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As the human footprint expands to meet societal energy needs, as do the impacts to wildlife. Raptors in particular are highly susceptible to anthropogenic caused mortality. Industry sectors are encouraged to offset these causes of mortality. Several options to mitigate these losses have been proposed, including raptor rehabilitation. However, its role as a conservation tool is untested. Currently no peer‐reviewed demographic analyses exist using post‐release data from rehabilitated raptors to evaluate its effectiveness at continental scales. Our objectives were to estimate annual survival of rehabilitated and wild raptors, and then use those estimates in demographic models to assess potential effects at individual and population levels. We hypothesized that rehabilitated raptors would survive similarly to their wild counterparts after an acclimation period, and that longer‐lived species (K‐selected) would benefit most from these releases. We used US Geological Survey Bird Banding Lab band‐recovery data (1974–2018) from 20 raptor species for modeling survival of rehabilitated individuals (n = 125 740) in comparison to wild birds (n = 1 913 352). Results from 17 species with adequate recovery data indicated that five species rehabilitated ≠ wild survival, two species had uncertain estimates, and 10 species rehabilitated ≈ wild survival by years two and three post‐release. We acquired admission (n = 69 707) and release (n = 25 740) data from 24 rehabilitation centers across the US (2012–2021). We integrated survival, fecundity and numbers of releases into demographic models. These models quantified the extent to which rehabilitated raptors may contribute to broader conservation efforts, especially in the context of individual take. All but two species, had measurable numbers of individuals added to the population regardless of the number of releases. The general pattern was for K‐selected species to yield larger benefits from rehabilitated supplementation to the population. These results provide evidence that rehabilitation may serve as mitigation tool to offset incidental take. [ABSTRACT FROM AUTHOR]

Traditional sports competition data management systems have poor security and reliability in management results. The author proposes a new sports competition data management system based on a network platform. Integrating internal and external services to handle system business, designing pages using JSP dynamic page technology. Utilize network platforms to transmit data, and verify the transmission of data on the network platform through hardware and software firewalls. Realize data transmission between the system and the client through the designed network platform. Analyzed data backup and recovery methods to ensure data security. Track important business and operations of the system through the log tracking layer, and provide UML modeling for competition data management. The experimental results show that the recovery speed of the system is significantly faster than SSH and MDA systems, indicating strong data recovery performance of the system. It has been proven that the competition data management results of the designed system are reliable and the system performance is strong. [ABSTRACT FROM AUTHOR]

In the federated learning scenario, the private data are kept local, and gradients are shared to train the global model. Because gradients are updated according to the private training data, the features of the data are encoded into gradients. Prior work proved the possibility of reconstructing the private training data based on gradients. However, only a small batch of images can be recovered, and the reconstruction quality, especially against the large batch size of images, is unsatisfactory. To improve the quality of reconstruction of a large batch of images, a generative gradient inversion attack based on a regulation term is designed, which is called fDLG. First, a regulation term that can avoid drastic variations within image regions is proposed, which is based on the cognition that changes between image pixels are gradual. The proposed regulation term encourages the synthesized dummy image to be piece-wise smooth. Second, generative adversarial networks are trained to improve the quality of the attack with the global model used as a discriminator. Simulation shows that large batches of images (128 images on CIFAR100, 256 images on MNIST) can be faithfully reconstructed at high resolution, and even large images from ImageNet can be reconstructed. [ABSTRACT FROM AUTHOR]

In the present times, the concept of cloud computing has played a significant role at the global level. With this approach, users can able to customize their services as per their needs. By having the connection of internet users can get able to serve various kinds of services like on-demand access, storage space, software building platforms, data recovery, etc., and pay only for that service that they have consumed. Enormous challenges in the cloud domain such as fault tolerance, energy efficiency, scheduling, resource provisioning, load balancing, etc. This paper is focused on load balancing domain. This can be defined as a redistribution of the workload among various available virtual machines in such an identical manner that would lead to a balanced state. This paper presents the evaluative and inclusive review of numerous load balancing (LB) methods. Quality of services(QoS) is vital role that contain various parameters to evaluate the load balancing methods in respect of makespan, speedup, cost, throughput, etc. This paper is highlighted numerous of load balancing methods with their brief explaination, platform used, different simulator and tools used by these methods and based on QoS parameters. [ABSTRACT FROM AUTHOR]

Single-cell RNA sequencing (scRNA-seq) has emerged as a powerful tool in genomics research, enabling the analysis of gene expression at the individual cell level. However, scRNA-seq data often suffer from a high rate of dropouts, where certain genes fail to be detected in specific cells due to technical limitations. This missing data can introduce biases and hinder downstream analysis. To overcome this challenge, the development of effective imputation methods has become crucial in the field of scRNA-seq data analysis. Here, we propose an imputation method based on robust and non-negative matrix factorization (scRNMF). Instead of other matrix factorization algorithms, scRNMF integrates two loss functions: L2 loss and C-loss. The L2 loss function is highly sensitive to outliers, which can introduce substantial errors. We utilize the C-loss function when dealing with zero values in the raw data. The primary advantage of the C-loss function is that it imposes a smaller punishment for larger errors, which results in more robust factorization when handling outliers. Various datasets of different sizes and zero rates are used to evaluate the performance of scRNMF against other state-of-the-art methods. Our method demonstrates its power and stability as a tool for imputation of scRNA-seq data. Author summary: It is still difficult to analyze scRNA-seq data because a significant portion of expressed genes have zeros. Gene expression levels can be restored through the imputation of scRNA-seq data, facilitating downstream analysis. To overcome this challenge, we propose an imputation method based on robust and non-negative matrix factorization (scRNMF). Instead of other matrix factorization algorithms, scRNMF integrates two loss functions: L2 loss and C-loss. Through the use of several simulated and real datasets, we perform an comprehensively evaluation of scRNMF against existing methods. scRNMF can enhance various aspects of downstream analysis, including gene expression data recovery, cell clustering analysis, gene differential expression analysis, and cellular trajectory reconstruction. The results of our study demonstrate that scRNMF is a powerful tool that can improve the accuracy of single-cell data analysis. [ABSTRACT FROM AUTHOR]

The newly constructed Bois d'Arc Lake Reservoir in Fannin County, Texas, USA, inevitably flooded a large ground surface area (67.34 km2) when the reservoir began impounding water in April 2021. Inside this (now) flooded area, land-based archaeological data recovery investigations discovered and documented several archaeological sites, now registered in the state of Texas; though, only two neighboring sites, namely, 41FN178 and 41FN244, are examined here. The first phase of archaeological testing at these sites included shovel testing, test unit excavations, and geoarchaeological trenching that yielded archaeological artifacts suggesting that Middle Caddo Indian peoples (AD 1200–1400) might have occupied this landscape. As the sites were recognized before the reservoir's impoundment phase, this merited a non-invasive, non-destructive, high-resolution near-surface geophysical study to map strategic areas within sites 41FN178 and 41FN244 that might yield potential shallow targets of archaeological context. The adopted geophysical survey comprised 3D direct current electrical resistivity imaging (ERI) and land horizontal magnetic gradiometry (HMG), each mapping a total surface area of 2133 and 15,640 m2, respectively. The combination of 3D ERI and land HMG surveys was instrumental in rapidly mapping the horizontal and vertical extent of shallowly buried anomalies within a large area prior to the completion of the dam and the beginning of water impoundment. Based on the geophysical insights, the outline of several Caddo houses with functional internal and external features (e.g., burnt cooking surfaces, storage pits, refuse pits, fired soil, ditches, a dump site, and a compound fence) are thought to exist within the uppermost 2 m of the Quaternary stratigraphy at both sites. At site 41FN244, 3D ERI found numerous resistive anomalies surrounding a conductive anomaly, collectively interpreted as a group of post-holes surrounding the remains of a Caddo house's inner clay floor. It also found a cluster of several resistive anomalies interpreted as midden or middens. The HMG survey carried across areas from which archaeological test units also yielded positive findings, at sites 41FN178 and 41FN244, identified numerous scattered monopolar and dipolar anomalies interpreted as post-molds of Caddo houses, compound enclosures or fences, and adjacent middens. Archaeological excavations guided by the geophysical results yielded significant cultural material and post-mold features at site 244, which validate the geophysical interpretation in a preliminary context. Additionally, several dispersed magnetic anomalies are thought to be shallowly buried hearths, burn cooking surfaces, storage pits, and ditches. The mapped magnetic anomalies agree with the location and distribution of previously found archaeological artifacts and the extent of resistive and conductive resistivity anomalies. Follow-up archaeological excavations of these geophysical anomalies have preliminarily confirmed interpretations. [ABSTRACT FROM AUTHOR]

We implement an inversion-based deblending method in the common midpoint gathers (CMP) as an alternative to the standard common receiver gather (CRG) domain methods. The primary advantage of deblending in the CMP domain is that reflections from dipping layers are centred around zero offsets. As a result, CMP gathers exhibit a simpler structure compared to common receiver gathers (CRGs), where these reflections are apex-shifted. Consequently, we can employ a zero-offset hyperbolic Radon operator to process CMP gathers. This operator is a computationally more efficient alternative to the apex-shifted hyperbolic Radon required for processing CRG gathers. Sparse transforms, such as the Radon transform, can stack reflections and produce sparse models capable of separating blended sources. We utilize the Radon operator to develop an inversion-based deblending framework that incorporates a sparse model constraint. The inclusion of a sparsity constraint in the inversion process enhances the focusing of the transform and improves data recovery. Inversion-based deblending enables us to account for all observed data by incorporating the blending operator into the cost function. Our synthetic and field data examples demonstrate that inversion-based deblending in the CMP domain can effectively separate blended sources. [ABSTRACT FROM AUTHOR]

Big data becomes the key for ubiquitous computing and intelligence, and Distributed Storage Systems (DSS) are widely used in large-scale data centers or in the cloud for efficient data management. However, the data on stored are likely to be unavailable due to hardware failures and cyberattacks, e.g. DDoS. Maximum Distance Separable (MDS) codes are commonly used for the recovery of faulty storage nodes or unavailable data. However, the recovery of data nodes usually involves access to multiple nodes, which introduces significant communication overheads to the DSS. In this paper, a new DSS based on the Redundant Residue Number System (RRNS) is proposed, where efficient recovery is enabled by applying the second version of Chinese Remainder Theorem (CRT-II). The complexity and network traffic of the proposed data protection scheme is analyzed theoretically and compared with that of traditional MDS based DSSs. Experimental results show that the proposed DSS achieves lower encoding complexity, lower recovery complexity and lower network traffic than the MDS based schemes. Although the proposed data protection scheme introduces computation overheads for the case on which there are no failing nodes, its complexity is still lower for scenarios with frequent data updates. In addition, the proposed scheme introduces additional advantages in terms of security and storage flexibility. [ABSTRACT FROM AUTHOR]

Regional studies are crucial for monitoring and managing the impacts of extreme climatic events. This phenomenon is particularly important in some areas, such as the Mediterranean region, which has been identified as one of the most responsive regions to climate change. In this regard, the analysis of large space-time sets of climatic data can provide potentially valuable information, although the datasets are commonly affected by the issue of missing data. This approach can significantly reduce the reliability of inferences derived from space-time data analysis. Consequently, the selection of an effective missing data recovery method is crucial since a poor dataset reconstruction could lead to misleading the decision makers' judgments. In the present paper, a methodology that can enhance the confidence of the statistical analysis performed on the reconstructed data is presented. The basic assumption of the proposed methodology is that missing data within certain percentages cannot significantly change the shape or parameters of the complete data distribution. Therefore, by applying several missing data recovery methods whose reconstructed dataset better overlaps the original dataset, larger confidence is needed. After the gap filling procedure, the temporal tendencies of the annual daily minimum temperature (T < 0 °C) were analysed in the Calabria region (southern Italy) by applying a test for trend detection to 8 temperature series over a 30-year period (1990–2019). The results showed that there was a constant reduction in the duration of frosty days, indicating the reliability of the effect of climate change. [ABSTRACT FROM AUTHOR]

Introduction: Intracerebral hemorrhage (ICH) is a severe manifestation of stroke, demonstrating notably elevated global mortality and morbidity. Thus far, effective therapeutic strategies for ICH have proven elusive. Currently, minimally invasive techniques are widely employed for ICH management, particularly using endoscopic hematoma evacuation in cases of deep ICH. Exploration of strategies to achieve meticulous surgery and diminish iatrogenic harm, especially to the corticospinal tract, with the objective of enhancing the neurological prognosis of patients, needs further efforts. Methods: We comprehensively collected detailed demographic, clinical, radiographic, surgical, and postoperative treatment and recovery data for patients who underwent endoscopic hematoma removal. This thorough inclusion of data intends to offer a comprehensive overview of our technical experience in this study. Results: One hundred fifty-four eligible patients with deep supratentorial intracerebral hemorrhage who underwent endoscopic hematoma removal were included in this study. The mean hematoma volume was 42 ml, with 74 instances of left-sided hematoma and 80 cases of right-sided hematoma. The median Glasgow Coma Scale (GCS) score at admission was 10 (range from 4 to 15), and the median time from symptom onset to surgery was 18 (range 2 to 96) h. The mean hematoma clearance rate was 89%. The rebleeding and mortality rates within 1 month after surgery were 3.2% and 7.8%, respectively. At the 6-month mark, the proportion of patients with modified Rankin Scale (mRS) scores of 0–3 was 58.4%. Conclusion: Both the reduction of surgery-related injury and the protection of the residual corticospinal tract through endoscopic hematoma removal may potentially enhance neurological functional outcomes in patients with deep ICH, warranting validation in a forthcoming multicenter clinical study. [ABSTRACT FROM AUTHOR]

Summary Ensuring data integrity in wireless sensor networks (WSNs) is crucial for accurate monitoring, yet missing data due to sensor faults present a significant challenge. This research introduces an innovative approach that integrates advanced data recovery techniques with leading‐edge methods to address this issue. The system begins by identifying and isolating fault nodes using a specialized algorithm that analyzes network behavior. By applying fuzzy density‐based spatial clustering of applications with noise (FDBSCAN), potential fault nodes are precisely located based on deviations from expected patterns. Subsequently, an intelligent missing data recovery mechanism powered by bidirectional long short‐term memory (Bi‐LSTM) networks takes action. The Bi‐LSTM model is trained on existing sensor data to capture intricate patterns and dependencies, enabling accurate prediction and reconstruction of missing values caused by identified faults. The synergy between Bi‐LSTM for missing data recovery and FDBSCAN for fault node detection comprehensively addresses the missing data problem in WSNs. In missing data recovery, it demonstrates low mean absolute deviation (MAD) ranging from 0.021 to 0.13 and mean squared deviation (MSD) ranging from 0.0025 to 0.05 across various missing data ratios. Data reliability remains consistently high at 96% to 98%, even with up to 80% missing data. For fault node detection, the approach achieves precision of 95.7%, recall of 96.3%, F1‐score of 96.1%, and accuracy of 97.4%, outperforming existing techniques. The computational cost during training is noted at 5.79 h, presenting a limitation compared to other methods. This research highlights the importance of integrating fault node detection into missing data recovery mechanisms, presenting an innovative solution for the advancement of WSNs. [ABSTRACT FROM AUTHOR]

As the demand for container technology and platforms increases due to the efficiency of IT resources, various workloads are being containerized. Although there are efforts to integrate various workloads into Kubernetes, the most widely used container platform today, the nature of containers makes it challenging to support persistence for memory-centric workloads like in-memory databases. In this paper, we discuss the drawbacks of one of the persistence support methods used for in-memory databases in a Kubernetes environment, namely, the data snapshot. To address these issues, we propose a compromise solution of using container checkpoints. Through this approach, we can perform checkpointing without incurring additional memory usage due to CoW, which is a problem in fork-based data snapshots during snapshot creation. Additionally, container checkpointing induces up to 7.1 times less downtime compared to the main process-based data snapshot. Furthermore, during database recovery, it is possible to achieve up to 11.3 times faster recovery compared to the data snapshot method. [ABSTRACT FROM AUTHOR]

Based on the assumption of photometric consistency, self-supervised monocular depth estimation has been widely studied due to the advantage of avoiding costly annotations. However, it is sensitive to noise, occlusion issues and photometric changes. To overcome these problems, we propose a multi-task model with a dual-attention-based cross-task feature fusion module (DCFFM). We simultaneously predict depth and semantic with a shared encoder and two separate decoders, aiming to improve depth estimation with the enhancement of semantic supervision information. In DCFFM, we fuse the cross-task features with both pixel-wise and channel-wise attention, which fully excavate and make good use of the helpful information from the other task mutually. We compute both of two attentions in a one-to-all manner to capture global information while limiting the rapid growth of computation. Furthermore, we propose a novel data augmentation method called data exchange & recovery (DE &R), which performs inter-batch data exchange in both vertical and horizontal direction so as to increase the diversity of input data. It encourages the network to explore more diversified cues for depth estimation and avoid overfitting. And essentially, the corresponding outputs are further recovered in order to keep the geometry relationship and ensure the correct calculation of photometric loss. Extensive experiments on the KITTI dataset and the NYU-Depth-v2 dataset demonstrate that our method is very effective and achieves better performance compared with other state-of-the-art works. [ABSTRACT FROM AUTHOR]

Federated learning (FL), celebrated for its privacy‐preserving features, has been revealed by recent studies to harbour security vulnerabilities that jeopardize client privacy, particularly through data reconstruction attacks that enable adversaries to recover original client data. This study introduces a client‐level handwriting forgery attack method for FL based on generative adversarial networks (GANs), which reveals security vulnerabilities existing in FL systems. It should be stressed that this research is purely for academic purposes, aiming to raise concerns about privacy protection and data security, and does not encourage illegal activities. Our novel methodology assumes an adversarial scenario wherein adversaries intercept a fraction of parameter updates via victim clients’ wireless communication channels, then use this information to train GAN for data recovery. Finally, the purpose of handwriting imitation is achieved. To rigorously assess and validate our methodology, experiments were conducted using a bespoke Chinese digit dataset, facilitating in‐depth analysis and robust verification of results. Our experimental findings demonstrated enhanced data recovery effectiveness, a client‐level attack and greater versatility compared to prior art. Notably, our method maintained high attack performance even with a streamlined GAN design, yielding increased precision and significantly faster execution times compared to standard methods. Specifically, our experimental numerical results revealed a substantial boost in reconstruction accuracy by 16.7%, coupled with a 51.9% decrease in computational time compared to the latest similar techniques. Furthermore, tests on a simplified version of our GAN exhibited an average 10% enhancement in accuracy, alongside a remarkable 70% reduction in time consumption. By surmounting the limitations of previous work, this study fills crucial gaps and affirms the effectiveness of our approach in achieving high‐accuracy client‐level data reconstruction within the FL context, thereby stimulating further exploration into FL security measures. [ABSTRACT FROM AUTHOR]

Cloud computing is an on-demand virtual-based technology to develop, configure, and modify applications online through the internet. It enables the users to handle various operations such as storage, back-up, and recovery of data, data analysis, delivery of software applications, implementation of new services and applications, hosting websites and blogs, and streaming of audio and video files. Thereby, it provides us many benefits although it is backlashed due to problems related to cloud security like data leakage, data loss, cyber attacks, etc. To address the security concerns, researchers have developed a variety of authentication mechanisms. This means that the authentication procedure used in the suggested method is multi-levelled. As a result, a better QKD method is offered to strengthen cloud security against different types of security risks. Key generation for enhanced QKD is based on the ABE public key cryptography approach. Here, an approach named CPABE is used in improved QKD. The Improved QKD scored the reduced KCA attack ratings of 0.3193, this is superior to CMMLA (0.7915), CPABE (0.8916), AES (0.5277), Blowfish (0.6144), and ECC (0.4287), accordingly. Finally, this multi-level authentication using an improved QKD approach is analysed under various measures and validates the enhancement over the state-of-the-art models [ABSTRACT FROM AUTHOR]

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Conducting assessments to understand the effects of changing environmental conditions on polar bear (Ursus maritimus) demography has become increasingly important to inform management and conservation. Here, we combined physical (2005–2007) and genetic (2017–2018) mark‐recapture with harvest recovery data (2005–2018) to estimate demographic rates of the Davis Strait polar bear subpopulation and examine the possible effects of climate, dynamic ice habitat, and prey resources on survival. Large sample sizes (e.g., 2,513 marked animals) allowed us to estimate temporal variation in annual survival rates using multistate mark‐recapture‐recovery models. We did not detect statistically significant effects of climate, ice habitat, and prey during the 13‐year study. Estimated total abundance in 2006 was 2,190, credible interval (CRI) [1,954, 2,454] and 1,944, CRI [1,593, 2,366] in 2018. Geometric mean population growth rate (0.99, 95% CRI [0.97, 1.01]) indicated the subpopulation may have declined slightly between 2006 and 2018. However, we did not detect a declining trend in survival or substantial change in reproductive metrics over this period. Given forecasts of major environmental change we emphasize the need to review monitoring programs for this subpopulation. [ABSTRACT FROM AUTHOR]

Today's digital data storage systems typically offer advanced data recovery solutions to address the problem of catastrophic data loss, such as software-based disk sector analysis or physical-level data retrieval methods for conventional hard disk drives. However, DNA-based data storage currently relies solely on the inherent error correction properties of the methods used to encode digital data into strands of DNA. Any error that cannot be corrected utilizing the redundancy added by DNA encoding methods results in permanent data loss. To provide data recovery for DNA storage systems, we present a method to automatically reconstruct corrupted or missing data stored in DNA using fountain codes. Our method exploits the relationships between packets encoded with fountain codes to identify and rectify corrupted or lost data. Furthermore, we present file type-specific and content-based data recovery methods for three file types, illustrating how a fusion of fountain encoding-specific redundancy and knowledge about the data can effectively recover information in a corrupted DNA storage system, both in an automatic and in a guided manual manner. To demonstrate our approach, we introduce DR4DNA, a software toolkit that contains all methods presented. We evaluate DR4DNA using both in-silico and in-vitro experiments.

Background and purpose: User interface environments of databases should be designed in such a way that, in addition to facilitating users' information-seeking behavior, they can provide appropriate strategies to solve their problems in the shortest possible time. The user interface allows users to get a proper understanding of the information base and they can take advantage of it. The closer the design of the user interface is to the mental patterns and information-seeking behavior of the users, the more effective and useful it is and the less the user's efforts. Despite the development of medical information technology in the field of user interface design, most medical information systems have not been able to meet users' expectations mainly due to: a lack of awareness of information needs, information behavior of users, and lack of understanding of its nature. This research aims to present a new model for effective information retrieval and increasing the desirability in the design of user interface environments of information databases. Materials and methods: This study is a mixed type with a quantitative and qualitative approach which has been done with the design of combined exploratory methods. In the qualitative part, the research community included expert professors and specialists, and in the quantitative part, specialized and research-oriented PhD students of the basic science fields of Mazandaran University of Medical Sciences. In the qualitative part, 12 people were included in the study with the purposeful sampling method, and with this number of interviews, information saturation occurred. In the quantitative part, stratified random sampling was used and 100 people were included in the study. The required data was collected through content analysis and researcher-developed questionnaires. The data was then analyzed using SPSS software version 22 and inferential statistics (Kolmogorov-Smirnov, Wilcoxon, Mann-Whitney, and structural analysis). Results: According to the IPMA analysis, the findings of the research showed that among the variables that explain the desirable information-seeking behavior in terms of importance, the highest rank in desirable information-seeking behaviors is related to the dimension of users' position with an emphasis on ability (with a significance coefficient of 0.437 and the lowest is related to the ease of access component (with a significance coefficient of 0.048) and in terms of performance, the highest rating is related to the user interface design dimension with an emphasis on the database efficiency variable (with a performance factor of 57.191) and the lowest was related to the ease component (with a performance factor of 40.793). Also, the proposed model was able to obtain the necessary validity and reliability. Conclusion: The presented model helps users of database user interface environments so that they can use the basic elements as much as possible to speed up work and save time. Also, the proposed model can provide the necessary ideas to the designers of database environments so that they can implement the appropriate features for better interaction with the databases to facilitate and understand as easily as possible for more users. considering that the proposed model has obtained the necessary validity and reliability, it can be claimed that this model will not only be able to answer the issues raised in the field of searching and retrieving information, but it can be used, criticized, and perfected in future researches as a basis for designing and implementing user interface environments.

The Gravity Recovery and Climate Experiment Follow-On (GRACE-FO) satellites are equipped with high-precision three-axis accelerometers to measure all non-gravitational accelerations acting on the satellites. Precise knowledge of these perturbations is a major prerequisite for successful Gravity Field Recovery (GFR). Unfortunately, after only one month in orbit the accelerometer on one of the two satellites produced decreasingly accurate measurements. Due to this, the GRACE-D accelerometer data has to be replaced by artificial data for the use in the GFR process. In most existing approaches, which are called transplants, this data is constructed from GRACE-C measurements. We are developing a GRACE-D accelerometer product that relies mainly on modelled acceleration data instead of a full transplant. By using physical models to generate acceleration data, we are able to better understand the accelerometer signal and its characteristics, which is beneficial for subsequent processes involving accelerometer data. We present the evaluation of our modelled data in terms of comparison to the official Science Data System (SDS) accelerometer data. For the first GRACE-FO satellite real accelerometer is used as a reference, but for the second satellite the main comparisons are done with respect to the ACH1B transplant data product. To get information about environmental changes and long-term effects data sets covering one year and nearly the whole mission duration are presented. In terms of gravity field solutions the performance of purely modelled accelerometer data for GRACE-D and a minimalistic transplant approach of estimated density values is compared to transplant products of other processing centres for the year 2019 as well as one month of 2023. Through this, the modelled solution can act as a cross-reference to the transplant, contributing to an improved implementation of the external perturbation characteristics and the influence of higher solar activity on our models and the subsequent gravity field solutions can be determined. Comparison of the artificial acceleration data to the real ACT1B data for GRACE-C showed that models related to the atmospheric drag are the limiting factors in our high-precision environment modelling approach. Nevertheless, it was still possible to generate monthly gravity field solutions with a combination of ACT1B data for GRACE-C and artificial data for GRACE-D that showed hydrological signals. Application of the transplant that substitutes the limiting modelled thermospheric density with estimated values obtained at GRACE-C positions reduces the residuals between artificial and real acceleration data significantly. Estimation of gravity fields showed that the improvements due to the transplant directly transfer into the gravity field solutions. Our minimalistic and physically motivated transplant is shown to be a valuable alternative to the transplant data from TUG and JPL. The GROOPS software is utilized to generate monthly gravity field solution to guarantee comparability of the presented solutions for the geodesy community. The presented results not only show the quality of our transplant product but also enabled us to identify potential areas of improvement in the data generation process and our in–house gravity field recovery tool. The ZARM GRACE-D transplant product, all radiation accelerations and estimated density is available for download at: https://www.zarm.uni-bremen.de/zarm_daten/. [ABSTRACT FROM AUTHOR]

In the realm of groundwater science, characterization of heterogeneous aquifers is pivotal for resolving diverse groundwater resource and engineering-related problems that require the detailed spatial distribution of hydraulic parameters. As research progresses, one hydraulic tomographical method, which is based on hydraulic travel time inversion, emerges as a promising and rapid method due to its robust and efficient calculation. In the field, the acquisition of hydraulic excitation and head observation data required for inversion is less time-consuming. Data collection from a single hydraulic test (such as a pumping test) typically takes only a few minutes or even a few tens of seconds. However, the field application of this method faces challenges. Hydraulic travel time is typically generated in the early stages of hydrogeological tests (e.g., early drawdown of a pumping test), yet accurate data may not be readily available because of the noise signals from test equipment, which can contaminate travel time signals, leading to inaccurate inversion results. A potential solution lies in utilizing the smooth head observation during the recovery period after the pump is turned off, which yields more accurate travel times for inversion calculations. In this paper, the mathematical development suggests that the travel time of the recovery phase aligns with that of the pumping phase when pumping reaches a steady or quasi-steady state. Subsequently, by employing Monte-Carlo simulations, 1200 realizations of two-dimensional heterogeneous confined aquifer models were generated for simulating pumping tests with different pumping durations. The calculated head data were then utilized to compute the travel time derived from drawdown data (t) and recovery data (t′), respectively. Comparisons showed that t is equal to t′ when drawdown reaches a steady or quasi-steady state. Conversely, when the pump is turned off before reaching a quasi-steady state, t differs from t′. However, results also indicate the fact that a decent hydraulic travel time diagnosis can be obtained, especially for the cases when travel times are smaller than 15 s. Given the statistical results of Monte-Carlo simulations, as well as experience during pumping tests in the field with different scenarios, using the recovery data from 60 s of pumping duration, or extended pumping durations of 100 s or 200 s as a more conservative alternative, can replace the aquifer characterization based on drawdown data. The new inversion strategy not only has less data uncertainty and equivalent inversion accuracy, but also can greatly enhance the repeatability of field tests and reduce the environmental impact of long-term pumping tests. [ABSTRACT FROM AUTHOR]

This article extends the celebrated Riemann–Hilbert (RH) method equipped with mixed spectrum to a new integrable system of three-component coupled time-varying coefficient complex mKdV equations (ccmKdVEs for short) generated by the mixed spectral equations (msEs). Firstly, the ccmKdVEs and the msEs for generating the ccmKdVEs are proposed. Then, based on the msEs, a solvable RH problem related to the ccmKdVEs is constructed. By using the constructed RH problem with mixed spectrum, scattering data for the recovery of potential formulae are further determined. In the case of reflectionless coefficients, explicit N-soliton solutions of the ccmKdVEs are ultimately obtained. Taking N equal to 1 and 2 as examples, this paper reveals that the spatiotemporal solution structures with time-varying nonlinear dynamic characteristics localized in the ccmKdVEs is attributed to the multiple selectivity of mixed spectrum and time-varying coefficients. In addition, to further highlight the application of our work in fractional calculus, by appropriately selecting these time-varying coefficients, the ccmKdVEs are transformed into a conformable time-fractional order system of three-component coupled complex mKdV equations. Based on the obtained one-soliton solutions, a set of initial values are assigned to the transformed fractional order system, and the N-th iteration formulae of approximate solutions for this fractional order system are derived through the variational iteration method (VIM). [ABSTRACT FROM AUTHOR]

In the realm of Ceph distributed storage systems, ensuring swift and symmetrical data recovery during severe data corruption scenarios is pivotal for data reliability and system stability. This paper introduces an innovative FPGA-based Multi-Dimensional Elastic Recovery Acceleration method, termed AMDER-Ceph. Utilizing FPGA technology, this method is a pioneer in accelerating erasure code data recovery within such systems symmetrically. By harnessing the parallel computing power of FPGAs and optimizing Cauchy matrix binary operations, AMDER-Ceph significantly enhances data recovery speed and efficiency symmetrically. Our evaluations in real-world Ceph environments show that AMDER-Ceph achieves up to 4.84 times faster performance compared with traditional methods, especially evident in the standard 4 MB block size configurations of Ceph systems. [ABSTRACT FROM AUTHOR]

Single-cell RNA sequencing (scRNA-seq) technology provides a means for studying biology from a cellular perspective. The fundamental goal of scRNA-seq data analysis is to discriminate single-cell types using unsupervised clustering. Few single-cell clustering algorithms have taken into account both deep and surface information, despite the recent slew of suggestions. Consequently, this article constructs a fusion learning framework based on deep learning, namely scGASI. For learning a clustering similarity matrix, scGASI integrates data affinity recovery and deep feature embedding in a unified scheme based on various top feature sets. Next, scGASI learns the low-dimensional latent representation underlying the data using a graph autoencoder to mine the hidden information residing in the data. To efficiently merge the surface information from raw area and the deeper potential information from underlying area, we then construct a fusion learning model based on self-expression. scGASI uses this fusion learning model to learn the similarity matrix of an individual feature set as well as the clustering similarity matrix of all feature sets. Lastly, gene marker identification, visualization, and clustering are accomplished using the clustering similarity matrix. Extensive verification on actual data sets demonstrates that scGASI outperforms many widely used clustering techniques in terms of clustering accuracy. [ABSTRACT FROM AUTHOR]

In this paper, a low-power Injection-Locked Clock and Data Recovery (ILCDR) using a 28 nm Ultra-Thin Body and Box-Fully Depleted Silicon On Insulator (UTBB-FDSOI) technology is presented. The back-gate auto-biasing of UTBB-FDSOI transistors enables the creation of a Quadrature Ring Oscillator (QRO) reducing both size and power consumption compared to an LC tank oscillator. By injecting a digital signal into this circuit, we realize an Injection-Locked Oscillator (ILO) with low jitter. Thanks to the good performance of this oscillator, we propose a low-power ILCDR with fast locking time and low jitter for burst-mode applications. The main novelty consists of the implementation of a complementary QRO based on back-gate control using FDSOI technology to realize a simple and efficient ILCDR circuit. With a Pseudo-Random Binary Sequence (PRBS7) at 868 Mbps, the recovered clock jitter is 26.7 ps (2.3% UIp-p) and the recovered data jitter is 11.9 ps (1% UIp-p). With a 0.6 V power supply, the power consumption is 318μW. All the results presented here are based on post-layout simulations, as no prototypes have been produced. Similarly, we can estimate the surface area of the chip (without the pad ring) at around 6600 μm2. [ABSTRACT FROM AUTHOR]

As wearable devices continue to proliferate, seamlessly integrating them into wireless body-area networks (WBANs) becomes increasingly crucial. Bodycoupled communication (BCC) emerges as a promising WBAN technology, utilizing the human body itself as a transmission channel. This paper presents a novel BCC transceiver designed for efficiency and miniaturization. The proposed transceiver prioritizes reliable data transmission with a convolutional encoder. It leverages a simple direct digital synthesizer (DDS) for frequency shift keying (FSK) modulation, minimizing chip area. At the receiver, a Viterbi decoder (VD) ensures accurate data recovery. This design shines in its resource efficiency. It occupies less than 1% of an Artix-7 FPGA, operates at 268.77 MHz with a mere 111 mW power consumption, and achieves a remarkable data rate of 13.78 Mbps. This translates to a hardware efficiency of 44.46 Kbps/slice, surpassing existing transceivers. Moreover, the BCC transceiver exhibits a stellar bit error rate (BER) of over 10-7 under realistic body channel conditions. Overall, this work presents a highly efficient BCC transceiver with significant improvements in chip area, power consumption, and data rate compared to existing designs. This paves the way for practical and miniaturized WBAN solutions for future wearable applications. [ABSTRACT FROM AUTHOR]

In the field of botany applied to archaeological and palaeoecological studies, the multi- and inter-disciplinary nature of this research produces a lack of data sharing and scattered articles in the specialty literature or in national and international journals. The vast production of archaeobotany and palynology data makes it necessary to develop a tool for the availability, accessibility, and dissemination of existing research. Many databases exist on palaeoecology, archaeobotany or pollen data. There are no collections focused on archaeological sites and human-induced environments and centred on Southern Europe and the Mediterranean. BRAIN - Botanical Records of Archaeobotany Italian Network is the first database listing sites from which all types of plant records are available in Italy and nearby Mediterranean regions. BRAIN represents the largest integrated collection of archaeo/palaeo-botanical data and a range of descriptive information that makes data recovery FAIR ready. This unique network hosts data on the availability of anthropogenic pollen, palynomorphs and plant macroremains in the same database, and experts of different research fields may contribute to it. [ABSTRACT FROM AUTHOR]

Technological developments coupled with socioeconomic changes are driving a rapid transformation of the fifth-generation (5G) cellular network landscape. This evolution has led to versatile applications with fast data-transfer capabilities. The integration of 5G with wireless sensor networks (WSNs) has rendered the Internet of Things (IoTs) crucial for measurement and sensing. Although 5G-enabled IoTs are vital, they face challenges in data integrity, such as mixed noise, outliers, and missing values, owing to various transmission issues. Traditional methods such as the tensor robust principal component analysis (TRPCA) have limitations in preserving essential data. This study introduces an enhanced approach, the weighted robust tensor principal component analysis (WRTPCA), combined with weighted tensor completion (WTC). The new method enhances data recovery using tensor singular value decomposition (t-SVD) to separate regular and abnormal data, preserve significant components, and robustly address complex data corruption issues, such as mixed noise, outliers, and missing data, with the globally optimal solution determined through the alternating direction method of multipliers (ADMM). Our study is the first to address complex corruption in multivariate data using the WTRPCA. The proposed approach outperforms current techniques. In all corrupted scenarios, the normalized mean absolute error (NMAE) of the proposed method is typically less than 0.2, demonstrating strong performance even in the most challenging conditions in which other models struggle. This highlights the effectiveness of the proposed approach in real-world 5G-enabled IoTs. [ABSTRACT FROM AUTHOR]

Recent advances in aerial robotics and wireless transceivers have generated an enormous interest in networks constituted by multiple compact unmanned aerial vehicles (UAVs). UAV adhoc networks, i.e., aerial networks with dynamic topology and no centralized control, are found suitable for a unique set of applications, yet their operation is vulnerable to cyberattacks. In many applications, such as IoT networks or emergency failover networks, UAVs augment and provide support to the sensor nodes or mobile nodes in the ground network in data acquisition and also improve the overall network performance. In this situation, ensuring the security of the adhoc UAV network and the integrity of data is paramount to accomplishing network mission objectives. In this paper, we propose a novel approach to secure UAV adhoc networks, referred to as the blockchain-assisted security framework (BCSF). We demonstrate that the proposed system provides security without sacrificing the performance of the network through blockchain technology adopted to the priority of the message to be communicated over the adhoc UAV network. Theoretical analysis for computing average latency is performed based on queuing theory models followed by an evaluation of the proposed BCSF approach through simulations that establish the superior performance of the proposed methodology in terms of transaction delay, data secrecy, data recovery, and energy efficiency. [ABSTRACT FROM AUTHOR]

Background and purpose: User interface environments of databases should be designed in such a way that, in addition to facilitating users' information-seeking behavior, they can provide appropriate strategies to solve their problems in the shortest possible time. The user interface allows users to get a proper understanding of the information base and they can take advantage of it. The closer the design of the user interface is to the mental patterns and information-seeking behavior of the users, the more effective and useful it is and the less the user's efforts. Despite the development of medical information technology in the field of user interface design, most medical information systems have not been able to meet users' expectations mainly due to: a lack of awareness of information needs, information behavior of users, and lack of understanding of its nature. This research aims to present a new model for effective information retrieval and increasing the desirability in the design of user interface environments of information databases. Materials and methods: This study is a mixed type with a quantitative and qualitative approach which has been done with the design of combined exploratory methods. In the qualitative part, the research community included expert professors and specialists, and in the quantitative part, specialized and research-oriented PhD students of the basic science fields of Mazandaran University of Medical Sciences. In the qualitative part, 12 people were included in the study with the purposeful sampling method, and with this number of interviews, information saturation occurred. In the quantitative part, stratified random sampling was used and 100 people were included in the study. The required data was collected through content analysis and researcher-developed questionnaires. The data was then analyzed using SPSS software version 22 and inferential statistics (Kolmogorov-Smirnov, Wilcoxon, Mann-Whitney, and structural analysis). Results: According to the IPMA analysis, the findings of the research showed that among the variables that explain the desirable information-seeking behavior in terms of importance, the highest rank in desirable informationseeking behaviors is related to the dimension of users' position with an emphasis on ability (with a significance coefficient of 0.437 and the lowest is related to the ease of access component (with a significance coefficient of 0.048) and in terms of performance, the highest rating is related to the user interface design dimension with an emphasis on the database efficiency variable (with a performance factor of 57.191) and the lowest was related to the ease component (with a performance factor of 40.793). Also, the proposed model was able to obtain the necessary validity and reliability. Conclusion: The presented model helps users of database user interface environments so that they can use the basic elements as much as possible to speed up work and save time. Also, the proposed model can provide the necessary ideas to the designers of database environments so that they can implement the appropriate features for better interaction with the databases to facilitate and understand as easily as possible for more users. considering that the proposed model has obtained the necessary validity and reliability, it can be claimed that this model will not only be able to answer the issues raised in the field of searching and retrieving information, but it can be used, criticized, and perfected in future researches as a basis for designing and implementing user interface environments. [ABSTRACT FROM AUTHOR]

Human activity recognition (HAR) and, more broadly, activities of daily life recognition using wearable devices have the potential to transform a number of applications, including mobile healthcare, smart homes, and fitness monitoring. Recent approaches for HAR use multiple sensors on various locations on the body to achieve higher accuracy for complex activities. While multiple sensors increase the accuracy, they are also susceptible to reliability issues when one or more sensors are unable to provide data to the application due to sensor malfunction, user error, or energy limitations. Training multiple activity classifiers that use a subset of sensors is not desirable, since it may lead to reduced accuracy for applications. To handle these limitations, we propose a novel generative approach that recovers the missing data of sensors using data available from other sensors. The recovered data are then used to seamlessly classify activities. Experiments using three publicly available activity datasets show that with data missing from one sensor, the proposed approach achieves accuracy that is within 10% of the accuracy with no missing data. Moreover, implementation on a wearable device prototype shows that the proposed approach takes about 1.5 ms for recovering data in the w-HAR dataset, which results in an energy consumption of 606 μJ. The low-energy consumption ensures that SensorGAN is suitable for effectively recovering data in tinyML applications on energy-constrained devices. [ABSTRACT FROM AUTHOR]

A low-power clock and data recovery circuit with a quarter rate operating at 10 GHz is presented. This circuit consists of a phase lock loop and an input data retiming circuit. The phase-locked loop includes an LC oscillator, a quarter-rate detector, a charge pump, and a low-pass filter. The output of the oscillator is applied to a two-bit counter, so the clock frequency is reduced to 2.5 GHz with eight different phases which applied to the phase detector to sample the input data in different phases. Each sampling is done in 12.5 picoseconds. The innovative application of this two-bit counter eliminates the requirement of the multiphase oscillator, thus helps to reduce overall power dissipation. The power consumption of the voltage control oscillator is about 5.83 mW. In addition, reducing the clock frequency improves the performance of the phase detector circuit. The total power dissipation of the proposed CDR is evaluated to be 10.9 mW from a 1.8 V supply. [ABSTRACT FROM AUTHOR]

With the increasing importance of data in modern business environments, effective data management and protection strategies are gaining increasing research attention. Data protection in a cloud environment is crucial for safeguarding information assets and maintaining sustainable services. This study introduces a system structure that integrates Kubernetes management platforms with backup and restoration tools. This system is designed to immediately detect disasters and automatically recover applications from another Kubernetes cluster. The experimental results show that this system executes the restoration process within 15 s without human intervention, enabling rapid recovery. This, in turn, significantly reduces the potential for delays and errors compared to manual recovery processes, thereby enhancing data management and recovery efficiency in cloud environments. Moreover, our research model predicts the CPU utilization of the cluster using Long Short-Term Memory (LSTM). The necessity of scheduling through this predict is made clearer through comparison with experiments without scheduling, demonstrating its ability to prevent performance degradation. This research highlights the efficiency and necessity of automatic recovery systems in cloud environments, setting a new direction for future research. [ABSTRACT FROM AUTHOR]

Multi-channel audio signals provide a better auditory sensation to the audience. However, missing data may occur in the collection, transmission, compression, or other processes of audio signals, resulting in audio quality degradation and affecting the auditory experience. As a result, the completeness of the audio signal has become a popular research topic in the field of signal processing. In this paper, the tensor nuclear norm is introduced into the audio signal completion algorithm, and the multi-channel audio signals with missing data are restored by using the completion algorithm based on the tensor nuclear norm. First of all, the multi-channel audio signals are preprocessed and are then transformed from the time domain to the frequency domain. Afterwards, the multi-channel audio with missing data is modeled to construct a third-order multi-channel audio tensor. In the next part, the tensor completion algorithm is used to complete the third-order tensor. The optimal solution of the convex optimization model of the tensor completion is obtained by using the convex relaxation technique and, ultimately, the data recovery of the multi-channel audio with data loss is accomplished. The experimental results of the tensor completion algorithm and the traditional matrix completion algorithm are compared using both objective and subjective indicators. The final result shows that the high-order tensor completion algorithm has a better completion ability and can restore the audio signal better. [ABSTRACT FROM AUTHOR]

Single‐cell RNA sequencing (scRNA‐seq) is a robust method for studying gene expression at the single‐cell level, but accurately quantifying genetic material is often hindered by limited mRNA capture, resulting in many missing expression values. Existing imputation methods rely on strict data assumptions, limiting their broader application, and lack reliable supervision, leading to biased signal recovery. To address these challenges, authors developed Bis, a distribution‐agnostic deep learning model for accurately recovering missing sing‐cell gene expression from multiple platforms. Bis is an optimal transport‐based autoencoder model that can capture the intricate distribution of scRNA‐seq data while addressing the characteristic sparsity by regularizing the cellular embedding space. Additionally, they propose a module using bulk RNA‐seq data to guide reconstruction and ensure expression consistency. Experimental results show Bis outperforms other models across simulated and real datasets, showcasing superiority in various downstream analyses including batch effect removal, clustering, differential expression analysis, and trajectory inference. Moreover, Bis successfully restores gene expression levels in rare cell subsets in a tumor‐matched peripheral blood dataset, revealing developmental characteristics of cytokine‐induced natural killer cells within a head and neck squamous cell carcinoma microenvironment. [ABSTRACT FROM AUTHOR]