Search Results (115)

Drought research is needed to understand the complex nature of drought phenomena and to develop effective management and mitigation strategies accordingly. This study presents a comprehensive regional frequency analysis (RFA) of 12-month meteorological droughts in the Kızılırmak Basin of Turkey using the L-moments approach. For this purpose, monthly precipitation data from 1960 to 2020 obtained from 22 meteorological stations in the basin are used. In the drought analysis, the Standard Precipitation Index (SPI), Z-Score Index (ZSI), China-Z Index (CZI) and Modified China-Z Index (MCZI), which are widely used precipitation-based indices in the literature, are employed. Here, the main objectives of this study are (i) to determine homogeneous regions based on drought, (ii) to identify the best-fit regional frequency distributions, (iii) to estimate the maximum drought intensities for return periods ranging from 5 to 1000 years, and (iv) to obtain drought maps for the selected return periods. The homogeneity test results show that the basin consists of a single homogeneous region according to the drought indices considered here. The best-fit regional frequency distributions for the selected drought indices are identified using L-moment ratio diagrams and ZDIST goodness-of-fit tests. According to the results, the best-fit regional distributions are the Pearson-Type 3 (PE3) for the SPI and ZSI, generalized extreme value (GEV) for the CZI, and generalized logistic distribution (GLO) for the MCZI. The drought maps obtained here can be utilized as a useful tool for estimating the probability of drought at any location across the basin, even without enough data for hydrological research. Full article

The reliability of data-driven approaches in generating landslide susceptibility maps depends on data quality, analytical method selection, and sampling techniques. Selecting optimal datasets and determining the most effective analytical methods pose significant challenges. This study assesses the performance of seven machine learning classifiers in the Himalayan region of the China–Pakistan Economic Corridor, utilizing statistical techniques and validation metrics. Thirteen geo-environmental variables were analyzed, including topographic (8), land cover (1), hydrological (1), geological (2), and meteorological (1) factors. These variables were evaluated for multicollinearity, feature importance, and their influence on landslide incidences. Our findings indicate that Support Vector Machines and Logistic Regression were highly effective, particularly near fault zones and roads, due to their effectiveness in handling complex, non-linear terrain interactions. Conversely, Random Forest and Logistic Regression demonstrated variability in their results. Each model distinctly identified landslide susceptibility zones ranging from very low to very high risk. Significant conditioning variables such as elevation, rainfall, lithology, slope, and land use were identified, reflecting the unique geomorphological conditions of the Himalayas. Further analysis using the Variance Inflation Factor and Pearson correlation coefficient showed minimal multicollinearity among the variables. Moreover, evaluations of Area Under the Receiver Operating Characteristic Curve (AUC-ROC) values confirmed the strong predictive capabilities of the models, with the Random Forest Classifier performing exceptionally well, achieving an AUC of 0.96 and an F-Score of 0.86. This study shows the importance of model selection based on dataset characteristics to enhance decision-making and strategy effectiveness. Full article

Estimating the pose of a large set of fixed indoor cameras is a requirement for certain applications in augmented reality, autonomous navigation, video surveillance, and logistics. However, accurately mapping the positions of these cameras remains an unsolved problem. While providing partial solutions, existing alternatives are limited by their dependence on distinct environmental features, the requirement for large overlapping camera views, and specific conditions. This paper introduces a novel approach to estimating the pose of a large set of cameras using a small subset of fiducial markers printed on regular pieces of paper. By placing the markers in areas visible to multiple cameras, we can obtain an initial estimation of the pair-wise spatial relationship between them. The markers can be moved throughout the environment to obtain the relationship between all cameras, thus creating a graph connecting all cameras. In the final step, our method performs a full optimization, minimizing the reprojection errors of the observed markers and enforcing physical constraints, such as camera and marker coplanarity and control points. We validated our approach using novel artificial and real datasets with varying levels of complexity. Our experiments demonstrated superior performance over existing state-of-the-art techniques and increased effectiveness in real-world applications. Accompanying this paper, we provide the research community with access to our code, tutorials, and an application framework to support the deployment of our methodology. Full article

The effective identification of reclaimed vegetation species is important for the subsequent management of ecological restoration projects in mining areas. Hyperspectral remote sensing has been used for identifying vegetation species. However, few studies have focused on mine-reclaimed vegetation. Even if there are studies in this field, the methods used by the researches are mainly traditional discriminant analyses. The environmental conditions of reclaimed mining areas lead to significant intraclass spectral differences in reclaimed vegetation, and there is uncertainty in the identification of reclaimed vegetation species using traditional classification models. In this study, in situ hyperspectral data were used to analyze the spectral variation in the reclaimed vegetation canopy in mine restoration areas and evaluate their potential in the identification of reclaimed vegetation species. We measured the canopy spectral reflectance of five vegetation species in the study area using the ASD FieldSpec 4. The spectral characteristics of vegetation canopy were analyzed by mathematically transforming the original spectra, including Savitzky–Golay smoothing, first derivative, reciprocal logarithm, and continuum removal. In addition, we calculated indicators for identifying vegetation species using mathematically transformed hyperspectral data. The metrics were submitted to a feature selection procedure (recursive feature elimination) to optimize model performance and reduce its complexity. Different classification algorithms (regularized logistic regression, back propagation neural network, support vector machines with radial basis function kernel, and random forest) were constructed to explore optimal procedures for identifying reclaimed vegetation species based on the best feature metrics. The results showed that the separability between the spectra of reclaimed vegetation can be improved by applying different mathematical transformations to the spectra. The most important spectral metrics extracted by the recursive feature elimination (RFE) algorithm were related to the visible and near-infrared spectral regions, mainly in the vegetation pigments and water absorption bands. Among the four identification models, the random forest had the best recognition ability for reclaimed vegetation species, with an overall accuracy of 0.871. Our results provide a quantitative reference for the future exploration of reclaimed vegetation mapping using hyperspectral data. Full article

One technique, especially in chaos-based cryptographic applications, is to include the message in the evolution of the dynamical system. This paper aims to find out if and to what extent the statistical behavior of the chaotic system is affected by the message inclusion in its dynamic evolution. The study is illustrated by the dynamical system described by the logistic map in cryptographic applications based on images. The evaluation of the statistical behavior was performed on an original scheme proposed. The Monte Carlo analysis of the applied Kolmogorov–Smirnov statistical test revealed that the dynamical system in the processing scheme with message inclusion does not modify its proper statistical behavior (revealed by definition relation). This was possible due to the proposed scheme designed. Namely, this scheme contains a decision switch which, supported by an appropriate choice of the magnitude of the scaling factor, ensures that the values of the dynamical system are maintained in the definition domain. The proposed framework for analyzing the statistical properties and for preserving the dynamical system behavior is one main contribution of this research. The message inclusion scheme also provides an enhancement with cryptographic mixing functions applied internally; the statistical behavior of the dynamical system is also analyzed in this case. Thus, the paper contributes to the theoretical complex characterization of the dynamical system considering also the message inclusion case. Full article

Using Uncrewed Aerial Vehicles (UAVs) to monitor the condition of nodal transportation assets—airports, seaports, heliports, vertiports, and cargo terminals—presents a transformative approach to traditional inspection methods. The focus on nodal assets rather than linear assets like roads, railways, bridges, and waterways fills a gap in addressing the dynamic challenges specific to transportation hubs. This study reviews scholarly literature on applying UAV-based remote sensing (URS) techniques to assess the condition of various transportation hubs, which are critical junctures in global logistics networks. Utilizing a systematic literature review framework, this study reviewed 486 publications from 2015 to 2023 to extract insights from the evolving discourse on URS applications. The findings suggest that these emerging methods resulted in substantial enhancements in time saving, cost efficiency, safety, and reliability. Specifically, this study presents evidence on how URS approaches can overcome the constraints of conventional inspection methods by enabling rapid, high-precision mapping and surveillance in complex and constrained environments. The findings highlight the role of UAVs in enhancing operational workflows and decision making in transportation planning and maintenance. By bridging the gap between traditional practices and innovative technology, this research offers significant implications for stakeholders in the field, advocating for a shift towards more dynamic, cost-effective, and precise asset management strategies. Full article

The complexity of historical data in financial markets and the uncertainty of the future, as well as the idea that investors always expect the least risk and the greatest return. This study presents a multi-period fractional portfolio model in a fuzzy environment, taking into account the limitations of asset quantity, asset position, transaction cost, and inter-period investment. This is a mixed integer programming NP-hard problem. To overcome the problem, an improved genetic algorithm (IGA) is presented. The IGA contribution mostly involves the following three points: (i) A cardinal constraint processing approach is presented for the cardinal constraint conditions in the model; (ii) Logistic chaotic mapping was implemented to boost the initial population diversity; (iii) An adaptive golden section variation probability formula is developed to strike the right balance between exploration and development. To test the model’s logic and the performance of the proposed algorithm, this study picks stock data from the Shanghai Stock Exchange 50 for simulated investing and examines portfolio strategies under various limitations. In addition, the numerical results of simulated investment are compared and analyzed, and the results show that the established models are in line with the actual market situation and the designed algorithm is effective, and the probability of obtaining the optimal value is more than 37.5% higher than other optimization algorithms. Full article

The rapid development and application of AMRs is important for Industry 4.0 and smart logistics. For large-scale dynamic flat warehouses, vision-based road recognition amidst complex obstacles is paramount for improving navigation efficiency and flexibility, while avoiding frequent manual settings. However, current mainstream road recognition methods face significant challenges of unsatisfactory accuracy and efficiency, as well as the lack of a large-scale high-quality dataset. To address this, this paper introduces IndoorPathNet, a transfer-learning-based Bird’s Eye View (BEV) indoor path segmentation network that furnishes directional guidance to AMRs through real-time segmented indoor pathway maps. IndoorPathNet employs a lightweight U-shaped architecture integrated with spatial self-attention mechanisms to augment the speed and accuracy of indoor pathway segmentation. Moreover, it surmounts the challenge of training posed by the scarcity of publicly available semantic datasets for warehouses through the strategic employment of transfer learning. Comparative experiments conducted between IndoorPathNet and four other lightweight models on the Urban Aerial Vehicle Image Dataset (UAVID) yielded a maximum Intersection Over Union (IOU) of 82.2%. On the Warehouse Indoor Path Dataset, the maximum IOU attained was 98.4% while achieving a processing speed of 9.81 frames per second (FPS) with a 1024 × 1024 input on a single 3060 GPU. Full article

Complex atypical endometrial hyperplasia (CAH) carries a high probability of cancer. The intraoperative evaluation of endometrial cancer in cases of CAH has not been reliable. The safety and sensitivity of sentinel lymph node (SLN) sampling has been validated. In our study, we aimed to evaluate the efficacy and safety of SLN sampling in CAH managed by the da Vinci robotic platform. A total of 113 patients with a preoperative diagnosis of CAH were included in this retrospective cohort study. All of them underwent a robot-assisted total laparoscopic hysterectomy and bilateral salpingo-oophorectomy, with 69 patients undergoing SLN sampling. A statistical analysis calculated the probability of cancer, the SLN map rate, and surgical complications. The predictors of cancer were evaluated. Descriptive statistics were used to summarize the results; comparative statistics were used to compare the cohorts; and logistical regression analysis was used to predict the risk. Forty-seven percent of the entire cohort was diagnosed with endometrial cancer. The median age was 63 years in the SLN cohort (N = 69) and 61 in the No-SLN cohort (N = 44) (p = 0.363). The median BMI was 34 Kg/m² in the SLN cohort and 40 in the No-SLN cohort (p = 0.004). The bilateral SLN map was 92.8%, and the unilateral SLN map rate was 7.2%. There were no grade-3–4 complications in the SLN cohort, and four grade-3–4 complications in the No-SLN group (p = 0.021). A preoperative diagnosis of CAH bordering on or unable to rule out cancer was the only predictor of cancer. Sentinel lymph node sampling has a high map rate and low complications in CAH. We recommend a prospective study investigating the clinical benefit of the procedure. Full article

Arguments inspired by algorithmic information theory predict an inverse relation between the probability and complexity of output patterns in a wide range of input–output maps. This phenomenon is known as simplicity bias. By viewing the parameters of dynamical systems as inputs, and the resulting (digitised) trajectories as outputs, we study simplicity bias in the logistic map, Gauss map, sine map, Bernoulli map, and tent map. We find that the logistic map, Gauss map, and sine map all exhibit simplicity bias upon sampling of map initial values and parameter values, but the Bernoulli map and tent map do not. The simplicity bias upper bound on the output pattern probability is used to make a priori predictions regarding the probability of output patterns. In some cases, the predictions are surprisingly accurate, given that almost no details of the underlying dynamical systems are assumed. More generally, we argue that studying probability–complexity relationships may be a useful tool when studying patterns in dynamical systems. Full article

The secure transmission of information is one of the most important topics in the field of information technology. Considering that images contain important visual information, it is crucial to create a safe platform for image transfer. One commonly employed tool to enhance the complexity and randomness in image encryption methods is the chaos system. The logistic and sine maps are utilized in encryption algorithms but these systems have some weaknesses, notably chaotic behavior in a confined area. In this study, to address these weaknesses, a hybrid system based on the Atangana–Baleanu fractional derivative is proposed. The various tests employed to evaluate the behavior of the new system, including the NIST test, histogram analysis, Lyapunov exponent calculation, and bifurcation diagram, demonstrate the efficiency of the proposed system. Furthermore, in comparison to the logistic and sine maps, the proposed hybrid exhibits chaotic behavior over a broader range. This system is utilized to establish a secure environment for the transmission of multiple images within an encryption algorithm, subsequently concealing them within a meaningful image. Various tools employed to assess the security of the proposed algorithm, including histogram analysis, NPCR, UACI, and correlation values, indicate that the proposed hybrid system has application value in encryption. Full article

In the past few decades, the transmission of data over an unsecure channel has resulted in an increased rate of hacking. The requirement to make multimedia data more secure is increasing day by day. Numerous algorithms have been developed to improve efficiency and robustness in the encryption process. In this article, a novel and secure image encryption algorithm is presented. It is based on a modified chaotic logistic map (CLM) that provides the advantage of taking less computational time to encrypt an input image. The encryption algorithm is based on Shannon’s idea of using a substitution–permutation and one-time pad network to achieve ideal secrecy. The CLM is used for substitution and permutation to improve randomness and increase dependency on the encryption key. Various statistical tests are conducted, such as keyspace analysis, complexity analysis, sensitivity analysis, strict avalanche criteria (SAC), histogram analysis, entropy analysis, mean of absolute deviation (MAD) analysis, correlation analysis, contrast analysis and homogeneity, to give a comparative analysis of the proposed algorithm and verify its security. As a result of various statistical tests, it is evident that the proposed algorithm is more efficient and robust as compared to previous ones. Full article

Simultaneous Localization and Mapping (SLAM), as one of the core technologies in intelligent robotics, has gained substantial attention in recent years. Addressing the limitations of SLAM systems in dynamic environments, this research proposes a system specifically designed for plant factory transportation environments, named GY-SLAM. GY-SLAM incorporates a lightweight target detection network, GY, based on YOLOv5, which utilizes GhostNet as the backbone network. This integration is further enhanced with CoordConv coordinate convolution, CARAFE up-sampling operators, and the SE attention mechanism, leading to simultaneous improvements in detection accuracy and model complexity reduction. While [email protected] increased by 0.514% to 95.364, the model simultaneously reduced the number of parameters by 43.976%, computational cost by 46.488%, and model size by 41.752%. Additionally, the system constructs pure static octree maps and grid maps. Tests conducted on the TUM dataset and a proprietary dataset demonstrate that GY-SLAM significantly outperforms ORB-SLAM3 in dynamic scenarios in terms of system localization accuracy and robustness. It shows a remarkable 92.59% improvement in RMSE for Absolute Trajectory Error (ATE), along with a 93.11% improvement in RMSE for the translational drift of Relative Pose Error (RPE) and a 92.89% improvement in RMSE for the rotational drift of RPE. Compared to YOLOv5s, the GY model brings a 41.5944% improvement in detection speed and a 17.7975% increase in SLAM operation speed to the system, indicating strong competitiveness and real-time capabilities. These results validate the effectiveness of GY-SLAM in dynamic environments and provide substantial support for the automation of logistics tasks by robots in specific contexts. Full article

In the logistics warehouse environment, the autonomous navigation and environment perception of the logistics sorting robot are two key challenges. To deal with the complex obstacles and cargo layout in a warehouse, this study focuses on improving the robot perception and navigation system to achieve efficient path planning and safe motion control. For this purpose, a scheme based on an improved Gmapping algorithm is proposed to construct a high-precision map inside a warehouse through the efficient scanning and processing of environmental data by robots. While the improved algorithm effectively integrates sensor data with robot position information to realize the real-time modeling and analysis of warehouse environments. Consequently, the precise mapping results provide a reliable navigation basis for the robot, enabling it to make intelligent path planning and obstacle avoidance decisions in unknown or dynamic environments. The experimental results show that the robot using the improved Gmapping algorithm has high accuracy and robustness in identifying obstacles and an effectively reduced navigation error, thus improving the intelligence level and efficiency of logistics operations. The improved algorithm significantly enhances obstacle detection rates, increasing them by 4.05%. Simultaneously, it successfully reduces map size accuracy errors by 1.4% and angle accuracy errors by 0.5%. Additionally, the accuracy of the robot’s travel distance improves by 2.4%, and the mapping time is reduced by nine seconds. Significant progress has been made in achieving high-precision environmental perception and intelligent navigation, providing reliable technical support and solutions for autonomous operations in logistics warehouses. Full article

Chaotic maps have been widely studied in the field of cryptography for their complex dynamics. However, chaos-based cryptosystems have not been widely used in practice. One important reason is that the following requirements of practical engineering applications are not taken into account: computational complexity and difficulty of hardware implementation. In this paper, based on the demand for information security applications, we modify the local structure of the three-dimensional Intertwining Logistic chaotic map to improve the efficiency of software calculation and reduce the cost of hardware implementation while maintaining the complex dynamic behavior of the original map. To achieve the goal by reducing the number of floating point operations, we design a mechanism that can be decomposed into two processes. One process is that the input parameters value of the original system is fixed to $2^k$ by Scale index analysis. The other process is that the transcendental function of the original system is replaced by a nonlinear polynomial. We named the new map as “Simple intertwining logistic”. The basic chaotic dynamic behavior of the new system for controlling parameter is qualitatively analyzed by bifurcation diagram and Lyapunov exponent; the non-periodicity of the sequence generated by the new system is quantitatively evaluated by using Scale index technique based on continuous wavelet change. Fuzzy entropy (FuzzyEn) is used to evaluate the randomness of the new system in different finite precision digital systems. The analysis and evaluation results show that the optimized map could achieve the designed target. Then, a novel scheme for generating pseudo-random numbers is proposed based on new map. To ensure its usability in cryptographic applications, a series of analysis are carried out. They mainly include key space analysis, recurrence plots analysis, correlation analysis, information entropy, statistical complexity measure, and performance speed. The statistical properties of the proposed pseudo random number generator (PRNG) are tested with NIST SP800-22 and DIEHARD. The obtained results of analyzing and statistical software testing shows that, the proposed PRNG passed all these tests and have good randomness. In particular, the speed of generating random numbers is extremely rapid compared with existing chaotic PRNGs. Compared to the original chaotic map (using the same scheme of random number generation), the speed is increased by 1.5 times. Thus, the proposed PRNG can be used in the information security. Full article