Imaginary dimensions in physics require an imaginary set of base Planck units and some negative parameter $c_n$ corresponding to the speed of light in vacuum $c$. The second, negative fine-structure constant $\alpha_2^{-1} \approx -140.178$ present in Fresnel coefficients for the normal incidence of electromagnetic radiation on monolayer graphene leads to the imaginary Planck units. Furthermore, it sets $c_n \approx -3.06 \times 10^8~\text{[m/s]}$. It follows that electric charges are the same in real and imaginary dimensions. Neutron stars and white dwarfs, objects emitting perfect black-body radiation, need energy exceeding their mass-energy equivalence ratios. Complex energies are defined in terms of real and imaginary Planck units. Their imaginary parts, inaccessible for direct observation, store the excess of these energies. It follows that black holes are fundamentally uncharged, charged micro neutron stars and white dwarfs with masses lower than $5.7275 \times 10^{-10}~[\text{kg}]$ are unphysical, and the radii of white dwarfs' cores are limited to $R_{\text{WD}} < 6.7933~G M_{\text{WD}}/c^2$. It is conjectured that the maximum atomic number $Z=238$. A black-body object is in the equilibrium of complex energies of masses, charges, and wavelengths if its radius $R_\text{eq} \approx 2.7665~G M_{\text{BBO}}/c^2$, which corrects the value of the photon sphere radius $R_{\text{ps}}=3G M/c^2$, taking into account the value(s) of the fine-structure constant(s), which is otherwise neglected in general relativity.

There must exist a reformulation of quantum field theory which does not refer to classical time. We propose a pre-quantum, pre-spacetime theory, which is a matrix-valued Lagrangian dynamics for gravity, Yang-Mills fields, and fermions. The definition of spin in this theory leads us to an eight dimensional octonionic space-time. The algebra of the octonions reveals the standard model; model parameters are determined by roots of the cubic characteristic equation of the exceptional Jordan algebra. We derive the asymptotic low energy value 1/137 of the fine structure constant, and predict the existence of universally interacting spin one Lorentz bosons, which replace the hypothesised graviton. Gravity is not to be quantized, but is an emergent four-dimensional classical phenomenon, precipitated by the spontaneous localisation of highly entangled fermions.

Deep neural network (DNN) watermarking is a potential approach for protecting the intellectual property rights of DNN models. Similar to classical watermarking techniques for multimedia content, the requirements for DNN watermarking include capacity, robustness, and transparency, and other factors. Studies have focused on robustness against retraining and fine-tuning. However, less important neurons in the DNN model may be pruned. Moreover, although the encoding approach in renders DNN watermarking robust against pruning attacks, the watermark is assumed to be embedded only into the fully connected layer in the fine-tuning model. In this study, we extended the method such that the model can be applied to any convolution layer of the DNN model and designed a watermark detector based on a statistical analysis of the extracted weight parameters to evaluate whether the model is watermarked. Using a nonfungible token mitigates the overwriting of the watermark and enables checking when the DNN model with the watermark was created.

In small data set learning, obtaining a large number of training samples from the source class for transfer learning is a challenge for fine-grained visual classification. Based on the fact that fine-grained concepts can be learned with very few samples, we use very few labeled samples (for example, three) in each category. However, due to the difficulty in distinguishing the subtle differences between fine-grained images, we propose a local structure information extraction method of SDFGVC based on spatial frequency features. The proposed learning module enhances the ability of the network to find significant regions and shows excellent performance in experiments on six datasets.

Registration of the flow pattern fine structure and the matter distribution of a free falling liquid drop in a target fluid at rest in the impact mode of coalescence, when the kinetic energy of the drop exceeds its available potential surface energy (APSE), was carried out by photo and video recording. The main attention was paid to the study of the flow structure at the initial stage of the cavity formation. To carry out color registration, the flow pattern was illuminated by several matrix LED and fiber optic sources of constant light. The planning of experiments and interpretation of the results were based on the properties of the complete solutions of the fundamental equations of fluid mechanics system, including the transfer and conversion of energy processes. Complete solutions of the system of equations describe large-scale flow components that are waves or vortices as well as thin jets (ligaments, filaments, fibers, trickles). In experiments, the jets are accelerated by the converted available potential surface energy (APSE) when the free surfaces of merging fluids were eliminated. The experiments were performed with the coalescence of water, solutions of alizarin ink, potassium permanganate, copper sulphate or iron sulphate drops in deep water. In all cases, at the initial contact, the drop begins to lose its continuity and breaks up into a thin veil and jets, the velocity of which exceeds the drop contact velocity. Small droplets, the size of which grows with time, are thrown into the air from spikes at the jet tops. On the surface of the liquid, the fine jets leave colored traces that form linear and reticular structures. Part of the jets penetrating through the bottom and wall of the cavity forms an intermediate covering layer. The forming layer jets are separated by interfaces of the target fluid. The processes of molecular diffusion equalize the density differences and form an intermediate layer with sharp boundaries in the target fluid. All noted structural features of the flow are also visualized when a fresh water drop isothermally spreads in the same tap water. The fast-changing and finely structured diffuse boundary of merging fluids, which at the initial stage has a complex and irregular shape, is gradually smoothed out by molecular diffusion processes. The similar flow patterns were observed in all performed experiments, however the geometric features of the flow depend on the individual thermodynamic and kinetic parameters of the contacting fluids.

Prolonged high-intensity grinding can modify the crystal structure of solid substances and/or induce chemical reaction, which is referred to as mechanochemical reaction. Such reactions can exert positive influences on hydrometallurgical processes, therefore, many researchers have applied mechanochemical reactions for metals dissolution from minerals. The mechanism of mechanochemical reaction has been investigated using solid analyses and simulations. Structural changes caused by mechanochemical reaction are not yet sufficiently clarified because the ground samples are amorphous. The objective of this study was to analyze structural changes of cerianite in weathered residual rare earth ore by mechanochemical reduction. Structural change was analyzed by x-ray absorption near-edge structure and extended x-ray absorption fine structure analysis at the cerium L_III- and K-edges. These analyses revealed that the structural change of cerianite in this ore induced by mechanochemical reduction involved oxygen vacancy production. The process of the oxygen vacancy formation was closely coupled with the quantum effect of localization–delocalization of the 4f electron of cerium.

Fine-tuning is an important technique in transfer learning that has achieved significant success in tasks that lack training data. However, as it is difficult to extract effective features for single-source domain fine-tuning when the data distribution difference between the source and the target domain is large, we propose a transfer learning framework based on multi-source domain called adaptive multi-source domain collaborative fine-tuning to address this issue. This approach utilizes multiple source domain models for collaborative fine-tuning, thereby improving the model's feature extraction capability in the target task. Specifically, this approach employs an adaptive multi-source domain layer selection strategy to customize appropriate layer fine-tuning schemes for the target task among multiple source domain models. The objective is to extract more efficient features. Furthermore, a novel multi-source domain collaborative loss function is designed to facilitate the precise extraction of target data features by each source domain model. Simultaneously, it works towards minimizing the output difference among various source domain models, thereby enhancing the adaptability of the source domain model to the target data. In order to validate the effectiveness of the proposed the adaptive multi-source domain collaborative fine-tuning framework, it is analyzed and compared with the most widely used fine-tuning methods by applying them to seven publicly available visual categorization datasets commonly employed in transfer learning. Experimental results demonstrate that, in comparison with the existing fine-tuning approaches, our method not only enhances the accuracy of feature extraction in the model but also provides precise layer fine-tuning schemes for the target task, resulting in state-of-the-art performance.

Our existence depends on a variety of constants which appear to be extremely fine-tuned to allow for the existence of life as we know it. These include the number of spatial dimensions, the strengths of the forces, the masses of the particles, the composition of the Universe and others. On the occasion of the 300^th anniversary of the death of G.W. Leibniz we discuss the question of whether we live in the "Best of all possible Worlds". The hypothesis of a multiverse could explain the mysterious fine tuning of so many fundamental quantities. Anthropic arguments are critically reviewed.

BACKGROUND: Endoscopic ultrasound-guided tissue acquisition (EUS-TA) is essential technique for sampling solid pancreatic lesions. However, the optimal needle type for achieving high histological diagnostic capability remains unclear. This study compares the histological diagnostic capabilities of the 19G FNA needle and the 22G FNB needle for solid pancreatic lesions. METHODS: This single-center, retrospective study was conducted in patients who underwent EUS-TA for solid pancreatic lesions. Patients were allocated to either Group A, utilizing the 19G FNA needle, or Group B, utilizing the 22G FNB needle. The primary outcome was the diagnostic accuracy based on histology alone. RESULTS: 143 patients were analyzed, with 61 in Group A and 82 in Group B. Diagnostic accuracy was significantly higher in Group B for both histology alone (85.4% vs. 68.9%, p=0.02) and overall diagnostic accuracy (85.4% vs. 73.8%, p=0.02). Logistic regression analysis revealed that lesion size ≥25 mm was an independent factor contributing to higher diagnostic accuracy (OR 3.32, 95% CI 1.28-8.64, p=0.01). Subgroup analysis showed that diagnostic accuracy for 15–25 mm lesions was significantly higher in Group B (90.0% vs. 63.2%, p=0.02). Additionally, trans-gastric punctures had higher accuracy in Group B (94.7% vs. 73.5%, p=0.01). CONCLUSIONS: Our study showed the overall superiority of the 22G FNB needle for EUS-TA of solid pancreatic lesions. However, 19G FNA needle remains a valuable option under specific conditions.

Flotation of small particles is one of the global challenges facing the mineral raw materials processing industry. Large amounts of non-ferrous and rare metals are lost in the flotation tailings in the form of mineral particles below 15 µm in size as a result of the low effectiveness of their capture by coarse bubbles generated in conventional flotation machines. The method of combined microflotation, which is being developed in recent years, uses not only coarse but also microbubbles (MB) produced in the stand-alone generator of air-in-water microdispersion, which serves as the flotation carriers. Depending on the dose of MB, the effect of their application may be both positive, but also negative. The theoretical analysis of various mechanisms of particle transfer onto the surface of coarse bubbles and further into the froth layer allowed to obtain the formula for the optimal MB dose f=2dddpρp, where dd is MB size; dp and ρp respectively are the size and the density of particles. Experiments performed on the copper ore flotation tailings at the Atalaya Mining (Spain) and Chaarat Kapan (Armenia) concentrators showed that, besides the optimal MB dose in the range of 1-3 ml/g, there is another optimal MB dose in the range of 10-20 ml/g, where the copper recovery increases by several percent compared to the reference test (f= 0). In the area between the optimal MB doses, the deep minimum in copper recovery is observed, which value is by several percent lower compared to the value in the reference test.

Scale inhibitors are deployed as preventive and rejuvenation operation in oil and gas industry when production operations are under threat or menace of scale blockage. The application of scale inhibitors is carried out through a method known as squeezing. In general, the squeeze process is governed by inhibitor-rock interaction which is described by adsorption/desorption isotherm. Most reservoirs produce loose sand grains or fine sand which float and flow within the pore spaces along with the squeezed scale inhibitors. Hypothetical reports have shown that not all scale inhibitors pumped into the formation adsorb onto the formation rock. A number of factors (irreversible adsorption, pH changes, competing ions, concentration and temperature) have been considered to affect the adsorption and return profile of these scale inhibitors. This review work examines the performances of most common scale inhibitors used in the oil and gas production activities, theoretical application in reservoirs and how loose fine sand grains affect the adsorption and desorption characteristics of squeezed scale inhibitors. Additionally, presented were overviews of previous reports on fine sand production and migration of fine sands through formation pores in reservoirs.

In this paper, we leverage state of the art models on Imagenet data-sets. We use the pre-trained model and learned weighs to extract the feature from the Dog breeds identification data-set. Afterwards, we applied fine-tuning and dataaugmentation to increase the performance of our test accuracy in classification of dog breeds datasets. The performance of the proposed approaches are compared with the state of the art models of Image-Net datasets such as ResNet-50, DenseNet-121, DenseNet-169 and GoogleNet. we achieved 89.66% , 85.37% 84.01% and 82.08% test accuracy respectively which shows the superior performance of proposed method to the previous works on Stanford dog breeds datasets.

The fluid seepage in local-saturated zone of subgrade promotes the migration of fine particles in the filler, resulting in the change of pore structure and morphology of the filler and the deformation of solid skeleton, which affects the fluid seepage characteristics. Repeatedly, the muddy interlayer, mud pumping and other diseases are finally formed. Based on the theory of two-phase seepage, the theory of porous media seepage, and the principle of effective stress in porous media, a two-phase fluid-solid coupling mathematical model in local-saturated zone of subgrade considering the effect of fine particles migration is established. The mathematical model is numerically calculated with the software COMSOL Multiphysics○R, the two-phase seepage characteristics and the deformation characteristics of the solid skeleton in local-saturated zone of the subgrade are studied. The research results show that due to the continuous erosion and migration of fine particles in local-saturated zone of the subgrade, the volume fraction of fine particles first increases then decreases and finally becomes stable with the increase of time. And the volume fraction of fine particles for the upper part of the subgrade is larger than that for the lower part of the subgrade. The porosity, the velocity of fluid, the velocity of fine particles, and the permeability show a trend of increasing first and then stabilizing with time; the pore water pressure has no significant changes with time. The vertical displacement increase first and then decrease slightly with the increase of time, and finally tend to be stable. For a filler with a larger initial volume fraction of fine particles, the maximum value of the volume fraction of fine particles caused by fluid seepage is larger, and the time required to reach the maximum value is shorter. It can be concluded that in actual engineering, the volume fraction of fine particles in the subgrade filler should be minimized on the premise that the filler gradation meets the requirements of the specification.

Against the background of the sky, imaging and perception of aircraft are cru- cial. Among these, the location of aircrafts is not a very difficult problem in general, because that deep learning algorithms and object detection models based on convolutional neural network(CNN) are ever-evolving. In order to have a better identification of various aircrafts, or to accurately locate different types of flying targets in the sky, it becomes significant to distinguish and recognize different types of aircrafts. One question is that, distinguishing and recognizing between sub-categories of aircrafts pose great challenges. Although fine-grained recognition focuses on exploring and studying such problems, aircrafts under different sub-categories and granularities lead us to rethink the application of features. We noticed that features in swin-transformer demonstrates the under- standing and processing of images, fully showcasing the encoding and indexing of information. Through this research and proposed approach, we discovered a better understanding of features encoding and use, which are inspired by the feature encoding and computation in swin-transformer. In our paper, our ap- proach has achieved effects on features encoded graphically, manifested from the architecture design and convolutional neural network computation, and outper- forms other famous fine-graiend classification models on this issue. Not only approach we proposed has demonstrated superior performance in fine-grained aicraft classification, but also the mechanisms of the feature encoding under different sample space partitions is revealed for this issue, which provides a re- search for the representation of aircraft fineness characteristics. The relationship between representation orientation of aircraft feature information under various grained divisions, shows that the recognition of different categories according to man-made, such as aircraft, can be achieved by means of the feature represen- tations studied in this paper, for more specific defined classification tasks and various man-made targets partition criteria, which may influences the princi- ple of design for calculation and feature extraction in fine-grained classification models.

Fine dust causes various disorders, including cardiovascular, neurological, renal, reproductive, motor, systemic, respiratory, and cancerous diseases. Therefore, it is necessary to study functional materials to prevent these problems. In this study we used various methods, including quantitative PCR, flow cytometry, ELISA, and Alizarin O staining, to study the beneficial effects of erucic acid against fine dust. Erucic acid and erucic acid-induced exosomes (EIE) strongly suppress the attenuation of osteocytic differentiation and inflammation induced by fine dust. Erucic acid effectively suppresses inflammation in fibroblasts exposed to fine dust. Notably, despite exposure to fine dust, EIE promoted osteocytic differentiation in adipose-derived stem cells (ASC) and osteogenesis in macrophages, in addition to phagocytosis. Dramatic upregulation of RunX2 and BMP7 by EIE indicates that it is a strong activator of osteocytic differentiation in ASC and protects them from the effects of fine dust. Moreover, EIE induces immune activity and acts as an osteogenic trigger for macrophages. Therefore, this study suggests the potential use of erucic acid and erucic acid-induced exosomes (EIE) as biomaterials to prevent environment-borne diseases.

These years have witnessed growing research on applying rheology in grinding and flotation treatment of finely disseminated ores. The slurry rheology has long been identified as the comprehensive effect of inter-particle interactions, including their aggregation (coagulation and flocculation) and dispersion states in slurry, which are more impactive under the fine-particle effect. In this regard, rheology has the potential to play a significant role in interpreting the flowing and deforming phenomenon of inter-particle aggregates, particle-bubble aggregates, and flotation froth. Though much attention has been paid to the rheological effect in industrial suspension, this has not been the case for mineral grinding and flotation for fine particles. The influential mechanism of rheology on the sub-processes of mineral processing has not been systemically concluded and revealed thoroughly, without which the underpinning mechanism for enhancing the processing efficiency has been difficult to discover. This paper reviews the current application and importance of rheology in the fine mineral processing, and the potential research direction in the field is proposed.

The identification and quantitative determination of wool and fine animal fibers are of great interest in the textile field because of significant price differences between them and common adulterations in raw and processed textiles. Since animal fibers have remarkable similarities in their chemical and physical characteristics, specific identification methods have been studied and proposed following advances in analytical technologies. The identification methods of wool and fine animal fibers are reviewed in this paper and the results of relevant studies are listed and summarized, starting from classical microscopy methods which are still used today not only in Small to Medium Enterprises but also in large industries, research studies and quality control laboratories. Particular attention has been paid to image analysis, Nir spectroscopy and proteomics which constitute the most promising technologies of quality control in the manufacturing and trading of luxury textiles and can find application in forensic science and archeology.

Objective: We aimed to clarify the differences in the association between re-admission for heart failure (HF) and left atrial (LA) overload indices by sex in heart failure and a preserved ejection fraction (HFpEF). Methods: We analysed 898 HFpEF patients hospitalized for acute decompensated HF. Blood tests and transthoracic echocardiography were performed before discharge. The primary endpoint was re-admission for HF during the first year. Results: The ratio of diastolic elastance to arterial elastance (p=0.014), a relative index of LA pressure overload, in men and LA volume index (LAVI, p=0.020) in women were significant for re-admission for HF during the first year in the multivariable Fine-Gray analysis. Stroke volume (SV)/LA volume (LAV), another index for LAV overload, was not a significant prognostic factor of re-admission for HF during this time. Conclusion: LA overload was an important prognostic factor for HF re-readmission during the first year after enrolment in patients with HFpEF, but the indices relating to LA overload differed by sex.

We investigated preschool-aged children’s understanding of early fractional tasks and how that performance correlates with fine motor skills and use of gestures while counting. Participants were 33 preschoolers aged 4 to 5 in two Southeastern public elementary schools. Children were tested individually in an interview-like setting. Mathematics tasks were presented in a paper and pencil format and the Grooved Pegboard test assessed fine motor skills. Finally, utilization of gestures was evaluated by taking a behavioral rating of the child’s hand morphology, accuracy of gestures, and synchrony of gestures and spoken word while performing a counting task. Results indicate performance on fractional reasoning tasks significantly predicts both fine motor ability and accuracy of gestures.

Our understanding and theoretical interpretation of observations in astrophysics and cosmology depends on our knowledge of the fundamental constants and their possible dependence on time and space. Atomic spectroscopy and radio astronomy give important information on the validity and stability of the fundamental constants. The possible dependence of the fine structure constant alpha on time and spatial direction is an active topic of research.Period doubling is a universal property of nonlinear dynamical systems, and the doubling is exact in principle. The value of the elementary charge squared can be calculated by the period doubling process from the Planck charge and thereby the value of alpha.If ‘old’ and ‘new’ electrons are identical, then the Planck charge, i.e. a set of natural constants, has remained constant over time. In this article we show that the value of alpha calculated from the Planck charge is 0.007 % smaller than the current accepted value of alpha.

Due to metal leaching and poor catalyst stability, the chemical industry’s fine chemical and pharmaceutical sectors have been historically reluctant to use supported transition metal catalysts to manufacture fine chemicals and active pharmaceutical ingredients. With the advent of new generation supported metal catalysts and flow chemistry, we argue in this study, this situation is poised to quickly change. Alongside heterogenized metal nanoparticles, both single-site molecular and single-atom catalyst will become ubiquitous. This study offers a critical outlook taking into account both technical and economic aspects.

Ultraviolet (UV) light with a wavelength of 254 nm has proven to be effective at inactivating microorganisms, and thus has been increasingly employed as a method of disinfection for indoor environments. Solar UV wavelengths (300 to 400 nm) are known to initiate the formation of secondary organic aerosol (SOA) particles from photo-oxidation of volatile organic compounds in the atmosphere, but germicidal wavelengths have not been extensively studied for indoor environments. In this work, toluene was exposed to 254 nm UV light in a laboratory photoreactor, with varying conditions of the air, the duration of UV exposure, and the duration of post-UV time. The number of particles formed in the fine particulate matter (PM2.5) size range was measured, and significant levels of particle formation were observed for UV exposure periods of as short as 5 minutes. The particle formation ranged from 2.4x10⁶ particles/m³ for 5 minutes of UV exposure, to 1449.8x10⁶ particles/m³ for 15 minutes of UV exposure. Particle formation was found to increase with increasing concentrations of gas phase toluene, and at relative humidity of approximately 20% and higher. Variations in the initial number of particles present did not appear to have a significant effect on the particle formation, suggesting that nucleation was not a controlling factor. However, tests in a commercial environment showed no significant detectable PM2.5 formation, indicating that SOA formation during the intermittent use of germicidal UV may not significantly affect indoor air quality.

Background: Food properties can directly influence individual dietary intake. Therefore, computer vision-based food recognition could be used to estimate meal contents for patients with metabolic diseases. Food recognition based on deep learning can create opportunities for breakthroughs in dietary interventions for personal health management and have rapidly emerged as dietary assessment strategies. Methods: This study proposed a methodology for automatic food recognition based on fine-grained visual classification, the High-temperaturE Refinement and Background Suppression network (HERBS). A technical investigation was conducted involved in the HERBS model on CNFOOD241 benchmark to verify the model effectiveness. The visualization analysis of HERBS was compared with the VGG16 and RepViT on CNFOOD241. Results: The system achieved classification accuracy of 82.72 % and 97.19 % in Top-1 accuracy and Top-5 accuracy respectively. Data showed that HERBS structure widened the attention area and outperformed VGG16 and RepViT on feature maps. Conclusions: Our findings elucidated that the proposed methodology using HERBS model establishes a new benchmark for SOTA performance in food recognition on CNFOOD 241 dataset. This study proved the feasibility of the proposed approach in a challenging food dataset. We further described a vision-language model structure consisting of a multi-modal vision model, language encoder and a multilayer perceptron classifier for food composition recognition.

This study investigates the permeability and California Bearing Ratio (CBR) of base course materials for flexible pavements, focusing on materials from local quarries in Querétaro and Puebla, Mexico. Recognizing the fundamental role of the base layer in load distribution and hydraulic conductivity, the research evaluates these properties at different grain sizes and moisture contents. The objective is to define an optimal range that maximizes both drainage and mechanical resistance, crucial to extending the useful life of the pavement. Four different quarries were selected for material sampling and extensive testing was performed to determine permeability and CBR values under different conditions. The findings reveal the need for broader experimentation and methodological improvements, particularly in the measurement of hydraulic conductivity, a parameter not yet regulated for these materials in Mexico. The results demonstrated permeability values ranging between 1.2810^-3 and 1.9010^-3 cm/s, highlighting the significant impact of the percentage of fine material and its plasticity on the hydraulic and mechanical performance. Despite achieving permeability rates lower than the optimal value suggested in American manuals (0.35 cm/s), even with a fine material content of less than 5%, the study highlights the potential of local materials to meet the international standards with proper processing and testing. This research contributes to the global body of knowledge in pavement engineering by linking local geomaterial properties to broader applicable engineering practices. It suggests that even locally sourced materials, when appropriately evaluated and used, can significantly influence overall pavement solutions, promoting sustainability and cost-effectiveness in pavement construction.

Sentiment analysis has become an important tool for understanding public opinion across languages and domains. Recently, there has been an increase in the number of studies on sentiment analysis in low-resource languages such as Kazakh. This is important to ensure that modern text analysis technologies are accessible to all users, regardless of their language background. The aim of the study is to create a sentiment analysis model for analyzing texts in Kazakh. As part of this work, we aim to use fine-tuning techniques on our own dataset for already existing models, thus improving their accuracy and efficiency for analyzing Kazakh language texts. This paper presents a manually collected dataset "KazIntTelCom" from the city information service 2GIS, consisting of user reviews, manually annotated by the authors taking into account the polarity of sentiment (i.e. negative positive or neutral). This dataset was used to fine-tune two pre-trained multi-lingual Transformer-based sentiment analysis models taken from the HuggingFace platform. The distillBERT and XLM-RoBERTa models were used for tuning. Also, the models were tested on the dataset "KazSAnDRA". The results show that accurate tuning even on a relatively small dataset gives a significant increase in performance, which is confirmed by an increase in the accuracy index by 20%-30%. In addition, false misses and false detections are analyzed, which allows us to identify directions for further improvement of the models. The contribution of this work, in addition to the dataset, is the analysis of model errors, which will help future developers to make more accurate settings of hyperparameters of training for sentiment analysis in Kazakh. These results are important for natural language processing and their adaptation to low-resource languages, promoting more inclusive and equitable access to modern analytical tools. Thus, this study demonstrates the effectiveness of the Transformers architecture for sentiment analysis in Kazakh and opens new opportunities for further model improvement.

Low-cost monitors have made possible for the first time measurements of long-term (months to years) potential indoor exposures to fine particles. Indoor and outdoor measurements made over nearly 5 years (2017-2021) by the largest network of low-cost monitors in the United States (PurpleAir) are compared to the prevalence of adult smokers in 1650 Zip codes within the three West Coast states of California, Oregon, and Washington. The results show that mean indoor exposures above the 75th percentile of adult smoking prevalence are more than 50% higher than those below the 25th percentile. Mean outdoor concentrations are also elevated, but by a smaller amount (20%). Both comparisons are significant at the p&lt;0.001 level. The elevation of the PM2.5 concentrations with increasing smoking prevalence is evidence of environmental disparities in income, education, and other socioeconomic indices..

Vibro-fluidized dry coal beneficiation involves the unavoidable mixing of -1 mm coal dust into the fluidized bed, which compromises the stability and homogeneity of the bed density and reduces the effectiveness of fine-grained coal separation. In order to understand the features of coal powder separation and mixing (-1 mm) in a vibrating fluidized bed, as well as to achieve a uniform and stable bed density, a wide-grained dense medium consisting of -1 mm coal dust and 0.3-0.15 mm magnetite powder was homogeneously mixed in this work. The degree of density separation in the entire bed was examined, as well as the local mixing properties of the Wide-grained Dense Medium. It was discovered that when low vibrational energy was introduced at the same gas velocity, particle separation was enhanced in comparison to the usual fluidized bed. Little bubbles were found to ensnare fine coal dust and migrate upward, increasing density separation. The introduction of high vibrational energy leads to particle disorder within the bed and an increase in the level of particle mixing. The beneficial effects of vibration on the bed are diminished when the gas velocity is gradually increased under specified vibration circumstances. This causes big bubbles to develop more frequently, which increases particle mixing. Among them, the 1-0.5 mm fine-grained coal exhibits the best mixing state; it is less impacted by variations in air velocity and vibration levels, whereas the -0.5 mm coal dust is more affected by each of these variables. Furthermore, a wide range of air velocity regulation without a significant segregation state change was discovered to be achievable by the particle system under vibration circumstances of f=25Hz A=1mm and f=25Hz A=2mm.

In this paper we will provide a new equation that explains why there are three generations or families of leptons and quarks, respectively. We will also explain why all those particles have the known mass ratios amongst their three respective generations and flavors. We will also tackle the problem of Yukawa couplings being arbitrary parameters in the Standard Model Higgs mechanism, which is a long standing problem do to their formulaic dependence on the Higgs Vacuum Expectation Value (VEV). We will attempt to solve this problem and provide a strong argument through an equation for Yukawa couplings of all leptons and quarks via a new methodology that depends on the running of the fine-structure constant on the Q scale, quantum numbers and the Weinberg angle (also on the Q scale). We will also make predictions for all three left-chiral neutrino mass eigenstates and we will provide upper limits for the three right-chiral neutrino mass eigenstates

The research was undertaken to study the way deformation behaves in ultrafine-grained (UFG) conducting Al-Zr alloys doped with Sc, Hf, and Yb. All in all, 8 alloys were studied with zirconium partially replaced by Sc, Hf, and/or Yb. Doping elements (X = Zr, Sc, Hf, Yb) in the alloys total 0.4 wt.%. The choice of doping elements is conditioned by possible precipitation of Al3X particles with L12 structure while annealing these alloys. Such particles provide higher thermal stability of a nonequilibrium UFG microstructure. Initial coarse-grained samples were obtained by induction casting. The UFG microstructure in the alloys was formed by Equal Channel Angular Pressing (ECAP) at 225°C. Superplasticity tests were carried out at temperatures ranging from 300 to 500 °C and strain rates varying between 3.310-4 and 3.310-1 s-1. The highest values of elongation to failure are observed in Sc-doped alloys. A UFG Al-0.2%Zr-0.1%Sc-0.1%Hf alloy has maximum ductility: at 450 °C and a strain rate of 3.310-3 s-1, relative elongation to failure reaches 765%. At the onset of superplasticity, stress–strain curves are characterized by a stage of homogeneous strain and a long stage of localized plastic flow. The dependence of homogeneous strain (eq) on test temperature in UFG Sc-doped alloys is increasing uniformly, which is not the case for other UFG alloys with eq(T) dependence peaking at 350-400 оС. Strain rate sensitivity coefficient of flow stress m is small and does not exceed 0.26-0.3 at 400-500 °C. In UFG alloys containing no scandium, m coefficient is observed to go down to 0.12-0.18 at 500 оС. It has been suggested that lower m values are driven by intensive grain growth and pore formation in large Al3X particles, which develop specifically at the ingot crystallization stage.

There has been considerable research on reconstructing 3D shapes from single-view images; however, preserving the detailed information of the input image remains difficult. In this paper, we propose the application of a gradient map to train a network, aimed at improving the visual quality of fine-grained details such as the thin and tiny components of generated shapes. Each gradient map was created from the original voxel data, and each value represented the amount of information per volume. Here, the gradient map was defined by several methods that mathematically quantify and represent the detailed structure of an object. By applying this map to the loss function in training, we could induce the network to intensively train partial details, such as thin and narrow parts. We demonstrated that the detailed information was well-recovered when a weight that is proportional to the gradient value was applied to the loss. Furthermore, it is expected that our method will contribute to the development of 3D technologies related to the construction of virtual space for simulation and new customer experience.

Text summarization remains a challenging task in the Natural Language Processing field despite the plethora of applications in enterprises and daily life. One of the common use cases is the summarization of web pages which has the potential to provide an overview of web pages to devices with limited features. In fact, despite the increasing penetration rate of mobile devices in rural areas, the bulk of those devices offer limited features in addition to the fact that these areas are covered with limited connectivity such as the GSM network. Summarizing web pages into SMS becomes, therefore, an important task to provide information to limited devices. This work introduces WATS-SMS, a T5-based French Wikipedia Abstractive Text Summarizer for SMS. It is built through a transfer learning approach. The T5 English pre-trained model is used to generate a French text summarization model by retraining the model on 25,000 Wikipedia pages then compared with different approaches in the literature. The objective is twofold: (1) to check the assumption made in the literature that abstractive models provide better results compared to extractive ones; and (2) to evaluate the performance of our model compared to other existing abstractive models. A score based on ROUGE metrics gave us a value of 52% for articles with length up to 500 characters against 34.2% for transformer-ED and 12.7% for seq-2seq-attention; and a value of 77% for articles with larger size against 37% for transformers-DMCA. Moreover, an architecture including a software SMS-gateway has been developed to allow owners of mobile devices with limited features to send requests and to receive summaries through the GSM network.

Is the extensive decline of insects partially due to an insect-specific feature of their functional organization that is disadvantageous for living in an industrialized environment? The unique way in which gases are supplied in insect tissues is such a special trait. It exposes cells directly to the gas phase, via tracheae that end in micro-tubes, the tracheoles, which have diameters in the same range as particles of ultra-fine dust transported by air of industrialized countries. Number and volume of these particles – calculated to be inhaled by honeybees, e.g., – are indicative to locally impede O2-uptake and CO2-release and thus to restrain physiological activities.

The problem of Yukawa couplings being arbitrary parameters in the Standard Model Higgs mechanism is a long standing one due to their formulaic dependence on the Higgs Vacuum Expectation Value. We will attempt to solve this problem and provide a strong argument that the Yukawa couplings of charged leptons and down type quarks are not arbitrary parameters in the SM. A new methodology for predicting the Yukawa couplings will be presented by using Compton wavelengths, the Rydberg Constant and g-factors of charged leptons instead of relying on the Higgs VEV. We will then proceed to rewrite this new method in terms of an empirical formula that depends on the running of the fine-structure constant on the Q scale, charge and lepton quantum numbers and g-factors to predict the values of the Yukawa couplings for all three generations of charged leptons and d-type quarks. We will also touch on the subject of neutrinos both as Majorana and Dirac fermions respectively and make a prediction for the lightest possible Majorana neutrino and the differences between Dirac neutrinos and anti-neutrinos. We conclude that the Yukawa couplings are not arbitrary parameters in the SM and that this new formula provides very accurate results.

This article uses a contemporary and revelatory case study to explore the relationship between three conversations in the innovation literature: design thinking, creativity in strategy and, the emerging area of art-thinking. Businesses are increasingly operating in a VUCA environment where they need to design better experiences for their customers and better outcomes for their firm and the Arts are no exception. Innovation, or more correctly growth through innovation, is a top priority for business and although there is no single, unifying blueprint for success at innovation, design thinking is the process that is receiving most attention and getting most traction. Design thinking teaches businesses to think with the creativity and intuition of a designer; to show a deep understanding of; and have empathy with the user. But design thinking has limitations. By placing the consumer at the very heart of the innovation process, design thinking can often lead to more incremental than radical ideas. Now there is a new perspective emerging, art-thinking, in which the objective is not to design a journey from current scenario; A to improved position: B. Art thinking requires the creation of B and spends more time in the open ended, problem space, staking out possibilities and looking for uncontested space. In Dublin, we examine a case of the oldest, largest and most prestigious fine art gallery and studio where most of the country’s best-known and successful visual artists both make, exhibit and sell their art. Graphic Studio Dublin is primarily a printmaking studio, established by artists over 60 years ago. It has facilities for woodblock, lino-print, silkscreen, intaglio and carborundum etching spread over four floors of a centrally located studio where the artists have access 24/7. But two years ago it found itself on the brink of collapse having borrowed heavily to invest in new facilities during the period of Ireland’s economic collapse. Its loans were sold to a vulture-fund who were about to foreclose in a move that would have seen a generation of Irish Artists displaced. A new board of directors was empaneled and they introduced some art thinking principles to bring the organisation back from the brink. They used an art-thinking mindset and design thinking tools to restore the fortunes of this venerable, artist-led institution and it worked.

Engineering properties of bedrock materials at Badlands National Park were used to develop models for Park managers to assess slope erosion and stability for fossil resource protection. Six fully instrumented sites were used to document slope conditions. Bedrock consisted of Oligocene White River Group rocks. Bulk erosion rates correlated to grain size with silty-sandy materials producing higher mass erosion rates as a function of the silt-to-clay ratio and plastic index. Data indicated that as grain size decreased, plastic index increased leading to a decrease in erodibility. These parameters were used to construct a grain-size proxy, ψ, that was substituted for grain size, D, in Bagnold’s entrainment equation and provided significant improvement in calculation of critical entrainment velocities for fine-grained materials. Hydraulic analyses of slope and pediment surface processes indicated surface roughness was a controlling factor and materials washed from rough steep slopes were effectively transported across smooth low-angle pediments with slope-to-pediment angle ratios of nearly 6:1. Slope stability modeling of ten slopes produced high factors of safety for all slopes, even under saturated conditions and was attributable to clay cohesion. All results were used to construct models that predicted years until net slope erosion equaled 2.5 cm (1 inch). Using these results, Park managers were advised to visit erosion-prone sites on a 1 to 6 year schedule, based on site geology and slope aspect, to adequately protect critical fossil resources from destruction.

The two-stage direct thermal contact (2sDTC) process is a rapid dewatering approach for fluid fine tailings (FFT) that is integrated into an oil sands bitumen extraction plant to reduce tailings pond storage and freshwater usage. In the first stage, combustion gas directly contacts atomized FFT, vaporizing water and yielding dry solids and steam-rich hot gas. The second stage involves pond effluent water directly contacting the steam-rich hot gas, recovering heat and condensing moisture. Case studies confirm the technical feasibility of the integration. Benefits include producing a dry discharge, reducing tailings water discharge and conserving an equivalent to 0.2 barrels of freshwater per barrel of oil produced. Most importantly, these benefits incur no additional energy cost to the plant as the integration eliminates the energy penalty and CO2 emissions associated with FFT dewatering. Further capacity enhancement can be achieved by using centrifuge-concentrated FFT. The study reveals that FFT concentrated to ~50 wt% solids could still maintain its pump-ability and be accommodated by the 2sDTC process, leading to an integration which could dewater more FFT annually and conserve more freshwater ( 0.58 barrels per barrel of oil produced), with the sole energy requirement being the power to drive the centrifuge machinery.

Accurate stock market predictions following earnings reports are crucial for investors. Traditional methods, particularly classical machine learning models, struggle with these predictions because they cannot effectively process and interpret extensive textual data contained in earnings reports and often overlook nuances that influence market movements. This paper introduces an advanced approach by employing Large Language Models (LLMs) instruction fine-tuned with a novel combination of instruction-based techniques and quantized low-rank adaptation (QLoRA) compression. Our methodology integrates ‘base factors’, such as financial metric growth and earnings transcripts, with ‘external factors’, including recent market indices performances and analyst grades, to create a rich, supervised dataset. This comprehensive dataset enables our models to achieve superior predictive performance in terms of accuracy, weighted F1, and Matthews correlation coefficient (MCC), especially evident in the comparison with benchmarks such as GPT-4. We specifically highlight the efficacy of the llama-3-8b-Instruct-4bit model, which showcases significant improvements over baseline models. The paper also discusses the potential of expanding the output capabilities to include a ‘Hold’ option and extending the prediction horizon, aiming to accommodate various investment styles and time frames. This study not only demonstrates the power of integrating cutting-edge AI with fine-tuned financial data but also paves the way for future research in enhancing AI-driven financial analysis tools.

Target recognition and fine-grained ship classification in remote sensing face the challenges of high inter-class similarity and sample scarcity. A transfer fusion-based ship image harmonization algorithm is proposed to overcome these challenges. This algorithm designs a feature transfer fusion strategy based on the combination of region aware instantiation and attention mechanism. Implement adversarial learning through image harmony generator and discriminator module to generate realistic remote sensing ship harmony images. Furthermore, the do-main encoder and domain discriminator modules are responsible for extracting feature representations of foreground and background, and further aligning ship foreground with remote sensing ocean background features through feature discrimination. Compared with other advanced image conversion techniques, our algorithm delivers more realistic visuals, improving classification accuracy for six ship types by 3% and twelve types by 2.94%, outperforming Sim2RealNet. Finally, a mixed dataset containing data augmentation and harmonizing samples and real data was proposed for the fine-grained classification task of remote sensing ships. Evaluation experiments were conducted on 8 typical fine-grained classification algorithms, and the accuracy of fine-grained classification for all categories of ships was analyzed. The experimental results show that the mixed dataset proposed in this paper effectively alleviates the long tail problem in real datasets, and the proposed remote sensing ship data augmentation framework performs better than state-of-the-art data augmentation methods in fine-grained ship classification tasks.

In this study, the distribution patterns of southern horse mackerel are examined using commercial fine-scale resolution data. Using landings by size category and VMS data from the Portuguese commercial bottom trawl fishery, which consistently targets horse mackerel, this study provides a comprehensive analysis of horse mackerel age distributions spanning a decade (2010-2020). Importantly, the study addresses potential biases in commercial effort data and establishes the usefulness of commercial bottom-trawl gear as a suitable method for sampling and evaluate southern horse mackerel stock dynamics. Ordered regression models were applied to allow for the modelling of the distribution of multiple age categories and investigate spatio-temporal migrations off the Portuguese coast. Southern horse mackerel shows a widespread age distribution range and stable abundance with indications of seasonal and spatial patterns in the distribution of specific age groups. The insights derived from this research contribute valuable knowledge for understanding the dynamics and distribution patterns of fish populations.

Fine-grained image classification remains an ongoing challenge in the computer vision field, which is particularly intended to identify objects within sub-categories. It is a difficult task since there is a minimal and substantial intra-class variance. The current methods address the issue by first locating selective regions with Region Proposal Networks (RPN), object localization, or part localization, followed by implementing a CNN Network or SVM classifier to those selective regions. This approach, however, makes the process simple by implementing a single-stage end-to-end feature encoding with a localization method, which leads to improved feature representations of individual tokens/regions by integrating the transformer encoder blocks into the Yolov5 backbone structure. These Transformer Encoder Blocks, with their self-attention mechanism, effectively captured the global dependencies and enabled the model to learn relationships between distant regions. This improved the model ability to understand context and captured long-range spatial relationships in the image. We also replaced the Yolov5 detection heads with three transformer heads at the output for object recognition using the discriminative and informative features maps from transformer encoder blocks. We established the potential of the single stage detector for the fine-grained image recognition task, by achieving state of the art 93.4% accuracy, as well as outperforming the existing Yolov5 model. The effectiveness of our approach is assessed using the Stanford car dataset, which includes 16,185 images of 196 different classes of vehicles with significantly identical visual appearances.

Currently available nationwide prediction models for fine particulate matter (PM_2.5) lack prediction confidence intervals and usually do not describe cross validated model performance at different spatiotemporal resolutions and extents. We used 41 different spatiotemporal predictors, including data on land use, meteorology, aerosol optical density, emissions, wildfires, population, traffic, and spatiotemporal indicators to train a machine learning model to predict daily averages of PM_2.5 concentrations at 0.75 sq km resolution across the contiguous United States from 2000 through 2020. We utilized a generalized random forest model that allowed us to generate asymptotically-valid prediction confidence intervals and took advantage of its usefulness as an ensemble learner to quickly and cheaply characterize leave-one-location-out CV model performance for different temporal resolutions and geographic regions. Using a variable importance metric, we selected 8 predictors that were able to accurately predict daily PM_2.5, with an overall leave-one-location-out cross validated median absolute error of 1.20 ug/m³, an R² of 0.84, and confidence interval coverage fraction of 95%. When considering aggregated temporal windows, the model achieved leave-one-location-out cross validated median absolute errors of 0.99, 0.76, 0.63, and 0.60 ug/m³ for weekly, monthly, annual, and all-time exposure assessments, respectively. We further describe the model’s cross validated performance at different geographic regions in the United States, finding that it performs worse in the Western half of the country where there are less monitors. The code and data used to create this model are publicly available and we have developed software packages to be used for exposure assessment. This accurate exposure assessment model will be useful for epidemiologists seeking to study the health effects of PM across the continental United States, while possibly considering exposure estimation accuracy and uncertainty specific to the the spatiotemporal resolution and extent of their study design and population.

Background: Particulate matter and urban air pollution affect the human body and can lead to death. Epidemiological studies should consider the exposure to pollutants and the diverse responses of individuals depending on their sensitivity to the pollutants. Methods: In this study, air pollution measurements were obtained hourly at measuring stations operated by the national and local governments to increase the reliability of the measured values. A β-ray absorption method was used to analyze the measurements of fine dust and determine the particulate matter content. Results: The air pollution data were log-converted to enable the comparison of data from different time periods. The comparison was made, focusing on the period of Seoul's fine dust policy. As a result, the cancer incidence rate also decreased after the policy. The data on individual characteristics were obtained from a survey of Seoul citizens conducted from 2015 to 2016 and using indicators such as the quality of life and social trust of Seoul citizens. Concludions: The survey on the living environment and residence indicated that 80% of the heads of households were men. Women had a greater dissatisfaction than men with the residential, economic, and social environments. The survey questions on well-being were related to the internal and external environment such as air pollution, noise, and fine dust.

Learning dental procedures is a complex task involving the development of fine motor skills. The reported use of theories and/ or evidence for designing learning activities to develop the fine motor skills needed for dental practice is limited. The aim of this review is to explore the available body of knowledge related to learning fine motor skills in dentistry. Evidence from studies investigating motor skill learning highlights the negative impact of self-focus and self-regulation on learning outcomes, particularly during the early stages of learning. The development of activities and schedules that enable novices to demonstrate characteristics similar to experts, without the reported long period of ‘deliberate practice’, is clearly of value. Outcomes of learning implicitly are important in dentistry because working under stressful conditions is common, either during undergraduate study or in practice. It is suggested that learning implicitly in the simulation stage can reduce disrupted performance when transitioning to clinical settings. Therefore, further investigation of effective methods for learning dental fine motor skills is indicated, using approaches that result in robust performance, even under stressful conditions.

Background: Lung cancer is one of the leading causes of morbidity and mortality worldwide with 25% of deaths due to lung cancer occurring in Europe. This study therefore sought to assess the burden of lung cancer by country and to evaluate the magnitude of fine Particulate Matter (PM2.5) and cigarette smoking by country in Europe. Methods: An ecological study nested on the World Health Organization air pollution database 2016 was conducted. We sampled 30 European Countries, with a total of 1625 mean annual samples of Particulate Matter (PM2.5) collected from 1625 designated sites (n = 1625). We further used the ‘World Health Disease Rankings’ database to extract Lung Cancer Morbidity and Mortality Rate by country. We used SAS version 9.4 to indicate the distribution of PM2.5 and Lung Cancer Mortality Rate. Results: Lung cancer Relative Risk (RR) was 1.0 in all never- smokers. RR for Ex-smokers for Adeno carcinoma was 3.5 in males and 1.1 in females, small cell carcinoma was 16.2 in males and 3.8 in females. RR for current smokers for Adeno carcinoma was 8.0 in males and 4.1 in females, small cell carcinoma was 57.9 in males and 18.2 in females. Mean annual PM2.5 by country ranged from 6.01 to 37.28µg/m3 whereas lung cancer mortality rate by country ranged from 19.67 to 54.26 deaths per 100,000 population. Conclusion: Cigarette smoking and exposure to both second hand smoke and high concentration of PM2.5 resulted into increased burden of lung cancer in Europe. Countries should re-strategize to reduce the burden of lung cancer in Europe.

Previous studies have reported certain sex differences in motor performance precision. The aim of the present study was to analyse sex differences in fine motor precision performance for both hands in different tests conditions. 220 Spanish participants (ages: 12-95) performed fine motor tasks - tracing over the provided models – lines of 40 mm for both hands, two sensory conditions (PV – proprioceptive-visual; P – proprioceptive only) and three movement types (F – frontal, T – transversal and S - Sagittal). Differences in line length (the task focused on precision) were observed through MANOVA analysis for all test conditions, both sexes and different age groups. Sex differences in precision were observed in F and T movement types (statistically significance level and higher Cohens’ d was observed in condition with vision). No any statistically significant differences were observed in both hands and sensory conditions in sagittal type. Sex differences in fine motor precision were more frequently observed in the PV sensory condition in the frontal movement type and less in the sagittal one.

With the advancement of technology, signal modulation types are becoming increasingly diverse and complex. The phenomenon of signal time-frequency overlap during transmission poses significant challenges for the classification and recognition of mixed signals, including poor recognition capabilities and low generality. This paper presents a recognition model for fine-grained analysis of mixed signal characteristics, proposing a Geometry Coordinate attention mechanism and introducing a low-rank bilinear pooling module to more effectively extract signal features for classification. The model employs a residual neural network as its backbone architecture and utilizes the Geometry Coordinate attention mechanism for time-frequency weighted analysis based on information geometry theory. This analysis targets multiple-scale features within the architecture, producing time-frequency weighted features of the signal. These weighted features are further analyzed through a low-rank bilinear pooling module, combined with the backbone features, to achieve fine-grained feature fusion. This results in a fused feature vector for mixed signal classification. Experiments were conducted on a simulated dataset comprising 39,600 mixed signal time-frequency plots. The model was benchmarked against a baseline using a residual neural network. The experimental outcomes demonstrated an improvement of 9% in the exact match ratio and 5% in the Hamming score. These results indicate that the proposed model significantly enhances the recognition capability and generalizability of mixed signal classification.

CD4 count recovery is the main goal for an HIV patient who initiated ART. Some patients respond positively to ART and attain CD4 count recovery, some patients might fail to recover their CD4 count due to non-adherence, treatment resistance, and virological failure, leading to HIV-related complications and death. The purpose of this study is to find the determinants of death in patients who failed to recover their CD4 count after initiating antiretroviral therapy. The data used in this study was obtained from KwaZulu-Natal, South Africa, where 2528 HIV-infected patients with a baseline CD4 count of 200 cells/ were initiated on ART. We used a Fine-Gray sub-distribution hazard and cumulative incidence function to estimate potential confounding factors of death, where CD4 count recovery was a competing event for failure due to death. Patients who were not suffering from tuberculosis were 1.33 times at risk of dying before attaining CD4 count recovery [aSHR 1.33; 95% CI (0.96-1.85] compared to those who had no tuberculosis. Rural patients had a higher risk of not recovering and leading to death [aSHR 1.97; 95%CI (1.57-2.47)] than those from urban areas. The patient’s tuberculosis status, viral load, regimen, baseline CD4 count, and location were significant contributors to death before CD4 count recovery. Intervention programs targeting HIV testing in rural areas for early ART initiation and promoting treatment adherence are recommended.

Self-compacting concrete (SCC) is a high-quality construction solution combining high fluidity, passing and filling ability with improved mechanical properties and durability. In the present study, the effect of incorporating alternative waste materials, such as two by-products of the ce-ment industry, namely cement kiln dust (CKD) and by-pass dust (BPD), into SCC as partial re-placement for traditional filler material was investigated. The produced compositions were compared with reference mixtures containing only marble powder (MP) as a filler. A series of tests including specific test methods for wet SCC, compressive, flexural and tensile splitting strength tests as well as drying shrinkage determination were conducted to evaluate the quality of the produced SCC in terms of fresh and hardened SCC properties. The use of alternative fine filler materials resulted in a high-performance sustainable SCC of low cement content. Specifi-cally, incorporating CKD into the SCC enhanced its rheological behavior and improved slightly its mechanical properties, while the use of BPD led to SCC mixtures of adequate rheological characteristics but with highly upgraded mechanical and physical properties

Drought poses a significant constraint on rice production, and in this study, we have discovered a novel drought-sensitive mutant, designated as dsm3, arising from the progenies of indica rice variety Zhonghui8015 treated with EMS. Under drought stress conditions, dsm3 exhibited characteristic withered leaf tips, accompanied by increased levels of MDA and H2O2, reduced Pn, and decreased activity of POD and SOD. Genetic analysis revealed that the withered leaf tip phenotype was governed by a single recessive gene, designated as Osdsm3.To begin with, Osdsm3 was initially mapped to the short arm of chromosome 1 through a cross involving dsm3 and 02428. Subsequently, utilizing a population of 2591 F2 individuals, we narrowed down the location of Osdsm3 to a 78Kb interval, encompassing 13 open reading frames (ORFs). Sequencing analysis unveiled a mutation (1275G→A) in the exon of the candidate gene (LOC_Os01g10680), leading to premature translation termination. Moreover, a quantitative RT-PCR assay demonstrated high expression of OsDSM3 in the panicle and sheath, with a significant upregulation of drought stress-related genes under drought conditions. Phylogenetic analyses indicated that Osdsm3 shares evolutionary homology with UNE1, an intracellular transport protein found in Arabidopsis thaliana. Subcellular studies further confirmed that OsDSM3 resides in the cytoplasm. In conclusion, the forthcoming cloning of Osdsm3 holds promise for delving deeper into the molecular mechanisms governing rice drought resistance.

Air pollution is a leading cause of death in the United States, and is associated with adverse health outcomes, including increased vulnerability to coronavirus disease 2019 (COVID-19). The AirBeam2 was used to measure particulate matter with a diameter of 2.5 micrometers or smaller (PM2.5), to investigate differences between indoor and ambient levels at seven private homes in New York during and after the COVID-19 lockdown. Measurements taken in 2020 winter, spring and fall and in fall 2022 showed that 90% of the time, indoor PM2.5 levels exceeded outdoor levels both during and after the COVID-19 lockdown, p = 0.03, and exceeded safety levels. Higher indoor PM2.5 levels attributed to little or no ventilation from cooking and smoke in the kitchen and basements, were significantly greater in fall than in winter. Higher ambient PM2.5 levels were attributed to vehicular traffic at a street-facing sampling site. PM2.5 sources identified in this study may help in devising control strategies to improve indoor air quality (IAQ), and consequently alleviate respiratory health effects. These findings may be used as a basis for in-house modifications including natural ventilation and use of air purifiers to reduce exposures, mitigate future risks, and prevent potential harm to vulnerable residents.

The cast grains of the 7055 aluminum alloy were refined by adding Sm + Er, and the proper heat treatment procedure was utilized to further precipitate the rare earth phase in order to increase the alloy's strength and toughness. The grain size, microstructure and phase were characterized by optical microscopes (OM), Scanning electron microscopy - energy spectrum (SEM-EDS), XRD diffractometer (XRD). The macroscopic hardness, yield strength and tensile strength of alloy materials were measured by hardness meter and universal electronic tensile machine. The results shown that the as-cast sample and the heat treatment sample all exist Al10Cu7Sm2 and Al8Cu4Er rare earth phase; But after heat treatment, the volume percentage of the rare earth phase dramatically increased and the dispersion was more unified. When 0.3wt.%Sm and 0.1wt.%Er were added, the grain size could be refined to 53μm. With the increase of the total content of rare earth elements, the refining effect first increased and then decreased. Under 410 oC solid solution for 2h + 150oC aging for 12h, the macroscopic hardness, yield strength, tensile strength and elongation of 0.3wt.%Sm + 0.1wt.%Er+7055 as-cast sample were 155.8HV, 620.5Mpa, 658.1Mpa, and 11.90%, respectively.

Herein is introduced a simple scalar field model derived from classical based kinetic energy, gravitational potential energy, and Special Relativity’s rest mass energy. By applying a classical orbit over the scalar field, relativistic effects are predicted. The scalar field is then applied to a classical model of the Hydrogen atom resulting in a relativistic effect equal to the binding energy of the Hydrogen atom. In addition, the model derives the fine structure constant due to the gravitational effect. The relativistic effects are then discretized in increments equal to the model’s gravitational induced constant. The discretization produces the Hydrogen atom spectral emissions and an angular moment equal to Planck’s reduced constant. The model is not presented as a replacement for current theory, rather it is for inspection and illustration of how a simplistic model may offer a fundamental bridge between the more complex, time proven theories of General Relativity and Quantum Mechanics.

A geometric Planck-scale model of quantum electrodynamics is tested against observations. Based on Dirac’s proposal to describe spin 1/2 particles as tethered objects, elementary fermions are conjectured to be fluctuating rational tangles with unobservable tethers. As Battey-Pratt and Racey showed, when such tangles propagate, they obey the free Dirac equation. Classifying rational tangles yields the observed spectrum of elementary fermions. Classifying deformations of tangle cores yields exactly three types of gauge interactions. They exchange three types of elementary gauge bosons and have the symmetry groups U(1), broken SU(2) and SU(3). The possible rational tangles for fermions, Higgs and gauge bosons allow only the observed Feynman diagrams. The complete Lagrangian of the standard model arises, including the Lagrangian of quantum electrodynamics. Over 50 tests of the tangle model are deduced. They include details on the perturbation expansion of the g-factor and the existence of limit values for electric and magnetic fields. Measurements agree with most tests, though this could change in the future. Some tests are genuine predictions that still need to be checked. In particular, the geometry of the process occurring at QED interaction vertices suggests an ab-initio estimate for the fine structure constant. In addition, the average geometric shapes of the elementary particle tangles suggest ab-initio lower and upper limits for the mass values of the electron and the other leptons.

We have recently proposed a pre-quantum, pre-space-time theory as a matrix-valued Lagrangian dynamics on an octonionic space-time. This pre-theory offers the prospect of unifying the internal symmetries of the standard model with gravity. It can also predict the values of free parameters of the standard model, because these parameters arising in the Lagrangian are related to the algebra of the octonions which define the underlying non-commutative space-time on which the dynamical degrees of freedom evolve. These free parameters are related to the algebra $J_3(\mathbb O)$ [exceptional Jordan algebra] which in turn is related to the three fermion generations. The exceptional Jordan algebra [also known as the Albert algebra] is the finite dimensional algebra of 3x3 Hermitean matrices with octonionic entries. Its automorphism group is the exceptional Lie group $F_4$. These matrices admit a cubic characteristic equation whose eigenvalues are real and depend on the invariant trace, determinant, and an inner product made from the Jordan matrix. Also, there is some evidence in the literature that the groups $F_4$ and $E_6$ could play a role in the unification of the standard model symmetries, including the Lorentz symmetry. The octonion algebra is known to correctly yield the electric charge values (0, 1/3, 2/3, 1) for standard model fermions, via the eigenvalues of a $U(1)$ number operator, identified with $U(1)_{em}$. In the present article, we use the same octonionic representation of the fermions to compute the eigenvalues of the characteristic equation of the Albert algebra, and compare the resulting eigenvalues with the known mass ratios for quarks and leptons. We find that the ratios of the eigenvalues correctly reproduce the [square root of the] known mass ratios for quarks and charged leptons. We also propose a diagrammatic representation of the standard model bosons, Higgs and three fermion generations, in terms of the octonions, exhibiting an $F_4$ and $E_6$ symmetry. In conjunction with the trace dynamics Lagrangian, the Jordan eigenvalues also provide a first principles theoretical derivation of the low energy value of the fine structure constant, yielding the value $1/137.04006$. The Karolyhazy correction to this value gives an exact match with the measured value of the constant, after assuming a specific value for the electro-weak symmetry breaking energy scale.

Stable isotope concentrations in the soil, rain and ground water have been used to trace the water extraction zones of plants in different environments. The need to identify the plant water use by plants in afforestation programs to control desertification increases the importance of sap water partitioning of plants in sand dune areas. However, the introduction of new plant covers exerts pressure on the water resources and can affect the local soil water conditions. In this study, we analyzed the isotope concentrations in rain, soil, sap, and ground water after the summer of 2010. Two experimental plots established in the Hailiutu catchment (Shaanxi province, northwest China) were selected to gather the water samples between September and October 2010. One plot is dominated by Salix bushes (Salix psammophila C. Wang \& Chang Y. Yang) and the other by the tree species Willow (Salix matsudana Koidz.). The total precipitation at the experimental site was 401 mm/yr during 2010, while 88.7 mm was collected in total for the period September to October. Willow trees transpired 12.82 kg/d being almost three times larger than Salix shrubs (4.57 kg/d). Despite the transpiration rates of both plant species and the few rain events in the region, the soil water beneath the plant covers is not depleted. Stable isotope signature of soil water beneath both covers shows the fractionation front in Salix at 20 cm depth and at Willow at 40 cm depth. However, soil water signature is closer to the groundwater than the collected rain water.

The popular appreciation of dry white wines is most frequently directed to young wines. However, present consumption trends comprise the valorisation of aged dry white wines. Therefore, the present work was aimed at the sensory analysis of these wines with different ages to define their sensory space and to understand which factors drive their quality evaluation by experienced tasters (critics, oenologists and students). Individuals were asked to evaluate several synthetic and aesthetic attributes and to characterise the analytic sensory profile through a Check-All-That-Apply (CATA) methodology. The quality evaluations were differently correlated with wine synthetic parameters according to the taster group. For both critics and oenologists, overall quality scores were driven by persistence and complexity. Moreover, quality was also highly correlated with power for critics and with balance for oenologists. Quality scores were highly correlated with wine browning (absorbance at 420 nm) for critics. The tasting panel showed a homogeneous analytic description of aroma, taste and mouthfeel consistent with wine age. The different ages could be associated to a continuous sensory space characterised by a decreasing perception of freshness and increase in mature and mellowed descriptors. All wines shared an austere in-mouth perception elicited by their acidity, saltiness, bitterness, smoothness and dryness. The age prediction showed that most tasters failed to guess the age wines with more than roughly 13 years old, indicating that tasters were not familiar with the sensory features of white wines with 17 to 46 years old. In conclusion, experienced tasters consistently described the sensory space and recognised the high quality of aged dry white wines. Education programs may use the defined sensory spaces according to aging and to expand the range of quality perception by consumers.

The high prevalence of thyroid nodules and increased availability of neck ultrasound have led to an increased incidence of diagnostic thyroid fine needle aspirations, with approximately 20% yielding indeterminate results. The recent availability of molecular tests has helped guide the clinical management of these cases. This paper aims to review and compare three main commercially available molecular cytology platforms in the U.S. – Afirma GSC, Thyroseq GC, and ThyGeNEXT+ThyraMIR. Sequential improvements of the Afirma GSC and Thyroseq GC tests have led to higher positive and negative predictive values, sensitivity, and specificity. Comparative studies revealed similar diagnostic performance between these tests, with considerations for factors such as cost and processing time. Thyroseq GC provides detailed genomic information and specific management recommendations. ThyGeNEXT+ThyraMIR, though less studied, presents promising results, particularly in miRNA analysis for weak driver mutations. Challenges in interpreting results include variations in reporting and the evolving nature of testing platforms. Questions persist regarding cost-effectiveness and the utility of ultrasound characteristics in selecting candidates for molecular testing. While molecular testing has primarily served diagnostic purposes, advancements in understanding genetic alterations now offer therapeutic implications. FDA-approved options target specific genetic alterations, signaling a promising future for tailored treatments.

The characteristics of response to stressful situations depend on a complex of factors such as personality traits, emotional regulation, health state, features of the nervous system, motor functions and proprioception. In cases of disruption of the brain's dopamine system due to disease or chemotherapy, hormone discharge due to stress, changes in emotions and proprioception may occur. The aim of our study was to find the relationship of stress resilence in verbal test and individual differences performed in proprioceptive test in onco-patients, sportsmen and group of control; as well as to observe the differences between these groups in the verbal stress resistance and individual differences proprioceptive tests. The results showed that onco-patients were less resistant as per verbal stress resistance outcomes for a major part of variables compared to other groups. The sportsmen were superior in verbal Risk and Control dimensions compared to other groups; but less in Positive Affectiveness. The proprioceptive test perfomance revealed the only statistically significant difference in performance in the dimension related to Mood, showing more Pessimism in the sportsmen group, but more Optimism in the oncopatients at temperamental (or intrisinc) level. The obtained results can be used by psychologists, therapists, coach and healthcare when needed to attend the specific needs of public, being pateints or sportsmen, etc.

Accurate and efficient medicinal plant image classification is of utmost importance as these plants produce a wide variety of bioactive compounds that offer therapeutic benefits. With a long history of medicinal plant usage, different parts of plants, such as flowers, leaves, and roots, have been recognized for their medicinal properties and are used for plant identification. However, leaf images are extensively used due to their convenient accessibility and are a major source of information. In recent years, transfer learning and fine-tuning, which use pre-trained deep convolutional networks to extract pertinent features, has emerged as an extremely effective approach for image identification problems. This study is leveraging the power of three component deep convolutional neural networks, namely VGG16, VGG19 and DenseNet201, to derive features from the input images of the medicinal plant dataset, containing leaf images of 30 classes. The models were compared and ensembled to make four hybrid models to enhance the predictive performance by utilizing the averaging and weighted averaging strategies. Quantitative experiments were carried out to evaluate the models on the Mendeley medicinal leaf dataset. The resultant ensemble of VGG19+DensNet201 with fine-tuning showcases enhanced capability in identifying medicinal plant images with an improvement of 7.43% and 5.8% compared with VGG19 and VGG16. Furthermore, VGG19+DensNet201 can outperform its standalone counterparts by achieving an accuracy of 99.12% on the test set. A thorough assessment with metrics such as accuracy, recall, precision, and f1-score firmly establishes the effectiveness of the ensemble strategy.

Natural gas hydrate has gained significant attention in recent years. To safely and sustainably exploit the natural gas from gas hydrate-bearing sediments, it is crucial to understand the long-term mechanical characteristics of the hydrate reservoir. In this study, the influence of hydrate and fine particles on the strain rate dependence of hydrate-bearing sediments under plane strain conditions has been studied. The experimental results show that the strain rate dependency of the mechanical properties of hydrate-bearing sediments is positively correlated with hydrate saturation instead of the morphology of hydrate in sediments. The residual strength of hydrate-bearing sediments is primarily controlled by the hydrate saturation and is independent of the strain rate. Changes in hydrate saturation, and fines content, can affect the relationship between the strain rate and shear band angle. Finally, the local volumetric expansion effect of hydrate-bearing sediments without fines content is more significant and shows a strong strain rate dependence characteristic. Overall, this study provides valuable insights into the long-term mechanical characteristics of hydrate reservoirs. These insights can contribute to the development of a constitutive model of hydrate-bearing sediments with time-dependence in the future, and which is meaningful to the exploitation of natural gas hydrate.

This paper presents a study of an automated system for identifying planktic foraminifera at the species level. The system uses a combination of deep learning methods, specifically Convolutional Neural Networks (CNNs), to analyze digital images of foraminifera taken at different illumination angles. The dataset is composed of 1437 groups of sixteen grayscale images, one group for each foraminifer, that are then converted to RGB images with various processing methods. These RGB images are fed into a set of CNNs, organized in an Ensemble Learning (EL) environment. The ensemble is built by training different networks using different approaches for creating the RGB images. The study finds that an ensemble of CNN models trained on different RGB images improves the system's performance compared to other state-of-the-art approaches. The proposed system was also found to outperform human experts in classification accuracy.

An effective method for coating textile fabrics with porous materials is proposed, and the removal rates of nitrogen oxides (NOx), sulfur oxides (SOx), and fine dust particles in the coated textile fabrics are evaluated. The textile fabrics made of polyester are used to effectively reduce fine dust particles through static electricity. Zeolite and coconut shell activated carbon are used as porous material to reduce SOx and NOx, respectively. The effects of the epoxy content and dilution solution types on the SOx removal rate of textile fabrics coated with zeolite are evaluated to determine the optimum coating conditions. In addition, the effects of external environmental conditions, such as washing and freeze thawing, on the SOx and NOx removal rates of the textile fabrics coated with porous materials using the optimum coating conditions are examined. The test results show that the SOx removal rate of textile fabrics coated with zeolite decreases with the increase in the epoxy content. The decrease is 2.9 times larger for textile fabrics coated using deionized water than those coated using isopropyl alcohol. After one wash, the SOx removal rate decreases dramatically. However, the decrease is reduced by 16% when the epoxy content ratio is increased by 0.5%. The effects of washing and freeze thawing on the SOx and NOx removal rates of textile fabrics coated using the deionized water diluted with the epoxy content ratio of 2% are minimal. Consequently, to maintain stable SOx and NOx removal rates under external environmental conditions such as washing and freeze thawing, 98% deionized water dilution and 2% epoxy content ratio are required for the optimum coating of textile fabrics with zeolite and coconut shell activated carbon.

Glacier dynamics play an important role in the mass balance of many glaciers, ice caps and ice sheets. In this study we exploit Radarsat-2 (RS-2) Wide Fine (WF) data to determine the surface speed of Svalbard glaciers in the winters of 2012/2013 and 2013/2014 using Synthetic Aperture RADAR (SAR) offset and speckle tracking. The RS-2 WF mode combines the advantages of the large spatial coverage of the Wide mode (150 x 150 km) and the high pixel resolution (9m) of the Fine mode and thus has a major potential for glacier velocity monitoring from space through offset and speckle tracking. Faster flowing glaciers (1.95 m d^-1 - 2.55 m d^-1) which are studied in detail are Nathorstbreen, Kronebreen, Kongsbreen and Monacobreen. Using our Radarsat-2 WF dataset, we compare the performance of two SAR tracking algorithms, namely the GAMMA Remote Sensing Software and a custom written MATLAB script (GRAY method) that has primarily been used in the Canadian Arctic. Both algorithms provide comparable results, especially for the faster flowing glaciers and the termini of slower tidewater glaciers. A comparison of the WF data to RS-2 Ultrafine and Wide mode data reveals the superiority of RS-2 WF data over the Wide mode data.

In order to apply RAP fine aggregate to High Modulus Asphalt Concrete(HMAC), this study first extracts aged asphalt from RAP fine aggregate through extraction and distillation, and adds it to high modulus modified asphalt in proportions of 20%, 40%, 60%, and 80%. The performance changes of the recycled asphalt were investigated using conventional performance tests and dynamic shear rheological tests (DSR). Microscopic techniques such as four‐component analysis and infrared spectroscopy were employed to explain macroscopic performance changes in terms of component and functional group changes in the asphalt. Using HMAC‐20 gradation as an example, uniaxial compression dynamic modulus tests, rutting tests, freeze‐thaw splitting tests, and low‐temperature bending tests were conducted to evaluate the performance changes of recycled asphalt mixture with different RAP fine aggregate contents through four indices: dynamic modulus, dynamic stability, tensile strength ratio (TSR), and maximum bending strain. The results indicate that for every 20% increase in aged asphalt, the penetration at 25°C, ductility at 25°C, and dynamic viscosity at 60°C decrease by approximately 6.2%, 15.4%, and 3.9%, respectively, while the softening point increases by approximately 13.3%. It is concluded that the addition of aged asphalt significantly affects the ductility at 25°C and the softening point. As the aged content increases, the complex shear modulus (G*) and phase angle (δ) of the recycled asphalt decrease, while the rutting factor increases. The four‐component analysis and infrared spectroscopy of the asphalt demonstrate that the incorporation of aged asphalt is merely a physical blending phenomenon, involving changes in the content of the four components without the formation of new functional groups (new peaks). When RAP fine aggregate is applied to high modulus recycled asphalt mixture, compared to high modulus modified asphalt mixture, the recycled mixtures exhibit higher dynamic modulus and dynamic stability, but significantly reduced water stability and low‐temperature performance.

The aging ability of dry white wines has been increasingly recognised. The present work aimed to understand which sensory factors drive their quality evaluation by experienced tasters. Individuals assessed several aesthetic, synthetic, and analytic flavour attributes, using dark tasting glasses. Wines originated from the grape varieties Alvarinho, Arinto, Sauvignon Blanc and Pinot Bianco with a wide range of ages. Basic physical-chemical analysis, browning (Abs 420 nm), elemental composition and a partial volatile fraction were also determined. The overall quality scores were a function of complexity and balance and were negatively influenced by the perception of faultiness. The different ages could be associated with a continuous sensory space characterised by a decreasing perception of freshness and an increase in the mellowed flavours. All wines shared an austere in-mouth perception elicited by their acidity, saltiness, bitterness, smoothness and dryness. Nevertheless, quality scores were similar from the youngest to the oldest-tasted wines (17 years old). The exception was a Sauvignon Blanc wine from a recent vintage that was judged as faulty due to the perception of earthiness. Overall, the tasted wines displayed an unexpected aging ability as demonstrated by the difference between the predicted and real wine ages. To improve the recognition of old wines, the metaphor “mellowed by age” is proposed to describe flavours resulting from beneficial aging instead of “oxidised by age”.

Accurate and robust deep learning models for facial expression recognition are challenging to achieve, given the diversity of human faces and variations in images, including different facial poses and lighting conditions. In this work, we proposed a clock-driven convolutional Spiking Neural Network (SNN) and STDP fine-tune architecture, meticulously calibrated its hyperparameters, and experimented with various optimization methods. The best model resulted was trained and evaluated on the Fer2013 and FER+ database, obtaining an accuracy of 61.87% and 79.97% without requiring auxiliary training data or face registration. To our best knowledge, the proposed SNN achieved comparable accuracy to CNNs of similar depth and possessed the advantages of low energy consumption and high computational efficiency. The computational efficiency of the proposed SNN is approximately three times that of CNNs. Along with this, we introduced the very recent cumulative spike guided encoder visualization technique and revealed the strong encoding capability of the proposed SNN.

In this article, we propose a machine learning based fine-grained vehicle classification method VehiClassNet, which effectively solves the challenges of precise vehicle recognition and classification in intelligent transportation systems by combining multi-scale feature extraction, cross modal fusion, and attention mechanisms. Our model demonstrated excellent performance on the Stanford Cars-196 dataset. The innovation of this study lies in its advanced multi-scale feature extraction, cross modal fusion, and attention mechanism. It not only improves classification accuracy, but also lays the foundation for further research on intelligent transportation systems and autonomous vehicle technology.

Accurate and robust deep learning models for facial expression recognition are challenging to achieve, given the diversity of human faces and variations in images, including different facial poses and lighting conditions. In this work, we proposed a clock-driven convolutional Spiking Neural Network (SNN) and STDP fine-tune architecture, meticulously calibrated its hyperpa-rameters, and experimented with various optimization methods. The best model resulted was trained and evaluated on the Fer2013 and FER+ database, obtaining an accuracy of 61.87% and 79.97% without requiring auxiliary training data or face registration. To our best knowledge, the proposed SNN achieved comparable accuracy to CNNs of similar depth and possessed advantages of low energy consumption and high computational efficiency. The computational efficiency of the proposed SNN is approximately three times that of CNNs. Along with this, we introduced the very recent cumulative spike guided encoder visualization technique and revealed the strong encoding capability of the proposed SNN.

Few-shot fine-grained image classification (FSFGIC) methods refer to machine learning methods which aim to classify images (e.g., bird species, flowers, and airplanes) belonging to subordinate object categories of the same entry-level category with only a few samples. It is worth to note that feature representation learning is used not only to represent training samples, but also to construct classifiers for performing various FSFGIC tasks. In this paper, starting from the definition of FSFGIC, a taxonomy of feature representation learning for FSFGIC is proposed. According to this taxonomy, we discuss key issues on FSFGIC (including data augmentation, local or/and global deep feature representation learning, class representation learning, and task specific feature representation learning). The existing popular datasets and evaluation standards are introduced. Furthermore, a novel classification performance evaluation mechanism is designed with a 0.95 confidence interval for judging whether the classification accuracy obtained by a certain specified method is good or bad. Moreover, current challenges and future trends of feature representation learning on FSFGIC are elaborated.

Improving the acquisition and retention of a new motor skill is of great importance. The present study (i) investigated the effects of difficulty manipulation strategies (gradual difficulty), combined with different modalities of feedback (FB) frequency on performance accuracy and consistency when learning a novel fine motor coordination task, and (ii) examined relationships between novel fine motor task performance and executive function (EF), working memory (WM), and perceived difficulty (PD). Thirty-six, right-handed, novice physical education students volunteered to participate in this study. Participants were divided into three progressive difficulty groups (PDG), 100% visual FB (FB1), 50% FB (FB2), and 33% FB (FB3). Progressive difficulty was increased by the manipulation of the distance to the target; 2 m, 2.37 m, and 3.56 m. Three FB modalities were investigated (i.e.: 100% visual FB (100% FB), 50% reduced feedback condition (50% RFB), and 33% reduced feedback conditions (33% RFB)). Performance assessments were conducted following familiarization, acquisition, and retention learning phases. Two stress-conditions of dart throws were investigated (i.e.: free condition (FC) and time pressure condition (TPC)). After the learning intervention, data showed that, under the free condition, the 100% FB group had a significant improvement in accuracy during all learning phases. Under time pressure condition, for the 50% RFB and the 33% RFB group, the measured variable (accuracy and consistency) showed a significant linear improvement in performance. The association between the percentage of RFB frequencies and the task difficulty (50% group) may be a more appropriate and manageable cognitive load compared to the 33% RFB and the 100% FB group. The present findings could have practical implications for practitioners because, while strategies are clearly necessary for improving learning, the efficacy of the process appears to be essentially based on the characteristics of the learners.

Glass and ceramics have a fundamental and crucial role in our lives due to their properties and aesthetic decoration. However, they create serious environmental problems, mainly due to their high occupation of landfills and harmful emissions. Both wastes could be utilized to reduce the natural resources' adverse environmental effects and exhaustion. With increasing environmental concerns to reduce solid waste as much as possible, the concrete industry has adopted several methods to achieve this goal. Hence this study examines the performance of self-compacted concrete (SCC) utilizing various percentages of recycled waste materials such as those deposited from glass and ceramic industries. The idea of utilizing recycled waste materials in concrete manufacturing has gained massive attention due to their impressive results in rheological and mechanical state. Recycled glass (RG) and ceramic waste powder (CWP) were utilized to replace fine aggregate and cement, respectively. Five mixes were designed, including the control mix, and the other four mixes with different dosages of RG and CWP as fine aggregate and cement replacement ranging between 5 to 25%. Mixes were tested for both rheological and mechanical properties to evaluate their compilment with SCC requirements as per codes and guidelines. The results revealed that 20%CWP or less as cement replacement and 10% or less of RG as fine aggregate replacement would provide suitable rheological properties along with mechanical ones. Utilizing recycled glass and ceramic waste powder provides strength like the mix designed with natural resources, which helps us structure economically and environmentally friendly.

We review time expansion wave theory and refine our model showing it to accurately match observed cosmological luminosity data. The theory augments general relativity to create a container within which quantum effects can be explained by treating time as an artefact of a fourth spatial dimension that we observe as the expansion of the universe. This leads us to explain mass creation, the speed of light limit, gravity, black holes without singularities and other macroscopic effects. Our model leads to an expansion velocity of 6.87 0.36 × 106 ms-1, which results in a Hubble expansion rate constant of 71.3 3.7 km/s/Mpc. Because of the finite window of time in which we exist, an extra a degree of freedom is available leading to interesting interpretations at the subatomic level. At this size range, then a spin is allowed within the time dimension explaining why wave particle duality occurs and explains the electromagnetic force and other quantum mechanical properties. It provides a solution to the double slit conundrum and can explain how quantum entangled partners can behave in a quantum way and pass seemingly time-defying information. The potential quantitative origin of the fine structure constant which governs the size of the electromagnetic force is also strongly indicated by this theory.

Abstract:The objects of fine-grained image categories(e.g., bird species) are various subclass under different categories. Because the differences between subclass are very subtle and most of them are concentrated in multiple local areas, the task of fine-grained image recognition is very challenging. At the same time, some fine-grained networks tend to focus on a certain region when judging the target category, resulting in the lack of other auxiliary regional features. To this end, Inception V3 is used as the backbone network, and an enhanced and complementary fine-grained image classification network is designed. While adopting the method of reinforcement learning to obtain more detailed fine grain image features, the complementary network can obtain the complementary discriminant area of the target through the method of attention erasure to increase the network's perception of the overall target. Finally, experiments are conducted on CUB-200-2011, FGVC Aircraft and Stanford dogs three open datasets. The experimental results show that the proposed model has better performance.

The rapidly evolving field of artificial intelligence (AI) continues to witness the introduction of innovative open-source pre-trained models, fostering advancements in various applications. One such model is Llama 2, an open-source pre-trained model released by Meta, which has garnered significant attention among early adopters. In addition to exploring the foundational elements of the Llama v2 model, this paper investigates how these early adopters leverage the capabilities of Llama 2 in their AI projects. Through a qualitative study, we delve into the perspectives, experiences, and strategies employed by early adopters to leverage Llama 2's capabilities. For the purpose of data analysis, the capabilities inherent in the Llama 2 model were employed to conduct keyword extraction from the context of the early adopters' case studies. The findings shed light on the model's strengths, weaknesses, and areas of improvement, offering valuable insights for the AI community and Meta to enhance future model iterations. Additionally, we discuss the implications of Llama 2's adoption on the broader open-source AI landscape, addressing challenges and opportunities for developers and researchers in the pursuit of cutting-edge AI solutions. The present study constitutes an early exploration of the Llama 2 pre-trained model, holding promise as a foundational basis for forthcoming research investigations.

The study aims to identify the fine particulate matter (PM2.5) hazard area, mitigation method, and possible sustainable development in a changing global climate. The critical environmental hazards are artificial light at night (ALAN) and air pollution with ambient PM2.5. People use nighttime outdoor environments for their needs, and the nocturnally migrating birds are attracted to urban ALAN during seasonal migration, which could increase the birds' exposure to PM2.5. A comparative study examines PM2.5 concentrations and the spatial correlation between ALAN and PM2.5 within urban versus rural areas. The author used the nighttime data of the artificial light on the Earth's surface and the PM2.5 level of concentration to estimate the extent of air pollution associated with PM2.5 in the ground-level atmosphere. The results can assist in determining the required PM2.5 control areas and designing and executing environmental conservation planning. Furthermore, the results of this study are not only beneficial to understanding accurately the regional differences of spatiotemporal PM2.5 emission dynamics and helpful for proposing alleviation policies in air pollution control and providing scientific support for regional sustainable development in changing climate. The integrated hazards of ALAN and air pollution are most significant and likely to increase within the urban and decrease within rural areas. This study was undertaken by the first author and built upon the context of the academic, scientific, and technological challenges to identify the PM2.5 concentration in urban and rural areas and the expected outcomes.

Low-cost particulate matter (PM) sensors offer an excellent opportunity to improve our knowledge about this type of pollution. Their size and their cost, which support multi-node network deployment, along with their temporal resolution, enable them to report fine spatio-temporal resolution for a given area. These sensors have known issues across performance metrics. Generally, the literature focuses on the PM mass concentration reported by these sensors but some models of sensors also report particle number concentrations (PNC) segregated into different PM size bins. In this study, 8 units of each Alphasense OPC-R1, Plantower PMS5003 and Sensirion SPS30 have been exposed, under controlled conditions, to short-lived peaks of PM generated using two different combustion sources of PM, exposing the sensors to different particle size distributions to quantify and better understand the low cost sensors performance across a range of relevant environmental ranges. The PNC reported by the sensors were analysed. This study showed that there is added value in directly using the PNC reported by the sensors instead of the mass concentrations, which could aid the efforts to calibrate these sensors to a known accuracy. It demonstrated that all sensors tested here could track the fine temporal variation of PNC, that the Alphasense OPC-R1 could closely follow the variations of size distribution between the two sources of PM, and it showed that particle size distribution and composition are more impactful on the sensors measurement than relative humidity.

Physical distancing and wearing a face mask are key interventions to prevent COVID-19. While this remains difficult to practice for millions of firefighters in fire engines responding to emergencies, the delayed forthcoming of evidence on the physical effectiveness of such safety interventions in this setting presents a major problem. In this field experimental study, we provided initial evidence to close this gap. We examined total aerosol burden in the cabin of a fire engine whilst manipulating crew size, natural ventilation, use of FFP2 respirators and use of SCBA full-face masks during 15-minute driving periods. At the same time, we controlled for crew activity and speaking, vehicle speed, cabin air temperature, pressure and humidity. Limiting the crew size, using FFP2 respirators and not donning SCBA full-face masks was associated with a reduction of the arithmetic mean of total aerosol burden of up to 49%. Natural ventilation as tested in this study was associated with both an increase and a decrease of total aerosol burden. This study provided initial evidence on the physical effectiveness of safety interventions in fire engines to reduce potential airborne transmission of SARS-CoV-2 through aerosols. More research about the physical and clinical effectiveness of such safety interventions is needed.

In the Standard Model of elementary particles, the rest mass, magnetic moment and electric charge of the electron are considered to be natural constants. In this article it is proposed that these properties can be calculated in a simple way from the corresponding Planck units using the well-known period doubling phenomenon in nonlinear dynamical systems. Periods corresponding to the values of the electron and positron properties belong to a subset of stable periods. The periodic structures of the rest energy and magnetic moment consist of three internal degrees of freedom, whereas the Coulomb energy of the electric charge consists of four. The number of period doublings for the elementary charge determines the value of the fine structure constant alpha.

A rigorous model for the electron is presented by generalizing the Coulomb's Law or Gauss's Law of electrostatics, using a unified theory of electricity and gravity. The permittivity of the free-space is allowed to be variable, dependent on the energy density associated with the electric field at a given location, employing generalized concepts of gravity and mass/energy density. The electric field becomes a non-linear function of the source charge, where concept of the energy density needs to be properly defined. Stable solutions are derived for a spherically symmetric, surface-charge distribution of an elementary charge. This is implemented by assuming that the gravitational field and its equivalent permittivity function is proportional to the energy density, as a simple first-order approximation, with the constant of proportionality referred to as the Unifield Electro-Gravity (UEG) constant. The stable solution with the lowest mass/energy is assumed to represent a ``static'' electron without any spin. Further, assuming that the mass/energy of a static electron is half of the total mass/energy of an electron including its spin contribution, the required UEG constant is estimated. More fundamentally, the lowest stable mass of a static elementary charged particle, its associated classical radius, and the UEG constant are related to each other by a dimensionless constant, independent of any specific value of the charge or mass of the particle. This dimensionless constant is numerologically found to be closely related to the the fine structure constant. This possible origin of the fine structure constant is further strengthened by applying the proposed theory to successfully model the Casimir effect, from which approximately the same above relationship between the UEG constant, electron's mass and classical radius, and the fine structure constant, emerges.

Plants produce above- and below-ground biomass. However, our understanding of both production and decomposition of below-ground biomass is poor, largely because of the difficulties of accessing study materials. Below-ground organic matter decomposition studies are scanty and especially rare in the tropics. Here, we used a litter bag experiment to quantify the mass loss and nutrients dynamics of decomposing twigs and fine roots from an arbuscular mycorrhizal fungal associated tree, Parashorea chinensis, in a tropical rain forest in Southwest China. Overall, twig litter decomposed 1.9 times faster than fine roots (decay rate (k) twig=0.255, root=0.134). The difference in decomposition rates can be explained by a difference in phosphorus (P) concentration, availability and use by decomposers or C quality. Both materials showed an increase in N concentration, with final measurements still higher than initial levels. This suggests N may not be available due to microbial immobilization. Both carbon and nitrogen dynamics were significantly predicted by mass loss and showed a negative and positive relationship, respectively. Our study results imply that fine roots carbon and nitrogen contribute more to soils organic matter and enlarge the resident time. Therefore, better understanding of carbon cycle requires better understanding of mechanisms governing below ground biomass decomposition.

The preparation of antibodies in the powder form without changing their physicochemical properties may enable their use in new drug delivery system therapies or for non-refrigerated storage. The variable domain of the heavy chain of heavy chain (VHH) antibodies is more suited for this purpose than conventional antibodies because of their high thermal stability and ability to refold. In this report, the fine droplet drying (FDD) process was selected as the powderization technique because of its favorable features, such as mild drying conditions and the generation of uniform particle sizes. The aggregation, binding, particle and in vitro inhalation properties of the prepared VHH antibody powder (VHHp) were evaluated. The amount of aggregated VHH antibody present in VHHp depended on the flow temperature during the FDD process, with higher temperatures yielding a high aggregation ratio. In contrast, no significant difference in binding activity was observed between each VHHp preparation and the native VHH antibody. Analysis of VHHp samples revealed that the particles were uniformly spherical with a single-micron size. VHHp showed a fine inhalation property in the inhalation property test. These findings suggest that the FDD process affords various VHH antibody powder formulations, including pharmaceutical formulations.

Extracting entities from large volumes of chicken epidemic texts is crucial for knowledge sharing, integration, and application. However, Named Entity Recognition (NER) encounters significant challenges in this domain, particularly due to the prevalence of nested entities and domain-specific named entities, coupled with a scarcity of labeled data. To address these challenges, we compiled a corpus from 50 books on chicken diseases, covering 28 different disease types. Utilizing this corpus, we developed a nested NER model, MFGFF-BiLSTM-EGP. This model integrates the Multiple Fine-Grained Feature Fusion (MFGFF) module with a BiLSTM neural network and employs an Efficient Global Pointer (EGP) for predicts the entity location encoding. In MFGFF module we designed three encoders, character encoder, word encoder and sentence encoder, this design effectively captures fine-grained features and improves the recognition accuracy of nested entities. Experimental results show that the model performs robustly, with F1 scores of 91.98%, 73.32%, and 82.54% on the CDNER, CMeEE V2, and CLUENER datasets, respectively, outperforming other commonly used NER models. Specifically, on the CDNER dataset, the model achieved an F1 score of 79.68% for nested entity recognition. This research not only advances the development of knowledge graph and intelligent question-answering system for chicken diseases but also provides a viable solution for extracting disease information that can be applied to other livestock species.

Lymphadenopathy is a common manifestation of both reactive and malignant diseases, and fine needle aspiration (FNA) is an effective and inexpensive screening method. It can prevent unnecessary invasive surgery and excisional biopsy, especially in benign cases. Unfortunately, the lack of universally accepted terminology for reporting results has hindered its widespread support. The Sydney system proposal for lymph node cytopathology categorization and reporting introduced five diagnostic categories to address the lack of universally accepted terminology for reporting results in lymphadenopathy. Our study analyzed 188 lymph node FNC samples from King Abdulaziz University Hospital, Saudi Arabia, examining clinical follow-up data, pathology records, patient information, and final diagnosis from January 2019 to December 2022. Most specimens were from axillary LNs, with 99.5% tissue correlation. The Sydney System Category classification identified 56.9% of cases as malignant, while 26.1% were benign. The final surgical specimen diagnosis revealed a higher percentage of malignant diagnoses, with the highest ROM in malignant/Category V. In conclusion, our study demonstrates that LN-FNAC offers high diagnostic accuracy for LN aspirates, with the Sydney approach potentially aiding risk stratification and achieving consistency in cytologic diagnosis, but further multi-centric research is needed.

Fine particulate matter (PM_2.5) can enter the lungs and cause premature death in millions of people worldwide every year. More and more international scholars are paying attention to the potential harm of PM_2.5 and how to reduce its negative impact. Reducing air pollution and its negative impacts is an important function of green spaces. Many scholars have conducted research on the impact of green spaces on PM_2.5. This article attempts to comprehensively review past related research. This is undoubtedly beneficial for people to better understand the impact of green spaces on PM_2.5 and optimize green spaces accordingly to reduce PM_2.5 concentration. This article first provides a detailed introduction to the impact of plants on PM_2.5 pollution from the perspective of the mechanism of action. Then, this article provides a review of the different impacts of green space types and morphological factors on the surrounding environment PM_2.5. In addition, this article outlines the range of the impact of green spaces on PM_2.5 in the environment. This article categorizes green spaces into different types such as road green spaces, commercial and residential green spaces, and parks and squares, and explores the impact of green spaces on PM_2.5 in the environment. This article analyzes the impact of various parts of a single plant on PM_2.5 in the environment from a single plant perspective. Finally, this article analyzes which factors in the surrounding environment can affect the impact of green spaces on PM_2.5 pollution in the environment. A review of relevant research is also beneficial for people to have a more comprehensive understanding of current research progress and guide future research to better carry out.

EUS–TA in unresectable pancreatic cancer requires not only a tissue diagnosis but also tissue collection in anticipation of comprehensive genomic profiling. However, the optimal puncture target remains controversial. Therefore, the Primary and Metastatic Lesions in Pancreatic Cancer (PRIMATE) study was designed to clarify the optimal target by comparing the success rates for meeting OncoGuide NCC Oncopanel (NOP) analysis criteria on pre-check primary and metastatic lesion specimens obtained during the same EUS–TA session in patients with invasive pancreatic ductal adenocarcinoma. In this ongoing prospective study, two specimens, each from primary and metastatic lesions, are obtained by EUS–TA (typically using a 19G fine-needle biopsy needle) in patients with invasive pancreatic ductal adenocarcinoma. The primary endpoint is the proportion of EUS–TA specimens that meet NOP analysis criteria during pre-check (i.e., tumor cellularity ≥ 20% and tissue area ≥ 4 mm2), which are then compared between primary and metastatic lesions. This study has been approved by the National Cancer Center Institutional Review Board (Research No. 2022-168). The results of this study will be reported at an international conference and published in an international peer-reviewed journal. The trial registration number is UMIN 000048966.

In arXiv:2111.15017v1 [1], Dias and Salamat posted some of the measured data for ac magnetic susceptibility of carbonaceous sulfur hydride, a material that was reported in Nature 586, 373 (2020) [2] to be a room temperature superconductor. They provided additional measured data in arXiv:2111.15017v2 [3]. Here I provide an analysis of these data. The results of this analysis indicate that the claim of ref. [2] that magnetic susceptibility measurements support the conclusion that the material is a room temperature superconductor is not supported by valid underlying data.

Image segmentation is a new challenge prob- lem in medical application. The use of medical imaging has become an integral part of research, as it allows us to see inside the human body without surgical intervention. Many researcher have studied brain segmentation. One stage method is used to segment the brain tissues. In this paper, we proposed the multi-stage generative ad- versarial network to solve the problem of information loss in the one-stage. We utilize the coarse-to-fine to improve brain segmentation using multi-stage generative adversar- ial networks (GAN). In the first stage, our model generated a coarse outline for (i) background and (ii) brain tissues. Then, in the second stage, the model generated outline for (i) white matter (WM), (ii) gray matter (GM) and (iii) cerebrospinal fluid (CSF). A good result can be achieved by fusing the coarse outline and refine outline. We conclude that our model is more efficient and accu- rate in practice for both infant and adult brain segmenta- tion. Moreover, we observe that multi-stage model is faster than prior models. To be more specific, the main goal of multi-stage model is to see the performance of the model in a few shot learning case where a few labeled data are available. For medical image, this proposed model can work in a wide range of image segmentation where the convolution neural networks and one-stage methods have failed.

Sediment cores were collected from a mudflow lobe (80 m water depth) offshore of the Mississippi River’s Southwest Pass in 2017 to better understand the sedimentology near the lobe entraining the SS Virginia shipwreck (sunk by a German U-Boat in 1942) and surrounding Mississippi River Delta Front. Core analyses included 210Pb/137Cs geochronology, granulometry, and X-radiography. Sediment accumulation rates (SAR) calculated from excess 210Pb activity in multicores are 0.22–0.29 cm/yr at seabed depths less than 20 cm and 0.29–0.51 cm/year at depths greater than 20 cm. 137Cs accumulation rates are ~0.15 to ~ 0.37 cm/year since 1954 and 1963 respectively. Sediment accumulation rates from 210Pb and 137Cs geochronology and indicators of relative sedimentation and bioturbation from X-radiographs suggest that rates of sediment accumulation near Virginia have declined since ca. the mid-20th century. This may be explained by the multi-decade downslope mass transport of the mudflow lobe in which the shipwreck is embedded, and decreases in sediment supply delivered offshore from the Mississippi River. Mass-transport calculations of the Virginia lobe derived from core properties and published lobe advection rates suggest downslope mass transport is far higher than sediment resupply from the Mississippi River, consistent with recent studies of delta retreat.

The space gravitational wave detection and drag free control requires the micro-thruster to have very low thrust noise within 0.1mHz~100mHz, which poses a great challenge to the ground thrust test. The evaluation and decoupling of thermal noise are the difficulties in the application of torsion balance for most thrusters dissipate heat in the experiment. The research has adopted a calibration scheme of micro-Newton thrust torsion balance. On the basis of Lisa Pathfinder's former research and using ideas inspired from PID control and multi time scale (MTS) for reference, the paper proposes to expand the state space of temperature to be applied on thrust prediction based on fine tree regression (FTR), to subtract the thermal noise filtered by transfer function fitted with z-domain vector fitting (ZDVF). The results show that the thrust amplitude thrust density in diurnal temperature fluctuation is decoupled from 24μN/Hz1/2 to 4.9μN/Hz1/2 at 0.11mHz. And the 1μN square wave modulations of electrostatic fins (ESF) is extracted from the simultaneously ambiguous background of temperature for PTC's heating and cooling. The FTR method is well demonstrated in thermal noise decoupling and can guide the design of thermal control and be extended to other physical quantities for noise decoupling.

The study of the concentrations and effects of fine particulate matter in urban areas have been of great interest to researchers in recent times. This is due to the acknowledgment of the far-reaching impacts of fine particulate matter on public health. Remote sensing data have been used to monitor the trend of concentrations of particulate matter by deriving aerosol optical depth (AOD) from satellite images. The Center for International Earth Science Information Network (CIESIN) has released the second version of its global PM_2.5 data with improvement in spatial resolution. This paper revisits the study of spatial and temporal variations in particulate matter in Saudi Arabia by exploring the cluster analysis of the new data. Cluster analysis of the PM_2.5 values of Saudi cities is performed by using Anselin local Moran’s I statistic. Also, the analysis is carried out at the regional level by using self-organizing map (SOM). The results show an increasing trend in the concentrations of particulate matter in Saudi Arabia, especially in some selected urban areas. The eastern and south-western parts of the Kingdom have significantly clustering high values. Some of the PM2.5 values have passed the threshold indicated by the World Health Organization (WHO) standard and targets posing health risks to Saudi urban population.

In urban areas like Chicago, daily life extends above ground level due to prevalence of high-rise buildings where residents and commuters live and work. This study examines the variation in fine particulate matter (PM2.5) concentrations across building stories. PM2.5 levels were measured using PurpleAir sensors, installed between April 8 and May 7, 2023, on floors one, four, six, and nine of an office building in Chicago. Additionally, data were collected from a public outdoor PurpleAir sensor on the fourteenth floor of a condominium located 800 meters away. Results show that outdoor PM2.5 concentrations peak at 14 meters height, then decline by 0.11 μg/m3 per meter elevation, especially noticeable from midnight to 8 AM under stable atmospheric conditions. Indoor PM2.5 concentrations increase steadily by 0.02 μg/m3 per meter elevation, particularly during peak work hours, likely caused by greater infiltration rates at higher floors. Both outdoor and indoor concentrations peak around noon. We find that indoor and outdoor PM2.5 are positively correlated, with indoor levels consistently remaining lower than outside levels. These findings align with previous research suggesting decreasing outdoor air pollution concentrations with increasing height. The study informs decision-making by community members and policymakers regarding air pollution exposure in urban settings.

Tactics, Techniques, and Procedures (TTPs) constitute the most valuable aspect of Cyber Threat Intelligence (CTI). However, TTPs are often implicit in unstructured text, necessitating manual analysis by field experts. Automating the classification of TTPs from unstructured text is a crucial task in contemporary research. MITRE ATT&CK serves as the de facto standard for studying TTPs. Existing research constructs classification datasets based on its procedural examples for tactics and techniques. However, due to a significant proportion of small sample categories, a long-tail phenomenon exists, leading to a highly imbalanced sample distribution. Consequently, more research concentrates on categories with relatively abundant samples. This paper proposes a method that combines ChatGPT data augmentation with Instruction Supervised Fine-Tuning of open large language models. This approach offers a solution for TTPs classification in few-shot learning scenarios, achieving coverage of 625 technical categories. The Precision, Recall, and F1 scores reach 86.2%, 89.9%, and 87.3%, respectively.

The transmission and analysis of data is one of the challenges of the 21st century. In the field of environmental measurement technology, existing broadband and wireless technologies have not been able to transmit data reliably and cost-effectively over long distances and in hard-to-reach places. LoRaWAN, an IoT technology, could be an energy-efficient, cost-effective and secure alternative as a narrowband technology in combination with battery-powered sensors and thus make an important contribution to the intelligent, largely wireless networking of objects, plants and machines (IoT), for example in the municipal sector. In addition to ecological and economic benefits, the quality of life in modern, intelligently networked cities can be enhanced by real time data acquisition. However, the prerequisite is that the quality of the data acquired via this method is sufficiently good. This paper therefore addresses the question of the quality of particulate matter data collected by low-cost sensors. To determine this, an SDS011 particulate matter sensor from Nova Fitness was ported to LoRaWAN. The sensor was installed next to a governmental measurement station. In a test that lasted five weeks, data from the SDS011 sensor were compared with those from the governmental station. Differences were identified and a correction approach was developed and applied. The efficiency of the approach was verified. Based on the results, it can be seen that the use of the low cost sensors has weaknesses. Problems can only be partially reduced. Nevertheless, the use of the low-cost sensors can be helpful for a flexible and cost effective collection of environmental data.

Every day around 93% of children under the age of 15 (1.8 billion children) breathe outdoor air that is so polluted it puts their health and development at serious risk. Due to the pandemic, however, ventilation of buildings using outdoor air has become an important safety technique to prevent the spread of COVID-19. With the mounting ev-idence suggesting that air pollution is impactful to human health and educational out-comes, this contradictory guidance may be problematic in schools with higher air pol-lution levels, but keeping kids COVID-19 free and in school to receive their education is now more pressing than ever. To understand if all schools in an urban area are ex-posed to similar outdoor air quality and if school infrastructure protects children equally indoors, we installed research grade sensors to observe PM2.5 concentrations in indoor and outdoor settings to understand how unequal exposure to indoor and out-door air pollution impacts indoor air quality among high- and low-income schools in Salt Lake City, Utah. Based on this approach, we found that during atmospheric inver-sions and dust events, there was a lag ranging between 35 to 73 minutes for the out-door PM2.5 concentrations to follow a similar temporal pattern as the indoor PM2.5. This lag has policy and health implications and may help to explain the rising concerns re-garding reduced educational outcomes related to air pollution in urban areas. These data and resulting analysis show that poor air quality may impact school settings, and the potential implications with respect to environmental inequality.

Cumulative effects of landscape disturbance in forested source water regions can alter the storage of fine sediment and associated phosphorus in riverbeds, shift nutrient dynamics and degrade water quality. Here, we examine longitudinal changes in major element chemistry and particulate phosphorus (PP) fractions of river-bed sediment in an oligotrophic river during environmentally sensitive low flow conditions. Study sites along 50 km of the Crowsnest River were located below tributary inflows from sub-watersheds and represent a gradient of increasing cumulative sedi-ment pressures across a range of land disturbance types (harvesting, wildfire, and municipal wastewater discharges). Major elements (Si2O, Al2O3, Fe2O3, MnO, CaO, MgO, Na2O, K2O, Ti2O, V2O5, P2O5), loss on ignition (LOI), PP fractions (NH4CI-RP, BD-RP, NaOH-RP, HCI-RP and NaOH(85)-RP) and absolute particle size were evaluated for sediments collected in 2016 and 2017. While total PP concentrations were similar across all sites, bioavailable PP fractions (BD-RP, NaOH-RP) increased downstream with increased concentrations of Al2O3 and MnO and levels of landscape disturbance. This study highlights the longitudinal water quality impacts of increasing landscape disturbance on bioavailable PP in fine riverbed sediments and shows how the convergence of climate (wildfire) and anthropogenic (sewage effluent, harvesting, agriculture) drivers can produce legacy effects on nutrients.

Estimating the infection risks in indoor environments comprises the assessment of the behavior of virus-laden aerosols, i.e. spreading, mixing, removal by air purifiers etc. A promising experimental approach is based on using non-hazardous surrogate aerosols of similar size, e.g. salt particles, to mimic the virus aerosol behavior. This manuscript addresses the issue how a successful transfer of such experiments can be accomplished. Corresponding experiments in two very different environments, a large community hall and a seminar room, with optional use of air purifiers in various constellations were conducted. While high particle concentrations are advantageous in terms of avoiding influence of background aerosol concentrations, it is shown that appropriate consideration of aggregation and settling are vital to theoretically describe the experimentally determined course of particle concentrations. A corresponding model equation for a well-mixed situation is derived and the required parameters are thoroughly determined in separate experiments independently. It is demonstrated that clean air delivery rates (CADR) of air purifiers determined with this approach may differ substantially from common approaches not explicitly taking into account aggregation.

To address the problems of wind abandonment in the regional integrated energy system (RIES), which occurs when cogeneration unit operates in a heat-determined electricity operation mode, and the low efficiency of the energy storage system in a low-temperature environment, we propose an optimal scheduling method for RIES based on fine energy storage and wind power dissipation. First, a fine energy storage model is established on the source side and the conditional value at risk(CvaR) theory is used to quantify the uncertainty of wind power; then a combined heat and power demand response mechanism is introduced on the load side to reduce the peak-to-valley difference between heat and power loads, and to promote the consumption of wind power. Finally, with the objective of minimizing the total cost of RIES optimal dispatch, the example is solved on the MATLAB platform. The simulation results show that compared with the traditional model, the proposed model is not only more adaptable to the low-temperature environment, but also can effectively improve the system economy and wind power consumption.

The flux growth of InVO4 bulk single crystals has been explored for the first time. The reported eutectic composition at a ratio of V2O5:InVO4 = 1:1 could not be used as a self-flux, since no sign of melting was observed up to 1100 °C. Crystals of InVO4 of typical size 0.5×1×7 mm3 were obtained using copper pyrovanadate (Cu2V2O7) as a flux, using Pt crucibles. XRD confirmed the orthorhombic Cmcm structure. Rests of flux material were observed on the sample surface, with occasional traces of Pt indicating some level of reaction with the crucible. X-ray absorption spectroscopy showed that oxidation states of indium and vanadium ions are +3 and +5, respectively. The size and high quality of the obtained InVO4 crystals makes them excellent candidates for the further study of their physical properties.

We present a novel passive satellite remote sensing approach for observing the three-dimensional distribution of aerosols emitted from wildfires. This method, called AEROS5P, retrieves vertical profiles of aerosol extinction from measurements of the TROPOMI satellite sensor onboard the Sentinel 5 Precursor mission. It uses a Tikhonov-Phillips regularization which iteratively fits cloud-free near-infrared and visible selected reflectances to simultaneously adjust the vertical distribution and abundance of aerosols. The information on the altitude of the aerosol layers is provided by TROPOMI measurements of the reflectance spectra at the oxygen A-band near 760 nm. In the present paper, we use this new approach for observing the daily evolution of the three-dimensional distribution of biomass burning aerosols emitted by Australian wildfires on 20-24 December 2019. Aerosol optical depths (AOD) derived by vertical integration of the aerosol extinction profiles retrieved by AEROS5P are compared with MODIS, VIIRS and AERONET coincident observations. They show a good agreement in the horizontal distribution of biomass burning aerosols, with a correlation coefficient of 0.86 and a mean absolute error of 0.2 with respect to VIIRS. A fair agreement is found between coincident transects of vertical profiles of biomass burning aerosols derived from AEROS5P and from the CALIOP spaceborne lidar. The mean altitude of these aerosols derived from these two measurements show a good agreement, with a small mean bias (185 m) and a correlation coefficient of 0.83. Moreover, AEROS5P observations reveal the height of injection of the biomass burning aerosols in 3D. The highest injection heights during the period of analysis are coincident with the largest fire radiative power derived from MODIS. Consistency is also found with respect to the vertical stability of the atmosphere.

Document classification is one of the most critical steps in the document analysis pipeline. There are two types of approaches for document classification, known as image-based and multimodal approaches. The image-based document classification approaches are solely based on the inherent visual cues of the document images. In contrast, the multimodal approach co-learns the visual and textual features, and it has proved to be more effective. Nonetheless, these approaches require a huge amount of data. This paper presents a novel approach for document classification that works with a small amount of data and outperforms other approaches. The proposed approach incorporates a hierarchical attention network(HAN) for the textual stream and the EfficientNet-B0 for the image stream. The hierarchical attention network in the textual stream uses the dynamic word embedding through fine-tuned BERT. HAN incorporates both the word level and sentence level features. While the earlier approaches rely on training on a large corpus (RVL-CDIP), we show that our approach works with a small amount of data (Tobacco-3482). To this end, we trained the neural network at Tobacco-3428 from scratch. Thereby, we outperform state-of-the-art by obtaining an accuracy of 90.3%. This results in a relative error reduction rate of 7.9%.