Search Results (53)

Perfect absorbers operating in the terahertz (THz) band are key enablers for next-generation wireless systems. However, conventional metal–dielectric designs suffer from Ohmic losses and limited reconfigurability. Here, we propose an all-dielectric indium antimonide (InSb) cylindrical pillar metasurface that achieves near-unity absorption at $f_0=1.83$ THz with a high quality factor of $Q=72.3$. Critical coupling between coexisting electric and magnetic dipoles enables perfect impedance matching, while InSb’s low damping minimizes energy loss. The resonance is tunable via temperature and magnetic bias at sensitivities of $S_T\approx 2.8\mathrm{GHz}·{\mathrm{K}}^{-1}$, $S_B^{\mathrm{TE}}\approx -132.7\mathrm{GHz}·{\mathrm{T}}^{-1}$, and $S_B^{\mathrm{TM}}\approx -34.7\mathrm{GHz}·{\mathrm{T}}^{-1}$, respectively, without compromising absorption strength. At zero magnetic bias ($B=0$), the metasurface is polarization-independent under normal incidence; under magnetic bias ($B\ne 0$), it maintains near-unity absorbance for both TE and TM, while the resonance frequency becomes polarization-dependent. Additionally, the 90% absorptance bandwidth ($\Delta f_{A\ge 0.9}$) can be modulated from 8.3 GHz to 3.3 GHz with temperature, or broadened from 8.5 GHz to 14.8 GHz under magnetic bias. This allows gapless suppression of up to 14 consecutive 1 GHz-spaced channels. This standards-agnostic bandwidth metric illustrates dynamic spectral filtering for future THz links and beyond-5G/6G research. Owing to its sharp selectivity, dual-mode tunability, and metal-free construction, the proposed absorber offers a compact and reconfigurable platform for advanced THz filtering applications. Full article

In this paper, the first demonstration of a highly strained In_0.8Ga_0.2As/In_0.2Ga_0.8Sb type-II superlattice structure grown on InAs substrates by molecular beam epitaxy (MBE) for long-wavelength infrared detection was reported. Novel methodologies were developed to optimize the As and Sb flux growth conditions. The quality of the epitaxial layer was characterized using multiple analytical techniques, including differential interference contrast microscopy, atomic force microscopy, high-resolution X-ray diffraction, and high-resolution transmission electron microscopy. The high-quality superlattice structure, with a total thickness of 1.5 μm, exhibited exceptional surface morphology with a root-mean-square roughness of 0.141 nm over a 5 × 5 μm² area. Single-element devices with PIN architecture were fabricated and characterized. At 77 K, these devices demonstrated a 50% cutoff wavelength of approximately 12.1 μm. The long-wavelength infrared PIN devices exhibited promising performance metrics, including a dark current density of 7.96 × 10⁻² A/cm² at −50 mV bias and a high peak responsivity of 4.90 A/W under zero bias conditions, both measured at 77 K. Furthermore, the devices achieved a high peak quantum efficiency of 65% and a specific detectivity (D*) of 2.74 × 10¹⁰ cm·Hz^1/2/W at the peak responsivity wavelength of 10.7 µm. These results demonstrate the viability of this material system for long-wavelength infrared detection applications. Full article

Mining and analyzing information from multiple sources—such as geophysics and geochemistry—is a key aspect of big data-driven mineral prediction. Clustering, which groups large datasets based on distance metrics, is an essential method in multidimensional data analysis. The Two-Step Clustering (TSC) approach offers advantages by handling both categorical and continuous variables and automatically determining the optimal number of clusters. In this study, we applied the TSC method to mineral prediction in the northeastern margin of the Jiaolai Basin by: (i) converting residual gravity and magnetic anomalies into categorical variables using Ward clustering; and (ii) transforming 13 stream sediment elements into independent continuous variables through factor analysis. The results showed that clustering is sensitive to categorical variables and performs better with fewer categories. When variables share similar distribution characteristics, consistency between geophysical discretization and geochemical boundaries also influences clustering results. In this study, the (3 × 4) and (4 × 4) combinations yielded optimal clustering results. Cluster 3 was identified as a favorable zone for gold deposits due to its moderate gravity, low magnetism, and the enrichment in F1 (Ni–Cu–Zn), F2 (W–Mo–Bi), and F3 (As–Sb), indicating a multi-stage, shallow, hydrothermal mineralization process. This study demonstrates the effectiveness of combining Ward clustering for variable transformation with TSC for the integrated analysis of categorical and numerical data, confirming its value in multi-source data research and its potential for further application. Full article

This study explores the application of machine learning (ML) techniques—gradient boosting (GB), ridge regression (RR), and support vector regression (SVR)—for estimating the consumption of energy (CE) and Blaine fineness (BF) in cement clinker grinding. This study utilizes key clinker grinding parameters, such as maximum ball size, ball filling ratio, clinker mass, rotation speed, and number of revolutions, as input features. Through comprehensive preprocessing, feature selection methods (mutual info regression (MIR), lasso regression (LR), and sequential backward selection (SBS)) were employed to identify the most significant variables for predicting CE and BF. The performance of the models was optimized using a grid search for hyperparameter tuning and validated using k-fold cross-validation (k = 10). The results show that all ML methods effectively estimated the target parameters, with SVR demonstrating superior accuracy in both CE and BF predictions, as evidenced by its higher R² and lower error metrics (MAE, MAPE, and RMSE). This research highlights the potential of ML models in optimizing cement grinding processes, offering a novel approach to parameter estimation that can reduce experimental effort and enhance production efficiency. The findings underscore the advantages of SVR, making it the most reliable method for predicting energy consumption and Blaine fineness in clinker grinding. Full article

Wuyi Shuixian tea, a premium oolong tea known for its complex floral-fruity aroma, exhibits significant quality variations across different grades. This study systematically analyzed the aroma characteristics and key fragrant compounds of four grades (Grand Prize SA, First Prize SB, Outstanding Award SC, and Non-award SD) using headspace solid-phase microextraction-gas chromatography-mass spectrometry (HS-SPME-GC-MS), odor activity value (OAV) analysis, and multivariate statistical methods. A total of 159 volatile compounds were identified, with similar compound categories but distinct concentration gradients between grades. OAV-splitting analysis (based on OAV ≥ 1 as the threshold for aroma activity) identified β-ionone (fruity), octanal (fatty), and linalool (floral) as core aroma-active contributors, as their OAV values significantly exceeded 10 in awarded grades (SA, SB, SC), indicating dominant roles in sensory perception. Notably, linalool, a floral marker, showed a concentration gradient (SA > SB > SC) and was absent in SD, serving as a critical determinant of grade differentiation. Orthogonal partial least squares-discriminant analysis (OPLS-DA) further distinguished awarded grades (SA, SB, SC) by balanced fruity, floral, and woody notes, while SD lacked floral traits and exhibited burnt aromas. This classification was supported by hierarchical clustering analysis (HCA) of volatile profiles and principal component analysis (PCA). Electronic nose data validated these findings, showing strong correlations between sensor responses (W5S/W2W) and key compounds like hexanal and β-ionone. This study elucidates the molecular basis of aroma-driven quality grading in Wuyi Shuixian tea, providing a scientific framework for optimizing processing techniques and enhancing quality evaluation standards. The integration of chemical profiling with sensory attributes advances precision in tea industry practices, bridging traditional grading with objective analytical metrics. Full article

To improve the recycling content of Reclaimed Asphalt Pavement (RAP), this paper utilizes the characteristic of aged and hardened asphalt in RAP materials by adopting the High-modulus Asphalt Mixture design method for high-RAP-content recycling. First, the basic technical performance, fatigue properties, rheological characteristics, and chemical functional groups of reclaimed asphalt, 30# hard asphalt, and Styrene-Butadiene-Styrene (SBS)-modified asphalt were analyzed. The results revealed significant similarities in various metrics between reclaimed and hard asphalt, demonstrating the feasibility of replacing hard asphalt with reclaimed asphalt in a High-modulus Asphalt Mixture design. Next, High-modulus Asphalt Mixtures, EME13, with different RAP contents (0%, 20%, 40%, 60%) were designed and compared with SBS-modified Sup13 mixtures. The results indicated that (1) as the RAP content increased, the high-temperature performance of EME13 improved by 20~60%, while its low-temperature and intermediate-temperature crack resistance slightly declined by 10~20%. The dynamic modulus in the low-frequency region increased by 3~6 times, whereas the high-frequency dynamic modulus decreased by 20~30%. RAP enabled EME13 to meet the modulus design requirements more readily for High-modulus Asphalt Mixtures. (2) Although the SBS-modified Sup13 exhibited superior pavement performance compared to EME13, its cost was significantly higher. EME13 with high RAP content demonstrated notable economic advantages despite slightly lower pavement performance than Sup13. This research provides a new technical approach for the high-content recycling of RAP materials. Full article

This study aims to analyze electrocardiogram (ECG) data for the classification of five cardiac rhythms: sinus bradycardia (SB), sinus rhythm (SR), atrial fibrillation (AFIB), supraventricular tachycardia (SVT), and sinus tachycardia (ST). While SR is considered normal, the other four represent types of cardiac arrhythmias. A range of methods is utilized, including the supervised learning technique K-Nearest Neighbors (KNNs), combined with dimensionality reduction approaches such as Principal Component Analysis (PCA) and Uniform Manifold Approximation and Projection (UMAP), a modern method based in Riemannian topology. Additionally, logistic regression was applied using both maximum likelihood and Bayesian methods, with two distinct prior distributions: an informative normal prior and a non-informative Jeffreys prior. Performance was assessed using evaluation metrics such as positive predictive value (PPV), negative predictive value (NPV), specificity, sensitivity, accuracy, and F1-score. Ultimately, the UMAP-KNN method demonstrated the best overall performance. Full article

Most modified asphalts require high-temperature shearing and prolonged mixing to achieve a uniform structure, often resulting in substantial exhaust gas pollution. This study explores the utilization of polyurethane (PU) as a warm mix asphalt modifier, leveraging its favorable compatibility with asphalt at lower temperatures to mitigate emissions. To address the inherent limitations of PU-modified asphalt mixtures, namely, poor low-temperature performance and susceptibility to water damage, silane coupling agents (SCAs) are introduced to reinforce the asphalt–aggregate interfacial strength. At the microscopic level, the optimal PU content (20.8%) was determined through analysis of micro-viscosity and radial distribution functions (RDFs). SCA effects on interfacial properties were assessed using adhesion work, adhesion depth, and interfacial thermal stability. At the macroscopic level, performance metrics—including strength, high-temperature resistance, low-temperature resistance, and water stability—were evaluated against a benchmark hot mix SBS-modified asphalt mixture. The results indicate that PU-modified asphalts exhibit superior high-temperature performance and strength but slightly lower low-temperature performance and insufficient water stability. The addition of SCAs improved both low-temperature and water stability attributes, enabling the mixtures to meet specification requirements. The simulation results suggest that KH-550, which chemically reacts with isocyanate groups (-OCN) in PU, exhibits a better interfacial reinforcement effect than KH-570. Therefore, KH-550 is recommended as the preferred SCA for PU-modified asphalt mixtures in practical applications. Full article

One-dimensional (1D) nanomaterials have attracted considerable attention in the fabrication of nano-scale optoelectronic devices owing to their large specific surface areas, high surface-to-volume ratios, and directional electron transport channels. Compared to 1D metal oxide nanostructures, 1D metal sulfides have emerged as promising candidates for high-efficiency photodetectors due to their abundant surface vacancies and trap states, which facilitate oxygen adsorption and dissociation on their surfaces, thereby suppressing intrinsic carrier recombination while achieving enhanced optoelectronic performance. This review focuses on recent advancements in the performance of photodetectors fabricated using 1D binary metal sulfides as primary photosensitive layers, including nanowires, nanorods, nanotubes, and their heterostructures. Initially, the working principles of photodetectors are outlined, along with the key parameters and device types that influence their performance. Subsequently, the synthesis methods, device fabrication, and photoelectric properties of several extensively studied 1D metal sulfides and their composites, such as ZnS, CdS, SnS, Bi₂S₃, Sb₂S₃, WS₂, and SnS₂, are examined. Additionally, the current research status of 1D nanostructures of MoS₂, TiS₃, ReS₂, and In₂S₃, which are predominantly utilized as 2D materials, is explored and summarized. For systematic performance evaluation, standardized metrics encompassing responsivity, detectivity, external quantum efficiency, and response speed are comprehensively tabulated in dedicated sub-sections. The review culminates in proposing targeted research trajectories for advancing photodetection systems employing 1D binary metal sulfides. Full article

A study was undertaken to investigate the effects of non-antibiotic additives—citric acid, synbiotics, and probiotics—administered through drinking water on broiler growth performance, carcass characteristics, and blood biochemical profiles. A total of 400 one-day-old Cobb 500 broiler chicks were randomly divided into four treatment groups: Control; no additives administered (CON); Citric acid @2.5 g/L water (CA); Synbiotic @0.2 g/L water (SB); Probiotic @0.5 g/L water (PB) and with each group having 4 replicates of 25 chicks. Growth performance metrics, such as body weight (BW), body weight gain (BWG), feed intake (FI), and feed conversion ratio (FCR), were recorded weekly. At the end of the trial, the probiotic-fed group had significantly higher BW (p = 0.018), BWG (p = 0.027), and an improved FCR (1.62) compared to the CON (1.74), CA (1.66), and SB (1.70) groups (p = 0.042). Biochemical parameters showed significant differences in total cholesterol (p = 0.013) and low-density lipoprotein (LDL) levels (p = 0.039), with the PB group showing higher levels. These results suggest that citric acid, synbiotics, and probiotic additives provided through drinking water can enhance broiler growth performance, with probiotics offering the most promising benefits. Full article

Under California’s Clean Miles Standard (or SB 1014), transportation network companies (TNCs) must transition to zero-emission vehicles by 2030. One significant hurdle for TNC drivers is the electric vehicle (EV) acquisition and operating costs versus an internal combustion engine (ICE) vehicle. This study therefore evaluates net TNC driving earnings through EV acquisition pathways—financing, leasing, and renting—along with EV-favoring policy options. Key metrics assessed include (1) total TNC income when considering service fees, fuel costs, monthly vehicle payments, etc., and (2) the time EVs take to reach parity with their ICE counterparts. Monthly comparisons illustrate the earning differentials between new/used EVs and gas-powered vehicles. Our analyses employing TNC data from 2019 to 2020 suggest that EV leasing is optimal for short-term low-mileage drivers; EV financing is more feasible for those planning to drive for TNCs for over two years; EV rentals are only optimal for higher mileages, and they are not an economical pathway for longer-term driving. Sensitivity analyses further indicate that EV charging price discounts are effective in shortening the time for EVs to reach cost parity over ICEs. Drivers may experience a total asset gain when reselling their TNC vehicle after two to three years. Full article

Background/Objectives: Methodologies for assessing behavior form the foundation of health promotion and disease prevention. Physical activity (PA) and sedentary behavior (SB) assessment methodologies have predominantly been developed for adults without an intellectual disability (ID), raising credibility concerns for adults with ID. The purpose was to synthesize the current state of assessment methodologies for quantifying PA and SB volume in the free-living setting for adults with an ID. Methods: Following PRISMA guidelines, eleven databases were searched through December 2023, yielding 8174 records. Data were extracted in Covidence (v.2.0), obtaining quantified PA and SB volume and assessment methodology characteristics across data collection and analysis, including tool(s) and technique(s) used, preparatory actions taken, instructions provided, and behavioral strategies employed during data collection. Results: Of the 8174 articles screened, 91 met the inclusion criteria. Common metrics included minutes/hours per day/week and steps per day/week. Despite 80% of the studies using objective techniques, substantial variation existed across studies regarding wearable models, sampling frequency and epoch length settings, calibration protocols, wearable placements, and data processing techniques. Limited studies provided instructions that did not exclusively rely on spoken language. Behavioral strategies varied, including self-monitoring, providing assistance or supervision, administering questionnaires verbally, issuing reminders, and offering monetary incentives. Conclusions: This review underscores the need for greater consistency and accessibility in PA and SB assessment methodology for adults with ID. Tailored preparation, instruction, and behavioral strategies may enhance assessment viability and suitability for adults with ID, with or without caregiver or researcher involvement in the free-living setting. Full article

Background: Children with spina bifida (SB) face an elevated risk of obesity, which necessitates precise methods for assessing body composition to ensure effective weight management. Conventional measures like BMI are inadequate for this population because of variations in growth patterns and skeletal structure. Bioelectrical impedance analysis (BIA) is a method that offers a clearer picture of body composition, yet its use in children with SB remains underexplored. Methods: Conducted on 57 children with SB and 28 healthy controls, with a median age of 11 years, this study evaluated anthropometrics, including BMI and BIA-derived metrics. The Hoffer’s scale to assess physical activity was applied in SB children. Results: Results showed that while 32% of SB patients were classified as overweight or obese based on BMI, 62% exhibited high body fat percentage via BIA. Fat-free mass, muscle and fat mass, and fat-to-muscle ratio (FMR) differed significantly compared to the reference group. Non-ambulators showed a higher median body fat mass percentage (25.9% vs. 17.8%, p = 0.01) and FMR (0.92 vs. 0.44, p = 0.003) in comparison to the community walkers. Conclusions: In SB children, BIA-measured fat mass is a better obesity indicator than BMI. Non-ambulatory, SB patients with obesity had the highest FMR values, indicating a higher risk for metabolic syndrome. Full article

Background: Wireless capsule endoscopy (WCE) has significantly advanced the diagnosis of gastrointestinal (GI) diseases by allowing for the non-invasive visualization of the entire small intestine. However, machine learning-based methods for organ classification in WCE often rely on color information, leading to decreased performance when obstacles such as food debris are present. This study proposes a novel model that integrates convolutional neural networks (CNNs) and long short-term memory (LSTM) networks to analyze multiple frames and incorporate temporal information, ensuring that it performs well even when visual information is limited. Methods: We collected data from 126 patients using PillCam™ SB3 (Medtronic, Minneapolis, MN, USA), which comprised 2,395,932 images. Our deep learning model was trained to identify organs (stomach, small intestine, and colon) using data from 44 training and 10 validation cases. We applied calibration using a Gaussian filter to enhance the accuracy of detecting organ boundaries. Additionally, we estimated the transit time of the capsule in the gastric and small intestine regions using a combination of a convolutional neural network (CNN) and a long short-term memory (LSTM) designed to be aware of the sequence information of continuous videos. Finally, we evaluated the model’s performance using WCE videos from 72 patients. Results: Our model demonstrated high performance in organ classification, achieving an accuracy, sensitivity, and specificity of over 95% for each organ (stomach, small intestine, and colon), with an overall accuracy and F1-score of 97.1%. The Matthews Correlation Coefficient (MCC) and Geometric Mean (G-mean) were used to evaluate the model’s performance on imbalanced datasets, achieving MCC values of 0.93 for the stomach, 0.91 for the small intestine, and 0.94 for the colon, and G-mean values of 0.96 for the stomach, 0.95 for the small intestine, and 0.97 for the colon. Regarding the estimation of gastric and small intestine transit times, the mean time differences between the model predictions and ground truth were 4.3 ± 9.7 min for the stomach and 24.7 ± 33.8 min for the small intestine. Notably, the model’s predictions for gastric transit times were within 15 min of the ground truth for 95.8% of the test dataset (69 out of 72 cases). The proposed model shows overall superior performance compared to a model using only CNN. Conclusions: The combination of CNN and LSTM proves to be both accurate and clinically effective for organ classification and transit time estimation in WCE. Our model’s ability to integrate temporal information allows it to maintain high performance even in challenging conditions where color information alone is insufficient. Including MCC and G-mean metrics further validates the robustness of our approach in handling imbalanced datasets. These findings suggest that the proposed method can significantly improve the diagnostic accuracy and efficiency of WCE, making it a valuable tool in clinical practice for diagnosing and managing GI diseases. Full article

Functional copy-number alterations (fCNAs) are DNA copy-number changes with concordant differential gene expression. These are less likely to be bystander genetic lesions and could serve as robust and reproducible tumor biomarkers. To identify candidate fCNAs in neuroendocrine tumors (NETs), we integrated chromosomal microarray (CMA) and RNA-seq differential gene-expression data from 31 pancreatic (pNETs) and 33 small-bowel neuroendocrine tumors (sbNETs). Tumors were resected from 47 early-disease-progression (<24 months) and 17 late-disease-progression (>24 months) patients. Candidate fCNAs that accurately differentiated these groups in this discovery cohort were then replicated using fluorescence in situ hybridization (FISH) on formalin-fixed, paraffin-embedded (FFPE) tissues in a larger validation cohort of 60 pNETs and 82 sbNETs (52 early- and 65 late-disease-progression samples). Logistic regression analysis revealed the predictive ability of these biomarkers, as well as the assay-performance metrics of sensitivity, specificity, and area under the curve. Our results indicate that copy-number changes at chromosomal loci 4p16.3, 7q31.2, 9p21.3, 17q12, 18q21.2, and 19q12 may be used as diagnostic and prognostic NET biomarkers. This involves a rapid, cost-effective approach to determine the primary tumor site for patients with metastatic liver NETs and to guide risk-stratified therapeutic decisions. Full article