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Detection of tiny object in complex environments is a matter of urgency, not only because of the high real-world demand, but also the high deployment and real-time requirements. Although many current single-stage algorithms have good detection performance under low computing power requirements, there are still significant challenges such as distinguishing the background from object features and extracting small-scale target features in complex natural environments. To address this, we first created real datasets based on natural environments and improved dataset diversity using a combination of copy–paste enhancement and multiple image enhancement techniques. As for the choice of network, we chose YOLOV5s due to its nature of fewer parameters and easier deployment in the same class of models. Most improvement strategies to boost detection performance claim to improve the performance of privilege extraction and recognition. However, we prefer to consider the combination of realistic deployment feasibility and detection performance. Therefore, based on the hottest improvement methods of YOLOV5s, we try to make adaptive improvements in three aspects, namely attention mechanism, head network, and backbone network. The experimental results proved that the decoupled head and Slimneck based improvements achieved, respectively, 0.872 and 0.849, 0.538 and 0.479, 87.5% and 89.8% on the mAP0.5, mAP0.5:0.95, and Precision metrics, surpassing the results of the baseline model on these three metrics: 0.705, 0.405 and 83.6%. This result suggests that the adaptively improved model can better meet routine testing needs without significantly increasing the number of parameters. These models perform well on our custom dataset and are also effective on images that are difficult to detect by naked eye. Meanwhile, we find that YOLOV8s, which also has the decoupled head improvement, has the results of 0.743, 0.461, and 87.17% on these three metrics. It proves that under our dataset, it is possible to achieve more advanced results with lower number of model parameters just by adding decoupled head. And according to the results, we also discuss and analyze some improvements that are not adapted to our dataset, which also provides ideas for researchers in similar scenarios: in the booming development of object detection, choosing the suitable model and adapting to combine with other technologies would help to provide solutions to real-world problems. [ABSTRACT FROM AUTHOR]

Trimethoprim (TMP) is a commonly used BCS class II antibiotic, the water solubility of which needs to be improved to overcome the limitations in oral administration. Herein, we combined TMP with pharmaceutically acceptable coformers (CCFs) by co-crystallization method and reported five new salts (two solvated salts and three unsolvated salts) of TMP. The crystal structures of the salts were determined by single-crystal X-ray diffraction. The supramolecular interactions in the crystal structures were theoretically investigated via molecular electrostatic potential surface (MEPs), Hirshfeld surface, and atoms-in-molecules (AIM) analyses of TMP and CCFs. The properties of the salts were experimentally characterized by using TGA, DSC, hot stage microscopy, and Fourier transform infrared (FTIR) spectroscopy. Furthermore, the equilibrium solubility and intrinsic dissolution rate (IDR) of the salts were determined and compared to that of bare TMP in buffers of pH 1.2 and 6.8. It was found that the equilibrium solubility and IDR of TMP–GA–2M (GA: gallic acid; M: methanol), TMP–SYA–0.16H (SYA: syringic acid; H: H2O), and TMP–OA (OA: orotic acid) dramatically increased in both buffers. The antibacterial activity measurement of these three salts against Escherichia coli revealed that the bacteriostasis rate of the three salts all increased by more than 40% and the minimum inhibitory concentrations (MICs) of salts all decreased compared to that of TMP. There may be drug synergism between the API and the CCF. Based on food safety considerations, TMP–SYA–0.16H and TMP–OA will have more potential prospects in further application of TMP. [ABSTRACT FROM AUTHOR]

Thermosalient (TS) crystals have gained considerable attention due to their potential applications in various fields, including in actuators, sensors, energy harvesting, and artificial muscles. Herein, co-crystallization was employed to construct TS crystals by forming a twisted angle between aromatic rings. Two multicomponent trimethoprim (TMP) TS cocrystals, TMP-25HBA and TMP-OA, were obtained. Differential scanning calorimetry (DSC) and variable-temperature powder X-ray diffraction (VT-PXRD) revealed that TMP-OA undergoes a solid-to-solid phase transition, while TMP-25HBA does not exhibit any phase transition. To the best of our knowledge, TMP-25HBA is the first multicomponent TS crystal without phase transition. The TS effect of both crystals is a result of the unit cell's anisotropic expansion. [ABSTRACT FROM AUTHOR]

The regulation of solid-state emission based on cocrystal engineering is an emerging strategy for developing next-generation luminescent materials. Here, three luminescent cocrystals (AA-DITFB, AA-TFTPA and AA-TCNB) of 9-anthraldehyde (9-AA) are reported, which exhibit a broad range of green-to-red emission. Tuning the photophysical properties of 9-AA via cocrystals is based on different mechanisms. Compared with single-component crystals, the difference in the emission properties of AA-DITFB and AA-TFTPA originates from the π–π interaction between chromophores. As for AA-TCNB, its prominent red-shifted emission is the result of the charge-transfer interaction, which is confirmed by infrared spectroscopy, "hole-electron" analysis and charge-transfer spectroscopy. This work not only reveals the relationship between molecular structure and fluorescent properties, but also proposes a strategy to develop multicolor luminescent systems with tunable efficiency and lifetime. [ABSTRACT FROM AUTHOR]

An extensive economic growth mode leads to resource depletion and environmental degradation. Green development is the best way to solve this problem. We analyzed the sample data of 41 cities in the Yangtze River Delta of China from 2007 to 2019. We used the spatial Durbin model and interaction term model to study the effects of regional financial agglomeration (FA) on green development and the moderating role of green technology innovation. The results show that FA promotes local green development but inhibits the green development of neighboring areas. Because the polarization effect of the growth pole is greater than its dispersion effect, cities with high levels of FA have a strong "siphon effect" on the surrounding areas. Green technology innovation positively moderates the promoting effect of FA on green development; the higher the level of green technology innovation and the degree of emphasis on it, the stronger the promoting effect. The government should encourage green finance to promote green technology innovation and promote the green development of the regional economy. These findings provide new insights for developing countries to achieve sustainable development under environmental constraints. [ABSTRACT FROM AUTHOR]

Fuzzy rule-based models (FRBMs) are sound constructs to describe complex systems. However, in reality, we may encounter situations, where the user or owner of a system only owns either the input or output data of that system (the other part could be owned by another user); and due to the consideration of data privacy, he/she could not obtain all the needed data to build the FRBMs. Since this type of situation has not been fully realized (noticed) and studied before, our objective is to come up with some strategy to address this challenge to meet the specific privacy consideration during the modeling process. In this study, the concept and algorithm of the collaborative fuzzy clustering (CFC) are applied to the identification of FRBMs, describing either multiple-input–single-output (MISO) or multiple-input–multiple-output (MIMO) systems. The collaboration between input and output spaces based on their structural information (conveyed in terms of the corresponding partition matrices) makes it possible to build FRBMs when input and output data could not be collected and used in unison. Surprisingly, on top of this primary pursuit, with the collaboration mechanism the input and output spaces of a system are endowed with an innovative way to comprehensively share, exchange, and utilize the structural information between each other, which results in their more relevant structures that guarantee better model performance compared with performance produced by some state-of-the-art modeling strategies. The effectiveness of the proposed approach is demonstrated by experiments on a series of synthetic and publicly available datasets. [ABSTRACT FROM AUTHOR]

Incomplete data are frequently encountered and bring difficulties when it comes to further processing. The concepts of granular computing (GrC) help deliver a higher level of abstraction to address this problem. Most of the existing data imputation and related modeling methods are of numeric nature and require prior numeric models to be provided. The underlying objective of this study is to introduce a novel and straightforward approach that uses information granules as a vehicle to effectively represent missing data and build granular fuzzy models directly from resulting hybrid granular and numeric data. The evaluation and optimization of this method are guided by the principle of justifiable granularity engaging the coverage and specificity criteria and carried out with the help of particle swarm optimization. We provide a collection of experimental studies using a synthetic dataset and several publicly available real-world datasets to demonstrate the feasibility and analyze the main features of this method. [ABSTRACT FROM AUTHOR]

It becomes apparent that there are no ideal numeric models. Bringing a concept of information granularity to the original numeric model makes it well aligned with the experimental data and helps deliver a better insight into the credibility of the results provided by the model. Information granularity is regarded as a crucial design asset being optimally allocated across the numeric parameters of the originally constructed model. The underlying objective of this study is to propose a concept of a granular output space and develop an optimization process of allocation of information granularity across this space. The optimization is carried out by optimizing output information granules produced by the granular model by considering a product of the essential criteria describing information granules, namely specificity and coverage. The detailed optimization procedure involving Particle Swarm Optimization (PSO) is presented. We stress a generality of the approach that cuts across a variety of classes of models. A collection of experimental studies involving interval information granules is reported demonstrating the main features of the proposed approach. [ABSTRACT FROM AUTHOR]

Fuzzy models are regarded as numeric constructs and as such are optimized and evaluated at the numeric level. In this study, we depart from this commonly accepted position and propose a granular evaluation of fuzzy models and present an augmentation of fuzzy models by forming information granules around numeric values of the parameters and constructions of the models. The concepts and algorithms of granular fuzzy models are discussed in the setting of Takagi–Sugeno rule-based architectures. We show how different protocols of forming and allocating information granules lead to the improvement of the granular performance of the models. Different from the standard numeric performance measure of fuzzy models coming in the form of the root mean squared error index, two performance measures are introduced that are pertinent to granular constructs, namely coverage and specificity. Furthermore, we propose a global indicator implied by these two measures, called an area under the curve, being computed for the characteristics of the granular model expressed in the coverage-specificity coordinates. A series of experimental studies is reported, which offers a comprehensive overview of the introduced performance measure criteria as well as the underlying realization of the granular fuzzy models. [ABSTRACT FROM PUBLISHER]

In recent years, granular fuzzy models have become an intensively studied category of fuzzy models. Granular fuzzy models help elevate the existing models to the higher level of abstraction subsequently making the resulting constructs in a better rapport with real-world systems. In contrast to numeric models, granular models produce results in the form of information granules (such as intervals, fuzzy sets, rough sets and alike). A number of studies have been focused on efficient designing granular fuzzy models where the information granularity has been formalized in the form of intervals. In this study, we propose a novel conceptual and algorithmic approach by elevating existing fuzzy models to granular fuzzy models in the form of fuzzy sets. Concentrating on Takagi-Sugeno fuzzy rule-based models (being commonly used in the literature), we establish a way of an optimal allocation of information granularity across the parameters of the original model and making them fuzzy sets (fuzzy numbers). As a result, the outputs of the granular fuzzy model become fuzzy sets. In the process of allocation of information granularity, we resort ourselves to some population-based meta-heuristic optimization techniques such as particle swarm optimization and differential evolution. To evaluate the performance of granular fuzzy models, we engage the principle of justifiable information granularity leading to the assessment and optimization of the coverage and specificity of the resulting fuzzy set. Besides, we involve the defuzzification (decoding) process to evaluate the impact of granular parameters on the quality of the numeric manifestation of the model. Comprehensive experimental studies involving synthetic and publicly available data sets are reported to demonstrate the performance of the granular fuzzy models. [ABSTRACT FROM AUTHOR]

Fuzzy rule-based models, due to modular architecture, have attracted attention and resulted in some practical implications because of their nonlinear characteristics and substantial interpretability. Data-driven fuzzy modeling is one of the most prevailing approaches, and the performance of such fuzzy models has been directly affected by a fundamental bias–variance dilemma. The concept and ensuing topologies of the ensemble strategy (bagging and boosting) offer an efficient method for constructing models to address this dilemma and to achieve a sound tradeoff. In this study, we design an ensemble fuzzy rule-based model in the setting of random forest and boosting mechanisms. To demonstrate the feasibility of the proposed method, we focus on the regression type of models. First, we design a method for assembling fuzzy rule-based models to improve the prediction accuracy. Second, we quantify the performance of the ensemble mechanism. To illustrate the effectiveness and discuss the main features of the proposed method, a series of publicly available datasets are considered in the experimental studies. • We design bagging and boosting mechanisms of assembling fuzzy rule-based models. • We quantify and analyze the performance of the ensemble mechanism. • We thoroughly study the predominant parameters of resulting ensemble models. [ABSTRACT FROM AUTHOR]