Due to the influence of background conditions, lighting conditions, occlusion issues and the image resolution, how to extract robust person features is one of the difficulties in ReID research. Vision in Transformers (ViT) has achieved significant results in the field of computer vision. However, the existing problems still limit its application in ReID due to slow extraction of person features and difficulty in utilizing local features of people. To solve the mentioned problems, we utilize Pyramid Vision Transformer (PVT) as the backbone of feature extraction and propose a PVT-based ReID method in conjunction with other studies. Firstly, some improvements suitable for ReID are used on the PVT backbone, and we establish a basic model by using powerful methods verified on CNN-based ReID. Secondly, in an effort to further promote the robustness of the person features extracted by the PVT backbone, two new modules are designed. (1) The local feature clustering (LFC) is recommend to enhance the robustness of person features by calculating the distance between local features and global feature to select the most discrete local features and clustering them. (2) The side information embeddings (SIE) are used to encode non-visual information and send it into the network for training to reduce its impact on person features. Finally, the experiments show that PVTReID has achieved excellent results in ReID datasets and are 20% faster on average than CNN-based ReID methods.

Coal gasification with carbon dioxide is a process for generating clean gaseous fuels and relieving greenhouse effect. Zhundong coal has high alkali and alkali earth metals (AAEMs) content, medium volatile and low ash in nature. Isothermal CO₂ gasification of char derived from Zhundong coal (R-char) and char from acid washing R-char (AR-char) are performed in thermo-gravimetric analyzer (TGA). The effect of AAEMs is investigated on the gasification behavior in the range of temperatures 1073 K to 1273 K. The carbon conversion increases rapidly with increasing reaction temperature and CO₂ concentration. R-char has high gasification rate and carbon conversion compared with AR-char. The accuracy of the free-model approach for calculating activation energy at different conversions is validated by compared with different kinetic models (volume reaction model, distributed activation energy model). Moreover, R-char gasification with CO₂ shows a compensation effect as the Arrhenius parameters (EA and k0) increase or decrease simultaneously.

Three kinds of inorganic alkali are introduced into tetraethylenepentamine (TPEA) and polyethyleneimine (PEI) modified MCM-41 as the CO2 adsorbents. FT-IR and TGA are used to characterize the surface groups and the thermal stability of adsorbents. Chemical titration method is used to measure the alkali amounts of adsorbents. Thermo-gravimetric analysis with 10%CO2/N2 as the simulated flue gas is used to test the CO2 adsorption performance of adsorbents. The results show that all three kinds of inorganic alkali containing adsorbents exhibit promoted CO2 adsorption capability than traditional TPEA and PEI modified samples. Ca(OH)2 and PEI modified samples exhibit the highest adsorption capacity and stable regeneration property. The introduction of inorganic alkali changes the chemical adsorption mechanism between CO2 and increases the effective alkali amounts due to its hydroxyl groups. The CO2 adsorption capacities have a linear dependence relation with the alkali amounts of adsorbents, indicating that alkali amount is a critical factor for novel adsorbents exploring.

Recent evidence has highlighted the critical role of memory cells in maintaining lifelong food allergies, thereby identifying these cells as therapeutic targets. IgG⁺ memory B cells replenish pools of IgE-secreting cells upon allergen exposure, which contract thereafter due to the short lifespan of tightly regulated IgE-expressing cells. Advances in the detection and highly dimensional analysis of allergen-specific B and T cells from allergic patients have provided insight on their phenotype and function. The newly identified Th2A and Tfh13 populations represent a leap in our understanding of allergen-specific T cell phenotypes, though how these populations contribute to IgE memory responses remains poorly understood. Within, we discuss the mechanisms by which memory B and T cells are activated, integrating knowledge from human systems and fundamental research. We then focus on memory reactivation; specifically, on the pathways of secondary IgE responses. Throughout, we identify areas of future research which will help identify immunotargets for a transformative therapy for food allergy.

Climate change is increasing air temperatures and altering the precipitation and hydrological re-gime on a global scale. Challenges arise when assessing the impacts of climate change on the local scale for water resources management proposes, especially for low mountain headwater catch-ments that not only serve as important water towers for local communities, but also have distinct hydrological characteristics. Until now, no low flow or hydrological drought studies have been carried out on the Lauter River. This study is unique in that it compares the Lauter River, a transboundary Rhine tributary with a nearby station on the River Rhine just below its confluence at the French-German border. The Lauter catchment is a mostly natural, forested catchment, however, its water course has been influenced by past and present cultural activities. Climate change disturbances cascade through the hydrologic regime right down to the local scale. As we are expecting more low flow events, the decrease in water availability could cause conflicts be-tween different water user groups in the Lauter catchment. However, the choice of different methods for identifying low flow periods may cause confusion for local water resources manag-ers. Using flow rate time series of the Lauter River between 1956 and 2022 we compare for the first time three low flow identification methods: the variable threshold method (VT), the fixed threshold method (FT), and the Standardized Streamflow Index (SSI). Similar analyses are applied and compared to the adjacent Maxau station on the Rhine River for the same time period. This study aims at 1) interpreting the differences amongst the various low flow identification methods and 2) revealing the differences in low flow characteristics of the Lauter catchment compared to the Rhine River. It appears that the FT reacts faster to direct climate or anthropogenic impacts whereas VT is more sensitive to indirect factors such as decreasing subsurface flow which is typical for small headwater catchments, such as the Lauter where flow dynamics react faster to flow disturbances. Abnormally low flow during the early Spring in tributaries such as the Lauter can help predict low flow conditions of the Rhine River during the following half year and espe-cially for the summer. The results could facilitate Early Warning of hydrological droughts and drought management for water users in the Lauter catchment and further downstream along some of the Rhine.

Euler’s product formula over the primes and Euler’s zeta function equate to enshrine the Fundamental Theorem of Arithmetic that every integer > 1 is the product of a unique set of primes. The product formula has no zero, and with a domain ≤1 Euler’s zeta diverges. Dirichlet’s eta function η(s), negates alternate terms of zeta, permitting convergence when s∈C and Re(s) < 1, and its non-trivial zeros {ρ}, have a deep relationship with the distribution of the primes. The Riemann Hypothesis is that all the non-trivial zeros have Re(ρ) = 1/2. This work examines the symmetries in a partial Euler’s zeta series with a complex domain equating it to the difference between two finite vector series whose matched terms have mirror-image arguments, but whose magnitudes differ when Re(s) ≠ 1/2. Analytical continuation generates a modified eta series η_l(s), in which every l^th term is multiplied by (1-l). If the integer l is appropriately determined by the Im(s), similar paired finite vector series have a difference that closely follows η_l (s) and their terminal vectors intersect in a unique way permitting zeros only when Re(s) = 1/2. Furthermore, those vectors tracking the derivatives of the series, have a special relationship permitting zeros of the differential only when Re(s) > 1/2.

This study considers an application of the first-order Grey Markov Model to foresee the values of Italian power generation in relation to the available energy sources. The model is used to fit data from the Italian energy system from 2000 to 2022. The integration of Markovian error introduces a random element to the model, which is able now to capture inherent uncertainties and misalignments between the Grey model predictions and the real data. This application provides valuable insights for strategic planning in the energy sector and future developments. The results show good accuracy of the predictions, which provides powerful information for the effective implementation of energy policies concerning the evolution of energy demand in the country. Despite advancements, Italy’s 2032 energy mix will still significantly rely on fossil fuels, emphasizing the need for sustained efforts beyond 2032 to enhance sustainability.

Electron Beam Melting (EBM) is an increasingly used Additive Manufacturing (AM) technique employed by many industrial sectors, including the medical device and aerospace industries. In EBM process monitoring, data analysis for processed layer quality evaluation is currently focused on the extraction of information from the raw data collected in-EBM process, i.e. thermal/ optical / electronic images, and the comparison between the collected data and the Computed Tomography (CT)/ microscopy images generated post-EBM process. This article postulates that a stack of bitmaps could be generated from the 3D model at a range of Z heights during file preparation of the EBM process, and serve as a reference image set. In-EBM process comparison between that and the workpiece images collected during the EBM process could then be used for quality assessment purposes. In addition, despite the extensive literature on 3D model slicing and contour generation for AM process preparation, no methods regarding image generation from cross sections of the 3D models have been disseminated in details. This article aims to address this by presenting a piece of 3D model-image generation software. The software is capable of generating binary 3D model reference images with user-defined Region-of-Interest (ROI) of the processing area, and Z heights of the model. It is envisaged that this 3D model-reference image generation ability opens up new opportunities in quality assessment for the in-process monitoring of the EBM process.

Facial recognition systems are widely used systems around the globe. Their applications in real world scenarios like law enforcement, surveillance, ID card identification, information security, access control and many more has made them very important in today’s world. These systems have shown promising results and excellent performance; however, these systems need tremendous number of facial images for training before they are implemented in the real-world scenarios. The problem arises when we face lack of training images due to which the performance of such systems decreases exponentially. The problem becomes even worse when there is only one image per subject for training our facial recognition system, the probe image might contain variation like illumination, expression and disguise which are hard to be accurately recognized. Generative models have achieved great success for handling the lack of training data by generating synthetic data that resembles the original data. We have created a new method by fine tuning the basic Conditional Generative Adversarial Network (CGAN) to generate synthetic facial images provided only single image per subject. These synthetic facial images contain six variations that are smile, anger, stare, illumination and disguise. We increased the size of the dataset and then applied a convolutional neural network for facial recognition, which improved the accuracy of our system from 0.76 to 0.99.

Paraphrase Generation is one of the most important and challenging tasks in the field of Natural Language Generation. The paraphrasing techniques help to identify or to extract/generate phrases/sentences conveying the similar meaning. The paraphrasing task can be bifurcated into two sub-tasks namely, Paraphrase Identification (PI) and Paraphrase Generation (PG). Most of the existing proposed state-of-the-art systems have the potential to solve only one problem at a time. This paper proposes a light-weight unified model that can simultaneously classify whether given pair of sentences are paraphrases of each other and the model can also generate multiple paraphrases given an input sentence. Paraphrase Generation module aims to generate fluent and semantically similar paraphrases and the Paraphrase Identification systemaims to classify whether sentences pair are paraphrases of each other or not. The proposed approach uses an amalgamation of data sampling or data variety with a granular fine-tuned Text-To-Text Transfer Transformer (T5) model. This paper proposes a unified approach which aims to solve the problems of Paraphrase Identification and generation by using carefully selected data-points and a fine-tuned T5 model. The highlight of this study is that the same light-weight model trained by keeping the objective of Paraphrase Generation can also be used for solving the Paraphrase Identification task. Hence, the proposed system is light-weight in terms of the model’s size along with the data used to train the model which facilitates the quick learning of the model without having to compromise with the results. The proposed system is then evaluated against the popular evaluation metrics like BLEU (BiLingual Evaluation Understudy):, ROUGE (Recall-Oriented Understudy for Gisting Evaluation), METEOR, WER (Word Error Rate), and GLEU (Google-BLEU) for Paraphrase Generation and classification metrics like accuracy, precision, recall and F1-score for Paraphrase Identification system. The proposed model achieves state-of-the-art results on both the tasks of Paraphrase Identification and paraphrase Generation.

Dynamics of human populations can be affected by various socio-economic factors through their influence on the natality and mortality rates, and on the migration intensity and directions. In this work we study an economic-demographic model which takes into account the dependence of the wealth production rate on the available resources. In the case of nonlocal consumption of resources the homogeneous in space wealth-population distribution is replaced by a periodic in space distribution for which the total wealth increases. For the global consumption of resources, if the wealth redistribution is small enough, then the homogeneous distribution is replaced by a heterogeneous one with a single wealth accumulation center. Thus, economic and demographic characteristics of nonlocal and global economies can be quite different in comparison with the local economy.

Human Face and facial parts are the most significant parts as it reveals a person’s true identity. It plays an important role in various biometric applications like crowd analysis, human tracking, photography, cosmetic surgery, etc. There are many techniques are available to detect a facial image. Among them, skin detection is the most popular one. The aim of this paper is to detect first the person's identity from facial image and finally check any spot present the the detected person. The first step is to detect the maximum skin region based on a combination method of RGB and HSV color space model. Next it is to verify the skin areas of human through machine learning approach. The Aggregated Channel Features (ACF) detector is used to identify the different facial parts like eye pairs, nose, and mouth. Bootstrap aggregation decision tree classifier is applied to classify the person’s identity based on Histogram Oriented Gradient (HOG) features value. The experimental results show that the proposed method gives the average 97% accuracy.

A method for generating fluoroscopic (time-varying) volumetric images using patient-specific motion models derived from 4-dimensional cone-beam CT (4D-CBCT) images is developed. 4D-CBCT images acquired immediately prior to treatment have the potential to accurately represent patient anatomy and respiration during treatment. Fluoroscopic 3D image estimation is done in two steps: 1) deriving motion models and 2) optimization. To derive motion models, every phase in a 4D-CBCT set is registered to a reference phase chosen from the same set using deformable image registration (DIR). Principal components analysis (PCA) is used to reduce the dimensionality of the displacement vector fields (DVFs) resulting from DIR into a few vectors representing organ motion found in the DVFs. The PCA motion models are optimized iteratively by comparing a cone-beam CT (CBCT) projection to a simulated projection computed from both the motion model and a reference 4D-CBCT phase, resulting in a sequence of fluoroscopic 3D images. Patient datasets were used to evaluate the method by estimating the tumor location in the generated images compared to manually defined ground truth positions. Experimental results showed that the average tumor mean absolute error (MAE) along the superior-inferior (SI) direction and the 95th percentile in two patient datasets were (2.29 mm and 5.79 mm) for patient 1 and (1.89 mm and 4.82 mm) for patient 2. This study has demonstrated the feasibility of deriving 4D-CBCT-based PCA motion models that have the potential to account for the 3D non-rigid patient motion and localize tumors and other patient anatomical structures on the day of treatment.

Plant-based protein sources derived from microalgae offer promise as low-carbon emission and nutrient-dense sources for human consumption. The current management of microalgae cultivation relies heavily on manual microscopic examination in order to identify desired and competing species, as well as predators. In this study, we trained and tested a transfer learning model modified from EfficientNetV2 B3 on 434 and 161 prospectively acquired images of the preferred Nannochloropsis sp microalgae and competitor Spirulina, respectively, and achieved &gt;98% classification for both species on tenfold cross-validation. The model was further enhanced with gradient-weighted class activation mapping, which allowed visualization of regions of the input images that were relevant to the classification, thereby improving its explainability. In this paper, we demonstrate that a simple deep transfer learning model can be used to identify microalgae species. The application addresses the practical need for robust automated monitoring of microalgae populations on industrial microalgae farms.

The unsupervised domain-adaptive vehicle reidentification approach aims to transfer knowledge from a labeled source domain to an unlabeled target domain, however there are knowledge differences between the target domain and the source domain. To reduce domain differences, existing unsupervised domain-adaptive rerecognition methods generally require access to the source domain data to assist in re-training the target domain model. But for security reasons, in many cases, data between different domains cannot communicate. To this end, this paper proposes an unsupervised domain-adaptive vehicle re-identification method based on source-free knowledge transfer. First, by constructing a passive domain knowledge migration module, the target domain is consistent with the source domain model output to train a generator to generate the "source-like samples". Then, it can effectively reduce the model knowledge difference and improve the model generalization performance. According to the experiment and testing of VeRi776 and VehicleID, two mainstream public data sets in this field, the rank-k and mAP indexes obtained are both improved, and are suitable for object re-recognition tasks without source data.

Cyber-Physical Systems form the new backbone of digital ecosystems. Their design can be coupled with engineering activities to facilitate dynamic adaptation and (re-)configuration. Behavior-oriented technologies enable highly distributed and while coupled operation of systems. Utilizing them for digital twins as self-contained design entities with federation capabilities makes them promising candidates to develop and run highly functional CPS. In this paper we discuss mapping CPS components to behavior-based digital twin constituents mirroring integration and implementation through subject-oriented models. These models, inspired by agent-oriented system thinking can be executed and increase transparency at design and runtime. Patterns recognizing environmental factors and operation details facilitate configuration of CPS. Subject-oriented runtime support enable dynamic adaptation and federated use.

The current health crisis derived from COVID 19 has become a serious threat to humanity. The collateral effects related to economic aspects are affecting companies, workers and, of course, families in general. The problems of financial insolvency are frequently present in debtors who have contracted a debt with their creditor. Therefore, the objective of the study is focused on modeling a debt restructuring proposal, through three hypothetical scenarios in which the debt is reconvened when some documents have expired and others are yet to expire. In each of the scenarios, the mathematical model is designed and subsequently corroborated in an Excel spreadsheet. The predominant variables in the restructuring are the adjustment rates used to update the past due promissory notes and the discount rates of those promissory notes that have not yet expired, capitalizations, transaction costs and, if applicable, whether the debt is in foreign currency. In this case, it is converted to the local currency, then the spot exchange rate is used.

Abstract: The diffusion of competition under the coexistence of multi-generation products has become one of the important challenges faced by enterprises in their daily and sustainable operations. At the same time, the competition between different brands has intensified the difficulty and complexity of decision-making in the process of multi-generation product operation, Therefore, based on the Norton-Bass model diffusion process, this paper introduces two marketing variables, dynamic price and quality level, builds a multi-generation product diffusion model under the dual-brand competition, and analyzes the impact of the company's revenue on the launch time to market, pricing, quality or technical level. By using system dynamics (SD) method, From the perspective of strong brand and weak brand enterprises, the competition diffusion model is built and simulated. The simulation shows that: i) When enterprises have the same brand competitiveness, reducing the pricing level cannot obtain more revenue, but diminish the overall revenue of the industry; Raising the pricing level can get more revenue, but also improve the revenue of competitors. ii) When the competitive strength of enterprises is different, the strong brand tends to maintain a stable pricing on the basis of improving the quality level, or slightly raise the price. Weak brands tend to raise the pricing of new products significantly on the basis of improving the quality level. iii) The launch time to market decision of new product is influenced by the degree of product quality upgrade. Therefore, the frequency of releasing new products should trade-off against the degree of technological upgrading of product quality. This paper provides the theoretical basis and new insights for the new product launch and operation decision of enterprises.

We investigate entropy generation in unsteady three-dimensional axisymmetric MHD nanofluid flow over a non-linearly stretching sheet. The flow is subject to thermal radiation and a chemical reaction. The conservation equations were solved using the spectral quasi-linearization method. The novelty of the work is in the study of entropy generation in three-dimensional axisymmetric MHD nanofluid and the choice of the spectral quasilinearization method as the solution method. The effects of Brownian motion and thermophoresis are also taken into account when the nanofluid particle volume fraction on the boundary in passively controlled. The results show that as the Hartman number increases, both the Nusselt number and the Sherwood number decrease whereas the skin friction increases. It is further shown that an increase in the thermal radiation parameter corresponds to a decrease in the Nusselt number. Moreover, entropy generation increases with the physical parameters.

Vehicle re-identification research under surveillance cameras has yielded impressive results. However, the challenge of Unmanned Aerial Vehicle (UAV)-based vehicle re-identification (ReID) presents a high degree of flexibility, mainly due to complicated shooting angles, occlusions, low discrimination of top-down features, and significant changes in vehicle scales. To address this, we propose a novel Dual Mixing Attention Network (DMANet) to extract discriminative features robust to variations in viewpoint. Specifically, we first present a plug-and-play Dual Mixing Attention Module (DMAM) to capture pixel-level pairwise relationships and channel dependencies, where DMAM is composed of Spatial Mixing Attention (SMA) and Channel Mixing Attention (CMA). First, the original feature is divided according to the dimensions of spatial and channel to obtain multiple subspaces. Then, a learnable weight is applied to capture the dependencies between local features in the mixture space. Finally, the features extracted from all subspaces are aggregated to promote their comprehensive feature interaction. Moreover, the DMAM can be readily inserted into backbone networks at any depth to improve vehicle discrimination. The experiments show that the proposed structure gains a better performance compared with the representative methods in the UAV-based vehicle ReID task. Our code and models will be publicly released.

In wireless sensor networks (WSNs), each sensor node can estimate the global parameter from the local data in distributed manner. This paper proposed a robust diffusion estimation algorithm based on minimum error entropy criterion with self-adjusting step-size, which are referred to as diffusion MEE-SAS (DMEE-SAS) algorithm. The DMEE-SAS algorithm has fast speed of convergence and is robust against non-Gaussian noise in the measurements. The detailed performance analysis of the DMEE-SAS algorithm is performed. By combining the DMEE-SAS with diffusion minimum error entropy (DMEE) algorithms, an Improving DMEE-SAS algorithm is proposed, in non-stationary environment where tracking is very important. The Improving DMEE-SAS algorithm can avoid insensitivity of the DMEE-SAS algorithm due to the small effective step-size near the optimal estimator, and obtain a fast convergence speed. Numerical simulations are given to verify the effectiveness and advantages of these proposed algorithms.

We develop the process of discounting when underlying rates follow a jump-diffusion process, that is, when, in addition of diffusive behavior, rates suffer a series of finite discontinuities located at random Poissonian times. Jump amplitudes are also random and governed by an arbitrary density. Such a model may describe the economic evolution specially when extreme situations occur (pandemics, global wars, etc.). When between jumps the dynamical evolution is governed by an Ornstein-Uhlenbeck diffusion process, we obtain exact and explicit expressions for the discount function and the long-run discount rate and show that the presence of discontinuities may drastically reduce the discount rate, a fact that has significant consequences for environmental planning. We also discuss as a specific example the case when rates are described by the continous time random walk.

Raman spectroscopy is a type of inelastic scattering that provides rich information about a sub-stance based on the coupling of the energy levels of their vibrational and rotational modes with incident light. It has been applied extensively in many fields. As there is an increasing need for remote detection of chemicals in planetary exploration and anti-terrorism, it is urgent to develop a compact and easily transportable fully automated remote Raman detection system for trace detection and identification of information with high-level confidence about the target’s compo-sition and conformation in real-time and for real field scenarios. Here, we present an unmanned vehicle-based remote Raman system, which includes a 266 nm air-cooling passive Q-switched nanosecond pulsed laser of high-repetition frequency, a gated ICMOS, and an unmanned vehicle. This system obtains good spectral signals from remote distances ranging from 3 m to 10 m for simulating realistic scenarios, such as aluminum plate, woodblock, paperboard, black cloth, and leaves, and even for detected amounts as low as 0.1 mg. Furthermore, a CNN-based algorithm is implemented and packaged into the recognition software to achieve fast and more accurate de-tection and identification. This prototype provides a proof-of-concept for an unmanned vehicle with accurate remote substance detection in real-time, which can be helpful for remote detection and identification of hazardous gas, explosives, their precursors, and so forth.

Diseases of cereals caused by pathogenic fungi can significantly reduce crop yields. Many cultures are exposed to them. The disease is difficult to control on a large scale, thus one of the relevant approaches is the crop field monitoring, which helps to identify the disease at an early stage and take measures to prevent its spread. One of the effective control methods is disease identification based on the analysis of digital images with the possibility of obtaining them in field conditions using mobile devices. In this work, we propose a method for the recognition of five fungal diseases of wheat shoots (leaf rust, stem rust, yellow rust, powdery mildew, and septoria), both separately and in combination, with the possibility of identifying the stage of plant development. A set of 2414 images of wheat fungi diseases (WFD2020) was generated, for which expert labeling was performed by the type of disease. WFD2020 data are available freely at http://wfd.sysbio.ru/. In the process of creating this set, a method was applied to reduce the degeneracy of the training data based on the image hashing algorithm. The disease recognition algorithm is based on the convolutional neural network with the EfficientNet architecture. The best accuracy (0.942) was shown by a network with a training strategy based on augmentation and transfer of image styles. The recognition method was implemented by the authors as a bot on the Telegram platform, which allows assessing plants by lesions in the field conditions.

Mineral identification is an important part of geological analysis. Traditional identification methods rely on either the experiences of the appraisers or the various measuring instruments and the methods are either easily influenced by the experiences or need too much work. To solve the above problems, there have been studies using image recognition and intelligent algorithms to identify minerals. But the current studies can not identify many minerals, and the accuracy is low. To increase the number of identified mineral categories and the accuracy, we proposed the method that uses both the mineral images and the Mohs hardness in the deep neural networks to identify the minerals. The experimental results showed that the method can reach 94.0% Top-1 accuracy and 99.9% Top-5 accuracy for 28 common minerals. The model that combines image and Mohs hardness together can identify more minerals and increase the accuracy using less training data.

Every biological image contains quantitative data that can be used to test hypotheses about how patterns were formed, what entities are associated with one another, and whether standard mathematical methods inform our understanding of biological phenomena. In particular, spatial point distributions and polygonal tessellations are particularly amendable to analysis with a variety of graph theoretic, computational geometric, and spatial statistical tools such as: Voronoi Polygons; Delaunay Triangulations; Perpendicular Bisectors; Circumcenters; Convex Hulls; Minimal Spanning Trees; Ulam Trees; Pitteway Violations; Circularity; Clark-Evans spatial statistics; Variance to Mean Ratios; Gabriel Graphs; and, Minimal Spanning Trees. Furthermore, biologists have developed a number of empirically related correlations for polygonal tessellations such as: Lewis’s Law (the number of edges of convex polygons are positively correlated with the areas of these polygons): Desch’s Law (the number of edges of convex polygons are positively correlated with the perimeters of these polygons); and Errara’s Law (daughter cell areas should be roughly half that of their parent cells’ areas). We introduce a new Pitteway Law that the number of sides of the convex polygons in a Voronoi tessellation of biological epithelia is proportional to the minimal interior angle of the convex polygons as angles less than 90 degrees result in Pitteway violations of the Delaunay dual of the Voronoi tessellation.

In recent years, pedestrian re-identification has gained a lot of interest due to its importance for many purposes such as security and safety. Many types of solutions have been proposed to solve this problem, where the majority are based on a features extraction such Convolution Neural Networks (CNNs). These approaches assume that only the identities that are in the training data can be recognized. The pedestrians in the training data are called In distribution (ID). However, in real world scenarios, new pedestrians and objects can appear in the scene and the model should detect them as Out Of Distribution (OOD). In our previous study , we proposed a pedestrian re-identification based on von-Mises Fisher (vMF) distribution. Each identity is embedded in the unit sphere as a compact vMF distribution far from other identities distributions. The embedding is done through a base CNN. Recently, proposed a framework called Virtual Outlier Synthetic (VOS), that detects OOD based on synthesising virtual outlier in the embedding space in an online manner. Their approach assumes that the samples from the same object maps to a compact space. This assumption aligns with the vMF based approach. Therefore, in this paper, we revisited the vMF approach and merged with VOS in order to detect OOD data points. We present our approach to merge both frameworks. We conducted several experiments to evaluate our proposed framework. Results showed that our framework was able to detect new pedestrian that do not exist in the training data in the inference phase. It also slightly helped to improve the re-identification performance.

This article addresses the urbanization model in valley bottom areas in the city of São Paulo, an-alyzed in its urban planning and socio-environmental aspects, and examines the hydrological impacts triggered by its implementation. It argues that sustainable occupation of urbanized hy-drographic basins should be based on environmental criteria in order to attain a sustainable water management regime that can ensure urban and environmental resilience. In the “Introduction,” the article emphasizes that urbanization in São Paulo has neglected the natural features of river basins, promoting the channeling of watercourses, which resulted in floods and inundations. “Theoretical Basics” brings in key concepts related to the environmental approach of drainage infrastructure with gray and green-blue systems. “Methods” details our qualitative research, bibliographical analyzes, referential case studies, and cartographical analyzes (geoprocessing), focusing on a case study in the Jaguaré creek basin. “Results” includes guidelines and recom-mendations for an urbanization model adjusted to valley bottom areas, applicable to new patterns of urban occupation or to the adjustment of existing patterns. “Discussion” explores the concept of “local sustainable self-development,” and “Conclusions” highlights the importance of an inte-grated, multisectoral, and sustainable planning, adjusted to the characteristics of river basins in valley bottom areas, promoting a socially responsible urban occupation, especially in the face of climate change.

The key links of face recognition are digital image preprocessing, facial feature extraction and pattern recognition, this article aimed at the current problem of slow speed and low recognition accuracy of face recognition , from the above three key links, on the basic of analyzing the therories of Fractional Differential Masks Operator (FDMO), Principal Component Analysis (PCA) and Support Vector Machine (SVM), design a kind of FDMO+PVA+SVM coupling algorithm that applies to face recognition to improve the speed and accuracy of it. To realize FDMO+PCA+SVM coupling algorithm, first, we should apply FDMO to face image processing binary marginalization, the purpose is getting face contour; Then, we apply PCA to get the feature of face image which is disposed by binary marainalization. At last, we can apply One-Against All of the SVM classifier and LibSVM software package to realize face recognition. Beside, the article with nine different coupling algorithm design four groups of experimental reaults on the ORL face database verified by comparative analysic FDMO+PCA+SVM coupling algorithm in the superiority of face recognition accuracy and speed.

Recently, Artificial Intelligence (AI)-based algorithms have revolutionized the medical image segmentation processes. Thus, the precise segmentation of organs and their lesions may contribute to an efficient diagnostics process and a more effective selection of targeted therapies as well as increasing the effectiveness of the training process. Thus, AI may contribute to the automatization of the image scan segmentation process and increase the quality of the resulting 3D objects, which may lead to the generation of more realistic virtual objects. In this paper, we focus on the AI-based solutions applied in the medical image scan segmentation, and intelligent visual-content generation, i.e. computer-generated three-dimensional (3D) images in the context of Extended Reality (XR). We consider different types of neural networks used with a special emphasis on the learning rules applied, taking into account algorithm accuracy and performance, as well as open data availability. This paper also attempts to summarize the current development of AI-based segmentation methods in medical imaging and intelligent visual content generation that are applied in XR. Finally, this paper concludes with possible developments and open challenges in AI application in Extended Reality-based solutions. Finally, the future lines of research and development directions of Artificial Intelligence applications both in medical image segmentation and Extended reality-based medical solutions are discussed.

Open-vocabulary learning has recently gained prominence as a means to enable image segmentation for arbitrary categories based on textual descriptions. This advancement has extended the applicability of segmentation systems to a broader range of generally purpose scenarios. However, current methods often revolve around specialized architectures and parameters tailored to specific segmentation tasks, resulting in a fragmented landscape of segmentation models. In response to these challenges, we introduce OVAMTSeg, a versatile framework designed for Open-Vocabulary and Multitask Image Segmentation. OVAMTSeg harnesses adaptive prompt learning to empower the model to capture category-sensitive concepts, enhancing its robustness across diverse multi-task and scenario contexts. Text prompts are employed to effectively capture semantic and contextual features of the text, while cross-attention and cross-modal interactions enable the fusion of image and text features. Furthermore, a transformer-based decoder is incorporated for dense prediction. Extensive experimental results underscore the effectiveness of OVAMTSeg, showcasing its state-of-the-art performance and superior generalization capabilities across three segmentation tasks. Notable achievements include a 47.5 mIoU in referring expression segmentation, 51.6 mIoU on Pascal-VOC with four unseen classes, 46.6 mIoU on Pascal-Context in zero-shot segmentation, 65.9 mIoU on Pascal-5i, and 35.7 mIoU on COCO-20i datasets for one-shot segmentation.

Pseudomonas aeruginosa is a Gram-negative bacillus included among the six "ESKAPE" microbial species with an outstanding ability to "escape" currently used antibiotics and developing new antibiotics against it is of the highest priority. Whereas minimum inhibitory concentration (MIC) values against Pseudomonas aeruginosa have been used previously for QSAR model development, disk diffusion results (inhibition zones) have not been apparently used for this purpose in the literature, and we decided to explore their use in this sense. We developed multiple QSAR methods using several machine learning algorithms (Support vector classifier, K Nearest Neighbors, Random Forest Classifier, Decision Tree Classifier, AdaBoost Classifier, Logistic Regression, and Naive Bayes Classifier). The main descriptors used in building the models belonged to the families of adjacency matrix, constitutional descriptors, first highest eigenvalue of Burden matrix, centered Moreau-Broto autocorrelation, and averaged and centered Moreau-Broto autocorrelation descriptors. A total of 32 models were built, of which 28 were selected and stacked to create a meta-model. In terms of balanced accuracy, the best performance was provided by KNN, SVM and AdaBoost algorithms, but the ensemble method had slightly superior results in nested cross-validation.

Person Re-identification is the task of recognizing comparable subjects across a network of nonoverlapping cameras. This is typically achieved by extracting from the source image a vector of characteristic features of the specific person captured by the camera. Learning a good set of robust, invariant and discriminative features is a complex task, often leveraging contrastive learning. In this article, we explore a different approach, learning the representation of an individual as the conditioning information required to generate images of the specific person starting from random noise. In this way we decouple the identity of the individual from any other information relative to a specific instance (pose, background, etc.), allowing interesting transformations from one identity to another. As generative models, we use the recent diffusion models that have already proven their sensibility to conditioning in many different contexts. The results presented in this article serve as a proof-of-concept. While our current performance on common benchmarks is lower than state-of-the-art techniques, the approach is intriguing and rich of innovative insights, suggesting a wide range of potential improvements along various lines of investigation.

The ongoing pandemic of the 2019 novel coronavirus disease (COVID-19) raises a global health crisis, which has resulted in 75,778 confirmed cases with 2130 deaths in China and beyond. Atypical symptom renders it challenging to earlier recognize the 2019-nCoV carrier with the potential ability of equivalent transmission. Therefore, it is needed to gain full spectrum of COVID-19. Here we report clustered COVID-19 cases of person-to-person transmission. The symptoms of typical pneumonia are shared by the two familial members, namely son (Patient 1) and father (Patient 2). Unexpectedly, an influenza-like illness (ILI) is also caused in Patient 3 having close contact with Patient 1 at personal dinner party. Combined with clinical and epidemiological study, chest computed tomography (CT) and molecular diagnosis demonstrate that all the three cases tested positive for COVID-19 with distinct symptoms by human-to-human transmission. To the best of knowledge, it closes in part (if not all), a missing gap of clinical repertoires of COVID-19 outbreaks and underlines the possibility that neglection of cryptic/asymptomatic/mild cold-like syndromes gives biased screen in the earlier stage of COVID-19 cases.

In this note, the toy-model of Fourth Generation quarks coupling to Standard Model particles through unknown Lagrangian has been figured. Eventually producing a 103 TeV vector gauge boson, which is a special gauge boson that appears in unification models and here as a mediator for WeakSM4 interaction. Another anomaly of different gauge boson has been reported.

Clinicians are often very sceptical about applying automatic image processing approaches, especially deep learning based methods, in practice. One main reason for this is the black-box nature of these approaches and the inherent problem of missing insights of the automatically derived decisions. In order to increase trust in these methods, this paper presents approaches that help to interpret and explain the results of deep learning algorithms by depicting the anatomical areas which influence the decision of the algorithm most. Moreover, this research presents a unified framework, TorchEsegeta, for applying various interpretability and explainability techniques for deep learning models and generate visual interpretations and explanations for clinicians to corroborate their clinical findings. In addition, this will aid in gaining confidence in such methods. The framework builds on existing interpretability and explainability techniques that are currently focusing on classification models, extending them to segmentation tasks. In addition, these methods have been adapted to 3D models for volumetric analysis. The proposed framework provides methods to quantitatively compare visual explanations using infidelity and sensitivity metrics. This framework can be used by data scientists to perform post-hoc interpretations and explanations of their models, develop more explainable tools and present the findings to clinicians to increase their faith in such models. The proposed framework was evaluated based on a use case scenario of vessel segmentation models trained on Time-of-fight (TOF) Magnetic Resonance Angiogram (MRA) images of the human brain. Quantitative and qualitative results of a comparative study of different models and interpretability methods are presented. Furthermore, this paper provides an extensive overview of several existing interpretability and explainability methods.

Identification of plants is a challenging task which aims to identify the family, genus, and species level according to morphological features. Automated deep learning-based computer vision algorithms are widely used for identifying plants and can help users to narrow down the possibilities. However, numerous morphological similarities between and within species make the classification difficult. In this paper, we tested a custom convolution neural network (CNN) and vision transformer (ViT) based models using the PyTorch framework to classify plants. We used a large dataset of 88K and 16K images for classifying plants at genus and species levels respectively. Our results show that for classifying plants at the genus level, ViT models perform better compared to CNN-based models ResNet50 and ResNet-RS-420, and other state-of-the-art CNN-based models suggested in previous studies on a similar dataset. The ViT model achieved top accuracy of 83.3% for classifying plants at the genus level. ViT models also perform better for classifying plants at the species level compared to CNN-based models ResNet50 and ResNet-RS-420, with a top accuracy of 92.5%. We show that the correct set of augmentation techniques plays an important role in classification success.

With the targets of petroleum exploration transferred to the deep and ancient strata, abundant oil and gas resources have been found in the Lower Paleozoic and older strata in central and western China. Due to complex evolutionary processes including multiple episodes of hydrocarbon accumulation and ubiquitously accompanied by secondary alterations, significant uncertainties were remained for the generation time and accumulation processes of these revealed petroleum. In this paper, relative pure Re and Os elements existing in the asphaltene fractions of Lower Cambrian solid bitumen collected from the Guangyuan area, western Sichuan Basin, SW China and another Middle–Lower Ordovician heavy oils in the Aiding area of the Tahe oilfield in the Tarim Basin, NW China were successfully obtained by the sample pre-treatments, and Re–Os isotopic analysis was subsequently carried out for the dating of these two petroleum. The Re–Os isotopic composition indicates a generation time of Guangyuan bitumen to locate between 572 Ma and 559 Ma, corresponding to the late Sinian period of Neoproterozoic era. By the means of Re–Os isochron ages, initial 187Os/188Os ratios, and carbon isotopic compositions, the Lower Cambrian bitumen is supposed to be originated from source rocks of the Doushantuo Formation in the Sinian strata and subsequently migrated into the reservoirs of Dengying Formation. These previously reserved petroleum had been transformed to present bitumen by the destruction of reservoirs caused by tectonic uplift. The Re–Os dating results of Middle–Lower Ordovician heavy oil of Tarim Basin supposed that it was formed between 450 Ma to 436 Ma, corresponding to the Late Ordovician–Early Silurian system, and the generated petroleum is likely to migrate into the Middle–Lower Ordovician karst reservoirs to form early oil reservoirs. With tectonic uplift, these oil reservoirs were degraded and reformed to be present heavy oil reservoirs.

As renewable energy generation prediction system has been introduced into the energy trading market, making a model to accurately predict the quantity of solar photovoltaic (PV) energy generation has become a significant problem. Moreover, to encourage an accurate prediction of the quantity of energy generation, an incentive system has been implemented for those who predict the quantity of solar PV energy under the error rate of 8%. Therefore, it has become more important to investigate and analyze current prediction technology numerically and develop more advanced prediction system. In this study, we tried to develop a better model to improve the accuracy of solar PV energy generation quantity by comparing three models made with gradient boosting machine (GBM), Model 1, Model 2, Model 3 respectively. Model 1 was built with the whole training data set without any additional preprocessing. After conducting some additional preprocessing procedure to predict solar energy generation more accurately, we made Model 2 with the whole training data set and Model 3 with only upper 10% of energy generation capacity. To compare the accuracy of three models, normalized mean absolute error (nMAE) was used as an evaluation index. The nMAE of Model 1 was 9.64% while the Model 2 showed 8.41%. Also, Model 3, which was constructed with the training set of upper 10% energy generation capacity, outperformed with the nMAE of 8.08%. For further study, to check the effectiveness of models constructed with GBM, a time series model, autoregressive integrated moving average (ARIMA), was also built and the nMAE was compared.

The recent detection of SARS-CoV-2 RNA in feces has led to speculation that it can be transmitted via the fecal-oral/ocular route. This review aims to critically evaluate the incidence of gastrointestinal (GI) symptoms, the quantity and infectivity of SARS-CoV-2 in feces and urine, and whether these pose an infection risk in sanitary settings, sewage networks, wastewater treatment plants, and the wider environment (e.g. rivers, lakes and marine waters). Overall, severe GI dysfunction is only evident in a small number of COVID-19 cases, with 11 ± 2% exhibiting diarrhea and 12 ± 3% exhibiting vomiting and nausea. In addition to these cases, SARS-CoV-2 RNA can be detected in feces from some asymptomatic, mildly- and pre-symptomatic individuals. Fecal shedding of the virus peaks in the symptomatic period and can persist for several weeks, but with declining abundances in the post-symptomatic phase. SARS-CoV-2 RNA is occasionally detected in urine, but reports in fecal samples are more frequent. The abundance of the virus genetic material in both urine (ca. 10²-10⁵ gc/ml) and feces (ca. 10²-10⁷ gc/ml) is much lower than in nasopharyngeal fluids (ca. 10⁵-10¹¹ gc/ml). There is strong evidence of multiplication of SARS-CoV-2 in the GI tract and infectious virus has occasionally been recovered from both urine and stool samples. The level and infectious capability of SARS-CoV-2 in vomit remain unknown. In comparison to enteric viruses transmitted via the fecal-oral route (e.g. norovirus, adenovirus), the likelihood of SARS-CoV-2 being transmitted via feces or urine appears lower due to the lower relative amounts of virus present in feces/urine. The biggest risk of transmission will occur in clinical and care home settings where secondary handling of people and urine/fecal matter occurs. In addition, while SARS-CoV-2 RNA genetic material can be detected by in wastewater, this signal is greatly reduced by conventional treatment. Our analysis also suggests the likelihood of infection due to contact with sewage-contaminated water (e.g. swimming, surfing, angling) or food (e.g. salads, shellfish) is extremely low or negligible based on very low predicted abundances and limited environmental survival of SARS-CoV-2. These conclusions are corroborated by the fact that over eight million global cases of COVID-19 have occurred, but exposure to feces or wastewater has never been implicated as a transmission vector.

Subgrid scale models for unresolved turbulent fluxes are investigated with a focus on combustion for space propulsion applications. An extension to the gradient model is proposed, introducing a dependency on the local burning regime. The dynamic behavior of the model's coefficients is investigated, and scaling laws are studied. The discussed models are validated using a DNS database of a high-pressure turbulent fuel-rich methane-oxygen diffusion flame. The operating point and turbulence characteristics are selected to resemble those of modern combustors for space propulsion applications to support the future usage of the devised model in this context.

This paper focuses on quantifying the uncertainty in the specific absorption rate values of brain induced by the uncertain positions of the electroencephalography electrodes placed on patient's scalp. To avoid running a large number of simulations, an artificial neural network architecture for uncertainty quantification involving high-dimensional data is proposed in this paper. The proposed method is demonstrated to be an attractive alternative to conventional uncertainty quantification methods because of its considerable advantage in the computational expense and speed.

Breast cancer is the second most common cancer worldwide, primarily affecting women, while histopathological image analysis is one of the possibilities used to determine tumor malignancy. Regarding image analysis, the application of deep learning has become increasingly prevalent in recent years. However, a significant issue is the unbalanced nature of available datasets, with some classes having more images than others, which may impact the performance of the models due to poorer generalisability. A possible strategy to avoid this problem is downsampling the class with the most images to create a balanced dataset. Nevertheless, this approach is not recommended for small datasets as it can lead to poor model performance. Instead, techniques such as data augmentation are traditionally used to address this issue. These techniques apply simple transformations, such as translation or rotation, to the images to increase variability in the dataset. Recently, Generative Adversarial Networks (GANs) have emerged, which can generate images from a relatively small training set. This work aims to enhance the model’s performance in classifying histopathological images by applying data augmentation using GANs instead of traditional techniques.

A tabletop system can facilitate multi-user collaboration in a variety of settings including small meetings, group work, and education and training exercises. The ability of identifying the users touching the table and their positions can promote collaborative work among participants, so methods have been studied that involve the attaching of sensors to the table or chairs or to the users themselves. An effective method of recognizing user actions without placing a burden on the user would be some type of visual process, so the development of a method that processes multi-touch gestures by visual means is desired. This paper describes the development of a multi-touch tabletop system using infrared image recognition for user position identification and presents the results of touch-gesture recognition experiments and a system usability evaluation. Using an FTIR touch panel and infrared light, this system picks up the shadow area of the user’s hand by infrared camera in relation to user touch operations and estimates user position by image recognition. The multi-touch gestures prepared for this system include an operation to change the direction of an object to face the user and a copy operation in which two users generate duplicates of an object. The average recognition rate of the change-direction gesture and copy gesture were found to be 96% and 85%, respectively. In addition, the system usability evaluation revealed that prior learning was easy and that system operations could be easily performed.

Noise reduction in medical images is a perplexing undertaking for the researchers in digital image processing. Noise generates maximum critical disturbances as well as touches the medical images quality, ultrasound images in the field of biomedical imaging. The image is normally considered as gathering of data and existence of noises degradation the image quality. It ought to be vital to reestablish the original image noises for accomplishing maximum data from images. Medical images are debased through noise through its transmission and procurement. Image with noise reduce the image contrast and resolution, thereby decreasing the diagnostic values of the medical image. This paper mainly focuses on Gaussian noise, Pepper noise, Uniform noise, Salt and Speckle noise. Different filtering techniques can be adapted for noise declining to improve the visual quality as well as reorganization of images. Here four types of noises have been undertaken and applied on medical images. Besides numerous filtering methods like Gaussian, median, mean and Weiner applied for noise reduction as well as estimate the performance of filter through the parameters like mean square error (MSE), peak signal to noise ratio (PSNR), Average difference value (AD) and Maximum difference value (MD) to diminish the noises without corrupting the medical image data.

: Tunnel instability and lining integrity are intimately tied to the creep properties of the surrounding rock. The acquisition of rock mass creep parameters is critical in ascertaining the appropriate timing for lining construction. Nevertheless, creep tests on rock specimens conducted in a controlled setting cannot be straightforwardly extrapolated to rock mass creep analysis. This study performs laboratory-based creep tests on mudstone samples and establishes the corresponding creep constitutive model. Integration of a Mohr-Coulomb element with the Burgers model in series serves to characterise the yield creep behaviour of the rock mass. Additionally, the research outlines a series of 100 orthogonal experiments utilising randomised creep parameters, and formulates a GA-BP neural network inversion model. Employing long-term deformation measurements from the crown and sidewalls of the project site, this study deduces the creep parameters of the mudstone in the investigated tunnel section and examines the prolonged deformation traits of the surrounding rock. Drawing on the deformation traits of the surrounding rock and the forces impacting the primary support and lining structures, this paper evaluates the earliest and latest viable lining casting periods and pinpoints an optimal timing interval for lining implementation. The methodologies employed herein can serve as a benchmark for analogous endeavors internationally.

As the demands in reliability and high-degree of automation in industrial applications are increasing exponentially, the current Industry 4.0 trend lies in migrating towards smarter technologies that increase flexibility and provide autonomous operation. To tackle this, manufacturers shifted towards Real-Time Ethernet communication which supports autonomous operation, faster sensor/actuator configuration and high degree of determinism. Nevertheless, the uninterrupted operating principle of legacy industrial systems as well as the system complexity and heterogeneity constitutes the transition to Real-Time Ethernet very challenging. This article proposes a novel method allowing to automate the generation of executable code for Real-Time Ethernet architectures based on high-level programming models. The method is based on a programming model and modular code artifacts for the hardware architecture, employing Real-Time Ethernet communication. Through an tool-supported algorithm the high-level model is linked to the architecture's communication primitives and interfaces. The tool-support reduces significantly the development and debugging effort for Real-Time Ethernet applications and increases the application reliability, since the generated code is based on verified programming models. Additionally, the tool creates dedicated files, which allow automated industrial network configuration without any human intervention. Finally, by embedding firewall policies into the code generation process, the method guarantees cyber-resilience for the Real-Time Ethernet architecture. We demonstrate the proposed method in Programmable Logic Controllers (PLCs) of a Public Power Corporation's Hydroelectric Power Plant, which control the temperature and rotor speed of a power generator. The results demonstrate the method's ability to generate rapidly trustworthy and fault-tolerant code for the autonomous plant operation. The hurdles in time and effort for developing, configuring and debugging Real-Time Ethernet applications can be minimized through a high-level programming model that allows automated generation of executable code that is reusable for similar applications. Such time and effort reduction may pave the way towards the Industry 4.0 area.

Recent advances in deep learning technology have led to increased interest in text-to-image technology, which enables computers to create images from text by simulating the human process of forming mental images. The GAN-based text-to-image technology involves the extraction of features from input text, which are combined with noise and then used as input to a GAN that generates images that are similar to the original images through competition between the generator and discriminator. Although generating images from English text is a mature area of research, text-to-image technology based on multilingualism, such as Korean, is still in its early stages of development. Webtoon is a digital comic format that allows comics to be viewed online. The creation process for webtoons is divided into story planning, content/sketching, coloring, and background drawing. Since each stage of webtoon production requires human intervention, it is both time-consuming and expensive. As a result, deep learning technologies such as automatic coloring and automatic line drawing are being used to reduce human involvement. However, there is a shortage of technology that can assist authors with story creation in webtoon production. Therefore, this study proposes a multilingual text-to-image model capable of generating webtoon images when presented with multilingual input text. The proposed model employs Multilingual BERT to extract feature vectors for multiple languages, and trains a DCGAN in conjunction with the images. The experimental results demonstrate that the model can generate images that are similar to the original images when presented with multilingual input text after training.

This review provides a comprehensive and updated information on chalcones, presenting their sources, methods of identification and properties putting emphasis on applications in agricultural industry. Vast exploitation of synthetic pesticides led to the weed and pest resistance issue, causing economic loss but more importantly health risk due to pesticide overuse. Because one of the aim of the European Green Deal 2030 is the Farm to Fork strategy which targets on reducing by 50% the use of chemical pesticides by 2030, and push towards natural pesticides which are more environmentally friendly and restore natural resources, chalcones and their derivatives seem to fit perfectly. They have herbicidal, fungicidal, bactericidal and antiviral properties. Moreover, chalcones are naturally occurring substances, known for their favorable health effect for years, working on many areas. This review is complementary to the previous ones describing chalcones use in agriculture, updates them but additionally shows methods of detection.

Precision agriculture is a farm management technology that involves sensing and then responding to the observed variability in the field. Remote sensing is one of the tools of precision agriculture. The emergence of small unmanned aerial vehicles (sUAV) have paved the way to accessible remote sensing tools for farmers. This paper describes the comparison of two popular off-the-shelf sUAVs: 3DR Iris and DJI Phantom 2. Both units are equipped with a camera gimbal attached with a GoPro camera. The comparison of the two sUAV involves a hovering test and a rectilinear motion test. In the hovering test, the sUAV was allowed to hover over a known object and images were taken every second for two minutes. The position of the object in the images was measured and this was used to assess the stability of the sUAV while hovering. In the rectilinear test, the sUAV was allowed to follow a straight path and images of a lined track were acquired. The lines on the images were then measured on how accurate the sUAV followed the path. Results showed that both sUAV performed well in both the hovering test and the rectilinear motion test. This demonstrates that both sUAVs can be used for agricultural monitoring.

False positives on multiparametric (mp)-MRI result in a large number of unnecessary biopsies in men with clinically insignificant diseases. This study investigates whether quantitative diffusion MRI can improve differentiation between false positives, true positives and normal tis-sue. Twenty-three patients underwent mp-MRI and Vascular, Extracellular and Restricted Diffu-sion for Cytometry in Tumours (VERDICT)-MRI, followed by transperineal biopsy. The patients were categorised into two groups following biopsy: (1) significant cancer - true positive (2) atro-phy/inflammation/high-grade prostatic intraepithelial neoplasia (PIN) - false positive. The clinical apparent diffusion coefficient (ADC) values of the lesions were obtained, and the intravoxel inco-herent motion (IVIM), diffusion kurtosis imaging (DKI) and VERDICT models were fitted using a deep learning approach. Significant differences (p < 0.05) between true positive and false positive lesions were found in ADC, IVIM perfusion fraction (f) and diffusivity (D), DKI diffusivity (DK) and kurtosis (K) and VERDICT intracellular volume fraction (fIC), extracellular-extravascular vol-ume fraction (fEES) and diffusivity (dEES) values. Significant differences between false positives and normal tissue were only found for the VERDICT fIC. These results demonstrate that model-based diffusion MRI could reduce the number of unnecessary biopsies due to false positive prostate lesions and shows promising sensitivity to benign diseases that mimic cancer.

As renewable energy generation prediction systems have been introduced into the energy trading market, accurate prediction of solar photovoltaic (PV) energy generation has become a crucial challenge for ensuring stable trade of variable energy. Therefore, it is essential to quantitatively investigate and analyze current prediction technology and develop more advanced prediction systems. In this study, three models for predicting PV energy generation were investigated. Multiple regression, random forest, and gradient boosting machine (GBM) models were con-structed for 15 utility-scale power plants throughout South Korea. Model performance was evaluated in terms of root mean square error (RMSE) and mean absolute error (MAE). The multiple regression-based model had an RMSE of 12.00% and MAE of 9.06%. The random forest model had an RMSE of 11.69% and MAE of 8.61%. The GBM model had an RMSE of 11.34% and MAE of 8.18%, indicating its superior accuracy among the three models in predicting PV energy generation. These findings provide valuable insights that may contribute to the development of advanced PV energy generation models and their application in grid operations.

The development of plant-based meat analogues has become a significant challenge for the food industry in recent years due to the increasing demand for sustainable and healthier proteins in the context of a global protein transition. Plant-based meat analogues imitate the visual, textural, and chemical properties of traditional meat products and are required to closely resemble meat to appeal to consumers. In addition, consumers demand natural, clean-label, and nutritional and healthy products. To address these challenges, the food industry must develop highly healthy, nutritious, and E-number-free meat analogue products. Understanding the functionality of each ingredient and its role in the food matrix is crucial to being a key player in the innovation of the meat analogues market. This review provides updated information on the primary ingredients utilized for the development of plant-based burger meat alternatives and their functionality. The key components of meat analogue burgers are outlined, including plant proteins, binding agents, fats and oils, flavorings, colorings, preservatives, fortificants, and clean-label considerations.

A rapid synthesis of chiral sulfoxide-functionalized meta-terphenyl derivatives by a 2,5-[C4+C2] ring transformation reaction of pyrylium salts with in-situ generated enantio-merically pure .alpha.-sulfinylacetaldehydes is described. This synthetic method demonstrates for the first time the use of .alpha.-sulfinylacetaldehydes in a reaction sequence initiated by nu-cleophilic attack of pyrylium salts by .alpha.-sulfinylcarbanions to generate chiral aromatic systems. The method presented shows broad applicability starting with various methyl sulfoxides and a number of functionalized pyrylium salts, furnishing meta-terphenyls with complex substitution patterns from readily accessible starting compounds.

This paper proposes a new approach for multi-focus images fusion based on Region Mosaicing on Contrast Pyramids (REMCP). A density-based region growing method is developed to construct a focused region mask for multi-focus images. The segmented focused region mask is decomposed into a mask pyramid, which is then used for supervised region mosaicking on a contrast pyramid. In this way, the focus measurement and the continuity of focused regions are incorporated and the pixel level pyramid fusion is improved at the region level. Objective and subjective experiments show that the proposed REMCP is more robust to noise than compared algorithms and can fully preserves the focus information of the multi-focus images meanwhile reducing distortions of the fused images.

Aircraft heat exchangers play a crucial role in maintaining thermal balance and ensuring that essential components operate efficiently and safely. In this context, it is highly relevant to gain knowledge about the deterioration and the predominant damage mechanisms of these components. In this study, two different non-destructive methods are employed to analyze the damage initiation of a heat exchanger part during fatigue test. On the one hand, Acoustic Emission technique is employed using membrane-free microphones capable of capturing a broad bandwidth. The acoustic events were classified by a machine learning algorithm to determine their source and damage mechanisms. On the other side, Digital Image Correlation (DIC) allowed the measurement of the strain evolution along the test and more precisely, redistributions were considered as damage indicators. As a result, it was identified that AE served as early damage indicator as the cumulative number of events was in good agreement with the severity of the damage. With respect to classification two clear clusters ascribed to different type of events were identified. In the case of DIC, strain redistributions gave clear indications of damage or deterioration but at a later stage compared to AE

One-step reactive melt processing (REx) via radical reaction was evaluated with the aim of improving the rheological properties of poly(ε-caprolactone) (PCL). In particular, a water-assisted REx was designed under the hypothesis of increasing crosslinking efficiency with water as a low viscous medium in comparison with a slower PCL macroradicals diffusion in the melt state. To assess the effect of dry vs. water-assisted REx on PCL, its structural, thermo-mechanical and rheological properties were investigated. Water-assisted REx resulted in increased PCL gel fraction compared to dry REx (from 1 to 34 %), proving the rationale under the formulated hypothesis. From dynamic mechanical analysis and tensile tests, the crosslink did not significantly affect the PCL mechanical performance. Dynamic rheological measurements showed that higher PCL viscosity was reached with increasing branching/crosslinking and the typical PCL Newtonian behavior was shifting towards a progressively more pronounced shear thinning. A complete transition from viscous- to solid-like PCL melt behavior was recorded, demonstrating that higher melt elasticity can be obtained as a function of gel content by controlled REx. Improvement in rheological properties offers the possibility of broadening PCL melt processability without hindering its recycling by melt processing.

The purpose of this paper is to explore the implementation of Retrieval-Augmented Gen- 14 eration (RAG) technology with open-source Large Language Models (LLMs). For this purpose, a 15 web-based application named PaSSER that integrates RAG with three models - Mistral:7b, 16 Llama2:7b: Orca2:7b, is developed. In its development various technologies are used, including 17 Blockchain for managing and storing assessment results of built-in testing modules. PaSSER em- 18 ploys a set of evaluation metrics for LLMs’ performance - METEOR, ROUGE, BLEU, Perplexity, 19 Cosine similarity, Pearson correlation, and F1 score. The specialized knowledge base is in the smart 20 agriculture domain. As an illustration, the paper presents results and analysis of two tests. First one 21 is assessing the performance of LLMs across different hardware configurations, the other is deter- 22 mining the model that delivers most accurate and contextually relevant responses within the RAG. 23 The paper also discusses the integration of blockchain technology with LLMs to manage and store 24 assessment results within a blockchain environment. The tests revealed that Mistral:7b demon- 25 strates the best results across most of the evaluated metrics. The discussion section comments on 26 technical and hardware considerations that affect the performance of LLMs. Future development 27 will focus in leveraging new larger pre-trained open-source LLMs, exploring different finetuning 28 approaches, and further integration with Blockchain and IPFS.

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

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

Kinetic modeling is an essential tool in systems biology research, enabling the quantitative analysis of biological systems and predicting their behavior. However, the development of kinetic models is a complex and time-consuming process. In this article, we propose a novel approach called KinModGPT, which generates kinetic models directly from natural language text. KinModGPT employs GPT-3 as a natural language interpreter and Tellurium as an SBML generator. We demonstrate the effectiveness of KinModGPT in creating SBML models from complex natural language descriptions of biochemical reactions. KinModGPT successfully generates valid SBML models from a range of natural language model descriptions of metabolic pathways, protein-protein interaction networks, and heat shock response. This article demonstrates the potential of KinModGPT in kinetic modeling automation.

Alberta Cancer Exercise (ACE) is an exercise oncology program that transitioned from in-person to online delivery during COVID-19. The purpose of this work was to understand participants’ experiences in both delivery modes. Specifically, survivors' exercise facilitators and barriers, delivery mode preference, and experience with program elements targeting behaviour change were gathered. A retrospective cohort design using explanatory sequential mixed methods was used. 57 participants completed a survey, and 19 subsequent, optional interviews were conducted. Most participants indicated preferring in-person programs (58%), followed by online (32%), and no preference (10%). There were significantly fewer barriers (i.e., commute time) (p<0.01), but also fewer facilitators (i.e., social support) (p<0.01), to exercising online. Four themes were generated from the qualitative data surrounding participant experiences in both delivery modes. Key differences in barriers and facilitators highlighted a more convenient experience online relative to a more socially supportive environment in-person. For future work that includes solely online, focusing on building social support and a sense of community will be critical to optimizing program benefits. Beyond the COVID-19 pandemic, results of this research will remain relevant as we aim to increase the reach of online exercise oncology programming to more underserved populations of individuals living with cancer.

Classification algorithms are very widely used algorithms for the study of various categories of data located in multiple databases that have real-world implementations. The main purpose of this research work is to identify the efficiency of classification algorithms in the study of breast cancer analysis. Mortality rate of women increases due to frequent cases of breast cancer. The conventional method of diagnosing breast cancer is time consuming and hence research works are being carried out in multiple dimensions to address this issue. In this research work, Google colab, an excellent environment for Python coders, is used as a tool to implement machine learning algorithms for predicting the type of cancer. The performance of machine learning algorithms is analyzed based on the accuracy obtained from various classification models such as logistic regression, K-Nearest Neighbor (KNN), Support Vector Machine (SVM), Naïve Bayes, Decision Tree and Random forest. Experiments show that these classifiers work well for the classification of breast cancers with accuracy>90% and the logistic regression stood top with an accuracy of 98.5%. Also implementation using Google colab made the task very easier without spending hours of installation of environment and supporting libraries which we used to do earlier.

This paper performs a comprehensive analysis of the wind energy potential of onshore regions in Greece with emphasis on quantifying the volume risk and the spatial covariance structure. Optimization techniques are employed to derive efficient wind capacity allocation plans (also known as generation portfolios) incorporating different yield aspirations. The generation profile of minimum variance and other optimal portfolios along the efficient frontier are subject to rigorous evaluation using a fusion of descriptive and statistical methods. In particular, principal component analysis is employed to estimate factor models and investigate the spatiotemporal properties of wind power generation, providing valuable insights into the persistence of volume risk. The overarching goal of the study is to employ a set of statistical and mathematical programming tools guiding investors, aggregators and policy makers in their selection of wind energy generating assets. The findings of this research challenge the effectiveness of current policies and industry practices, offering a new perspective on wind energy harvesting with a focus on the management of volume risk.

One of the most important problems in the presence of epidemics and pandemics is an accurate prediction and preparedness. Scholars and experts argue that future pandemics and/or epidemics are almost inevitable events and is not whether next pandemics will happen, but when a new heath emergency will emerge. Epidemiologic models for prediction of Coronavirus Disease 2019 (COVID-19) have shown many limitations because of unpredictable dynamics of the new viral agent SARS-CoV-2 in environment and society. The main goals of this study are twofold: first, the analysis of anthropogenic activities and factors that may trigger pandemic threats; second, the planning of new directions for strategies to reduce risks that a pandemic threat emerges and/or in the initial phase to reduce vast diffusion and negative impact of new viral agents that can generate hazards and problems in public health, environment and socioeconomic systems. In particular, the investigation and understanding of sources and driving factors concerning the emergence and diffusion of new pandemics have critical aspects for strategic actions of forecast, prevention and preparation of effective policy responses to cope with next pandemic crises and health emergencies. Insights here endeavor, whenever possible, to clarify these problems to increase the knowledge of the sources and factor determining the emergence of new viral agents in order to design optimal response policies to face next pandemic diseases similar to COVID-19. .

High resolution information on nucleotide arrangement in the genome of any or-ganism is critical to understand its genetic makeup, function, and evolutionary his-tory. For years, DNA sequencing has provided crucial breakthroughs in the field of genomics and molecular biology. With the advent of technology, Next Generation Sequencing has now transformed the whole genome sequencing prospects, thus unveiling the novel and interesting features of an organism genome that can behold. Next generation sequencing platforms offer realistic approach to identify and anal-yses whole genome with high throughput, accuracy, scalability in a very short time span. Information obtained can be further used in improving our existing healthcare systems, designing novel and personalized therapeutic modules, making the envi-ronment sustainable and proactively preparing ourselves for future pandemics and outbreaks. This article discusses the various NGS technologies and approaches, their advantages, drawbacks, and futuristic usage in different fields. Though still in nascent stages and marred with flaws, with ongoing advancement, NGS will indeed have promising impacts in the future.

In order to improve contrast and restore color for underwater image captured by camera sensors without suffering from insufficient details and color cast, a fusion algorithm for image enhancement in different color spaces based on contrast limited adaptive histogram equalization (CLAHE) is proposed in this article. The original color image is first converted from RGB color space to two different special color spaces: YIQ and HSI. The color space conversion from RGB to YIQ is a linear transformation, while the RGB to HSI conversion is nonlinear. Then, the algorithm separately operates CLAHE in YIQ and HSI color spaces to obtain two different enhancement images. The luminance component (Y) in the YIQ color space and the intensity component (I) in the HSI color space are enhanced with CLAHE algorithm. The CLAHE has two key parameters: Block Size and Clip Limit, which mainly control the quality of CLAHE enhancement image. After that, the YIQ and HSI enhancement images are respectively converted backward to RGB color. When the three components of red, green, and blue are not coherent in the YIQ-RGB or HSI-RGB images, the three components will have to be harmonized with the CLAHE algorithm in RGB space. Finally, with 4 direction Sobel edge detector in the bounded general logarithm ratio operation, a self-adaptive weight selection nonlinear image enhancement is carried out to fuse YIQ-RGB and HSI-RGB images together to achieve the final fused image. The enhancement fusion algorithm has two key factors: average of Sobel edge detector and fusion coefficient, and these two factors determine the effects of enhancement fusion algorithm. A series of evaluate metrics such as mean, contrast, entropy, colorfulness metric (CM), mean square error (MSE) and peak signal to noise ratio (PSNR) are used to assess the proposed enhancement algorithm. The experiments results showed that the proposed algorithm provides more detail enhancement and higher values of colorfulness restoration as compared to other existing image enhancement algorithms. The proposed algorithm can suppress effectively noise interference, improve the image quality for underwater image availably.

A simple design of an ultrathin six-band polarization-insensitive terahertz perfect metamaterial absorber (PMMA) composed of a metal cross-cave-patch resonator (CCPR) placed over a ground plane was proposed and investigated numerically. The numerical simulation results demonstrate that the average absorption peaks are up to 95% at six resonance frequencies with high quality-factors (>65). In addition, the absorption properties can be kept stability for both normal incident transverse magnetic (TM) and transverse electric (TE) waves. The physical mechanism behind the observed high level absorption is illustrated by the electric and power loss density distributions. The different absorption mainly originates from the higher order multipolar and multipolar plasmon resonance of the structure, which are sharp different to the most previous studies of the PMMAs. Furthermore, the resonance absorption of the PMMA can be tunable by varying the geometric parameters of the unit cell.

A simple design of an ultrathin six-band polarization-insensitive terahertz perfect metamaterial absorber (PMMA) composed of a metal cross-cave-patch resonator (CCPR) placed over a ground plane was proposed and investigated numerically. The numerical simulation results demonstrate that the average absorption peaks are up to 95% at six resonance frequencies with high quality-factors (>65). In addition, the absorption properties can be kept stability for both normal incident transverse magnetic (TM) and transverse electric (TE) waves. The physical mechanism behind the observed high level absorption is illustrated by the electric and power loss density distributions. The different absorption mainly originates from the higher order multipolar and multipolar plasmon resonance of the structure, which are sharp different to the most previous studies of the PMMAs. Furthermore, the resonance absorption of the PMMA can be tunable by varying the geometric parameters of the unit cell.

The Eurasian grapevine (Vitis vinifera L.) is the most widely grown horticultural crop in the world and is important for the economy of many countries. In the wine production chain, grape varieties play an important role as they directly influence the authenticity and classification of the product. Identifying the different grape varieties is therefore fundamental for quality control and inspection activities, as well as for regulating production. Currently, ampelography and molecular analysis are the main approaches to identifying grape varieties. However, both methods have limitations. Ampelography is subjective and prone to errors and is experiencing enormous difficulties as ampelographers are increasingly scarce. On the other hand, molecular analyses are very demanding in terms of cost and time. In this scenario, Deep Learning (DL) and Machine Learning (ML) methods have emerged as a classification alternative to deal with the scarcity of ampelographs and avoid molecular analyses. In this study, the most recent and current methods for identifying grapevine varieties using DL classification-based approaches are presented through a systematic literature review. The classification pipeline of the 31 studies found in the literature was described, highlighting its pros and cons. Most of the studies used DL-based models trained with leaf images acquired in a controlled environment at a maximum distance of 1.2 metres to classify grape varieties. In addition, there is a large gap between practical applications and the datasets used: a great lack of varieties, limited data acquired in the field and a lack of tests on plants under adverse conditions. Potential directions for improving this area of research were also presented.

In the sphere of urban renewal in historic districts, preserving and innovatively reinterpreting traditional architectural styles remains a primary research focus. However, the modernization and adaptive reuse of traditional buildings often necessitate changes in their functionality. To cater to the demands of tourism in historic districts, many traditional residential buildings require conversion to commercial use, resulting in a mismatch between their external form and internal function. This study explores an automated approach to transform traditional residences into commercially viable designs, offering an efficient and scalable solution for the modernization of historic architecture. We developed a methodology based on diffusion models, focusing on a dataset of nighttime shopfront facades. By training a Low-Rank adaptation (LoRA) model and integrating the ControlNet model, we enhanced the accuracy and stability of generated images. The methodology’s performance was validated through qualitative and quantitative assessments, optimizing batch size, repetition, and learning rate configurations. These evaluations confirmed the method’s effectiveness. Our findings significantly advance the modern commercial style transformation of historical architectural facades, providing a novel solution that maintains aesthetic and functional integrity, thereby fostering breakthroughs in traditional de-sign thinking and exploring new possibilities for the preservation and commercial adaptation of historical buildings.

Sunflower is one of the oil crops and its planting areas was listed behind Soybean, peanut and rapeseeds in China. It will encounter different kind of diseases during planting, thus limited the seeds yield. Fusarium species are the major threat to different crops nowadays, it is very important to identify the species caused diseases on different hosts, so that the potential control measures may be explored. In Inner mongolia, Sunflower Disk Rot (SDR) was found in different sunflower planting field, the average infection ratio around 8.50%, typically in some verieties, its infection ratio reach to 11.67%. The diseased receptacle were collected from different locations and Koch’s postulate was conducted. The results showed that Fusarium verticillioides was the main causal agent caused SDR. Fungicide toxicity tests shown that Fludioxonil and Flutolanil were screened out from six fungicides for controlling F. verticillioides, with EC50 values of 0.05 µg/mL (R = 0.9825) and 0.96 µg/mL (R = 0.9964) , respectively. This is the first report of SDR caused by F. verticillioides, and it will alert sunflower reseachers to include SDR in the disease list, so as to control sunflower diseases with integrated management strategy successfully.

Bioactive peptides are sequences of amino acids, cut from the parent protein with en-zymes or high pressure processing that can impart a tertiary health benefit to the con-sumer that goes beyond basic human nutrition. This study explores mesopelagic fish in-cluding Maurolicus muelleri and Benthosema glaciale as sources of peptides with an-ti-inflammatory, heart health beneficial, antioxidant and other bioactivities. Bioactive peptides were generated post-catch using different enzymatic hydrolysis processes ap-plied to mesopelagic species caught from experimental cruises targeting these fish. Maurolicus muelleri and Benthosema glaciale were the dominant species processed. Bi-oactive hydrolysates included a Maurolicus muelleri Alcalase hydrolysate and a Maurolicus muelleri autolysis. Hundreds of peptide sequences were identified using mass spectrome-try from active hydrolysates. In silico analysis was used to rank identified peptide se-quences in terms of overall bioactivity using Peptide Ranker, anti-inflammatory activity using PrepAIP, ability to impart Umami flavours with Umami-MRNN and dipeptidyl peptidase IV inhibitory potential using AntiDMPpred . Thirty peptides were selected fol-lowing in silico analysis. Several of the selected peptides had sequence homology with peptides derived from other fish proteins including Atlantic salmon (peptide QCPLHR-PWAL) and thin-lipped mullet (peptide FDAFLPM). Seven peptides predicted to have an-ti-inflammatory, anti-type 2 diabetes or Umami potential using in silico strategies were chemically synthesized and their anti-inflammatory activities confirmed using in vitro bioassays with COX-1 and COX-2 enzymes. The peptide QCPLHRPWAL inhibited COX-1 and COX-2 by 82.90% (+/-0.54) and 53.84% respectively and had a selectivity index greater than 10. This peptide warrant further research as a novel anti-inflammatory/pain relief peptide. Other bioactivities associated with DPP-IV inhibition and Umami flavours de-velopment are discussed. Identified peptides offer potential for use in functional foods or topical agents to prevent pain and inflammation associated with diseases like psoriasis, arthritis and inflammatory bowel disease, however further research in vivo is required.

The abilities of quantitative description of noise are restricted due to its origin and only the statistical and spectral analysis methods can be applied while an exact time evolution cannot be defined or predicted. It emphasizes the challenges faced in many applications including communication systems where noise can play, on one hand, a vital role impacting signal-to-noise ratio but possessing, on the other hand, unique properties such as infinite entropy (infinite information capacity), exponentially decaying correlation function and so on. Despite the deterministic nature of chaotic systems, the predictability of chaotic signals is limited for a short time window putting them close to random noise. In this article, we propose and experimentally verify an approach to achieve Gaussian-distributed chaotic signals by processing the outputs of chaotic systems. The mathematical criteria, on which the main idea of the study is based on, is the Central Limit Theorem which states that the sum of a large number of independent random variables with similar variances approaches a Gaussian distribution. The study involves more than 40 mostly three-dimensional continuous-time chaotic systems (Chua’s, Lorenz’s, Sprott’s, memristor-based, etc.) whose output signals are analyzed according to criteria that encompass the probability density functions of a chaotic signal itself and its envelope and phase, statistical and entropy-based metrics such as skewness, kurtosis, and entropy power. We found that two chaotic signals of Chua’s and Lorenz’s systems exhibited superior performance across the chosen metrics. Furthermore, our focus extended to determining the minimum number of independent chaotic signals necessary to yield a Gaussian-distributed combined signal. The analytic and experimental results indicate that the sum of at least three non-Gaussian chaotic signals closely approximates Gaussian distribution. It allows generation of reproducible Gaussian-distributed deterministic chaos by modeling simple chaotic systems.

Apis mellifera, especially weak ones, are highly vulnerable to Carpoglyphus lactis mites, which can rapidly infest and consume stored pollen, leading to weakened colonies and potentially colony collapse. This study aimed to ascertain and investigate the prevalence of this mite in honeybee colonies across 9 provinces in the Republic of Korea (ROK). A total of 615 honeybee colonies were collected, from 66 apiaries during the spring and 58 apiaries during the summer of 2023. A 1,242 bp segment of the the Cytochrome c oxidase subunit 1 (COI) gene was amplified using the polymerase chain reaction method. The detection levels of C. lactis in the honeybees were compared between winter and summer. Based on the COI sequence analysis, the nucleotide sequence similarity of C. lactis mites isolated in the ROK with those from China (NC048990.1) was found to be 99.5%, and with those from the United Kingdom (KY922482.1) was 99.3%. This study is the first report of C. lactis in Korean apiaries. The findings in this study demonstrate a significantly higher detection rate in winter, which is 4.1 times greater than that in summer (p &lt; 0.001). Furthermore, the results underscore the usefulness of molecular diagnostic techniques for detecting C. lactis mites.

This paper presents a system for identification of wind features, such as gusts and wind shear. These are of particular interest in the context of energy-efficient navigation of Small Unmanned Aerial Systems (UAS). The proposed system generates real-time wind vector estimates and a novel algorithm to generate wind field predictions. Estimations are based on the integration of an off-the-shelf navigation system and airspeed readings in a so-called direct approach. Wind predictions use atmospheric models to characterize the wind field with different statistical analyses. During the prediction stage, the system is able to incorporate, in a big-data approach, wind measurements from previous flights in order to enhance the approximations. Wind estimates are classified and fitted into a Weibull probability density function. A Genetic Algorithm (GA) is utilized to determine the shaping and scale parameters of the distribution, which are employed to determine the most probable wind speed at a certain position. The system uses this information to characterize a wind shear or a discrete gust and also utilizes a Gaussian Process regression to characterize continuous gusts. The knowledge of the wind features is crucial for computing energy-efficient trajectories with low cost and payload. Therefore, the system provides a solution that does not require any additional sensors. The system architecture presents a modular decentralized approach, in which the main parts of the system are separated in modules and the exchange of information is managed by a communication handler to enhance upgradeability and maintainability. Validation is done providing preliminary results of both simulations and Software-In-The-Loop testing. Telemetry data collected from real flights, performed in the Seville Metropolitan Area in Andalusia (Spain), was used for testing. Results show that wind estimation and predictions can be calculated at 1 Hz and a wind map can be updated at 0.4 Hz. Predictions show a convergence time with a 95% confidence interval of approximately 30 s.

This paper introduces an innovative energy circuit that challenges conventional understandings of energy conservation, marking a significant departure from traditional frameworks. Seeking to enhance energy generation within existing paradigms, this innovation holds promise for addressing pressing global issues such as the energy crisis and environmental sustainability, while also stimulating scientific inquiry. In contrast to prevalent misconceptions rooted in philosophical and scientific constraints, this study refutes the notion of generating energy from nothing and questions the viability of perpetual motion machines as conceptual models. Instead, it proposes a novel circuit design derived from an unconventional electrical short circuit, which is shown to disrupt the established principles of energy conservation. This circuit offers a transformative approach with diverse applications, including self-recharging capabilities for electric vehicles, bolstering microgrid infrastructure, and facilitating the integration of renewable energy sources. Transcending conventional scientific boundaries, this paper prompts philosophical reflections on the evolving nature of scientific exploration. Ultimately, the energy circuit represents a pivotal step towards mitigating the global energy crisis by reducing reliance on finite resources and fostering sustainability. The paper concludes by illustrating the circuit's potential to revolutionize energy systems and contribute to a more resilient and sustainable future.

Deep neural networks have been extensively researched in the field of document image classification to improve classification performance and have shown excellent results. However, there is little research in this area that addresses the question of how well these models would perform in a real-world environment, where the data the models are confronted with often exhibits various types of noise or distortion. In this work, we present two separate benchmark datasets, namely RVL-CDIP-D and Tobacco3482-D, to evaluate the robustness of existing state-of-the-art document image classifiers to different types of data distortions that are commonly encountered in the real world. The proposed benchmarks are generated by inserting 21 different types of data distortions with varying severity levels into the well-known document datasets RVL-CDIP and Tobacco3482, respectively, which are then used to quantitatively evaluate the impact of the different distortion types on the performance of latest document image classifiers. In doing so, we show that while the higher accuracy models also exhibit relatively higher robustness, they still severely underperform on some specific distortions, with their classification accuracies dropping from ~90% to as low as ~40% in some cases. We also show that some of these high accuracy models perform even worse than the baseline AlexNet model in the presence of distortions, with the relative decline in their accuracy sometimes reaching as high as 300-450% that of AlexNet. The proposed robustness benchmarks are made available to the community and may aid future research in this area.

Since the first description of COVID-19 infection, among clinical manifestations of the disease including fever, dispnea, cough, fatigue, it was observed a high incidence of thromboembolic events potentially evolving towars ARDS and COVID-associated-coagulopathy (CAC).The hypercoagulation state is based on an interaction between thrombosis and inflammation. The so-called CAC represents a key aspect in the genesis of organ damage from SARS-CoV-2. The prothrombotic status in COVID-19 disease can be explained by the increase of coagulation levels of D-dimer, lymphocytes, fibrinogen, IL-6 and prothrombin time. Several mechanisms have been hypothesized to explain this hypercoagulable process such as inflammatory cytokine storm, platelet activation, endothelial dysfunction and stasis for a long time. The purpose of this narrative review is to provide an overview of the current knowledge on the pathogenic mechanisms of coagulopathy that may characterize COVID-19 infection and inform on new areas of research. New vascular therapeutic strategies are also reviewed.

Background: Microscopic examination of acid-fast stained sputum smears for detecting mycobacterial acid-fast bacilli (AFB) remains the most economical and readily available method for laboratory diagnosis of tuberculosis (TB). However, this conventional approach has limitations, including low sensitivity and labor-intensive procedures. Methods: An automated microscopy system incorporating artificial intelligence (AI) and machine learning for AFB identification was evaluated. The study was conducted at an Infectious Disease Hospital in Jiangsu Province, China, utilizing an intelligent microscope system (TB-Scan, Wellgen Medical, Kaohsiung). A total of 1,000 sputum smears were included in the analysis, with the system capturing digital microscopic images and employing an image recognition model to automatically identify and classify AFB. Referee technicians provided the gold standard for discrepant results. Results: The automated system demonstrated an overall accuracy of 95.00% (950/1,000), sensitivity of 91.24% (177/194), and specificity of 95.91% (773/806). Notably, the system identified 21 smears as positive that were previously reported as negative, with referee technicians confirming 17 of these as true positive and retracting the test results accordingly. Recalculating the performance, the accuracy increased to 96.70% (967/1,000), sensitivity to 91.94% (194/211), and specificity to 97.97% (773/789), with a false negative rate of 8.06% (17/211) and a false positive rate of 2.03% (16/789). Conclusions: The incorporation of AI and machine learning into an automated microscopy system demonstrated the potential to enhance the sensitivity and efficiency of AFB detection in sputum smears compared to conventional manual microscopy. This approach holds promise for widespread application in TB diagnostics and potentially other fields requiring labor-intensive microscopic examination.

Accurately predicting the power of solar power generation can greatly reduce the impact of the randomness and volatility of power generation on the stability of the power grid system, which is beneficial for the balanced operation and optimized dispatch of the power grid system, and reduces operating costs. Solar PV power generation depends on weather conditions, which are prone to large fluctuations under different weather conditions. Its power generation is characterized by randomness, volatility and intermittency. Recently, the demand for further investigation and effective use on the uncertainty of short-term solar PV power generation prediction has been getting increasing attention in many application of renewable energy sources. In order to improve the predictive accuracy of output power of solar PV power generation and develop a precise predictive model, the authors worked predictive algorithms for the output power of a solar PV power generation system. Moreover, since short-term solar PV power forecasting is one of the important aspects for optimizing the operation and control of renewable energy systems and electricity markets, this review focuses on the predictive models of solar PV power generation, which can be verified in the daily planning and operation of a smart grid system. In addition, the predictive methods in the reviewed literature are classified according to the input data source used for accurate predictive models, and the case studies and examples proposed are analyzed in detail. The contributions, advantages and disadvantages of the predictive probabilistic methods are compared. Finally, the future studies of short-term solar PV power forecasting is proposed.

Saikosaponin (SS) a is a compound with various pharmacological properties and easily degraded into SSb1 and SSg in acid condition. In present work another two new degradation products of SSa formed under acid hydrolytic condition were detected and isolated by analytical and semi-preparative liquid chromatography technology, furthermore their structures were characterized as hydroxyl-saikosaponin a and SSb2 by spectral analysis, which is not only essential in stability-indicating method development and validation but also could be used as a worst case to assess the analytical method performance of SSa. Moreover their structural transformation pathways were also proposed.

The conformity assessment of products or a measured value with the given standards is carried out based on the global risk of producers and consumers calculation. A product may conform to specifications but be falsely rejected as non-conforming. This is about the producer's risk. If a product doesn't meet the requirements but is falsely accepted as conforming, that poses a risk to the consumer. The conventional approach to risk assessment, which yields only a single numerical value for the global risk of producers and consumers, is naturally extended, and utilized for assessing risk in measurement models with linear regression. The outcomes of the two-dimensional extension, along a moderate scale, are the parabolas with an opening upwards. Risk surfaces were obtained through three-dimensional extension over the area limited by the moderate scale and guard band axes. Four models with different ranges of tolerance intervals were used to test this innovative method of risk assessment in linear regression. The corresponding standard measurement uncertainties are determined by applying a simplified measurement model with the use of comprehensive data on the measurement performance and by determining measurement uncertainty derived from consideration of the functional relationship obtained by linear regression analysis. Models that utilize information from linear regression analysis to determine measurement uncertainty are biased towards risks at the edges of the moderate scale. Testing the model's performances with metrics related to the confusion matrix, such as the F1 score, further substantiated this assertion. The diagnostic odds ratio has been proven to be extremely effective in identifying the curve along the guard band axis, along which the global risks of producers and consumers are at their lowest.

Dynamic optical structural color is always desired in various display applications and usually involves active materials. Full-color generation, especially bi-directional full-color generation in both reflective and transmissive modes, without any active materials included has been rarely investigated. Herein, we demonstrate a scheme of bi-directional full-color generation based on a plasmonic metasurface modulated by rotation of the polarization angle of the incident light without varying the geometry and the optical properties of the materials and the environment where the metasurface resides. The metasurface unit cell consists of plasmonic modules aligning in three directions and is patterned in a square array. The metasurface structural color device generates full colors in both reflection and transmission. Based on the proposed polarization-dependent structural color, information encoding of three multiplexed animal images and quick-responsive (QR) codes is demonstrated to verify the efficient information encoding and decoding of the proposed scheme. The animals can be seen under different polarization incidence and the QR codes can be successfully decoded by the polarization rotation in transmission. The proposed bi-directional full-color generation metasurface has great potential in applications such as kaleidoscope generation, anti-counterfeiting, dynamic color display, and optical information encoding.

With the development of organizational biology and forced from the EU Cosmetic Regulation (EU/1223/2009) with the complete ban of animal testing for cosmetic purposes, 3D skin models have been widely developed due to their similar physiological structure and metabolic function to native human tissue. 3D skin models for in vitro cosmetic evaluation are still promising alternative methods for evaluating phototoxicity, corrosivity and irritation. These models have been used for early screening and later validation of toxicity and efficacy evaluation by many well-known cosmetic companies. 3D skin models have become an important tool in cosmetic evaluation research. This article reviews the development of 3D skin models and their application in cosmetic evaluation. An outlook on the current application and future technological development directions in the cosmetic field is also presented.

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

The use of privileged scaffolds as starting point for the construction of libraries of bioactive compounds is a widely used strategy in drug discovery and development. Scaffold decoration, morphing and hopping are additional techniques that allow to modify the privileged framework chosen and better explore the chemical space around it. In this study, two series of highly functionalized pyrimidine and pyridine derivatives were synthesized by scaffold morphing approach of triazine compounds obtained previously as antiviral agents. Newly synthesized azines were evaluated against lymphoma, hepatocarcinoma, and colon epithelial carcinoma cells showing in five cases acceptable to good anticancer activity associated to low cytotoxicity on healthy fibroblasts. Finally, ADME in vitro studies were conducted on the best derivatives of the two series showing good passive permeability and resistance to metabolic degradation.

The article presents the method of identification of dynamic models for different flight states of a rotary-wing UAV. Experimental flights were conducted to obtain data necessary for the identification process. Such models can later be implemented in simulations to represent the behavior of real-life objects. Simulation of UAV swarms is the first stage of developing a swarm system, where prototyping with physical models is problematic. Therefore, obtaining accurate models is crucial for the simulation process to be reliable. Also, verification of obtained models was performed to make sure that they were identified correctly. In particular, the presented method was proven effective and successfully used in some applications.

Around the world, every year, about 17 million people death cause happen due to CardioVascular Diseases (CVD). As per clinical records, primarily sufferers exhibit myocardial infarctions and Heart Failures (HF). Creatinine is a Musculo - skeletal waste product. The kidneys filter creatinine from the blood and excrete it through the urine in a healthy body. High creatinine levels can suggest renal problems. Elevated Serum Creatinine (SC) has been well established in the HF. Patients’ electronic medical records can be used to quantify symptoms and other related clinical laboratory test values, which would then be utilized to direct biostatistics exploration to uncover patterns and associations that doctors would otherwise miss. The latest American Heart Association guidelines for 1500 mg/d sodium tend to be sufficiently relevant for patients with stage A and B with HF. In this article, we used a dataset of the year 2015 of heart patients records of 299 patients. The present paper used the data analytic and statistical tools to verify the significant differences between alive and dead patients’ SC and Serum Sodium (SS). It also demonstrates the impact of significant features on abnormal SC and SS on the Survival-Status levels. The Age-Group feature, which is derived from age attribute and, Ejection Fraction (EF), anemia, platelets, Creatinine Phosphokinase (CPK), Blood-Pressure (BP), gender, diabetes, and smoking-status were utilized to determine the potential contributing features to mortality with Cox regression model. The Kaplan Meier plot was used to investigate the overall pattern of survival concerning age-group. During pre-processing of the dataset, Age and SS were removed due to multicollinear features during performing machine learning algorithms experiments. This paper also predicted patients’ survival, age group, and gender using supervised machine learning classifiers. Detection of significant features would help in making informed decisions to balance the lifestyle of heart patients. The author revealed that the patient’s follow-up months, as well as SC, EF, CPK, and platelets, are sufficient key features to predict heart patient survival using Random Forest (RF) stratified 10-fold CV method with accuracy (96%) with 5% Standard Deviation (SD) from medical records dataset. We identified the age-group and gender of the patient, and the RF model outperformed others with the best accuracy 96% and 94% in both cases having 11% SD. Also, prominent features such as CPK, SC, follow-up month, platelets, and ejection were found to be significant factors in predicting the patient’s age-group. Smoking habits, CPK, platelets, follow-up month, and SC of each patient were discovered to be significant predictors of patient gender. The hypothetical study proved that SC and SS making substantial differences in the survival of patients (p &lt; 0.05) and failed to reject that anemia, diabetes, and BP making a significant impact on the creatinine and sodium of each patient (p &gt; 0.05). With χ2(1) = 8.565, the Kaplan Meier plot revealed that mortality was high in the extremely elder age-group. The finding has possible effects on clinical practice and becomes a new medical support system when predicting whether a patient can survive a heart attack or not. The doctor should primarily concentrate on follow-up month, SC and EF, CPK, and platelet count since the aim is to understand whether a patient survives after HF.

Banana wilt caused by Fusarium oxysporum f. sp. cubense tropical race 4 (Foc TR4) is a devastating fungal disease. Biocontrol strategies owned immense potential in inhibiting the spread of Foc TR4. Here, thirty actinobacteria were isolated from soils and screened for their antagonistic activity against Foc TR4. Strain SCA4-21T exhibited broad-spectrum antifungal activity against nine phytopathogenic fungi including Foc TR4. The strain was identified as the genus Streptomyces according to phenotypic characteristics and 16S rRNA gene phylogenetic analysis. Multi-locus sequence analysis based on five house-keeping gene alleles (atpD, gyrB, recA, rpoB, and trpB) revealed that strain SCA4-21T clustered into S. hygroscopicus subsp. hygroscopicus NBRC 13472T with 100% of bootstrap value. Average nucleotide identity and dDDH were 98.49% and 44.30% above the threshold of novel species. Hence, strain SCA 4-21T represented a novel species within the genus Streptomyces, named as Streptomyces luomodiensis sp. nov.. The type strain is SCA4-21T (=GDMCC4.340T= JCM36555T). By the CAZymes analysis, 348 carbohydrate-active enzymes were detected, including 15 chitinases and 8 β-1,3-glucanases. The fermentation broth of strain SCA4-21T showed high activities of chitinase and β-1,3-glucanase, contributing to the antifungal activity. Our results showed an innovative potential biocontrol agent for managing plant fungal diseases, specifically banana fusarium wilt.

LuBO3 crystallizes in the calcite type (CaCO3) structure and is a widely applied inorganic host for luminescent materials and scintillators. Even though many scientific works have been published concerning the optical properties of rare earth doped LuBO3, so far, no study of the emission spectra as function of the excitation energy of such orthoborates has been conducted. Therefore, this work deals with the photoluminescence of RE doped LuBO3 with RE = Ce3+, Eu3+, Gd3+, or Tb3+, while an emphasize is laid on the energy dependence of these four luminescent compounds.

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

Despite the successful introduction of oral cholera vaccines, Zambia continues to experience multiple, sporadic, and protracted cholera outbreaks in various parts of the country. While vaccines have been useful in staying the cholera outbreaks, the ideal window for re-vaccinating individual’s resident in cholera hotspot areas remains unclear. Using a prospective cohort study design, 225 individuals were enrolled, re-vaccinated with two doses of Shanchol™ regardless of previous vaccination and followed-up for 90 days. Bloods collected at baseline before re-vaccination, at day 14 prior to second dosing and subsequently on days 28, 60, and 90. Vibriocidal assay was performed on samples collected at all five time points. Our results showed that anti-LPS and vibriocidal antibody titers increased at day 14 after re-vaccination and decreased gradually at 28, 60 and 90 across all the groups. Seroconversion rates were generally comparable in all treatment arms. We therefore conclude that vibriocidal antibody titers generated in response to re-vaccination still wane quickly irrespective of previous vaccination status. However, despite the observed decline, the levels of vibriocidal antibodies remained elevated over baseline values across all groups, an important aspect for Zambia where there is no empirical evidence as to the ideal time for re-vaccination.

Central nervous system (CNS) myelin has a crucial role in accelerating the propagation of action potentials and providing trophic support to the axons. Defective myelination and lack of myelin regeneration following demyelination can both lead to axonal pathology and neurodegeneration. Energy deficit has been evoked as an important contributor to various CNS disorders, including multiple sclerosis (MS). This suggests that dysregulation of energy homeostasis in oligodendroglia may be an important contributor to myelin dysfunction and lack of repair observed in the disease. This article will focus on energy metabolism pathways in oligodendroglial cells and highlight differences dependent on the maturation stage of the cell. In addition, it will emphasize that the use of alternative energy sources by oligodendroglia may be required to save glucose for functions that cannot be fulfilled by other metabolites, thus ensuring sufficient energy input for both myelin synthesis and trophic support to the axons. Finally, it will point out that neuropathological findings in a subtype of MS lesions likely reflect defective oligodendroglial energy homeostasis in the disease.

The AREB/ABF (ABA response element binding) proteins in plants are vital for plant stress responses, but the understanding of AREB/ABFs in orchid species, an important traditional medicinal and ornamental plants, is limited. Here, twelve AREB/ABF genes were identified within three orchids’ complete genome and classified into three groups (groups I, II, and III) via a phylogenetic analysis, which was further supported by the analysis of their conserved motifs and gene structures. The cis-elements analysis showed that hormone response elements followed by light and stress responses were widely rich in AREB/ABFs. A prediction analysis of orchid ABRE/ABF-mediated regulatory network was further constructed through cis-regulatory elements (CREs) analysis of their promoter regions and revealed that several transcriptional factor (TF) gene family were abundant as the potential regulators for orchid AREB/ABFs. Expression profile analysis based on public transcriptomic data showed that most AREB/ABF genes have distinct tissue-specific expression patterns in orchid plants. Additionally, DcaABI5 as the homolog of ABA INSENSITIVE 5 (ABI5) from Arabidopsis was selected for further analysis. The results showed that Arabidopsis transgenic overexpressing DcaABI5 could rescue the ABA insensitive phenotype in the mutant abi5. Collectively, these findings will provide valuable information on AREB/ABF genes in orchids.

As Artificial Intelligence (AI) technology, particularly Machine Learning algorithms, becomes increasingly ubiquitous, our abilities to understand and interpret AI and machine learning algorithms are becoming increasingly desirable. Visualization has been used as a common tool to view and understand complex machine learning processes. In this paper, we will focus on developing a general framework for the visualization of machine learning models as a scalar valued multi-dimensional function to help users understand how the models behave over different viewing spaces. We first give a formal definition of the visualization problem. Interpolation-based morphing and subspace sampling techniques are applied to generate various renderings through projections and cross-sections of the model space as 3D surfaces or heatmap images. This method will be applied to two real-world datasets and applications: the diagnosis of Alzheimer's Disease (AD) using a human brain networks dataset and a real-world benchmark dataset for predicting home credit default risks. The visualizations show that different machine learning algorithms can behave quite differently under different conditions.

Today, air quality is one of the global concerns that governments are facing. One of the main air pollutants is the particulate matter (PM) that affects human health. This article presents the modeling of a purification system by means of negative air ions (NAIs) for air pollutant removal, using computational intelligence methods. The system uses a high voltage booster output to ionize air molecules from stainless steel electrodes; its particle-capturing efficiency reaches up to 97%. With two devices (5 x 2 x 2.5 cm), 2 trillion negative ions are produced per second, and the particulate matter (PM 2.5) can be reduced from 999 to 0 mg / m3 in a period of approximately 5 to 7 minutes (in a 40 x 40 x 40 cm acrylic chamber). This negative ion generator is a viable and sustainable alternative to reduce polluting emissions, with beneficial effects on human health.

(1) Background: Colon polyps are common protrusions in the colon’s lumen with potential risks of developing colorectal cancer. Early detection and intervention of these polyps are vital for reducing colorectal cancer incidence and mortality rates. This research aims to evaluate and compare the performance of three machine-learning image classification models’ performance in detecting and classifying colon polyps. (2) Methods: The performance of three machine learning image classification models, Google Teachable Machine (GTM), Roboflow3 (RF3), and You Only Look Once version 8 (YOLOV8), in the detection and classification of colon polyps were evaluated. The study used a dataset of colonoscopy images of normal colon, polyps, and resected polyps. The study assessed the models’ ability to correctly classify the images into their respective classes using precision, recall, and F1 score generated from confusion matrix analysis and performance graphs. (3) Results: All three models successfully distinguished between normal colon, polyps, and resected polyps in colonoscopy images. GTM achieved the highest accuracies: 0.99, with consistent precision, recall, and F1 scores of 1.00 for the `normal’ class, 0.97 - 1.00 for `polyps,’ and 0.97 - 1.00 for `resected polyps.’ While GTM exclusively classified images into these three categories, both YOLOV8 and RF3 extended their capabilities to detect normal colonic tissue, polyps, and resected polyps, with YOLOV8 and RF3 achieving overall accuracies of 0.84 and 0.87, respectively. (4) Conclusions: Machine learning, particularly models like GTM, shows promising results in ensuring comprehensive detection of polyps during colonoscopies.

Objective: To map the literature pertaining to adult Person-Centered Rehabilitation (PCR).Data Sources: Following our previously published scoping review protocol, data were identified through: three major databases, snowball searches and expert consultation.Study Selection: Two independent reviewers identified English-language papers which addressed adult physical rehabilitation and met one or more of the six pre-defined inclusion categories for PCR content. Data Extraction: Two independent reviewers extracted key characteristics of included papers (e.g. aims, methods, participants). Quantitative methods (descriptive statistics, regression analysis) and qualitative content analyses were used to synthesize the results. Data synthesis: Of 5084 unique records initially screened, 145 papers were included: 111 empirical, including 11 systematic reviews. Empirical papers had data from 13498 clients and 3849 providers, in total. Yearly publications grew exponentially from 2000 to 2017 (r²= 0.65; p<0.01). Publications were unevenly distributed by countries (e.g. United States’ publications per population size was 45 times lower than New Zealand’s). Most papers focused in more than one profession, setting-type or health conditions, respectively 57.2%, 66.2%, and 60.7%. Finally, more than half of the empirical papers (53.2%) studied implementation of PCR approaches, including its effect. Conclusion: This scoping review synthesizes key characteristics and publication trends in the PCR literature, which is mainly empirical and growing exponentially over time. Stakeholders can use the identified trends, gaps, and literature map to guide further PCR research, and both clinical and organizational practices. The high prevalence of papers focused on multiple professions, settings-type, or health conditions reinforces the need for developing a transdisciplinary, trans-service model of PCR, which will be derived from a thematic analysis of this body of literature.

Cement industry is one of the highest energy consuming industries. The quantity of fuel and energy needed accounts for most of the cost of cement manufacturing. Thermal power plants generate electricity but are harmful and ineffective by nature. As a backup mechanism to account for main grid failures, batteries can be utilized. In this paper the first ever investigation on battery’s depth of discharge (DOD) for four different kinds of battery technologies is carried out in the framework of cement industry. The intended battery technologies are the lead-acid battery (LA), lithium-ion battery (Li-ion), vana-dium redox battery (VR), and nickel–iron battery (Ni-Fe). Four hybrid energy generation models (HEGMs) for five cement plants of Pakistan using the HOMER pro software are proposed. Cement plants includes Askari Cement Plant, Wah (ACPW); Bestway Cement Plant, Kalar Kahar (BCPKK); Bestway Cement Plant, Farooqia (BCPF); Bestway Cement Plant, Hattar (BCPH); and DG Cement Plant, Chakwal (DGCPC). HEGM-1 comprises of a diesel generator (DGen), a photovoltaic system (PV), a converter, and a battery system. HEGM-2 comprises of a PV system, a converter, and a battery system. HEGM-3 is the grid-connected version of HEGM-1 and HEGM-4 is the grid-connected version of HEGM-2. A base-model consisting of grid only is used as a reference. A multi-criteria decision analysis (MCDA) is performed by formulating a cumulative objective function (COF) which includes net present cost (NPC), levelized cost of energy (LCOE), and greenhouse gas (GHG) emissions. The principal objective is the maximization of COF while simultaneously minimizing the objectives (NPC, LCOE and GHG emissions), based on optimal battery technology and DOD. The results reveal that VR is the most suitable battery technology with 10% DOD. It is achieved for DGCPC with HEGM-3 with 61.49% of NPC, 78.62% of LCOE and 84.00% of GHG emissions reduction as com-pared to the base model.