Search Results (4,056)

As Android devices become more prevalent, their security risks extend beyond software vulnerabilities to include critical hardware weaknesses. This paper provides a comprehensive and systematic review of hardware-related vulnerabilities in Android systems, which can bypass even the most sophisticated software defenses. We compile and analyze an extensive range of reported vulnerabilities, introducing a novel categorization framework to facilitate a deeper understanding of these risks, classified by affected hardware components, vulnerability type, and the potential impact on system security. The paper addresses key areas such as memory management flaws, side-channel attacks, insecure system-on-chip (SoC) resource allocation, and cryptographic vulnerabilities. In addition, it examines feasible countermeasures, including hardware-backed encryption, secure boot mechanisms, and trusted execution environments (TEEs), to mitigate the risks posed by these hardware threats. By contextualizing hardware vulnerabilities within the broader security architecture of Android devices, this review emphasizes the importance of hardware security in ensuring system integrity and resilience. The findings serve as a valuable resource for both researchers and security professionals, offering insights into the development of more robust defenses against the emerging hardware-based threats faced by Android devices. Full article

Accurate estimation of the state of health (SOH) of batteries for automotive applications, particularly in electric vehicle battery management systems (EV-BMS), remains a critical study area to ensure battery system availability. This paper proposes a comprehensive SOH estimation method that transcends traditional approaches based on estimating the available capacity using the integral of the battery current or estimating the increase in internal resistance. The SOH estimator employs a partial discharge method (PDM) and a linear state-of-charge (SOC) observer based on an equivalent electrical circuit model (ECM), utilizing readily available manufacturer data and designed for real-time applications. The proposed method was tested and validated using three different automotive battery technologies and a real-time simulation on the OPAL-RT platform. The simulations involved voltage and current measurements of pulsed-discharge current profiles under temperature-controlled conditions and an electric vehicle driving profile. The results showed a high accuracy in SOH estimation, with a maximum standard deviation of approximately 0.03497 V for lithium-ion batteries, representing about 7.124% of the mean value of the SOH estimator output. For other technologies, the standard deviations were even lower, all below 0.61% of their respective mean values. These outcomes demonstrate the reliability and accuracy of our method, making it suitable for real-time SOH estimation in EV-BMSs. Full article

Soil water movement is energy-dependent and is influenced by various management practices. It can be understood by measuring the soil water potential (SWP); however, the influence of cover crops (CCs) on SWP is not currently well understood. The objective of this study was to assess the effects of CCs on SWP before and after termination in order to understand their effects on soil water availability for the subsequent cash crop. The experimental design was a completely randomized design with two levels of CCs (CCs vs. no cover crop [NC]) with three replicates. The SWP sensors were buried at 0–10, 10–20, and 20–30 cm depths before CCs were planted. Additionally, soil samples were collected at the aforementioned depths just before CC termination for soil organic carbon (SOC), bulk density (BD), and saturated hydraulic conductivity (K_sat) analysis. Results showed that CCs increased SOC and K_sat, and significantly lowered BD compared with NC management. Before termination, CC plots had significantly lower SWP values compared with NC management, suggesting that the transpirational needs of the CCs can lead to lower water content. After termination, CC management also resulted in lower SWP, suggesting that CCs can increase water-use efficiency by improving soil health parameters. However, effective planning is required for CC implementation, especially in semiarid and arid regions. Full article

Recent studies have focused on accuracy as the key state of charge (SoC) estimation algorithms’ performance metrics, whereas just a few of them compare algorithms in terms of energy efficiency. Such a comparison is important when selecting an algorithm that should be implemented on a resource-constrained, low-power embedded system. In this paper, recursive model-based SoC estimation algorithms, such as the extended Kalman filter, have been identified as well-suited solutions for implementation on an embedded platform, providing a good compromise between estimation accuracy and computational complexity that is correlated to energy consumption. Assuming that a decrease in the estimation rate will result in a decrease in both accuracy and energy consumption of the estimator, a theoretical analysis has been conducted to establish how these two metrics depend on the estimation rate. To verify results obtained in theory, two extended Kalman filter-based SoC estimation algorithms of different complexities have been implemented and compared in terms of accuracy, quantified by root mean square error (RMSE), and energy consumption. The obtained results confirm that for a selected type of recursive model-based SoC estimation algorithm, it is possible to achieve an optimal algorithm estimation rate in the sense of satisfactory accuracy and acceptable energy consumption. The analysis and results presented in this paper establish a foundation for a future development of energy-efficient algorithms for SoC estimation in applications where the energy consumption of the estimation process is comparable to the energy consumption of the complete system. Full article

Trauma recovery research requires the development of instruments that capture gender-based violence (GBV) survivor recovery phases. The salutogenic concepts in Antonovsky’s Sense of Coherence (SOC) (manageability, comprehensibility, and meaning) could help capture trauma recovery stages, but the factorial structure of the SOC-13 has remained problematic. Moreover, most SOC revisions generally abandon the original intent of the SOC-13, developing scales that capture essential but different aspects of positive psychology. This study used mixed methods to develop the Sense of Meaning Inventory (SOMI), preserving the original concepts but updating the language, removing cultural idioms, and revising the response scales to stabilize the subscales. The qualitative phase evaluated and updated the items of the scale while retaining the original concepts. The quantitative phase conducted a two-sample psychometrics reliability and validity evaluation of the new scale with GBV survivors, finding a three-factor solution. This scale may be more amenable for international research and theory testing in GBV and other health conditions. Full article

This paper presents an optimization method for hybrid energy systems based on Model Predictive Control (MPC), Long Short-Term Memory (LSTM) networks, and Kolmogorov–Arnold Networks (KANs). The proposed method is applied to a high-altitude wind energy work umbrella control system, where it aims to enhance the stability and efficiency of energy utilization. The work umbrella system integrates wind and solar energy sources, with energy stored in a battery and used to control the umbrella’s operations. The MPC framework is employed to optimize control actions by solving a finite-horizon optimization problem, ensuring the battery State of Charge (SOC) remains within an optimal range. The LSTM network provides accurate predictions of environmental conditions, including wind speed and solar irradiance, which are essential for MPC’s decision-making process. To address complex nonlinearities in the system, the KAN is utilized to model and approximate these dynamics, refining the LSTM predictions. The integration of these advanced control strategies enables the system to handle varying operational conditions and maintain optimal performance. The case study demonstrates the effectiveness of the MPC-LSTM-KAN approach, revealing improvements in the SOC stability, energy efficiency, and operational endurance of the high-altitude wind energy work umbrella system. The results indicate that this hybrid optimization method offers a robust solution for managing hybrid energy systems in dynamic environments. Full article

Modern embedded nanoelectronic devices, particularly in safety-critical sectors, require high dependability throughout their lifecycle. To address this, designers have started integrating extra circuitry for on-device self-testing, such as the Logic Built-In Self-Test (LBIST). However, while automatic test equipment (ATE) ensures exhaustive testing during manufacturing, in-field testing capabilities are limited. This study introduces a novel methodology for in-field data collection of failure information from LBIST engines and a subsequent logic diagnosis strategy to facilitate failure analysis of field returns. The information is collected from key-on and key-off self-tests, executed by central processing units (CPUs) with a fixed seed and different frequency configurations, primarily addressing transition delay (TRN) faults. The proposed approach capitalizes on the constrained in-field configurability of LBIST and does not require a custom architecture, making it highly practical and readily applicable to real-world devices. The logic diagnosis strategy significantly reduces the number of candidate faults by exploiting the first failing pattern index found during the in-field testing and data collection. Reducing fault candidates could enhance accuracy during failure analysis, especially when field return devices exhibit a “No Trouble Found” (NTF) behavior. The experimental results are reported for ITC’99 benchmarks and an industrial automotive system-on-chip (SoC) produced by STMicroelectronics, with about 20 million gates. Full article

The state of charge (SOC) is a characteristic parameter that indicates the remaining capacity of electric vehicle batteries. It plays a significant role in determining driving range, ensuring operational safety, and extending the service life of battery energy storage systems. Accurate SOC estimation can ensure the safety and reliability of vehicles. To tackle the challenge of precise SOC estimation in complex environments, this study introduces an improved forgetting factor recursive least squares (IFFRLS) method, which integrates the Golden Jackal optimization (GJO) algorithm with the traditional FFRLS method. This integration is grounded in the formulation of a lithium battery state equation derived from a second-order RC equivalent circuit model. Additionally, the research utilizes the interactive multiple model unscented Kalman filter (IMMUKF) algorithm for SOC estimation, with experimental validation conducted under various conditions, including hybrid pulse power characterization (HPPC), urban dynamometer driving schedule (UDDS), and real underwater scenarios. The experimental results demonstrate that the SOC estimation method of lithium batteries based on IFFRLS-IMMUKF exhibits high accuracy and a favorable temperature applicability range. Full article

Soil erosion and the loss of soil organic carbon (SOC) pools are considered serious environmental problems in undulating landscapes on loess covers, accompanied in some areas, such as south Poland, by the physical degradation of chernozemic soils. The aim of the present study was to identify the scale and reasons for spatial variation of the SOC pools in the intensely cultivated Luvic Phaeozems in one of the unique patches of chernozemic soils in Poland. This study, carried out in a soil catena located in the undulating Carpathian Foreland in south-east Poland, has demonstrated that the SOC pools can greatly differ on a very small scale, even in relatively less differentiated landscapes and in soils classified into the same group. The scale and reasons for the differentiation of the SOC pools depend on the method (depth) of calculation. The spatial differences were smaller and were mainly related to the SOC concentrations and the bulk density of the topsoil horizons, when calculated for depths of 0–30 cm and 0–50 cm. On the other hand, the SOC pools calculated for the 0–100 cm soil layer differed most significantly between the profiles in the catena, representing a continuous growing trend from the uppermost towards the lowermost part of the catena, and were clearly related to the total thickness of the humus horizon(s). The latter findings confirm that sheet erosion has a major impact on the spatial variation of SOC pools in an agricultural landscape. However, soil morphology and the distribution of SOC across the soil profiles suggest additional influences from historical pedogenesis and modern farming technology. The presence of black, thick and humus-rich chernic horizons in all soils across the catena indicates that modern farming must not degrade the soils, but, on the contrary, it can help in the restoration of even neo-formation of chernozemic soils (Phaeozems), if oriented towards the conservation of humus content, soil structure, and biological activity. Full article

Sustainability in agroecosystems relies on the optimized use of resources to achieve consistent yields while maintaining or improving soil health. The monitoring of soil quality is crucial when changes from rainfall-fed to irrigated crop systems occur. The objective of this study was to assess the impact of different crop successions in the Mediterranean area under irrigation and different technical practices. The soil nitrogen–phosphorous–potassium (NPK) and soil organic carbon (SOC) balances were observed in four fields with irrigated annual crops in a two-year succession timeframe, namely, sunflower–maize (P1), sunflower–clover (P2), maize–sunflower (P3), and alfalfa–alfalfa (P4). The SOC and nutrient balance, integrating the total irrigation, mineral fertilizers, and exported yield, was calculated for each farm. Except for maize–sunflower succession (P3), all fields presented a negative SOC balance at the end of the two-year crop succession, indicating losses from 2.84 to 4.91 Mg SOC ha⁻¹ y⁻¹. While in N-fixing plants the soil N decreased, in the remaining crops a surplus was observed, possibly leading to future N losses. The continuous depletion of soil P revealed a potential underestimation of this nutrient. Soil K appears to be related to specific crop management practices, namely, crop residue incorporation after harvest. In annual irrigated crops under Mediterranean conditions, crop succession can induce soil fertility degradation if conservation practices are absent. Full article

It remains unclear what changes have occurred in the distribution pattern of and trend in soil organic carbon (SOC) in China against the background of climate and land use change. Clarifying the dynamic changes in SOC and their driving factors in different regions of China is therefore crucial for assessing the global carbon cycle. In this study, we collected and supplemented a large amount of soil organic carbon density (SOCD) data in China from 2001 to 2020 and extracted data on environmental covariates (ECs) for the corresponding years. A random forest model was used to estimate the SOCD at a depth of 0–20 cm and 0–100 cm in China for the years 2001, 2005, 2010, 2015, and 2020, and we explored the trend of SOCD changes and their key driving factors. The results showed the following: (1) Compared with previous studies, the predictive ability of the 0–100 cm depth model was greatly improved; the coefficient of determination (R²) was 0.61 and Lin’s concordance correlation coefficient (LCCC) was =0.76. (2) From 2001 to 2020, China’s soil organic carbon stocks (SOCS) were 38.11, 39.11, 39.88, 40.16, and 41.12 Pg C for the 0–20 cm depth and 110.49, 112.67, 112.80, 113.06, and 114.96 Pg C for the 0–100 cm depth, respectively. (3) The effects of temperature and precipitation on SOCD in China showed obvious regional variability, and land use changes had mainly positive effects on SOCD in all regions of China, which was related to the large-scale implementation of ecological protection and restoration and the policy of returning farmland to forests and grasslands in China. This study provides strong scientific support for addressing climate change and rationalizing the use of land resources. Full article

Brown spot of pear (BSP), caused by Stemphylium vesicarium, is one of the most dangerous pear fungal diseases, being responsible for huge losses in production. Currently, in order to increase its containment, chemical control is implemented in conjunction with agronomic techniques able to reduce BSP inoculum sources (e.g., orchard grass sanitation, litter removal or application of biocontrol agents). Regardless, despite the introduction of agronomic practices, the complete control of the disease is still rarely possible, which suggests that other sources of S. vesicarium inoculum that are currently neglected may be involved. The aim of this study is to investigate the possible wintering forms of Pleospora allii/S. vesicarium on pear wood and whether any spore-forming productions (conidial or ascosporic) might infect the green tissues of the plant in the following spring. Symptomatic fragments of woody tissue from a commercial pear orchard (in Ferrara, Emilia-Romagna, Italy) with a high BSP pressure (~40% incidence) were analysed. The results prove that pseudothecia and the maturation of ascospores of P. allii also develop on one-year-old branch cankers of pear trees, thus representing an additional source of inoculum. In conclusion, the pruning of affected branches and removal of relative residues should be preventatively performed in order to improve BSP management and control. Full article

One of the most important functions of the battery management system (BMS) in battery electric vehicle (BEV) applications is to estimate the state of charge (SOC). In this study, several machine and deep learning techniques, such as linear regression, support vector regressors (SVRs), k-nearest neighbor, random forest, extra trees regressor, extreme gradient boosting, random forest combined with gradient boosting, artificial neural networks (ANNs), convolutional neural networks, and long short-term memory (LSTM) networks, are investigated to develop a modeling framework for SOC estimation. The purpose of this study is to improve overall battery performance by examining how BEV operation affects battery deterioration. By using dynamic response simulation of lithium battery electric vehicles (BEVs) and lithium battery packs (LIBs), the proposed research provides realistic training data, enabling more accurate prediction of SOC using data-driven methods, which will have a crucial and effective impact on the safe operation of electric vehicles. The paper evaluates the performance of machine and deep learning algorithms using various metrics, including the R2 Score, median absolute error, mean square error, mean absolute error, and max error. All the simulation tests were performed using MATLAB 2023, Anaconda platform, and COMSOL Multiphysics. Full article

Background/Objectives: Serous ovarian carcinoma (SOC) is the most common subtype of epithelial ovarian cancer, with high-grade (HGSOC) and low-grade (LGSOC) subtypes presenting distinct clinical behaviours. This study aimed to evaluate histopathologic features in SOC, correlating these with prognostic outcomes, and explore the potential clinical implications. Methods: We analysed 51 SOC cases for lymphovascular space invasion (LVSI), tumour border configuration (TBC), microvessel density (MVD), tumour budding (TB), the tumour–stroma ratio (TSR), the stromal type, tumour-infiltrating lymphocytes (TILs), and tertiary lymphoid structures (TLSs). A validation cohort of 54 SOC cases from The Cancer Genome Atlas (TCGA) was used for comparison. Results: In the discovery set, significant predictors of aggressive behaviour included LVSI, high MVD, high TB, and low TILs. These findings were validated in the validation set where the absence of TLSs, lower peritumoural TILs, immature stromal type, and low TSR were associated with worse survival outcomes. The stromal type was identified as an independent prognostic predictor in SOC across both datasets. Inter-observer variability analysis demonstrated substantial to almost perfect agreement for these features, ensuring the reproducibility of the findings. Conclusions: The histopathological evaluation of immune and stromal features, such as TILs, TLSs, TB, TSR, and stromal type, provides critical prognostic information for SOC. Incorporating these markers into routine pathological assessments could enhance risk stratification and guide treatment, offering practical utility, particularly in low-resource settings when molecular testing is not feasible. Full article

Background/Objectives: This study evaluated the clinical utility of a virtual reality (VR)-based kitchen error task for children (VKET-C) to assess functional cognition in children. Methods: In total, 38 children aged 7–12 years were included, comprising 23 typically developing (TD) children and 15 children with developmental disabilities (DDs), including autism spectrum disorder, attention deficit hyperactivity disorder, and intellectual disability. While performing the VKET-C, performance errors were analyzed. The Stockings of Cambridge (SOC) and Spatial Working Memory (SWM) tasks from the Cambridge Neuropsychological Test Automated Battery (CANTAB) were used to assess cognitive function. The Brunner–Munzel test was performed to compare performance errors between the TD and DD groups, and correlations between performance errors and cognitive measures were analyzed. Results: Omission and commission errors were significantly different between the groups (p < 0.001), with no significant difference in motor errors (p > 0.05). Omission errors were correlated with the initial thinking time mean (ITMN) in all items of the SOC task and the between errors (BE) of the SWM task. Commission errors were correlated with the ITMN in the difficult items of the SOC task and the BE of the SWM task. Additionally, motor errors were significantly correlated with problems solved in minimum moves (PSMM) and ITMN in the difficult items of the SOC task and BE in the SWM task. Conclusions: The VKET-C shows promise as an effective tool for assessing executive function and working memory in children with DDs, offering an engaging and ecologically valid alternative to traditional methods. Full article