Search Results (73)

Generally, most inversion approaches model the seabed as a stack of range-independent homogeneous layers with unknown geoacoustic parameters and layer numbers. In our previous study, we established a layered geoacoustic seabed model based on sub-bottom profiler data to characterize low-frequency (100–500 Hz) airgun signal propagation at short ranges (0–20 km). However, when applying the same model to simulate high-frequency (500–1000 Hz) explosive sound signal propagation, it failed to adequately reproduce the observed significant transmission loss phenomenon. Through systematic analysis of transmission loss (including water column sound speed profiles, seabed topography, and sediment properties), this study proposes a range-dependent layered geoacoustic model using the Range-dependent Acoustic Model–Parabolic Equation (RAM-PE). Stepwise inversion implementation has successfully explained the observed experimental phenomena. To generalize the proposed model, this study further introduces a trans-dimensional inversion framework that automatically resolves sediment property interfaces along propagation paths. The method effectively combines prior information with trans-dimensional inversion techniques, providing improved characterization of range-dependent seabed environments. Full article

The container loading problem (CLP) has a broad spectrum of applications in industry and has been studied for over 60 years due to its high complexity. This paper addresses a realistic single-container loading scenario with practical constraints, including orientation limitations, maximum stacking weight, static stability, overall container weight limit, and fractional loading for multiple drop-off points (multidrop). We propose an open-source decision support system (DSS) implemented on a widely used platform (MS Excel®), which employs a heuristic algorithm to find efficient loading solutions under these constraints. The DSS uses a multi-start randomized constructive algorithm based on a maximal residual space representation. The constructive phase builds the loading pattern in vertical layers (columns or walls), while respecting all practical constraints. The performance of the proposed heuristic is validated through extensive computational experiments on classical benchmark instances, comparing its results against the recent state-of-the-art methods. We also analyze the impact of multi-drop constraints on utilization metrics. The DSS features an interactive interface for creating/loading instances, visualizing step-by-step packing patterns, and displaying key statistics, thus providing a user-friendly decision tool for practitioners. Full article

This paper presents the calibration process involved in a planar active phased array antenna operating in the K-band (17.7–20.2 GHz). The array consists of eight columns, each containing a 1 × 8 subarray of patch antennas. To enhance the antenna bandwidth, a double-stacked patch structure is employed. We analyze the challenges encountered when measuring active antennas. Additionally, we discuss the solutions and calibration techniques used to improve the array performance. Finally, we present the results of the optimal calibration approach, comparing simulated and measured data, both with and without calibration, to evaluate the improvements achieved. Full article

The crystal structures, interactions, and contacts analyses of four N-(ferrocenylalkyl)benzene-carboxamide derivatives are described as the N-(ferrocenylmethyl)benzenecarboxamide 4a, N-(ferrocenylmethyl)-2,6-difluorobenzenecarboxamide 4e, N-(ferrocenylmethyl)pentafluorobenzenecarboxamide 4f and N-(ferrocenylethyl)-4-fluorobenzenecarboxamide 5. Intermolecular amide⋯amide hydrogen-bonding interactions as 1D intermolecular chains are present in all four crystal structures, with N⋯O distances ranging from 2.819 (2) to 2.924 (3) Å. Three of the crystal structures have one molecule per asymmetric unit, except the phenyl 4a, which has Z’=2. In the structure of 4a, Fc(C-H)⋯(phenyl) and _phenylC-H⋯π(C₅H₄) ring interactions dominate the interaction landscape, together with (1:1) face-to-face (phenyl)⋯(phenyl) and (C₅H₅)⋯(C₅H₅) ring stacked pairs (Fc = ferrocenyl moiety). In 4e, interlocking ferrocenyls, short C-H⋯(C-F) and C-H⋯O hydrogen bonds are the only additional notable intermolecular interactions. In the pentafluorophenyl derivative 4f, a remarkable selection of interactions is present with interwoven 1D ferrocenyl⋯(C₆F₅) stacking and C-H⋯F interactions; molecules aggregate forming impressive 1D columns comprising intertwined (Fc⋯C₆F₅⋯)_n ring stacking. In the ethyl bridged system 5, C-H⋯F and C-H⋯π (arene) contacts with (4-fluorobenzene) ring⋯ring pairs combine and stack about inversion centres. The reported para-F substituted structure REYWOU (4d) is used for comparisons with the 4a, 4e, 4f, and 5 crystal structures. In view of the rich interaction chemistry, contacts enrichment analyses of the Hirshfeld surface highlights several interesting features in all five ferrocenylalkylcarboxamide structures. Full article

By leveraging the high correlation between multi-ping echo data, low-rank and sparse decomposition methods are applied for reverberation suppression. Previous methods typically perform decomposition on the vectorized multi-ping echograph, which is obtained by stacking beamforming outputs from all directions in the same column. However, when the multi-ping correlation of beamforming outputs from different directions varies significantly due to the time-varying nature of the underwater acoustic channel, it becomes challenging to precisely capture the variations of the reverberation background. As a result, the performance of reverberation suppression is degraded. To alleviate this issue, we attempt to decompose the matrix formed by multi-ping beamforming outputs in different directions individually. The accelerated alternating projections method is used to estimate the steady reverberation for moving target detection. By exploiting the differences in spatio-temporal dimensions between moving targets and reverberation fluctuations, a weighted spatio-temporal density method with adaptive thresholding is used to further extract the target echoes. Field data were utilized to validate the effectiveness of the proposed method, and the experimental results demonstrated its superior robustness in an unstable reverberation-limited environment, maintaining an accurate estimation of steady reverberation. Full article

One of the most complex and prevalent diseases is heart disease (HD). It is among the main causes of death around the globe. With changes in lifestyles and the environment, its prevalence is rising rapidly. The prediction of the disease in its early stages is crucial, as delays in diagnosis can cause serious complications and even death. Machine learning (ML) can be effective in this regard. Many researchers have used different techniques for the efficient detection of the disease and to overcome the drawbacks of existing models. Several ensemble models have also been applied. We proposed a stacking ensemble model named NCDG, which uses Naive Bayes, Categorical Boosting, and Decision Tree as base learners, with Gradient Boosting serving as the meta-learner classifier. We performed preprocessing using a factorization method to convert string columns into integers. We employ the Synthetic Minority Oversampling TEchnique (SMOTE) and BorderLineSMOTE balancing techniques to address the issue of data class imbalance. Additionally, we implemented hard and soft voting using voting classifier and compared the results with the proposed stacking model. For the Artificial Intelligence-based eXplainability of our proposed NCDG model, we use the SHapley Additive exPlanations (SHAP) technique. The outcomes show that our suggested stacking model, NCDG, performs better than the benchmark existing techniques. The experimental results of our proposed stacking model achieved the highest accuracy, F1-Score, precision and recall of 0.91, 0.91, 0.91 and 0.91, respectively, and an execution time of 653 s. Moreover, we have also utilized K-Fold Cross-Validation method to validate our predicted results. It is worth mentioning that our prediction results and their validation strongly coincide with each other which proves our approach to be symmetric. Full article

A series of triphenylene (TP) compounds—denoted 3,6-THTP-DiCnOH—bearing four hexyloxy ancillary chains and two variable-length alkoxy chains terminally functionalized with hydroxyl groups have been synthesized and characterized. The shorter homologs revealed mesogenic characteristics, giving rise to thermotropic mesophases in which π-stacked columns of H-bound dimers self-organize yielding superstructures. Molecular-scale models are proposed to account for their structural features. The three studied compounds yielded supramolecular gels in methanol; their ability to gelify higher alcohols was found to be enhanced by the presence of water. The intermediate homolog also gelled n-hexane. Compared to their isomeric 2,7-THTP-DiCnOH analogs, the 3,6-derivatives showed a higher tendency to give rise to LC phases (wider thermal ranges) and a lower organogelling ability (variety of gelled solvents, lower gels stabilities). The overall results are analyzed in terms of different kinds of competing H-bonds: intramolecular, face-to-face dimeric, lateral polymeric, and solvent–TP interactions. Full article

Thanks to recent developments in spectrophotometric instruments, the spectra, quantum yields (Φ_f), and lifetimes (τ_f) of photoluminescence from organic and inorganic compounds can be readily determined not only in solution but also in the solid state. It is known that naphthalene emits fluorescence in solution, but not in the solid state. In a previous paper, we reported that solid-state emission can be seen from biaryl compounds comprised of chromophores that show no emission in the solid state. In this work, we prepared diphenylnaphthalenes (DPNs), and the spectra and the Φ_f and τ_f values of fluorescence were determined in solution and the solid state, as well as the crystallographic features. The 2,6-Diphenylnphthalene (26DPN) showed solid-state emission in the wavelength region for longer than those in solution, while the emission spectra of the others in the solid state were similar to those in solution. The crystal structure of 26DPN belonged to a herringbone motif, whereas those of the others were column-stacked structures. Based on these spectroscopic and crystallographic facts, the relationship between the crystal motif and the emission features in the solid state is discussed. Full article

Graphite made from coal will not only widen the graphite mineral resource, but also significantly improve the value of coal utilization. In this study, anthracite coal was heated in the temperature range of 500 to 2900 °C to study the size increase of nanometric graphite crystallites from anthracite to real graphite. The carbon content rapidly increases to 99.2% when heated from room temperature to 1600 °C, and then gradually increases to 100% when the treated temperature increases to 2900 °C. The FTIR results show that methyl, methylene, and aromatic hydrocarbon, preexisting in the raw anthracite, were preserved in the JZS-500 sample, but that when the treated temperature ≥ 1000 °C, these C-H bonds almost disappear. The basic structural units (nano graphitic carbon) grow into distorted columns, and the basic structural units and micro-columns re-oriented and coalesced to form local molecular oriented domains with the temperature increase from anthracite to JZS-1500. When the temperature ≥ 1600 °C, amorphous carbon, onion-like carbon, turbostratic layers, and graphitic carbon co-occur within the graphitized coals. At the sub-micron scale, carbonization is a homogenous process, whereas graphitization is a heterogenous process. The average graphite crystalline size (L_a, lateral extension; L_c, stacking height) rapidly increases as the treatment temperature increases from 1600 to 2300 °C. Three coal structural transformation stages were classified according to the nanometric carbon structural evolution with temperature. This study will contribute to the efficient and value-added utilization of coal to make graphite materials. Full article

The water retention capacity of porous materials is crucial in various geotechnical and environmental engineering applications such as slope stability analysis, landfill management, and mining operations. Filtered tailings stacks are considered an alternative to traditional tailings dams. Nevertheless, the mechanical behaviour and stability of the material under different water content conditions are of concern because these stacks can reach considerable heights. The water behaviour in these structures is poorly understood, particularly the effects of the water content on the stability and potential for liquefaction of the stacks. This study aims to investigate the water retention and flow characteristics of compacted iron ore tailings in high columns to better understand their hydromechanical behaviour. The research used 5 m high columns filled with iron ore tailings from the Quadrilátero Ferrífero region in Minas Gerais, Brazil. The columns were prepared in layers, compacted, and instrumented with moisture content sensors and suction sensors to monitor the water movement during various stages of saturation, drainage, infiltration, and evaporation. The sensors provided consistent data and revealed that the tailings exhibited high drainage capacity. The moisture content and suction profiles were effectively established over time and revealed the dynamic water retention behaviour. The comparison of the data with the theoretical soil water retention curve (SWRC) demonstrated a good correlation which indicates that there was no hysteresis in the material response. The study concludes that the column setup effectively captures the water retention and flow characteristics of compacted tailings and provides valuable insights for the hydromechanical analysis of filtered tailings stacks. These findings can significantly help improve numerical models, calibrate material parameters, and contribute to the safer and more efficient management of tailings storage facilities. Full article

Using rapeseed straw as a raw material and potassium bicarbonate (KHCO₃) and urea (CO(NH₂)₂) as modification reagents, the pyrolysis raw materials were mixed in a certain proportion, and the unmodified biochar GBC800, KHCO₃-modified biochar KGBC800, and (KHCO₃)/(CO(NH₂)₂) co-modified biochar N-KGBC800 were, respectively, prepared using the one-pot method at 800 °C. The physicochemical properties, such as surface morphology, pore characteristics, functional group distribution, and elemental composition of the three biochars, were characterized, and the adsorption performance and mechanism of the typical antibiotic tetracycline (TC) in water were studied. The results showed that the surface of GBC800 was smooth and dense, with no obvious pore structure, and both the specific surface area and total pore volume were small; the surface of KGBC800 showed an obvious coral-like three-dimensional carbon skeleton, the number of micropores and the specific surface area were significantly improved, and the degree of carbonization and aromatization was enhanced; N-KGBC800 had a coral-like three-dimensional carbon skeleton similar to KGBC800, and there were also many clustered carbon groups. The carbon layer changed significantly with interlayer gaps, presenting a multi-level porous structure. After N doping, the content of N increased, and new nitrogen-containing functional groups were formed. When the initial TC concentration was 100 mg/L, pH ≈ 3.4, the temperature was 25 °C, and the dosage of the three biochars was 0.15 g/L, the adsorption equilibrium was reached before 720 min. The adsorption capacities of GBC800, KGBC800, and N-KGBC800 for TC were 16.97 mg/g, 294.86 mg/g, and 604.71 mg/g, respectively. Fitting the kinetic model to the experimental data, the adsorption of TC by the three biochars was more in line with the pseudo-second-order adsorption kinetic model, and the adsorption isotherm was more in line with the Langmuir model. This adsorption process was a spontaneous endothermic reaction, mainly chemical adsorption, specifically involving multiple adsorption mechanisms such as pore filling, electrostatic attraction, hydrogen bonds, n−π interaction, Lewis acid–base interaction, π−π stacking, or cation −π interaction between the aromatic ring structure of the carbon itself and TC. A biochar-adsorption column was built to investigate the dynamic adsorption process of tetracycline using the three biochars against the background of laboratory pure water and salt water. The adsorption results show that the Thomas model and the Yoon–Nelson model both provide better predictions for dynamic adsorption processes. The modified biochars KGBC800 and N-KGBC800 can be used as preferred materials for the efficient adsorption of TC in water. Full article

Voltage stacking is a technique in which multiple integrated circuits are stacked in series between the supply voltage instead of in parallel, thus improving the energy efficiency of the power distribution network. Unfortunately, voltage stacking presents stability challenges for integrated circuits within the stack. A first-order model to quantify variability, stability, and power metrics for an array of voltage-stacked asynchronous integrated circuits is presented. Voltage variability and power consumption are accounted for and discussed. Limitations of the model are identified outside of the nominal behavior. The number of columns in the architecture, chip leakage, and supply voltage are shown to be the key contributors to the stability, performance, and energy efficiency of a system of voltage-stacked asynchronous processors. A higher leakage to active power ratio, though usually avoided by chip designers, is shown to improve stability and be key in designing stacks without external balancing. Outputs of the model enable system and chip designers to evaluate first-order trade-offs in energy efficiency, performance, and system cost. These fundamental data allow designers to make informed design and optimization trade-offs between asynchronous voltage-stacked architectures and the integrated circuits used therein. Analysis of this model shows that various voltage-stacked configurations, such as one with a 48 V supply using 100 rows and 11 columns, can be designed with less than 10% voltage variation per chip, mitigating the need for external voltage balancing. Full article

Low-light imaging capabilities are in urgent demand in many fields, such as security surveillance, night-time autonomous driving, wilderness rescue, and environmental monitoring. The excellent performance of SPAD devices gives them significant potential for applications in low-light imaging. This article presents a 64 (rows) × 128 (columns) SPAD image sensor designed for low-light imaging. The chip utilizes a three-dimensional stacking architecture and microlens technology, combined with compact gated pixel circuits designed with thick-gate MOS transistors, which further enhance the SPAD’s photosensitivity. The configurable digital control circuit allows for the adjustment of exposure time, enabling the sensor to adapt to different lighting conditions. The chip exhibits very low dark noise levels, with an average DCR of 41.5 cps at 2.4 V excess bias voltage. Additionally, it employs a denoising algorithm specifically developed for the SPAD image sensor, achieving two-dimensional grayscale imaging under 6 × 10⁻⁴ lux illumination conditions, demonstrating excellent low-light imaging capabilities. The chip designed in this paper fully leverages the performance advantages of SPAD image sensors and holds promise for applications in various fields requiring low-light imaging capabilities. Full article

One expanding area of bioinformatics is medical diagnosis through the categorization of biomedical characteristics. Automatic medical strategies to boost the diagnostic through machine learning (ML) methods are challenging. They require a formal examination of their performance to identify the best conditions that enhance the ML method. This work proposes variants of the Voting and Stacking (VC and SC) ensemble strategies based on diverse auto-tuning supervised machine learning techniques to increase the efficacy of traditional baseline classifiers for the automatic diagnosis of vertebral column orthopedic illnesses. The ensemble strategies are created by first combining a complete set of auto-tuned baseline classifiers based on different processes, such as geometric, probabilistic, logic, and optimization. Next, the three most promising classifiers are selected among k-Nearest Neighbors (kNN), Naïve Bayes (NB), Logistic Regression (LR), Linear Discriminant Analysis (LDA), Quadratic Discriminant Analysis (QDA), Support Vector Machine (SVM), Artificial Neural Networks (ANN), and Decision Tree (DT). The grid-search K-Fold cross-validation strategy is applied to auto-tune the baseline classifier hyperparameters. The performances of the proposed ensemble strategies are independently compared with the auto-tuned baseline classifiers. A concise analysis evaluates accuracy, precision, recall, F1-score, and ROC-ACU metrics. The analysis also examines the misclassified disease elements to find the most and least reliable classifiers for this specific medical problem. The results show that the VC ensemble strategy provides an improvement comparable to that of the best baseline classifier (the kNN). Meanwhile, when all baseline classifiers are included in the SC ensemble, this strategy surpasses 95% in all the evaluated metrics, standing out as the most suitable option for classifying vertebral column diseases. Full article

This article presents a technique to carry out fault classification using an equal-angle integer-period array convolutional neural network (EAIP-CNN) to process the electrostatic signal of working roller bearings. Firstly, electrostatic signals were collected using uniform angle sampling to ensure the angle intervals between two adjacent data points stayed the same and the signal length was fixed to a pre-determined number of rotation cycles. Then, this one-dimensional signal was transformed into a two-dimensional matrix, where the component of each row was the signal in one period, and the ordinate value of each row represented the corresponding rotation period. Therefore, the row and column indexes of the matrix had a specific meaning instead of simply splitting and stacking the data. Finally, the matrixes were utilized to train the CNN network and test the classification performance. The results show that the classification rate using this technique reaches 95.6%, which is higher than that of 2D CNNs without equal-angle integer-period arrays. Full article