Search Results (59)

In HPC systems and hyper-scale data centers, the adoption of high-performance NVMe SSDs and high-speed networks has shifted storage bottlenecks to the network stack. Under high-concurrency workloads, frequent interrupt processing exhausts CPU resources while protocol-level control–data dependencies in the NVMe over TCP write path introduce additional serialization penalties. Existing optimizations either require specialized hardware, dedicate CPU cores to user-space polling, or apply semantically blind batching that delays time-sensitive control messages. We present SENS, a Semantic-aware NVMe over TCP Scheduler embedded within the NVMe over TCP driver of the Linux kernel. SENS combines two mechanisms: (1) PDU vectorization, which aggregates discrete Protocol Data Units into memory vectors before network transmission, amortizing per-I/O system call overhead and reducing soft-interrupt frequency; and (2) instruction-aware dispatch, which detects control PDUs such as R2T and triggers an early flush of the aggregation window, mitigating the serialization penalty on the write path. A prototype evaluation with physical NVMe SSDs and 100 GbE networks shows that SENS saturates the SSD throughput ceiling using 4–5 CPU cores, halving the host-side core budget compared to the native TCP driver. With a RAMDisk backend that removes storage-media constraints, SENS sustains up to 2.5× higher concurrent IOPS. These results show that exposing storage-protocol semantics to the batching layer improves the scalability of NVMe over TCP without additional hardware. Full article

Motivated by heterogeneous data distributions and task-dependent aggregation requirements in federated learning, we study information-theoretic secure aggregation of linear functions over a two-hop hierarchical network. The system comprises an aggregation server, an intermediate layer of U relays, and $UV$ users, where each relay serves a disjoint cluster of V users. Each relay observes all uplink transmissions within its cluster and forwards a coded message to the server. The server is authorized to compute a prescribed linear function F of the users’ inputs with zero error, while being prevented from learning any additional information about an unauthorized linear function G. Moreover, each relay must obtain no information about any non-trivial linear function $B_u$ of the inputs in its own cluster. We define the communication rates on both hops as the number of transmitted symbols per input symbol. By deriving matching information-theoretic converse and achievability bounds, we fully characterize the optimal communication rates and propose an explicit linear coding scheme that achieves the resulting optimal region. Our results demonstrate that hierarchical architectures can attain optimal communication rates while substantially reducing the server-side masking burden, thereby enabling scalable secure aggregation of authorized linear functions. Full article

Reversible data hiding (RDH) is a key technique in secure multimedia applications, enabling the exact recovery of both embedded data and the original cover content. To further enhance security and embedding capacity, this paper proposes a dual-image reversible data hiding (DIRDH) method based on absolute moment block truncation coding (AMBTC). In the proposed scheme, two identical AMBTC-decoded images are exploited as twin covers, and secret bits are adaptively embedded into paired pixels using a variable embedding rate. To ensure data integrity, a lightweight Hash-based Message Authentication Code (HMAC) mechanism is integrated, allowing reliable detection of tampering without additional side information. Experimental results demonstrate that the proposed method achieves high embedding capacity while preserving good visual quality and provides effective authentication against representative tampering cases, including pixel modification, noise addition, and cropping. These contributions highlight the advantages of combining DIRDH with AMBTC, offering a practical and secure solution for high-capacity reversible data hiding. Full article

At the current stage, indoor Ultra-Wideband (UWB) positioning systems often encounter challenges in achieving high localization accuracy under non-line-of-sight (NLOS) conditions within workshop environments when employing the Double-Sided Two-Way Ranging (DS-TWR) algorithm. To address this issue, a positioning optimization method based on the DS-TWR algorithm is proposed. By streamlining message exchanges between nodes, the method reduces node energy consumption and shortens ranging time, thereby enhancing system energy efficiency and response speed. Furthermore, to improve positioning accuracy in workshop NLOS environments, an Adaptive Kalman Filtering algorithm is introduced. This algorithm dynamically evaluates the influence of obstruction information caused by NLOS conditions on the covariance of observation noise and adaptively adjusts the filtering gain of the signals accordingly. Through this approach, the system can effectively eliminate invalid positioning information in signals, mitigate the adverse effects of NLOS conditions on positioning accuracy and achieve more precise localization. Experimental results demonstrate that the proposed optimization algorithm achieves substantial performance improvements in both static and dynamic positioning experiments under workshop NLOS conditions. Specifically, the algorithm not only enhances system positioning accuracy but also further strengthens the real-time ranging precision of the DS-TWR algorithm. Full article

Urban air pollution and emission reduction commitments have stimulated interest in cleaner vehicle technologies in Latin America, yet hybrid vehicle penetration in Ecuador, particularly in Guayaquil, remains limited. This study analyzes technical and commercial determinants of purchase intention using a mixed-methods design that combines a survey of 384 consumers with interviews of 20 dealership representatives. Within this male-dominated sample, Spearman’s rank correlation coefficients ($\rho$) (all two-sided tests yielded $p<0.001$) indicate that technical attributes show stronger associations with purchase intention than commercial variables: technology and performance ($\rho =0.65$) and maintenance ($\rho =0.61$) are the most influential, followed by Social Influence ($\rho =0.53$); public policies ($\rho =0.48$) and purchase price ($\rho =0.45$) display moderate effects. Overall, 51.5% of respondents report a favorable intention to purchase a hybrid vehicle in the short to medium term. Interviews confirm an information gap on tax incentives at the point of sale and underscore the potential of financing schemes to mitigate upfront cost barriers. Findings suggest that, in this market, narratives emphasizing long-term operating savings and reliability are more persuasive than generic sustainability messages. We discuss implications for dealership communication, targeted credit programs, and public policy instruments with information campaigns to accelerate sustainable mobility transitions in urban Ecuador. While price is widely cited as decisive (84.2%), variation in technical attributes explains more of the variation in stated purchase intention than price within our sample. The survey sample was collected at an auto show and dealerships and is predominantly male (87.5%). Thus, results describe a male-skewed subset of potential buyers and should not be generalized to households or the broader consumer base. Full article

The present study utilized two prominent emotion theories to investigate intention and behavior involved in propagating threatening social media messages. Participants were randomly assigned to different blocks of tweets/Xs with the same word count but different topics/sentiments. The topics in Study 1 (N = 619) were neutral and illegal border crossing, whereas the topics in Study 2 (N = 577) were the virulent risk of COVID-19 and the potential risks of newly developed vaccines. Dissemination intention was gauged by the number of tweets that participants wanted to share. Participants were also asked to summarize the messages to observe their behavioral engagement with the information, specifically through time spent on the task and the number of words written. An intention–behavior disjoint was found under all threatening topics and on both sides of the political divide. Fearful participants showed engaging intentions (wanted to share more tweets) but disengaging behaviors (wrote fewer words and submitted their summaries sooner). The necessary and sufficient conditions for the intention–behavior disjoint seemed to be the presence of threatening contents and subjective fear. Communicating risks can spark interest, but it is important not to burden the audience with too much fear, or they may stop spreading the word. Full article

Living systems cannot rely on random intermolecular approaches toward cell crowding, and hidden mechanisms must be present to favor only those molecular interactions required explicitly by the biological function. Electromagnetic messaging among proteins is proposed from the observation that charged amino acids located on the protein surface are mostly in adjacent sequence positions and/or in spatial proximity. Molecular dynamics (MD) simulations have been used to predict electric charge proximities arising from concerted motions of charged amino acid side chains in two protein model systems, human ubiquitin and the chitinolytic enzyme from Ostrinia furnacalis. This choice has been made for their large difference in size and sociality. Protein electrodynamics seems to emerge as the framework for a deeper understanding of the long-distance interactions of proteins with their molecular environment. Our findings will be valuable in orienting the design of proteins with specific recognition patterns. Full article

Vehicular ad hoc networks (VANETs) can bolster road safety through the proactive dissemination of emergency messages (EMs) among vehicles, effectively reducing the occurrence of traffic-related accidents. It is difficult to transmit EMs quickly and reliably due to the high-speed mobility of VANET and the attenuation of the wireless signal. However, poor network design and high vehicle mobility are the two most difficult problems that affect VANET’s network performance. The real-time traffic situation and network dependability will also be significantly impacted by route selection and message delivery. Many of the current works have undergone studies focused on forwarder selection and message transmission to address these problems. However, these earlier approaches, while effective in forwarder selection and routing, have overlooked the critical aspects of communication overhead and excessive energy consumption, resulting in transmission delays. To address the prevailing challenges, the proposed solutions use edge computing to process and analyze data locally from surrounding cars and infrastructure. EDGE-RSUs are positioned by the side of the road. In intelligent transportation systems, this lowers latency and enhances real-time decision-making by employing proficient forwarder selection techniques and optimizing the dissemination of EMs. In the context of 5G-enabled VANET, this paper introduces a novel routing protocol, namely, the supercluster-based urban multi-hop broadcast and best forwarder selection protocol (UMB-BFS). The improved twin delay deep deterministic policy gradient (IT3DPG) method is used to select the target region for emergency message distribution after route selection. Clustering is conducted using modified density peak clustering (MDPC). Improved firefly optimization (IFO) is used for optimal path selection. In this way, all emergency messages are quickly disseminated to multiple directions and also manage the traffic in VANET. Finally, we plotted graphs for the following metrics: throughput (3.9 kbps), end-to-end delay (70), coverage (90%), packet delivery ratio (98%), packet received (12.75 k), and transmission delay (57 ms). Our approach’s performance is examined using numerical analysis, demonstrating that it performs better than the current methodologies across all measures. Full article

In most filtering algorithms involving measurement data association, handling the complex computations due to multiple hypotheses is necessary. This paper introduces a novel Trajectory Poisson Multi-Bernoulli (TPMB) filter for tracking extended targets, facilitated by a synergy between the model and the data. This filter can track extended targets under unknown process and measurement noise. Initially, on the model-driven side, we compute multi-model transition probabilities using the posterior probabilities from models at two consecutive time points with the targets in high maneuverability state. The accuracy of the tracking algorithm is improved by calculating the improved Interacting Multiple Model (IMM) transition probability at each time step. For the data-driven aspect, the Gate-control Belief Propagation (GBP) is set in the message- passing algorithm to reduce the running time of false hypothesis associations. Thus, it is unnecessary to consider all message information when computing the likelihood matrix for target-measurement associations. Subsequently, the posterior density function of the Adaptive Square Root Cubature Kalman Filter (ASCKF) is constructed to adaptively estimate unknown process and measurement noises, while importance sampling in the current particle filter further mitigates particle degradation. Experiments demonstrate that our algorithm reduces the running time of data associations, alleviates particle degradation, and more accurately tracks maneuvering targets under nonlinear conditions and estimates their states. Full article

With the gradual increase in the proportion of new energy sources in the power grid, there is an urgent need for more flexible resources to participate in short-term regulation. The impact of communication network channel quality will continue to magnify, and factors such as communication latency may directly affect the efficiency and effectiveness of resource regulation. In this context of a large number of flexible demand-side resources accessing the grid, this article proposes a bidirectional channel delay measurement method based on MQTT (Message Queuing Telemetry Transport). It can effectively evaluate the real-time performance of communication links, considering that resources mainly access the grid through the public network. Subsequently, focusing on two typical types of resources on the demand side, namely, split air conditioners and central air conditioners, this article proposes an assessment method for correcting the response capabilities of air conditioning resources considering communication latency. Experimental simulations are conducted, and the results demonstrate that under given communication conditions, this method can more accurately estimate the response capability of air conditioners. This provides a basis for formulating more reasonable scheduling strategies, avoiding excessive or insufficient resource regulation caused by communication issues, and aiding the power grid in achieving precise scheduling. Full article

Android phones are widely recognised as the most popular mobile phone operating system. Additionally, tasks like browsing the internet, taking pictures, making calls, and sending messages may be completed with ease in daily life because of the functionality that Android phones offer. The number of situations in which users are harmed by unauthorised access to data emerging from these processes is growing daily. Because the Android operating system is open source and generated applications are not thoroughly reviewed before being released onto the market, this scenario has been the primary focus of hackers. Therefore, technologies to distinguish between malware and benign Android applications are required. CNN-based techniques are proven to produce important and successful outcomes when applied to Android malware detection on images. The CICMalDroid 2020 dataset, which is currently utilised in the literature, was used for this purpose. The features of the apps in the dataset were obtained using the AndroPyTool tool, and faster analysis files of 17,089 Android applications were obtained using the parallel execution technique. Permissions, intents, receivers, and services were used as static analysis features in this article. After these features were obtained, as data preprocessing, the ones with a grand total equal to 1 for each feature in the whole dataset were excluded in order to exclude the features that were specially created by the applications themselves. For each of the features specified for each application, a comma-separated text was obtained according to the usage status of the application. The BERT method was used to digitise the pertinent texts in order to create a unique embedding vector for every feature. Following the digitisation of the vectors, picture files were produced based on the length of each feature. To create a single image file, these image files were combined side by side. Finally, these image files were classified with CNNs. Experimental results were obtained by applying CNNs to the dataset used in the study. As a result of the experiments, a CNN with two outputs provided the highest performance with an accuracy of 91%, an F1-score of 89%, a Recall of 90%, and a Precision of 91%. Full article

Currently, cryptographic hash functions are widely used in various applications, including message authentication codes, cryptographic random generators, digital signatures, key derivation functions, and post-quantum algorithms. Notably, they play a vital role in establishing secure communication between servers and clients. Specifically, servers often need to compute a large number of hash functions simultaneously to provide smooth services to connected clients. In this paper, we present highly optimized parallel implementations of Lightweight Secure Hash (LSH), a hash algorithm developed in Korea, on server sides. To optimize LSH performance, we leverage two parallel architectures: AVX-512 on high-end CPUs and NVIDIA GPUs. In essence, we introduce a word-level parallel processing design suitable for AVX-512 instruction sets and a data parallel processing design appropriate for the NVIDIA CUDA platform. In the former approach, we parallelize the core functions of LSH using AVX-512 registers and instructions. As a result, our first implementation achieves a performance improvement of up to 50.37% compared to the latest LSH AVX-2 implementation. In the latter approach, we optimize the core operation of LSH with CUDA PTX assembly and apply a coalesced memory access pattern. Furthermore, we determine the optimal number of blocks/threads configuration and CUDA streams for RTX 2080Ti and RTX 3090. Consequently, in the RTX 3090 architecture, our optimized CUDA implementation achieves about a 180.62% performance improvement compared with the initially ported LSH implementation to the CUDA platform. As far as we know, this is the first work on optimizing LSH with AVX-512 and NVIDIA GPU. The proposed implementation methodologies can be used alone or together in a server environment to achieve the maximum throughput of LSH computation. Full article

Background and Objectives: Vaccination is one means of SARS-CoV-2 prevention and control. However, despite the effectiveness of vaccination, adverse reactions continue to require vigilance and monitoring. The researchers emphasize the possibility that some of the reported side effects may be psychological in origin. Based on this hypothesis, the main goal of this study was to evaluate the emotional dispositions of healthcare workers who experienced emotions before vaccination and adverse reactions after vaccination. Materials and Methods: This study was conducted between February and May 2021 in the Kaunas Clinics of the University of Health Sciences. A total of 2117 employees of the clinic departments who were vaccinated with two doses of the Pfizer–BioNTech vaccine participated in this study. Statistical analysis was performed on the data using IBM SPSS Statistics^®. Results: Most participants (74.5%) experienced systemic (including local) adverse events; 16.5% experienced only local adverse events, and 9.1% experienced no adverse events. The frequency of systemic (including local) adverse events reduced with increasing age (p < 0.05). The main emotions that participants experienced before vaccination were anxiety (37.88%) and happiness (39.02%). Systemic (including local) adverse events occurred 1.26 times more frequently in women than men (77.44% vs. 61.6%, p < 0.05), while local adverse events occurred 1.4 times more often in male participants than in female participants (21.39% vs. 15.27%, p < 0.05). Among the respondents who did not experience adverse events, the most common emotion felt was happiness (25.5%), and most of the participants who experienced systemic (including local) adverse events felt anxiety (42.6%). Conclusions: The information about vaccination and potential adverse events should be targeted at younger persons. It is recommended that women, more than men, should receive professional counseling from psychologists or psychotherapists. The public dissemination of positive messages about the benefits and safety of vaccines prior to a vaccination campaign may alleviate the tension or anxiety felt regarding potential adverse events. Healthcare specialists—both those who work directly with vaccines and those who do not—should maintain a positive psychological attitude towards vaccination, as this can increase patient satisfaction with the benefits of vaccines. Full article

When communicating their CSR (Corporate Social Responsibility) activities many companies predominantly release positive information. However, relying solely on positive information holds potential risks. Brands are consequently confronted with the dilemma of whether to exclusively disclose positive details about their environmental CSR activities (i.e., one-sided messages) or opt for voluntary disclosure of positive and negative information (i.e., two-sided messages). Existing literature distinguishes moderately and highly relevant types of negative information. However, prior research has predominantly overlooked the investigation of highly relevant negative CSR information. Therefore, our primary objective is to explore the impact of two-sided messages encompassing highly relevant negative CSR information in comparison to one-sided messages. We conducted four online experiments in two countries with different brands (study 1 (Germany, Mercedes-Benz, n = 457); study 2 (Germany, Porsche, n = 431); study 3 (USA, Mercedes-Benz, n = 468) and study 4 (USA, Tesla, n = 465)). The results reveal that two-sided messages with the disclosure of highly relevant negative CSR information lead to negative effects in comparison to one-sided messages with only positive information. Consequently, brands should exercise caution in communicating highly relevant negative CSR aspects. Our findings offer notable theoretical insights and practical implications. Full article

Background: Rehabilitation programs applied in cases of COVID-19-related stroke should counteract not only the effects of the stroke but also the effects of long-term COVID-19. As the molecular processes underlying these cases are still not fully understood, and evidence-based clinical outcomes are scarcely documented, there is a valid need to gather information and develop rehabilitation strategies for these patients. The risks, already clarified in the case of stroke, need to be assessed taking into account the coincidence of the two diseases. Endothelial injuries and emboli that develop after the hypercoagulable state of COVID-19 may take longer to heal, and complications may occur during exercise. This case study attempts to determine what the rehabilitation of a COVID-19-related stroke patient should include. The participant was a 64-year-old male with ischemic right middle cerebral artery stroke, left-side hemiplegia, and middle cerebral artery stenosis, and the CT showed a well-defined area of hypoattenuation in the basal ganglia territory involving the right lentiform nucleus, the anterior and posterior limbs of the internal capsule, and the dorsal part of the external capsule. His NIHSS score was 14, and he registered 15 points on the Barthel index. The patient had a COVID-19 infection two weeks before the stroke event. Methods: Conventional physical therapy was combined with adaptive ballistic strength training, a high-intensity interval training regimen, and manual treatment for myofascial release throughout the chronic recovery phase. Our primary goals were gait rehabilitation, muscle strengthening, weakness management, as well as spasticity reduction, while three different rehabilitation approaches were adopted in a single rehabilitation program to improve the outcome and long-term functional recovery of the patient. Results: The patient progressed in almost every aspect of the assessment criteria. This combined approach’s main success was improved gait speed, gait quality, and improved cardiovascular fitness. Take-away message: In the case of a stroke caused by COVID-19, where the endothelium cells are compromised, HIIT may be questionable due to the poor vascular condition. Based on our results, the low-volume HIIT approach proved appropriate and effective. Full article