Visibility Improvement Research Articles

Evaluating community-embedded elder care: A case study of supply and demand in Hangzhou, China.

With China's urbanization and demographic shifts significantly affecting elder care, this study examines the alignment of community-based elder care services in Hangzhou's Hemu Community. It addresses the gap in understanding how these services meet the needs of an aging population in a rapidly changing social context. A comprehensive mixed-methods approach was utilized, involving interviews with community residents, observations of elder care service operationsand thorough document analysis. The objective was to evaluate the adequacy, utilizationand satisfaction levels concerning these elder care services. The research identified that, although the services generally meet the community's needs, there are notable challenges in content specificity, quality assuranceand promotional efforts. These challenges lead to resistance among older adults. Additionally, issues with service visibility and the lack of robust feedback mechanisms were noted. This study highlights the critical need for a cooperative strategy involving government, community organizations, social entitiesand businesses in enhancing elder care services. It suggests that improvements in service visibility, content qualityand targeted promotion are crucial to address the evolving needs of the aging population and to reduce reluctance in service utilization.

Solving supply chain management issues with AI and Big Data analytics for future operational efficiency

This review paper examines the use of artificial intelligence (AI) and big data analytics in solving supply chain management (SCM) issues and enhancing future operational efficiency. The primary objective is to synthesize existing research and provide a comprehensive overview of how these technologies are revolutionizing SCM. The paper systematically reviews recent literature on AI and big data applications in SCM, focusing on key areas such as demand forecasting, inventory management, and logistics optimization. By analyzing various studies and case examples, it highlights the transformative effects of these technologies on supply chain processes. The review covers a range of AI techniques including machine learning, deep learning, and predictive analytics, as well as the role of big data in capturing and processing large volumes of supply chain-related information. Key findings from the literature indicate significant improvements in supply chain visibility, decision-making accuracy, and operational efficiency. AI and big data analytics enable more precise demand forecasting, better inventory control, and optimized logistics, leading to cost reductions and enhanced responsiveness to market fluctuations. The review also discusses the challenges and considerations for implementing these technologies, such as data quality, integration complexity, and the need for specialized skills. The paper emphasizes the critical role of AI and big data analytics in addressing contemporary SCM issues and fostering future-ready supply chains. It underscores the necessity for organizations to adopt these technologies to stay competitive and achieve long-term operational excellence. The insights provided serve as a valuable resource for researchers and practitioners aiming to leverage AI and big data for supply chain innovation. Keywords: Artificial Intelligence (AI), Big Data Analytics (BDA), Supply Chain Management (SCM), Predictive Analytics, Internet of Things (IoT), Blockchain Technology, Automation, Machine Learning, Inventory Management, Logistics Optimization, Supply Chain Visibility, Real-time Monitoring, Data Quality, System Integration, Risk Management, Operational Efficiency, Strategic Decision-making, Sustainability, Supply Chain Resilience, Emerging Technologies.

Visibility Improvement Effect through Installation of Cylindrical Pedestrian Traffic Lights

Visibility Improvement Effect through Installation of Cylindrical Pedestrian Traffic Lights

BBMS: Investigation to Single Colour Image Visibility Improvement in Turbid Media through Biorthogonal Wavelet Based Depth-map Estimation

Electronic imaging needs good quality, high resolution (HR) digital images for highlighting finer details of the image. Normally images captured by ordinary digital camera are post pro cessed with available software instead of high cost CMOS sensor phased camera. Problem commonly faced with these images may get degraded due to the scattering media which deteriorates contrast, shifts colour, and make overall image whitish. Information from the distant objects suffer from poor medium transmission as well as noise amplification. Biorthogonal wavelet denoising (BWD), compress sparsely in frequency -time space, removes noise from depth estimation and makes the transmission smooth. Moreover, single image recovery is a serious challenge and ill posed problem. The recovered image improves compare to degraded image significantly and dis cards possibility of false edge detection as well as prevent colour shift and low contrast. Good quality images are found with objective evaluation. Moreover, different wavelets, thresholds, and decomposition levels have been studied and compared. Time complexity is linear due to wavelet domain analysis which makes the technique fast, reliable, and visually pleasing.

Enhancing Visibility Through Image Defogging

This project introduces a method for enhancing visibility in foggy images through histogram equalization. By leveraging the LAB color space, the method separates luminance and chrominance components, applying histogram equalization solely to the luminance channel. This process effectively enhances contrast and brightness while preserving color fidelity. Experimental results demonstrate significant visibility improvements in foggy scenes, with computational efficiency suitable for real-time applications. This approach holds promise for enhancing visibility in critical domains like transportation safety and outdoor surveillance. Keywords: Fog Removal, Atmospheric Haze, Luminance Channel, Image Enhancement, Noise Reduction ,Histogram Equalisation, Color Fidelity.

Current challenges in the visibility improvement of urban Chongqing in Southwest China: From the perspective of PM2.5-bound water uptake property over 2015–2021

The visibility (VIS) improvement remains challenging such as in Southwest China, although the aerosol loading was reduced markedly. More in-depth understanding of interactions between visibility and aerosol physicochemical properties is required. This study systematically investigated mechanisms behind the interannual variation of low visibility at urban Chongqing from the aspect of PM2.5-bound optical enhancements over a relatively large timescale of 2015–2021. Results show that VIS <10 km frequently exceeded 50% in 2021 despite remarkable emission reductions since 2015, with the most severe challenge in visibility improvement faced in winter. Generally, the annual mean aerosol hygroscopicity and light extinction capacity were promoted during 2015–2021, as demonstrated by a higher optical enhancement factor (f(RH)), aerosol liquid water content per unit PM2.5 mass (i.e., ALWC/PM2.5), and dry mass extinction efficiency (MEEdry) observed for 2021 in comparison to previous years. A pronounced ascending trend existed in the annual mean fraction of nitrate (fNO3−), which even surpassed organics and sulfate to be the predominant composition in PM2.5. A similar evolution of fNO3− was observed during the visibility degradation specifically when VIS <10 km, highlighting the importance of highly hydrophilic inorganics in the haze formation. Given that the higher level of fNO3− normally introduced an elevated ALWC/PM2.5 which likely enhanced secondary aerosol formation (e.g., sulfate, nitrate, and secondary organics) via multiphase reactions, the combined effects could further exacerbate the visibility degradation particularly under highly humid and polluted conditions. Stricter controls such as on NOx and volatile organic precursors would be beneficial for the visibility improvement in China.

Enhancement of weak signals by applying a suppression method to high-intense methyl and methylene signals of lipids in NMR spectroscopy.

Lipids play crucial roles in human biology, serving as energy stores, cell membranes, hormone production, and signaling molecules. Accordingly, their study under lipidomics has advanced the study of living organisms. 1-Dimensional (D) and 2D NMR methods, particularly 1D 1H and 2D 1H-1H Total Correlation Spectroscopy (TOCSY), are commonly used in lipidomics for quantification and structural identification. However, these NMR methods suffer from low sensitivity, especially in cases of low concentrated molecules such as protons attached to hydroxy, esters, aliphatic, or aromatic unsaturated carbons. Such molecules are common in complex mixtures such as dairy products and plant oils. On the other hand, lipids have highly populated fractions of methyl and methylene groups that result in intense peaks that overwhelm lower peaks and cause inhomogeneities in 2D TOCSY spectra. In this study, we applied a method of suppression to suppress these intense peaks of methyl and methylene groups to detect weaker peaks. The suppression method was investigated on samples of cheese, butter, a mixture of lipids, coconut oil, and olive oil. A significant improvement in peak sensitivity and visibility of cross-peaks was observed, leading to enhanced comparative quantification and structural identification of a greater number of lipids. Additionally, the enhanced sensitivity reduced the time required for the qualitative and comparative quantification of other lipid compounds and components. This, in turn, enables faster and more reliable structural identification and comparative quantification of a greater number of lipids. Additionally, it reduces the time required for the qualitative, and comparative quantification due to the enhancement of sensitivity.

Evaluasi Bisnis Proses Rekrutmen dan Seleksi Karyawan PT Sumber Alfaria Trijaya Tbk

This research evaluates and improves the efficiency of the employee recruitment and selection process at PT Sumber Alfaria Trijaya Tbk, a leading retail company. The research focus involves marketing strategies for job vacancies, optimizing selection tests, and implementing information technology. Research methods involve document analysis, interviews, and observation. The results show potential improvements in job vacancy visibility, selection test adjustments, and use of human resource information systems.

Paper] Construction of 3D Image and Visibility Improvement of 2D Image Outside 3D Viewing Zone by Edge-Based Depth-Fused 3D Display Using Aerial Image and Rear Display

Paper] Construction of 3D Image and Visibility Improvement of 2D Image Outside 3D Viewing Zone by Edge-Based Depth-Fused 3D Display Using Aerial Image and Rear Display

Changes in wintertime visibility across China over 2013–2019 and the drivers: A comprehensive assessment using machine learning method

Effective emission reduction measures have largely lowered the particulate concentration in China, but low-visibility events still occur frequently, greatly affecting people's daily life, travel, and health. In the context of carbon neutrality strategy and climate change, the mechanisms governing visibility changes may be undergoing a transformation. To address this critical issue, we have undertaken a comprehensive assessment by employing a novel approach that combines site observations, model-derived datasets, and machine learning techniques. Our analysis of the dataset shows varying degrees of improvement in wintertime visibility in regions such as North China, South China, and the Fenwei Plain over 2013–2019, but an unexpected deterioration (approximately 1 km yr−1) in central and southern China (CSC). We further elucidate key roles of PM2.5 reduction in these regions with visibility improvement; whereas the unsatisfactory visibility trend in CSC was caused by combined effect of relative humidity (RH) increase (47 %), aerosol hygroscopicity (κ) enhancement (9 %), and boundary layer (BLH) reduction (8 %), which greatly overwhelms the effect of PM2.5 reduction recently. Moreover, the study reveals a growing influence of RH on the wintertime visibility, reaching 40 % ± 24 % to the total contribution in 2019, while that of PM2.5 declined to 18 % ± 19 % and is expected to further diminish with emission reduction. Note those often-neglected factors-temperature, wind speed, BLH, and κ, account for over 40 % of the total contribution. Though the importance of aerosol hygroscopic growth to visibility was found decreasing recently, it retains non-negligible impacts on driving inter-annual visibility trends. This study yields innovative insights for air pollution control, underscoring the imperative of region-specific strategies to mitigate low-visibility events.

Clinical Application of Dark-blood Imaging in Head and Neck CT Angiography: Effect on Image Quality and Plaque Visibility

The aim of this study was to explore the potential of a newly developed dark-blood imaging technique to improve image quality and plaque visibility in head and neck computed tomography (CT) angiography. Patients who underwent triphasic head and neck CT angiography scans from August 2021 to March 2023 were retrospectively enrolled (mean age 67.23±10.81 [SD] years, range 43-85 years, 64.7% male). The CT protocol consists of pre-contrast, arterial and delayed phases. Dark-blood images were postprocessed with the contrast-enhancement boost (CE-boost) technique. The quantitative assessment involved evaluating the CT value, image noise, contrast-to-noise ratio (CNR), and signal-to-noise ratio (SNR) of calcified plaque and non-calcified plaque. The plaque CNR relative to the vessel lumen (CNRplaque-lumen), vessel wall (CNRplaque-wall), and adjacent muscle (CNRplaque-muscle) was respectively calculated. Two experienced radiologists independently evaluated the CT images (5, best; 1, worst) by four characteristics including calcified plaque visibility, non-calcified plaque visibility, diagnostic confidence, and overall image quality. Inter-rater variability was also evaluated. The artery stenosis rate and plaque burden on dark-blood images were measured and compared with arterial phases. The intraclass correlation coefficient (ICC) was used for consistency analysis. The diagnostic accuracy of dark-blood images for the stenosis rate was evaluated by the area under the curve (AUC). A total of 43 patients with 54 calcified plaques and 34 non-calcified plaques were assessed in this study. When compared with pre-contrast and delayed phase, dark-blood images yielded significantly higher CNRplaque-lumen and CNRplaque-muscle of calcified (219.79±159.20 and 181.23±112.12, respectively) and non-calcified (30.30±29.11 and 6.28±4.75, respectively) plaques (all p<0.001). Calcified plaque SNR of dark-blood showed equal or slightly lower than other phases (p>0.05 or p=0.02). A major increase was observed in the non-calcified plaque SNR of dark-blood compared to the arterial phase (5.56±3.71 vs. 4.23±3.56, p=0.02), although there were no apparent differences compared to pre-contrast and delayed phases (p>0.05). In subjective analyzes, the calcified plaque visibility (4.99±0.07), non-calcified plaque visibility (4.62±0.48), overall image quality (4.81±0.34), and diagnostic confidence (4.74±0.36) in dark-blood images dominated the highest scores (p<0.001). The subjective scores of radiologists exhibited good consistency (all kappa value>0.7). The dark-blood image and the arterial phase image exhibited good consistency in identifying the stenosis rate (p<0.001). In the evaluation of plaque burden, the interobserver agreement for dark-blood images was higher compared to arterial phase images (ICC = 0.870 vs. 0.729). Compared to conventional triphasic head and neck CT angiography, the CE-boost derived dark-blood imaging demonstrated a significant improvement in image quality and visibility for both calcified and non-calcified plaque assessment.

Deep learning enables time-efficient soft tissue enhancement in CBCT: Proof-of-concept study for dentomaxillofacial applications

PurposeThe use of iterative and deep learning reconstruction methods, which would allow effective noise reduction, is limited in cone-beam computed tomography (CBCT). As a consequence, the visibility of soft tissues is limited with CBCT. The study aimed to improve this issue through time-efficient deep learning enhancement (DLE) methods. MethodsTwo DLE networks, UNIT and U-Net, were trained with simulated CBCT data. The performance of the networks was tested with three different test data sets. The quantitative evaluation measured the structural similarity index measure (SSIM) and the peak signal-to-noise ratio (PSNR) of the DLE reconstructions with respect to the ground truth iterative reconstruction method. In the second assessment, a dentomaxillofacial radiologist assessed the resolution of hard tissue structures, visibility of soft tissues, and overall image quality of real patient data using the Likert scale. Finally, the technical image quality was determined using modulation transfer function, noise power spectrum, and noise magnitude analyses. ResultsThe study demonstrated that deep learning CBCT denoising is feasible and time efficient. The DLE methods, trained with simulated CBCT data, generalized well, and DLE provided quantitatively (SSIM/PSNR) and visually similar noise-reduction as conventional IR, but with faster processing time. The DLE methods improved soft tissue visibility compared to the conventional Feldkamp-Davis-Kress (FDK) algorithm through noise reduction. However, in hard tissue quantification tasks, the radiologist preferred the FDK over the DLE methods. ConclusionPost-reconstruction DLE allowed feasible reconstruction times while yielding improvements in soft tissue visibility in each dataset.

DEEP LEARNING-BASED IMAGE DEHAZING AND VISIBILITY ENHANCEMENT FOR IMPROVED VISUAL PERCEPTION

In recent years, image dehazing has gained significant attention in the field of computer vision and image processing due to its crucial role in enhancing visibility and improving visual perception. The presence of haze in images captured under adverse weather conditions or polluted environments poses a challenge to various computer vision applications, such as autonomous driving, surveillance, and satellite imagery. Traditional image dehazing methods often struggle to achieve optimal results, particularly in complex scenes with varying degrees of haze and intricate details. The need for a robust and efficient dehazing approach has become imperative for addressing real-world challenges in computer vision applications. Despite the advancements in traditional methods, a research gap exists in developing a comprehensive solution that can handle diverse atmospheric conditions and complex scenes effectively. The integration of deep learning techniques presents an opportunity to bridge this gap, leveraging the power of neural networks to learn and adapt to intricate patterns in hazy images. This research proposes a novel deep learning-based approach for image dehazing and visibility enhancement. A Convolutional Neural Network (CNN) architecture is designed to learn complex relationships between hazy and clear images, allowing the model to effectively remove haze and enhance visibility. The network is trained on a diverse dataset encompassing various atmospheric conditions and scene complexities to ensure generalization. Experimental results demonstrate the superior performance of the proposed deep learning approach compared to traditional methods. The model exhibits robustness in handling challenging scenarios, achieving significant improvements in image clarity, contrast, and overall visibility. The findings highlight the potential of deep learning in addressing the limitations of existing dehazing techniques.

Boosting SNR of cascaded FBGs in a sapphire fiber through a rapid heat treatment.

This Letter reports the performance of femtosecond (fs) laser-written distributed fiber Bragg gratings (FBGs) under high-temperature conditions up to 1600°C and explores the impact of rapid heat treatment on signal-to-noise ratio (SNR) enhancement. FBGs are essential for reliable optical sensing in extreme temperature environments. Comprehensive tests demonstrate the remarkable performance and resilience of FBGs at temperatures up to 1600°C, confirming their suitability for deployment in such conditions. The study also reveals significant fringe visibility improvements of up to ∼10 dB on a 1-m-long sapphire optical fiber through rapid heat treatment, representing a first-time achievement to the best of our knowledge. These enhancements are vital for improving the SNR and overall performance of optical fiber systems in extreme temperatures. Furthermore, the research attains long-term stability for the cascaded FBGs over a 24-hr period at 1600°C. This research expands our understanding of the FBG behavior in high-temperature environments and opens avenues for developing robust optical fiber systems for energy, aerospace, oil and gas, and high-temperature distributed sensing applications.

Linkage between digital supply chain, supply chain innovation and supply chain dynamic capabilities: an empirical study

PurposeThere has been limited research that has explored the connection between digital supply chain (DSC) and SC innovation and SC dynamic capabilities. This paper aims to examine the mediating effect of SC innovation on the relationship between DSC and SC dynamic capabilities.Design/methodology/approachThe research model and hypotheses were tested, employing (Statistical Package of Social Sciences) SPSS 25.0 and (Analysis of Moment Structures) AMOS 24.0 on data drawn from the Chinese manufacturing enterprises.FindingsThe study reveals that DSC has a significant positive effect on SC innovation and SC dynamic capabilities. SC innovation also has a significant positive effect on SC dynamic capabilities. Besides, the authors' research illustrates that SC innovation partially mediates the relationship between DSC and SC dynamic capabilities.Research limitations/implicationsSince the results are derived from the data collected from China, it may not, therefore, be generalized to other settings. Moreover, future research could consider other contextual variables such as “environmental uncertainty” and “Government's Reward-Penalty Mechanism,” which may influence SC dynamic capabilities.Practical implicationsThe study provides practical insights for senior executives and managers in the manufacturing industry. Managers should emphasize the investment of advanced digital technologies and tools (DTTs) and improvement of SC visibility and collaboration. In the digital age, companies should pay attention to the introduction of advanced technologies, tools and processes and focus on cultivating an innovative spirit to promote SC dynamic capabilities, thereby enhancing competitive advantages.Originality/valueThe paper illustrates that DSC is of great significance to improving SC dynamic capabilities. This study reveals compelling insights for firms to enhance SC innovation and dynamic capabilities by using DSC as an enabler.

Lean Six Sigma for Visibility Improvement: Case Study at Department of Social Support

This paper addresses quality management in the public sector, namely the application of Lean Six Sigma (LSS) methods to increase the visibility of the flow of procedures in the administration of public institutions. Visibility of administrative procedures flow helps the citizen to better understand how an administrative process begins, how it develops, who is involved, how long it lasts and when it ends. In order to achieve the intended result of this study, public institutions at the local level, respectively municipality and the center for social issues, are observed. The current state of visibility of the procedures has been identified, defects are listed, measured, the causes of the defects are learned, the action plan has been proposed and the change of the situation has been verified. The methodology applied in this study is based on the LSS - DMADV technique (Define, Measure, Analyse, Design, and Verify). Thanks to DMADV methodology, a practical solution has been proposed that improves the visibility of procedures in the municipal administration. The public sector should consider investing in the application of LSS methods in order to increase creativity among employees for the quality flow of information by making them clear, cheep, simple and fast for citizens.

Acousto-optic modulator-based improvement in imaging through scattering media.

Reduced visibility is a common problem when light traverses through a scattering medium, and it becomes difficult to identify an object in such scenarios. What we believe to be a novel proof-of-principle technique for improving image visibility based on the quadrature lock-in discrimination algorithm in which the demodulation is performed using an acousto-optic modulator is presented here. A significant improvement in image visibility is achieved using a series of frames. We have also performed systematic imaging by varying the camera parameters, such as exposure time, frame rate, and series length, to investigate their effect on enhancing image visibility.

Illumination-Adaptive Unpaired Low-Light Enhancement

Supervised networks address the task of low-light enhancement using paired images. However, collecting a wide variety of low-light/clean paired images is tedious as the scene needs to remain static during imaging. In this paper, we propose an unsupervised low-light enhancement network using context-guided illumination-adaptive norm (CIN). Inspired by coarse to fine methods, we propose to address this task in two stages. In stage-I, a pixel amplifier module (PAM) is used to generate a coarse estimate with an overall improvement in visibility and aesthetic quality. Stage-II further enhances the saturated dark pixels and scene properties of the image using CIN. Different ablation studies show the importance of PAM and CIN in improving the visible quality of the image. Next, we propose a region-adaptive single input multiple output (SIMO) model that can generate multiple enhanced images from a single low-light image. The objective of SIMO is to let users choose the image of their liking from a pool of enhanced images. Human subjective analysis of SIMO results shows that the distribution of preferred images varies, endorsing the importance of SIMO-type models. Lastly, we propose a low-light road scene (LLRS) dataset having an unpaired collection of low-light and clean scenes. Unlike existing datasets, the clean and low-light scenes in LLRS are real and captured using fixed camera settings. Exhaustive comparisons on publicly available datasets, and the proposed dataset reveal that the results of our model outperform prior art quantitatively and qualitatively.

Hindered visibility improvement despite marked reduction in anthropogenic emissions in a megacity of southwestern China: An interplay between enhanced secondary inorganics formation and hygroscopic growth at prevailing high RH conditions

The PM2.5-bound visibility improvement remains challenging in China despite vigorous control on anthropogenic emissions in recent years. One critical issue could exist in the distinct physicochemical properties especially of secondary aerosol components. Taken the COVID-19 lockdown as an extreme case, we focus on the relationship between visibility, emission cuts, and secondary formation of inorganics with changing optical and hygroscopic behaviors in Chongqing, a representative city characterized with humid weather and poor diffusion conditions in Sichuan Basin, southwest of China.It is found that the increased secondary aerosol abundance (e.g., PM2.5/CO and PM2.5/PM10 as a proxy) with enhanced atmospheric oxidative capacity (e.g., O3/Ox, Ox = O3 + NO2), combined with insignificant meteorological dilution effect, might partly offset the benefit on the improved visibility from substantial reduction in anthropogenic emissions during the COVID-19 lockdown. This is in line with the efficient oxidation rates of sulfur and nitrogen (i.e., SOR, NOR), increasing more significantly with PM2.5 and relative humidity (RH) in comparison to O3/Ox. The resulted larger fraction of nitrate and sulfate (i.e., fSNA) would promote the optical enhancement (i.e., f(RH)) and mass extinction efficiency (MEE) of PM2.5, especially under highly humid conditions (e.g., RH > 80 %, with approximately half of the occurrence frequency). This could further facilitate secondary aerosol formation via aqueous-phase reaction and heterogeneous oxidation, likely due to enhanced water uptake and enlarged size/surface area upon hydration. In combination of gradually increased atmospheric oxidative capacity, this positive feedback would in turn inhibit the visibility improvement particularly at high RH environment.Considering the current air pollution complex status over China, further work on the formation mechanisms of major secondary species (e.g., sulfate, nitrate, and secondary organics), size-resolved chemical and hygroscopic properties, together with their interactions are highly recommended. Our results are hoping to assist in the atmospheric pollution complex mitigation and prevention in China.

Effect of Sub-Surface Damage Layer Removal by Sublimation Etching of 4H-SiC Bulk Wafers on PL Imaging of Crystal Defect Visibility

Improving the visibility of defects in nitrogen-doped 4H-SiC (0001) bare wafers by photoluminescence imaging (PLI) is essential for improving the epitaxial growth process and device yields. This study proposes sub-surface damage (SSD) introduced during the mechanical process of SiC wafers as a new factor in reducing defect visibility in PL images. To verify the effect of SSD, we observed the surface of a SiC wafer, which was thermally etched at about 3 μm. As a result, dramatic defect visibility improvement was observed when the surface roughness was sufficiently flat (Ra &lt; 0.3 nm) after thermal etching. Thus, the results suggest that defect visibility in PL images can be improved by controlling SSD and surface roughness. Using the background noise reduction effect of the SSD removal, not only PLI but also many other wafer surface inspections are expected to be improved.