Effectively mapping and monitoring rubber plantation is still changing. Previous studies have explored the potential of phenology features for rubber plantation mapping through a pixel-based approach (pixel-based phenology approach). However, in fragmented mountainous Xishuangbanna, it could lead to noises and low accuracy of resultant maps. In this study, we investigated the capability of an integrated approach by combining phenology information with an object-based approach (object-based phenology approach) to map rubber plantations in Xishuangbanna. Moderate Resolution Imaging Spectroradiometer (MODIS) data were firstly used to acquire the temporal profile and phenological features of rubber plantations and natural forests, which delineates the time windows of defoliation and foliation phases. Landsat images were then used to extract a phenology algorithm comparing three different approaches: pixel-based phenology, object-based phenology, and extended object-based phenology to separate rubber plantations and natural forests. The results showed that the two object-based approaches achieved higher accuracy than the pixel-based approach, having overall accuracies of 96.4%, 97.4%, and 95.5%, respectively. This study proved the reliability of a phenology-based rubber mapping in fragmented landscapes with a distinct dry/cool season using Landsat images. This study indicated that the object-based phenology approaches can effectively improve the accuracy of the resultant maps in fragmented landscapes.

This paper proposes an encryption scheme for high pixel density images. Based on the application of the quantum random walk algorithm, the Long short-term memory (LSTM) can effectively solve the problem of low efficiency of the quantum random walk algorithm in generating large-scale pseudorandom matrices, and further improve the statistical properties of the pseudorandom matrices required for encryption. The LSTM is then divided into columns and fed into the LSTM in order for training. Due to the randomness of the input matrix, the LSTM cannot be trained effectively, so the output matrix is predicted to be highly random. The LSTM prediction matrix of the same size as the key matrix is generated based on the pixel density of the image to be encrypted, which can effectively complete the encryption of the image. In the statistical performance test, the proposed encryption scheme achieves an average information entropy of 7.9992, an average number of pixels changed rate (NPCR) of 99.6231%, an average uniform average change intensity (UACI) of 33.6029% and an average correlation of 0.0032. Finally, various noise simulation tests are also conducted to verify its robustness in real-world applications where common noise and attack interference are encountered.

The land use land cover (LULC) map is extensively employed for different purposes. The LULC maps provided by the Portuguese National Geographic Information System (SNIG) are not ideal for continuous assessment and regular scrutiny. Machine learning (ML) algorithms applied in remote sensing (RS) data has been proven effective in image classification, object detection, and semantic segmentation. Previous studies have shown that Random Forest (RF) and Support Vector Machine (SVM) consistently achieved high accuracy for land classification. In this study, the application of RF and SVM classifiers, and object-based (OBIA) and pixel-based (PBIA) approaches with Sentinel-2A imagery was evaluated using Google Earth Engine (GEE) platform for land cover classification of a burned area in the Serra da Estrela Natural Park (PNSE), Portugal. This aimed to detect the land cover change and closely observe the burned area and vegetation recovery after the 2022 wildfire. The combination of RF and OBIA achieved the highest accuracy in all evaluation metrics. At the same time, a comparison with the Normalized Difference Vegetation Index (NDVI) map and Conjunctural Land Occupation Map (COSc) of 2023 year indicated that the SVM and PBIA map resembled the maps better.

It is very difficult to accurately divide farmland and woodland in Gaofen 2 (GF-2) remote sensing image, because their single plant coverage is very small, and their spectra are very similar. The ratio of spatial resolution and one plant’s coverage area must be fully taken into account when designing the Convolutional Neural Network structure for extracting them from GF-2 image. We establish a Convolutional Encode Neural Networks model (CENN), The first layer has two sets of convolution kernels to learn the characteristics of farmland and woodland respectively, while the second layer is the encoder to encode the characteristics by transfer function, which can map the results to the corresponding category number. In the training stage, samples of farmland, woodland, and other categories are categorically used to train CENN, as soon as training is accomplished, CENN would acquire enough ability to accurately extract farmland and woodland from remote sensing images. The final extraction result is obtained by implementing per-pixel segmentation of images used to train the CENN. CENN is compared and analyzed with others such as Deep Belief Network (DBN), Full Convolutional Network (FCN), Deeplab Model. The results of experiments show that CENN can more accurately mine the characteristics of farmland and woodland, and it achieves its goal of extracting farmland and woodland with high precision from GF-2 images.

Environmental factors, such as drought-stress, significantly impact maize growth and productivity worldwide. To improve yield and quality, effective strategies for early detection and mitigation of drought-stress in maize are essential. This paper presents a detailed analysis of three imaging trials conducted to detect drought-stress in maize plants using an existing, custom-developed, low cost, high throughput phenotyping platform. We propose a pipeline for early detection of water stress in maize plants using a Vision Transformer classifier and analysis of distributions of near-infrared (NIR) reflectance from the plants. We also explored suitable regions on the plant that are more sensitive to drought-stress and show that the region surrounding the youngest expanding leaf (YEL) and the stem can be used as a more consistent alternative to analysis involving just the YEL. Our results show good separation between well-watered and drought-stressed trials for two out of the three imaging trials both in terms of classification accuracy from data-driven features as well as through analysis of histograms of NIR reflectance.

Cascading landslides represent a dynamic and hazardous geological phenomenon, char-15 acterized by a sequential chain of slope failures triggered by various factors such as heavy rainfall, 16 seismic activity, or anthropogenic activities. These events can amplify the damage caused by the 17 initial trigger and propagate instability along a slope, often resulting in significant environmental 18 and societal impacts. The Morino-Rendinara cascading landslide, situated in the Ernici mountains 19 along the border of Abruzzo and Lazio region (Italy), serves as a notable example of the complexities 20 and devastating consequences associated with such events. In March 2021, a substantial debris flow 21 event in Morino obstructed the Liri River, marking the latest step in a series of landslide events 22 characterized by a complexity far beyond initial expectations. This study employs a multidiscipli-23 nary approach, combining conventional techniques and advanced technologies, to understand the 24 complexities of the Morino-Rendinara landslide. Field activities, UAV image acquisition, SAR inter-25 ferometry based on SENTINEL-1 images and pixel offset analysis based on high-resolution Google 26 Earth images, offer insights into the geological and hydrogeological setting of the unstable slope, 27 landslide geometry, mechanism and kinematics. To supplement the analysis a specific FEM slope 28 stability analyses is used to reconstruct the deep geometry of the system emphasizing the modula-29 tion action of groundwater flow to the slope stability

River monitoring has the potential to grow substantially with present-day available affordable and locally sourced hardware. Yet river monitoring networks are still under pressure due to lack of resources, difficulties with maintenance, and rapidly changing conditions which in part may be due to climate change. We advocate that to turn around this trend, monitoring stations must rely on local people, work with locally available devices, work without contact with water and operate through openly available knowledge. River observations with camera videos have this potential. IP cameras, drones or smartphones are widely available as observation platforms. The scientific methods are well established in literature. Yet current attempts to establish scalable open-source software solutions that can be operated anywhere are lacking. In fact, currently available software is either research-oriented, more aimed at incidental observations, is restricted to single-use licenses, entirely proprietary, or restricted to operations through a third-party Software as a Service. To overcome this obstacle, we present OpenRiverCam, a free and open-source software ecosystem for river observations that can fulfill a wide variety of use cases and business cases through a well-documented and simple to use Application Programming Interface, service workflows, cloud scalability and interoperability, and options to extend to several applications within the hydrological, hydrodynamic, environmental and geospatial domains. We demonstrate its current technical abilities through three different case studies that all originate from a user perspective. We discuss the future developments to meet further requirements, which include documentation, widely available training materials and embedding in curricula, and further hardware and software developments.

Cloud Computing is a promising and emerging technology that is rapidly being adopted by many IT companies due to a number of benefits that it provides, such as large storage space, low investment cost, virtualization, resource sharing, etc. Users are able to store a vast amount of data and information in the cloud and access it from anywhere, anytime on a pay-per-use basis. Since many users are able to share the data and the resources stored in the cloud, there arises a need to provide access to the data to only those users who are authorized to access it. This can be done through access control schemes which allow the authenticated and authorized users to access the data and deny access to unauthorized users. In this paper, a comprehensive review of all the existing access control schemes has been discussed along with analysis. Keywords: role-based access control, attribute-based access control, attribute-based encryption

The integration of robotics and image processing has led to the realization of robot autonomy in dynamic environments through the provision of visual feedback. This paper presents the application of parallel and open-link robots in palletizing and shape drawing tasks as enhanced by visual feedback from image processing. In determining the set of joint angles that could be used to reach the desired position and orientation of the end effector, the geometric approach in which the spatial geometry of the robotic arms was decomposed into several plane geometry problems was employed. Image processing techniques were used to enhance the performance of the robotic manipulators. In one approach, Color-based segmentation was used to distinguish between different objects in the workspace by using predefined color markers as references in the L*a*b color space. Classification of each pixel in the workspace image was then done by calculating the Euclidean distance between that pixel and a predefined color marker. A second approach employed Edge detection to identify the boundaries of objects within the workspace image by employing the Hough Transform mathematical model to detect the abrupt changes in the image brightness pixel-wise. The pixel locations from Hough were then sorted sequentially to outline the detected object. The integration of image processing with the robotic tasks was expected to improve the precise detection of the position of objects as well as the outline of geometric shapes. The incorporation of visual feedback allowed for dynamic robot manipulation in which prior knowledge of the workspace was not requisite. This led to improved pick and place as well as shape detection as applied in palletizing and shape drawing tasks actuated by the parallel and serial link manipulators, respectively.

Currently, maritime traffic is increasing with economic growth in several regions worldwide. However, this growth in maritime traffic has led to increased risk of marine accidents. These accidents have a higher probability of occurring in regions where geographical features, such as islands, are present. Further, the positioning of rescue ships in a particular ocean region with a high level of maritime activity is critical for rescue operations. This paper proposes a method for determining an optimal set of locations for stationing rescue ships in an ocean region with numerous accident sites in the Wando islands of South Korea. The computational challenge in this problem is identified as the positioning of numerous islands of varying sizes located in the region. Thus, the proposed method combines a clustering-based optimization method and an image processing approach that incorporates flood filling to calculate the shortest distance between two points in the ocean that detours around the islands. Experimental results indicate that the proposed method reduces the distance from rescue ships and each accident site by 5.0 km compared to the original rescue ship locations. Thus, rescue time is reduced.

Protrusive defects on the color filter of thin-film transistor (TFT) liquid crystal displays (LCDs) frequently damage the valuable photomask. An fast method using side-view illuminations associated with digital charge-couple devices (CCDs) to detect the protrusive defect in the four substrates, which are the black matrix (BM), red, green, and blue. Between the photomask and substrate, the depth of field (DOF) is normally 300 μm for the proximity-type aligner; we select the four substrates to evaluate the detectability in the task. The experiment is capable of detecting measurements of 300 μm and even lower than 100 μm can be assessed successfully. The maximum error of the measurement is within 6% among the four samples. Furthermore, the uncertainty analysis of three standard deviations is conducted. Thus, the method is cost-effective to prevent damage for valuable photomasks in the flat panel display industry.

With the rapid development of satellite remote sensing technology, the volume of image datasets in many application areas is growing exponentially and the demand for Land-Cover and Land-Use change remote sensing data is growing rapidly. It is thus becoming hard to efficiently and intelligently retrieve the change information that users need from massive image databases. In this paper, content-based image retrieval is successfully applied to change detection and a content-based remote sensing image change information retrieval model is introduced. First, the construction of a new model framework for change information retrieval in a remote sensing database is described. Then, as the target content cannot be expressed by one kind of feature alone, a multiple-feature integrated retrieval model is proposed. Thirdly, an experimental prototype system that was set up to demonstrate the validity and practicability of the model is described. The proposed model is a new method of acquiring change detection information from remote sensing imagery and so can reduce the need for image pre-processing, deal with problems related toseasonal changes as well as other problems encountered in the field of change detection. Meanwhile, the new model has important implications for improving remote sensing image management and autonomous information retrieval.

The progress in markerless technologies is bringing clinicians tools to rapidly evaluate tests such as Timed Up and Go (TUG) without compromising the time of the assessment, yet it raises questions about the potential trade-off in accuracy compared to traditional marker-based systems, suggesting that the precision of these evaluations is not yet conclusively established. The OpenCap system is an easy tool to process 3D kinematics with two iPhone cameras placed in front of the subject. Previous research has primarily focused on walking and movement towards the cameras with the subject facing the cameras (WF) and thus has been unable to disentangle the differences in the movement directions. We have compared the OpenCap system with a standard marker-based system for both walking directions. In OpenCap, we found significantly worse results capturing walking facing away from the cameras (RW). Compared to the marker-based system, our findings indicate troubles measuring kinematic parameters but provide good accuracy in measuring spatial-temporal parameters. These insights pave the way for refining markerless assessment technologies, potentially enhancing their utility in clinical settings.

The quantification of sea surface temperature (SST) through space platforms has revolutionized how we obtain information at a global level. However, the main disadvantage of obtaining SST with satellite images consists of its inherent coarse spatial resolution. One solution could be the use of downscaling algorithms to create sequences of matrices at a higher resolution. We used the same SST data source from the MODIS-Aqua sensor at three spatial resolutions of 9 km, 4.5 km, and 1 km in the Gulf of California. Based on an open-source algorithm, the original SST images were downscaled to 4.5 km, 1 km, 500 m, 250 m, and 125 m per pixel scales. Results indicate a strong linear relationship between the original SST-MODIS data and the modeled data for all spatial resolutions. This study demonstrates the feasibility of an open-source downscaling algorithm to enhance the spatial resolution of SST images in a marginal sea.

A 'little brother' of pain, itch is an unpleasant sensation that creates a specific urge to scratch. To date, various machine-learning based image classifiers (MBICs) have been proposed for quantitative analysis of itch-induced scratch behaviour of laboratory animals in an automated, non-invasive, inexpensive and real-time manner. In spite of MBICs' advantages, the overall performances (accuracy, sensitivity and specificity) of current MBIC approaches remains inconsistent, with their values varying from ~50% to ~99%, for which the reasons underlying have yet to be investigated further, both computationally and experimentally. To look into the variation of the performance of MBICs in automated detection of itch-induced scratch, this article focuses on the experimental data recording step, and reports here for the first time that MBICs' overall performance is inextricably linked to the sharpness of experimentally recorded video of laboratory animal scratch behaviour. This article furthermore demonstrates for the first time that a linearly correlated relationship exists between video sharpness and overall performance (accuracy and specificity, but not sensitivity) of MBICs, and highlight the primary role of experimental data recording in rapid, accurate and consistent quantitative assessment of laboratory animal itch.

The digital era has significantly amplified the volume of online reviews, presenting both opportunities and challenges in harnessing insights from consumer feedback. Aspect-Based Sentiment Analysis (ABSA) has emerged as a crucial tool for distilling sentiments from these reviews, providing valuable data for enhancing product and service quality. This study introduces an advanced hybrid model, AdvSentiNet, which integrates adversarial training into the state-of-the-art framework to elevate the precision of sentiment detection at the aspect level. By employing an adversarial network, where a generative model competes against a classifier by crafting highly realistic synthetic samples, we aim to bolster the model's resilience against varied data samples. This innovative approach, unexplored in its entirety within the realm of ABSA, demonstrated remarkable performance improvements on benchmark datasets. For instance, accuracy on the SemEval 2015 dataset escalated from 81.7% to 82.5%, and for the SemEval 2016 dataset, it surged from 84.4% to 87.3%.

Designing motion representations for the problem of 3D human action recognition from skeleton sequences is an important yet challenging task. An effective representation should be robust to noise, invariant to viewpoint changes and result in a good performance with low-computational demand. Two main challenges in this task include how to efficiently represent spatio-temporal patterns of skeletal movements and how to learn their discriminative features for classification task. This paper presents a novel skeleton-based representation and a deep learning framework for 3D action recognition using RGB-D sensors. We propose to build an action map called SPMF (Skeleton Posture-Motion Feature), which is a compact image representation built from skeleton poses and their motions. An Adaptive Histogram Equalization (AHE) algorithm is then applied on the SPMF to enhance their local patterns and form an enhanced action map, namely Enhanced-SPMF. For learning and classification tasks, we exploit Deep Convolutional Neural Networks based on the DenseNet architecture to learn directly an end-to-end mapping between input skeleton sequences and their action labels via the Enhanced-SPMFs. The proposed method is evaluated on four challenging benchmark datasets, including both individual actions, interactions, multiview and large-scale datasets. The experimental results demonstrate that the proposed method outperforms previous state-of-the-art approaches on all benchmark tasks, whilst requiring low computational time for training and inference.

Deep learning methods have been widely used in PolSAR image classification. To learn the polarimetric information, many deep learning methods expect to learn high-level semantic features from original PolSAR data. However, only original data cannot learn multiple scattering features and complex structures for extremely heterogeneous terrain objects. In addition, deep learning methods always cause edge confusion due to the high-level features. To overcome these shortages, we propose a double-channel CNN network combined with an edge-preserving MRF model(DCCNN-MRF) for PolSAR image classification. Firstly, to combine complex matrix data and multiple scattering features together, a double-channel convolution network(DCCNN) is developed, which consists of a Wishart-based complex matrix and multi-feature subnetworks. The Wishart-based complex matrix network can learn the statistical characteristics and channel correlation well, and the multi-feature network can learn high-level semantic features well. Then, a unified network framework is designed to fuse two kinds of features to enhance advantageous features and reduce redundant ones. Finally, an edge preserving MRF model is designed to combine with the DCCNN network. In the MRF model, a sketch map-based edge energy function is designed by defining adaptive weighted neighborhood for edge pixels. Experiments are conducted on four real PolSAR data sets with different sensors and bands. Experimental results demonstrate the effectiveness of the proposed DCCNN-MRF method.

Obtaining classification confidence at the pixel level is a challenging task for accuracy assessment in remote sensing image classification. Among the various methods for estimating classification confidence at the pixel level, interpolation-based methods have drawn special attention in the literature. Even though they have been widely recognized in the literature, their usefulness has not been rigorously evaluated. This paper conducts a comprehensive evaluation of three interpolation-based methods: local error matrix method, bootstrap method, and geostatistical method. We applied each of the three methods to three representative datasets with different spatial resolutions, spectral bands, and the number of classes. We then derive the estimated classification confidence and true classification confidence and compared the results with each other using both exploratory data analysis (bi-histogram) and statistical analysis (Willmott's d and Binned classification quality). The results indicate that the three interpolation methods provide some interesting insights on various aspects of estimating per-pixel classification confidence. Unfortunately, the interpolation assumes that classification confidence is smooth across the space, which is usually not true in practice. In other words, interpolation-based methods have limited practical use.

Timely and accurate acquisition of information on distribution of the crop planting structure in Loess Plateau of eastern Gansu Province, one of the most important agricultural areas in western China, is crucial for promoting fine management of agriculture and ensuring food security. In this study, the Object-Based Image Classification (OBIC), the Random Forest (RF) and Convolutional Neural Network (CNN) models were employed to classify the crop planting structure of four representative test areas in Qingyang City in a precise manner. Firstly, different optimal segmentation scales for various crops were selected using the Estimation of Scale Parameter 2 (ESP2) tool and the Ratio of Mean Difference to Neighbors(ABS) to Standard Deviation (RMAS)model. The images were then segmented through multiresolution segmentation combined with the Canny Edge Detection algorithm. Secondly, the L1 regularized logistic regression model was utilized to select and optimize 39 spatial feature factors including spectral, textural, geometric, and index features, in conjunction with phenological factors. Lastly, under the multi-level classification framework, the Random Forest (RF) classifier and Convolutional Neural Network (CNN) model, combined with object-based multiresolution segmentation, was used to classify the crop planting structure. The findings show that: Thanks to the Canny Edge Detection algorithm, we can obtain a more complete boundary of the segmented objects and improve the separability. The optimal segmentation scales for corn, vegetables, and buckwheat were found to be 55, 70, and 35, respectively, while wheat and apple had optimal segmentation scales of 65. In addition to phenological characteristics, the number of selected spatial features for corn, vegetables, buckwheat, wheat, and apple were 9, 7, 16, 12, and 10, respectively. The CNN model demonstrated high consistency with the RF model in the classification results, but the accuracy of RF model is higher than that of CNN model on the whole. The overall accuracy of classification using RF model in four test areas registered 91.93%, 94.92%, 89.37% and 90.68%, respectively. This paper introduced crop phenological factors, effectively improving the extraction precision of shattered agricultural planting structure in the Loess Plateau of eastern Gansu Province. Its findings have important application value in crop monitoring, management, food security and other related fields.

Bangladeshi construction industry suffers a lot of safety and accidental issues than other developing countries in the world. Among many of these, accident of construction project goes far beyond and shape a horrific figure of death for every year. The aims of this study is that analysis and discussion of causes of accident at construction project in Bangladesh. A widespread statistical data collection and data analysis take place to identify the causes and design the questionnaire. The questionnaire-based survey was used to elicit the attitude of four stakeholders as workers, owners, consultants, and contractors towards passive causes of fatal accident at construction site. These study also identify 77 passive causes under 14 major groups and ranked them based on Relative Importance Index (RII). The top 5 major group of causes are (1) Management related, (2) Consultant related, (3) Technology related, (4) Labour related and (5) Contractor related. The top 5 passive causes are: (1) Unaware of safety-related issue, (2) Lack of personal protective equipment, (3) Lack of safety eliminating/ avoiding design, (4) Unfit equipment, (5) Lack of knowledge and training on equipment.

The coseismic surface three-dimensional deformation fields were significant for quantified the geometric and kinematic characteristics of earthquake rupture faults. Traditional geodetic techniques were constrained by their intrinsic limitations, such as InSAR, which could only extract far-field deformation fields due to its incoherence, and GNSS, which could only acquire deformations at discrete points. The recently developing optical pixel correlation technique based on high resolution remote sensing images could acquire the near-field coseismic horizontal deformation. In this study, the vertical displacement field of the 2022 Mw 6.6 Menyuan earthquake was obtained by differencing the pre- and post-earthquake DEMs from GaoFen-7 stereo satellite images. Further, the line-of-sight (LOS) and azimuthal (AZI) directions far-field deformations by InSAR, the horizontal near-field deformation by optical pixel correlation based on pre- and post-earthquake GF-2/7 images, and the vertical deformation by differencing the pre- and post-earthquake DEMs, were incorporated to comprehensively solve the complete three-dimensional deformation fields of the 2022 Mw 6.6 Menyuan earthquake. Firstly, the vertical displacement field by differencing DEMs indicated there were significant vertical displacements about 1 m at the bend region, which was induced by the local compressive stress. In addition, at the epicenter on the middle segment of ruptured Lenglongling fault (LLLF), the maximum lifting occurred on the southern sides of the main and secondary faults exceeded 2 m. Secondly, the three-dimensional deformation fields solved by multiple deformation data demonstrated that the near-field deformation field calculated by optical pixel correlation method could quantify the displacements distributed over the rupture fault zone, which were not available on the InSAR deformation maps. Finally, the surface 2D strain derived from the displacement maps calculated by optical pixel correlation revealed high strain concentration on the rupture fault zone. Our study focused on the complete surface three-dimensional deformation estimation from multiple far- and near-field deformation data, and provided a new perspective for a deeper understanding of the characteristics of coseismic surface deformation and the rupture pattern of the fault.

The object detection task is usually affected by complex backgrounds. In this paper, a new image object detection method is proposed, which can perform multi-feature selection on multi-scale feature maps. By this method, a bidirectional multi-scale feature fusion network is designed to fuse semantic features and shallow features to improve the detection effect of small objects in complex backgrounds. When the shallow features are transferred to the top layer, a bottom-up path is added to reduce the number of network layers experienced by the feature fusion network, reducing the loss of shallow features. In addition, a multi-feature selection module based on the attention mechanism is used to minimize the interference of useless information on subsequent classification and regression, allowing the network to adaptively focus on appropriate information for classification or regression to improve detection accuracy. Because the traditional five-parameter regression method has severe boundary problems when predicting objects with large aspect ratios, the proposed network treats angle prediction as a classification task. The experimental results on the DOTA dataset, the self-made DOTA-GF dataset and the HRSC 2016 dataset show that, compared with several popular object detection algorithms, the proposed method has certain advantages in detection accuracy.

In this contribution, we present a high-speed multiplex grating spectrometer based on a spectral coding approach that is founded on principles of compressive sensing. The spectrometer employs a single-pixel InGaAs detector to measure the signals encoded by an amplitude spatial light modulator (digital micromirror device, DMD). This approach leads to a speed advantage and multiplex sensitivity advantage atypical for standard dispersive systems. Exploiting the 18.2 kHz pattern rate of the DMD, we demonstrate 4.2 ms acquisition times for full spectra with a bandwidth of 450 nm (5250 cm-1 – 4300 cm-1; 1.9 µm – 2.33 µm). Due to the programmability of the DMD, spectral regions of interest can be chosen freely, thus reducing acquisition times further, down to the sub-millisecond regime. The adjustable resolving power of the system accessed by means of computer simulations is discussed, quantified for different measurement modes, and verified by comparison with a state-of-the-art Fourier-transform infrared spectrometer. We show measurements of characteristic polymer absorption bands in different operation regimes of the spectrometer. The theoretical multiplex advantage of 8 was experimentally verified by comparison of the noise behavior of the spectral coding approach and a standard line-scan approach.

Decision making as a result of system dynamics analysis requires, in practice, a straightforward and systematic modelling capability as well as a high-level of customisation and flexibility to adapt to situations and environments that may vary very much from each other. While in general terms a completely generic approach could be not as effective as ad-hoc solutions, the proper application of modern technology may facilitate agile strategies as a result of a smart combination of qualitative and quantitative aspects. In order to address such a complexity, we propose a knowledge-based approach that integrates the systematic computation of heterogeneous criteria with open semantics. The holistic understanding of the framework is described by a reference architecture and the proof-of-concept prototype developed can support high-level system analysis, as well as it suitable within a number of applications contexts - i.e. as a research/educational tool, communication framework, gamification and participatory modelling. Additionally, the knowledge-based philosophy, developed upon Semantic Web technology, increases the capability in terms of holistic knowledge building and re-use via interoperability. Last but not least, the framework is designed to constantly evolve in the next future, for instance by incorporating more advanced AI-powered features.

This study proposes a new algorithm termed rain cell identification and tracking (RCIT) to identify and track rain cells from high resolution weather radar data. Previous algorithms have limitations when tracking non-consequent rain cells owing to their use of maximum correlation coefficient methods and their lack of an alternative way to handle the variation stages of rain cells during their life cycles. To address these deficiencies, various methods are implemented in the new algorithm. These include the particle image velocimetry (PIV) method for motion estimation and the rain cell matching rule to obtain the stage changes of rain cells. High resolution (5-min and 1-km) radar reflectivity data from three rainy days over the German federal state North Rhine Westphalia (NRW) are used to evaluate the proposed algorithm. The performance of the new algorithm is compared with a radar reflectivity map and verified by two object-oriented methods: structure–amplitude–location (SAL) and geometric index. The verification results suggest that the performance of the new algorithm is good. Application of the RCIT algorithm to the selected cases shows that the inner structure of rainfall events in the experimental region present extreme value distributions, with most rainfall events having a short duration with less intensity. The new algorithm can effectively capture the stage changes of rain cells during their life cycles. The proposed algorithm can serve as the basis for further hydro-meteorological applications such as spatial and temporal analysis of rainfall events and short-term flood forecasting.

Traditional high pressure mechanical compressors account for over half of the car station’s cost, have insufficient reliability and are not feasible for a large-scale fuel cell market. An alternative technology, employing a two-stage, hybrid system based on electrochemical and metal hydride compression technologies, represents an excellent alternative to conventional compressors. The high-pressure stage, operating at 100-875 bar, is based on a metal hydride thermal system. A techno-economic analysis of the metal hydride system is presented and discussed. A model of the metal hydride system was developed, integrating a lumped parameter mass and energy balance model with an economic model. A novel metal hydride heat exchanger configuration is also presented, based on mini-channel heat transfer systems, allowing for effective high-pressure compression. Several metal hydrides were analyzed and screened, demonstrating that one selected material, namely (Ti_0.97Zr_0.03)_1.1Cr_1.6Mn_0.4, is likely the best candidate material to be employed for high-pressure compressors under the specific conditions. System efficiency and costs were assessed based on the properties of currently available materials at industrial levels. Results show that the system can reach pressures on the order of 875 bar with thermal power provided at approximately 150 °C. The system cost is comparable with the current mechanical compressors and can be reduced in several ways as discussed in the paper.

For Visually impaired People (VIPs), the ability to convert text to sound can mean a new level of independence or the simple joy of a good book. With significant advances in Optical Character Recognition (OCR) in recent years, a number of reading aids are appearing on the market. These reading aids convert images captured by a camera to text which can then be read aloud. However, all of these reading aids suffer from a key issue – the user must be able to visually target the text and capture an image of sufficient quality for the OCR algorithm to function – no small task for VIPs. In this work, a Sound-Emitting Document Image Quality Assessment metric (SEDIQA) is proposed which allows the user to hear the quality of the text image and automatically captures the best image for OCR accuracy. This work also includes testing of OCR performance against image degradations, to identify the most significant contributors to accuracy reduction. The proposed No-Reference Image Quality Assessor (NR-IQA) is validated alongside established NR-IQAs and this work includes insights into the performance of these NR-IQAs on document images.

Wafer-scale blue micro-light-emitting diode (micro-LED) arrays are fabricated with a pixel size of 12 μm, a pixel pitch of 15 μm, and a pixel density of 1692 pixels per inch, achieved by optimizing the properties of e-beam-deposited and sputter-deposited indium tin oxide (ITO). Although the sputter-deposited ITO (S-ITO) films exhibit a densely packed morphology and lower resistivity compared to those of the e-beam-deposited ITO (E-ITO) films, the forward voltage (VF) values of a micro-LED with the S-ITO films are higher than those with the E-ITO films. The VF values for a single pixel and for four pixels with E-ITO films are 2.82 V and 2.83 V, respectively, while the corresponding values for S-ITO films are 3.50 V and 3.52 V. This was attributed to ion bombardment damage and nitrogen vacancies in the p-GaN layer. Surprisingly, the VF variations of a single pixel and of four pixels with the optimized E-ITO spreading layer from five different regions are only 0.09 V and 0.10 V, respectively. This extremely uniform VF variation is suitable for realizing micro-LED displays to be used in AR and VR applications, circumventing the bottleneck in the development of long-lifespan and high-brightness organic LED devices for industrial mass production.

Sewage surveillance can be used as an effective complementary tool for detecting pathogens in local communities, providing insights into emerging threats and aiding in the monitoring of outbreaks. In this study using qPCR and whole genomic sewage surveillance, we detected Mpox virus along with other viruses, in municipal and hospital wastewaters in Belo Horizonte, Brazil over a 9 months period (from July 2022 till March 2023). MPXV DNA detection rates varied in our study, with 19.6% (11 out of 56 samples) detected through the hybrid capture method of whole genome sequencing and 20% (12 out of 60 samples) through qPCR. In hospital wastewaters, the detection rate was higher at 40% (12 out of 30 samples), compared to 13.3%(4 out of 30 samples) in municipal wastewaters. This variation could be attributed to the relatively low number of MPXV cases reported in the city, which ranged from 106 to 341 cases during the study period, and to dilution effects, given that each of the two wastewater treatment plants (WWTP) investigated serves approximately 1.1 million inhabitants. Additionally, nine other virus families were identified in both hospitals and municipal wastewaters including Adenoviridade, Astroviridae, Caliciviridae, Picornaviridade, Polyomaviridae, Coronaviridae (which includes SARS-CoV-2), Herspesviridae, Papillomaviridae and Flaviviridae (notably including Dengue). These findings underscore the potential of genomic sewage surveillance as a robust public health tool for monitoring a wide range of viruses circulating in both community and hospitals environments including MPXV.

This paper presents a robust method based on graph topology to find the topologically correct and consistent subset of inter-robot relative pose measurements for multi-robot map fusion. However, the absence of good prior gives a severe challenge to distinguish the inliers and outliers, and wrong loop closures can seriously corrupt the fused global map. Existing works mainly rely on the consistency of spatial dimension to select inter-robot measurements, which does not always hold. In this paper, we propose a fast inter-robot loop closure selection method that integrates the consistency and topology relationship of measurements, which both conform to the continuity characteristics of similar scenes and spatiotemporal consistency. The traditional high-dimensional consistency matrix is decomposed into the sub-matrix blocks corresponding to the overlapping trajectory area. Building on this logic, a clustering method involving topology correctness of inter-robot loop closures is introduced to split the entire measurement set into multiple clusters. We define the weight function to find the maximum cardinality subset with topologically correct and consistent, then convert the weight function to a maximum clique problem in the graph and solve it. We evaluate the performance of our method in a simulation and in a real-world experiment. Compared to state-of-the-art methods, the results show that our method can achieve competitive performance in accuracy while reducing computation time by 75%.

High levels of meat consumption are increasingly being criticised for ethical, environmental, and social reasons. Plant-based meat substitutes have been identified as healthy sources of protein that, in comparison to meat, offer a number of social, environmental and health benefits and may play a role in reducing meat consumption. However, there has been a lack of research on the role they can play in the policy agenda and how specific meat substitute attributes can influence consumers to replace partially replace meat in their diets.

Background: MR image classification in datasets collected from multiple sources is complicated by inconsistent and missing DICOM metadata. Therefore, we aimed to establish a method for the efficient automatic classification of MR brain sequences. Methods: Deep convolutional neural networks (DCNN) were trained as one-vs-all classifiers to differentiate between six classes, T1 weighted (w), contrast-enhanced T1w, T2w, T2w-FLAIR, ADC, and SWI. Each classifier yields a probability, allowing threshold-based and relative probability assignment while excluding images with low probability (label: unknown, open-set recognition problem). Data from three high-grade glioma (HGG) cohorts was assessed; C1 (320 patients, 20101 MRI images) was used for training, while C2 (197, 11333) and C3 (256, 3522) were for testing. Two raters manually checked images through an interactive labeling tool. Finally, MR-Class' added value was evaluated via radiomics models' performance for progression-free survival (PFS) prediction in C2, utilizing the concordance index (C-I). Results: Approximately 10% of annotation errors were observed in each cohort between the DICOM series descriptions and the derived labels. MR-Class accuracy was 96.7% [95%-Cl: 95.8, 97.3] for C2 and 94.4% [93.6, 96.1] for C3. 620 images were misclassified; Manual assessment of those frequently showed motion artifacts or alterations of anatomy by large tumors. Implementation of MR-Class increased on average the PFS model C-I by 14.6% compared to a model trained without MR-Class. Conclusions: We provide a DCNN-based method for sequence classification of brain MR images and demonstrate its usability in two independent HGG datasets.

The feedback collection and analysis has remained an important subject matter since long. The traditional techniques for student feedback analysis are based on questionnaire-based data collection and analysis. However, the student expresses their feedback opinions on online social media sites, which need to be analyzed. This study aims at the development of fuzzy-based sentiment analysis system for analyzing student feedback and satisfaction by assigning proper sentiment score to opinion words and polarity shifters present in the input reviews. Our technique computes the sentiment score of student feedback reviews and then applies fuzzy-logic module to analyze and quantify student’s satisfaction at the fine-grained level. The experimental results reveal that the proposed work has outperformed the baseline studies as well as state-of-the-art machine learning classifiers.

Spectral photon counting cone-beam CT imaging is challenged by individual pixel response behaviour, which leads to noisy projection images and subsequent image artefacts like rings. Existing methods to correct for this either use calibration measurements, like signal-to-thickness calibration (STC), or perform post-processing ring artefact correction of sinogram data or scan reconstructions, without taking the pixel response explicitly into account. Here we present a novel post-processing method (DAC-shifting), which explicitly measures the current pixel response using flat field images, and subsequently corrects the projection data. The DAC-shifting method was evaluated with a repeat series of spectral photon counting imaging (Medipix3) of a phantom with different density inserts, and in iodine K-edge imaging. The method was also compared against PMMA based STC. The DAC-shifting method was shown to be effective in correcting individual pixel response, and was robust against detector instability. On the contrary, STC correction showed varying results, which was almost as good as DAC-shifting when data was acquired just after STC calibration, but worse with larger time differences. In K-edge imaging, DAC-shifting provides a sharper attenuation peak, and more uniform CT values, which is expected to benefit iodine concentration quantification.

There is a need for cost-effective and non-invasive methods of monitoring feeding behavior in livestock operations, considering the significant impact of feed costs on economic efficiency and assisting in detecting health issues of group-fed animals. This paper proposes using deep learning-based computer vision techniques to detect pen-fed beef cattle feeding behavior using Mask Region-based Convolutional Neural Network (RCNN). A deep learning model was pre-trained on the Common Objects in Context (COCO) dataset to generate cattle instance segmentation. Manually defined feed bunk polygons are compared with these segmentation masks to derive feeding time for each bunk. A full day’s worth of video data and the corresponding physical sensor data are collected for the experiment. By benchmarking the computer vision detected data with physical ground truth over random time segments from morning to evening (thus various lighting conditions), the optimal thresholds for Mask RCNN are determined to be 0.7 for bounding boxes and 0.1 for masks. Using these parameters. The reports suggest that the computer vision system achieved a precision of 87.2% and a recall of 89.1%, signifying precise detection of feeding events. Our study, to the best of our knowledge, was one of the first investigations of instance segmentation on feeding time sense, which combines deep learning methods with traditional computer vision logistics, reporting on feeding time data collection and processing, camera testing and adjustment, and performance evaluation. Future research directions include computer vision applied in feed grading and animal re-identification for individual production analysis.

How a species is represented by marketers of animal-based products both reflects and shapes how consumers think about that animal. By examining the explicit statements, and implicit messages encoded in the imagery on supermarket egg boxes, this paper explores how hens are represented by whole egg retailers. Content analysis reveals two prominent messages purveyed through eggbox graphics, namely those pertaining to hen welfare and human health. The later disenfranchises hens from their products by focusing on the nutritional value of eggs, whereas the former reflects a public concern for the welfare of egg-laying hens. Although claims of improvements in welfare practices are undoubtedly exploited as marketing tools, they serve to raise awareness and drive competitors to adopt similar practices. Welfare claims are a direct response to public concerns about the plight of hens, and may positively influence industry welfare standards. However, idyllic depictions displayed on eggboxes also lull consumers into the belief that those eggs are an ethically sound food choice, regardless of the actual standard of living experienced by the hens.

The purpose of the article is to determine the peculiarities of using of teaching elements of research-based training at the Institute of Human Sciences of Borys Grinchenko Kyiv University. Based on the focus group methodology, the authors identify the key methodological characteristics of research-based training, which have been put into basis of analysis of educational programs for the purpose of determining the application of tasks that contribute to the development of research skills of students. The study used a method of focus group. Its purpose was to obtain the necessary information from the participants to describe the methodological basis and justification of methods, forms, indicators, etc. of research-based training system among people who are competent, have experience in this field. After that, the method of "theoretical sampling" was used, which enabled to formulate generalized characteristics according to the results of focus groups. The practical value of the study is determination of the methodological characteristics of research-based training which is the basis for the application of tasks by university teachers that promote the development of research competence of students. The research is one of the first attempts to determine the methodological characteristics of research-based training in Ukraine.

In an increasingly complex world there is a real, urgent need for methodologies to enable engineers to model complex sociotechnical systems, as these now seem to describe the majority of systems in use today. This is of course exacerbated by the increasing involvement and augmentation with “black box” AI contributions. Hollnagel produced a methodology, (FRAM) which did allow the analyst in-sights into these systems’ behaviour, but the model-based system engineering applications demand numbers and a quantitative ap-proach. In the last 10 years, this original approach developed to model systems as sets of interactive, interdependent “functions”, (abstracted from agent or component details), has been further developed to the point where it can take the basic data and structures from the current component focussed system engineering “models”, and can pull it all together into dynamic models, (as opposed to static, fixed System Theoretic Process Accimaps), from which analysts can discern how they really work in practice, and predict the emergent behaviours characteristic of complex systems. This paper describes how the FRAM methodology has now been extended to provide these extra, essential attributes. It also describes its implementation using an open-source software, freely available for use and verification on the GitHub site.

Various renewable energy sources such as wind power and photovoltaic (PV) have been increasingly integrated into the power system through power electronic converters in recent years. However, power electronic converter-driven stability issues under specific circumstances, for instance, modal resonances might deteriorate the dynamic performance of the power systems or even threaten the overall stability. In this paper, the integration impact of a hybrid renewable energy source (HRES) system on modal interaction and converter-driven stability is investigated in an IEEE 16-machine 68-bus power system. Firstly, an HRES system is introduced, which consists of full converter-based wind power generation (FCWG) and full converter-based photovoltaic generation (FCPV). The equivalent dynamic models of FCWG and FCPV are then established, followed by the linearized state-space modeling. On this basis, converter-driven stability analyses are performed to reveal the modal resonance mechanisms of the interconnected power systems and the modal interaction phenomenon. Additionally, time-domain simulations are conducted to verify effectiveness of dynamic models and support the converter-driven stability analysis results. To avoid detrimental modal resonances, an optimization strategy is further proposed by retuning the controller parameters of the HRES system. The overall results demonstrate the modal interaction effect between external AC power system and the HRES system and its various impacts on converter-driven stability.

Gender has been a rarely addressed aspect in digital game-based learning (DGBL). Likewise, mixed results have been presented regarding the effects according to gender and the conditions that generate these effects. The present work studies the effects of a drill-and-practice mathe-matical game on primary students. The study focused on an analysis by gender, measuring learning performance, and motivation in the practice activity. Also, two instructional mechanics were considered regarding the question answering, a multiple-try (MTF) and a single-try (STF) condition, to search for possible differences. A total of 81 students from 4 courses and 2 schools participated in the experiment. The study's main findings were that: (a) the girls outperformed boys in terms of students' learning gains, (b) girls presented lower levels of competence and autonomy than boys, (c) in the MTF, girls presented lower levels of autonomy but no differences in competence contrasted to boys, (d) in the STF, girls presented lower levels of competence but no differences in autonomy against boys, and (e) no differences existed in interest, effort and value among gender overall o per instructional condition. This study enhances the knowledge of gender differences under diverse instructional settings, in particular providing insights into possible differences by gender when varying the number of attempts provided to students.

Natural image matting is an essential technique for image processing that enables various applications, such as image synthesis, video editing, and target tracking. However, the existing image matting methods may fail to produce satisfactory results when computing resources are limited. Sampling-based methods can reduce the dimensionality of the decision space and therefore reduce computational resources by employing different sampling strategies. While these approaches reduce computational consumption, they may miss an optimal pixel pair when the number of available high-quality pixel pairs is limited. To address this shortcoming, we propose a novel multi-criterion sampling strategy that avoids missing high-quality pixel pairs by incorporating multi-range pixel pair sampling and high-quality samples selection method. This strategy is employed to develop a multi-criterion matting algorithm via Gaussian process, which searches for the optimal pixel pair by using the Gaussian process fitting model instead of solving the original pixel pair objective function. Experimental results demonstrate that our proposed algorithm outperforms other methods even with 1% computing resources, and achieves alpha matte results comparable to those yielded by the state-of-the-art optimization algorithms.

The processing of cloud free geo-referenced imagery is one of the preliminary processing step of any land application. This letter describe the methodology developed to obtain a seamless cloud free composite of Africa for 2016 using Sentinel-2A data at 10 meters resolution freely available from the European Space Agency. The method is based on an hybrid method resulting from the merging of the two most robust time series methods namely the "darkest pixel" and the "maximum NDVI" previously developed with AVHRR time series.

Paper-based microfluidic analysis devices (μPADs) have attracted attention as a cost-effective platform for point-of-care testing (POCT), food safety, and environmental monitoring. Recently, three-dimensional (3D)-μPADs have been developed to improve the performance of μPADs. For accurate diagnosis of diseases, however, 3D-μPADs need to be developed to simultaneously detect multiple biomarkers. Here, we report a 3D-μPADs platform for the detection of multiple biomarkers that can be analyzed and diagnosed with a smartphone. The 3D-μPADs were fabricated using a 3D digital light processing printer and consisted of a sample reservoir (300 µL) connected to 24 detection zones (of 4 mm in diameter) through 8 microchannels (of 2 mm in width). With the smartphone application, eight different biomarkers related to various diseases were detectable in concentrations ranging from normal to abnormal conditions: glucose (0–20 mmol/L), cholesterol (0–10 mmol/L), albumin (0–7 g/dL), alkaline phosphatase (0–800 U/L), creatinine (0–500 µmol/L), aspartate aminotransferase (0–800 U/L), alanine aminotransferase (0–1000 U/L), and urea nitrogen (0–7.2 mmol/L). These results suggest that 3D-µPADs can be used as a POCT platform for simultaneous detection of multiple biomarkers.

In the software development process, the decision-maker (DM) has a range of problems inherent to its function. Wrong choices during software planning can bring great risk to the project. Therefore, the planning of software releases to be delivered to the customer should be well done. This is not an easy task because releases are made up of many requirements that contain complex variables that must be considered, such as precedence, cost, requirement stability, among other features that make the requirements-selection process challenging. To make this process less exhaustive, DM can use tools that facilitate this work. In software engineering, we can find fields of research specialists in this context, such as Search-Based Software Engineering (SBSE). The SBSE makes use of advanced metaheuristics to search for optimal solutions or the closest to it. In this work, we try to use another field of research to solve this same problem type, the Verbal Decision Analysis (VDA). To do this, we elaborate a workflow that will use the same source data, execute two solutions using the two search fields (SBSE and VDA) and compare the results. In the end, we evaluated and commented on the results.

In this paper, we delve into the realm of aspect-level sentiment analysis, a sophisticated analytical task focused on pinpointing and interpreting the sentiment directed towards specific elements within a sentence. Traditional methods in this domain, primarily based on neural networks, have often overlooked the critical role of syntactic structures in sentences. To bridge this gap, we have developed the Syntax-Enhanced Sentiment Graph Network (SentiSyn). This pioneering model represents a significant step forward in aspect-level sentiment analysis, bringing to the forefront the utilization of word dependency relationships to enrich sentiment analysis. SentiSyn stands out by its innovative use of a dependency graph, a tool that meticulously maps out the intricate web of syntactic relationships surrounding a target aspect in a sentence. This approach allows SentiSyn to effectively capture and channel sentiment-related characteristics that are deeply rooted in the syntactic context of the aspect target. By doing so, SentiSyn unlocks a deeper understanding of sentiment dynamics in textual content, enabling a more nuanced and accurate sentiment analysis. Our comprehensive experimental evaluation of SentiSyn showcases its remarkable capabilities. When combined with advanced embedding techniques like GloVe and BERT, SentiSyn demonstrates a superior performance edge over several existing sentiment analysis methods. This performance leap is not just incremental; it represents a significant enhancement in the field of sentiment analysis, underscoring the importance of syntactic context in understanding sentiments. Furthermore, our analysis delves into how SentiSyn effectively leverages these embeddings to gain a more profound and contextually rich insight into sentiment dynamics. The results from our tests indicate that SentiSyn, with its unique approach to integrating syntactic structures and advanced embeddings, sets a new benchmark in aspect-level sentiment analysis, offering both enhanced accuracy and deeper sentiment understanding.

Background: Serious games are conceptualized as a broad topic and overlap segments of more modern forms of education: e-learning, edutainment, game-based learning, and digital game-based learning. Serious Games aligns with digitalization and the modern era and creates novel opportunities for learning and assessment in medical education. Escape rooms, a type of serious games, merge mental and physical aspects to reinforce critical skills useful in daily life. It challenges logic and reasoning and demands careful analysis of situations to correlate and solve different stages of the escape room under pressurized, timed conditions. Furthermore, it serves as an adequate environment to build problem-solving skills, communication skills, and leadership skills through the collaboration of people to achieve a common goal. The aim of this study was to investigate the applications of escape rooms in Medical Education. Method: This study investigated the applications of escape rooms in medical education. Serious games are expanding in education and have attained great relevance due to their intriguing and intrinsically motivating attributes. Within serious games, we focused on escape rooms in which participants are locked in a room, faced with puzzles that must be solved to ‘escape the room’. Compiling the data from the first 100 hits of medical application of escape rooms, we found 72 cases and categorized them by year, specialty, participants structure, simulation experience, and design. Results: We reported on escape rooms in medical education by the year in which they were reported, the medical specialty, the participant structure, grouped or individual, the experience design; real, hybrid, or digital, and the modality of the delivery. 72% of the escape rooms focused on four main areas: nursing education (25.0%), emergency medicine (22.2%), pharmacy (12.5%), and interprofessional education (12.5%). Most of the escape rooms had a group-based physical design and little attention was given to provide a detailed description of the design considerations, such as the pathway type (linear, semi-linear, open). Conclusion: Escape rooms are applied in a wide range of medical education areas. In Medical Education, group-based on-site escape rooms with a focus on nursing, emergency medicine, pharmacy and interprofessional education dominates the implementation landscape. To further advance the field, stronger emphasis on making explicit the design considerations will advance the research and inform implementations.

Lactobacillus plantarum has been observed to play a crucial role in shaping the sensory properties of chili sauce. However, the specific taste-active metabolites responsible for the desirable flavor profile of chili sauce remain inadequately characterized. This study employed a combination of metabolomics and web-based computational tools analysis to investigate the dynamic changes in taste-active metabolites during chili sauce fermentation. Initially, metabolites were rapidly annotated using a feature-based molecular network, leading to the tentative annotation of 206 metabolites, of which a significant proportion had not been previously reported. Subsequently, the VirtualTaste tool identified dihydrosphingosine, lactic acid, isoleucine, phytosphingosine, and gluconic acid as potential taste markers for quality control. Finally, pathway enrichment analysis revealed that these components were primarily associated with amino acid tRNA, phenylalanine, tyrosine, and tryptophan biosynthesis, as well as sphingolipid metabolism. This study provides valuable insights into the mechanisms underlying the formation of the distinctive flavor of chili sauce.

Segmentation of a region of interest is an important pre-processing step for many colour image analysis techniques. Similarly segmentation of plant in digital images is an important preprocessing step in phenotying plants by image analysis. In this paper we present an analytical study to statistically determine the suitability of colour space representation of an image to best detect plant pixels and separate them from background pixels. Our hypothesis is that the colour space representation in which the separation of the distributions representing plant pixels and background pixels is maximized would be the best for detection of plant pixels. The two classes of pixels are modelled as a Gaussian mixture model (GMM). In our GM modelling we don't make any prior assumption about the number of Gaussians in the model. Rather a constant bandwidth mean-shift filter is used to cluster the data and the number of clusters and hence the number of Gaussians is automatically determined. Here we have analysed following representative colour spaces like $RGB$, $rgb$, $HSV$, $Ycbcr$ and $CIE-Lab$. This is because these colour spaces represent several other similar colour spaces and also an exhaustive study of all the colour space will be too voluminous. We also analyse the colour space feature from the two-class variance ratio perspective and compare the results of our hypothesis with this metric. The dataset for this empirical study consist of 378 digital images of plants and their manual segmentation. Dataset consist of various species of plants (arabidopsi, tobacco, wheat, rye grass etc.) imaged under different lighting conditions, indoor and outdoor, controlled and uncontrolled background. In results we obtain better segmentation of the plants in $HSV$ colour space, which is supported by its Earth mover distance (EMD) on the GMM distribution of plant and background pixels.

Background: Metabolic flux analysis (MFA) is an indispensable tool in metabolic engineering. The simplest variant of MFA relies on an overdetermined stoichiometric model of the cell’s metabolism under the pseudo-steady state assumption, to evaluate the intracellular flux distribution. Despite its long history, the issue of model error in the overdetermined MFA, particularly misspecifications of the stoichiometric matrix, has not received much attention. Method: We evaluated the performance of statistical tests from linear least square regressions, namely Ramsey RESET test, F-test and Lagrange multiplier test, in detecting model misspecifications in the overdetermined MFA, particularly missing reactions. We further proposed an iterative procedure using the F-test to correct such an issue. Result: Using Chinese hamster ovary and random metabolic networks, we demonstrated that: (1) a statistically significant regression does not guarantee high accuracy of the flux estimates, (2) the removal of a reaction with a low flux magnitude can cause disproportionately large biases in the flux estimates, (3) the F-test could efficiently detect missing reactions, and (4) the proposed iterative procedure could robustly resolve the omission of reactions. Conclusion: Our work demonstrated that statistical analysis and tests could be used to systematically assess, detect and resolve model misspecifications in the overdetermined MFA.

In this paper, we present a groundbreaking methodology in the realm of aspect-level sentiment analysis, which capitalizes on the advanced capabilities of graph-based neural networks. Our approach, distinguished as the Aspect Correlation Graph Network (ACGN), represents a significant departure from conventional models. These traditional models often analyze aspects in isolation, failing to capture the intricate web of sentiment relationships that may exist within a single sentence. ACGN, however, is designed to address this gap by employing a sophisticated bidirectional attention mechanism, integrated with positional encoding. This unique combination not only enhances the model's ability to focus on relevant parts of the sentence but also aids in constructing detailed, aspect-focused representations. These representations are particularly crucial for understanding the nuanced interplay of sentiments associated with different aspects. Central to our model's architecture is the incorporation of a graph convolutional network. This network serves as a pivotal component in mapping and analyzing the complex network of sentiment correlations that can exist among various aspects within sentences. Through this integration, ACGN is able to unearth and interpret the subtle and often overlooked sentiment dynamics that traditional models might miss. Our comprehensive evaluations of the Aspect Correlation Graph Network, conducted using the SemEval 2014 datasets, have yielded promising results. These findings demonstrate a clear and significant advancement over the capabilities of existing models. Particularly, the results underscore the critical importance and utility of recognizing and understanding the connections between sentiments of different aspects in text analysis. This insight opens new avenues in the field of sentiment analysis, suggesting a broader application potential of ACGN in various contexts where understanding nuanced sentiment relationships is key. Overall, our study not only introduces a novel approach in aspect-level sentiment analysis but also sets a new standard for future research in this area. By highlighting the integral role of inter-aspect sentiment connections, ACGN paves the way for more sophisticated and accurate sentiment analysis tools, capable of handling the complexities of natural language with greater finesse and precision.

In this paper, we introduce the application of Type-I fuzzy inference systems (FIS) as an alternative to improve the prioritization in the FMECA analysis applied in cyber-power grids. Classical FMECA assesses the risk level through the Risk Priority Number (RPN). The multiplication between three integer numbers computes this, called risk factors, representing the severity, occurrence, and detectability of each failure mode and are defined by a team of experts. The RPN does not consider any relative importance between the risk factors and may not necessarily represent the real risk perception of the FMECA team members, usually expressed by natural language; this is the main FMECA shortcoming criticized in the literature. Our approach considers fuzzy variables defined by FMECA experts to represent the uncertainty associated with the human language and a rule base consisting of 125 fuzzy rules to represent the risk perception of the experts. To test our approach, we select a cyber-power grid previously analyzed by the authors using the classical FMECA. The results reveal our proposed fuzzy approach as promissory to represent the uncertainty associated with expert knowledge and to perform an accurate prioritization of failure modes in the context of electrical power systems.

Badminton tactics refer to the techniques and strategies employed by players to win a match. Analyzing these tactics can help players improve their performance and outsmart their opponents. To study the tactics of top players, we use a gaming tree to analyze matches between two of the most powerful badminton players in history: Lin and Lee. By employing the Nash Equilibrium, we can discover the most beneficial strategies for both players, which reflect their most powerful techniques. Additionally, with the help of this gaming tree, we can precisely predict how players will implement their tactics. Empirical experimental results demonstrate that our proposed method not only evaluates and identifies each player's weaknesses and strengths but also has powerful capabilities to predict their tactics.

Background: Alzheimer's disease causes great changes, with the prefrontal cortex be-ing frequently damaged zones, affecting physical and cognitive behavioural and compromising autonomy and quality of life. Objective: To evaluate the effectiveness of feedback-based tech-nology on the improvement of physical-cognitive capacities in Alzheimer. Methods: Forty pa-tients with Alzheimer were recruited from the Santa Elena Alzheimer's Association, Seville, of which 15 ended up being pilot participants, divided randomly into controls (8) and experi-mental (7). They were evaluated with: Cognitive Mini-Examination Scale, Oddball Test and At-tention Network, Berg Scale, Tinetti, Timed up and Go and Geriatric Deterioration Scale. The experimental group was treated with feedback-based technology under the supervision of physiotherapists, during 16 thirty-minute sessions, twice a week. The control group performed their usual care. Result: A significant improvement was observed in the experimental group (Cognitive Mini-Examination and Tinetti). In the group of control, a worsening of physical and motor skills was evidenced because of the degenerative disease. Discussion/Conclusion: This treatment could be a strategy to keep the patient in early stages for longer, and improve quali-ty of life delaying dependency. It creates intergenerational bonds and use at home to promote treatment adherence.

Background: It is clear that breastfeeding is the gold standard of infant feeding because of the many advantages it offers to both the child and the mother. Objective: to identity the main reasons for cessation breastfeeding declares by the mother themselves during the first year. Design: A prospective cohort study was conducted, recruiting 969 newborns in a third level hospital in Spain. The main maternal variables studied were: maternal age, parity, educational level, work occupation, smoking habit, gestational age, birth, weigh, feeding type, and duration of breastfeeding. All the participants were followed for a year to determinate the duration of breastfeeding and to know the reason of the abandonment. Results: At 6 months, the percentage of maternal lactation was cut in half and only 24.6% of these mothers maintain. Mainly 15.80% of the mothers decide to give up the exclusive maternal lactation of their own free desire, and 15.41% because they suspect hypogalactia. The work cause is the third reason of abandonment in both cases. Conclusions: Our results show the need to improve the health policies of promotion, protection and support the initiation of breastfeeding. In particular, our results show the importance of the work factor with particular emphasis on improving conciliation measures.

Vehicle driveability is one of the important vehicle attributes in range-extender electric vehicles due to the electric motor torque characteristics at low-speed events. The process of validating and rectifying vehicle driveability attributes is typically utilised by a physical vehicle prototype that can be expensive and required several design iterations. In this paper, a model-based energy method to assess vehicle driveability is presented based on a high-fidelity 49 degree-of-freedom powertrain and vehicle systems. Multibody dynamics components were built according to their true centre of gravity relative to the vehicle datum for providing an accurate system interaction. The work covered a frequency at less than 20 Hz. The results that consisted of the component frequency domination are structured and examined to identify the low-frequency sensitivity based on different operating parameters such as a road surface coefficient. An energy path technique was also implemented on the dominant component by decoupling its compliances to study the effect on the vehicle driveability and low-frequency response. The outcomes of the research provided a good understanding of the interaction across the sub-systems levels. The powertrain rubber mounts were the dominant components that controlled the low-frequency contents (< 15.33 Hz) and can change the vehicle driveability quality.

The engagement of the arresting hook with the arresting cable is a critical maneuver that is essential to the safe operation of aircraft landing on aircraft carrier. A comprehensive understanding of the engagement process dynamics is necessary to optimize landing performance and ensure the safety and efficiency of carrier operations. In this paper, an efficient and accurate simulation and analysis method is presented for studying the arresting hook engaging arresting cable process. The Finite Element Method and Multibody Dynamics (FEM-MBD) approach is employed. By establishing a rigid-flexible coupling model encompassing the aircraft frame, arresting hook, carrier deck and arresting gear system, the dynamic model for the engagement process is obtained. The model incorporates multiple coordinate systems to effectively capture the relative motion between the rigid and flexible components, enabling a thorough understanding of the dynamics characteristics. The analysis conducted in this paper takes into account various factors, including the material properties of the components, the characteristics of the arresting gear system, and the state of the aircraft during the engagement process. The analysis method is verified by comparing the simulation results with experiments of arresting hook rebound obtained from reference. Finally, simulations are performed to analyze the engagement process under different touchdown points and rolling angle of aircraft. The simulation results provide valuable insights into the distribution of stresses during the arresting hook and cable engagement, the center of gravity variations, as well as the response of the tire touch and rollover cable under specific scenarios. The proposed rigid-flexible coupling arresting dynamics model in this paper enables effective analysis of the dynamic behavior during arresting hook engaging arresting cable. The results obtained from this analysis offer valuable insights into the performance of the engagement process, which can be used to improve the design of carrier-based aircraft and techniques for carrier landing.

In the development of the safety-critical systems, it is important to perform Failure Modes and Effects Analysis (FMEA) process to identify potential failures. However, traditional FMEA activities tend to be considered difficult and time-consuming tasks. To compensate for the difficulty of the FMEA task, various types of tools are used to increase the quality and the effectiveness of the FMEA reports. This paper explains an Automatic FMEA tool which integrates the Model-based Design (MBD), FMEA, and Simulated Fault Injection techniques in a single environment. The Automatic FMEA tool has the following advantages compared to the existing FMEA analysis tool. First, the Automatic FMEA tool automatically generates FMEA reports compared to the traditional spreadsheet-based FMEA tools. Second, the Automatic FMEA tool analyzes the causality between the failure modes and the failure effects by performing model-based fault injection simulation. In order to demonstrate the applicability of the Automatic FMEA, we used the electronic fuel injection system (EFI) Simulink model. The results of the Automatic FMEA were compared to that of the legacy FMEA.

Plantation-based farmer cooperatives play an important role in promoting agricultural modernization and increasing farmer income, and the management of business risks of such cooperatives not only influences the development of the cooperatives themselves, but also concerns the interests of farmers. Based on the survey data of 226 sample cooperatives from Guizhou province, this article uses factor analysis to measure business risks of plantation-based farmer cooperatives and carries out empirical tests on influence factors of business risks through structural equation model. According to the research results, the ordering of business risks of plantation-based farmer cooperative based on their significance is as follows: market risk > policy risk > natural risk > technical risk > management risk. Influence factors including policy environment, economic environment, social service environment, technical environment, resource endowment of the cooperative and manager characteristics have significant positive influence on the cooperative, with economic environment and manager characteristics generating relatively stronger influence and social service environment generating relatively weaker influence. Through this research, several policy implications on driving high-quality development of plantation-based farmer cooperative are made, including specifying supportive policies of plantation-based farmer cooperative, improving economic environment of agricultural market, upgrading agricultural infrastructure, depending reform of land transfer system, and enhancing internal management of the cooperative.

Orbit and clock products are used in real-time GNSS precise point positioning without knowing their quality. This study develops a new approach to detect orbit and clock errors through comparing geometry-free and geometry-based wide-lane ambiguities in PPP model. The reparameterization and estimation procedures of the geometry-free and geometry-based ambiguities are described in detail. The effects of orbit and clock errors on ambiguities are given in analytical expressions. The numerical similarity and differences of geometry-free and geometry-based wide-lane ambiguities are analyzed using different orbit and clock products. Furthermore, two types of typical errors in orbit and clock are simulated and their effects on wide-lane ambiguities are numerically produced and analyzed. The contribution discloses that the geometry-free and geometry-based wide-lane ambiguities are equivalent in terms of their formal errors. Although they are very close in terms of their estimates when the used orbit and clock for geometry-based ambiguities are precise enough, they are not the same, in particular, in the case that the used orbit and clock, as a combination, contain significant errors. It is discovered that the discrepancies of geometry-free and geometry-based wide-lane ambiguities are coincided with the actual time-variant errors in the used orbit and clock at the line-of-sight direction. This provides a quality index for real-time users to detect the errors in real-time orbit and clock products, which potentially improves the accuracy of positioning.

In today's business world, many companies have realized that consumer feedback evaluations play a crucial part in shaping the company's future activities. The hotel and tourism industries are well-known examples where the user feedback has become crucial. Even though most accommodations want their clients to express their opinions about their services, these reviews are nevertheless carefully reviewed by them. This manual approach is time demanding and error prone, the level of expertise of the individual analyzing the evaluations determines the quality of the manual analysis. This research focuses on the development of a tool-based support system that analyzes text-based customer evaluations and then highlights the maintenance issues identified in the reviews, removing the need to manually evaluate and analyze the customer reviews. The process includes data preparation, sentiment analysis, and a fully trained deep learning model to extract critical insights. Using sentiment analysis techniques after data preprocessing and trained deep learning model, an accuracy of 96\% was achieved, and this paper discusses how this technology helps to meet the needs of accommodation management by completing activities efficiently. This might save management time and helps to save money without losing clients, allowing them to enhance their additional sales.

Experimental methodologies for fatigue lifetime prediction are time-intensive and susceptible to environmental variables. While the cohesive zone model is popular for predicting adhesive fatigue lifetime, entropy-based methods have also shown promise. This study aims to provide an understanding of the durability characteristics of CFRP adhesive joints by incorporating an entropy damage model within the context of the finite element method (FEM), examined the effects of different adhesive layer thicknesses on SLS models. As the adhesive layer increased in thickness, DAMAGE VARIABLES initially rose and then declined, peaking at 0.3mm. This finding provides crucial understanding into the stress behavior at the resin-CFRP interface and the resin's fatigue mechanisms.

Background: There is poor evidence about sensitivity to chemotherapy according to microsatellite instability (MSI)/mismatch repair (MMR) status in endometrial cancer (EC). Methodology: The RAME study is a retrospective analysis aiming to assess response to chemotherapy in MSI-high (h) /deficient (d) MMR and MSI- low (l)/proficient (p)MMR EC patients. Primary endpoints were recurrence-free survival (RFS) for patients with localized disease and progression-free survival (PFS) and overall survival (OS) in patients with advanced/recurrent disease. Results: 312 patients treated between 2010-2022 in 4 high volume Multicenter Italian Trial in Ovarian cancer and gynecological malignancies (MITO) centers were selected. 239 patients had endometrioid EC (76.6%), 151 had FIGO stage I at diagnosis (48.9%) and 71 were MSI-h/dMMR (22.8%). Median age was 65 (range 31–91) years. Among patients with localized disease, median RFS was 100.0 months (95%CI 59.4–140.7) for MSI-l/pMMR and 120.9 months (60.0-181.8) for MSI-h/dMMR (p=0.39). Seventy-seven patients received first-line chemotherapy for advanced/recurrent disease. Patients with MSI-h/dMMR ECs had a significantly worse OS (p=0.039). In patients receiving platinum-based chemotherapy, no statistically significant difference in PFS (p=0.21) and OS (p=0.057) were detected, although PFS and OS were numerically longer in the MSI-l/pMMR population. Conclusion: Patients with metastatic MSI-h/dMMR EC receiving first-line chemotherapy had a significantly worse OS.

Information overload is the biggest challenges nowadays for any website, especially the e-commerce website. However, this challenge arises for the fast growth of information on the web (WWW) with easy access to the internet. Collaborative filtering based recommender system is the most useful application to solve the information overload problem by filtering relevant information for the users according to their interests. But, the existing system faces some significant limitations like as data sparsity, low accuracy, cold-start and malicious attacks. To alleviate the mentioned issues, the relationship of trust incorporates in the system where it can be between the users or items, and such system is known as the trust-based recommender system (TBRS). From the user perspective, the motive of the TBRS is to utilize the reliability between the users to generate more accurate and trusted recommendations. However, the aim of the paper is to present a comparative analysis of different trust metrics in the context of the type of trust definition of TBRS. Also, the study accomplishes on twenty-four trust metrics in terms of the methodology, trust properties &amp; measurement, validation approaches, and the experimented dataset.

The Gulf of Mannar, a UNESCO World Biosphere Reserve, faces severe overfishing and habitat degradation threats. In this study, we investigate the pivotal role of ecosystem services in sustaining local livelihoods and overall well-being. By conducting a comprehensive survey of 480 respondents across two districts, we gathered extensive data on demographics, livelihoods, fishing practices, reliance on ecosystem services, and community management participation. The analysis reveals a critical dependence on the Gulf's resources (income, food security, traditions) with gender disparities (men fish, women in pre/post-harvest), but fishing is only allowed in the 10km buffer zone (not the core zone). The findings emphasize the promise of community-based strategies, such as Marine Protected Areas and reviving co-management committees, for achieving sustainable fisheries management. However, we also identify gaps, including the need for more nuanced well-being indicators and improved models for community management participation. To address these challenges, we advocate for sustainable fishing practices, tackling social inequities, especially gender disparities in resource access and decision-making, and investing in healthcare, education, and social safety nets for fishing communities. Promoting alternative livelihoods can alleviate pressure on fish stocks, and empowering local communities through capacity building and community-based management initiatives is crucial for ensuring the long-term sustainability of both the Gulf of Mannar ecosystem and the well-being of its dependent communities. This multifaceted approach holds significant promise for balancing ecological health with human prosperity.

In today's business world, many companies have realized that consumer feedback evaluations play a crucial part in shaping the company's future activities. The hotel and tourism industries are well-known examples where the user feedback has become crucial. Even though most accommodations want their clients to express their opinions about their services, these reviews are nevertheless carefully reviewed by them. This manual approach is time demanding and error prone, the level of expertise of the individual analyzing the evaluations determines the quality of the manual analysis. This research focuses on the development of a tool-based support system that analyzes text-based customer evaluations and then highlights the maintenance issues identified in the reviews, removing the need to manually evaluate and analyze the customer reviews. The process includes data preparation, sentiment analysis, and a fully trained deep learning model to extract critical insights. Using sentiment analysis techniques after data preprocessing and trained deep learning model, an accuracy of 96% was achieved, and this paper discusses how this technology helps to meet the needs of accommodation management by completing activities efficiently. This might save management time and helps to save money without losing clients, allowing them to enhance their additional sales.

Background: Three drug-combinations, ipilimumab-nivolumab (Ipi-Nivo), pembrolizumab-axitinib (Pembro-Axi) and avelumab-axitinib (Ave-Axi), have received regulatory approvals in USA and Europe for the treatment of metastatic renal cell carcinoma with clear cell component (mRCC). However, no head-to-head comparison data are available to identify the best option. Therefore, we aimed to compare these new treatments in the first-line setting. Methods: We conducted a systematic search in Pubmed, the Cochrane library and clinicaltri-al.gov website from January 2015 to October 2019, for any randomized controlled trials of treatment-naïve patients with mRCC. The process was performed according to PRISMA guide-lines. We performed a Bayesian network meta-analysis with two different approaches. The out-comes for analysis were overall survival, progression-free survival, and objective response rate. Results: Our search identified 3 published phase 3 randomized clinical trials (2835 patients). In the contrast-based model, Ave-Axi (SUCRA: 83%) and Pembro-Axi (SUCRA: 80%) exhibited the best ranking probabilities for PFS. For OS, Pembro-Axi (SUCRA: 96%) was the most pref-erable option against Ave-Axi and Ipi-Nivo. Objective response rate analysis showed Ave-Axi as the best (SUCRA= 94%) and Pembro-Axi as second best option. In the parametric models, risk of progression was comparable for Ave-Axi and Ipi-Nivo, whereas Pembro-Axi exhibited a lower risk during the first 6 months of treatment and a higher risk afterward. Furthermore, Pembro-Axi exhibited a net advantage in terms of OS over the two other regimens, while Ave-Axi was the least preferable option. Conclusions: Overall evidences suggested pembrolizumab plus axitinib may be the best option.

This paper focuses on the crucial role that remote sensing plays in divining land features. Data that is collected distantly provides information in spectral, spatial, temporal and radiometric domains, with each domain having the specific resolution to information collected. Diverse sectors such as hydrology, geology, agriculture, land cover mapping, forestry, urban development and planning, oceanography and others are known to use and rely on information that is gathered remotely from different sensors. In the present study, IRS LISS IV Multi-spectral data is used for land cover mapping. It is known, however, that the task of classifying high-resolution imagery of land cover through manual digitizing consumes time and is way too costly. Therefore, this paper proposes accomplishing classifications by way of enforcing algorithms in computers. These classifications fall in three classes: supervised, unsupervised, and object-based classification. In the case of supervised classification, two approaches are relied upon for land cover classification of high-resolution LISS-IV multispectral image. These approaches are Maximum Likelihood and Support Vector Machine (SVM). Finally, the paper proposes a step-by-step procedure for optical image classification methodology. This paper concludes that in optical data classification, SVM classification gives a better result than the ML classification technique.

In Pakistan, 2018 National Nutrition Survey reported 40% of under five children were stunted. This study assesses the effectiveness of nutritional supplementation in reducing stunting among under-five children in two rural districts in Sindh, Pakistan. This was a mixed-method quasi-experimental study with intervention and control population. 3397 and 3277 under-five children participated in the baseline and end-line surveys. Both study areas were matched for demographic and economic circumstances. The participants received nutrient supplements underpinned by nutrition behaviour change communication for appropriate complementary feeding practices and hand hygiene targeted at primary caregivers during the first 1000 days of an infant's life. The impact was assessed using the difference in difference analysis with kernel propensity score matching to adjust the differences among control and intervention populations.

A single paragraph of about 200 words maximum. For research articles, abstracts should give a pertinent overview of the work. We strongly encourage authors to use the following style of structured abstracts, but without headings: (1) Background: Place the question addressed in a broad context and highlight the purpose of the study; (2) Methods: briefly describe the main methods or treatments applied; (3) Results: summarize the article’s main findings; (4) Conclusions: indicate the main conclusions or interpretations. The abstract should be an objective representation of the article, and it must not contain results that are not presented and substantiated in the main text and should not exaggerate the main conclusions. Python, a versatile programming language, holds vast potential for Sentiment Analysis (SA). Leveraging the Requests and TextBlob libraries, we have crafted a user-friendly code that enables Industrial Engineers (IE) and managers, particularly those who are new to Python, to extract and analyze sentiment efficiently. We aim to provide IE/managers in service companies requiring SA capabilities with a simple yet effective Python solution. While machine learning resources like PyTorch/TensorFlow are commonly utilized in SA, offering pre-built algorithms and tools for training, and implementing machine learning models, we sought to exploit Python's versatility by integrating additional web-scraping libraries. Thus, by using a lexicon-based approach, we intend to deliver an informative and practical article. The code in this article is twofold; firstly, it can be easily adapted by IE/managers possessing basic Python skills; secondly, we aim to inspire junior IE/managers to develop their own customized coding solutions tailored to specific organizational needs.

Traditional Romanian food products are an integral part of local culture and spirituality. These food traditions have been formed over the centuries and represent a particularly important part for the development of a circular economy in rural areas. In order to contribute to the development of this sector of activity; we conducted a study on consumer perception of traditional Romanian food products. The purpose of this work was to identify the structure of the studies and the future directions of research related to the image of traditional food products through the bibliometric study as well as the identification of consumer trends of these products through a survey based on the questionnaire. The results obtained indicate that traditional Romanian food products are consumed by the majority of respondents participating in the survey and that this sector of activity has continuity in its development and presents a clear interest among citizens.

Chemical plants are characterized by the management and operation of various hazardous materials; thus, accident risk is inherently high. Nevertheless, the incidence of chemical accidents in Korea has effectively stabilized, with no marked increase attributable to the implementation of systematic safety management procedures based on process safety management system principles. In June 2023, the Ministry of Employment and Labor in Korea unveiled the Serious Injury and Fatality (SIF) report, meticulously cataloging 4,432 instances of grave accidents spanning six years (2016–2021). The compilation comprises 1,834 and 2,574 cases within the manufacturing and affiliated sectors and the construction sector, respectively. This study provides an overview of each accident, and elucidates the causes, and recommends preventative strategies. The objective was to galvanize companies across various industrial landscapes and fortify their intrinsic safety management protocols by using accident data delineated per sector. Sixteen specific accident cases from the SIF report, all of which occurred in facilities regulated by process safety management (PSM), were investigated. Technical analysis was conducted, focusing on explosion accidents within the organic catalyst packaging process, notable for their unique patterns of occurrence. This analysis extended to an examination of the reproducibility of such accidents. Employing two system analysis methodologies, system theoretic accident model and process and barrier-based systemic cause analysis technique, this study provides insights into the causative factors of the accidents and formulates comprehensive strategies for the prevention of future accidents. The findings will contribute significantly to reducing the risk of accidents within the catalyst packaging domain and assist in the development of robust recurrence prevention measures.

Using the potent tools of information geometry (IG), the current research analyses the information geometrics of the five-dimensional HTBFC Manifold. By enabling a unique revolutionary analysis for the performance of Human-Trust Based Feedback Control (HTBFC), the integration of information geometry into HTBFC facilitates the relativistic analysis of the five-dimensional HTBFC manifold. Both the two- and three-dimensional manifold subcases are investigated. This work provides a ground-breaking approach to IG unification of Human- Machine Interactions (HMIs). Some potential applications of HTBFC to robotics are presented.Conclusions are provided, along with some emerging open problems and recommendations for future research.

Blockchain technology has been gaining great interest from a variety of sectors including healthcare, supply chain, and cryptocurrencies. However, Blockchain suffers from its limited ability to scale (i.e. low throughput and high latency). Several solutions have been proposed to tackle this. In particular, sharding proved that it is one of the most promising solutions to Blockchain’s scalability issue. Sharding can be divided into two major categories: (1) Sharding-based Proof-of-Work (PoW) Blockchain protocols, and (2) Sharding-based Proof-of-Stake (PoS) Blockchain protocols. The two categories achieve good performances (i.e. good throughput with a reasonable latency), but raise security issues. This article focuses on the second category. In this paper, we start by introducing the key components of sharding-based PoS Blockchain protocols. Then, we briefly introduce two consensus mechanisms, namely PoS and practical Byzantine Fault Tolerance (pBFT), and discuss their use and limitations in the context of sharding-based Blockchain protocols. Next, we provide a probabilistic model to analyze the security of these protocols. More specifically, we compute the probability of committing a faulty block and measure the security by computing the number of years to fail. Finally, we evaluate the effectiveness of the proposed model via numerical analysis.

The increasing adoption of student-centred learning environs by the institutions of higher education in business, has led to new mechanisms and methodologies such as the Simulation-Based Learning (SBL), Problem-Based Learning (PBL) and Challenge-Based Learning (CBL). Literature has shown evidence of raising interest in these methodologies towards a more experiential paradigm with regard to upcoming events from the surrounding real-world context. This paper presents a Systematic Bibliographic Literature Review (SBLR) with respect to the integration and application of these innovative and collaborative methodologies in higher education. It aimed to identify its relevant advantages, challenges, and consequences for the future of business education. Bibliographic databases were searched for documents published up to May 2024 to categorize key topics discussed in the literature on SBL, PBL and CBL. The review process identified 89 empirical and non-empirical papers on SBL, PBL and CBL, business related paperwork, which resulted from the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) method. PRISMA is a framework with evidence-based data designed to help authors perform a systematic literature review. Data analysis revealed diverse subthemes for those methodologies uses in business higher education institutions. The research questions “What are the advantages, challenges and implications of SBL, PBL and CBL altogether, in the training of business students?” guided the study. The central output of the research is the discovery of the varying uses of SBL, PBL and CBL overall, to cope with diverse demands arising within environmental context. Future research directions are suggested.

The influence of cuff inflations on nighttime measurements during 24h ambulatory blood pressure (BP) measurements is unknown. We investigated the potential effect of cuff-inflations on asleep parameters using measurements taken simultaneously with a cuffless device using pulse-transit-time (PTT). On the first day of measurement, standard cuff-based 24h BP and cuf-fless measurements were simultaneously performed on right and left arms (CUFF/PTT-D). 1-2 days after the first measurement, the cuffless device was worn alone (PTT-D). Only data from the cuffless device was analyzed. The following mean asleep parameters were analyzed: systolic and diastolic BP, arousals, sleep efficiency, total arousals, arousal per hour and desaturations. 21 in-dividuals were prospectively enrolled. Mean (SD) age was 47(±15) years and 57% were female. Mean systolic asleep BP for the during CUFF/PTT-D and during PTT-D were 131(±21) and 131(±26) mmHg respectively. Mean diastolic asleep BP values during CUFF/PTT-D and during PTT-D were 80(±14) and 84(±14) mmHg respectively (p=0.860, p=0.100 respectively). Systolic and diastolic asleep mean difference was 0.1 (±18.0) and -3.6 (±9.8) mmHg, respectively. There were significantly more total arousals during PTT-D (p=0.042). There was no significant difference seen in sleep efficiency (p=0.339) or desaturations (p=0.896) between the two measurements. We could not show any significant impact from cuff inflations during asleep PTT-D measurements.

In space application, hybrid pixel detectors of the Timepix family have been considered mainly for measurement of radiation levels and radiation dosimetry in low earth orbits. By the example of the Space Application of Timepix Radiation Monitor (SATRAM), we demonstrate the unique capabilities of Timepix-based miniaturized radiation detectors for particle separation. Using a novel method for proton spectrum reconstruction, we were able to measure the spectrum of protons trapped in the inner Van-Allen radiation belt, for the first time with a single-layer detector. We assess the measurement stability and the resiliency of the detector to the space environment demonstrating that, even though degradation is observed, data quality has not been affected significantly over more than 10 years. Based on the SATRAM heritage and the capabilities of the latest-generation Timepix-series chips, we discuss their applicability for use in a compact magnetic spectrometer for deep-space mission or in the high radiation environment of the Jupiter radiation belts, and their capability for use as single-layer X- and γ-ray polarimeter. The latter was supported by measurement of the polarization of scattered radiation in a laboratory experiment, where a modulation of 80% was found.

With the COVID-19 pandemic behind us, the U.S. Food and Drug Administration (FDA) has approved 55 new drugs in 2023, a figure consistent with the number authorized in the last five years (53 per year on average). Thus, 2023 is the second-best FDA harvest after 2018 (59 approvals) in all the series. Monoclonal antibodies (mAbs) continue to be the class of drugs with most approvals, with an exceptional 12, a number that makes it the most outstanding year for this class. As in 2022, five proteins/enzymes have been approved in 2023. However, no antibody–drug conjugate (ADC) has been released onto the market. With respect to TIDES (peptides and oligonucleotides), 2023 has proved a spectacular year, with a total of nine approvals, corresponding to five peptides and four oligonucleotides. Natural products continue to be the best source of inspiration for drug development, with 10 new products on the market. Three drugs in this year’s harvest are pegylated, which may indicate the return of pegylation as a method to increase the half-lives of drugs, after the withdrawal of peginesatide from the market in 2013. Following the trends of recent years, two bispecific drugs have been authorized in 2023. As in preceding years, fluorine and/or N-aromatic heterocycles are present in most of the drugs. Herein, the 55 new drugs approved by the FDA in 2023 are analyzed exclusively on the basis of their chemical structure. They are classified as the following: biologics (antibodies, proteins/enzymes); TIDES (peptide and oligonucleotides); combined drugs; pegylated drugs; natural products; nitrogen aromatic heterocycles; fluorine-containing molecules; and other small molecules.

Bridge load testing is crucial in evaluating bridge load-carrying capacity and construction quality. However, conventional techniques have many limitations such as the need for direct contact and low acquisition frequency. This study provides a comprehensive examination of the static and dynamic load tests of a multi-span cable-stayed bridge utilizing a ground-based radar inter-ferometer (GB-radar). The case study was conducted at the Fifth Nanjing Yangtze River Bridge (FNYRB), which is recognized as the world's first lightweight steel-concrete composite cable-stayed bridge, with two spans measuring 600 m. To enhance measurement accuracy, a method was proposed to detect and recover radar phase jumps caused by vehicle motion. Furthermore, precise geometry projection was employed to acquire vertical displacements from GB-radar which were taken along the line-of-sight (LOS) direction. Moreover, during the static load test, a continuous deformation was observed with a maximum pace of 0.31 mm/min, indicating a post-construction settlement caused by soft soil consolidation. This case study highlights the high sampling frequency, high measurement accuracy, and exceptional weatherproof ability of GB-radar, thereby demonstrating its potential to be an alternative to the structural health monitoring (SHM) system.

Empowering teachers with Critical Thinking and understanding of sustainability is vital for guiding students’ informed decisions and actions in today’s world. Higher education needs innovative, student-centered methods like case-based teaching in order to promote Critical Thinking and the principles of Education for Sustainable Development. The aim of the study was to explore and evaluate pre-service teachers' Critical Thinking when engaged with case studies in a practicum preparation course centered on Education for Sustainable Development. Nine undergraduate students in Teacher Education participated in the study and answered four case studies. The case studies’ responses were analyzed through a bottom-up content analysis revealing eight higher-order categories. While approaching the case studies, student-teachers followed thinking processes similar to the processes proposed by the iterative models of Critical Thinking as perceived by Garrison (1991) and/or Brookfield (1987). Nevertheless, in some cases, pre-service teachers’ disengagement was evident. The rubric assessment highlighted that the responses fell under three profiles, namely the “Deficient Critical Thinker”, the “Emerging Critical Thinker” and the “Competent Critical Thinker”. The findings emphasize the need for targeted and individualized teaching interventions in teacher higher education programs aiming at Education for Sustainable Development complying with the learning needs of student-teachers’ Critical Thinking profiles.

The inherent capabilities of the K-means++ algorithm to approximate system dynamics within complex systems are subjected by constructing a network structure that captures interconnections among identified components, extending its original purpose as a data clustering method and transforming it into a tool for systems analysis. Leveraging advanced measurement technologies and sophisticated data collection systems, the K-means++ algorithm unveils hidden relationships among components and identifies critical elements. This study explored the algorithm's potential, facilitating the identification of critical components to enhance system operation, control, optimization, and decision-making and examining the practicality and resiliency of the method in real-world application with noisy and limited data. A case study conducted on power systems (IEEE 39-bus and IEEE 300-bus systems) exemplifies K-means++'s capacity to accurately identify critical components and approximate system dynamics, supported by performance metrics affirming its effectiveness and robustness in system analysis through measurements of bus similarity within clusters based on standard deviation and comparison of net tie-line flows in equivalent networks with the original network across scenarios. Performance metrics, including the Silhouette Score, Davies-Bouldin Index, and Variation of Information (VI) score, further validated K-means++'s performance, yielding reliable and consistent results.

In 2019, Chinese government implemented the first round of National Centralized Drug Procurement (NCDP) pilot (so-called "4+7" policy) in mainland China, achieved a prominent price reduction of 52% on average for 25 bidding winning products. Under cross-price elasticity theory, the price behavior of pharmaceutical enterprises for policy-related drugs might change. This study used drug purchasing data from the Centralized Drug Procurement Survey in Shenzhen 2019, and applied single-group Interruption Time Series (ITS) design to examine the impact of "4+7" policy on the drug price index (DPI) of policy-related drugs. The ITS analysis showed that the DPI of winning (-0.183 per month, p&lt;0.0001) and non-winning (-0.034 per month, p=0.046) products significantly decreased after the implementation of "4+7" policy. No significant difference was found for the immediate change of DPI for alternative drugs (p=0.537), while a significant decrease in change trend was detected in the post-"4+7" policy period (-0.003 per month, p=0.014). The DPI of the overall policy-related drugs significantly decreased (-0.261 per month, p&lt;0.0001) after "4+7" policy. These findings indicate that the price behavior of pharmaceutical enterprises changed under NCDP policy, while the price linkage effect is still limited. It is necessary to further expand the scope of centralized purchased drugs and strengthen the monitoring of related drugs regarding price change and consumption structure.

In this study, the biodegradation properties of leather treated with various finishing chemicals were evaluated in order to enhance the sustainability of leather processing. We applied advanced analytical techniques including FT-IR, thermogravimetric analysis (TGA), and solid-state NMR spectroscopy. Leather samples treated with different polymers, resins, bio-based materials, and traditional finishing agents were subjected to a composting process under controlled conditions to measure their biodegradability. The findings revealed that bio-based polyurethane finishes and acrylic wax exhibited biodegradability, while traditional chemical finishes like isocyanate and nitrocellulose lacquer showed moderate biodegradation levels. The results indicated significant differences in the biodegradation rates and the impact on plant germination and growth. Some materials, such as black pigment, nitrocellulose lacquer and wax, were beneficial for plant growth, while others, such as polyurethane materials, had adverse effects. These results support the use of eco-friendly finishes to reduce the environmental footprint of leather production. Overall, this study underscores the importance of selecting sustainable finishing chemicals to promote eco-friendly leather manufacturing practices.

The objective of the study was to investigate the gap between data and evidence-based decisions among healthcare professionals considering the enormous amount of individual and aggregate data collected. Our study assessed the capacity, skills, and knowledge of the Ministry of Health leadership staff to understand data management, analysis, utilization, and dissemination. Three key components were assessed: 1) Knowledge through true/false questions, 2) Level of Skill (and Competency) using a Likert scale, and 3) Understanding of Key Concepts and Tools based on a Likert scale. The 183 study respondents were diverse healthcare professionals from Kenya, Tanzania, and Rwanda. Majority of respondents had not received any training on data management, analysis, interpretation, and utilization techniques, further there was a significant difference between those who had received training versus those who had not(p=0.005). The respondents were competent in work-related experiences but lacked skills and knowledge on: data concepts and tools, study designs, and types of data analysis. These findings explain the gap between data management, analysis, utilization, and dissemination among health professional’s cadre. To enhance service delivery and optimal provision of health care, it is imperative to have all health care professionals receive a well-designed training on data management, analysis, interpretation, and utilization.

The COVID-19 pandemic has posed a significant global threat, leading to several initiatives for its control and management. One such initiative involves wastewater-based epidemiology, which has gained attention for its potential to provide early warning of virus outbreaks and real-time information on its spread. In this study, water samples from two wastewater treatment plants (WWTPs) located at the south east of Spain (Region of Murcia) namely Murcia, and Cartagena, were analyzed by RT-qPCR, Phylogenetic Analysis, and Machine Learning Approach. The aim was to determine whether SARS-CoV-2 detection in the WWTPs of these two cities could serve as a proxy for the virus's spread in the population. The results confirmed that the levels of SARS-CoV-2 in these wastewater samples changed concerning the number of SARS-CoV-2 cases detected in the population and variant occurrences were in line with clinical reported data. Additionally, the phylogenetic analysis showed that samples obtained in close sampling times exhibited a higher similarity than those obtained more distantly in time. A second analysis using a machine learning approach based on the mutations found in the SARS-CoV-2 spike protein was also conducted. Hierarchical Clustering (HC) was used as an efficient unsupervised approach for data analysis. Results indicated that samples obtained in October 2022 in Murcia and Cartagena were significantly different, which corresponded well with the different virus variants circulating in the two locations. The proposed methods in this study are adequate for comparing the Accumulated Natural Vector (ANV) of the SARS-CoV-2 sequences as a preliminary evaluation of potential changes in the variants that are circulating in a given population at a specific time point.

The early-mid Paleocene Farewell Formation is stratigraphically distributed across the southern Taranaki Basin (STB) which is also encountered within the Maui Gas Field. Using available 3D seismic and well log data, a challenging task to delineate the spatial distribution and geobody patterns of the potential reservoir sands of the formation was performed. Object based modeling coupled with sequential indicator simulation were used to analyze the spatial distribution of facies configuration and a conceptual model was developed based on the outputs from the structurally- modeled grids. The facies modeling followed a hierarchical object-based mechanism which was set to perform with constraints like channel geometry and heterogeneity within the formation. The resultant 3D geobody model showed that the distributary channels, mainly braided geobodies flowed from northeast cutting through several regional normal-fault systems to the southwest. Overbank facies was adhered to the fringe of the channels whereas the floodplain facies was at the periphery of the model. Meandering channel-sand facies were mostly observed at the center of the model flowing in a more random manner, occupying major flow directions of northwest to southwest and southeast to northwest within the model.

Artificial intelligence (AI), deep learning (DL) or machine learning (ML) is the ability of computer systems, machines and enginery to work devise procedures like humans. These technologies provide opportunities and possibilities to advance diagnostics and in planning also in the field of human medicine and dentistry. The purpose of this literature review was to ascertain the applicability and significance of AI, as well as to highlight its uses in maxillofacial surgery. The primary inclusion criterion for this publication was an original paper written in English focusing on the use of AI, DL or ML in maxillofacial surgery. The sources of information were PubMed, Scopus and Web of Science, and the queries were made on 31st of December 2023. The search strings used were “artificial intelligence maxillofacial surgery”, “machine learning maxillofacial surgery” and “deep learning maxillofacial surgery”. Following the removal of duplicates, all remaining publications that were returned by the searches and were screened by three independent operators to minimize the risk of bias. The analysis of publications from 1992 to 2023 identified certain records, of which 324 were finally selected. These were calculated according to the year of publication with a continuous increase (excluding 2012 and 2013) of R2 = 0.9295. Generally, in orthognathic dentistry and maxillofacial surgery, artificial intelligence and machine learning have gained popularity over the past few decades. When we included the keywords "planning in maxillofacial surgery" and "planning in orthognathic surgery", the set of published papers significantly increased to the number of 7535 publications. The first publication appeared in 1965, with an increasing trend (excluding 2014-2018), with an R2 value of 0.8642. These tools have been found useful for diagnosis, treatment planning in head and neck surgical oncology, cosmetic and aesthetic surgery, and in oral pathology. In orthognathic surgery, they have been utilised for diagnosis, treatment planning, assessment of treatment needs, cephalometric analyses, and orthognathic surgeries, among other applications. The review confirms that the current use of artificial intelligence and machine learning in maxillofacial surgery is focused mainly on the evaluation of digital diagnostic methods especially radiology, treatment plans and postoperative results. However, as these technologies are integrated in maxillofacial surgery and robotic surgery in head and neck region, it is expected that in the future they will be gradually utilized to plan and comprehensively evaluate the success of maxillofacial surgeries.

On using a 3D model to teach students a topic in astronomy, feedback was evaluated via a questionnaire. Analysis of students' responses to the questions revealed that user interface had a significant impact on their attitudes, which in turn could increase users' willingness to use immersive technology in e-learning. The results also showed that improving visual features through immersive technology and augmented reality tools were effective. The results confirm all hypotheses about positive relationships between facilitating conditions and usability of metaverse technology for students' attention and study motivation.

In recent years there has been an increasing interest in developing novel materials based on natural biopolymers as a renewable alternative of petroleum-based plastics. The availability of proteins derived from agricultural by-products, along with their favourable properties, has fostered a renewed interest in protein-based materials, promoting research in innovative technologies. In this study we propose the use of rapeseed protein-rich meal as main ingredient for the preparation of novel sustainable materials combining excellent environmental properties such as biodegradability and renewable origin. Application as sustainable products in the present high-tech society needs modification of basic native properties of these natural compounds. The original route proposed in this paper consists in the preparation via compression moulding of flexible biomaterials stabilized by crosslinkers/chain extenders. Investigation of the effects of different denaturing and disulfide bond reducing agents, crosslinkers, and preparation conditions on the material mechanical behaviour demonstrated that the novel materials have appreciable strength and stiffness. The results show the potential of utilizing full meal from vegetable by-products to prepare protein-based materials with granted ecofriendly characteristics and mechanical properties adequate to specific structural applications.

In this paper, the stability control of the inverted pendulum on a cart with a disturbance forces has been done using observer based and full state feedback H2 controllers. The Lagrangian equation has been used to model the system equation of motions and linearized the system to the unstable upward position. Comparison of the system stability has been simulated by comparing the proposed controllers using Matlab/Scripts and a promising results has been analyzed successfully.

Based on the perspective of complex directed weighted network analysis, this paper constructs a static resilience evaluation framework for trade networks from two dimensions: structural resilience and node resilience. It studies the evolution of static resilience in the upstream, midstream, downstream, and recycling trade networks of global wood forest products from 2002 to 2021. The study finds that the total trade volume of global wood forest products has increased by 58.0%, 77.3%, 71.2%, and 77.2% in the upstream, midstream, downstream, and recycling sectors, respectively. The downstream trade network exhibits efficient information transmission and resource mobility capabilities. All four types of trade networks exhibit scale-free characteristics, with core countries such as China, the United States, and Germany playing a dominant role in the network and playing a crucial role in network robustness, but also increasing network vulnerability. In terms of unweighted assortativity, all four networks exhibit the characteristic of "small countries relying on hubs," while the weighted assortativity of the downstream network is positive, indicating enhanced network resilience. In terms of node resilience, countries exhibit significant differences and dynamic changes. China demonstrates strong resilience and adaptability, while the United States maintains its core position. China's influence in the downstream network has significantly increased, and India has rapidly emerged in the recycling network. To enhance network resilience, it is necessary to increase diversity and redundancy, establish effective emergency response mechanisms, and promote the diversification and green transformation of the supply chain.

Background: Immunoglobulin A nephropathy (IgAN) is a complex autoimmune disease, and the exact pathogenesis remains to be elucidated. Methods: We conducted summary data-based Mendelian randomization (SMR) analysis and performed functional mapping and annotation using FUMA to explore genetic loci that are po-tentially involved in the pathogenies of IgAN. Both analyses used summarized data of a recent genome-wide association study (GWAS) on IgANs, which included 477,784 Europeans (15,587 cases and 462,197 controls) and 175,359 East Asians (71 cases and 175,288 controls). We performed separate SMR analysis using CAGE and GTEx eQTL data. Results: Using the CAGE eQTL data, our SMR analysis identified 32 probes tagging 25 unique genes that were pleiotropically/potentially causally associated with IgAN, with the top three probes being ILMN_2150787 (tagging HLA-C, PSMR=2.10×10-18), ILMN_1682717 (tagging IER3, PSMR=1.07×10-16) and ILMN_1661439 (tagging FLOT1, PSMR=1.16×10-14). Using GTEx eQTL data, our SMR analysis identified 24 probes tagging 24 unique genes, with the top three probes being ENSG00000271581.1 (tagging XXbac-BPG248L24.12, PSMR=1.44×10-10), ENSG00000186470.9 (tagging BTN3A2, PSMR=2.28×10-10), and ENSG00000224389.4 (tagging C4B, PSMR=1.23×10-9). FUMA analysis identified 3 independent, significant and lead SNPs, 2 genomic risk loci and 39 genes. Conclusion: We identified many genetic variants/loci that are potentially involved in the patho-genesis of IgAN.

: The cyber-physical gap (CPG) is the difference between the 'real' state of the world and the way the system perceives it. This discrepancy often stems from the limitations of sensing and data collection technologies and capabilities, and is an inevitable issue in any cyber-physical system (CPS). Ignoring or misrepresenting such limitations during system modeling, specification, design, and analysis can potentially result in systemic misconceptions, disrupted functionality and performance, system failure, severe damage, and potential detrimental impacts on the system and its environment. We propose CPG-Aware Modeling & Engineering (CPGAME), a conceptual model-based approach for capturing, explaining, and mitigating the CPG, on top of and in sync with the conventional system model, and as an inherent systems engineering activity. This approach enhances the systems engineer’s ability to cope with CPGs, mitigate them by design, and prevent erroneous decisions, actions, and hazardous implications. CPGAME is a generic, conceptual approach, specified and demonstrated with Object Process Methodology (OPM). OPM is a holistic conceptual modeling paradigm for multidisciplinary, complex, dynamic systems, which is also ISO-19450. We analyze the 1979 Three Miles Island 2 nuclear accident as a prime example of the disastrous consequences of unmitigated CPGs in complex systems.

Many theoretical models of iron-based superconductors have been proposed but Tc calculations based on the models are usually missing. We have chosen two models of iron-based superconductors in the literature and then compute the Tc values accordingly: Recently two models have been announced which suggest that superconducting electron concentration involved in the pairing mechanism of iron-based superconductors may have been underestimated, and that the antiferromagnetism and the induced xy potential may even have a dramatic amplification effect on electron-phonon coupling. We use bulk FeSe, LiFeAs and NaFeAs data to calculate the Tc based on these models and test if the combined model can predict the superconducting transition temperature (Tc) of the nanostructured FeSe monolayer well. To substantiate the recently announced xy potential in the literature, we create a two-channel model to separately superimpose the dynamics of the electron in the upper and lower tetrahedral plane. The results of our two-channel model support the literature data. While scientists are still searching for a universal DFT functional that can describe the pairing mechanism of all iron-based superconductors, we base on the ARPES data to propose an empirical combination of DFT functional for revising the electron-phonon scattering matrix in the superconducting state, which ensures that all electrons involved in iron-based superconductivity are included in the computation. Our computational model takes into account this amplifying effect of antiferromagnetism and the correction of the electron-phonon scattering matrix together with the abnormal soft out-of-plane lattice vibration of the layered structure, which allows us to calculate theoretical Tc values of LiFeAs, NaFeAs and FeSe as a function of pressure that correspond reasonably well to the experimental values. More importantly, by taking into account the interfacial effect between an FeSe monolayer and its SrTiO3 substrate as an additional gain factor, our calculated Tc value is up to 91 K high, and provides evidence that the strong Tc enhancement recently observed in such monolayers with Tc reaching 100 K may be contributed from the electrons within the ARPES range.

One of the efficient data mining tools is density-based clustering, including the density grid-based clustering. However, a common drawback always existing in clusters made by the density grid-based method is the existence of weakly connected grids deriving mainly from noise. Appearing such an unwanted connection with a high frequency reduces the accuracy of the obtained cluster data space (CDS) and its application efficiency. Here, we present an essential improvement to overcome this problem. First, we describe a concept of the weak-connected grid cell (WCG) and present a fuzzy-type approximation to depict the density-based distribution of data points at grid nodes. Then, we propose a strategy of searching WCG for density grid-based clustering (SWCG-DGB) to set up a CDS, filter noise, and tune the created CDS. A buffer is deployed during this phase to collect border points and filter noise, which improves the computational time significantly, especially for noisy datasets. Results from numerical surveys reflected the compared efficiency of this method in clustering validity, including the accuracy of the number of clusters.

Mycobacterium tuberculosis (Mtb) is the etiological agent of tuberculosis (TB), a disease that alt-hough preventable and curable, remains a global epidemic due to the emergence of resistance and a latent form responsible for a long period of treatment. Drug discovery in TB is a challenging task due to the heterogeneity of the disease, the emergence of resistance and an uncomplete knowledge of the pathophysiology of the disease. The limited permeability of the cell wall and the presence of multiple efflux pumps remain a major barrier to achieve effective intracellular drug accumulation. While the complete genome sequence of Mtb has been determined and several potential protein targets have been validated, the lack of adequate models for in vitro and in vivo studies is a limit-ing factor in TB drug discovery programs. In current therapeutic regimens, less than 0.5% of bac-terial proteins are targeted being the biosynthesis of the cell wall and the energetic metabolism two of the most important processes exploited for TB chemotherapeutics. This review provides an overview on the current challenges in TB drug discovery and emerging Mtb druggable proteins, and how chemical probes for protein profiling enabled the identification of new targets and bi-omarkers, paving the way to disruptive therapeutic regimens and diagnostic tools.

Lake Rawapening, Semarang Regency, Indonesia, has incorporated a holistic plan in its management practices. However, despite successful target achievements, some limitations remain that a review of its management plan is needed. This paper identifies and analyzes existing lake management strategies as a standard specifically in Lake Rawapening by exploring various literature, both legal frameworks and scholarly articles indexed in Google Scholar and published in Water by MDPI about lake management in many countries. There are two major types of lake management, namely pillar-based and object-based. While the former is the foundation of a conceptual paradigm that does not comprehensively consider the roles of finance and technology in the lake management, the latter indicates the objects to manage so as to create standards or benchmarks for the implementation of various programs. Overall, Lake Rawapening management should include more programs on erosion-sedimentation control and monitoring of operational performance using information systems.

Nature-based solutions (NbS) are increasingly recognised for their potential to address both the climate and biodiversity crises. These outcomes are interdependent, and both rely on the capacity of NbS to support and enhance the health of an ecosystem: its biodiversity, the condition of its abiotic and biotic elements, and its capacity to function normally despite environmental change. However, while understanding of ecosystem health outcomes of nature-based interventions for climate change mitigation is growing, the outcomes of those implemented for adaptation remain poorly understood with evidence scattered across multiple disciplines. To address this, we conducted a systematic review of the outcomes of 109 nature-based interventions for climate change adaptation using 33 indicators of ecosystem health across eight broad categories (e.g. diversity, biomass, ecosystem functioning and population dynamics). We showed that 88% of interventions with positive outcomes for climate change adaptation also reported measurable benefits for ecosystem health. We also showed that interventions were associated with a 67% average increase in local species richness. All eight studies that reported benefits in terms of both climate change mitigation and adaptation also supported ecosystem health, leading to a triple win. However, there were also trade-offs, mainly for forest management and creation of novel ecosystems such as monoculture plantations of non-native species. Our review highlights two major limitations of research to date. First, only a limited selection of metrics are used to assess ecosystem health and these rarely include key aspects such as functional diversity and habitat connectivity. Second, taxonomic coverage is poor: 67% of outcomes assessed only plants and 57% did not distinguish between native and non-native species. Future research addressing these issues will allow the design and adaptive management of NbS to support healthy and resilient ecosystems, and thereby enhance their effectiveness for meeting both climate and biodiversity targets.

Currently, an interactive environmental planning analysis system platform based on " Three Lines One Permit " (TLOP) is being developed to support environmental planning, construction project approval, and the application of TLOP outcome data in Guangzhou. The main objective is to provide governments, businesses and the public with environmental planning analysis tools to determine the site of construction projects. The platform is using the system architecture of the browser and server. Its core functions are interactive environmental planning analysis tool for construction project and the results display tool supporting map viewing. It provides users with a large number of detailed geospatial data and TLOP results data access and environmental planning analysis functions. This article describes the system architecture and implementation of the system platform and has a case study illustrating the system functionality. At present, the platform has been deployed and trial-operated. The content of the analysis framework is constantly expanding. This promotes the matching of environmental planning and analysis with local conditions. This will implement the application of TLOP and improve the efficiency of project construction and the level of ecological environment planning and management.

The paper presents the design and the implementation of different advanced control strategies that are applied to a nonlinear model of a thermal unit. A data–driven grey–box identification approach provided the physically meaningful nonlinear continuous–time model, which represents the benchmark exploited in this work. The control problem of this thermal unit is important since it constitutes the key element of passive air conditioning systems. The advanced control schemes analysed in this paper are used to regulate the outflow air temperature of the thermal unit by exploiting the inflow air speed, whilst the inflow air temperature is considered as an external disturbance. The reliability and robustness issues of the suggested control methodologies are verified with a Monte–Carlo analysis for simulating modelling uncertainty, disturbance and measurement errors. The achieved results serve to demonstrate the effectiveness and the viable application the suggested control solutions to air conditioning systems. The benchmark model represents one of the key issues of this study, which is exploited for benchmarking different model–based and data–driven advanced control methodologies through extensive simulations. Moreover, this work highlights the main features of the proposed control schemes, while providing practitioners and heating, ventilating and air conditioning engineers with tools to design robust control strategies for air conditioning systems.