Real-world Scenarios Research Articles

An Effective Method for Estimating the Population Mean That Utilizes Dual Auxiliary Information

To improve the effectiveness of population estimators, researchers have recently implemented dual supplementary information. They employed traditional rankings, the empirical cumulative distribution function, and indicator functions as supplementary sources of information in their analysis. An improved family of population mean estimators is introduced in this article, which utilizes the relative ranks of the auxiliary information’s configurations to incorporate the relevant information. A first-order approximation is employed to derive the mathematical expressions for the bias and the mean-squared error (MSE) of the proposed family of estimators. The empirical analysis is investigated to demonstrate the practicality of the proposed estimators in real-world scenarios. Additionally, the theoretical conclusions are effectively validated by the Monte Carlo simulation integration. Our results unequivocally indicate that the proposed family of estimators surpasses their current counterparts.

Relationship between age and subfoveal choroidal thickness and its clinical implications

The retrospective study by Lei et al is an investigation of the relationship between age and subfoveal choroidal thickness (SFCT) in Chinese patients with proliferative diabetic retinopathy. Elements of the study design prevent the generalizability of the study findings, limiting their clinical implications. We recommend consideration of stricter eligibility criteria, other variables like duration of diabetes, interpretation of gender-differences in SFCT, longitudinal follow-up, use of newer choroidal flow indices, comparison of values with normal controls, subgroup analysis to determine the effect of prior treatment, as well as consideration of various real-world scenarios in future studies.

Semantic Web-Driven Targeted Adversarial Attack on Black Box Automatic Speech Recognition Systems

The susceptibility of Deep Neural Networks (DNNs) to adversarial attacks in Automatic Speech Recognition (ASR) systems has drawn significant attention. Most work focuses on white-box methods, but the assumption of full transparency of model architecture and parameters is unrealistic in real-world scenarios. Although several targeted black-box attack methods have been proposed in recent years, due to the complexity of ASR systems, they primarily rely on query-based approaches with limited search capabilities, leading to low success rates and noticeable noise. To address this, we propose DE-gradient, a new black-box approach using differential evolution (DE), a population-based search algorithm. Inspired by Semantic Web ideas, we introduce modulation noise to preserve semantic coherence while enhancing imperceptibility. In experiments on two public datasets, DE-gradient improved attack success rates by 19% and increased the signal-to-noise ratio (SNR) of silent parts from 27 dB to 54 dB, establishing a strong baseline for evaluating black-box adversarial attacks in ASR systems.

Subambient daytime radiative cooling of vertical surfaces.

Subambient daytime radiative cooling enables temperatures to passively reach below ambient temperature, even under direct sunlight, by emitting thermal radiation toward outer space. This technology holds promise for numerous exciting applications. However, previous demonstrations of subambient daytime radiative cooling require surfaces that directly face the sky, and these cannot be applied to vertical surfaces that are ubiquitous in real-world scenarios such as buildings and vehicles. Here, we demonstrate subambient daytime radiative cooling of vertical surfaces under peak sunlight using a hierarchically designed, angularly asymmetric, spectrally selective thermal emitter. Under peak sunlight of about 920 watts per square meter, our emitter reaches a temperature that is about 2.5°C below ambient temperature, corresponding to a temperature reduction of about 4.3° and 8.9°C compared with a silica-polymer hybrid radiative cooler and commercial white paint, respectively.

Generative AI for Consumer Behavior Prediction: Techniques and Applications

Generative AI techniques, such as Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), and transformers, have revolutionized consumer behavior prediction by enabling the synthesis of realistic data and extracting meaningful insights from large, unstructured datasets. However, despite their potential, the effectiveness of these models in practical applications remains inadequately addressed in the existing literature. This study aims to investigate how generative AI models can effectively enhance consumer behavior prediction and their implications for real-world applications in marketing and customer engagement. By systematically reviewing 31 studies focused on these models in e-commerce, energy data modeling, and public health, we identify their contributions to improving personalized marketing, inventory management, and customer retention. Specifically, transformer models excel at processing complicated sequential data for real-time consumer insights, while GANs and VAEs are effective in generating realistic data and predicting customer behaviors such as churn and purchasing intent. Additionally, this review highlights significant challenges, including data privacy concerns, the integration of computing resources, and the limited applicability of these models in real-world scenarios.

Secure Search of Smart Home Data Based on Access Control Encryption

Abstract: In recent years, the increasing variety of smart home devices, such as smart locks, surveillance cameras, and various sensors, has led to a significant rise in related data. To facilitate the sharing of smart home data, manufacturers store these data on the Internet of Things (IoT). However, since IoT servers are not fully trustworthy and the data often contain sensitive commercial information, any leakage could result in a commercial crisis for manufacturers. To address security concerns, manufacturers encrypt the data before storing them on the IoT. A challenge arises: how can users search for target data in encrypted form? This paper proposes a new search encryption scheme for smart home data, addressing the limitations of existing attribute-based encryption (ABE) searchable encryption schemes. The proposed scheme integrates access control trees and bilinear mapping technology, supporting multiple access policies including AND gates, OR gates, and threshold gates. Experimental results demonstrate a comprehensive analysis of the attribute-based searchable encryption protocol under a multi-authority architecture, covering four dimensions: correctness, security, complexity, and real-world application scenarios. In specific scenarios, when a data user, out of privacy concerns, is unwilling to reveal all attribute information to a single authority during a query for shared datathus avoiding identity disclosureor when the users identity information is managed by multiple independent authorities, this protocol proves highly effective. It ensures smooth data sharing while maintaining data security for both data owners and users, fully safeguarding data privacy and shareability.

A review of epilepsy detection and prediction methods based on EEG signal processing and deep learning

Epilepsy is a chronic neurological disorder that poses significant challenges to patients and their families. Effective detection and prediction of epilepsy can facilitate patient recovery, reduce family burden, and streamline healthcare processes. Therefore, it is essential to propose a deep learning method for efficient detection and prediction of epileptic electroencephalography (EEG) signals. This paper reviews several key aspects of epileptic EEG signal processing, focusing on epilepsy detection and prediction. It covers publicly available epileptic EEG datasets, preprocessing techniques, feature extraction methods, and deep learning-based networks used in these tasks. The literature is categorized based on patient independence, distinguishing between patient-independent and non-patient-independent studies. Additionally, the evaluation methods are classified into general classification indicators and specific epilepsy prediction criteria, with findings organized according to the prediction cycles reported in various studies. The review reveals several important insights. Despite the availability of public datasets, they often lack diversity in epilepsy types and are collected under controlled conditions that may not reflect real-world scenarios. As a result, signal preprocessing methods tend to be limited and may not fully represent practical conditions. Feature extraction and network designs frequently emphasize fusion mechanisms, with recent advances in Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) showing promising results, suggesting that new network models warrant further exploration. Studies using patient-independent data generally produce better results than those relying on non-patient-independent data. Metrics based on general classification methods typically perform better than those using specific epilepsy prediction criteria, though future research should focus on the latter for more accurate evaluation. Epilepsy prediction cycles are typically kept under 1 h, with most studies concentrating on intervals of 30 min or less.

Positioning Sensor Data Mining and Correlation Analysis Based on Multi-mode Radio Frequency Identification

Real-time positioning and sensor data mining are critical for various Internet of things (IoT) applications, but current approaches face challenges such as limited accuracy, high cost, and energy inefficiency. This paper proposes a novel fusion intelligent structure that combines radio frequency identification (RFID) technology and wireless sensor networks (WSN) to enable accurate and efficient positioning and data mining. The proposed method consists of two key components: an energy heterogeneity strategy for optimizing network energy consumption, and a data association feature analysis technique for mining sensor data. The results demonstrated that the proposed approach achieved superior positioning accuracy compared to other algorithms, with an average error of less than 0.300 under various conditions. The data detection accuracy was also high, with a maximum false detection rate of 3%. Furthermore, the proposed energy heterogeneity strategy significantly improved network energy utilization and stability. Real-world scenario testing validated the practicality of the proposed approach, with an average positioning error of less than 0.500 meters. These results validate the effectiveness of the proposed RFID-WSN fusion structure for real-time positioning and sensor data mining, providing a promising solution for IoT applications.

Photosynthetic efficiency and water retention in okra (Abelmoschus esculentus) contribute to tolerance to single and combined effects of drought and heat stress.

The co-occurrence of drought and heat significantly hampers plant productivity. Although their impacts are well studied, these studies have been based on the effects of individual stressors rather than their combined influence. Okra is crucial for food and nutritional security and livelihoods in many regions, yet it remains under-researched and unimproved. Okra has been proven to be sensitive to both drought and heat stress. This study employed a cost-effective phenotyping method to assess key traits characterising the diversity of okra morphophysiological responses to independent and interactive heat-drought stresses. This study aimed to understand okra responses to stress, identify stress-resilient traits, and characterise okra genotypes. We also addressed the need to examine interactive stress effects, which mirror real-world scenarios more accurately than single-stress studies. Sixty-three okra genotypes were subjected to heat, drought, or concurrent heat-drought stress at the seedling stage in improvised climate-controlled chambers. The germplasm exhibited significant variations in response to the various stresses. The broad-sense heritability was high (> 0.60) for traits such as chlorophyll content, plant biomass, performance indices, electrolyte leakage, and total leaf area. Drought stress alone had a more pronounced effect than heat stress alone, and the adverse impact was worsened under combined heat and drought stress. The interactive impact of drought and heat was more likely additive than antagonistic or synergistic. A positive and strong relationship was observed between photosynthetic efficiency parameters such as the Fv/Fm ratio, chlorophyll content, relative water index, and biomass parameters such as dry shoot weight. The 63 okra genotypes were classified into three distinct clusters, suggesting potential for future breeding efforts. Okra genotype considered to be tolerant or climate resilient (such as GH170, V1060831, GH174, V1060874, and GH106) to drought and heat, maintained enhanced photosynthetic efficiency and high internal water potential, possibly reducing osmotic and oxidative damage. This study revealed some mechanisms underlying the adaptation of okra genotypes to independent and combined heat and drought stress. The results provide a basis for breeding efforts to develop climate-resilient okra varieties.

EDMF: A New Benchmark for Multi-Focus Images with the Challenge of Exposure Difference

The goal of the multi-focus image fusion (MFIF) task is to merge images with different focus areas into a single clear image. In real world scenarios, in addition to varying focus attributes, there are also exposure differences between multi-source images, which is an important but often overlooked issue. To address this drawback and improve the development of the MFIF task, a new image fusion dataset is introduced called EDMF. Compared with the existing public MFIF datasets, it contains more images with exposure differences, which is more challenging and has a numerical advantage. Specifically, EDMF contains 1000 pairs of color images captured in real-world scenes, with some pairs exhibiting significant exposure difference. These images are captured using smartphones, encompassing diverse scenes and lighting conditions. Additionally, in this paper, a baseline method is also proposed, which is an improved version of memory unit-based unsupervised learning. By incorporating multiple adaptive memory units and spatial frequency information, the network is guided to focus on learning features from in-focus areas. This approach enables the network to effectively learn focus features during training, resulting in clear fused images that align with human visual perception. Experimental results demonstrate the effectiveness of the proposed method in handling exposure difference, achieving excellent fusion results in various complex scenes.

UNIQUE: A Framework for Uncertainty Quantification Benchmarking.

Machine learning (ML) models have become key in decision-making for many disciplines, including drug discovery and medicinal chemistry. ML models are generally evaluated prior to their usage in high-stakes decisions, such as compound synthesis or experimental testing. However, no ML model is robust or predictive in all real-world scenarios. Therefore, uncertainty quantification (UQ) in ML predictions has gained importance in recent years. Many investigations have focused on developing methodologies that provide accurate uncertainty estimates for ML-based predictions. Unfortunately, there is no UQ strategy that consistently provides robust estimates about model's applicability on new samples. Depending on the dataset, prediction task, and algorithm, accurate uncertainty estimations might be unfeasible to obtain. Moreover, the optimum UQ metric also varies across applications, and previous investigations have shown a lack of consistency across benchmarks. Herein, the UNIQUE (UNcertaInty QUantification bEnchmarking) framework is introduced to facilitate a comparison of UQ strategies in ML-based predictions. This Python library unifies the benchmarking of multiple UQ metrics, including the calculation of nonstandard UQ metrics (combining information from the dataset and model), and provides a comprehensive evaluation. In this framework, UQ metrics are evaluated for different application scenarios, e.g., eliminating the predictions with the lowest confidence or obtaining a reliable uncertainty estimate for an acquisition function. Taken together, this library will help to standardize UQ investigations and evaluate new methodologies.

Analysis of impact of limb segment length variations during reinforcement learning in four-legged robot.

Crawling robots are becoming increasingly prevalent in both industrial and private applications. Despite their many advantages over other robot types, they have complex movement mechanics. Artificial intelligence can simplify this by reinforcement learning. This process requires configuring the training environment and defining input parameters, including a robot model for movement training. To translate the virtual results into real-world scenarios, a 3D model with appropriate mechanical parameters must be developed.These parameters can vary significantly between multiple mechanical configurations, which will further impact the reinforcement learning process of such a robot. For this reason, it was decided to test which limb configurations would work best in this process. Initially, various kinematic types of walking robots were analysed, drawing on the anatomy of mammals, reptiles, and insects for the biological model. The reptilian model was chosen for its balance of stability, dynamics, and energy efficiency. The article reviews the preparation of robot models and the configuration of the Unity3D development environment using the ML-Agents toolkit. The experiment examined how different limb lengths affect training, resulting in movement algorithms for various quadruped robot configurations using artificial neural networks. Based on the numerical results, the best configuration was the default, with the same length of the tibia as the thigh, achieving a reward function value of 883.9 and an episode length of 245.5. Taking into account the same criteria, the least efficient configuration was definitely the one characterised by the shortest thigh and the longest tibia among those considered. In its case, the reward function reached a value of only 526.2 with an episode lasting 999.0, which means that it never achieved the intended goal.

A dual branch attention network based on practical degradation model for face super resolution.

In recent years, the field of face super-resolution (FSR) has advanced rapidly. However, complex degradation factors in real-world scenarios can severely deteriorate image quality, significantly affecting the reconstruction performance of FSR methods. Currently, there is a lack of research on degradation modeling for real-world facial images, which impacts the generalization ability of existing FSR methods. In this paper, a practical degradation model based on hybrid degradation processes is proposed to select multiple degradation processes including Gaussian noise, Rayleigh noise, Motion blur, Salt-and-Pepper noise, and Mean blur through a stochastic strategy to more realistically simulate the effect of image distortion in real scenarios. We also design a dual-branch attention network called DBANet for face super-resolution and conduct experiments on the SCUT_FBP, Helen and PFHQ datasets, achieving satisfactory results. Our proposed model is effective in handling image distortion under different degradation modalities, which improves the robustness of super-resolution reconstruction. This study introduces an innovative approach to the field of face image super-resolution, which has the potential for a wide range of practical applications. The code of DBANet will be available at https://github.com/bxzha/DBANet .

Risky hybrid foraging: The impact of risk, reward value, and prevalence on foraging behavior in hybrid visual search.

In hybrid foraging, foragers search for multiple targets in multiple patches throughout the foraging session, mimicking a range of real-world scenarios. This research examines outcome uncertainty, the prevalence of different target types, and the reward value of targets in human hybrid foraging. Our empirical findings show a consistent tendency toward risk-averse behavior in hybrid foraging. That is, people display a preference for certainty and actively avoid taking risks. While altering the prevalence or reward value of the risky targets does influence people's aversion to risk, the overall effect of risk remains dominant. Additionally, simulation results suggest that the observed risk-averse strategy is suboptimal in the sense that it prevents foragers from maximizing their overall returns. These results underscore the crucial role of outcome uncertainty in shaping hybrid foraging behavior and shed light on potential theoretical developments bridging theories in decision making and hybrid foraging. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Oneiros: The Sports Club: navigating the growth path

Learning outcomes After completion of the case study, the students will be able to analyze the challenges and opportunities associated with business expansion in any business, assess the significance of leveraging existing strengths versus exploring new markets for organizational growth, evaluate the implications of regional consolidation versus national expansion strategies, develop strategic thinking and decision-making skills in a competitive business landscape and understand the nuances of market dynamics, brand recognition and operational challenges in diverse geographical regions. Case overview/synopsis Oneiros – The Sports Club stood poised at a pivotal juncture, grappling with a strategic quandary that encapsulates the divergent visions of its leadership. The narrative unfolded in Surat, a vibrant city in the state of Gujarat where contrasting viewpoints champion the familiar stability of local success against the lure of uncharted state and national territories. Ms Hemali Shah advocated for consolidating the club’s triumphs in Gujarat, emphasizing on the parameters of brand loyalty and operational mastery. In stark contrast, Mr Robin Patel envisioned a bold expansion strategy across the state, aiming for brand recognition and a paradigm shift to regional prominence. Caught amidst these competing visions, Mr Sumit Lathia who was aspiring for the club’s national presence navigated the complexities of market dynamics and business model, oscillating between preserving familiarity and embracing the allure of ambition. This case study highlights the nuanced strategic dilemmas faced by Oneiros, offering a captivating exploration of growth strategies in a competitive landscape. With insights from various perspectives within the organization, this case study navigates the complexities of growth, market dynamics and the balance between familiarity and ambition. This case study offers valuable insights and practical applications for students pursuing regular Master of Business Administration (MBA) and executive MBA programs, as well as undergraduate and postgraduate studies in entrepreneurship and strategic management. By examining the strategic decisions and operational challenges faced by Lathia and Oneiros – The Sports Club, students can gain a deeper understanding of key concepts such as public–private partnerships, market expansion strategies, customer segmentation and revenue diversification. Complexity academic level This case study is positioned within the decision-making or business development modules of the curriculum. It serves as a platform to apply theoretical concepts of strategic decision-making, market analysis and growth strategies to a real-world scenario. This case study prompts students to critically evaluate expansion dilemmas and devise strategic solutions. Supplementary materials Teaching notes are available for educators only. Subject code Strategy.

DESAT: A Distance-Enhanced Strip Attention Transformer for Remote Sensing Image Super-Resolution

Transformer-based methods have demonstrated impressive performance in image super-resolution tasks. However, when applied to large-scale Earth observation images, the existing transformers encounter two significant challenges: (1) insufficient consideration of spatial correlation between adjacent ground objects; and (2) performance bottlenecks due to the underutilization of the upsample module. To address these issues, we propose a novel distance-enhanced strip attention transformer (DESAT). The DESAT integrates distance priors, easily obtainable from remote sensing images, into the strip window self-attention mechanism to capture spatial correlations more effectively. To further enhance the transfer of deep features into high-resolution outputs, we designed an attention-enhanced upsample block, which combines the pixel shuffle layer with an attention-based upsample branch implemented through the overlapping window self-attention mechanism. Additionally, to better simulate real-world scenarios, we constructed a new cross-sensor super-resolution dataset using Gaofen-6 satellite imagery. Extensive experiments on both simulated and real-world remote sensing datasets demonstrate that the DESAT outperforms state-of-the-art models by up to 1.17 dB along with superior qualitative results. Furthermore, the DESAT achieves more competitive performance in real-world tasks, effectively balancing spatial detail reconstruction and spectral transform, making it highly suitable for practical remote sensing super-resolution applications.

Creating a Quasi-Resonant Induction Cooktop Integrating Zero-Voltage Switching (ZVS) and Load Management

This study aims to elucidate the development and construction of a durable induction cooktop, with key considerations including efficiency, power customization, and safety features. The intricate processes involved in crafting a 3.5 kW induction burner are thoroughly examined, encompassing simulations for quasi-resonant inverters, the meticulous selection of induction coils and capacitors, the implementation of practical Analog-to-Digital Converter (ADC) filtration, pulse width modulation (PWM) driving techniques, and the integration of protection mechanisms. Leveraging an ARM-based microcontroller enabled the attainment of diverse objectives such as Zero-Voltage Switching (ZVS), safeguarding IGBTs, facilitating user interaction through a user-friendly interface, and enabling load detection capabilities. Furthermore, the capability to gauge and adjust output power based on user preferences was incorporated. Subsequently, rigorous testing was conducted to evaluate the functionality and applicability of the device in real-world scenarios.

Fractals as Pre-Training Datasets for Anomaly Detection and Localization

Anomaly detection is crucial in large-scale industrial manufacturing as it helps to detect and localize defective parts. Pre-training feature extractors on large-scale datasets is a popular approach for this task. Stringent data security, privacy regulations, high costs, and long acquisition time hinder the development of large-scale datasets for training and benchmarking. Despite recent work focusing primarily on the development of new anomaly detection methods based on such extractors, not much attention has been paid to the importance of the data used for pre-training. This study compares representative models pre-trained with fractal images against those pre-trained with ImageNet, without subsequent task-specific fine-tuning. We evaluated the performance of eleven state-of-the-art methods on MVTecAD, MVTec LOCO AD, and VisA, well-known benchmark datasets inspired by real-world industrial inspection scenarios. Further, we propose a novel method to create a dataset by combining the dynamically generated fractal images creating a “Multi-Formula” dataset. Even though pre-training with ImageNet leads to better results, fractals can achieve close performance to ImageNet under proper parametrization. This opens up the possibility for a new research direction where feature extractors could be trained on synthetically generated abstract datasets mitigating the ever-increasing demand for data in machine learning while circumventing privacy and security concerns.

Blind 3D Video Stabilization with Spatio-Temporally Varying Motion Blur

Video stabilization is a challenging task that attempts to compensate for the overall frame shake during video acquisition. Existing three-dimensional video stabilization methods aim at modeling camera perspective projection through either data-driven training or explicit motion estimation. However, the above methods are difficult to effectively solve the issue of shaky videos with abrupt object movements, resulting in local motion blur in the direction of the movement. This phenomenon is prevalent in real-world scenarios featuring foreground blind motion scenes. Unfortunately, directly combining stabilization and deblurring methods poses challenges when dealing with this situation. In the video, the intensity of motion blur undergoes continuous changes, and the direct combination method inadequately utilizes spatiotemporal information, providing insufficient clues for cross-frame compensation. To alleviate this problem, the Cross-frame-temporal Module framework is proposed to address blind motion blur induced by various conditions, which utilizes cross-frame temporal features to estimate depth maps and camera motion. In this framework, a Blur Transform Network (BTNet) is designed to adapt to spatially varying motion blur, which transforms local regions according to the impact of blur intensities to adapt to the effects of non-uniform motion blur; furthermore, our Temporal-Aware Network (TANet) further suppresses motion blur by leveraging cross-frame temporal features. In addition, the limited availability of pair-training video data containing motion blur limits the application of this approach in practice. The Cross-frame-temporal Module framework adopts an un-pretrained in-test training strategy. Extensive experimental results have demonstrated that our method outperforms state-of-the-art methods.

A Lightweight Barcode Detection Algorithm Based on Deep Learning

For existing situations of missed detections, false detections, and repeated detections in barcode detection algorithms in real-world scenarios, a barcode detection algorithm based on improved YOLOv8 is proposed. The EfficientViT block based on a linear self-attention mechanism is introduced into the backbone of the original model to enhance the model’s attention to barcode features. In the model’s neck, linear mapping and grouped convolution are used to improve the C2f module, and the ADown convolution block is utilized to modify the model’s downsampling, which reduces the model’s parameters and computational cost while improving the efficiency of model feature fusion. Finally, the reconstruction of the model’s detection head and the modification of the loss function are implemented to enhance the model’s training quality and reduce the model’s error in barcode detection. Experimental results indicate that the improved model exhibits an increase of 1.8% in recall rate and 1.9% in mAP50:95 for barcode localization and classification. The FPS is improved by 40 frames per second. The model’s parameter count is reduced by 74.2%, and FLOPs are decreased by 79.6%. Furthermore, the proposed model outperforms other models in terms of model size and barcode detection accuracy.