Robust Recovery Research Articles

Remote physiological signal recovery with efficient spatio-temporal modeling.

Contactless physiological signal measurement has great applications in various fields, such as affective computing and health monitoring. Physiological measurements based on remote photoplethysmography (rPPG) are realized by capturing the weak periodic color changes. The changes are caused by the variation in the light absorption of skin surface during systole and diastole stages of a functioning heart. This measurement mode has advantages of contactless measurement, simple operation, low cost, etc. In recent years, several deep learning-based rPPG measurement methods have been proposed. However, the features learned by deep learning models are vulnerable to motion and illumination artefacts, and are unable to fully exploit the intrinsic temporal characteristics of the rPPG. This paper presents an efficient spatiotemporal modeling-based rPPG recovery method for physiological signal measurements. First, two modules are utilized in the rPPG task: 1) 3D central difference convolution for temporal context modeling with enhanced representation and generalization capacity, and 2) Huber loss for robust intensity-level rPPG recovery. Second, a dual branch structure for both motion and appearance modeling and a soft attention mask are adapted to take full advantage of the central difference convolution. Third, a multi-task setting for joint cardiac and respiratory signals measurements is introduced to benefit from the internal relevance between two physiological signals. Last, extensive experiments performed on three public databases show that the proposed method outperforms prior state-of-the-art methods with the Pearson's correlation coefficient higher than 0.96 on all three datasets. The generalization ability of the proposed method is also evaluated by cross-database and video compression experiments. The effectiveness and necessity of each module are confirmed by ablation studies.

AN ANALYSIS OF GST IMPLEMENTATION AND TRENDS IN REVENUE COLLECTION FROM 2017-18 TO 2023-24

India’s Goods and Services Tax (GST), implemented on July 1, 2017, revolutionized the country's indirect tax system by replacing a complex array of taxes with a unified consumption tax. This study analyzes the implementation, features, and trends of GST from fiscal year 2017-18 to 2023-24, utilizing secondary data from the Economic Survey of India 2023-24. The total GST revenue has shown significant growth, rising from ₹5,39,601 crores in 2017-18 to ₹15,18,127 crores in 2023-24. The GST structure operates under a dual system, with Central Goods and Services Tax (CGST) and State Goods and Services Tax (SGST) being key components, alongside the Integrated Goods and Services Tax (IGST) for inter-state trade. High-contributing states like Maharashtra and Karnataka have strengthened their positions, while states like Himachal Pradesh and Uttarakhand face challenges. The analysis also reveals fluctuations in revenue due to the COVID-19 pandemic, with a robust recovery in subsequent years. Key findings underscore the need for targeted interventions in lower-performing regions, improvements in tax administration, and policy adjustments to address the declining share of cess. This research highlights the pivotal role of GST in enhancing tax compliance and broadening the tax base, laying the groundwork for sustainable economic growth in India. KEY WORDS: Goods and Services Tax (GST), Tax Compliance, Indirect Taxation, Economic Growth, Public Finance

Contrast-Enhanced Ultrasound of Mouse Models of Hindlimb Ischemia Reveals Persistent Perfusion Deficits and Distinctive Muscle Perfusion Patterns

ObjectiveMouse models of hindlimb ischemia (HLI) are used to study peripheral arterial disease and evaluate novel therapies. Contrast-enhanced ultrasound (CEUS) is a noninvasive perfusion measurement technique that is increasingly being employed in these models. The objective of this study was to evaluate two models of severe HLI by CEUS to characterize perfusion recovery and muscle perfusion patterns. MethodsMice undergoing double femoral artery ligation were measured by CEUS and laser Doppler perfusion imaging (LDPI) at baseline and 1–150 d postsurgery. A second group undergoing femoral artery ligation and excision was measured 1–28 d postsurgery. ResultsBy LDPI, both surgeries showed robust perfusion recovery by 14 d postsurgery. However, by CEUS only a ∼40% perfusion recovery plateau was reached in either group. These results are consistent with our previous work, employing a less severe single femoral artery ligation, that showed perfusion in the ischemic limb does not return to normal by 150 d postsurgery. Cluster analysis of muscle perfusion patterns indicated 3–5 different patterns at day 1 postsurgery. The double ligation model yielded significantly less variable perfusion patterns, suggesting that it can provide more reproducible results. ConclusionContrary to LDPI, perfusion as measured by CEUS never fully recovers after hindlimb surgery, even when followed 28–150 d postsurgery. Individual mice can manifest different patterns of muscle perfusion to the same surgery, but these patterns are conserved within and between different surgical techniques. These results may have significant implications for the evaluation of novel therapeutics to treat PAD in mice.

DFC-Igloo: A Dynamic Functional Connectome Learning Framework for Identifying Neurodevelopmental Biomarkers in Very Preterm Infants

Background and ObjectiveVery preterm infants are susceptible to neurodevelopmental impairments, necessitating early detection of prognostic biomarkers for timely intervention. The study aims to explore possible functional biomarkers for very preterm infants at born that relate to their future cognitive and motor development using resting-state fMRI. Prior studies are limited by the sample size and suffer from efficient functional connectome (FC) construction algorithms that can handle the noisy data contained in neonatal time series, leading to equivocal findings. Therefore, we first propose an enhanced functional connectome construction algorithm as a prerequisite step. We then apply the new FC construction algorithm to our large prospective very preterm cohort to explore multi-level neurodevelopmental biomarkers. MethodsThere exists an intrinsic relationship between the structural connectome (SC) and FC, with a notable coupling between the two. This observation implies a putative property of graph signal smoothness on the SC as well. Yet, this property has not been fully exploited for constructing intrinsic dFC. In this study, we proposed an advanced dynamic FC (dFC) learning model, dFC-Igloo, which leveraged SC information to iteratively refine dFC estimations by applying graph signal smoothness to both FC and SC. The model was evaluated on artificial small-world graphs and simulated graph signals. ResultsThe proposed model achieved the best and most robust recovery of the ground truth graph across different noise levels and simulated SC pairs from the simulation. The model was further applied to a cohort of very preterm infants from five Neonatal Intensive Care Units, where an enhanced dFC was obtained for each infant. Based on the improved dFC, we identified neurodevelopmental biomarkers for neonates across connectome-wide, regional, and subnetwork scales. ConclusionThe identified markers correlate with cognitive and motor developmental outcomes, offering insights into early brain development and potential neurodevelopmental challenges.

Comprehensive data security and compliance framework for SMEs

Small and Medium-sized Enterprises (SMEs) are increasingly relying on cloud platforms to support critical business operations, making effective disaster recovery (DR) strategies essential for ensuring business continuity. This review proposes a robust disaster recovery framework tailored for SMEs, designed to minimize downtime and data loss in the event of a system failure, cyberattack, or natural disaster. The framework integrates advanced cloud technologies to create a cost-effective, scalable solution that aligns with the resource constraints of SMEs while providing enterprise-grade resilience. Key components of the disaster recovery framework include cloud-based data replication, automated backup solutions, and geo-redundant storage to ensure that data is continuously available and recoverable. This model employs real-time data synchronization and incremental backups to minimize Recovery Point Objectives (RPO), ensuring that critical data is not lost during an unexpected outage. Additionally, the framework leverages automated failover mechanisms to achieve low Recovery Time Objectives (RTO), allowing businesses to restore operations quickly after an interruption. Cloud orchestration tools such as AWS Elastic Disaster Recovery or Azure Site Recovery are utilized to automate disaster recovery processes, reducing manual intervention and improving the speed of recovery. The framework also incorporates regular testing of disaster recovery plans, using simulation tools to identify weaknesses and optimize response times. For SMEs, cost-effectiveness and ease of management are crucial. The framework emphasizes a pay-as-you-go model for cloud resources, allowing businesses to scale their disaster recovery solutions as they grow without incurring excessive upfront costs. By providing continuous monitoring and proactive threat detection, this disaster recovery framework ensures that SMEs can maintain uninterrupted business operations on cloud platforms, thereby enhancing resilience and mitigating the financial and operational risks associated with data loss and system downtime.

Enhancing NO2 sensing performance through interface engineering in Cs2AgBiBr6/SnO2/ZnO-NRs sensor

Developing sensors that operate under ambient conditions with minimal energy consumption, ensuring a robust response and efficient recovery to toxic NO2 gas, can help take appropriate and corrective measures to protect human health from exposure to hazardous environments. In this study, we have successfully designed a gas sensor device based on a sandwich structure of Cs2AgBiBr6/SnO2/ZnO-nanorods (CABB/SnO2/ZnO-NRs) for detecting NO2 at room temperature. The insertion of the SnO2 layer between the CABB and ZnO-NRs, which works as a hole blocker, not only improves significantly the sensor stability from a few hours to more than a month but also offers the energy step for electron transfer from ZnO-NRs to CABB. The CABB layer further enhances the charge separation, transport, and conductivity from the perovskite to the ZnO surface, actively participating in the gas-sensitive reaction. The response of CABB/SnO2/ZnO-NRs sensors to 100 ppm NO2 is about 9 times higher than that of ZnO-NRs, showing a significantly improved NO2 sensing performance.

Guaranteed matrix recovery using weighted nuclear norm plus weighted total variation minimization

This work presents a general framework regarding the recovery of matrices equipped with hybrid low-rank and local-smooth properties from just a few measurements consisting of linear combinations of the matrix entries. Concretely, we consider the problem of robust low-rank matrix recovery using Weighted Nuclear Norm plus Weight Total Variation (WNNWTV) minimization. First of all, based on a new restricted isometry property, we prove that the WNNWTV method possesses an error bound consisting of a low-rank approximation term, a total variation approximation term, and an observation error term. It should be noted that although there are many models considering both properties, there are very few recoverable error theories about such models. Specifically, the theoretical error bound provides an automatic mechanism to reducing regularization parameters with no need for cross-validation while keeping almost the same selection result with commonly used cross-validation technique. Subsequently, the proposed method is reformulated into a regularized unconstrained problem, and we study its optimization aspects in detail based on the Alternating Direction Method of Multipliers (ADMM). Extensive experiments on synthetic data and two applications, i.e. hyperspectral image recovery and dynamic magnetic resonance imaging recovery verified our theories and proposed algorithms.

Robust magnetic vivianite recovery from digested sewage sludge: Evaluating resilience to sludge dry matter and particle size variations

Phosphorus recovery via vivianite extraction from digested sludge has recently gained considerable interest. The separation of vivianite was demonstrated earlier at the pilot scale, and operational parameters were optimized. In this study, we tested the robustness of this technology by changing the sludge characteristics, such as dry matter, and via that, sludge viscosity, and vivianite particle size. It was proven that the main factor influencing recovery was the concentration of vivianite in the feed. The technology can extract vivianite even when the sludge has higher dry matter (1.8% - 3.3%) and, therefore, higher viscosity. Smaller vivianite sizes (< 10 µm) can still be recovered but at a lower rate. This made magnetic separation applicable to a wide range of wastewater treatment plants.

Monetary and fiscal policies in Brazil and the behavioral approach

PurposeThis study aims to elucidate the dynamics of monetary and fiscal policy interactions in Brazil, focusing on the impacts of positive shocks in government consumption and interest rates. By comparing rational and behavioral agent responses, it clarifies how these frameworks influence gross domestic product (GDP), inflation, private and government consumption and nominal interest rates.Design/methodology/approachThe study employs a new Keynesian dynamic stochastic general equilibrium (DSGE) model with Bayesian estimation from 2000Q1 to 2022Q4, capturing rational and behavioral behaviors with adjustments for Brazilian economic idiosyncrasies. Impulse response functions (IRF) assess the dynamic effects of policy shocks, providing a comparative analysis of the two frameworks.FindingsBehavioral agents show greater initial sensitivity to policy shocks, causing more pronounced fluctuations in GDP, inflation and private consumption compared to rational agents. Over time, the behavioral approach leads to a more robust recovery, while the rational approach results in a quicker return to equilibrium but less pronounced long-term recovery. The study also finds fiscal policy can partially offset the negative impacts of monetary tightening, with a more delayed effect in the behavioral model.Originality/valueThis paper provides insights into the interplay between monetary and fiscal policies under different agent expectations, emphasizing the importance of incorporating behavioral elements into macroeconomic models to better capture policy dynamics in emerging markets.

The Role of Length of Nerve Grafts in Combination with Free Functional Muscle Transplantation for Brachial Plexus Injury: A Single-Center Experience.

Extensive lesions of the brachial plexus, or late cases, require free functional muscle grafts because the expected recovery time exceeds the critical threshold of 1.5 years, beyond which irreversible damage may be expected in the distal nerve stump and in the muscle. The reconstructive concept consists of a two-stage procedure where, in the first step, a nerve transfer is performed (from ipsi- or contralateral donor nerves). In the second step, after successful axonal regeneration within the graft has been confirmed, a free muscle transfer is performed. These grafts often exceed 40 cm in length, particularly for contralateral transfers. The purpose of this study was to assess whether robust motor recovery could be supported by such long nerve grafts. From April 2004 to April 2023, a total of 327 free functional muscle transfers were performed, the nerve graft length ranging from 0 cm (direct coaptation) to 90 cm (serial grafts). Motor recovery was evaluated 1.5 years after surgery according to the MRC scale. A total of 208 patients were available for follow up. Direct coaptation yielded the best results, with 83% of patients reaching an M3 or M4 level of muscle strength. With the application of long (30-60 cm) grafts, 73% of the patients were classified as M3 or M4. The application of serial nerve grafts, however, only resulted in 18% of patients achieving a motor recovery rating of M3. These findings demonstrate that robust motor regeneration is supported by long (30-60 cm) nerve grafts, whereas serial nerve grafting results in a marked reduction in the quality of regeneration.

A robust underwater polarization image recovery based on Angle of Polarization with low-rank and sparse decomposition

Underwater imaging method based on the Angle of Polarization (AoP) is extremely popular due to its ability to remove the backscattered light effectively. However, existing methods overlook a potentially important phenomenon: the AoP contains significant noise in the real world, which leads to inaccurate estimations of key parameters. In this study, we propose a novel robust underwater polarization image recovery method based on AoP with low-rank and sparse decomposition. Based on the analysis of polarization, the AoP of backscattering exhibits low-rank characteristics, whereas the AoP of noise and targets demonstrates sparsity. Considering different sparsity of AoP, we combine Robust Principal Component Analysis and Alternating Direction Multiplier Method (RPCA-ADMM) with an adaptive threshold shrinkage method for low-rank and sparse decomposition, the AoP of the backscattering is obtained. Moreover, according to the low-rank component of the AoP, the Degree of Linear Polarization (DoLP) of backscattering can be effectively and automatically estimated without considering whether the background region exists, relying on a frequency prior strategy. Finally, the degraded image is recovered utilizing underwater polarization imaging model. Extensive experimental results demonstrate that the recovered images from the proposed method have abundant detail, high contrast, and minimal color distortion, which is competitive with most state-of-the-art methods.

Effect of Bioactive Ingredients on Urinary Excretion of Aflatoxin B1 and Ochratoxin A in Rats, as Measured by Liquid Chromatography with Fluorescence Detection.

Aflatoxin B1 (AFB1) and ochratoxin A (OTA) are highly toxic mycotoxins present in food and feed, posing serious health risks to humans and animals. This study aimed to validate an efficient and cost-effective analytical method for quantifying AFB1 and OTA in rat urine using immunoaffinity column extraction and liquid chromatography with fluorescence detection (IAC-LC-FD). Additionally, the study evaluated the effect of incorporating fermented whey and pumpkin into the feed on the urinary excretion of these mycotoxins. The limits of detection and quantification were determined to be 0.1 µg/kg and 0.3 µg/kg, respectively, for both mycotoxins in feed, and 0.2 ng/mL and 0.6 ng/mL, respectively, in urine. The method demonstrated robust recovery rates ranging from 74% to 119% for both AFB1 and OTA in both matrices. In feed samples, the levels of AFB1 and OTA ranged from 4.3 to 5.2 µg/g and from 5.4 to 8.8 µg/g, respectively. This validated method was successfully applied to analyze 116 urine samples from rats collected during the fourth week of an in vivo trial. The results indicated that the addition of fermented whey and pumpkin to the feed influenced mycotoxin excretion in urine, with variations observed based on the sex of the rats, type of mycotoxin, and exposure dosage.

Robust Recovery of Optimally Smoothed Polymer Relaxation Spectrum from Stress Relaxation Test Measurements.

The relaxation spectrum is a fundamental viscoelastic characteristic from which other material functions used to describe the rheological properties of polymers can be determined. The spectrum is recovered from relaxation stress or oscillatory shear data. Since the problem of the relaxation spectrum identification is ill-posed, in the known methods, different mechanisms are built-in to obtain a smooth enough and noise-robust relaxation spectrum model. The regularization of the original problem and/or limit of the set of admissible solutions are the most commonly used remedies. Here, the problem of determining an optimally smoothed continuous relaxation time spectrum is directly stated and solved for the first time, assuming that discrete-time noise-corrupted measurements of a relaxation modulus obtained in the stress relaxation experiment are available for identification. The relaxation time spectrum model that reproduces the relaxation modulus measurements and is the best smoothed in the class of continuous square-integrable functions is sought. Based on the Hilbert projection theorem, the best-smoothed relaxation spectrum model is found to be described by a finite sum of specific exponential-hyperbolic basis functions. For noise-corrupted measurements, a quadratic with respect to the Lagrange multipliers term is introduced into the Lagrangian functional of the model's best smoothing problem. As a result, a small model error of the relaxation modulus model is obtained, which increases the model's robustness. The necessary and sufficient optimality conditions are derived whose unique solution yields a direct analytical formula of the best-smoothed relaxation spectrum model. The related model of the relaxation modulus is given. A computational identification algorithm using the singular value decomposition is presented, which can be easily implemented in any computing environment. The approximation error, model smoothness, noise robustness, and identifiability of the polymer real spectrum are studied analytically. It is demonstrated by numerical studies that the algorithm proposed can be successfully applied for the identification of one- and two-mode Gaussian-like relaxation spectra. The applicability of this approach to determining the Baumgaertel, Schausberger, and Winter spectrum is also examined, and it is shown that it is well approximated for higher frequencies and, in particular, in the neighborhood of the local maximum. However, the comparison of the asymptotic properties of the best-smoothed spectrum model and the BSW model a priori excludes a good approximation of the spectrum in the close neighborhood of zero-relaxation time.

Diet outperforms microbial transplant to drive microbiome recovery post-antibiotics.

High-fat, low-fiber Western-style diets (WD) induce microbiome dysbiosis characterized by reduced taxonomic diversity and metabolic breadth, which in turn increases risk for a wide array of metabolic, immune and systemic pathologies. Recent work has established that WD can impair microbiome resilience to acute perturbations like antibiotic treatment, although we know little about the mechanism of impairment and the specific host consequences of prolonged post-antibiotic dysbiosis. Here, we characterize the trajectory by which the gut microbiome recovers its taxonomic and functional profile after antibiotic treatment in mice on regular chow (RC) and WD, and find that only mice on RC undergo a rapid successional process of recovery. Metabolic modeling indicates that RC diet promotes the development of syntrophic cross-feeding interactions, while on WD, a dominant taxon monopolizes readily available resources without releasing syntrophic byproducts. Intervention experiments reveal that an appropriate dietary resource environment is both necessary and sufficient for rapid and robust microbiome recovery, whereas microbial transplant is neither. Furthermore, prolonged post-antibiotic dysbiosis in mice on WD renders them susceptible to infection by the intestinal pathogen Salmonella enterica serovar Typhimurium. Our data challenge widespread enthusiasm for fecal microbiota transplant (FMT) as a strategy to address dysbiosis and demonstrate that specific dietary interventions are, at minimum, an essential prerequisite for effective FMT, and may afford a safer, more natural, and less invasive alternative to FMT.

Building Back Better: Exploring the Resilience of Schools in Disaster Recovery

As natural and human-made disasters escalate globally, the urgency to rebuild resilient communities is increasingly evident. With this study, Building Back Better: Exploring the Resilience of Schools in Disaster Recovery explores the experiences, disaster recovery, challenges, coping mechanisms, and insights of three school administrators and four teachers designated as school Disaster Risk Reduction coordinators in the Monkayo East District of Davao de Oro. This study utilized qualitative phenomenological study to delve into the lived experiences of educational professionals during post-disaster recovery. This research aims to gain a deep understanding of the challenges, coping mechanisms, and insights from a purposive sample of four teachers designated as school Disaster Risk Reduction coordinators and three school administrators who have directly engaged with the recovery processes following natural disasters. The data were gathered through an in-depth face-to-face interview and used an audio recorder to record the discussion. The significant finding from the study is the multifaceted and long-lasting impact of disasters on students' learning environments. This finding highlights how disasters not only cause physical damage to school infrastructure but also disrupt the educational process, widen achievement gaps, and affect the emotional well-being of students. The disruption of normal routines and the psychological stress experienced by students underscore the critical need for comprehensive recovery strategies, including academic support and mental health resources, to help students regain stability and continue their education effectively. This emphasizes the importance of preparedness and resilience in educational institutions to mitigate the impact of such crises. This study highlights the need for timely assessments, repairs, and coordination with local authorities to ensure the structural integrity of school facilities. It underscores the importance of disaster preparedness and the implementation of robust recovery strategies to mitigate the impact on the educational environment and maintain a safe and conducive learning space. This focus on infrastructure is foundational, as it directly influences the ability of schools to function effectively post-disaster. Teachers and school heads are pivotal in disaster risk reduction within educational institutions. They create safety plans, organize drills, and educate students on emergency procedures, fostering a culture of preparedness. School heads provide leadership, while teachers act as first responders, guiding students to safety and offering necessary care. Collaboration between teachers, school heads, and stakeholders is crucial for a resilient school environment. By involving the entire school community in planning and implementing safety measures, schools can effectively respond to disasters, ensuring the safety and well-being of everyone involved. This partnership promotes a proactive approach to disaster preparedness, making safety a priority in all school activities.

ЕФЕКТИВНІ СХОВИЩА ДАНИХ ДЛЯ РІШЕНЬ МАШИННОГО НАВЧАННЯ

Awareness of the importance of using effective data warehouses to develop machine learning solutions in today's world is becoming increasingly relevant in connection with the growth of data volumes and the growing need for accurate and productive models. The article identifies major vital requirements for data storage systems to ensure effective data management in building machine learning solutions. The study underscores the practical implications of rapid data access and minimal latency, reliable storage systems with robust backup and recovery mechanisms, high-level security, flexibility in handling different data types, and cost-effectiveness. These factors are not just theoretical considerations but directly impact the efficiency and success of machine learning solutions. Together with describing the concepts of block storage and object storage, the article also compares major cloud platforms—AWS (Amazon Web Services), Microsoft Azure, and Google Cloud Platform (GCP)—each providing a comprehensive range of flexible and customizable storage, security, and data processing services. These platforms enable users to manage their information resources flexibly and efficiently, offering unique features that cater to the specific needs of machine learning applications. Understanding the nuances of each provider's offerings empowers engineers and researchers to make informed decisions that align with their unique requirements. The article provides a thorough overview of the key considerations and available options, guiding strategic decisions to support machine learning initiatives effectively. Also the importance of staying up to date with the evolution of major platforms offerings is emphasized. In conclusion, selecting the proper data storage solution is critical for enhancing the performance, security, and cost-efficiency of machine learning models.

Robust tensor recovery via a nonconvex approach with ket augmentation and auto‐weighted strategy

AbstractIn this article, we introduce a nonconvex tensor recovery approach, which employs the powerful ket augmentation technique to expand a low order tensor into a high‐order one so that we can exploit the advantage of tensor train (TT) decomposition tailored for high‐order tensors. Moreover, we define a new nonconvex surrogate function to approximate the tensor rank, and develop an auto‐weighted mechanism to adjust the weights of the resulting high‐order tensor's TT ranks. To make our approach robust, we add two mode‐unfolding regularization terms to enhance the model for the purpose of exploring spatio‐temporal continuity and self‐similarity of the underlying tensors. Also, we propose an implementable algorithm to solve the proposed optimization model in the sense that each subproblem enjoys a closed‐form solution. A series of numerical results demonstrate that our approach works well on recovering color images and videos.

Robust image tamper detection and recovery with self-embedding watermarking using SPIHT and LDPC

In today’s digital landscape, the pervasive use of digital images across diverse domains has led to growing concerns regarding their authenticity and reliability. The potential for malicious manipulation of these images underscores the critical need to develop robust methods for detecting tampering and ensuring their integrity. Fragile watermarking has been found to have extensive applications for tamper detection and recovery. An image watermarking technique for tamper detection, correction, and recovery is presented in this study. The proposed method employs a self-embedding method to generate the reference watermark from the original image, which has the advantage of superior tamper detection, localization, recovery capabilities, and robustness against attacks. Set Partitioning in the Hierarchical Tree (SPIHT) algorithm is applied to generate a reference watermark of the original image. Low-Density Parity Check (LDPC) is employed for error correction, providing higher-quality reconstruction to recover the original image. Schur decomposition processed the watermarked image blocks to generate authentication bits for each block to enhance tampering detection. The proposed watermarking method was evaluated using PSNR, SSIM, BER, and NC metrics for grayscale and colored images. The technique demonstrated high robustness in tamper detection and recovery against various malicious attacks. Comparative analysis with existing methods shows the efficacy of the proposed method.

Associations Between Drug Overdose Mortality and Recovery Ecosystems in the United States: A County-Level Analysis Using a Novel Index.

Communities with robust recovery ecosystems could reduce negative outcomes associated with substance use disorders (SUDs) and facilitate the recovery process. This cross-sectional study examined the relationship between drug overdose mortality rates in the United States and the strength of county-level recovery ecosystems, as measured by the Recovery Ecosystem Index (REI). The REI assesses the strength of county-level recovery ecosystems in the United States. Comprised of 14 indicators across 3 component classes, overall and component scores ranging from "one" (strongest) to "five" (weakest) were calculated for each county using standardized values of the indicators. County-level analyses included: (1) correlational analyses between drug overdose mortality rates (n = 2076) and REI scores (overall score and by component); and (2) quadrant analysis (n = 2076), dividing counties based on their drug overdose mortality rates and overall REI scores. Drug overdose mortality rates were inversely related to REI overall, SUD treatment component, and continuum of SUD support component scores, indicating that lower (stronger) scores corresponded to higher rates. Conversely, REI infrastructure and social component scores were positively related to rates. Counties were relatively evenly distributed across quadrants, with 26% (n = 537) with a strong REI score and high overdose mortality rate, 24% (n = 489) with a strong REI score and low overdose mortality rate, 20% (n = 409) with a weak REI and high overdose mortality rate, and 31% (n = 641) with a weak REI and low overdose mortality rate. REI scores were generally inversely associated with drug overdose mortality rates in US counties, suggesting that communities have stronger recovery systems and services as the burden of SUD increases. Given relative variation in the scale of drug overdose mortality and strength of recovery ecosystems among counties, results could guide the identification of communities where the need for expanded recovery systems and services may be particularly critical.

Fiscal Policy and Economic Resilience: The Impact of Government Consumption Alongside Oil and Non-Oil Revenues on Saudi Arabia’s GDP during Crises (1969–2022)

This study investigates the impact of Government Consumption (GC) on Saudi Arabia’s GDP during major economic crises from 1969 to 2022, focusing on periods marked by fluctuations in oil and non-oil revenues. By integrating these revenue streams, the research provides a more comprehensive analysis of fiscal policy effectiveness during economic downturns. Using an Autoregressive Distributed Lag (ARDL) model, the study reveals the complex role of Government Consumption (GC) in stabilizing and stimulating the Saudi economy amidst revenue volatility. Key findings indicate that while GC does not significantly influence GDP in the short term, its long-term effectiveness varies across different crises. Specifically, GC has acted as a buffer against immediate economic shocks during certain crises while providing a stimulus for economic recovery in others. During the 2020 COVID-19 pandemic, timely fiscal measures significantly boosted GDP, underscoring the importance of adaptive and proactive fiscal policies. Conversely, the 2014–2016 oil price collapse demonstrated that GC alone was insufficient to counteract economic downturns, emphasizing the need for diversified revenue strategies. These findings underscore the dual role of GC in economic stabilization and recovery. During the COVID-19 pandemic, GC played a crucial role in both mitigating negative economic impacts and supporting recovery efforts, showcasing its effectiveness in times of global disruptions. This demonstrates GC’s capability as an immediate buffer against economic shocks and a stimulus for economic recovery. In contrast, during the 2014–2016 oil price collapse, GC was less effective, indicating the limitations of relying solely on government spending without broader economic diversification. This highlights the necessity of diversified revenue strategies to complement fiscal measures for long-term economic resilience. The robustness of the findings was ensured through various diagnostic tests, including checks for autocorrelation, heteroskedasticity, and stationarity of residuals. The absence of significant autocorrelation and heteroskedasticity, along with the stationarity of differenced variables, confirms the validity of the econometric models used. The study contributes to the discourse on fiscal policy in oil-dependent economies by illustrating the critical role of diversified revenue strategies and adaptive fiscal measures in enhancing economic resilience. Recommendations are offered for policymakers to optimize fiscal strategies, ensuring robust economic recovery and long-term stability in volatile markets. This research highlights the necessity for Saudi Arabia to refine its fiscal policies towards greater economic diversification and stability.