Search Results (152)

The accurate prediction of secondary structures of proteins (SSPs) is a critical challenge in molecular biology and structural bioinformatics. Despite recent advancements, this task remains complex and demands further exploration. This study presents a novel approach to SSP prediction using atom-centric substructural multilevel neighborhoods of atoms (MNA) descriptors for protein molecular fragments. A dataset comprising over 335,000 SSPs, annotated by the Dictionary of Secondary Structure in Proteins (DSSP) software from 37,000 proteins, was constructed from Protein Data Bank (PDB) records with a resolution of 2 Å or better. Protein fragments were converted into structural formulae using the RDKit Python package and stored in SD files using the MOL V3000 format. Classification sequence–structure–property relationships (SSPR) models were developed with varying levels of MNA descriptors and a Bayesian algorithm implemented in MultiPASS software. The average prediction accuracy (AUC) for eight SSP types, calculated via leave-one-out cross-validation, was 0.902. For independent test sets (ASTRAL and CB513 datasets), the best SSPR models achieved AUC, Q3, and Q8 values of 0.860, 77.32%, 70.92% and 0.889, 78.78%, 74.74%, respectively. Based on the created models, a freely available web application MNA-PSS-Pred was developed. Full article

The identification of natural products (NPs) has played a pivotal role in drug discovery and shaped the evolution of synthetic compounds (SCs). However, the extent to which NPs have historically influenced the structural characteristics of SCs remains unclear. In this study, we conducted a comprehensive, time-dependent chemoinformatic analysis to investigate the impact of NPs on the structural evolution of SCs. The physicochemical properties, molecular fragments, biological relevance, and chemical space of the molecules from the Dictionary of Natural Products were compared in a time series fashion with a synthetic compound collection sourced from 12 databases. Our findings reveal that NPs have become larger, more complex, and more hydrophobic over time, exhibiting increased structural diversity and uniqueness. Conversely, SCs exhibit a continuous shift in physicochemical properties, yet these changes are constrained within a defined range governed by drug-like constraints. SCs possess a broader range of synthetic pathways and structural diversity, albeit with a decline in biological relevance. The chemical space of NPs has become less concentrated compared to that of SCs. In conclusion, our study suggests that the structural evolution of SCs is influenced by NPs to some extent; however, SCs have not fully evolved in the direction of NPs. Full article

The defossilisation of the agricultural sector is driven by intense worldwide academic research on non-fossil, renewable and energy-efficient agriculture, and the acknowledgment of the need for sustainable farming practices. For this purpose, not only technical transformations but also socio-technical system changes towards sustainability need to take place in a co-evolutionary manner. This paper investigates structural and qualitative characteristics of the knowledge produced by research on fossil-energy-free agriculture. We provide evidence on the worldwide research directions, as well as investigate whether academic research and publicly funded research projects foster knowledge creation for the desired transformation. Bibliographic maps are constructed using a query-based methodology as social networks to investigate the efficiency of the EU-funded research to achieve the goals set for the 2050 EU Green Deal. The H2020-funded papers are further analysed with dictionary-based text analysis to quantify the relative emphasis of different types of knowledge in the text. This approach is eventually used to relate transformational capacity to project profiles in the European Union, to evaluate past funding schemes and to improve the shape of future research programs on renewable and sustainable agriculture. Full article

With the exponential growth of online review platforms, understanding user preferences and interests in the tourism domain has become increasingly critical for businesses and service providers. However, extracting meaningful insights from the vast amount of available data poses a significant challenge. Traditional methods often struggle to capture the nuanced and hierarchical nature of user interests within the tourism domain. This paper pioneers the integration of domain information modeling technology into the realm of online review information mining, presenting a novel approach to constructing a user tourism interest model. Unlike existing methods, which primarily rely on flat or simplistic representations of user data, our approach leverages the hierarchical structure inherent in tourism domain information modeling. By harnessing big data within the tourism domain, we construct hierarchical tourism attributes and apply a conditional random field model along with an affective dictionary to facilitate the hierarchical mining of user travel interest information. This culminates in the establishment of a comprehensive user travel interest model using advanced information modeling techniques. Building upon this foundation, we further propose a dynamic user travel interest model, showcasing its adaptability and responsiveness to changing user preferences. Finally, we validate the accuracy and effectiveness of our model through simulation experiments within a user travel recommendation system, demonstrating significant improvements over traditional methods. Full article

Kernel matching pursuit (KMP) requires every step of the searching process to be global optimal searching in the redundant dictionary of functions in order to select the best matching signal structure. Namely, the dictionary learning time of KMP is too long. To solve the above drawbacks, a rough dataset was divided into some small-sized dictionaries to substitute local searching for global searching by using the property superiority of dynamic clustering performance, which is also superior in the intuitionistic fuzzy c-means (IFCM) algorithm. Then, we proposed a novel technique for KMP based on IFCM (IFCM-KMP). Subsequently, three tests including classification, effectiveness, and time complexity were carried out on four practical sample datasets, the conclusions of which fully demonstrate that the IFCM-KMP algorithm is superior to FCM and KMP. Full article

Since ancient times, dams have been built to store water, control rivers, and irrigate agricultural land to meet human needs. By the end of the 19th century, hydroelectric power stations arose and extended the purposes of dams. Today, dams can be seen as part of the renewable energy supply infrastructure. The word dam comes from French and is defined in dictionaries using words like strange, dike, and obstacle. In other words, a dam is a structure that stores water and directs it to the desired location, with a dam being built in front of river valleys. Dams built on rivers serve various purposes such as the supply of drinking water, agricultural irrigation, flood control, the supply of industrial water, power generation, recreation, the movement control of solids, and fisheries. Dams can also be built in a catchment area to capture and store the rainwater in arid and semi-arid areas. Dams can be built from concrete or natural materials such as earth and rock. There are various types of dams: embankment dams (earth-fill dams, rock-fill dams, and rock-fill dams with concrete faces) and rigid dams (gravity dams, rolled compacted concrete dams, arch dams, and buttress dams). A gravity dam is a straight wall of stone masonry or earthen material that can withstand the full force of the water pressure. In other words, the pressure of the water transfers the vertical compressive forces and horizontal shear forces to the foundations beneath the dam. The strength of a gravity dam ultimately depends on its weight and the strength of its foundations. Most dams built in ancient times were constructed as gravity dams. An arch dam, on the other hand, has a convex curved surface that faces the water. The forces generated by the water pressure are transferred to the sides of the structure by horizontal lines. The horizontal, normal, and shear forces resist the weight at the edges. When viewed in a horizontal section, an arch dam has a curved shape. This type of dam can also resist water pressure due to its particular shape that allows the transfer of the forces generated by the stored water to the rock foundations. This article takes a detailed look at hydraulic engineering in dams over the millennia. Lessons should be learned from the successful and unsuccessful applications and operations of dams. Water resource managers, policymakers, and stakeholders can use these lessons to achieve sustainable development goals in times of climate change and water crisis. Full article

In the research into semantic sentiment analysis, researchers commonly use some factor rules, such as the utilization of emotional keywords and the manual definition of emotional rules, to increase accuracy. However, this approach often requires extensive data and time-consuming training, and there is a need to make the system simpler and more efficient. Recognizing these challenges, our paper introduces a new semantic sentiment analysis system designed to be both higher in quality and more efficient. The structure of our proposed system is organized into several key phases. Initially, we focus on data training, which involves studying emotions and emotional psychology. Utilizing linguistic resources such as HowNet and the Chinese Knowledge and Information Processing (CKIP) techniques, we develop emotional rules that facilitate the generation of sparse representation characteristics. This process also includes constructing a sparse representation dictionary. We can map these back to the original vector space by resolving the sparse coefficients, representing two distinct categories. The system then calculates the error compared to the original vector, and the category with the minimum error is determined. The second phase involves inputting topics and collecting relevant comments from internet forums to gather public opinion on trending topics. The final phase is data classification, where we assess the accuracy of classified issues based on our data training results. Additionally, our experimental results will demonstrate the system’s ability to identify hot topics, thus validating our semantic classification models. This comprehensive approach ensures a more streamlined and effective system for semantic sentiment analysis. Full article

In a hyperspectral image, there is a close correlation between spectra and a certain degree of correlation in the pixel space. However, most existing low-rank representation (LRR) methods struggle to utilize these two characteristics simultaneously to detect anomalies. To address this challenge, a novel low-rank representation with dual graph regularization and an adaptive dictionary (DGRAD-LRR) is proposed for hyperspectral anomaly detection. To be specific, dual graph regularization, which combines spectral and spatial regularization, provides a new paradigm for LRR, and it can effectively preserve the local geometrical structure in the spectral and spatial information. To obtain a robust background dictionary, a novel adaptive dictionary strategy is utilized for the LRR model. In addition, extensive comparative experiments and an ablation study were conducted to demonstrate the superiority and practicality of the proposed DGRAD-LRR method. Full article

Spectral computed tomography (CT)-reconstructed images often exhibit severe noise and artifacts, which compromise the practical application of spectral CT imaging technology. Methods that use tensor dictionary learning (TDL) have shown superior performance, but it is difficult to obtain a high-quality pre-trained global tensor dictionary in practice. In order to resolve this problem, this paper develops an algorithm called tensor decomposition with total generalized variation (TGV) for sparse-view spectral CT reconstruction. In the process of constructing tensor volumes, the proposed algorithm utilizes the non-local similarity feature of images to construct fourth-order tensor volumes and uses Canonical Polyadic (CP) tensor decomposition instead of pre-trained tensor dictionaries to further explore the inter-channel correlation of images. Simultaneously, introducing the TGV regularization term to characterize spatial sparsity features, the use of higher-order derivatives can better adapt to different image structures and noise levels. The proposed objective minimization model has been addressed using the split-Bregman algorithm. To assess the performance of the proposed algorithm, several numerical simulations and actual preclinical mice are studied. The final results demonstrate that the proposed algorithm has an enormous improvement in the quality of spectral CT images when compared to several existing competing algorithms. Full article

As an immensely important characteristic of natural images, the nonlocal self-similarity (NSS) prior has demonstrated great promise in a variety of inverse problems. Unfortunately, most current methods utilize either the internal or the external NSS prior learned from the degraded image or training images. The former is inevitably disturbed by degradation, while the latter is not adapted to the image to be restored. To mitigate such problems, this work proposes to learn a hybrid NSS prior from both internal images and external training images and employs it in image restoration tasks. To achieve our aims, we first learn internal and external NSS priors from the measured image and high-quality image sets, respectively. Then, with the learned priors, an efficient method, involving only singular value decomposition (SVD) and a simple weighting method, is developed to learn the HNSS prior for patch groups. Subsequently, taking the learned HNSS prior as the dictionary, we formulate a structural sparse representation model with adaptive regularization parameters called HNSS-SSR for image restoration, and a general and efficient image restoration algorithm is developed via an alternating minimization strategy. The experimental results indicate that the proposed HNSS-SSR-based restoration method exceeds many existing competition algorithms in PSNR and SSIM values. Full article

Hyperspectral (HS) data, encompassing hundreds of spectral channels for the same area, offer a wealth of spectral information and are increasingly utilized across various fields. However, their limitations in spatial resolution and imaging width pose challenges for precise recognition and fine classification in large scenes. Conversely, multispectral (MS) data excel in providing spatial details for vast landscapes but lack spectral precision. In this article, we proposed an adaptive learning-based mapping model, including an image fusion module, spectral super-resolution network, and adaptive learning network. Spectral super-resolution networks learn the mapping between multispectral and hyperspectral images based on the attention mechanism. The image fusion module leverages spatial and spectral consistency in training data, providing pseudo labels for spectral super-resolution training. And the adaptive learning network incorporates spectral response priors via unsupervised learning, adjusting the output of the super-resolution network to preserve spectral information in reconstructed data. Through the experiment, the model eliminates the need for the manual setting of image prior information and complex parameter selection, and can adjust the network structure and parameters dynamically, eventually enhancing the reconstructed image quality, and enabling the fine classification of large-scale scenes with high spatial resolution. Compared with the recent dictionary learning and deep learning spectral super-resolution methods, our approach exhibits superior performance in terms of both image similarity and classification accuracy. Full article

Background and Objectives: Speech audiometry employs standardized materials, typically in the language spoken by the target population. Language-specific nuances, including phonological features, influence speech perception and recognition. The material of speech audiometry tests for the assessment of word recognition comprises lists of words that are phonemically or phonetically balanced. As auditory perception is influenced by a variety of linguistic features, it is necessary to develop test materials for the listener’s mother tongue. The objective of our study was to compose and evaluate new lists of Lithuanian words to assess speech recognition abilities. Materials and Methods: The main criteria for composing new lists of Lithuanian words included the syllable structure and frequency, the correlation between consonant and vowel phonemes, the frequency of specific vowel and consonant phonemes, word familiarity and rate. The words for the new lists were chosen from the Frequency Dictionary of Written Lithuanian according to the above criteria. Word recognition was assessed at different levels of presentations. The word list data were analyzed using a linear mixed-effect model for repeated measures. Results: Two hundred bisyllabic words were selected and organized into four lists. The results showed no statistically significant difference between the four sets of words. The interaction of the word list and presentation level was not statistically significant. Conclusions: Monaural performance functions indicated good inter-list reliability with no significant differences between the word recognition scores on the different bisyllabic word lists at each of the tested intensities. The word lists developed are equivalent, reliable and can be valuable for assessing speech recognition in a variety of conditions, including diagnosis, hearing rehabilitation and research. Full article

This study aims to analyze the perceptions of construction experts in the Chilean construction industry regarding emotional intelligence. This exploratory and qualitative study is based on data collected through semi-structured interviews with construction professionals. The collected data were analyzed using a qualitative content analysis (QCA) technique to leverage how emotional intelligence (EI) is perceived by professionals in Chilean construction projects. A review, coding, and categorization were carried out within each transcribed interview, which allowed the elaboration of coding dictionaries and corresponding frequency tables to identify emerging topics. Our main results indicate that interviewees perceived EI as a tool to help them face the problems in Chilean construction projects. According to the interviewees, the benefits of implementing EI training are mainly the socio-emotional development and the transfer of critical skills to confront the challenges related to workers’ interactions. The main barriers are related to the work culture, lack of awareness of EI among construction workers, economic interests, and gender factors. The literature points to limited studies on understanding emotional intelligence in the construction sector, particularly in South America. This study contributes to responding to the need to explore and provide knowledge on emotional intelligence in the construction sector in the context of a South American country. This study contributes to exploring and understanding how EI is understood among workers in Chilean construction projects. In practicality, construction managers may use our findings to design training programs that leverage EI to improve the management of construction projects. Full article

In the realm of artificial intelligence, knowledge graphs have become an effective area of research. Relationships between entities are depicted through a structural framework in knowledge graphs. In this paper, we propose to build a domain-specific medicine dictionary (DSMD) based on the principles of knowledge graphs. Our dictionary is composed of structured triples, where each entity is defined as a concept, and these concepts are interconnected through relationships. This comprehensive dictionary boasts more than 348,000 triples, encompassing over 20,000 medicine brands and 1500 generic medicines. It presents an innovative method of storing and accessing medical data. Our dictionary facilitates various functionalities, including medicine brand information extraction, brand-specific queries, and queries involving two words or question answering. We anticipate that our dictionary will serve a broad spectrum of users, catering to both human users, such as a diverse range of healthcare professionals, and AI applications. Full article

Hyperspectral image (HSI) reconstruction from RGB input has drawn much attention recently and plays a crucial role in further vision tasks. However, current sparse coding algorithms often take each single pixel as the basic processing unit during the reconstruction process, which ignores the strong similarity and relation between adjacent pixels within an image or scene, leading to an inadequate learning of spectral and spatial features in the target hyperspectral domain. In this paper, a novel tensor-based sparse coding method is proposed to integrate both spectral and spatial information represented in tensor forms, which is capable of taking all the neighboring pixels into account during the spectral super-resolution (SSR) process without breaking the semantic structures, thus improving the accuracy of the final results. Specifically, the proposed method recovers the unknown HSI signals using sparse coding on the learned dictionary pairs. Firstly, the spatial information of pixels is used to constrain the sparse reconstruction process, which effectively improves the spectral reconstruction accuracy of pixels. In addition, the traditional two-dimensional dictionary learning is further extended to the tensor domain, by which the structure of inputs can be processed in a more flexible way, thus enhancing the spatial contextual relations. To this end, a rudimentary HSI estimation acquired in the sparse reconstruction stage is further enhanced by introducing the regression method, aiming to eliminate the spectral distortion to some extent. Abundant experiments are conducted on two public datasets, indicating the considerable availability of the proposed framework. Full article