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A bstract Composite Higgs Models are often constructed including fermionic top partners with a mass around the TeV scale, with the top partners playing the role of stabilizing the Higgs potential and enforcing partial compositeness for the top quark. A class of models of this kind can be formulated in terms of fermionic strongly coupled gauge theories. A common feature they all share is the presence of specific additional scalar resonances, namely two neutral singlets and a colored octet, described by a simple effective Lagrangian. We study the phenomenology of these scalars, both in a model independent and model dependent way, including the bounds from all the available searches in the relevant channels with di-boson and di-top final states. We develop a generic framework which can be used to constrain any model containing pseudo-scalar singlets or octets. Using it, we find that such signatures provide strong bounds on the compositeness scale complementary to the traditional EWPT and Higgs couplings deviations. In many cases a relatively light scalar can be on the verge of discovery as a first sign of new physics.

A bstract The search for a Dark Matter particle is the new grail and hard-sought nirvana of the particle physics community. From the theoretical side, it is the main challenge to provide a consistent and model-independent tool for comparing the bounds and reach of the diverse experiments. We propose a first complete classification of minimal consistent Dark Matter models, abbreviated as MCDMs, that are defined by one Dark Matter weak multiplet with up to one mediator multiplet. This classification provides the missing link between experiments and top-down models. Consistency is achieved by imposing renormalisability and invariance under the full Standard Model symmetries. We apply this paradigm to the fermionic Dark Matter case. We also reconsider the one-loop contributions to direct detection, including the relevant effect of (small) mass splits in the Dark multiplet. Our work highlights the presence of unexplored viable models, and paves the way for the ultimate systematic hunt for the Dark Matter particle.

This paper presents a deep learning method for image dehazing and clarification. The main advantages of the method are high computational speed and using unpaired image data for training. The method adapts the Zero-DCE approach (Li et al. in IEEE Trans Pattern Anal Mach Intell 44(8):4225–4238, 2021) for the image dehazing problem and uses high-order curves to adjust the dynamic range of images and achieve dehazing. Training the proposed dehazing neural network does not require paired hazy and clear datasets but instead utilizes a set of loss functions, assessing the quality of dehazed images to drive the training process. Experiments on a large number of real-world hazy images demonstrate that our proposed network effectively removes haze while preserving details and enhancing brightness. Furthermore, on an affordable GPU-equipped laptop, the processing speed can reach 1000 FPS for images with 2K resolution, making it highly suitable for real-time dehazing applications.

We consider a variational approach to the problem of structure + texture decomposition (also known as cartoon + texture decomposition). As usual for many variational problems in image analysis and processing, the energy we minimize consists of two terms: a data-fitting term and a regularization term. The main feature of our approach consists of choosing parameters in the regularization term adaptively. Namely, the regularization term is given by a weighted p ( · ) -Dirichlet-based energy ∫ a ( x ) | ∇ u | p ( x ) , where the weight and exponent functions are determined from an analysis of the spectral content of the image curvature. Our numerical experiments, both qualitative and quantitative, suggest that the proposed approach delivers better results than state-of-the-art methods for extracting the structure from textured and mosaic images, as well as competitive results on image enhancement problems.

The aim of this study was to research the cost effectiveness of creating forest reclamation complexes on slopes, as well as to determine the patterns of their orographic dynamics, taking into account environmental aspects in arid conditions. With the help of modeling agroforestry landscapes, we established forest plantations created from Lanceolate ash (Fraxinus lanceolata) in arid climatic conditions on sloping lands, the cost of planting of which is EUR 1202–EUR 1453 per ha of forest. The specific capital intensity of the arrangement of land use by forest stands is EUR 24–EUR 63 per hectare of afforested plot, while 5–11% accounts for the cost of logging of forest care and 2–30% for the inclusion of a hydraulic element in forest reclamation systems. The monetary equivalent of the return on these investments in the form of prevented damage from soil erosion and air pollution is EUR 333–EUR 940 per hectare of afforested plot per year. This economic effect increases with the growth of the protective forest cover of the plot (by reducing the interband space) by almost 3 times. The benefit–cost ratio for all forest reclamation strategies on slopes is greater than 1, which confirms the high efficiency and expediency of capital investments in forest reclamation activities on slope lands to preserve the land resources of various regions.

The economic meaning of measures to water wetlands based on calculations of the economic value of their ecosystem goods and services is insufficiently studied in Russia. In this regard, it is difficult for decision-making authorities to adopt these measures as a strategy for sustainable management of natural resources. The purpose of the research is a monetary assessment of the regional benefits from ecosystem services of wetlands that the local community of the Lower Volga region will receive in connection with the rehabilitation of the Volga–Akhtuba floodplain. The study presents the magnitude and structure of these ecosystem services. The methodology of their economic assessment is given. It is established that by the period of full restoration of the hydrological regime of the Volga–Akhtuba floodplain (2035), the economic value of provisioning services of its wetlands, taking into account inflation and regional pricing, will be USD 87 ha−1 year−1, the economic value of cultural services—USD 77 ha–1 year−1, the economic value of regulation and maintenance services—USD 106 ha−1 year−1. The data obtained indicate the high importance of wetland irrigation measures for the Lower Volga region and allow us to consider them as a means of improving the quality of the environment and solving social problems of the region by decision-making authorities involved in the sustainable management of its development.

Mobile robot navigation in diverse environments is challenging due to varying terrain properties. Underlying surface classification improves robot control and navigation in such conditions. This paper presents an adaptive surface classification system using proprioceptive energy consumption data. We introduce an energy coefficient, calculated from motor current and velocity, to quantify motion effort. This coefficient’s dependency on motion direction is modeled for known surface types using discrete cosine transform. A probabilistic classifier, enhanced with memory, compares real-time coefficient values against these models to identify known surfaces. A neural network-based detector identifies encounters with previously unknown terrains by recognizing significant deviations from known models. Upon detection, a least squares method identifies the new surface’s model parameters using data gathered from specific motion directions. Experimental results validate the approach, demonstrating high classification accuracy for known surfaces (91%) and robust detection (96.2%) and identification (MAPE < 3%) of unknown surfaces.

A bstract We have explored Natural Supersymmetry (NSUSY) scenarios with low values of the μ parameter which are characterised by higgsino-like Dark Matter (DM) and compressed spectra for the lightest MSSM particles, χ 1 0 , χ 2 0 and χ 1 ± . This scenario could be probed via monojet signatures, but as the signal-to-background ratio (S/B) is low we demonstrate that the 8 TeV LHC cannot obtain limits on the DM mass beyond those of LEP2. On the other hand, we have found, for the 13 TeV run of the LHC, that by optimising kinematical cuts we can bring the S/B ratio up to the 5(3)% level which would allow the exclusion of the DM mass up to 200(250) GeV respectively, significantly extending LEP2 limits. Moreover, we have found that LUX/XENON1T and LHC do play very complementary roles in exploring the parameter space of NSUSY, as the LHC has the capability to access regions where DM is quasi-degenerate with other higgsinos, which are challenging for direct detection experiments.

A bstract Leptonic signatures of Dark Matter (DM) are one of the cleanest ways to discover such a secluded form of matter at high energy colliders. We explore the full parameter space relevant to multi-lepton (2- and 3-lepton) signatures at the Large Hadron Collider (LHC) from representative minimal consistent models with scalar and fermion DM. In our analysis, we suggest a new parametrisation of the model parameter spaces in terms of the DM mass and mass differences between DM and its multiplet partners. This parametrisation allows us to explore properties of DM models in their whole parameter space. This approach is generic and quite model-independent since the mass differences are related to the couplings of the DM to the Standard Model (SM) sector. We establish the most up-to-date LHC limits on the inert 2-Higgs Doublet Model (i2HDM) and Minimal Fermion DM (MFDM) model parameter spaces, by using the complementary information stemming from 2- and 3-lepton signatures. We provide a map of LHC efficiencies and cross-section limits for such 2- and 3-lepton signatures allowing one to easily make model-independent reinterpretations of LHC results for analogous classes of models. We also present combined constraints from the LHC, DM relic density and direct search experiments indicating the current status of the i2HDM and MFDM model.