similarity between points. If euclidean distance between points in embedded space is below a threshold t₁ value we consider them as similar points and if it is greater than a certain threshold t₂ we consider them as dissimilar points. Using this we collect a batch of similar and dissimilar points which are then used as a constraints for ITML algorithm and learn a metric. To prove this concept we have tested our approach on various UCI machine learning datasets. In second approach we propose Incremental Diusion Maps by updating SVD in a batch-wise manner.

An analysis of the processes of plastic deformation and acoustic relaxation in a high-entropy alloy Al0.5CoCrCuFeNi was carried out. The following have been established: dominant dislocation defects; types of barriers that prevent the movement of dislocations; mechanisms of thermally activated movement of various elements of dislocation lines through barriers at room and low temperatures. Based on modern dislocation theory, quantitative estimates have been obtained for the most important characteristics of dislocations and their interaction with barriers.

Highly efficient, broadband and distinctively structured electromagnetic wave (EMW) absorption materials exhibit huge exceptions with multi-functions in both military and civilian fields. Searching the relationship between structural and EMW performance is beneficial to develop outstanding microwave absorption materials, and has become an attractive research topic. In this study, we briefly reviewed the different EMW absorption mechanisms and key influence factors, then, present the latest developments in low-dimensional (LD) EMW absorption materials. We provide a comprehensive discussion of remarkable examples and summarize the main information related to the structure-function relationship of dimensional design and structural engineering in order to facilitate the rapid evolution of this fascinating field.

The stepwise hydrogenation of the C=C bond and C=O group of acrolein on Au₃ and Au₅ model systems is investigated using the density functional theory(DFT) PW91 functional. Our results show that the C=C hydrogenation is more favorable than that of C=O bond on Au₃ with the barriers of the rate-determining step being 0.35 and 0.62 eV respectively. On the other hand, the C=O reduction is preferred over the hydrogenation of the C=C bond on Au₅. The corresponding barriers of the rate-determining steps are 0.45 and 0.54 eV, respectively. This demonstrated that the second hydrogenation step controls the reaction on both Au₃ and Au₅ for C=O and C=C hydrogenation and the C=O hydrogenation on Au₅ is preferred over the hydrogenation of the C=C bond, which is helpful to address the reactivity of small size-selected supported gold clusters.

Low-dimensional carbon-based (LDC) materials have attracted extensive research attentions in electrocatalysis because of their unique advantages such as structural diversity, low cost, and chemical tolerance. They have been widely used in a broad range of electrochemical reactions to relief environmental pollution and energy crisis. Typical examples include hydrogen evolution reaction (HER), oxygen evolution reaction (OER), oxygen reduction reaction (ORR), carbon dioxide reduction reaction (CO₂RR), and nitrogen reduction reaction (NRR). Traditional “trial and error” strategies seriously slowed down the rational design of electrocatalysts for these important applications. Recent studies show that the combination of density functional theory (DFT) calculations and experimental research is capable of accurately predicting the structures of electrocatalysts, thus could reveal the catalytic mechanisms. Herein, current well-recognized collaboration methods of theory and practice are reviewed. The history of modern DFT, commonly used calculation methods, and basic functionals are briefly summarized. Special attention is paid to descriptors that are widely accepted as a bridge links the structure and activity, and the breakthroughs for high-volume accurate prediction of electrocatalysts. Importantly, correlating multiple descriptors are used to systematically describe the complicated interfacial electrocatalytic processes of LDC catalysts. In addition, machine learning and high-throughput simulations are crucial in assisting the discovery of new multiple descriptors and reaction mechanisms. This review will guide the further development of LDC electrocatalysts for extended applications from the aspect of DFT computations.

The reinforcing ability of the fillers results in significant improvements in properties of polymer matrix at extremely low filler loadings compared to conventional fillers. In view of this, present review article describes the different methods used in preparation of different rubber nanocomposites reinforced with nanodimensional individual carbonaceous fillers, such as graphene, expanded graphite, single walled carbon nanotubes, multiwalled carbon nanotubes and graphite oxide, graphene oxide and hybrid fillers consisting combination of individual fillers. This is followed by review of mechanical properties (tensile strength, elongation at break, Young modulus, and fracture toughness) and dynamic mechanical properties (glass transition temperature, crystallization temperature, melting point) of these rubber nanocomposites. Finally, Payne and Mullin Effects have also been reviewed in rubber filled with different carbon based nanofillers.

Abstract: Two novel low-dimensional molecular magnetic materials were prepared by a self-assembling of 3d- and 5d-metal complexes. These are the first neutral heterobimetallic cyanobridged compounds involving one anisotropic Mn(III) Schiff base complex and one octacyanotungstate(V) per molecular unit. A slow diffusion of the constituents’ solutions leads to a formation of the 0D crystalline complex 1 due to coordination of a water molecule to the Mn center prevents a polymer formation. A rapid mixing of reagents results in a precipitation of the microcrystalline powder of the complex 2, which on a totality of experimental data possess 1D polymeric structure. The magnetic studies have shown that antiferromagnetic exchange interactions are dominating in 1 J/k_B= -13.1(7) K, D=-3.0(1.3) K, zJ'= -0.16(20) K and g_av=2.00(1); while the presence of the significant intramolecular Mn(III)–W(V) ferromagnetic couplings through cyanide bridges is characteristic for 2 (J/k_B = 46.1(5) K g_Mn = 2.11(3), fixed g_W = 2.0). Due to the weak interchain interactions, zJ'/k_B = –0.8(2) K, compound 2 is a metamagnet with the Néel temperature of 9.5 K undergoing a spin-flip transition at 2 kOe. The slow magnetization dynamics of 2 was investigated at DС field of 0 and 2 kOe, giving the values of τ₀ 32(15) and 36(15) ps respectively, well within the range typical for SCMs. The respective ∆_τ/k_B values were 48.4(1.2) and 44.9(1.0) K.

One of the oldest problems in physics is that of calculating the motion of N particles under a specified mutual force: the N-body problem. Much is known about this problem if the specified force is non-relativistic gravity, and considerable progress has been made by considering the problem in one spatial dimension. Here I review what is known about the relativistic gravitational N-body problem. Reduction to one spatial dimension has the feature that gravitational radiation is absent, thereby allowing for a clear comparison between the physics of one-dimensional relativistic and non-relativistic self-gravitating systems. After describing how to obtain a relativistic theory of gravity coupled to N point particles, I discuss in turn the 2-body, 3-body, 4-body, and N-body problems. Quite general exact solutions can be obtained for the 2-body problem, unlike the situation in general relativity in 3 spatial dimensions for which only highly specified solutions exist. The 3-body problem exhibits mild forms of chaos, and provides one of the first theoretical settings in which relativistic chaos can be studied. For N4 other interesting features emerge. Relativistic self-gravitating systems have a number of interesting problems awaiting further investigation, providing us with a new frontier for exploring relativistic many-body systems.

Carotid atherosclerotic lesions are a major cause of cerebrovascular disease (CVD). Identification and quantification of carotid plaques are important for categorizing the vulnerability of plaques for rupture and assessing the impact of treatments. The irregularity of plaque surface is associated with previous plaque rupture and plays an important role in the risk of stroke. Thus, the aim of this study is to develop and validate novel vulnerability biomarkers from three-dimensional ultrasound (3DUS) images by analyzing the surface morphological characterization of carotid plaque using fractal geometry features. 3D box-counting and 3D blanket are the two types of 3D fractal dimension that were employed to describe the smoothness of plaques. This fractal dimension analysis tool was used to evaluate the effect of atorvastatin using 3DUS carotid images, which were acquired from 6 patients treated with atorvastatin with 80 mg daily and 5 patients with placebo. The Student's T Test results showed that those two fractal features were effective for detecting the statin-related changes in carotid atherosclerosis with p<0.0068 and p<0.015 respectively, suggesting that 3D fractal dimension measurements can be used effectively to analyze the surface characteristics of carotid plaques, especially for evaluating the impact of the treatment.

Obesity is associated with serious comorbidities and economic implications. Bariatric surgery, most commonly Roux-en-Y gastric bypass and sleeve gastrectomy, are effective options for weight loss and improvement of obesity-related comorbidities. With the growing obesity epidemic, there has been a concomitant rise in bariatric surgeries, particularly in sleeve gastrectomy, which has been the most widely performed bariatric surgery since 2013. Gastroesophageal reflux disease (GERD) is highly prevalent in obese individuals, can significantly impact quality of life and may lead to serious complications. Obesity and GERD both improve with weight loss. However, as the incidence of sleeve gastrectomy rises, recent data has revealed a risk of exacerbation of pre-existing GERD or the development of de novo GERD following sleeve gastrectomy. We performed a detailed review of GERD post-sleeve gastrectomy, including its overall incidence, pathophysiology and current treatment paradigms.

This study extends the CGAN-based MODIS cloud vertical-profile (64×64-scene, about 70km width×15km height) retrieval technique developed by Leinonen et al. (2019) [1] to construct seamless 3D cloud fields for the MODIS granules [2]. Firstly, the accuracy and spatial continuity of the Leinonen et al. [1] retrievals are statistically evaluated. Then, according to the characteristics of the retrieval error, a spatially overlapping-scene ensemble generation method and a bi-directional Ensemble Binning Probability Fusion (CGAN-BEBPF) technique are developed, which improves the CGAN retrieval accuracy and support to construct seamless 3D clouds. The CGAN-BEBPF technique involves three steps: cloud masking, intensity scaling, and optimal value selection. It ensures adequate coverage of the low reflectivity areas while preserving the high reflectivity cloud cores. The technique is applied to retrieve the 3D cloud fields of Typhoon Chaba and a multi-cell convective system. The cloud structures of the CGAN-BEBPF results are highly consistent with the ground-based radar observations. It can retrieve weak clouds at the top levels of convective storms and hurricanes that were missed by ground-based radars and fill the gaps of the ground-based radar lower-level PPI scan. Overall, CGAN-BEBPF can be automated to retrieve the rich informative 3D structures of typhoons and severe convective systems over the oceans along the MODIS swath, which certainly presents great value for improving maritime and near-shore typhoons and convection prediction and other cloud-sensitive applications.

The growing expansion of the economy and population has resulted in an increased inclination towards the utilization of groundwater resources. Conducting a geophysical survey is a widely employed method for subsurface mapping and the detection of groundwater. A geophysical study was conducted in Nanshan township, Huarong County, located in the Hunan province of the South-Central region of China. The investigation involved the utilization of a 2D Electrical Resistivity Imaging technique employing forward and inverse pole-dipole electrode array. A total of six survey lines were established, each with an electrode distance from the nearest measuring point to exceeding 800 m. The maximum current electrode separation was utilized in this setup. The spacing between the electrical resistivity sounding points was established at regular intervals. The findings from the exploration indicate the existence of multiple faults within the surveyed region. The study additionally identified two regions of structural failure, which occurred due to the convergence of faults oriented in different directions. This convergence led to the fracturing of rocks, an increase in water content, and a decrease in resistivity. The findings from the exploration were utilized in the formulation of five verification target boreholes. The results of this study offer significant insights that can inform future investigations into groundwater exploration endeavors within the region.

A PLS-based quality prediction method is proposed for batch processes using two-dimensional extended windows. To realize the adoption of information in the direction of sampling time and batch, a new definition region of support (ROS), k-i-back-extended region of support (KIBROS), is proposed to establish the extended window, which including the data of the previous sampling times in the same batch, the data of the same sampling time in the historical batches, the data of the previous sampling times in the historical batches and the data of the later sampling times in the historical batches. According to these regions of support, extended windows are established, and different models are proposed based on the extended windows for batch processes quality prediction. Further, using a typical batch process as an example, the injection molding process, the proposed quality prediction method is experimentally verified, proving that the proposed methods have higher prediction accuracy than the traditional method and the prediction stability is also improved.

Keywords: olfactory rehabilitation; chronic pain; fMRI;

Plastics, once regarded as a revolutionary material shaping modern society, now poses an unprecedented threat to our environment. Household solid waste sorting stations produce several fractions, one of which contains a high concentration of Low-Density Polyethylene (LDPE) film waste (packaging, sunscreen film, etc.). This fraction is difficult to recycle because it contains quite a lot of impurities. Usually, it is sent to the cement factories that burn it together with other fuels. However, with some processing techniques such catalytic pyrolysis this fraction could be valorized. In this paper the experiments which were carried out at a laboratory scale installation, in batches with a processing capacity of 1-3 kg of waste. The pyrolysis reactor is connected to a distillation column giving the possibility to separate the fractions. The gaseous and liquid fractions were characterized by GC-FID-TCD (gases) and GC-MS (liquids) analysis. Natural catalysts such as bentonite or clinoptilolite were studied and used in the melting of plastic mass to simplify the process as much as possible. To test the activity of the catalysts, the pyrolysis of LDPE granules was initially studied. It was found that natural zeolites are much more active than bentonite and that a minimum concentration of 5-10% is needed to have a positive effect on the composition of the fractions (increasing the weight of the light fractions C1-C6, C6-C10 and C11-C13) in relation to the heavy fractions C13-C20 and C20+. Catalytic pyrolysis gives a completely different distribution of light hydrocarbons. The best catalyst selected from LDPE lab experiments, was then tested upon the pyrolysis of plastic film waste obtained by a waste treatment plant. The research objective reported in this paper was to obtain a fraction of combustible gases in the largest possible proportion, which can be much easier exploited by burning in an engine that drives an electric generator.

There is a growing interest in the endocannabinoid system and the gut microbiota. Both areas have been suggested to play a role in endometriosis, however this is yet to be studied comprehensively. The purpose of this narrative review is to identify the gaps in endometriosis research in the context of these two areas. It underscores the limited knowledge regarding the involvement of the endocannabinoid system and gut microbiota in endometriosis. Further research in these areas is essential to unlock the therapeutic potential of cannabis and enhance the understanding of endometriosis pathogenesis.

This paper proposes an enhanced Quantum Key Distribution (QKD) protocol by incorporating a Three-Dimensional Angular Momentum Representation (3D AMR) to address vulnerabilities inherent in traditional two-dimensional polarization states-based systems. The 3D AMR method expands the state space, potentially increasing security against sophisticated quantum attacks. We present a theoretical and computational framework that transitions from the conventional two-dimensional polarization to a three-dimensional angular momentum space, which inherently increases the complexity and security of the quantum states used in QKD.

Vibration signal analysis is regarded as a fundamental approach in diagnosing faults in rolling bearings, and recent advancements have shown notable progress in this domain. However, the presence of substantial background noise often results in the masking of these fault signals, posing a significant challenge for researchers. In response, an Adaptive Denoising Autoencoder (ADAE) approach is proposed in this paper. The data representations are learned by the encoder through convolutional layers, while the data reconstruction is performed by the decoder using deconvolutional layers. Both the encoder and decoder incorporate adaptive shrinkage units to simulate denoising functions, effectively removing interfering information while preserving sensitive fault features. Additionally, dropout regularization is applied to sparsify the network and prevent overfitting, thereby enhancing the overall expressive power of the model. To further enhance ADAE's noise resistance, shortcut connections are added. Evaluation using publicly available datasets under scenarios with known and unknown noise demonstrates that ADAE effectively enhances the signal-to-noise ratio in strongly noisy backgrounds, facilitating accurate diagnosis of faults in rolling bearings.

The interest in lipid composition profiling is significantly increasing as research reveals the immense importance of lipids in medicine, plant science, food and agriculture. However, lipidomic analysis requires high-end specialty equipment. We used two-dimensional thin-layer chromatography (2D TLC) as a readily available, low-cost tool for basic lipidomic profiling of lipid classes in algal samples as model: Chlamydomonas reinhardtii, Auxenochlorella protothecoides, and Euglena gracilis. Algal lipid extracts were separated on a 2D-TLC plate, and TLC analysis was followed by scraping individual TLC spots off the plate, and a subsequent liquid chromatography separation and tandem mass spectrometry (LC-MS/MS) analysis. For comparison, crude lipid extracts were also injected directly to the LC-MS/MS system. Lipid class annotation was achieved by a combination of accurate mass, retention time information, neutral loss and fragment ion analysis by MS2Analyzer, and by matching spectra to LipidBlast MS/MS library. Overall, we were able to identify 15 lipid classes, and to adequately profile the lipid classes in all three organisms. This TLC method is thus suggested as an accessible tool for lipid class profiling of algal, plant, and food lipids, alike, when a rapid and simple analysis is required, e.g., for screening purposes.

Additive manufacturing (AM) is an emerged layer-by-layer manufacturing process. However, its broad adoption is still hindered by limited material options, different fabrication defects, and inconsistent part quality. Material extrusion (ME) is one of the most widely used AM technologies, and, hence, is adopted in this research. Low-cost metal ME is a new and AM technology used to fabricate metal composite parts using sintered metal infused filament material. Since the involved materials and process are relatively new, there is a need to investigate the dimensional accuracy of ME fabricated metal parts for real-world applications. Each step of the manufacturing process, from the material extrusion to sintering, might significantly affect the dimensional accuracy. This research provides a comprehensive analysis of dimensional changes of metal samples fabricated by the ME and sintering process, using statistical and machine learning algorithms. Machine learning (ML) methods can be used to assist researchers in sophisticated pre-manufacturing planning and product quality assessment and control. This study compares linear regression to neural networks in assessing and predicting the dimensional changes of ME made components after 3D printing and sintering process. The prediction outcomes using a neural network performed the best with the highest accuracy as compared to regression. The findings of this study can help researchers and engineers to predict the dimensional variations and optimize the printing and sintering process parameters to obtain high quality metal parts fabricated by the low-cost ME process.

Additive manufacturing processes offer various options to fabricate metal parts, which show numerous ecological and technological advantages compared to formative and subtractive manufacturing methods. Due to quick availability and high accuracy, they are particularly suitable to produce complex prototypes. Metal fused deposition modeling is a relatively new material extrusion additive manufacturing technique based on the widely used fused filament deposition process. This study focuses on the Inconel 625 metal filament from Markforged Inc., it consists of a mixture of metal powder and a binder system, the latter being removed during washing and sintering. In this article, the mechanical properties of printed and sintered Inconel were investigated using tensile testing specimens with three different infill patterns, solid, triangular, and gyroid, printed in flatwise and upright direction. In addition, investigations into the dimensional accuracy of the printed specimens and the sintering shrinkage were carried out. The results showed that neither the dimensional accuracy nor the sintering shrinkage was significantly influenced by the printing orientation. In the context of lightweight engineering, the structures proofed beneficial especially within the elastic region. Generally, triangular infill patterns resulted in higher stiffness, while gyroids led to more ductile specimens. A mass-related evaluation of tensile testing elucidates that with aid of the infill structures weight savings of 40 % resulted in mechanical performance decreased by only 20 % on average.

The effect of wave forces on the structural safety of fixed oscillating water-column (OWC) devices is critical. In this study, a three-dimensional numerical model of an oscillating water column (OWC) device is constructed using the computational fluid dynamics software OpenFOAM. Subsequently, the hydrodynamic performance of the numerical model is comprehensively validated. Finally, the hydrodynamic performance data are analyzed in detail to obtain meaningful conclusions. The results indicate that the horizontal wave force on the OWC device is approximately 6.6 to 7.9 times greater than the vertical wave force, whereas the lateral wave force is relatively small. Both the horizontal and vertical wave forces decrease as the relative water depth increases under a constant wave period and height. The horizontal wave force increases linearly with the wave height, whereas the vertical wave force increases with the square of the wave height. In addition, the highest dynamic water pressure is observed at the interface between the water surface and device, both within and outside the front wall of the gas chamber. The dynamic water pressure at different locations on the front chamber increases and subsequently decreases as the wave frequency increases.

Real-time object detection remains a pivotal topic in the realm of computer vision. Balancing the accuracy and speed of object detectors poses a formidable challenge for both academic researchers and industry practitioners. While recent transformer-based models have showncase the prowess of the attention mechanism, delivering remarkable performance improvements over CNNs, their computational demands can hinder their effectiveness in real-time detection tasks. In this paper, we elect to use YOLOX as our robust starting point and introduce a series of effective enhancements, culminating in a new high-performance detector named YOLOAX. To further exploit the power of the attention mechanism, we devise multi-dimensional attention-based modules that activate CNNs, emphasizing the most pertinent regions and bolstering the capacity to learn the most informative image representations from feature maps. Moreover, we introduce a novel leading label assignment strategy called STA, along with a groundbreaking loss function named GEIOU Loss, to further refine our detector's performance. We provide extensive ablation studies on the COCO and PASCAL VOC 2012 detection datasets to validate our proposed methods. Remarkably, our YOLOAX is trained solely on the MS-COCO dataset from scratch, without leveraging any prior knowledge, which achieves an impressive 54.2% AP on the COCO 2017 test set while maintaining a real-time speed of 72.3 fps, surpassing YOLOX by a significant margin of 3.0% AP. Our source code and pre-trained models are openly accessible at https://github.com/KejianXu/yoloax.

This research focuses on the Hopf bifurcation occurring at a singular point with high-order degeneracy in a class of three-dimensional systems. Based on center manifold theorem, by expanding the technique for determining singular point values of degenerate singularities in two-dimensional systems, the formal series method to determine the singular point values at the high-order degenerate critical point is discussed. Furthermore, a class of specific three-dimensional differential systems with high-order degenerate singularities is explored. By computing the singular point values and determining center conditions, we proved that a minimum of 5 small-amplitude limit cycles in the vicinity of the origin. This research provides new perspectives on the center problem and limit cycle bifurcation in high-dimensional systems with high-order degenerate singular points.

The aims of the study are: a) Describe the reactogenicity of WHO-approved two mRNA (Pfizer-BioNTech, Moderna) and two non-RNA vaccines (Oxford-AstraZeneca, Sinovac) among lactating mother and baby pairs; and b) compare and contrast the reactogenicity between mRNA and non-mRNA vaccines. A cross-sectional, self-reported survey was conducted amongst 1784 lactating women who received COVID-19 vaccinations. The most common maternal adverse reaction was a local reaction at the injection site; the largest minority of respondents, 43.7% (780/1784), reported experiencing worse symptoms when receiving the second dose compared to the first dose. There were no major reported adverse effects or behavioural changes in the breastfed infants. Among the respondents who received non-mRNA COVID-19 vaccinations, a majority reported no change in lactation but those who did more commonly reported an increase in milk supply, decrease in milk supply and pain in the breast. The more commonly reported lactation changes (fluctuations in breastmilk supply and pain in the breast) for the non-mRNA vaccines were similar to that of respondents who received mRNA vaccines. Our study, with a large cohort and wide geographical and racial mix, further augments earlier reported findings that COVID-19 vaccines are safe for breastfeeding mothers and her children.

Motivated by diffraction experiments on the $\left(2\sqrt{3}\times \sqrt{3}\right)\mathrm{R}{30}^{\circ }$ reconstructed Si(111) due to deposition of rare earth elements (Dy, Tb) and silicide formation we analyse the splitting and non-splitting of superstructure spots. For this purpose, we model diffraction patterns for one dimensional structures generated by the binary surface technique and use supercell models to keep the analysis simple. Diffraction pattern are calculated in the framework of the kinematical diffraction theory and they are analyzed as a function of the domains and domain boundaries. Basic properties of the diffraction pattern are analyzed for model systems of a two-fold and a three-fold periodicity. The rules derived from these calculations are applied to the "real-world" system of Si(111)-($\left(2\sqrt{3}\times \sqrt{3}\right)\mathrm{R}{30}^{\circ }$)-RESi$2_{}$ (RE = Dy or Tb). Depending on the combination of domains and domain boundaries of different types a plethora of different features are observed in the diffraction patterns. These are analyzed to determine the sizes of both domain boundaries and domains from experimentally observed splitting of specific superstructure spots.

This paper is devoted to the study of multi-dimensional integral transform with Fox H-function in the kernel in weighted spaces integrable functions in the domain R+n with positive coordinates. Mapping properties such as the boundedness, the range, the representations of the considered transformation are established.

The implementation of sentinel lymph node (SLN) biopsy is changing the scenario in the surgical treatment of early-stage cervical cancer and the oncologic safety of replacing bilateral pelvic lymphadenectomy with SLN biopsy is currently under investigation. One of the undisputed values of SLN biopsy is its diagnostic accuracy in detecting low-volume metastases (LVM) via pathologic ultrastaging. In early-stage cervical cancer, the reported incidence of LVM ranges from 4 to 20%. The prognostic impact and the role of adjuvant treatment in patients with LVM is still unclear. Some non-prespecified analyses in prospective studies showed no impact on the oncologic outcomes compared to node-negative disease. However, the heterogeneity of the studies, the differences in the disease stage and the use of adjuvant treatment, and the concomitant pelvic lymphadenectomy (PLND) make any conclusion on this topic hard. Current guidelines suggest considering micrometastasis (MIC) as a node-positive disease, while isolated tumor cells (ITC) as a node-negative disease, with a low level of evidence. This review aims to highlight the unanswered questions about the definition, identification, prognostic and therapeutic role of LVM and underline the present and future challenges we are facing. We hope this review will guide further research, giving robust evidence on LVM and their impact on clinical practice.

High dimensional statistical analysis for the yield of large-scale circuits is quite difficult due to expensive simulations, especially for the memory circuits with high sigma requirement (e.g., SRAM). In this paper, we developed an efficient sparse additive model to substitute simulations. To fit high sigma region accurately, the modeling center is moved to near failure boundary searched by scaling the shape of sampling function. To solve the model efficiently, the process variables are grouped by standard cells so that the model can be solved by our developed blockwise greedy algorithm. The experiments on the 28nm memory circuits validate that our method achieves high accuracy and efficiency compared with other state-of-art works.

To improve carbon efficiency for urban logistics service system composed of a third-party logistics service provider (3PLs) and an e-business enterprise, low-carbon operation game between them was studied. Considering low carbon technology investment cost and sales expansion effect of low carbon level, profit functions for both players were constituted. Based on their different bargaining capabilities, totally 5 types of game scenarios were designed. Through analytical solution, Nash Equilibria under varied scenarios were obtained. By analyzing these equilibria, 4 major propositions were given, in which, some key variables and system performance index were compared. Results show that the best system yields could only be achieved under the fully cooperative situation; limited cooperation only for carbon emission reduction would not benefit the system performance improvement; E-business enterprise-leading game’s performance overtook 3PLs-leading ones.

Dark energy and dark matter are described as different manifestations of vacuum energy in a new cyclic universe framework of Extra Dimensional Symmetry (EDS) that doubles large spatial dimensions with microscopic partners. In this testable framework, vacuum energy, constrained by a Planck density constraint, exists in a gravitationally dark state where actual gravitational constant G_o = 0G. While a small repulsive component is constrained by an asymptotically evolving asymmetry to the active state where G_o = 2G, some components in the inert state are enabled to gravitationally attract by neutrino induced gravitational constant with light and shear activated heavy components. Observational and experimental tests are briefly discussed.

Macrophages undergo different cellular states upon activation that can be hyporesponsive (tolerated) or hyperresponsive (primed or trained) to subsequent stimuli. Epigenetic modifications are known to play a key role in determining these cellular states. However, little is known about the role of signaling pathways that lead to these epigenetic modifications. Here, we examined the effects of various inhibitors targeting key signaling pathways induced by lipopolysaccharide (LPS) on tolerance and priming in the murine macrophages. We found that a prolonged inhibition (>18 h) of the MEK1/2-ERK signaling axis reversed tolerance and primed cells in expressing IL-1β and other inflammatory cytokines such as IL-6, TNFα, and CXCL10. Ectopic expression of catalytically active and inactive MEK1 mutants suppressed and enhanced IL-1β expression, respectively. Primed cells by the MEK1/2 inhibitor U0126 expressed higher levels of gene sets associated with immune responses and cytokine/chemokine production but lower levels of genes with cell cycle progression, chromosome organization, and heterochromatin formation than non-primed cells. Of interest, expression of the histone 3 lysine 9 (H3K9) methyltransferase Suv39h1 and the H3K9 methylation reader Cbx5 was substantially suppressed, whereas the H3K9 demethylase Kdm7a was enhanced, suggesting a role of the MEK1/2-ERK signaling axis in H3K9 demethylation. The H3K9 trimethylation levels in the genomic regions of IL-1β, TNFα, and CXCL10 were decreased by U0126. Also, the H3K9 methyltransferase inhibitor BIX01294 mimicked and overexpression of Cbx5 prevented the U0126 training effects in both RAW264.7 cells and bone marrow-derived macrophages. Collectively, these data suggest that prolonged inhibition of the MEK1/2-ERK signaling axis reverses tolerance and primed macrophages likely through decreasing H3K9 methylation levels.

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.

The challenging terrain and deep ravines characteristic of mountainous regions often result in slower path planning and suboptimal flight paths for Unmanned Aerial Vehicles (UAVs) when traditional meta-heuristic optimization algorithms are employed. This study investigates the application of an Improved Enhanced Snake Optimizer (IESO) for three-dimensional path planning in a simulated rugged mountainous terrain. The initialization process in the enhanced snake optimizer is refined by integrating the Chebyshev chaotic map. Additionally, a non-monotonic factor is introduced to modulate the "temperature," and a boundary condition is incorporated into the dynamic opposition learning mechanism. These modifications collectively reduce the likelihood of population convergence to local optima during optimization. The feasibility of IESO is validated through time complexity and global convergence analyses. Comparative simulation experiments benchmarked IESO against five state-of-the-art optimization algorithms across test functions and path-planning simulated scenarios. The experimental results indicate that the enhanced algorithm achieves superior optimization precision, faster convergence speeds, and improved quality in the planned trajectories by 30%.

In this work, an analog front-end (AFE) circuit for Electrocardiogram (ECG) detection system has been designed, implemented, and investigated in an industry-standard Cadence simulation framework using advanced technology node of 45 nm. The AFE consists of an instrumentation amplifier, a Butterworth band-pass filter (with fifth-order low-pass and second-order high-pass sections), and a second-order notch filter- all are based on two-stage, Miller-compensated operational transconductance amplifiers (OTA). The OTAs have been designed employing the $g_m/I_D$ methodology. Both the pre-layout and post-layout simulation were carried out. The layout consumes an area of 0.0058 mm$^2$ without the resistors and capacitors. Analysis of various simulation results were carried out for the proposed AFE. The circuit demonstrates a post-layout bandwidth of 239 Hz with a variable gain between 44 to 58 dB, a notch depth of -56.4 dB at 50.1 Hz, a total harmonic distortion (THD) of -59.65 dB (less than 1\%), an input referred noise spectral density of $

Herein, we present the design technique of a resonant rectifier for piezoelectric (PE) energy harvesting. We propose two diode equivalents to reduce the voltage drop in the rectifier operation, a minuscule-drop-diode equivalent (MDDE) and a low-drop-diode equivalent (LDDE). The diode equivalents are embedded in resonant rectifier integrated circuits (ICs), which use symmetric bias-flip to reduce the power wasted for charging and discharging the internal capacitance of a PE transducer. The self-startup function is supported by synchronously generating control pulses for the bias-flip from the PE transducer. Two resonant rectifier ICs, using both MDDE and LDDE, are fabricated in a 0.18 μm CMOS process and their performances are characterized under external and self-power conditions. Under the external-power condition, the rectifier using LDDE delivers an output power POUT of 564 μW and a rectifier output voltage VRECT of 3.36 V with a power conversion efficiency (PCE) of 90.1%. Under self-power conditions, the rectifier using MDDE delivers a POUT of 288 μW and a VRECT of 2.4 V with a corresponding PCE of 74.6%. The result shows that the power extraction capability of the proposed rectifier is 5.9 and 3.0 times higher than that of a conventional full-bridge rectifier.

In order to further control the equivalent circulating density,a new type of annular pres-sure-reducing downhole tool for high temperature and high pressure wells-liquid-absorbing ECD tool is designed.Through this tool,the bottom hole pressure of the equivalent circulating drilling fluid can be close to its hydrostatic pres-sure,therebyachieving a deeper drilling depth.The tool is mainly composed of screw mo-tor,vortex blade,annular seal,universal joint and drill string.Its working principle is mainly to use the liquid absorption effect of the tool and the hydraulic energy of the circu-lating fluid extracted by the screw motor to convert it into mechanical energy,and to gen-erate suction force to improve the flow energy of the drilling fluid in the bottom hole an-nulusand reduce the equivalent circulation density.Based on ANSYS-FLUENT analysis andsimulation,the change of pressure drop characteristics under different drilling fluid density,displacement and tool size was simulated.The simulation results show that thedepressurization effect is 1.0MPa when the drilling fluid density is 1.2g/cm3. When the drilling fluid density is 1.5g/cm3,the depressurization is about 1.7MPa,and when thedrill-ing fluid density is 1.8g/cm3,the depressurization is about 1.9MPa.When the drilling fluid density is 2.0g/cm3,the pressure drop is about 2.3MPa.When the flow rate is from 1500-2500L/min,the maximum pressure can be reduced by 2.3MPa.When the viscosity is 40mPa•S and 60mPa•S,the pressure drop almost does not change.The increase of viscosi-ty has little effect on the pressure drop,and it is mainly affected by the flow rate under equal displacement.

Bone drilling is a common procedure used to create pilot holes for inserting screws to secure implants for fracture fixation. However, dis process can increase bone temperature, teh excessive heat can lead to cell death and thermal osteonecrosis, potentially causing early fixation failure or complications. We conducted a three-dimensional dynamic elastoplastic finite element model to evaluate teh propagation and distribution of heat during bone drilling and assess teh thermally TEMPeffected zone (TAZ) dat may lead to thermal necrosis. dis model investigates teh parameters influencing bone temperature during bone drilling, including drill diameter, rotational speed, feed force, and predrilled hole. Teh results indicate dat our FE model is sufficiently accurate in predicting teh temperature rise TEMPeffect during bone drilling. Teh maximum temperature decreases exponentially wif radial distance. When teh feed forces are 40 and 60 N, teh maximum temperature does not exceed 45 °C. However, wif feed forces of 10 and 20 N, both teh maximum temperature exceeds 45 °C wifin a radial distance of 0.2 mm, indicating a high-risk zone for potential thermal osteonecrosis. Wif teh two-stage drilling procedure, where a 2.5 mm pilot hole is pre-drilled, teh maximum temperature can be reduced by 14 °C. dis suggests dat higher feed force and rotational speed and/or using a two-stage drilling process could mitigate bone temperature elevation and reduce teh risk of thermal osteonecrosis during bone drilling.

Despite the fact that COVID-19 treatment and management are now considerably regulated, SARS-CoV-2 is still one of the leading causes of death in 2022. The availability of COVID-19 vaccines, FDA-approved antivirals, and monoclonal antibodies in low-income countries still poses an issue to be addressed. Natural products, particularly traditional Chinese medicines (TCMs) and medicinal plant extracts (or their active component) have challenged the dominance of drug repurposing and synthetic compound libraries in COVID-19 therapeutics. Abundant resources and excellent antiviral performance make natural products a relatively cheap and easily available alternative for COVID-19 therapeutics. Here, we deliberately review the anti-SARS-CoV-2 mechanisms of the natural products, their potency (pharmacological profiles), and application strategies for COVID-19 intervention. In light of their advantages, this review is intended to acknowledge the potential of natural products as COVID-19 therapeutic candidates.

Rapid and accurate detection of road cracks is of great significance for road health monitoring, but currently this work is mainly completed through manual site surveys. Low-altitude UAV remote sensing can obtain images with centimeter or even subcentimeter ground resolution, which provides a new efficient and economical approach for rapid crack detection. Nevertheless, crack detection networks face challenges such as edge blurring and misidentification due to the heterogeneity of road cracks and the complexity of the background. To address these issues, we propose a real-time edge reconstruction crack detection network (ERNet) which adopts multi-level information aggregation to reconstruct crack edges and improve the accuracy of segmentation between the target and background. To capture global dependencies across spatial and channel levels, we propose an efficient bilateral decomposed convolutional attention module (BDAM) that combines depth separable convolution and dilated convolution to capture global dependencies across spatial and channel levels. To enhance the accuracy of crack detection, we use a coordinate-based fusion module that integrates spatial, semantic, and edge reconstruction information. In addition, we propose an automatic measurement of crack information for extracting the crack trunk and its corresponding length and width. Experimental results demonstrate that our network achieves the best balance between accuracy and inference speed.

Many fields of human activity benefit from the ability to create images of obscured objects placed behind walls and to map their displacement in a noninvasive way. Usually, imaging devices like Synthetic Aperture Radars (SAR) and Ground Penetrating Radars (GPR) use expensive dedicated electronics which results in prohibitive prices. This paper presents the experimental implementation and the results obtained from an imaging system capable of performing SAR imaging and interferometric displacement mapping of targets located behind walls, as well as 3D GPR imaging using a low-cost general-purpose radar sensor. The proposed solution uses for the RF section of the system a K-band microwave radar sensor module implementing Frequency Modulated Continuous Wave operation. The low-cost sensor was originally intended for simple presence detection and ranging for domestic applications. The proposed system was tested in several scenarios and proved to operate as intended for a fraction of the cost of a commercial imaging device.

Background: In response to inflammation, the absorption of nutritional iron is restricted. Multiple inflammation-related signaling pathways can lead to ferroptosis, an iron-dependent mechanism of programmed cell death. Since the pathophysiological significance of the presence and uptake of iron in chronic inflammation is still unknown, we tested the effect of a low iron diet on the clinical course of arthritis in the mouse model of collagen-induced arthritis (CIA). Methods: Six- to eight-week-old male DBA/1 mice were fed either a normal or a low iron diet starting three weeks before arthritis induction. From day four after the collagen-booster made on day 25, the development of arthritis was regularly monitored until the end of the experiment (day 34), using a standard clinical arthritis score. The complete blood count was determined as well as Fe2+, Fe3+, oxidized and reduced glutathione (GSH and GSSG) and malondialdehyde (MDA) in whole paw tissue by ELISA. Quantitative PCR was performed from whole paw tissue for glutathione peroxidase 4 and other key regulator genes of iron metabolism and ferroptosis. We used nonparametric tests to com-pare markers of iron metabolism and ferroptosis as well as nonlinear regression models for longitu-dinal data for signs of arthritis. Results: Mice fed a low iron diet showed a significantly less severe course of arthritis compared to mice fed a normal iron diet (p

In this paper we present a city-scale (possibly global) air pollution network made of low-cost sensors for particulate matter concentration in the air. The components of presented system are based on our research and experience from previous studies focused on air quality instruments and sensors for addressing urban air pollution. Sensor nodes are produced locally and distributed over a city of Sarajevo, Bosnia and Herzegovina. The system collects, calibrates and displays particulate metal concentrations in real-time. In comparison to the available measurements from governmental institutions, our system demonstrated good agreement of measured parameters, with several advantages including: higher resolution in space and time, lower costs, both horizontal and vertical measurements and many more. One of the capabilities of the system is the real-time air pollution city map with animations. By installing multiple sensor nodes over a slope (cable car), we receive vertical measurements of temperature, humidity and particulate matter concentration in real time, which gives a valuable insight into the dynamics of temperature inversion episodes and air pollution below the inversion layer.

Glucose is a simple sugar molecule. The sugar molecule is chemically symbolised as C6H12O6. This means that the glucose molecule contains 6 carbon atoms (C), 12 hydrogen atoms (H) and 6 oxygen atoms (O). In human blood, the molecule glucose circulates as blood sugar. Normally after eating or drinking, our body breaks down the sugars in food and uses them to obtain energy for our cells. To do this, our pancreas produces insulin. Insulin "pulls" sugar from the blood and puts it into the cells for use. If someone has diabetes, their pancreas can't produce enough insulin. As a result, the level of glucose in the blood rises. This can lead one to many potential complications, including blindness, disease, nerve damage, amputation, stroke, heart attack and damage to blood vessels etc. In this study, a non-invasive method for monitoring blood glucose has been developed and is therefore easily usable. The device developed can measure the glucose level continuously and eliminates the disadvantages of the invasive system. Near-IR sensors (optical sensors) were used to estimate the concentration of glucose in the blood; these sensors have a wavelength of 940 nm. The sensor is placed on the tip of the finger and the output of the optical sensor is then connected to the microcontroller at the analogue input. Another sensor used, but only to provide more medical information was the heartbeat sensor. After processing and regression analysis, the glucose level is predicted, and data is sent via the Bluetooth network to a developed APP. The results of the implemented device were compared with available invasive methods. The study consists of a hardware platform inserted into an armband and then a small black parallelepiped-shaped box in which the optical sensors are inserted. The hardware consists of a microcontroller, a Near-IR optical sensor, a heartbeat sensor and a Bluetooth module.

A proposed low-temperature forging method aims to enhance stainless steel bearings by creating a martensitic subsurface layer, significantly boosting bearing fatigue life due to increased surface hardness. This technique induces beneficial residual stresses, particularly in axial bearings, streamlining their construction and improving machine elements. Challenges persist, especially with radial bearings, but simplicity in axial bearing forging promotes compact, resource-efficient facility construction. Future research will focus on applying this technique to axial bearing washers, potentially replicating success in other bearing components. Despite the energy ex-penditure for cooling during forging, the substantial increase in bearing fatigue life offsets this, enhancing overall longevity and reliability of critical machine components. Integration of this forging technique into bearing fabrication appears seamless, offering a promising trade-off be-tween energy use and enhanced performance.

The growing interest in benchtop NMR systems is visible in a vast number of applications in science and industry. Low-field nuclear magnetic resonance (LF-NMR) became an instant in technologies that productions are dependent on the reliable structure identification, grade and purity control, chemical process inspection or reaction monitoring. Beyond the benefits of being cost-, space-, maintenance- and user friendly, insufficient sensitivity and low signal resolution are the main drawbacks, due to which the low-field NMR (LF-NMR) in the fields of organic chemistry and synthesis remains limited to solely academic teaching purposes. Herein, we discuss the possibilities of LF-NMR for the needs of organic chemistry and synthesis on a set of compounds 1-4 achieved as the products of student’s laboratory course and organic derivatives 5-8 that are from our research interests. Chemical structure determination of all compounds 1-8 presented herein was performed using the benchtop-type NMR instrument operating at 60 MHz (Pulsar Oxford Ltd.). Structure of novel compounds 5-7 prepared under the aims of our research was characterized by combined 1H/19LF-NMR and compared with an appropriate high-field NMR data from Bruker Ascend Instrument (400 MHz). Finally, possibilities of the low-field NMR as an analytical tool for the research purposes in the fields of organic chemistry and synthesis are documented on a set of 1H and 2D 1H-1H COSY for azomethine 8.

In response to the fairness issue arising from the unequal delay of vehicles in different phases at intersections, considering the actual situation of small and variable delays for vehicles in low-saturation intersection phases, this paper proposes the concept of "sacrificing efficiency for fairness." Firstly, the universality of unfair delay phenomena at intersection phases is explained, especially at low-saturation intersections where the fluctuation in phase delays is 1.87 times higher than at other intersections. Then, a fairness evaluation index is constructed using information entropy, and the feasibility of the proposed approach is demonstrated. Subsequently, a signal optimization model that balances efficiency and fairness is proposed. Finally, the proposed model is validated through case studies, showing that it not only simultaneously considers efficiency and fairness but also has minimal impact on efficiency. Moreover, the changes to timing schemes in the efficiency model are much smaller compared to the model that only considers fairness. Sensitivity analysis reveals that the model performs better under low-saturation intersection conditions.

Low-energy cosmic rays (LECRs) play a crucial role in the formation of planetary systems, and detecting and reconstructing the properties of early LECRs is essential for understanding the mechanisms of planetary system formation. Given that LECRs interact with the surrounding medium to produce nuclear de-excitation line emissions with energy mainly within 0.1–10 MeV, which are unaffected by stellar wind modulation, these gamma-ray emissions can accurately reflect the properties of LECRs. This study introduces an innovative method for using gamma-ray emissions to infer LECR properties. We employed the Parker transport equation to simulate the propagation and spectral evolution of LECRs in a protoplanetary disk and calculated the characteristic gamma-ray emissions resulting from interactions between LECRs and disk material. These gamma-ray emissions encapsulate the spectral information of LECRs, providing a powerful tool to reconstruct the cosmic ray environment at that time. This method, supported by further theoretical developments and observations, will fundamentally enhance our understanding of the impact of CRs on the origin and evolution of planetary systems and address significant scientific questions regarding the cosmic ray environment at the origin of life.

This paper presents a compact wideband circularly polarized cross dipole antenna with low-pass filter response. It consists of two pairs of folded crossed dipole arms printed separately on both sides of the top substrate, and the two dipole arms on the same surface are connected by an annular phase-shifting delay line to generate circular polarization. A bent metal square ring and four small metal square rings around the cross dipoles are employed to introduce new resonant frequencies, effectively extending the impedance and axial ratio bandwidth. Four square patches printed on the middle substrate are connected to the ground plane by the vertical metal plates, in order to reduce the antenna height. Thus, a compact wideband circularly polarized antenna is realized. In addition, a transmission zero can be introduced at the upper frequency stopband by the bent metal square rings, without using extra filter circuits. For verification, the proposed model is implemented and tested. The overall size of the model is 90mm×90mm×33mm(0.37λ0×0.37λ0×0.14λ0, λ0 denotes the center operating frequency). The measured impedance bandwidth and 3 dB axial ratio bandwidth are 53.3% and 41%, respectively. More than 15 dB upper-band radiation suppression level is realized, indicating a good low-pass filter response.

Background: this interventional pilot study aimed to evaluate the short-term (3 years) efficacy of focal laser ablation (FLA) in treating the index lesion of low-intermediate risk prostate cancer, along with assessing the safety of the procedure (ClinicalTrials.gov ID NCT04045756). Methods: forty patients aged between 46 and 86 with histologically proven organ-confined prostate cancer and low to intermediate progression risk were included. FLA was performed under percutaneous fusion magnetic resonance/ultrasound guidance in a Day Hospital setting under local anesthesia. Patients underwent regular clinical assessments, PSA measurements, post-procedure MRI scans, and biopsies at 36 months or if positive findings were detected earlier. Statistical analyses were conducted to assess trends in PSA levels and cavity dimensions over time. Results: forty patients were initially included, with 15 lost to follow-up. At 36 months, a mean PSA reduction of 60% was observed, and 80% of MRI scans showed no signs of in-field clinically significant residual/recurrent cancer. Biopsies at 36 months revealed no malignant findings in 20 patients. No deterioration in sexual function or urinary symptoms was recorded. Conclusions: FLA appears to be safe, feasible and effective in the index lesion treatment of low-intermediate risk prostate cancer, with a high rate of tumor eradication and preservation of quality of life.

A low-cost bio-mass-derived carbon substrate has been employed to synthesize MoS₂@carbon composites through a hydrothermal method. Carbon fibers derived from natural cotton provide a three-dimensional and open framework for the uniform growth of MoS₂ nanosheets, thus constructing hierarchically coaxial architecture. The unique structure could synergistically benefit fast Li-ion and electron transport from the conductive carbon scaffold and porous MoS₂ nanostructures. As a result, the MoS₂@carbon composites, when served as anodes for Li-ion batteries, exhibit a high reversible specific capacity of 820 mAh g^-1, high-rate capability (457 mAh g^-1 at 2 A g^-1), and excellent cycling stability. The superior electrochemical performance makes the MoS₂@carbon composites to be low-cost and promising anode materials for Li-ion batteries.

Air pollution has created a lot of problems in the developed and developing countries. To avoid or reduce these problems, constant monitoring of the air should be ensured. The conventional techniques is costly because it requires a lot of money and time consuming. Biomonitoring has been the alternative method. Moss, lichens and plants are biomonitors available to entrap air pollutants. The aim of this paper is to discuss one of the ways of monitoring air pollution – Moss bag technique. To do this, types, choice, preparation, handling of bags after preparation of moss were discussed. From the literatures consulted, it was discovered that there were differences in the techniques used by the researchers. In all, the use of mosses with emphasis on the employment of moss bag have proved to be a powerful tool in airborne particulate and toxic elements. To conclude, developing countries should focus more on this technique because it will reduce cost of air monitoring.

Background: Breastfeeding is essential for optimal infant health, offering numerous benefits such as infection prevention and enhanced bonding. Despite its importance, challenges exist, particularly for mothers of preterm and very low birth weight (VLBW) infants, necessitating effective lactation support strategies. Initiatives like the Baby Friendly Hospital Initiative (BFHI) and Kangaroo Mother Care (KMC) aim to promote breastfeeding success, while donor human milk (DHM) serves as an alternative bridge while the mother is supported to build up her supply. Adequate lactation support, counseling, and education are vital antenatally, during NICU stays and after discharge to improve breastfeeding rates and infant health outcomes, ultimately contributing to global efforts to reduce neonatal mortality and achieve sustainable development goals. This study explores existing research on factors influencing mothers’ ability to produce breast milk and sustain breastfeeding, with the aim of enhancing lactation support and improving infant health outcomes.Methods/Design: A systematic review will be conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) checklist. Searches will be performed in the following databases: PubMed, Cochrane Library, Cumulative Index to Nursing and Allied Health Literature (CINAHL), MEDLINE Ovid via EBSCOhost, Google Scholar, and WILEY online Library) to identify and incorporate primary research articles related to the topic published between 2014 and 2024. Authors will search peer-reviewed articles, gray literature sources, and reference lists to identify eligible studies. Independent abstract and full-text screening by two reviewers will follow the title screening. The analysis of data will be carried out using Review Manager software (version 5.3.5). The I² test will be used to quantify the amount of unexplained heterogeneity. Potential biases related to study size (such as publication bias) will be examined through the application of several techniques, including funnel plots, Egger’s test, Begg’s test, and Trim and Fill analysis.Discussion: Our review represents a singular exploration into exclusive breastfeeding among very low birth weight (VLBW) infants in low- and middle-income countries (LMICs), focusing on breastfeeding assistance methods. With VLBW defined as less than 1500 g, and most low birth weight (LBW) babies born in LMICs, there is a critical need to address feeding challenges and promote optimal nutrition. Despite updated WHO guidelines in 2022, there is still a lack of comprehensive advice for this vulnerable population. Peer support interventions and health worker counseling have shown promise in increasing breastfeeding continuation rates in LMICs, but challenges persist due to workforce shortages and inadequate documentation. Promoting early postpartum milk expression is crucial for milk production and infant health. Our review aims to fill the gap in understanding VLBW mothers’ breastfeeding experiences in LMICs, shedding light on the effectiveness of breastfeeding support methods and identifying areas for further research. Systematic review registration: CRD42024547864

Due to the retrognathic mandible, an orthodontist frequently observes skeletal class II malocclusion in growing patients. The preferred technique for analyzing and monitoring the temporomandibular joint alterations caused by the functional device is magnetic resonance imaging [MRI]. The study aimed to assess and compare changes in the articular disc's dimension and location before and following the use of various class II correctors to correct the class II molar relation using MRI technology. Methods: 39 adolescent patients aged 15-23 years old [24 girls and 15 boys] who were diagnosed with skeletal Class II division 1 malocclusion based on clearly defined criteria underwent treatment with either the power scope II [PS], Sabbagh Advanced Repositioning Appliance [SARA] and or Dynaflex [DF] with fixed appliances. For fixed functional treatment, the average length of treatment was 5-7 months. The changes in the articular disc dimension and position and condylar position were assessed. The study was done at the Department of Orthodontics at the College of Dentistry, Hawler Medical University in Erbil City. Results: Group DF has a significant effect on the condylar position index and posterior joint space on the closed mouth and disc length on both open and close mouth techniques, and in the group SARA, disc position, and condylar position index on closed mouth technique have shown that significant differences were observed. Conclusion: Class II malocclusions can be effectively corrected by fixed functional appliances. The statistical significance forward movement of the condyle in the glenoid fossa and the posterior movement of the articular disc concerning the condylar head was observed. The mechanism of action of functional appliances appears to include the forward migration of the C-GF complex.

Graphene was synthesized through a two-step pyrolysis method using waste candlenut (Aleurites moluccanus) shells as the precursor. Cerium (Ce)/graphene composites were prepared via an impregnation technique. The resulting graphene and Ce/graphene were characterized using various analytical methods, including Scanning Electron Microscopy with Energy-Dispersive Spectroscopy (SEM-EDS), X-ray Diffraction (XRD), X-ray Photoelectron Spectroscopy (XPS), Transmission Electron Microscopy (TEM), Thermo Gravimetric Analysis (TGA), Fourier Transform Infrared (FTIR) spectroscopy, Cyclic Voltammetry (CV), and Linear Sweep Voltammetry (LSV). The bio-carbon produced predominantly exhibited a graphene structure with flat carbon morphology and an interlayer distance of 0.33 nm. This structural information is supported by XRD data, which shows a broad and weak peak at 2θ = 26° corresponding to the C (002) plane, indicative of graphene presence. FTIR, XPS, and Raman spectroscopy further confirmed the presence of graphene through the detection of Csp2 aromatic bonds and the characteristic D, G, and 2D peaks. Notably, the performance of cerium can be enhanced by the incorporation of graphene, attributed to the large surface area and chemical interactions between Ce and graphene. Consequently, candlenut-derived graphene shows potential as a supportive material for modifying the properties of cerium, thereby opening avenues for various advanced applications, such as sustainable and high-performance energy storage systems.

A large core size and bending resistance are very important properties of mid-infrared energy transfer fibers, but large core sizes usually lead to a deterioration of bending properties. A negative-curvature nested node-free anti-resonant hollow-core fiber (AR-HCF) based on quartz is proposed. It was made by adding a nested layer to a previous AR-HCF design to provide an additional anti-resonance region while keeping the gap between adjacent tubes strictly correlated to the core diameter to produce a node-free structure. These features improve the fiber’s bending resistance while achieving a larger core diameter. The simulation results show that the radial air-glass anti-resonant layer is increased by the introduction of the nested anti-resonant tube, and the weak interference overlap between the fiber core and the cladding mode is reduced, so the fiber core’s limiting loss and the sensitivity to bending are effectively reduced. When the capillary wall thickness t of the fiber is 0.71 μm, the core diameter D is 70 μm, the ratio of the inner diameter of the cladding capillary to the core diameter d/D is 0.62, and the diameter of the nested tube is d0=29 μm, the fiber has a lower limiting loss at the wavelength of 2.79 μm, and the limiting loss is 3.28×10-4 dB/m. At the same time, the optimized structure also has good bending resistance. When the bending radius is 30 mm, the bending loss is only 4.72×10-2 dB/m. To our knowledge, this is the first research to achieve an anti-bending low-loss micro-structure hollow fiber with a bending radius of less than 30 mm in the 2.79 μm band. An anti-bending low-loss anti-resonant hollow-core fiber with this structure constitutes a reliable choice for the light guiding system of a 2.79 μm band erbium laser therapy instrument.

Laser Photoacoustic Spectrometers (LPAS), which provide photoacoustic spectra for detecting food frauds or adulteration, and fluorescence-based systems for recognition of contamination by heavy metal ions in water are two important families of sensors for health and environmental purposes. Both sensor families require one or more low-frequency Lock-In Amplifiers (LIAs) to extract the signal of interest from background noise. In the cited applications, the required LIA frequency is quite low (up to 1kHz), and this leads to a simplification of the hardware with consequent good results in portability, economy, reduced size, weight and low-cost characteristics. The present system, called ENEA DSP Box Due, is based on a very inexpensive microcontroller proto-board and can replace four commercial LIAs, resulting in significant savings in both cost and space. Furthermore, it incorporates a dual-channel oscilloscope and a sinusoidal function generator. This article outlines the architecture of the ENEA DSP Box Due, its electrical characterization and its applications within a project concerning laser techniques for food and water safety.

Pore structure characterization and fractal analysis have great significance for understanding and evaluating tight limestone reservoirs. In this work, the pore structure of tight limestone is characterized combined with low-temperature nitrogen adsorption (LTNA) and low-field nuclear magnetic resonance (NMR), and the fractal dimension of tight limestone pore structure is discussed based on LTNA and NMR data. The results indicate that the pores of tight limestone have H3 and H4 types, the pore size distribution (PSD) of H3 type is a wave distribution ranging from 2 to 10 nm, and the PSD of H4 type is a unimodal distribution ranging from 2 to 10 nm. The transverse relaxation time (T2) spectrum of tight limestone shows single peak (DF), double peak (SF) and three peak (TF), and the T2 spectrums for micropores, mesopores and macropores range from 0.1 to 10 ms, from 10 to 100 ms, and greater than 100 ms, respectively. The LTNA fractal dimension of tight limestone (DL) ranges between 2.4446 and 2.7688, with an average of 2.5729, and the NMR fractal dimensions of micropores (DNMR1), mesopores (DNMR2) and macropores (DNMR3) are distributed between 0.3744 to 1.1293, 2.4263 to 2.9395 and 2.6582 to 2.9989, respectively. Moreover, there is no correlation between fractal dimension and permeability and porosity, a negative correlation between DL and average pore radius, a positive correlation between DL and specific surface area, and a positive correlation between DNMR2 and DNMR3 and micropore content, while DNMR2 and DNMR3 are negatively correlated with the content of mesopores and macropores.

Objective: We report a case of acute pancytopenia and liver injury after concomitant administration of low-dose methotrexate (MTX), and high-dose esomeprazole and metamizole. Case summary: A 71-year-old patient with chronic systemic idiopathic erosive arthritis was admitted after a pelvic ring fracture. After hospital admission, she received MTX in addition to esomeprazole and metamizole. Subsequent laboratory tests revealed pancytopenia and elevated liver enzymes. The follow-up clinical examinations were unremarkable, with the exception of sub-febrile temperatures. Further investigations did not detect a definitive etiology. Due to the suspicion of methotrexate-induced hematotoxicity and hepatotoxicity, antagonizing therapy with calcium folinate was administered, after which the blood counts and liver parameters normalized. Discussion: Due to the administration of high-dose metamizole immediately before the administration of low-dose MTX, the pre-existing hematotoxic pharmacodynamic effect of MTX was acutely enhanced by that of metamizole, although folic acid was administered preemptively. In addition, the concomitant administration of high-dose esomeprazole and normal dose torasemide resulted in a pharmacokinetic interaction with MTX by decreasing its renal secretion and elimination, further enhancing the concentration-dependent hematotoxic and hepatotoxic side effects of MTX. Conclusions: The relatively high demand for analgesics in patients with chronic rheumatic diseases already being treated with MTX and proton pump inhibitors necessitates that clinicians consider drug-drug interactions as potential causes of adverse drug reactions after the administration of metamizole or nonsteroidal anti-inflammatory drugs. In this category of patients, it is strongly recommended to switch to analgesics of other classes.

Background: Stoma closure is a widely performed surgical procedure, with 6295 undertaken in England in 2018 alone. This procedure is associated with significant complications; incisional hernias are the most severe, occurring in 30% of patients. Complications place considerable financial burden on the NHS; hernia costs are estimated at GBP 114 million annually. As recent evidence (ROCSS, 2020) found that prophylactic meshes significantly reduce rates of incisional hernias following stoma closure surgery, an evaluation of this intervention vs. standard procedure is essential. Methods: A cost-utility analysis (CUA) was conducted using data from the ROCSS prospective multi-centre trial, which followed 790 patients, randomly assigned to mesh closure (n=394) and standard closure (n=396). Quality of life was assessed using mean EQ-5D-5L scores from the trial, and costs in GBP using UK-based sources over a 2-year time horizon. Results: The CUA yielded an incremental cost-effectiveness ratio (ICER) of GBP 128,356.25 per QALY. Additionally, two univariate sensitivity analyses were performed to test the robustness of the model. Conclusion: The results demonstrate an increased benefit with mesh prophylaxis, but at an increased cost. Although the intervention is cost-ineffective and greater than the ICER threshold of GBP 30,000/QALY (NICE), further investigation into mesh prophylaxis for at risk population groups is needed.

Fatigue and dyspnea are the most commonly presented long-term complaints in individuals previously infected with SARS-CoV-2. The purpose of the study was to comprehensively evaluate diaphragm muscle function in COVID-19 survivors, as well as investigate whether potential diaphragm dysfunction will contribute to physical functioning impairment. 46 patients qualified to pulmonary rehabilitation were examined. Diaphragm muscle function parameters were evaluated using ultrasonography, while severity of dyspnea, aerobic capacity and amount of energy used by the body during physical activity were assessed by 6-minute walk test, mMRC scale and Metabolic Equivalent Task (MET), respectively. We identified 69.5% patients with diaphragm atrophy and 6.5% with diaphragm paralysis. Percentage of atrophy was not related to age, gender, BMI index, oxygen therapy use during COVID-19 infection course and severity of disease. Patients with presence of cough, fever and without loss of smell during COVID-19 course had a significantly greater values of diaphragm inspiratory thickness, while patients with cough and no smell disorders had a significantly lower percentage of diaphragm atrophy. Diaphragm functional parameters were strongly associated with selected variables of person's exercise tolerance, such as distance in 6-minute walk test, saturation level, fatigue and exertion in Borg scale. In conclusion, diaphragm muscle dysfunction is a serious long-term post-COVID aftermath and can be viewed as a major contributing factor of prolonged functional impairments.

Low carbon supply chain management (LCSCM) strategies are required in the Indian manufacturing industries due to rapidly growing energy usage for financial and environmental concerns. LCSCM technologies face a number of challenges that limit their adoption. Eleven hurdles were selected from the literature as well as the recommendations of business and academic experts. These barriers are defined by a number of factors, including a lack of information, financial worries, environmental concerns, and governmental limits, among others. In addition, efforts are being made to quantify the most optimal relevant barriers using Multi criteria decision making (MCDM) techniques such as interpretive structural modeling (ISM) and decision-making trail and evaluation laboratory (DEMATEL). MICMAC analysis is used to estimate the sensitivity and priority of barriers based on dependency power and driving power. One of these barriers is a needed factor; three are driving elements; four barriers are linked factors; and three are self-sufficient factors. The findings of this study can help academic specialists, industry representatives, and practitioners in their ongoing attempts to adopt 'LCSCM techniques' in Indian manufacturing industries. The model is appropriately explained and applies the link between the barriers.The objective of this paper is the Evaluation and selection of most influential of Barriers to Adopt Low Carbon Supply Chain Management (LCSCM) in the Indian Manufacturing Sector is interrelated.

The prolonged static sitting at the workplace is considered as one of the main risks for development of musculoskeletal disorders (MSDs) and adverse health effects. Factors such as poor posture and extended sitting are perceived to be a reason for conditions such as lumbar hyperlordosis and lower back pain (LBP), even though the scientific explanation of this relationship is still unclear and raises disputes in the scientific community. This publication proposes the low back pain assessment (LBPA) dataset collected through experiment with 100 participants and consisting of photogrammetric images with intentionally placed body markers, calculated postural angles and tags correct and incorrect posture assessed by habilitated rehabilitator, as well as questionnaire-based self-reports regarding the occurrence of LBP and similar symptoms among the participants. Machine learning models trained with this data are employed for recognizing incorrect body postures. Two scenarios have been elaborated for modeling purposes: scenario 1, based on natural body posture tagged as correct and incorrect, and scenario 2, based on incorrect body postures, corrected additionally by the rehabilitator. The achieved accuracies of respectively 75.3% and 85% for both scenarios reveal the potential for future research in enhancing awareness and actively managing posture-related issues that elevate the likelihood of developing lower back pain symptoms.

A dynamic view is conjectured for not only the universe but also the underlying theories in contrast to the convectional pursuance of single unification theory. As the 4 -d spacetime evolves dimension by dimension via the spontaneous symmetry breaking mechanism, supersymmetric mirror models consistently emerge one by one at different energy scales and scenarios involving different sets of particle species and interactions. Starting from random Planck fluctuations, the time dimension and its arrow are born in the time inflation process as the gravitational strength is weakened under a 1-d model of a “timeron” scalar field. The “ timeron” decay then starts the hot big bang and generates Majorana fermions and U(1) gauge bosons in 2-d spacetime. The next spontaneous symmetry breaking results in two space inflaton fields leading to a double space inflation process and emergence of two decoupled sectors of ordinary and mirror particles. In fully extended 4-d spacetime, the supersymmetric standard model with mirror matter before the electroweak phase transition and the subsequent pseudo-supersymmetric model due to staged quark condensation as previously proposed are justified. A set of principles are postulated under t his new framework. In particular, new understanding of the evolving supersymmetry and Z2 or generalized mirror symmetry is presented.

Our hypothesis that controlled ozone applications interfere into the redox balance of the biological organism (first published in 1998 with a preclinical trial on protecting the liver from CCl4 intoxication), has been verified over the last 2 decades in reactive oxygen species (ROS)-induced mitochondrial pathologies, such as rheumatoid arthritis, osteoarthritis, aging processes, diabetes-2 and in prevention of intoxications. Low-dose ozone acts as a redox bioregulator: The restoration of the disturbed redox balance is comprehensible in a number of preclinical and clinical studies by a remarkable increase of the antioxidant repair markers, here mainly shown as glutathione increase and a reduction in oxidative stress markers, mainly malondialdehyde. The mechanism of action is shown, relevant data are displayed, evaluated and comprehensively discussed: The repair side of the equilibrium increases by 21% up to 140% compared to the non-ozone treated groups and depending on the indication; the stress markers are simultaneously reduced, the redox system regains its balance.

X-ray Fluorescence Computed Tomography (XFCT) is an emerging non-invasive imaging technique providing molecular-level data, gaining attention for high-resolution imaging. However, increased sensitivity with benchtop X-ray sources raises radiation exposure. Artificial Intelligence (AI), particularly deep learning (DL), has revolutionized medical imaging by delivering high-quality images in the presence of noise. In XFCT, traditional methods rely on complex algorithms for noise reduction, but AI holds promise in addressing high-dose concerns. We present an optimized SCUNet model for noise reduction in low-concentration XRF images. Compared to higher-dose images, our method’s effectiveness is evaluated. While various denoising techniques exist for X-ray and CT, few address to XFCT. The DL model is trained and assessed using the augmented data, focusing on background noise reduction. Image quality is measured using PSNR and SSIM, comparing outcomes with 100% X-ray dose images. Results show the proposed algorithm achieves high-quality images from low-dose and low-contrast agents, with a maximum PSNR of 39.05 and SSIM of 0.86. The model outperforms BM3D, NLM, DnCNN, and SCUNet in both visual inspection and quantitative analysis, particularly in high-noise scenarios. This indicates the potential of AI, specifically the SCUNet model, in significantly improving XFCT imaging by mitigating the trade-off between sensitivity and radiation exposure.

The emergence of Artificial Intelligence (AI) driven Keyword Spotting (KWS) technologies has revolutionized human to machine interaction. Yet, the challenge of end-to-end energy efficiency, memory footprint and system complexity of current Neural Network (NN) powered AI-KWS pipelines has remained ever present. This paper evaluates KWS utilizing a learning automata powered machine learning algorithm called the Tsetlin Machine (TM). Through significant reduction in parameter requirements and choosing logic over arithmetic based processing, the TM offers new opportunities for low-power KWS while maintaining high learning efficacy. In this paper we explore a TM based keyword spotting (KWS) pipeline to demonstrate low complexity with faster rate of convergence compared to NNs. Further, we investigate the scalability with increasing keywords and explore the potential for enabling low-power on-chip KWS.

(1) Background: This study aimed to compare the effect of polyetheretherketone (PEEK) and titanium abutments on implant prosthetic systems and supporting bone using three-dimensional finite element analysis (FEA); (2) Methods: A three-dimensional finite element analysis was conducted using CATIA and Abaqus software to model mandibular first molar implant systems with titanium and PEEK abutments. Under external loading conditions, finite element analysis was conducted for the stresses in implant components and surrounding bones of each group; (3) Results: The implant fixture of the PEEK model exhibited the highest von Mises stress (VMS). The lowest VMS was observed in the abutment screw of the titanium model. Both implant systems demonstrated similar stress distribution and magnitudes in cortical and cancellous bones; (4) Conclusion: PEEK abutments show similar stress distribution in surrounding bone compared to titanium. However, PEEK absorbs the stresses within the implant system and exhibits the highest VMS values due to its low mechanical and physical properties. Therefore, PEEK abutments need improved mechanical properties for better clinical application.

The use of chemical pesticides in agriculture contributes to soil, water and air pollution, biodiversity loss and can harm non-target species. The European Commission has already established a Harmonized Risk Indicator to quantify the progress in reducing the risks linked to pesticides. Therefore, there is an increasing need to promote biopesticides or so-called low-risk pesticides (LRP). Extract from Melaleuca alternifolia – tea tree oil (TTO) is known for its antiseptic, antimicrobial, antiviral, antifungal, and anti-inflammatory properties. Tea tree oil has been extensively studied in pest management as well as in the pharmaceutical and cosmetic industry and there are already products based on its active substances on the market. This review focuses on the overall evaluation of tea tree oil in terms of effectiveness and safety as a biopesticide for the first time. The collected data can be an added value for further evaluation of tea tree oil in terms of the authorization extension as a fungicide in 2026.

Drug resistance in melanoma is a major hindrance in cancer therapy. Growth hormone (GH) plays a pivotal role in contributing to resistance to chemotherapy. Knocking down or blocking the GH receptor has been shown to sensitize the tumor cells to chemotherapy. Extensive studies have demonstrated that exosomes, a subset of extracellular vesicles, play an important role in drug resistance by transferring key factors to sensitize cancer cells to chemotherapy. In this study, we explore how GH modulates exosomal cargoes from melanoma cells and their role in drug resistance. We treated the melanoma cells with GH, doxorubicin, and GHR antagonist, pegvisomant, and analyzed the exosomes released. Additionally, we administered these exosomes to recipient cells. The GH-treated melanoma cells released exosomes with elevated levels of ABC transporters (ABCC1 and ABCB1), N-cadherin and MMP2, enhancing drug resistance and migration in recipient cells. GHR antagonism reduced these exosomal levels, restoring drug sensitivity and attenuating migration. Overall, our findings highlight a novel role of GH in modulating exosomal cargoes that drive chemoresistance and metastasis in melanoma. This understanding provides insights to the mechanisms of GH in melanoma chemoresistance and suggests GHR antagonism as a potential therapy to overcome chemoresistance in melanoma treatment.

Mangroves offer vital ecological advantages including air and water filtration, coastal and estuarine habitat provision, sediment stabilization, and wave energy absorption. Their intricate root systems play a key role in safeguarding shorelines from tsunamis and erosive storms by dissipating wave energy. Moreover, mangroves shield against boat wakes and wind-waves, bolstering shoreline defense. Wave dissipation is a function of forest width, tree diameter, and forest density. Restoration efforts of juvenile mangroves in Florida’s Indian River Lagoon (IRL) aim to reduce wave energy in areas vulnerable to erosion. Physical model testing of wave dissipation through mangroves is limited due to the complexity in representing the mangrove structure, where prop roots are non-uniform in both diameter and location. Previous studies have quantified wave dissipating effects through use of scaled and parameterized mangrove structures. This study measures the dissipation effects of live mangroves in a wave flume, forced by conditions representative of the IRL. These measurements are used to validate a parameterized dowel model. Error between wave attenuation factors for the live mangrove and dowel system was on average 2.5%. Validation of the modularized dowel system allowed for further parameterized testing to understand forest structure effects, such as sediment stabilization and wave attenuation.

Single-phase-to-ground fault in non-effectively grounded systems poses serious public safety concern. Along with increasing deployment of low-voltage (LV) sensors, new opportunities and ways are created for solving this traditional problem in distribution systems. A new approach, identifying primary single-phase-to-ground line fault and locating it through measurement data from smart sensors deployed in the secondary side of distribution transformers, is proposed in this paper. When a fault occurs in primary distribution feeder, the transient aerial mode voltage (AMV) of each LV smart sensor is calculated. The fault line can be identified by an abrupt change value of AMV at LV side. Next, by finding the position of the maximum value of this AMV from LV sensors on the fault line, the fault location can be pinpointed to two neighboring distribution transformers. The proposed method has been proven to be efficient and reliable by simulation studies.

Abstract: Efforts for carbon emission reduction have been identified as a major climate change mitigation target in the Paris Agreement. The 26th Conference of Parties to the United Nations Framework Convention on Climate Change highlighted the worldwide effort for cleaner and greener mining, urging the mineral industry to embrace greener operational emissions reduction technologies and strategies. Ghana pledged at the Paris Conference to reduce CO2 emissions by 15% and increase renewable energy share to 10% of the national energy mix. Here the fundamental question is how Ghana’s mining industry can contribute to CO2 emission reduction efforts. This paper investigates barriers to low-carbon emission policy implementation among mining companies in Ghana. The questionnaire survey was conducted among Ghana’s four large-scale gold mining companies. As the sample number of available companies is limited, we used a descriptive analysis to assess the responses. The results show that the companies expressed widely different ways to respond to the issues of cost, a government policy/regulations for carbon emission reduction, and the adoption/acquisition of low-carbon technologies. Those companies that are relatively less prepared for this endeavor emphasized the uncertainties of securing sufficient energy in case of using low-carbon technologies. They also expressed a need for more trained workers to handle low-carbon technologies. All companies agreed with a expected high cost of adopting low-carbon options. There was a communication gap with the relevant government ministry about low-carbon mining options where the government should be able to facilitate the involvement of othe low technology providers, such as solar PV installation.

This paper seeks to model the crack propagation in of AAs (aluminum-alloys) 2524-T3 and 7050-T7452 subjected to fatigue loading at low temperature. Fatigue crack growth tests were performed on AAs 2524-T3 and 7050-T7452 subjected to constant-amplitude and actual random-spectra loading histories at room temperature of about 25 °C and at cryogenic temperature of -70 °C, respectively, to determine their crack growth properties and residual lives. The interaction mechanisms between cryogenic temperature and the fatigue load were deduced on the basis of the results of fractographic analysis. Temperature-dependent residual lives under actual random-spectrum load history were modeled based on a modified accumulation damage rule by accounting for the load interaction. Good correlation was achieved between the predictions and actual experiments, demonstrating the practical and effective use of the proposed method.

Dielectric barrier discharge (DBD) plasma has several applications in different fields. One of these fundamental applications is medical usages, where various methods are employed to improve the plasma treatment process. The combination of different gases is one of the important strategies to improve the performance of plasma in treatment. In this paper, the optimized plasma parameters for one-dimensional radiofrequency discharge produced at low pressures in a helium gas combination is studied. In this research, the optimal combination of H₂O and He is identified to attain the highest amount of reactive oxygen species (ROS). Considered mixture are 5,10,15 and 20 precent of H₂O for one dimensional helium gas discharge. The results show that the parameters of the output plasma are highly dependent on the composition of the input gases. It is found that the greatest concentrations of H+, He+, Hes (excited helium), and OH/ densities are observed when the H₂O percentage is 10%. Moreover, the density distributions of various species and the temperatures of electrons are numerically calculated during the electrical discharge process. These findings provide useful knowledge on how to optimize plasma parameters for biomedical applications, which may lead to improved treatment results in several therapeutic areas.

The increasing regulatory pressure to monitor and reduce GHG emissions and air pollutants requires cost-effective methods for their surveillance. The most common technique used for scientific investigations on gas concentration monitoring in barns are accurate but expensive and with a complex maintenance. This research study analysed the potential use of a low-cost portable measurement devices for the measurement of ammonia (NH3) and carbon dioxide (CO2) concentrations in an open dairy barn. A comparison between gas concentrations acquired at different heights from the floor by using portable devices and those acquired by a photoacoustic infrared multigas spectroscope (i.e., reference measurement) in the same sampling locations was carried out to determine the precision of the low-cost portable devices. The performance of low-cost portable devices was statistically analysed by the application of the one-way analysis of variance, correlation analysis and regression analysis. The results showed a significant difference between gas concentration values at various heights from the floor for both NH3 and CO2. The correlations between concentrations acquired by the low-cost portable device and the INNOVA were statistically significant (r=0.83; P&lt;0.001) for gas concentrations monitored at 0.4 m from the floor. Compared with the reference measurement device, the low-cost devices were effective in the monitoring of NH3 concentrations at 0.40 m from the floor though it underestimated them in the barn at increasing of the height from the floor, whereas the device was not adequate for CO2 concentrations. In detail, the relative measurement error of the low-cost devices compared to INNOVA was reduced close to the floor during NH3 concentration measurements. Within these limitations, this device could be useful for monitoring NH3 concentration in the barn and to assess variation in NH3 concentrations mainly related to the animal occupied zone. Further efforts are needed in this field of research to identify low-cost device that could simplify emission estimation from open dairy barns.

Preschoolers with disabilities and their caregivers have been neglected in health and social service provision in most low-income countries and arguably too in low resourced areas of more affluent nations. Yet as this rapid review of published literature identifies, there are low-cost, evidence-based strategies to address their needs that can be implemented in communities by local people. Five key features of the necessary supports are examined. First the leadership functions required to create and implement the support services. Second, the family-centred, home-based support provided to caregivers and the personnel undertaking this form of support. Third, providing opportunities for peer support to flourish and encouraging the formation of advocacy groups across families. Fourth, mobilizing the support of significant groups within the community; notably traditional healers and leaders; health services and poverty alleviation initiatives. Fifth, devising ways in which preschool educational opportunities can be offered to children as a prelude to their inclusion in primary schools. The review serves a further purpose. It provides an example of how public health researchers and academics could achieve more rapid implementation of evidence-based knowledge into existing and new support services through dissemination to community practitioners.

Several Lung diseases can result in lung damage structural changes, with lung transplantation as a only therapeutic option when the disease reaches an advanced progression, unfortunately, the success of lung transplantation is limited. In this context, lung bioengineering is considered a potential therapeutic alternative and the current approach is based on the use of the natural extracellular matrix (ECM) of the decellularized lung. The use of undifferentiated cells to seed an ECM can be a more practical strategy for lung bioengineering but the sterilization of the organ for this recellularization process is still a challenge. In this sense, Low-Level Laser Therapy (LLLT) can emerge as a potential treatment alternative. Therefore, the objective of this study was to verify if LLLT, can optimize lung recelularization with mesenchymal stem cells by cytokine modulation. For this, we decellularized lungs from C57BL/6 mice perfusing SDS 1% via pulmonary artery, sterilized with LLL (660nm, 100mW, 30s). Whole lung scaffolds were recellularized: (1) Recellularized Lung (2) Recellularized Lung + LLLT. Mesenchymal stem cells derived from human tooth pulp (DPSc) were seeded into lungs and incubated for 24 hours at 37ºC, 5% CO₂, in a bioreactor-like conditions, maintaining CPAP at 20cmH2O, 90% O2, and LLL at wavelength of 808nm, 100mW, 30s. Culture medium were analyzed at 24 hours. H&amp;E, immunostaining, SEM and ELISA assay were performed. We produced viable biological scaffolds absent of DNA content, preserving glycosaminoglycans and collagen I, III and IV, fibronectin, laminin and elastin and lung structure (SEM). The levels of IL-6 and IL-8 were stable during the 24 hours of culture. IFN-γ levels showed significant difference in the DPSc and DPSc+LLLT group in the 24-hour evaluation. The levels of IL-10 decreased after LLLT. These findings highlight LLLT's role in cytokine regulation and contamination prevention during lung recellularization with mesenchymal stem cells. Overall, these results demonstrated that LLLT's modulated cytokine levels and could prevent contamination during the lung recellularization process with mesenchymal stem cells, offering promise for enhanced therapeutic strategies in lung bioengineering.

Micromobility has gained attention from policymakers, industry stakeholders, and academia; however, a comprehensive conceptualization of micromobility is still missing. Existing definitions are largely vehicle-centric: either listing modes or detailing vehicle characteristics. This paper addresses this gap by developing a ‘beyond vehicles’, multi-dimensional conceptualization of micromobility, accompanied by a novel socio-technical definition. Through a review of related concepts, combined with an analysis of the use and definitions of the term micromobility in publications, this study establishes a new conceptualization of micromobility. It incorporates human, social, and cultural dimensions, considers environmental, economic, infrastructure, vehicle technology, regulatory and policy aspects, and considerations for public health. Our definition of micromobility encompasses a wide range of mobility options typically used for shorter trips and manoeuvrable by an individual without motor assistance, at least for short distances. These modes are characterized by their ‘micro’ attributes, including low energy demand, environmental impact, and road space use relative to automobility. The conceptualization incorporates a range of micromobility modes, including fully human-powered (including walking), partially motor-assisted, and fully powered options. These modes typically operate at speeds not exceeding 25 to 32 kilometres per hour (or 45 km/h for faster options), weigh (typically substantially) below 350 kilograms and often yield significant (public) health benefits. Trip length is generally less than 15 kilometres, and daily distances under 80 kilometres. Importantly, our definition includes the practices, policies, cultures, and infrastructures that emerge around the use of micromobility options and shape their uptake. This proposed conceptualization significantly broadens the prevailing vehicle-focus in micromobility debates towards a socio-technical perspective. Embracing a widely accepted conceptualisation of micromobility would offer several advantages, including robust design standards, legislation, and evaluation metrics and methods. Additionally, this paper highlights the pivotal role micromobilities can play in transcending the limitations of automobility, towards more sustainable and equitable mobility futures.

This work is driven by a practical question, corrections of Artificial Intelligence (AI) errors. Systematic re-training of a large AI system is hardly possible. To solve this problem, special external devices, correctors, are developed. They should provide quick and non-iterative system fix without modification of a legacy AI system. A common universal part of the AI corrector is a classifier that should separate undesired and erroneous behavior from normal operation. Training of such classifiers is a grand challenge at the heart of the one- and few-shot learning methods. Effectiveness of one- and few-short methods is based on either significant dimensionality reductions or the blessing of dimensionality effects. Stochastic separability is a blessing of dimensionality phenomenon that allows one-and few-shot error correction: in high-dimensional datasets under broad assumptions each point can be separated from the rest of the set by simple and robust linear discriminant. The hierarchical structure of data universe is introduced where each data cluster has a granular internal structure, etc. New stochastic separation theorems for the data distributions with fine-grained structure are formulated and proved. Separation theorems in infinite-dimensional limits are proven under assumptions of compact embedding of patterns into data space. New multi-correctors of AI systems are presented and illustrated with examples of predicting errors and learning new classes of objects by a deep convolutional neural network.

The three-dimensional printing of scaffolds is an interesting alternative to the traditional techniques of periodontal regeneration. This technique uses computer assisted design and manufacturing after CT scan. After 3D modelling, individualized scaffolds are printed by extrusion, selective laser sintering, stereolithography, or powder bed inkjet printing. These scaffolds can be made of one or several materials such as natural polymers, synthetic polymers, or bioceramics. They can be monophasic or multiphasic and tend to recreate the architectural structure of the periodontal tissue. In order to enhance the bioactivity and have a higher regeneration, the scaffolds can be embedded with stem cells and/or growth factors. This new technique could enhance a complete periodontal regeneration. This review summarizes the application of 3D printed scaffolds in periodontal regeneration. The process, the materials and designs, the key advantages and prospects of 3D bioprinting are highlighted, providing new ideas for tissue regeneration.

This study aims to investigate the interfacial tension, emulsion stability, and the impact on oil displacement effectiveness of mixed systems containing nonionic surfactants and amphoteric surfactants at different ratios. To achieve this, three different emulsion stability formulations were prepared by mixing betaine and nonionic surfactants in ratios of 1:1, 3:2, and 3:1 respectively. By adjusting the proportions of betaine and nonionic surfactants, variations in interfacial tension and emulsion formation among the three mixed systems were studied to analyze the mechanism of interaction between betaine and nonionic surfactants and evaluate their effects on crude oil displacement and enhanced oil recovery in reservoirs. Subsequently, microscopic and macroscopic oil displacement experiments were conducted on the three mixed systems, along with experiments on emulsion flow resistance. The aim was to verify the oil displacement effectiveness of different mixed systems and further understand their potential applications in reservoirs. By comprehensively analyzing the experimental results, optimal ratios and conditions for the mixed systems were determined. The experimental results indicate that the emulsification system formed by mixing betaine and nonionic surfactants at a ratio of 3:1 achieves molecular synergies through electrostatic interactions and mixed adsorption with betaine, resulting in a compact arrangement of interface membranes and demonstrating a synergistic effect in reducing interfacial tension to an ultra-low value of 10 -4 mN/m. Additionally, as the proportion of betaine surfactant increases, the viscosity of the aqueous phase liquid film increases, the rate of thinning of the liquid film slows down, the rate of water release slows down, and the stability of the emulsion increases. Microscopic oil displacement experiments verified that the three mixed systems increase oil washing efficiency through low interfacial tension and emulsification stripping action, thereby increasing the oil displacement mechanism's coefficient. Emulsion flow resistance experiments demonstrated that as the proportion of betaine surfactant increases, the system exhibits better plugging ability. However, macroscopic oil displacement experiments revealed differences in compatibility between different emulsion stability solutions and rocks with different permeabilities. The mixed system of betaine and nonionic surfactants at a ratio of 3:1 exhibits the best emulsification plugging ability in rocks with a permeability of 109mD, while the 1:1 mixed system performs best in rocks with a permeability of 12mD. This holds significant theoretical and practical significance for reservoir development and enhanced production.

A temperature-modulated pyroelectricity measurement system for a small single crystal is developed and applied to standard sample measurements performed on a thin single crystal of lithium niobate. The modulation measurement is based on the AC technique, in which the temperature of the sample is periodically oscillated, and the synchronized pyroelectric signal is extracted using a lock-in amplifier. Temperature modulation is applied by irradiating periodic light on the sample placed in the heat exchange gas. To apply this technique to the transparent reference sample, a commercially available black resin is coated on the sample’s surface to absorb the light energy and transmits it to the specimen. The experimental results are analyzed using a two-layer heat transfer model to verify the effect of the light-absorbing layer as well as the non-contact temperature modulation system.

Background: The effectiveness of the anti-COVID-19 vaccine remains a matter to be clarified. Methods: The humoral and cellular immunity after the administration of three doses of the Pfizer–BioNTech and Oxford AstraZeneca vaccines against SARS-CoV-2 over one year and the appearance of post-vaccination COVID-19 have been studied. Results: Anti-SARS-CoV-2 IgG and IgA antibodies showed a progressive increase throughout the vaccination period. This increase was greatest after the third dose. The highest levels were observed in subjects who had anti-SARS-CoV-2 antibodies prior to vaccination. There was an increase in CD4+ αβ, CD8+ γδ and TEM CD8+ γδ T cells, and a decrease in apoptosis in CD4+CD8+ and CD56+ αβ and γδ T cells. Post-vaccination SARS-CoV-2 infection was greater than 60%. The symptoms of COVID-19 were very mild and were related to γδ T cell deficit, specifically CD8+ TEMRA and CD56+ γδ TEM, as well as lower pre-vaccine apoptosis levels. Conclusions: Those results demonstrate the important role that γδ T cells play in SARS-CoV-2 vaccine immunization.

Developing new ways to estimate probabilities can be valuable for science, statistics, and engineering. By considering the information content of different output patterns, recent work invoking algorithmic information theory has shown that a priori probability predictions based on pattern complexities can be made in a broad class of input-output maps. These algorithmic probability predictions do not depend on a detailed knowledge of how output patterns were produced, or historical statistical data. Although quantitatively fairly accurate, a main weakness of these predictions is that they are given as an upper bound on the probability of a pattern, but many low complexity, low probability patterns occur, for which the upper bound has little predictive value. Here we study this low complexity, low probability phenomenon by looking at example maps, namely a finite state transducer, natural time series data, RNA molecule structures, and polynomial curves. Some mechanisms causing low complexity, low probability behaviour are identified, and we argue this behaviour should be assumed as a default in the real world algorithmic probability studies. Additionally, we examine some applications of algorithmic probability and discuss some implications of low complexity, low probability patterns for several research areas including simplicity in physics and biology, a priori probability predictions, Solomonoff induction and Occam's razor, machine learning, and password guessing.

Climate change, increasing global food needs, and decreasing global resources brought a new era to human every-day live with a strong turn to circular economy and sustainability. Food packaging and preservation is an important global sector where enchanced research is carried out. New methods such as active packaging and chemical preservatives replacement are of major interest. Many researchers were published studies about active packaging films, replacement of chemical preservatives with natural originated extracts, and novel incorporation procedures. Sequentially to our and other scientists’ previous work, in this paper we present the results of the improvement which was achieved by incorporating the developed nanohybrid of natural-halloysite/carvacrol-extract, the pure natural carvacrol extract and the commonly used low-density-polyethylene. The well known corona-treatment procedure was employed to produced the optimum final film and the results shown that the low-density-polyethylene incorporated with natural-halloysite/carvacrol-extract nanohybrid and coated external with extra carvacrol extract exhibited aproximatelly 100% higher Young-Modulus values, slightly decreased ultimate strength by 20%, and almost stable elonation at break properties. The water-vapor and oxygen barrier increased by 40%. Finally, the antioxidant activity of the corrona-treated film increased by 28% compared to the similar but untreated product because of the lower release rate of the carvacrol.

Using the local bijectivity of Keller maps, we give a proof of two-dimensional Jacobian conjecture.

In this work, we explore a the different forms of a new type of modified gravity, namely f(φ) gravity. We construct the Big Rip type for the energy density and the curvature of the universe. We show that dark energy is a result of the transformation of the field φ mass (dark matter) to energy. In addition, we provide that Ω_{m}≈0,050, Ω_{DM}≈0,2, Ω_{DE}≈0,746, is in excellent agreement with observation data. We explore a generalized formalism of braneworld modified gravity. We also construct a new field equations, which generalize the Einstein field equations. We provide a relation between the extra dimension in 3-brane with the vacuum energy density. We show that the energy density of matter depends directly on the number of dimensions. We manage to find the value of the Gauss-Bonnet coupling α=1/4 which is a good agreement with the results in the literature, this correspondence creates a passage between f(R) gravity and Gauss-Bonnet gravity, this comparison leads to a number of bulk dimensions equal to D=10¹²¹+4.

Establishing macrometastases at distant organs is a highly challenging process for cancer cells, with extremely high attrition rates. A very small percentage of disseminated cells have the ability to dynamically adapt to their changing micro-environments through reversibly switching to another phenotype, aiding metastasis. Such plasticity can be exhibited along one or more axes – epithelial-mesenchymal plasticity (EMP) and cancer stem cells (CSCs) being the two most studied, and often tacitly assumed to be synonymous. Here, we review the emerging concepts related to EMP and CSCs across multiple cancers. Both processes are multi-dimensional in nature; for instance, EMP can be defined on morphological, molecular and functional changes, which may or may not be synchronized. Similarly, self-renewal, multi-lineage potential, and anoikis and/or therapy resistance may not all occur simultaneously in CSCs. Thus, arriving at rigorous functional definitions for both EMP and CSCs is crucial. These processes are dynamic, reversible, and semi-independent in nature; cells traverse the inter-connected high-dimensional EMP and CSC landscapes in diverse paths, each of which may exhibit a distinct EMP-CSC coupling. Our proposed model offers a potential unifying framework for elucidating the coupled decision-making along these dimensions and highlights a key set of open questions to be answered.

Endometriosis occurs when endometrial-like tissue grows outside the uterine cavity, leading to pelvic pain, infertility, and increased risk of ovarian cancer. The present study describes the optimization and characterization of cellular spheroids as building blocks for Kenzan scaffold-free method biofabrication and proof-of-concept models of endometriosis and the endometriotic microenvironment. The spheroid building blocks must be a specific diameter (~500 m), compact, round, and smooth to withstand Kenzan biofabrication. Under optimized spheroid conditions for biofabrication, the endometriotic epithelial-like cell line, 12Z, expressed high levels of estrogen-related genes and secreted high amounts of endometriotic inflammatory factors that were independent of TNF stimulation. Heterotypic spheroids, composed of 12Z and T-HESC, an immortalized endometrial stromal cell line, self-assembled into a biologically relevant pattern, consisting of epithelial cells on the outside of the spheroids and stromal cells in the core. 12Z spheroids were biofabricated into large three-dimensional constructs alone, with HEYA8 spheroids, or as heterotypic spheroids with T-HESC. These three-dimensional biofabricated constructs containing multiple monotypic or heterotypic spheroids represent the first scaffold-free biofabricated in vitro models of endometriosis and the endometriotic microenvironment. These efficient and innovative models will allow us to study the complex interactions of multiple cell types within a biologically relevant microenvironment.

Zinc oxide (ZnO) nanostructures have been studied extensively in the past years due to the novel electronic, photonic, mechanical and electrochemical properties. Recently, more attention has been paid to assemble nanoscale building blocks into three dimensional (3D) complex hierarchical structures, which not only inherit the excellent properties of the single building blocks but also provide potential applications in the bottom-up fabrication of functional devices. This review article focuses on 3D ZnO hierarchical nanostructures, and summarizes major advances in the solution phase synthesis, applications in environment, and electrical/electrochemical devices. We present the principles and growth mechanisms of ZnO nanostructures via different solution methods, with an emphasis on rational control of the morphology and assembly. We then discuss the applications of 3D ZnO hierarchical nanostructures in photocatalysis, field emission, electrochemical sensor, and lithium ion batteries. Throughout the discussion, the relationship between the device performance and the microstructures of 3D ZnO hierarchical nanostructures will be highlighted. This review concludes with a personal perspective on the current challenges and future researches.

With the development of higher voltage power grid, the high and low voltage parallel loops are emerging, which lead to energy losses, even threaten the security and stability of power system. The multi-infeed HVDC configurations widely appearing in AC/DC interconnected power system make this situation even worse. Aimed at energy saving and system security, a decision optimization method for power grid operating condition with high and low voltage parallel loops is proposed in this paper. Firstly, considering hub substation distribution and power grid structure, parallel loop opening schemes are generated with GN algorithm. Then, candidate opening schemes are preliminarily selected from all these generated schemes based on a filtering index. Finally, with the influence on power system security, stability and operation economy in consideration, an evaluation model for candidate opening schemes is founded based on analytic hierarchy process (AHP). And a fuzzy evaluation algorithm is used to find the optimal scheme. Simulation results of New England 39-bus system and an actual power system validate the effectiveness and superiority of this proposed method.

Introduction: The disease caused by Coronavirus-2019 (COVID-19) was initially described in December 2019. Severe acute respiratory syndrome type 2 (Sars-Cov-2) is responsible for an important multisystem inflammatory spectrum. Cardiovascular conditions have been reported with an estimated frequency of 8-28%. In this context, transthoracic echocardiography associated with the analysis of the two-dimensional Global Longitudinal Strain (GLS) of the left ventricle derived from Speckle Tracking emerges as a promising modality. Objective: This study aims to evaluate the applicability of GLS and segmental deformity assessment in detecting subclinical myocardial dysfunction in patients that recovered from COVID-19 infection. Methods: The observational study involved 18 patients (mean age 52 years) with recent evidence of COVID-19 infection that underwent detailed echocardiographic evaluation with GLS, and cardiac magnetic resonance (CMR) according to segmental assessment results. The degree of correlation between the methods was analyzed and a systematic literature review was also conducted. Results: The mean left ventricular ejection fraction (LVEF) was 57.50 ± 9.98%. Only 16.6% of patients had reduced LVEF. The average GLS was – 18.70 ± 3.54% and in 44% of cases only one myocardial segment was affected, particularly the inferolateral basal. CMR revealed inflammatory cardiomyopathy. Discussion: Subclinical myocardial involvement is associated with major adverse cardiac events. Literature review supports the use of GLS for detecting early cardiac involvement. Conclusion: Speckle-tracking echocardiography has been shown to have clinical utility in a variety of settings and to offer superior prognostic value with a potential to enhance subclinical detection of myocardial changes in patients after COVID-19 infection.

Abstract: Short-chain fatty acids (SCFAs) are the major microbial metabolites produced in the gut through the fermentation of non-digestible fibers. SCFAs act through free fatty acid receptor 2 and 3 (FFAR2 and 3), which are known to be expressed on glucagon-like peptide-1 (GLP-1)-secreting L-cells. This pathway is suggested to protect the intestinal mucosa. We aimed to investigate how lack of fiber affects the production of SCFAs and the vulnerability of the co-lon. Additionally, the involvement of FFAR2 and 3 in GLP-1 secretion and intestinal protection. SCFA concentrations in luminal contents from chow- or fiber-free diet-fed mice were analysed by LC‒MS. SCFAs were applied to isolated perfused colons from FFAR2/3 double KO and WT mice, and the secretion of GLP-1 was measured. Dextran sulfate sodium (DSS) colitis was induced in chow- or fiber-free diet-fed mice as well as in FFAR2/3 KO and WT mice. The removal of die-tary fiber significantly decreased total colonic SCFA concentrations, with specific decreases in acetate, propionate, and butyrate. SCFAs significantly increased colonic GLP-1 secretion but not in FFAR2/3 KO mice. In the DSS model, the removal of dietary fiber increased the severity and delayed recovery. Colitis severity was independent of FFAR2 and FFAR3 signaling.

In autonomous driving systems, high-speed and real-time image processing, along with object recognition, are crucial technologies. This paper builds upon the research achievements in in-dustrial item sorting systems and proposes an object recognition and sorting system for auton-omous driving. In industrial sorting lines, goods sorting robots often need to work at high speeds to efficiently sort large volumes of items. This poses a challenge to the robot's real-time vision and sorting capabilities, making it both practical and economically viable to implement a real-time and low-cost sorting system in a real-world industrial sorting line. Existing sorting systems have limitations such as high cost, high computing resource consumption, and high power consump-tion. These issues lead to the fact that existing sorting systems are typically used only in large industrial plants. In this paper, we design a high-speed, low-cost, low-resource-consumption FPGA (Field-Programmable Gate Array) based item sorting system that achieves similar perfor-mance to current mainstream sorting systems at a lower cost and consumption than existing sorting systems. The recognition part employs a morphological recognition method, which segments the image using a frame difference algorithm and then extracts the color and shape features of the items. To handle sorting, a six-degree-of-freedom robotic arm is introduced in the sorting segment. The improved cubic B-spline interpolation algorithm is employed to plan the motion trajectory and consequently control the robotic arm to execute the corresponding actions. Through a series of experiments, this system achieves an average recognition delay of 25.26ms, ensures smooth operation of the gripping motion trajectory, minimizes resource consumption, and reduces implementation costs.

In order to design efficient and sustainable office spaces and to automate lighting, heating and air circulation in these facilities, solving the challenge of occupancy prediction is crucial. In office spaces where large areas need to be observed, multiple sensors must be used for full coverage. In these cases, it is normally important to keep the costs low, but also to make sure that the privacy of the people who use such environments are preserved. Low-cost and low-resolution heat (thermal) sensors can be very useful to build solutions that address these concerns. However, they are extremely sensitive to noise artifacts which might be caused by heat prints of the people who left the space or by other objects which are either using electricity or exposed to sunlight. There are some earlier solutions for occupancy prediction that employ low-resolution heat sensors, however, they have not addressed nor compensate for such heat artifacts. Therefore, in this paper, we present a low-cost and low-energy consuming smart space implementation to predict the number of people in the environment based on whether their activity is static or dynamic in time. We use a low-resolution (8×8) and non-intrusive heat sensor to collect data from an actual meeting room. We propose two novel workflows to predict the occupancy; one based on computer vision and one based on machine learning. Besides comparing the advantages and disadvantages of these different workflows, we use several state-of-the-art explainability methods in order to provide a detailed analysis of the algorithm parameters and how the image properties influence the resulting performance. Furthermore, we analyze noise resources which affect the heat sensor data. We hope that our analysis brings light into understanding how to handle very low-resolution heat images in these environments. The presented workflows could be used in various domains and applications other than smart offices, where occupancy prediction is essential, e.g., for elderly care.

The three-dimensional Ising model on the m$\times$n$\times$l cubic lattice with the screw boundary condition along the ${\bf X}$ direction and the periodic boundary conditions along both ${\bf Y}$ and ${\bf Z}$ directions is exactly solved by using the $2^{mn}$-dimensional representation of the rotation group in $2mn$-dimensions, similar to the Kaufman's spinor approach in two dimensions. The exact partition function is obtained from two sets of $2^{mn-1}$ eigenvalues of the $2^{mn}$-dimensional transfer matrix {\bf V} corresponding to the even and odd eigenvectors, respectively. Such the eigenvalues are determined by the angles of the 2mn-dimensional rotation associated with {\bf V}.