Search Results (10,192)

In recent years, computational intelligence techniques have significantly contributed to the automation and optimization of trading strategies. Despite the increasing sophistication of predictive models, classical technical indicators such as dual Simple Moving Averages (2-SMA) remain popular due to their simplicity and interpretability. This work proposes an adaptive trading system that combines the 2-SMA strategy with a learning-based metaheuristic optimizer known as the Learning-Based Linear Balancer ($L{B}^2$). The objective is to dynamically adjust the strategy’s parameters to maximize returns in the highly volatile cryptocurrency market. The proposed system is evaluated through simulations using historical data of the BTCUSDT futures contract from the Binance platform, incorporating real-world trading constraints such as transaction fees. The optimization process is validated over 34 training/test splits using overlapping 60-day windows. Results show that the $L{B}^2$-optimized strategy achieves an average return on investment (ROI) of 7.9% in unseen test periods, with a maximum ROI of 17.2% in the best case. Statistical analysis using the Wilcoxon Signed-Rank Test confirms that our approach significantly outperforms classical benchmarks, including Buy and Hold, Random Walk, and non-optimized 2-SMA. This study demonstrates that hybrid strategies combining classical indicators with adaptive optimization can achieve robust and consistent returns, making them a viable alternative to more complex predictive models in crypto-based financial environments. Full article

Addressing the lack of dynamic monitoring methods for assessing the combined toxicity of mixed pesticides, this study developed a fluorescent probe, CCHL, specifically responsive to leucine aminopeptidase (LAP). The probe utilized Cy7-COOH (CCH) as the fluorophore, with fluorescence recovery triggered by enzymatic hydrolysis. Spectral characterization confirmed a linear response between the probe and LAP activity within a concentration range of 0–0.9 μg/mL (R² = 0.992), along with excellent selectivity in the presence of coexisting biomolecules. Application experiments demonstrated that the combination of chlorfenapyr and beta-cyfluthrin significantly reduced LAP activity, revealing a notable antagonistic effect. The novel sensing strategy developed here provides a real-time, visualized analytical tool for evaluating the combined effects of mixed pollutants, demonstrating significant potential for environmental toxicology monitoring. Full article

We analysed the socioeconomic, behavioural, and protection factors (PFs) influences on the HIV combined prevention (CP) strategy among Brazilian Amazonian men who have sex with men (MSMs). PFs are resources that reduce the effect of adversity and help people maintain their well-being. Methods: Cross-sectional study employing a convenient sample of MSMs living in the metropolitan region of Belém. A questionnaire containing socioeconomic, behavioural, PFs, and behaviour/knowledge concerning CP questions was used. “Behaviour/knowledge concerning CP” was defined as a dependent variable and received a maximum score of 16 points. The Mann–Whitney and Kruskal–Wallis tests and multiple linear regression were employed. Results: Our sample comprised 384 MSMs scoring an average of 7.83 points (±1.9). Contributing to lower scores were “not talking about sex life with confidants”, “not talking with work colleagues about personal life and sexually transmissible infections”, and “not participating in non-governmental organisations.” On the other hand, “not being happy in the neighbourhood of residency” contributed to higher scores. Conclusion: Peer support and social inclusion are essential for increasing MSMs’ access to CP. Full article

Expandable polystyrene (EPS) nozzle covers can be used to replace traditional metal nozzle covers due to their excellent mechanical properties, as well as being lightweight and ablatable. As an important part of the solid rocket motor, the nozzle cover needs to be designed according to the requirements of the overall system. This study lays a theoretical foundation for the engineering design and performance optimization of the EPS nozzle cover. In this paper, the method of combining test research and numerical simulation is used to explore the pressure bearing capacity of EPS nozzle covers with different thicknesses under linear load. Firstly, the quasi-static tensile, compression and shear tests of EPS materials were carried out by universal testing machine, and the key parameters such as stress-strain curve, elastic modulus and yield strength were obtained; Based on the experimental data, the constitutive model of EPS material with respect to density is fitted and modified; The VUMAT subroutine of the material was written in Fortran language, and the mechanical properties of the nozzle cover with different material model distribution schemes and different thicknesses were explored by ABAQUS finite element numerical simulation technology. The results indicate that the EPS nozzle cover design based on the two material model allocation schemes better aligns with practical conditions; when the end thickness of the EPS nozzle cover exceeds 3 mm, the opening pressure formula for the cover based on the pure shear theory of thin-walled circular plates becomes inapplicable; the EPS nozzle cover exhibits excellent pressure-bearing capacity and performance, with its pressure-bearing capacity showing a positive correlation with its end thickness, and an EPS nozzle cover with a 9 mm end thickness can withstand a pressure of 7.58 MPa (under internal pressure conditions); the pressure-bearing capacity of the EPS nozzle cover under internal pressure conditions is higher than under external pressure conditions, and when the end pressure-bearing surface thickness increases to 9 mm, the internal pressure-bearing capacity is 3.13 MPa higher than under external pressure conditions. Full article

This work describes a non-enzymatic electrochemical glucose biosensor combining for the first time molecularly imprinted polymers (MIPs) for glucose concentration and gold nanoparticles (AuNPs) on screen-printed carbon electrodes (SPEs), where both MIPs and AuNPs were assembled in situ. Electrochemical impedance spectroscopy (EIS) was used to evaluate the analytical performance of the sensor, which has a linear range between 1.0 µM and 1.0 mM when standard solutions are prepared in buffer. Direct measurement of glucose was performed by chronoamperometry, measuring the oxidation current generated during direct glucose oxidation. The selectivity was tested against ascorbic acid and the results confirmed a selective discrimination of the electrode for glucose. Overall, the work presented here represents a promising tool for tracking glucose levels in serum. The use of glucose MIP on the electrode surface allows the concentration of glucose, resulting in lower detection limits, and the use of AuNPs reduces the potential required for the oxidation of glucose, which increases selectivity. In addition, this possible combination of two analytical measurements following different theoretical concepts can contribute to the accuracy of the analytical measurements. This combination can also be extended to other biomolecules that can be electrochemically oxidised at lower potentials. Full article

The stock market plays a crucial role in the financial system, with its price movements reflecting macroeconomic trends. Due to the influence of multifaceted factors such as policy shifts and corporate performance, stock prices exhibit nonlinearity, high noise, and non-stationarity, making them difficult to model accurately using a single approach. To enhance forecasting accuracy, this study proposes a hybrid forecasting framework that integrates wavelet denoising, multi-head attention-based BiLSTM, ARIMA, and XGBoost. Wavelet transform is first employed to enhance data quality. The multi-head attention BiLSTM captures nonlinear temporal dependencies, ARIMA models linear trends in residuals, and XGBoost improves the recognition of complex patterns. The final prediction is obtained by combining the outputs of all models through an inverse-error weighted ensemble strategy. Using the CSI 300 Index as an empirical case, we construct a multidimensional feature set including both market and technical indicators. Experimental results show that the proposed model clearly outperforms individual models in terms of RMSE, MAE, MAPE, and R². Ablation studies confirm the importance of each module in performance enhancement. The model also performs well on individual stock data (e.g., Fuyao Glass), demonstrating promising generalization ability. This research provides an effective solution for improving stock price forecasting accuracy and offers valuable insights for investment decision-making and market regulation. Full article

Accidental surface surcharge will generate additional load in the stratum, which then leads to unfavorable impacts on the underlying shield tunnel. This paper proposes a probabilistic analysis method to address this problem. In this framework, an improved soil–tunnel interaction model considering the nonlinearity of the subgrade is established at first, and the Newton–Raphson iterative solution algorithm is employed to acquire tunnel responses. Then, the random field models of the initial stiffness and the ultimate reaction of the subgrade are constructed to realize the spatial variability of soil properties. Finally, with the aid of the Monte Carlo Simulation method, the probabilistic analyses on tunnel responses are performed by combining the improved soil–tunnel interaction model and the random field model of subgrade parameters. The applicability and the superiority of the improved soil–tunnel interaction model are validated by a historical case from Shanghai Metro Line 9. The results prove that the traditional linear foundation model will overestimate the bearing capacity of the subgrade, thereby leading to overly optimistic assessments of surcharge-induced tunnel responses. This shortcoming could be addressed by the improved nonlinear soil–tunnel interaction model. The influences of spatial variability of soil properties on tunnel responses are nonnegligible. The stronger the uncertainties of subgrade parameters, in terms of the initial stiffness and the ultimate reaction concerned in this work, the higher the failure risk of the shield tunnel subjected to the surcharge. The failure modes of the tunnel subjected to the surcharge are controlled by the longitudinal curvature radius of the tunnel within the current assessment criteria, which means if this evaluation indicator can be restricted within the allowable value, then the opening of the circumferential joint and the longitudinal settlement can also meet the requirements. Compared with the influences of the uncertainty of the subgrade ultimate reaction, the spatial variability of the subgrade initial stiffness has greater influences on tunnel failure risk under the same conditions. An increase in the range of surcharge will raise the risk of tunnel failure, while the influence of tunnel burial depth is just the opposite. Full article

This paper presents a power-aware Reconfigurable Parameterizable Pseudorandom Pattern Generator (RP-PRPG) for a number of applications, including built in self-testing (BIST) and cryptography. Linear Feedback Shift Registers (LFSRs) are broadly utilized in pattern generation due to their efficiency and simplicity. However, the diversity of generated patterns, as well as their power consumption, improves through circuit modifications. This work explores enhancements to LFSR structures to achieve broader range of patterns with reduced power consumption for BIST-based applications. The proposed circuit constructed on the LFSR platform can be programmed to generate patterns with varying degrees of different LFSR configurations. Diverse set of patterns of any circuit arrangement can be created using any characteristic polynomial and by utilizing the reseeding capacity of the circuit. The circuit combines a double-tier linear feedback circuit with zero forcing methods, resulting in more than 70% transition reduction, thus significantly lowering power dissipation. The behaviour of the proposed circuit is assessed for characteristic polynomials with degrees ranging from 4 to 128 using various Linear Feedback Shift Register (LFSR) topologies. For reconfigurable HDL and ASIC synthesis, the power-aware RP-PRPG can be used to generate an efficient set of stream ciphers as well as applications involving the scan-for-test protocol. Full article

Thermocouples can be combined into thermopiles to sense heat differences or achieve localized heating and cooling. However, integrating them into textiles using yarns is not straightforward, and chemical methods face challenges like complex processing, poor scalability, and voltage non-uniformity. This study employs conventional weaving to fabricate textile-based thermocouples and thermopiles for wearable sensing and potential cooling applications, with a focus on protective clothing. Using stainless steel and nickel-coated carbon yarns, we demonstrate a more stable thermocouple than those made with chemical or welded methods, with minimal fabric damage. Four conductive yarns, stainless steel, carbon fiber (CF), and nickel-coated carbon fiber (NiFC), were woven and laser-cut to form thermocouples using three different binding types to connect them. Inox1–NiFC was the most efficient thermocouple, achieving the highest Seebeck coefficient of 21.87 µV/K with Binding 3. Binding 3 also reduced contact resistance by 66% across all configurations. Slightly lower but comparable performance was seen with Inox1–NiFC/Binding 2 (21.83 µV/K) and Inox2–NiFC/Binding 1 (15.79 µV/K). In contrast, FC-based thermocouples showed significantly lower Seebeck values: 5.67 µV/K (Inox2–FC/Binding 2), 5.43 µV/K (Inox1–FC/Binding 3), and 5.06 µV/K (Inox2–FC/Binding 1). A woven thermopile with three junctions made with the optimal binding and thermocouple combination generated an average of 55.54 µV/K and about 500 µV at small temperature differences (4–5 °C), with a linear voltage response suitable for sensing. While thermal sensing proved effective, Peltier cooling needs further optimization. This method offers a stable, low-cost, and scalable platform for textile-integrated thermoelectric systems, with strong potential for use in uniforms and other protective garments. Full article

This study investigates the deterioration of skid resistance and surface macrotexture following preventive maintenance using micro-milling techniques. Field data were collected from 31 asphalt pavement sections located across four climatic zones in Texas. The data encompasses a variety of surface types, milling depths, operational speeds, and drum configurations. A standardized data collection protocol was followed, with measurements taken before milling, immediately after treatment, and at 3, 6, 12, and 18 months post-treatment. Skid number and Mean Profile Depth (MPD) were used to evaluate surface friction and texture characteristics. The dataset was reformatted into a time-series structure with 930 observations, including contextual variables such as climatic zone, treatment parameters, and baseline surface condition. A comparative modeling framework was applied to predict the deterioration trends of both skid resistance and macrotexture over time. Eight regression models, including linear, tree-based, and ensemble methods, were evaluated alongside a time series Transformer model. The results show that the Transformer model achieved the highest prediction accuracy for skid resistance (R² = 0.981), while Random Forest performed best for macrotexture prediction (R² = 0.838). The findings indicate that the degradation of surface characteristics after preventive maintenance is non-linear and influenced by a combination of environmental and operational factors. This study demonstrates the effectiveness of data-driven modeling in supporting transportation agencies with pavement performance forecasting and maintenance planning. Full article

Globally, the mutations around forest landscapes continue to draw significant scientific interest, despite fragmented evidence on the socio-ecological outcomes linked to this process. This knowledge gap is evident in the Congo Basin—one of the world’s major ecosystems. To contribute towards addressing the knowledge gap, this study analyzed two decades of forest landscape mutations and the socio-ecological transformation-cum-outcomes linked to the process in Cameroon. A mixed-methods approach was employed, combining remote sensing-based land use/land cover (LULC) analysis (using multi-date Landsat imagery at 30 m resolution) with household surveys involving 100 randomly selected forest-dependent households across three forest blocks: Ebo, Ndokbou, and Makombé for ground truthing. Survey data were analyzed using descriptive statistics and combined spatial analysis to reveal the following. Firstly, forest cover has significantly increased within the 20-year period; this involved a 104.01% increase between 2004 and 2014, and an additional 47.27% between 2014 and 2024. In that vein, agricultural land declined by more than 20%, whereas settlement and water bodies increased by 226.4% and 376.2%, respectively. Secondly, forest landscape mutations in the Yabassi Forest Area were primarily driven by a convergence of social (notably population growth at 57% and livelihood diversification), economic (agricultural expansion and timber exploitation), political (tenure ambiguity and development-driven land conversion), and environmental (climate variability at 36% and ecological restoration efforts) forces. These interwoven drivers shaped the land use change process, revealing how the human-environment feedback defines landscape trajectories in complex and non-linear ways. Thirdly, while the ecological outcomes of forest mutations were largely positive—with significant gains in forest cover, the social outcomes were skewed towards the negative. Communities experienced both improvements in livelihoods and infrastructure (66%), but also faced land conflicts (67%), the loss of traditional access (69%), and resource-based insecurity. By applying the socio-ecological systems (SES) framework, this study provides novel insights on how governance, ecological processes, and human behavior co-evolve in forest landscapes. The findings do not only edify the SES framework but also challenge the mainstream position about forest decline by highlighting areas of recovery. The evidence informs adaptive forest governance processes in the Congo Basin and similar contexts. Further research should investigate the institutional and adaptive mechanisms that influence these dynamics across the Congo Basin. Full article

Polarization-sensitive photodetection is critical for advanced optical systems, yet achieving simultaneous high-fidelity recognition of the circularly polarized (CP) and linearly polarized (LP) light with compact designs remains challenging. Here, we use COMSOL 5.6 software to demonstrate a silicon metasurface-integrated MCT photodetector that resolves both CP and LP signals through a single ultrathin platform. The device deciphers LP states via four orientation-specific linear gratings for differential detection, while chiral symmetric silicon nanostructures enable direct CP discrimination with an exceptional extinction ratio of 30 dB. The proposed architecture combines two breakthroughs: (1) superior polarization reconstruction capability, achieved via the synergy of grating-induced polarization selectivity and chiral near-field enhancement, and (2) a fabrication-simplified process that eliminates multilayer stacking or complex alignment steps. This work establishes a new paradigm for miniaturized, high-performance polarization optics, with potential applications in polarization imaging, quantum communication, and hyperspectral sensing. Full article

Deep-learning-based multiple label chest X-ray classification has achieved significant success, but existing models still have three main issues: fixed-scale convolutions fail to capture both large and small lesions, standard pooling is lacking in the lack of attention to important regions, and linear classification lacks the capacity to model complex dependency between features. To circumvent these obstacles, we propose CONVFCMAE, a lightweight yet powerful framework that is built on a backbone that is partially frozen (77.08 % of the initial layers are fixed) in order to preserve complex, multi-scale features while decreasing the number of trainable parameters. Our architecture adds (1) an intelligent global pooling module that is learnable, with $1\times 1$ convolutions that are dynamically weighted by their spatial location, and (2) a multi-head attention block that is dedicated to channel re-calibration, along with (3) a two-layer MLP that has been enhanced with ReLU, batch normalization, and dropout. This module is used to enhance the non-linearity of the feature space. To further reduce the noise associated with labels and the imbalance in class distribution inherent to the NIH ChestXray14 dataset, we utilize a combined loss that combines BCEWithLogits and Focal Loss as well as extensive data augmentation. On ChestXray14, the average ROC–AUC of CONVFCMAE is 0.852, which is 3.97 percent greater than the state of the art. Ablation experiments demonstrate the individual and collective effectiveness of each component. Grad-CAM visualizations have a superior capacity to localize the pathological regions, and this increases the interpretability of the model. Overall, CONVFCMAE provides a practical, generalizable solution to the problem of extracting features from medical images in a practical manner. Full article

This narrative review examines the genomic-adjusted radiation dose (GARD), a biologically informed framework developed to personalize radiotherapy by integrating tumor-specific genomic data. GARD combines the radiosensitivity index (RSI), based on gene expression, with the linear quadratic model to estimate patient-specific radiation effect. Since its introduction in 2017, GARD has demonstrated prognostic value across multiple cancer types in retrospective studies. This review summarizes key studies evaluating GARD across various tumor types and clinical contexts. Emerging trials, including a Phase II trial in HPV-positive oropharyngeal cancer, aim to validate GARD-guided dosing in precision radiotherapy. Future efforts may focus on refining RSI, addressing tumor heterogeneity, and validating GARD-guided dosing in prospective settings. Full article

Based on the G85 high-fill subgrade project in east Gansu Province, this study conducts one-dimensional compression tests in the laboratory on both disturbed and in situ-compacted loess. Through the combination of the test results of remolded soil, compaction standards for each layer of the subgrade fill are established, and quality inspections of the compacted subgrade are performed. The experimental results demonstrate that the compression deformation of remolded loess exhibits a positive correlation with compaction degree and a negative correlation with moisture content. Under constant compaction degree conditions, axial pressure and deformation follow a linear relationship, whereas under fixed conditions, the relationship adheres to a quadratic trend. Specimen void ratios show minimal variation within the 25–100 kPa stress range but undergo significant reduction between 100 and 400 kPa. Under an axial compressive load of 100–200 kPa, the compression coefficient at a height of 10 m within the subgrade ranges from 0.163 to 0.171 MPa⁻¹. At a height of 6 m, it ranges from 0.177 to 0.183 MPa⁻¹, and at 1 m, from 0.183 to 0.186 MPa⁻¹. These values indicate that the compaction quality throughout the subgrade corresponds to a low compressibility level. However, the compaction quality near the slopes on both sides is slightly lower than that along the centerline of the subgrade. Overall, the compaction quality meets the required standards. Full article