Search Results (17,950)

Combining products of natural origin with different mechanisms of action on insect herbivores may provide an alternative among methods of plant protection against pests that are less risky for the environment. The aim of the study was to evaluate the effectiveness of mixtures of Thuja occidentalis L. essential oil and diatomite (EO + DE) compared to each substance separately in reducing economically important pests such as black bean aphid (BBA) Aphis fabae Scop., Colorado potato beetle (CPB) Leptinotarsa decemlineata Say., and pea leaf weevil (PLW) Sitona lineatus L. The effects on mortality (all pests) and foraging intensity (CPB and PLW) were tested. The improvement in effectiveness using a mixture of EO + DE versus single components against BBA was dose- and the developmental stage-dependent. The effect of enhancing CPB foraging inhibition through DE addition was obtained at a concentration of 0.2% EO (both females and males of CPB) and 0.5% EO (males) in no-choice experiments. In choice experiments, mixtures EO + DE with both 0.2% and 0.5% EO concentrations resulted in a significant reduction in CPB foraging. A significant strengthening effect of EO 0.5% through the addition of DE at a dose of 10% against PLW males was observed in the no-choice experiment, while, when the beetles had a choice, the synergistic effect of a mixture of EO 0.5% and DE 10% was also apparent in females. In conclusion, the use of DE mixtures with EO from T. occidentalis appears to be a promising strategy. The results support the idea of not using doses of EO higher than 0.5%. Full article

“The Internet of Things” is a relatively new idea that refers to objects that can connect to the Internet and exchange data. The Internet of Things (IoT) enables novel interactions between objects and people by interconnecting billions of devices. While there are many IoT-related products, challenges pertaining to their effective implementation, particularly the lack of knowledge and confidence about security, must be addressed. To provide IoT-based enterprises with a platform for efficiency and sustainability, this study aims to identify the critical elements that influence the growth of a successful company integrated with an IoT system. This study proposes a decision support tool that evaluates the influential features of IoT using the Pythagorean Fuzzy and Interval Grey approaches within the Analytical Hierarchy Process (AHP). This study demonstrates that security, value, and connectivity are more critical than telepresence and intelligence indicators. When both strategies are used, market demand and information privacy become significant indicators. Applying the Pythagorean Fuzzy approach enables the identification of sensor networks, authorization, market demand, and data management in terms of importance. The application of the Interval Grey approach underscores the importance of data management, particularly in sensor networks. The indicators that were finally ranked are compared to obtain a good coefficient of agreement. These findings offer practical insights for promoting sustainability in enterprise operations by optimizing IoT infrastructure and decision-making processes. Full article

Radar is one of the primary means of monitoring maritime targets. Compared to electro-optical systems, radar offers the advantage of all-weather, day-and-night operation. However, existing radar target detection algorithms predominantly achieve binary detection (i.e., determining the presence or absence of a target) and are unable to accurately classify target types. This limitation is particularly significant for coastal-deployed maritime surveillance radars, which must contend with not only maritime vessels but also various land-based and island targets within their monitoring range. This paper aims to enhance the informational breadth of existing binary detection methods by proposing a land–sea classification method of radar targets based on dynamic dense connections. The core idea behind this method is to merge the interlayer output features of the network and to augment and weigh them through dynamic convolutional combinations to improve the feature extraction capability of the network. The experimental results demonstrate that the proposed attribute recognition method outperforms current deep network architectures. Full article

This contribution aims to honour Guido Barbiellini’s profound interest in the interpretation and impact of quantum mechanics by examining the implications of the so-called before–before Experiment on quantum entanglement. This experiment was inspired by talks and discussions with John Bell at CERN. This was during the years when John and Guido co-worked, promoting the mission of the laboratory: “to advance the boundaries of human knowledge”. As the experiment uses measuring devices in motion, it can be considered a complement to entanglement experiments using stationary measuring devices, which have meanwhile been awarded the 2022 Nobel Prize in Physics. The before–before Experiment supports the idea that the quantum realm exists beyond space and time and that the quantum state is a real mental entity concerning choices. As it also leads us to a better understanding of the ‘quantum collapse’ and the measurement process, we pay homage to Guido’s work on detectors, such as his collaborations on the DELPHI experiment at CERN, on cosmic ray detection at the International Space Station, and gamma-ray astrophysics during a large NASA space mission. Full article

This paper discusses the integrated technology of CO₂ adsorption and catalysis, which combines adsorption and catalytic conversion, simplifies the traditional process, reduces energy consumption, and improves efficiency. The traditional carbon capture technology has the problems of high energy consumption, equipment corrosion, and absorbent loss, while the integrated technology realizes the adsorption, conversion, and catalyst regeneration of CO₂ in a single reaction system, avoiding complex desorption steps. Through micropore confinement and surface electron transfer mechanism, the technology improves the reactant concentration and mass transfer efficiency, reduces the activation energy, and realizes the low-temperature and high-efficiency conversion of CO₂. In terms of materials, MOF-based composites, alkali metal modified oxides, and carbon-based hybrid materials show excellent performance, helping to efficiently adsorb and transform CO₂. However, the design and engineering of reactors still face challenges, such as the development of new moving bed reactors. This technology provides a new idea for CO₂ capture and resource utilization and has important environmental significance and broad application prospects. Full article

We describe some properties of the hyperbolically symmetric black hole (hereafter referred to as the $HSBH$) proposed a few years ago. We start by explaining the main motivation behind such an idea, and we determine the main differences between this scenario and the classical black hole (hereafter referred to as the $CBH$) scenario. Particularly important are the facts that, in the $HSBH$ scenario, (i) test particles in the region inside the horizon experience a repulsive force that prevents them from reaching the center, (ii) test particles may cross the horizon outward only along the symmetry axis, and (iii) the spacetime within the horizon is static but not spherically symmetric. Next, we examine the differences between the two models of black holes in light of the Landauer principle and the Hawking results on the eventual evaporation of the black hole and the paradox resulting thereof. Finally, we explore what observational signature could be invoked to confirm or dismiss the model. Full article

This study explores the role of egoistic and altruistic values on green real estate purchase intention among young consumers in Canada aged between 20 and 40 years. In addition, this study examines the mediating effects of pro-environmental self-identity between social consumption motivation and green real estate purchase intention. A quantitative cross-sectional research design with an explanatory nature is employed. A total of 432 participating consumers in Canada, comprising 44% men and 48% women, with a graduate educational background accounting for 46.7%, and the ages between 24 and 35 contributing 75.2%, were part of the study, and the data collection used a survey method with a purposive sampling, followed by a respondent-driven method. Descriptive and inferential statistics were performed on the scales used for the study variables. A structural equational model and path analysis were conducted to derive the results, and the relationships were positive and significant. The study results infer the factors contributing to green real estate purchase intention, including altruistic value, egoistic value, social consumption motivation, and pro-environmental self-identity, with pro-environmental self-identity mediating the relationship. This study emphasizes the relevance of consumer values in real estate purchasing decisions, urging developers and marketers to prioritize ethical ideas, sustainable practices, and building a feeling of belonging and social connectedness. Offering eco-friendly amenities and green construction methods might attract clients, but creating a secure area for social interaction is critical. To the best of the authors’ knowledge, this research is the first to explore the role of egoistic and altruistic values on purchase intention, mainly in the housing and real estate sector, with the target consumers being young consumers in Canada. Full article

The paradigm-shift idea of murburn concept is no hypothesis but developed directly from fundamental facts of cellular/ecological existence. Murburn involves spontaneous and stochastic interactions (mediated by murzymes) amongst the molecules and unbound ions of cells. It leads to effective charge separation (ECS) and formation/recruitment of diffusible reactive species (DRS, like radicals whose reactions enable ATP-synthesis and thermogenesis) and emission of radiations (UV/Vis to ELF). These processes also lead to a chemo-electromagnetic matrix (CEM), ascertaining that living cell/organism react/function as a coherent unit. Murburn concept propounds the true utility of oxygen: generating DRS (with catalytic and electrical properties) on the way to becoming water, the life solvent, and ultimately also leading to phase-based macroscopic homeostatic outcomes. Such a layout enables cells to become simple chemical engines (SCEs) with powering, coherence, homeostasis, electro-mechanical and sensing–response (PCHEMS; life’s short-term “intelligence”) abilities. In the current review, we discuss the coacervate nature of cells and dwell upon the ways and contexts in which various radiations (either incident or endogenously generated) could interact in the new scheme of cellular function. Presenting comparative evidence/arguments and listing of systems with murburn models, we argue that the new perceptions explain life processes better and urge the community to urgently adopt murburn bioenergetics and adapt to its views. Further, we touch upon some distinct scientific and sociological contexts with respect to the outreach of murburn concept. It is envisaged that greater awareness of murburn could enhance the longevity and quality of life and afford better approaches to therapies. Full article

Existing heuristic algorithms are based on inspiration sources and have not yet done a good job of basing themselves on optimization principles to minimize and utilize historical information, which may lead to low efficiency, accuracy, and stability of the algorithm. To solve this problem, a personalized-template-guided intelligent evolutionary algorithm named PTG is proposed. The core idea of PTG is to generate personalized templates to guide particle optimization. We also find that high-quality templates can be generated to guide the exploration and exploitation of particles by using the information of the population particles when the optimal value remains unchanged, the knowledge of population distribution changes, and the dimensional distribution properties of particles themselves. By conducting an ablation study and comparative experiments on the challenging CEC2022 test and CEC2005 test functions, we have validated the effectiveness of our method and concluded that the stability and accuracy of the solutions obtained by PTG are superior to other algorithms. Finally, we further verified the effectiveness of PTG through four engineering problems. Full article

A concept towards current measurement in low and medium voltage power distribution networks is presented. The concentric magnetic field around the current-carrying conductor should be measured using a nitrogen-vacancy quantum magnetic field sensor. A bottleneck in current measurement systems is the readout electronics, which are usually based on optically detected magnetic resonance (ODMR). The idea is to have a hardware that tracks up to four resonances simultaneously for the detection of the three-axis magnetic field components and the temperature. Normally, expensive scientific instruments are used for the measurement setup. In this work, we present an electronic device that is based on a Zynq 7010 FPGA (Red Pitaya) with an add-on board, which has been developed to control the excitation laser, the generation of the microwaves, and interfacing the photodiode, and which provides additional fast digital outputs. The T1 measurement was chosen to demonstrate the ability to read out the spin of the system. Full article

Background: The aim of this review is to summarize and evaluate the properties of antibacterial polysaccharides for application in dental implantology to identify knowledge gaps and provide new research ideas. Methods: The electronic databases PubMed, Medline, ProQuest, and Google Scholar were used to search for peer-reviewed scientific publications published between 2018 and 2025 that provide insights to answer research questions on the role of antibacterial polysaccharides in combating pathogens in dental implantology without triggering immune reactions and inflammation. Further research questions relate to the efficacy against various dental pathogens and the understanding of the antibacterial mechanism, which may enable the development of functionalized polysaccharides with long-term antibacterial activity. Results: Biomedical implants have revolutionized medicine but also increased the risk of infections. Implant infections are a major problem in implantology and lead to implant failure and replacement. An antibacterial coating could be an excellent strategy to extend the lifespan of implants and improve the quality of the patient’s life. Bacterial resistance to antibiotics poses significant challenges for researchers, forcing them to search for new ways to prevent bacterial infections in implantology. Antibacterial natural polymers have recently received considerable research attention due to their long-term antibacterial activity. Polysaccharides from marine sources, such as chitosan and alginate, or pectin, xanthan, etc., from various plants, appear to be promising biopolymers for such applications in implantology due to their antibacterial activity, biocompatibility, and osteogenic properties. The antibacterial activity of these natural biopolymers depends on their chemical and physical properties. Nanopolysaccharides exhibit higher antibacterial activity than conventional polysaccharides, but their toxicity to human cells must be considered. Their antibacterial activity is based on the disruption of bacterial DNA or RNA synthesis, increased cell wall permeability, membrane disruption, and cytoplasmic leakage. Conclusions: Polysaccharides are a class of natural polymers with a broad spectrum of biological activities. They exhibit antioxidant, immunomodulatory, anticoagulant, anticancer, anti-inflammatory, antibacterial, and antiviral activity. Furthermore, polysaccharides are non-cytotoxic and exhibit good biocompatibility with osteogenic cells. Bactericidal polysaccharides are attractive new antibacterial materials against implant infections and open up new perspectives in implantology. Full article

To address the issues in existing 3D point cloud facet generation networks, specifically, the tendency to produce a large number of empty facets and the uncertainty in facet count, this paper proposes a novel deep learning framework for robust facet segmentation. Based on the generated facet set, two exploratory applications are further developed. First, to overcome the bottleneck where inaccurate empty-facet detection impairs the downsampling performance, a facet-abstracted downsampling method is introduced. By using a learned facet classifier to filter out and discard empty facets, retaining only non-empty surface facets, and fusing point coordinates and local features within each facet, the method achieves significant compression of point cloud data while preserving essential geometric information. Second, to solve the insufficient precision in organ segmentation within crop point clouds, a facet growth-based segmentation algorithm is designed. The network first predicts the edge scores for the facets to determine the seed facets. The facets are then iteratively expanded according to adjacent-facet similarity until a complete organ region is enclosed, thereby enhancing the accuracy of segmentation across semantic boundaries. Finally, the proposed facet segmentation network is trained and validated using a synthetic dataset. Experiments show that, compared with traditional methods, the proposed approach significantly outperforms both downsampling accuracy and instance segmentation performance. In various crop scenarios, it demonstrates excellent geometric fidelity and semantic consistency, as well as strong generalization ability and practical application potential, providing new ideas for in-depth applications of facet-level features in 3D point cloud analysis. Full article

This study presents a reanalysis of existing data to clarify how the visual system processes simple dynamic Glass patterns (GPs), with a particular focus on translational configurations. By combining datasets from previous studies, we apply a mixed-effects modeling approach—which offers advantages over the statistical methods used in previous studies—to investigate the contributions of pattern update rate and number of unique frames to perceptual sensitivity. Our findings indicate that the number of unique frames is the most robust predictor of discrimination thresholds, supporting the idea that the visual system integrates global form information across multiple frames—a process consistent with spatiotemporal summation. In contrast, the pattern update rate showed a weaker, though statistically significant, effect. This suggests that faster updates help preserve temporal consistency between frames, facilitating global form extraction. These results align with previous observations on complex dynamic GPs, where discrimination thresholds decrease with more unique frames, suggesting that the summation of form signals across time plays a key role in form–motion perception. By adopting a mixed-effects modeling approach, our reanalysis provides new insights into the mechanisms underlying global form perception in dynamic GPs. Full article

In compressed sensing, it is believed that the L₀ norm minimization is the best way to enforce a sparse solution. However, the L₀ norm is difficult to implement in a gradient-based iterative image reconstruction algorithm. The total variation (TV) norm minimization is considered a proper substitute for the L₀ norm minimization. This paper points out that the TV norm is not powerful enough to enforce a piecewise-constant image. This paper uses the limited-angle tomography to illustrate the possibility of using the L₀ norm to encourage a piecewise-constant image. However, one of the drawbacks of the L₀ norm is that its derivative is zero almost everywhere, making a gradient-based algorithm useless. Our novel idea is to replace the zero value of the L₀ norm derivative with a zero-mean random variable. Computer simulations show that the proposed L₀ norm minimization outperforms the TV minimization. The novelty of this paper is the introduction of some randomness in the gradient of the objective function when the gradient is zero. The quantitative evaluations indicate the improvements of the proposed method in terms of the structural similarity (SSIM) and the peak signal-to-noise ratio (PSNR). Full article

In this paper, we study the classification problem of short-time-window steady-state visual evoked potentials (SSVEPs) and propose a novel deep convolutional network named EEGResNet based on the idea of residual connection to further improve the classification performance. Since the frequency-domain features extracted from short-time-window signals are difficult to distinguish, the EEGResNet starts from the filter bank (FB)-based feature extraction module in the time domain. The FB designed in this paper is composed of four sixth-order Butterworth filters with different bandpass ranges, and the four bandwidths are 19–50 Hz, 14–38 Hz, 9–26 Hz, and 3–14 Hz, respectively. Then, the extracted four feature tensors with the same shape are directly aggregated together. Furthermore, the aggregated features are further learned by a six-layer convolutional neural network with residual connections. Finally, the network output is generated through an adaptive fully connected layer. To prove the effectiveness and superiority of our designed EEGResNet, necessary experiments and comparisons are conducted over two large public datasets. To further verify the application potential of the trained network, a virtual simulation of brain computer interface (BCI) based quadrotor control is presented through V-REP. Full article