Search Results (36)

In recent years, many chaos-based encryption algorithms have been proposed. Many of these are based on established designs and populate their S-boxes with values derived from chaotic maps, following conventional implementation strategies to enable comparison with their original non-chaotic counterparts. In contrast, this work proposes a novel approach: a Chaos-Based Substitution Box (CB-SBox) implementation, in which conventional ROM-based S-boxes are replaced by a digital circuit that directly executes a selected chaotic map. This method enables the construction of S-boxes with long word lengths through an FPGA-based programmable circuit that allows for variable S-box lengths, facilitating the analysis of S-boxes of varying sizes, and ultimately enhancing security, particularly for larger S-boxes, as demonstrated by increased resistance to linear and differential cryptanalysis. Furthermore, the proposed CB-SBox achieves reductions in both area and power consumption compared to size-comparable ROM-based S-boxes. A 19-bit chaos-based S-box consumes just 0.0238% of the area and 0.0241% of the power required by an equivalent ROM-implemented S-box while providing the same level of security. The inherent unpredictability of non-linear chaotic behavior causes the proposed chaos-based S-boxes to exhibit non-bijective characteristics, making them well suited for application in non-invertible cryptographic primitives, such as hash functions and Feistel networks. The proposed CB-SBox is implemented in a Feistel network as described in the literature, and the results are provided. Full article

Over the past two decades, cardiovascular disease (CVD) prevention guidelines have progressively lowered LDL-C targets to <70 mg/dL for high-risk individuals based on the assumption of a linear relationship between LDL-C levels and CVD risk. However, the available evidence challenges this premise. Multiple studies demonstrate a weak or inconsistent association between LDL-C levels and atherosclerosis progression at the individual patient-level. Systematic reviews supporting the linearity assumption have notable limitations, including extrapolation beyond observed LDL-C ranges and potential ecological fallacy, as meta-regression analyses rely on study-level data, while patient-level data within the same trials often show no association between LDL-C reduction and CVD outcomes. Moreover, randomized controlled trials explicitly designed to assess LDL-C targets have yielded inconclusive and biased results. LDL-C itself is a heterogeneous marker, with particle size and composition influencing its atherogenicity. The cardiovascular benefits of lipid-lowering therapies may arise in part from pleiotropic effects unrelated to LDL-C lowering. Additionally, several studies indicate that higher LDL-C levels are paradoxically associated with longevity in elderly populations that is equal to or even greater than that of the general population. Collectively, this body of evidence raises questions about the validity of current LDL-C targets < 70 mg/dL in high-risk patients. Full article

In real-world applications, many complex problems can be formulated as mathematical optimization challenges, and efficiently solving these problems is critical. Metaheuristic algorithms have proven highly effective in addressing a wide range of engineering issues. The differentiated creative search is a recently proposed evolution-based meta-heuristic algorithm with certain advantages. However, it also has limitations, including weakened population diversity, reduced search efficiency, and hindrance of comprehensive exploration of the solution space. To address the shortcomings of the DCS algorithm, this paper proposes a multi-strategy differentiated creative search (MSDCS) based on the collaborative development mechanism and population evaluation strategy. First, this paper proposes a collaborative development mechanism that organically integrates the estimation distribution algorithm and DCS to compensate for the shortcomings of the DCS algorithm’s insufficient exploration ability and its tendency to fall into local optimums through the guiding effect of dominant populations, and to improve the quality of the DCS algorithm’s search efficiency and solution at the same time. Secondly, a new population evaluation strategy is proposed to realize the coordinated transition between exploitation and exploration through the comprehensive evaluation of fitness and distance. Finally, a linear population size reduction strategy is incorporated into DCS, which significantly improves the overall performance of the algorithm by maintaining a large population size at the initial stage to enhance the exploration capability and extensive search of the solution space, and then gradually decreasing the population size at the later stage to enhance the exploitation capability. A series of validations was conducted on the CEC2018 test set, and the experimental results were analyzed using the Friedman test and Wilcoxon rank sum test. The results show the superior performance of MSDCS in terms of convergence speed, stability, and global optimization. In addition, MSDCS is successfully applied to several engineering constrained optimization problems. In all cases, MSDCS outperforms the basic DCS algorithm with fast convergence and strong robustness, emphasizing its superior efficacy in practical applications. Full article

Competitive dynamics in dry bulk terminals necessitate efficient planning and scheduling to optimize operations. This study focuses on the productivity of stackers and reclaimers by developing a mathematical optimization model to enhance scheduling efficiency. A mixed-integer linear programming (MILP) model was formulated to minimize the maximum completion time (makespan) of operations while ensuring smooth material flow and resource utilization. Given the computational complexity of real-world scenarios, a novel hybrid genetic algorithm (GA) was proposed. This algorithm integrates tabu search to generate a high-quality initial population size and employs innovative chromosome designs that respect operational constraints, such as equipment availability, material flow continuity, and sequencing restrictions. This hybrid approach balances exploration and exploitation, improving solution convergence and robustness. Computational experiments using real data from a Moroccan dry bulk terminal validated the algorithm’s efficiency and effectiveness. Performance indicators such as makespan reduction, equipment utilization, and computational efficiency were analyzed. The results demonstrate that the hybrid GA significantly reduced processing times and improved resource efficiency compared to conventional methods. Additionally, the algorithm showed scalability across different operational scenarios, confirming its adaptability to dynamic terminal conditions. These findings highlight the potential of advanced optimization techniques to enhance decision making and improve operational productivity in dry bulk terminals. Full article

In this study, firstly, the balance between the exploration and exploitation capabilities of the weighted mean of vectors (INFO) algorithm was developed using the fitness–distance balance (FDB) method. Then, the FDB-INFO algorithm was developed with a hyper-heuristic method to create the beginning optimal population by using Linear Population Reduction Success History-based Adaptive Differential Evolution (LSHADE) and a novel Hyper-FDB-INFO algorithm was presented. Finally, the developed Hyper-FDB-INFO algorithm was applied to solve the optimal placement and sizing of FACTS devices for the optimal power flow (OPF) problem incorporating wind energy sources. Moreover, determining the placement and sizing of FACTS devices is an additional problem to minimize the total cost of generation and reducing the power losses of the power system. The experimental results showed that the Hyper-FDB-INFO algorithm is a more effective solver than the SHADE-SF, INFO, FDB-INFO and Hyper-INFO algorithms for wind power and FACTS devices integrating the OPF problem. Full article

European Union cargo and container ports are under pressure to reduce GHG emissions and achieve carbon neutrality by 2050, as mandated by the European Commission. The pace of progress varies among ports. This study examined the characteristics influencing GHG reduction measures in European cargo and container ports and their implications for sustainable investing. The methods used in this study, such as linear regression models to analyze predictive variables, can be applied in sustainable investing to assess which factors most strongly predict a company’s environmental, social, and governance performance. Using linear regression models to analyze data from the 33 busiest European ports, we identified five predictive variables: port size, cargo mix, surrounding population density, access to the sea, and the economic wealth of the host country. Our findings revealed that the port size significantly correlates with the adoption of measures to reduce scope 1, 2, and 3 emissions. This study underscores the importance of contextual and operational factors in evaluating sustainability efforts across sectors. The results contribute to drawing parallels with the field of sustainable investing within finance. This offers valuable insights for sustainable investing, emphasizing the importance of considering various contextual and operational factors when evaluating the sustainability efforts of entities in different sectors. Full article

Loss of hand functions, often manifesting in the form of weakness or spasticity from conditions like stroke or multiple sclerosis, poses challenges in performing activities of daily living (ADLs). The broad area of rehabilitation robotics provides the tools and knowledge necessary for implementing efficient restorative therapies. These therapies aim to improve hand functionality with minimal therapist intervention. However, the human hand evolved for various precision and power gripping tasks, with its intricate anatomy featuring a large number of degrees of freedom—up to 31—which hinder its modeling in many rehabilitation scenarios. In the process of designing prosthetic devices, instrumented gloves, and rehabilitation devices, there is a clear need to obtain simplified rehabilitation-oriented hand models without compromising their representativeness across the population. This is where the concept of kinematic reduction, focusing on specific grasps, becomes essential. Thus, the objective of this study is to uncover the intra-finger dependencies during finger flexion/extension by analyzing a comprehensive database containing recorded trajectories for 23 different functional movements related to ADLs, involving 77 test subjects. The initial phase involves data wrangling, followed by correlation analysis aimed at selecting 116 dependency-movement relationships across all grasps. A regularized generalized linear model is then applied to select uncorrelated predictors, while a linear mixed-effect model, with reductions based on both predictor significance and effect size, is used for modeling the dependencies. As a final step, agglomerative clustering of models is performed to further facilitate flexibility in tradeoffs in hand model accuracy/reduction, allowing the modeling of finger flexion extensions using 5–15 degrees of freedom only. Full article

The escalating prevalence of carbohydrate metabolism disorders (CMDs) prompts the need for early diagnosis and effective markers for their prediction. Hyperglycemia, the primary indicator of CMDs including prediabetes and type 2 diabetes mellitus (T2DM), leads to overproduction of reactive oxygen species (ROS) and oxidative stress (OxS). This condition, resulting from chronic hyperglycemia and insufficient antioxidant defense, causes damage to biomolecules, triggering diabetes complications. Additionally, aging itself can serve as a source of OxS due to the weakening of antioxidant defense mechanisms. Notably, previous research indicates that miR-196a, by downregulating glutathione peroxidase 3 (GPx3), contributes to insulin resistance (IR). Additionally, a GPx3 decrease is observed in overweight/obese and insulin-resistant individuals and in the elderly population. This study investigates plasma GPx3 levels and miR-196a expression as potential CMD risk indicators. We used ELISA to measure GPx3 and qRT-PCR for miR-196a expression, supplemented by multivariate linear regression and receiver operating characteristic (ROC) analysis. Our findings included a significant GPx3 reduction in the CMD patients (n = 126), especially in the T2DM patients (n = 51), and a decreasing trend in the prediabetes group (n = 37). miR-196a expression, although higher in the CMD and T2DM groups than in the controls, was not statistically significant, potentially due to the small sample size. In the individuals with CMD, GPx3 levels exhibited a negative correlation with the mass of adipose tissue, muscle, and total body water, while miR-196a positively correlated with fat mass. In the CMD group, the analysis revealed a weak negative correlation between glucose and GPx3 levels. ROC analysis indicated a 5.2-fold increased CMD risk with GPx3 below 419.501 ng/mL. Logistic regression suggested that each 100 ng/mL GPx3 increase corresponded to a roughly 20% lower CMD risk (OR = 0.998; 95% CI: 0.996–0.999; p = 0.031). These results support the potential of GPx3 as a biomarker for CMD, particularly in T2DM, and the lack of a significant decline in GPx3 levels in prediabetic individuals suggests that it may not serve reliably as an early indicator of CMDs, warranting further large-scale validation. Full article

Opioid prescribing and dispensing from emergency departments is a noteworthy issue given widespread opioid misuse and diversion in many countries, contributing both physical and economic harm to the population. High patient numbers and the stochastic nature of acute emergency presentations to emergency departments (EDs) introduce challenges for prescribers who are considering opioid stewardship principles. This study investigated the effect of changes to electronic prescribing software on prescriptions with an auto-populated quantity of oxycodone immediate release (IR) from an Australian tertiary emergency department following the implementation of national recommendations for reduced pack sizes. A retrospective review of oxycodone IR prescriptions over two six-month periods between 2019 and 2021 was undertaken, either side of a software adjustment to reduce the default quantities of tablets prescribed from 20 to 10. Patient demographic details were collected, and prescriber years of practice calculated for inclusion in linear mixed effects regression modelling. A reduction in the median number of tablets prescribed per prescription following the software changes (13.5 to 10.0, p < 0.001) with little change in the underlying characteristics of the patient or prescriber populations was observed, as well as an 11.65% reduction in the total number of tablets prescribed. The prescriber’s years of practice, patient age and patient sex were found to influence increased prescription sizes. Reduced quantity of oxycodone tablets prescribed was achieved by alteration of prescribing software prefill parameters, providing further evidence to support systems-based policy interventions to influence health care providers behaviour and to act as a forcing function for prescribers to consider opioid stewardship principles. Full article

Recognizing how populations fluctuate over time is a crucial factor in determining the environmental elements affecting population persistence. However, the limited information on wild bee populations complicates the estimation of the impact of anthropogenic threats leading to changes in population size. To address this, we conducted a study capturing and monitoring nine species of wild bees through monthly samplings over four years. Tray traps were placed in permanent plots, and capture records were used to determine population size (N) and density (D). A generalized linear model (GLM) was employed to determine how the use of traps affected bee species captures. The families Apidae and Halictidae represented the most captures. Apis mellifera, the Lasioglossum (Dialictus spp.) complex, and Macrotera sinaloana exhibited the largest number of captures and highest population density. Most species (77.7%) showed a tendency to remain constant over the years and to have a higher number of captures in the spring months. Moreover, yellow traps were the most effective in capturing bee individuals. We suggest that the availability of essential resources and the reduction in environmental stressors positively affected the capture of wild bee populations. Full article

Landscape fires emit smoke that contains particulate matter (PM) that can be harmful to human health. Prescribed fires or hazard reduction burns (HRBs) and wildfires can substantially reduce air quality in populated areas. While HRBs reduce the size and PM output of future wildfires, they also produce PM. There is a critical question of whether conducting HRBs adds to or reduces the total PM (prescribed + wildfire PM) exposure of populations, which has important community health implications. We modelled mean 24 h PM_2.5 in Sydney from fire type, 24 h active regional fire area and weather predictors. We compared HRB and wildfire area effects via an interaction between fire area and fire type, and we found that a non-linear effect of fire area was most appropriate. We conducted a trade-off analysis by using the model to predict increased HRB area scenarios and calculated the number of HRB and wildfire days with ‘Fair’ or worse air quality under each scenario. Regional HRBs and wildfire areas had similar effects on PM_2.5 in Sydney. Increasing regional HRB area produced substantial increases in HRB exceedance days (>12.5 µgm⁻³) in Sydney but only a small reduction in wildfire exceedance days. Our results indicate that small fires in the region have a higher per-hectare impact on PM levels in Sydney, and consequently, increasing regional HRB area would result in more poor air quality days overall (HRB + wildfire days) in Sydney. Full article

This study provides comprehensive results on the current status of inbreeding depression for traits upon which sheep are selected for the herdbook in Germany. A total of 30 sheep breeds from the OviCap national database met the inclusion criteria for the present analysis regarding the depth and completeness of pedigrees and the number of animals with phenotypic data. We analyzed heritabilities and inbreeding depression for the three breeding objective traits of wool quality, muscling conformation and exterior. Heritabilities were across all breeds of moderate size, with estimates of 0.18 for wool quality and muscling conformation and of 0.14 for exterior. The models employed to estimate linear regression slopes for individual and ancestral inbreeding rates also account for non-genetic effects and the additive genetic effect of the animal. Inbreeding depression was obvious for all three traits when we averaged the estimates across all 30 sheep breeds. Inbreeding depression was significant for wool quality for only a few breeds, whereas for muscling conformation, 14/30 breeds achieved significant estimates. A 1% increase in inbreeding decreased the mean of all three traits across all sheep breeds by 0.33% of their standard deviation. Positive effects due to ancestral inbreeding were only significant in very few breeds in the different traits. Across all 30 sheep breeds, there were indications that purging effects (a reduction in negative effects of inbreeding depression due to selection for heterozygotes) may play a role for the exterior. The results of this study should help for reviewing breeding programs, particularly for sheep breeds with critical effective population sizes and increasing rates of inbreeding, with regard to the selection policy and selection intensity applied. Full article

The aim of this study was to analyze the therapeutic results and survival of patients with myelofibrosis treated with ruxolitinib in comparison with a group on standard therapy. It is a cross-sectional, retrospective, non-interventional, real-life study that was performed between January 2000 and February 2023. Patients treated between 2000 and 2016, before the introduction of ruxolitinib, constituted the control group (n = 45), while those treated after May 2016, after ruxolitinib inclusion, constituted the active group (n = 66). Demographic characteristics, clinical indicators, the severity of the disease, and survival were explored using Kaplan–Meier survival analyses. Spearman’s correlation, linear regression, and other statistical analyses were performed. According to the Kaplan–Meier analysis, there was a 75.33% reduction in the fatality risk in the sample. On a general-population level, the fatality risk in the group treated with ruxolitinib varied between 7.9% and 77.18% compared to that of the risk in the control group. There was a decrease in blood parameters (leukocytes, hemoglobin, and platelets) and spleen size. During the first six months, the spleen size of the patients on ruxolitinib decreased by 6%, and during the second six months, it decreased by another 9%. This study shows that patients in a real-life clinical setting treated with ruxolitinib exhibited improved clinical signs of the disease, had a lower symptom severity, and survived longer than patients on standard therapy before ruxolitinib’s entrance into the national market. The improvements correlate with those reported in randomized clinical trials. Full article

This paper presents a study on the modeling and control of an aero-engine within the full flight envelope using the Takagi–Sugeno (T-S) fuzzy theory. A highly accurate aero-engine small deviation state variable model (SVM) was developed using the adaptive differential evolution, based on numerous successes through history, with the linear population size reduction (L-SHADE) algorithm. The affinity propagation (AP) clustering algorithm was then implemented to realize the division of flight envelopes based on the gap metric between the SVMs at each working point. By solving the membership parameters using the L-SHADE algorithm, the T-S fuzzy model was obtained, which has flight conditions as premises and engine linear SVM as consequences. Furthermore, based on the established T-S fuzzy model and T-S control theory, a controller design method is proposed. The simulation results show that the T-S fuzzy model has high accuracy and good generalization capability within the flight envelope, and the proposed control method can guarantee the asymptotic stability of the system, subject to external disturbance and time delay. Full article

The development of the real estate industry inevitably consumes large amounts of fossil energy and makes great contributions to China’s carbon emissions. However, very few research studies have explored the intrinsic link and influence mechanisms between the rapidly growing real estate sector and carbon emissions in China. Hence, this study investigated the impact of real estate development on carbon emissions using a differential generalized method of moments and dynamic panel threshold models. The empirical results show that: (1) There is a non-linear relationship between real estate development and China’s carbon emissions, first promoting and then inhibiting them with the increasing level of real estate development, but it will take a long time to reach the latter stage in the future; (2) The threshold effect of economic development levels on carbon emissions was identified with a threshold value of 9.904, and the positive impact of real estate development on China’s carbon emissions is more significant in economically backward areas; (3) The threshold effect of population sizes on carbon emissions was identified with a threshold value of 7.839, and in areas with larger populations, the positive impact of real estate development on China’s carbon emissions is more significant. The findings above extend the carbon emission literature by clarifying the threshold role of the economic development level and population size between real estate development and carbon emissions. Furthermore, the findings of this study are instructive for China to formulate energy-saving and emission-reduction policies according to local conditions and will ultimately contribute to achieving the goal of “carbon peaking” and “carbon neutrality”. Full article