Stack Architecture Research Articles

HDRSA-Net: Hybrid dynamic residual self-attention network for SAR-assisted optical image cloud and shadow removal

Clouds and shadows often contaminate optical remote sensing images, resulting in missing information. Consequently, continuous spatiotemporal monitoring of the Earth’s surface requires the efficient removal of clouds and shadows. Unlike optical satellites, synthetic aperture radar (SAR) has active imaging capabilities in all weather conditions, supplying valuable supplementary information for reconstructing missing regions. Nevertheless, the reconstruction of high-fidelity cloud-free images based on SAR-optical data fusion remains challenging due to differences in imaging mechanisms and the considerable contamination from speckle noise inherent in SAR imagery. To solve the aforementioned challenges, this paper presents a novel hybrid dynamic residual self-attention network (HDRSA-Net), aiming to fully exploit the potential of SAR images in reconstructing missing regions. The proposed HDRSA-Net comprises multiple dynamic interaction residual (DIR) groups organized into an end-to-end trainable deep hierarchical stacked architecture. Specifically, the omni-dimensional dynamic local exploration (ODDLE) module and the sparse global context aggregation (SGCA) module are used to form a local–global feature adaptive extraction and implicit enhancement. A multi-task cooperative optimization loss function is designed to ensure that the results exhibit high spectral fidelity and coherent spatial structures. Additionally, this paper releases a large dataset that can comprehensively evaluate the reconstruction quality under different cloud coverages and various types of ground cover, providing a solid foundation for restoring satisfactory sensory effects and reliable semantic application value. In comparison to the current representative algorithms, the presented approach exhibits effectiveness and advancement in reconstructing missing regions with stability. The project is accessible at: https://github.com/RSIIPAC/LuojiaSET-OSFCR.

Optimization and analysis of Si/SiGe strained vertically stacked heterostructure on insulator FeFinFET for high performance analog and RF applications

As semiconductor technology advances, the exploration of novel materials and device architectures becomes imperative to meet the growing demands of integrated circuits for analog and radio-frequency (RF) applications. In this paper, various advanced technologies have been amalgamated such as integration of ferroelectric layer in multigate FinFET along with the adaptation of SOI technology. Further strain technology is also used which employs a tri-layered strained-silicon channel system with the help of SiGe to form Vertically Stacked Heterostructure on Insulator Ferroelectric based FinFET (VS-HOI-FeFinFET) and on comparison with baseline FeFinFET, it is found to show remarkable improvements in terms of various measured parameters such as drain current, switching ratio, threshold voltage and subthreshold swing. Subsequently, gate stacking architecture is incorporated in VS-HOI-FeFinFET to further optimize the device performance. The four different configurations C1 to C4 are taken in terms of four different combinations of gate stack materials considered for gate oxide such as C1(SiO2+Al2O3), C2(SiO2+HfO2), C3(Al2O3), and C4(Al2O3+HfO2). It is found that the static and analog performance of VS-HOI-GS-FeFinFET enhance sequentially from configuration C1 to C4 such as switching ratio is enhanced upto around 5 times, DIBL and quality factor are improved by around 41% and 58% respectively along with significant improvement in device efficiency, early voltage, intrinsic gain, output conductance and output resistance. Subsequently performance optimization of VS-HOI-GS-FeFinFET with variation in mole fraction of germanium is also explored for various analog metrics. Further, several RF parameters are also explored and it is observed that the gain frequency product (GFP) and gain transconductance frequency product (GTFP) are augmented by around three times in magnitude along with 16% reduction in the unity gain cut off frequency in C4 configuration, exhibiting its ability of high frequency amplification with minimized noise distortion thus makes the device suitable for various high performance Analog and RF applications.

A novel framework for multiple thermokarst hazards risk assessment and controlling environmental factors analysis on the Qinghai-Tibet Plateau

Due to the influence of climate warming, the degradation of permafrost on the Qinghai-Tibet Plateau (QTP) has become evident. The formation of thermokarst hazards induced by the degradation of ice-rich permafrost has a significant impact on infrastructure construction and local ecology; therefore, it is necessary to assess its risk. Currently, high-precision and harmonized assessment tools for multiple thermokarst hazards risk assessment are lacking, and the mechanisms governing the environmental interactions of thermokarst hazards have not been fully clarified. In this study, a novel multiple thermokarst hazards risk assessment framework was proposed by combining stacking machine learning and potential environmental factors to assess the risk of thermokarst hazards in the Yangtze River source region (YRSR). In addition, model performance was improved by model optimization. Finally, structural equation modeling (SEM) was used to assess the controlling environmental factors for the thermokarst hazards in the YRSR. The results show that slope and precipitation contribute the most to the modeling accuracy of thermokarst lakes and thaw slumps, respectively. Model optimization improved the base model modeling accuracy by approximately 2 % ∼ 7 %, with XGBoost having the highest sensitivity to model optimization and the highest modeling accuracy. In terms of the ensemble strategy, the stacking model and ensemble model significantly improved the risk mapping accuracy, and the stacking model was better than the ensemble model, with accuracies of 92.39 % and 93.36 % for thermokarst lakes and thaw slumps, respectively. Compared with previous results, the results of this study are more representative of the YRSR. Finally, via SEM, terrain factors and soil factors were identified as controlling environmental factors for the risk of thaw slumps and thermokarst lakes, respectively. This study proposes a high-precision risk assessment method for thermokarst hazards in permafrost regions, and contributes to a deeper understanding of the interaction mechanisms between thermokarst hazards and environmental factors.

Novel Strategy for Human Deep Vein Thrombosis Diagnosis Based on Metabolomics and Stacking Machine Learning.

Deep vein thrombosis (DVT) is a serious health issue that often leads to considerable morbidity and mortality. Diagnosis of DVT in a clinical setting, however, presents considerable challenges. The fusion of metabolomics techniques and machine learning methods has led to high diagnostic and prognostic accuracy for various pathological conditions. This study explored the synergistic potential of dual-platform metabolomics (specifically, gas chromatography-mass spectrometry (GC-MS) and liquid chromatography-mass spectrometry (LC-MS)) to expand the detection of metabolites and improve the precision of DVT diagnosis. Sixty-one differential metabolites were identified in serum from DVT patients: 22 from GC-MS and 39 from LC-MS. Among these, five key metabolites were highlighted by SHapley Additive exPlanations (SHAP)-guided feature engineering and then used to develop a stacking diagnostic model. Additionally, a user-friendly interface application system was developed to streamline and automate the application of the diagnostic model, enhancing its practicality and accessibility for clinical use. This work showed that the integration of dual-platform metabolomics with a stacking machine learning model enables faster and more accurate diagnosis of DVT in clinical environments.

A stacking machine learning model for predicting pullout capacity of small ground anchors

Small ground anchors are widely used to fix securing tents in disaster relief efforts. Given the urgent nature of rescue operations, it is crucial to obtain prompt and accurate estimations of their pullout capacity. In this study, a stacking machine learning (ML) model is developed for the rapid estimation of pullout capacity offered by small ground anchors used for temporary tents, leveraging cone penetration data. The proposed stacking model incorporates three ML algorithms as the base regression models: K-nearest neighbors (KNN), support vector regression (SVR), and extreme gradient boosting (XGBoost). A dataset comprising 119 in-situ anchor pullout tests, where the cone penetration data were measured, is utilized to train and assess the stacking model performance. Three metrics, i.e., coefficient of determination (R2), mean absolute error (MAE), and root mean square error (RMSE), are employed to evaluate the predictive accuracy of the proposed model and compare its performance against four popular ML models and an empirical formula to highlight the advantages of the proposed stacking approach. The results affirm that the proposed stacking model outperforms other ML models and the empirical approach as achieving higher R2 and lower MAE and RMSE and more predicted data points falling within 20% error line. Thus, the proposed stacking model holds promising potential as a solution for efficiently predicting the pullout capacity of small ground anchors.

Improving Shear Strength Prediction in Steel Fiber Reinforced Concrete Beams: Stacked Ensemble Machine Learning Modeling and Practical Applications

Existing machine learning (ML) models often encounter challenges in accurately predicting the shear strength of steel fiber reinforced concrete (SFRC) beams, mainly due to a lack of generalization. This study introduces an advanced stacked ensemble ML architecture to overcome this limitation by utilizing a comprehensive data set of 394 experimental observations and a 20-feature matrix. The model exhibits exceptional performance with a mean absolute error of 0.391 and a correlation coefficient (R2) of 93.7%, and surpasses traditional ML algorithms. Furthermore, a sensitivity analysis of the developed model yields that shear strength is highly responsive to the shear span-to-effective depth ratio, with an increase from 1 to 4 resulting in a significant reduction (about 50%) in strength. Increasing the percentage of longitudinal steel from 1 to 2% leads to a 14.6% gain, whereas doubling its yield strength has a more modest 3.7% effect. Increasing the compressive strength of concrete from 25 to 50 MPa, notably increases the shear strength by 19.6%. Fiber length, diameter, and aspect ratio exhibit varying impacts, with shear strength most influenced by the fiber volume fraction, which leads to a peak enhancement of 30.7% at 2% fibrous volume; however, the tensile strength of fibers minimally affects the shear strength. Additionally, this research presents a simplified empirical model to predict the shear strength of SFRC beams based on the key determinants. This model employs the iterative Gauss–Newton algorithm, demonstrates reasonable predictive capability, and boasts an R2 of 83.3% and mean prediction-tested strengths of around 1.039. The practical implications of these findings are substantial for the construction industry as they enable a more accurate and reliable design of SFRC beams, optimize material usage, and potentially reduce construction costs as well as enhance structural safety.

Improved pore structure prediction based on a stacking machine learning model for low-permeability reservoir in Tazhong area, Tarim Basin

Stacking is an ensemble machine learning method designed to improve overall performance by combining the predictions of multiple base learners. The core idea of Stacking is to use the output of different base learners as input and make final predictions through a meta-learner, allowing the model to benefit from the strengths of various base learners and adapt better to complex data patterns. Predicting pore structures in low permeability is a sophisticated nonlinear modeling issue affected by multiple factors, including sedimentary and diagenetic effects on the structure. An individual model to predict pore structure is not satisfactory. In this paper, we applied a fusion of random forest and Adaboost models as base learners, which emerges as the cornerstone of this research, showcasing remarkable versatility and integration of diverse learner strengths. The Silurian system in Tazhong area features a vast expanse of lithological reservoirs with low abundance and high heterogeneity. Capillary pressure plays a crucial role in the distribution of these less permeable reservoirs by pore structure type influence. Aiming to predict the pore structure for the low-permeability reservoir in the field, which is characterized by low abundance and high heterogeneity, the study strategically integrates geological theory into specialized databases and harnesses machine learning methods. The resulting machine learning modeling dataset leverages geological factors and logging response characteristics to strengthen the ensemble machine learning stacking model. This sophisticated model not only achieves exceptional accuracy but also undergoes rigorous validation by analyzing predicted pore structure types alongside production data. The model's average accuracy was 79.4%, which practically hit the test set's accuracy threshold, meaning that the model was in an optimal state. In essence, the proficiency of the stacking model in predicting pore structure types serves as a transformative force, offering significant advancements in hydrocarbon distribution predictions and paving the way for more streamlined and effective future EOR activities.

A 64 × 128 3D-Stacked SPAD Image Sensor for Low-Light Imaging.

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.

The Technology Stack and System Architecture of the University of Cologne Virtual Campus

The Technology Stack and System Architecture of the University of Cologne Virtual Campus

Leveraging Stacking Framework for Fake Review Detection in the Hospitality Sector

Driven by motives of profit and competition, fake reviews are increasingly used to manipulate product ratings. This trend has caught the attention of academic researchers and international regulatory bodies. Current methods for spotting fake reviews suffer from scalability and interpretability issues. This study focuses on identifying suspected fake reviews in the hospitality sector using a review aggregator platform. By combining features and leveraging various classifiers through a stacking architecture, we improve training outcomes. User-centric traits emerge as crucial in spotting fake reviews. Incorporating SHAP (Shapley Additive Explanations) enhances model interpretability. Our model consistently outperforms existing methods across diverse dataset sizes, proving its adaptable, explainable, and scalable nature. These findings hold implications for review platforms, decision-makers, and users, promoting transparency and reliability in reviews and decisions.

Muscle Strength Preservation During Repeated Sets of Fatiguing Resistance Exercise: A Secondary Analysis.

Nuzzo, JL. Muscle strength preservation during repeated sets of fatiguing resistance exercise: A secondary analysis. J Strength Cond Res 38(6): 1149-1156, 2024-During sustained or repeated maximal voluntary efforts, muscle fatigue (acute strength loss) is not linear. After a large initial decrease, muscle strength plateaus at approximately 40% of baseline. This plateau, which likely reflects muscle strength preservation, has been observed in sustained maximal isometric and repeated maximal isokinetic contractions. Whether this pattern of fatigue occurs with traditional resistance exercise repetitions with free weights and weight stack machines has not been overviewed. Here, the aim was to determine whether the number of repetitions completed across 4 or more consecutive repetitions-to-failure tests exhibits the same nonlinear pattern of muscle fatigue. A secondary analysis was applied to data extracted as part of a recent meta-analysis on repetitions-to-failure tests. Studies were eligible if they reported mean number of repetitions completed in 4-6 consecutive repetitions-to-failure tests at a given relative load. Twenty-nine studies were included. Overall, the results show that the number of repetitions completed in consecutive repetitions-to-failure tests at a given load generally decreases curvilinearly. The numbers of repetitions completed in sets 2, 3, 4, 5, and 6 were equal to approximately 70, 55, 50, 45, and 45% of the number of repetitions completed in set 1, respectively. Longer interset rest intervals typically attenuated repetition loss, but the curvilinear pattern remained. From the results, a chart was created to predict the number of repetitions across 6 sets of resistance exercise taken to failure based on the number of repetitions completed in set 1. The chart is a general guide and educational tool. It should be used cautiously. More data from a variety of exercises, relative loads, and interset rest intervals are needed for more precise estimates of number of repetitions completed during repeated sets of fatiguing resistance exercise.

Geographic Characteristics and Meteorological Factors Dominate the Variation of Chlorophyll‐a in Lakes and Reservoirs With Higher TP Concentrations

AbstractNutrients such as phosphorus and nitrogen lead to extensive growth of harmful algae in lakes and reservoirs, which results in eutrophication. The driving mechanism of primary productivity change in lakes and reservoirs at a wide spatial and temporal scale remains largely unknown. We establish a water quality database using a stacking machine learning model, including monthly chlorophyll‐a (Chl‐a), total nitrogen (TN) and phosphorus (TP) concentrations in 255 lakes and 332 reservoirs from 1980 to 2018. We find an increase in the number of lakes and reservoirs at risk of algal blooms, with approximately 2.66% exhibiting Chl‐a concentrations exceeding 30 μg L−1 by 2018. Variations in Chl‐a concentrations were not entirely synchronized at the individual and regional levels with TN and TP concentrations, or their stoichiometric ratios, as estimated by a hierarchical linear model spanning 1980 to 2018. Further, we discovered unexpected effects of geographical features (i.e., latitude, longitude, and slope) and meteorological factors (i.e., air temperature) on Chl‐a concentrations in the lakes and reservoirs with higher TP concentrations (>0.041 mg L−1 for lakes and >0.027 mg L−1 for reservoirs), through the use of multiple regression trees and structural equation model analysis. Our findings underscore the importance of (a) implementing flexible nutrient pollution control approaches based on nutrient ecoregions that consider geographical variations, and (b) developing mitigation and adaptation strategies to address the uncertain risks posed by climate change in the prevention and control of eutrophication in lakes and reservoirs.

Firefly algorithm-based LSTM model for Guzheng tunes switching with big data analysis

Guzheng tune progression involves intricately harmonizing melodic motif transitions. Effectively navigating this vast creative possibility space to expose musically consistent elaborations presents challenges. We develop a specialized large long short-term memory (LSTM) model for generating musically consistent Guzheng tune transitions. First, we propose novel firefly algorithm (FA) enhancements, e.g., adaptive diversity preservation and adaptive swim parameters, to boost exploration effectiveness for navigating the vast creative combinatorics when generating Guzheng tune transitions. Then, we develop a specialized stacked LSTM architecture incorporating residual connections and conditioned embedding vectors that can leverage long-range temporal dependencies in Guzheng music patterns, including unsupervised learning of concise Guzheng-specific melody embedding vectors via a variational autoencoder, encapsulating unique harmonic signatures from performance descriptors to provide style guidance. Finally, we use LSTM networks to develop adversarial generative large models that enable realistic synthesis and evaluation of Guzheng tunes switching. We gather an extensive 10+ hour corpus of solo Guzheng recordings spanning 230 musical pieces, 130 distinguished performing artists, and 600+ audio tracks. Simultaneously, we conduct thorough Guzheng data analysis. Comparative assessments against strong baselines over systematic musical metrics and professional listeners validate significant generation fidelity improvements. Our model achieves a 63 % reduction in reconstruction error compared to the standard FA optimization after 1000 iterations. It also outperforms baselines in capturing characteristic motifs, maintaining modality coherence with under 2 % dissonant pitch errors, and retaining desired rhythmic cadences. User studies further confirm the superior naturalness, novelty, and stylistic faithfulness of the generated tune transitions, with ratings close to real data.

Test-retest reliability and concurrent validity of knee extensor strength measured by a novel device incorporated into a weight stack machine vs. handheld and isokinetic dynamometry.

The current clinical gold standard for assessing isometric quadriceps muscle strength is an isokinetic dynamometer (IKD). However, in clinics without an IKD, clinicians default to using handheld dynamometers (HHD), which are less reliable and accurate than the IKD, particularly for large muscle groups. A novel device (ND) was developed that locks the weight stack of weight machines, and measures forces applied to the machine, turning this equipment into an isometric dynamometer. The objectives of this study were to characterize the test-retest reliability of the ND, determine the within-day and between-days inter-rater reliability and concurrent validity compared with that of the HHD, in healthy volunteers (HV) and individuals with knee osteoarthritis (OA) for measuring knee extensors isometric muscle force. 29 healthy (age = 28.4 ± 7.4 years) and 15 knee OA (age = 37.6 ± 13.4 years) participants completed three maximum force isometric strength testing trials on dominant side knee extensor muscles on three devices (ND, HHD, and IKD) in two separate sessions by two raters. The maximum force (Fmax) produced, and the force-time series were recorded. Reliability and validity were assessed using Intraclass Correlation Coefficient (ICC), Bland-Altman Plots, Pearson's r, and cross-correlations. The ND demonstrated excellent test-retest reliability (ICC2,3 = 0.97). The within-day (ICC2,3 = 0.88) and between-day inter-rater reliability (ICC2,3 = 0.87) was good for HHD. The ND showed excellent within-day (ICC2,3 = 0.93) and good between-day (ICC2,3 = 0.89) inter-rater reliability. The Bland-Altman analysis revealed HHD systematic bias and underestimation of force particularly with quadriceps force values exceeding 450 N. Mean differences were found in maximum force between HHD vs. IKD (MDabs = 58 N, p < .001) but not the HHD vs. ND (MDabs = 24 N, p = .267) or ND vs. IKD (MDabs = 34 N, p = .051). The concurrent validity of Fmax (r = 0.81) and force-time curve correlation (0.96 ± 0.05) were the highest between the ND and IKD. The ND's test-retest reliability and concurrent validity make it a potential strength assessment tool with utility in physical therapy and fitness settings for large muscle groups such as the knee extensors.

Performance evaluation of deep learning approaches for fault diagnosis of rotational mechanical systems using vibration, sound, and acoustic emission signals

The present study emphasizes an optimized deep learning algorithm for gearbox fault detection using vibration, sound, and acoustic emission signals. Statistical and acoustic features are extracted from these signals, and various neural network algorithms are explored. The supervised deep feed forward neural network (DFFNN) demonstrates excellent performance with vibration signals but limited accuracy with sound and acoustic emission signals. To address this, unsupervised algorithms are optimized and compared with vibration-based classification. The findings show that unsupervised neural networks, particularly the auto-encoder and stacked auto-encoder architectures, achieve improved classification accuracy by leveraging the unique characteristics of acoustic emission signals. The unsupervised models also effectively overcome the vanishing gradient problem via regularization, enhancing their training efficiency. The stacked auto-encoder, with multiple layers of encoders and decoders, reduces computation time by 40% and memory consumption. These optimized algorithms hold promise for automated fault detection systems. The auto-encoder and stacked auto-encoder, utilizing vibration, sound, and acoustic emission signals, offer enhanced classification accuracy and can facilitate real-time monitoring of rotating mechanical systems. However, further optimization is needed to maximize their performance. In a nutshell, the supervised DFFNN excels in utilizing vibration signals for fault detection, while the unsupervised models exploit the distinctive characteristics of acoustic emission signals. Future research will focus on refining these algorithms to enhance their effectiveness. Implementing these optimized deep learning approaches can lead to autonomous fault detection systems, eliminating the need for continuous human supervision.

Observation of dichotomic field-tunable electronic structure in twisted monolayer-bilayer graphene

Twisted bilayer graphene (tBLG) provides a fascinating platform for engineering flat bands and inducing correlated phenomena. By designing the stacking architecture of graphene layers, twisted multilayer graphene can exhibit different symmetries with rich tunability. For example, in twisted monolayer-bilayer graphene (tMBG) which breaks the C2z symmetry, transport measurements reveal an asymmetric phase diagram under an out-of-plane electric field, exhibiting correlated insulating state and ferromagnetic state respectively when reversing the field direction. Revealing how the electronic structure evolves with electric field is critical for providing a better understanding of such asymmetric field-tunable properties. Here we report the experimental observation of field-tunable dichotomic electronic structure of tMBG by nanospot angle-resolved photoemission spectroscopy (NanoARPES) with operando gating. Interestingly, selective enhancement of the relative spectral weight contributions from monolayer and bilayer graphene is observed when switching the polarity of the bias voltage. Combining experimental results with theoretical calculations, the origin of such field-tunable electronic structure, resembling either tBLG or twisted double-bilayer graphene (tDBG), is attributed to the selectively enhanced contribution from different stacking graphene layers with a strong electron-hole asymmetry. Our work provides electronic structure insights for understanding the rich field-tunable physics of tMBG.

A Fullstack Web Application to Provide On-demand Household Services

Abstract: A Fullstack Web Application to Provide On-demand Household Services is a paper focused on designing and creating a full stack web application. The application aims to help users find nearby household service providers, such as electricians, plumbers, gardeners, carpenters etc. Users can easily browse the available services, book the appointments, track the location of service professionals, and share feedback about completed work. For service professionals, the application offers tools to manage appointments and track earnings. The application incorporates advanced technologies like Blockchain for secure storage of customer ratings, reviews, and work history generated by service professionals. Additionally, Machine Learning is utilized to develop chatbots for real-time user assistance. Few technological domains which are covered in this application are Full Stack development, Blockchain technology and Machine Learning. In summary, the goal of this application is to improve the skilled workforce connection system, making it efficient, secure, and engaging for all users

NiCuAg: An electrochemically-synthesised trimetallic stack for CO2 reduction

Electrochemical CO2 reduction is a promising technique for the production of desirable hydrocarbons without the need to resort to fossil resources. However, high overpotentials and poor selectivity remain a challenge for CO2 electro-reduction, especially for deep reduction by more than two electrons. One apparently attractive approach for breaking the scaling relations caused by simultaneous CO2 reduction pathways and for achieving deeper reduction is the use of multi-metallic electrodes, where several promising metal catalysts are present in close proximity. Herein, noting the activity shown by Ni, Cu and Ag for CO2 electroreduction when used individually, we set out to synthesise a tri-metallic “stack” catalyst, NiCuAg, and then to test this for electrochemical CO2 reduction. The stack architecture was successfully generated and the trimetallic NiCuAg system did show improved Faradaic efficiency for the reduction of CO2 to formic acid when compared to the bare Ni and bimetallic NiCu controls under some select conditions. However, the two-layer NiCu stack and bare Ni exhibited consistently higher Faradaic efficiencies than NiCuAg for deeper CO2 electroreduction to methanol and ethanol, indicating that the combination of three individually promising metals does not necessarily translate into superior catalytic performance for deep carbon dioxide reduction. [Display omitted]

Campus Management System

The Campus Management System (CMS) represents a pivotal advancement in educational technology, offering an integrated software solution tailored to the complex needs of modern educational institutions. This comprehensive system is designed to revolutionize administrative tasks, enhance efficiency, and foster a more organized campus environment. Through seamless automation of various processes, CMS empowers administrators, faculty, and students to navigate campus operations with ease. Utilizing Full Stack Development, CMS ensures seamless integration of front-end and back-end components, resulting in a user-friendly interface accessible to all stakeholders. This approach streamlines administrative tasks, simplifying processes such as student enrolment, course management, and resource allocation. Furthermore, CMS harnesses the power of Machine Learning (ML) algorithms to optimize key processes. These ML algorithms enable intelligent automation of tasks such as resume evaluation, eligibility criteria assessment, and course recommendations based on student performance. By analyzing vast amounts of data, CMS empowers institutions to make data-driven decisions, enhancing the overall educational experience for students. The integration of technology within educational institutions through CMS represents a significant step forward in campus management. By leveraging Full Stack Development and Machine Learning algorithms, CMS offers a robust solution to the challenges faced by modern educational institutions, ultimately paving the way for a more efficient, effective, and student-centric campus environment.

Stacking architecture for collimated backlight using cylindrical lens sheet with linear light sources or edge-lit/direct-lit BLU.

Highly collimated and directional backlights are essential for realizing advanced display technologies such as autostereoscopic 3D displays. Previously reported collimated backlights, either edge-lit or direct-lit, in general still suffer unsatisfactory form factors, directivity, uniformity, or crosstalk etc. In this work, we report a simple stacking architecture for the highly collimated and uniform backlights, by combining linear light source arrays and carefully designed cylindrical lens arrays. Experiments were conducted to validate the design and simulation, using the conventional edge-lit backlight or the direct-lit mini-LED (mLED) arrays as light sources, the NiFe (stainless steel) barrier sheets, and cylindrical lens arrays fabricated by molding. Highly collimated backlights with small angular divergence of ±1.45°∼±2.61°, decent uniformity of 93-96%, and minimal larger-angle sidelobes in emission patterns were achieved with controlled divergence of the light source and optimization of lens designs. The architecture reported here provides a convenient way to convert available backlight sources into a highly collimated backlight, and the use of optically reflective barrier also helps recycle light energy and enhance the luminance. The results of this work are believed to provide a facile approach for display technologies requiring highly collimated backlights.