Search Results (260)

Unmanned Surface Vehicle (USV) clusters operating in maritime environments face dynamic communication conditions, including varying sea states, electromagnetic interference, and satellite denial, that render static communication topologies suboptimal. Existing approaches assess link quality through single indicators, typically the SNR, and lack mechanisms for automatic topology adaptation. This paper presents a multi-layer adaptive communication framework that achieves a mean communication quality score of 0.72 (vs. 0.51–0.66 for baselines), a message delivery rate of 94.1% under benign conditions, and a failure recovery time of 3.2 s (vs. 5.8–8.4 s for baselines) across five communication failure scenarios. The framework integrates three layers: a weighted multi-indicator communication quality metric fusing the SNR, packet loss rate, latency, and link stability into a unified score; a topology utility function that selects among centralized, distributed, and hierarchical topologies by optimizing a quality–threat–overhead objective; and a multi-modal backup communication manager with physics-based underwater acoustic, optical line-of-sight, and multi-hop relay fallback modes. Simulation results demonstrate consistent improvements over single-indicator and static-topology baselines, with particularly strong performance under satellite denial and jamming scenarios where multi-modal backup communication sustains delivery rates above 85% under simulated conditions. In summary, the framework demonstrates consistent improvements across all metrics (communication quality, delivery rate, recovery time) relative to four baselines, with the largest gains observed under the most challenging conditions (satellite denial and jamming). We emphasize that the framework adaptively selects among pre-defined canonical topologies (star, mesh, tree) based on real-time conditions rather than synthesizing optimal topologies de novo—a distinction between topology management and topology optimization. Full article

Introduction: Prelacteal feeding, the practice of giving newborns substances other than breast milk within the first few days of life, remains a common yet harmful practice in many low- and middle-income countries, including Ethiopia. No evidence in Ethiopia indicates that mHealth can help improve prelacteal feeding. This study aimed to evaluate the effect of mobile health (mHealth) intervention on reducing prelacteal feeding practices and improving antenatal care (ANC) and postnatal care (PNC) utilization among mothers in South Ethiopia. Methods: A cluster-randomized controlled trial (CRT) was conducted in rural areas of South Ethiopia. A total of 20 clusters were selected using simple random sampling for intervention (mHealth) and control groups, each containing 340 women. Mothers in the intervention group received automated weekly SMS messages and reminders on exclusive breastfeeding, prelacteal feeding risks, ANC, and PNC. Mothers were only selected if they could read, write, and use mobile phones. Results: The mHealth intervention significantly reduced prelacteal feeding practice (AOR = 0.19, 95% CI: 0.06–0.58); p < 0.05). Higher ANC visits related to decreased prelacteal feeding (AOR = 0.28, 95% CI: 0.21–0.39; p < 0.001). The log count of ANC visit increased by 0.14 among intervention groups (IRR = 1.15, 95% CI: 1.06–1.25; p < 0.001). The PNC time was delayed 2.05 days among controls (β = −2.05, 95% CI: −2.66–−1.42; p < 0.001). Maternal and partner education, postnatal time, and ANC visits influenced prelacteal feeding. Conclusions: This finding might suggest that mHealth can reduce prelacteal feeding practices and improve maternal healthcare behaviors such as ANC attendance and timely PNC. These findings highlight the potential of mobile health interventions in promoting healthy maternal and infant practices in rural settings, where healthcare access is limited. Further research is needed to explore the long-term impacts of such interventions on maternal and child health outcomes. Multi-level analysis reduced variability. However, an unexplained variance could be reduced by including more cluster-level variables. Full article

Efficient Distributed Computing is still a major challenge, especially in networks composed of very-low-resource embedded systems, e.g., tiny microcontrollers deployed in sensor networks. This work will, firstly, address the design and implementation of event-driven and real-time capable low-resource Virtual Machines (VMs) tightly coupled to communication-centric systems, and secondly, address messaging and routing in mesh-grid networks. The distributed VM network herein forms one big virtual computer executing typically the same program on each node, but processing different data with different control states. The VM provides an integrated program code compiler and an optimized Bytecode processor. The programming language of the VM supports channel-based communication, multi-tasking, and event-based (asynchronous) data processing following the CSP model. The VM fits in microcontrollers with only a few kB of RAM and ROM. A major part of this work is dedicated to network messaging (supported by the VM, too) and routing in two-dimensional mesh-grid networks with a varying degree k of communication ports per node (connectivity degree k), and especially considering the odd but technical relevant case, k = 3, which introduces challenges in message routing that are solved herein. This study demonstrates the performance and suitability of our VM approach for distributed sensor networks performing distributed Machine Learning and clustering by using local sensor data only. Full article

Protein–ligand binding affinity prediction underpins structure-based drug discovery, yet random partitions of public benchmarks overestimate generalisation due to protein-family and ligand leakage, and the marginal value of explicit pocket-geometry descriptors over atom-level graph neural networks remains unclear. We computed a 59-dimensional discrete differential geometry descriptor on the ligand-aware solvent-excluded surface of 3285 PDBBind v2020 complexes, combining curvature distributions, the leading sixteen Laplace–Beltrami eigenvalues and a ten-point heat-kernel signature, and evaluated it in gradient-boosted tree pipelines across progressively stricter split regimes and two leak-proof external benchmarks, together with four mechanistically distinct injection strategies in a SchNet-style graph neural network. The descriptor lifted Pearson correlations by 0.111 on cluster-disjoint testing, 0.258 on LP-PDBBind DataSAIL S2 and 0.365 on CASF-2016, while in isolation reaching 0.456 to 0.594 on external benchmarks, on a par with X-Score and AutoDock Vina (version 1.2). TreeSHAP attribution localised the dominant signal to the heat-kernel signature. The four graph neural network injection strategies produced no statistically significant lift, indicating that distance-based message passing on atomic coordinates already captures much of the geometric content. Pocket-surface discrete differential geometry, therefore, offers an interpretable, leakage-robust and lightweight feature class for early-stage virtual screening, and motivates hybrid mesh-to-atom architectures. Full article

In remote areas where sugar beets are grown on a large scale, inadequate ground-based communication networks can easily lead to information silos in farmland, as well as technical challenges such as uneven node power consumption and short lifespans during the long-term operation of wireless sensor networks. To address these challenges, a real-time field environment monitoring system for sugar beet fields based on the Beidou satellite system and ZigBee wireless sensor networks has been developed, employing a three-tier architecture comprising a perception layer, a network layer, and an application layer. The system uses ARM as the core of the data acquisition nodes and integrates sensors for temperature, humidity, light intensity, atmospheric pressure, and dissolved oxygen with a Beidou positioning module. Field data are aggregated via a ZigBee mesh network and transmitted remotely using a dual-link Beidou short message protocol. To prevent uneven energy consumption in ZigBee networks, an improved energy-balanced routing algorithm, Energy-Balanced Low-Energy Adaptive Clustering Hierarchy (EB-LEACH), is proposed. By optimizing cluster head election, adaptive competition radius mechanisms, and inter-cluster multi-hop routing strategies through multi-factor weighting, the algorithm achieves a globally balanced distribution of network energy consumption. Our experimental tests demonstrate that, compared to the traditional LEACH protocol, this algorithm increases the number of rounds until the first node fails by 87.3%, extends the network half-life by 110.48%, and improves total packet delivery by 118.3%. Our test results indicate that the improved routing algorithm performs better, and the accuracy of the sensor measurements meets the practical requirements for environmental monitoring in sugar beet fields. Full article

Tobacco use remains a major public health challenge in Jordan, where cigarette smoking and waterpipe use are both common and dual use is increasingly prevalent. Community pharmacies are highly accessible healthcare settings, yet structured smoking-cessation services remain underutilized. This study evaluated the clinical effectiveness and implementation of Dual-Quit Digital, a pharmacist-delivered cessation counseling program tailored to the type of tobacco used, paired with a 6-month automated WhatsApp^® (Menlo Park, CA, USA) follow-up system. We conducted a pragmatic, two-arm, parallel-group, Hybrid Type 2 cluster randomized controlled trial in 16 community pharmacies in Jordan, randomized 1:1 to intervention or usual care. A total of 320 adult tobacco users were enrolled (160 per arm). The intervention combined a structured in-pharmacy pharmacist consultation, tailored behavioral support, phenotype-stratified pharmacotherapy support, and 6 months of semi-automated WhatsApp^® follow-up with telepharmacy escalation for predefined red-flag responses. The control arm received usual care, consisting of opportunistic brief advice and standard over-the-counter sales without proactive follow-up. The primary outcome was biochemically verified continuous abstinence at 6 months, defined as exhaled carbon monoxide (CO) < 10 ppm and analyzed using intention-to-treat principles. Secondary outcomes included 7-day point prevalence abstinence (PPA) at 3 and 6 months, 30-day PPA at 6 months, both-product abstinence among baseline dual users, pharmacotherapy uptake and adherence, and implementation-relevant outcomes, including service reach, feasibility of recruitment, and digital engagement metrics. All 16 pharmacies were retained, and all 320 randomized participants were included in the intention-to-treat analysis. At 6 months, CO-verified continuous abstinence was achieved by 26.3% of participants in the intervention arm compared with 11.3% in the control arm (adjusted odds ratio [aOR] 2.84, 95% CI 1.55–5.18; p < 0.001). The intervention also improved 7-day PPA at 3 months (33.1% vs. 15.6%; aOR 2.68, 95% CI 1.56–4.60; p < 0.001), 7-day PPA at 6 months (30.6% vs. 14.4%; aOR 2.62, 95% CI 1.48–4.62; p = 0.001), and 30-day PPA at 6 months (28.1% vs. 11.9%; aOR 2.89, 95% CI 1.59–5.24; p < 0.001). Among baseline dual users, both-product abstinence was higher in the intervention arm (21.9% vs. 7.8%; aOR 3.30, 95% CI 1.12–9.75; p = 0.026). Pharmacotherapy initiation was more frequent in the intervention arm (72.5% vs. 28.1%; p < 0.001), as was self-reported adherence for at least 8 weeks among initiators (56.0% vs. 26.7%; p = 0.002). In the intervention arm, active patient response rates to scheduled WhatsApp^® messages remained substantial, with 88.1% responding at Week 1, 73.8% at Week 4, 67.5% at Month 3, and 61.3% at Month 6; 145 red-flag triggers were captured from 62 participants, and 84.1% of escalations resulted in successful pharmacist follow-up within 48 h. The Dual-Quit Digital model significantly improved smoking-cessation outcomes compared with usual care and proved operationally feasible. These findings support integrating phenotype-stratified pharmacist counselling, pharmacotherapy support, and low-burden digital follow-up as a pragmatic cessation model for Jordan and similar settings. Full article

This paper presents a parallel genetic algorithm (GA) for the planning of power distribution networks considering harmonics. Power distribution systems are generally operated in a radial configuration, supplemented by tie switches that enable network reconfiguration during unexpected outages or planned maintenance. They can also include distributed generators (DGs), capacitor banks (CBs), and soft open points (SOPs) to lower distribution losses and improve the voltage profile. Some of the loads and DG units may be nonlinear, generating harmonic currents in the system, polluting the power, and increasing losses. This paper makes use of a parallel GA to find an optimized configuration, optimized location, and sizing of DGs, CBs, and SOPs to lower real power distribution losses while considering harmonics and the physical constraints of the network. The proposed algorithm uses a solution encoding based on the minimum spanning tree to guarantee the radial topology of candidate solutions. It uses the backward–forward power flow method to compute the fundamental voltages and a decoupled harmonic power flow for the harmonic components. The algorithm is parallelized on a small computer cluster using the Message Passing Interface (MPI) to reduce its execution time. The proposed solver is validated on distribution systems ranging from 16 to 880 buses. The results show that simultaneously optimizing the topology, the DGs, the CBs, and the SOPs results in reducing power losses by 37% to 93%, improving the overall efficiency of the distribution system. The parallelization using MPI allows for a 90.9× speedup on a 96-core cluster. Full article

Background: Despite the availability of safe vaccines, Ethiopia’s human papillomavirus (HPV) vaccine uptake remains suboptimal, particularly among out-of-school girls (OOSGs). This study examines the effect of multi-channel demand generation messages in two districts to determine which interventions most effectively improve uptake. Methods: A convergent mixed-methods design was employed across four districts in the Somali and South Ethiopia regions, with Jigjiga and Derashe serving as intervention sites and Gode and Kolango Zuria as controls. For the quantitative component, 950 sample households were recruited using cluster sampling. The qualitative inquiry involved 27 in-depth interviews (IDIs) and 16 focus group discussions (FGDs) within the intervention sites. Results: A total of 950 caregivers and 1134 girls completed the survey. Awareness was significantly higher among caregivers (AOR: 4.42; 95% CI: (3.06, 6.39)) and girls (AOR: 7.63; 95% CI: (3.49, 16.67)) in intervention sites, as well as among in-school girls (AOR: 13.46; 95% CI: (4.09, 41.90)). The mean vaccination coverage reached 71%, with significantly higher rates in intervention sites (AOR: 4.07; 95% CI: (2.29, 7.23)) and among in-school girls (AOR: 47.16; 95% CI: (20.23, 109.9)). Interpersonal communication—via teachers, peers, community health workers and vehicle-mounted promotion—was more effective in influencing awareness, attitude and uptake. Barriers for OOSGs included limited access to vaccination sites, low campaign awareness, misconceptions and gender-related issues. Conclusions: Appropriate demand generation strategies effectively enhance HPV awareness and vaccine uptake, yet a significant equity gap remains, as only one-third of OOSGs received the vaccine compared with 85% of in-school girls. Targeted interventions are recommended for OOSGs focused on both access to service and context-specific demand creation to address this disparity. Full article

There are numerous ways in which generative artificial intelligence (GAI) can be applied in the teaching and learning process. This paper presents one application in the Business Decision Analysis (BDA) course. BDA is considered as the most challenging course in the Graduate Study Program in Economic Entrepreneurship at the University of Zagreb Faculty of Organisation and Informatics; consequently, the teachers continuously analyse possibilities to make the course more attractive for students. The innovative teaching activity at BDA was implemented as a betting shop during the first colloquium (which accounts for 50% of the overall grade). In the activity, GAI analysed learning management system (LMS) data of students’ results (attendance, self-assessment test results, logs in the system) of the initial (pre-course) test, as well as their results of the pub quiz (activity organised a week before the colloquium as a preparatory activity). GAI analysed all the data and predicted the number of points each student will achieve. Additionally, GAI calculated the risk index, average growth (among self-assessment tests) and learning consistency for each student. Finally, GAI created a message for each student that explained what went well in their learning activity, what could be improved, and included a motivational note for the test. The rule was: if a student achieved a higher result than the GAI predicted, the teacher would buy a chocolate for that student. More than 60% percent of students achieved a higher score than was predicted. Surprisingly, exceeding the expected result was not in correlation with the risk indices determined by the GAI. Cluster analysis identified four student profiles consistent with the correlation results, showing weak overall agreement between the predicted and achieved scores, except in the male subgroup, while higher predicted scores were associated with higher average growth and lower risk indices. Qualitative analysis of the GAI application in teaching yielded positive comments, as students perceived the activity as helpful, motivating, and engaging, and would have liked more similar activities. 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

The proliferation of generative artificial intelligence (AI) as an autonomous recommendation agent fundamentally challenges traditional paradigms of marketing communication. As AI systems increasingly mediate consumer–brand relationships, understanding how artificial agents construct persuasive discourse—distinct from human communicators—becomes critical for developing effective dual-channel marketing strategies. Grounded in Source Credibility Theory and the Computers Are Social Actors (CASA) paradigm, this study investigates the semantic and structural divergence between AI-generated product recommendations and human influencer marketing messages in social commerce contexts. Employing a mixed-methods computational approach integrating term frequency analysis, TF-IDF weighting, Latent Dirichlet Allocation (LDA) topic modeling, and BERT-based contextualized semantic embedding analysis (KR-SBERT), we examined 330 Instagram influencer posts and 541 AI-generated responses concerning inner beauty enzyme products—a hybrid category combining functional health claims with hedonic beauty appeals—in the Korean social commerce market. AI-generated responses were collected through a systematically designed query protocol with empirically grounded prompts derived from actual consumer search behaviors, and analytical robustness was verified through sensitivity analyses across multiple parameter thresholds. Our findings reveal a fundamental divergence in persuasive architecture: human influencers construct experiential narratives exhibiting message characteristics typically associated with peripheral-route cues (sensory descriptions, emotional testimonials, social context), while AI recommendations employ systematic, evidence-based discourse exhibiting message characteristics typically associated with central-route argumentation (functional mechanisms, ingredient specifications, objective criteria). Topic modeling identified four distinct thematic clusters for each source type: human discourse centers on embodied experience and relational consumption, whereas AI discourse organizes around informational utility and rational decision support. Jensen–Shannon Divergence analysis (JSD = 0.213 bits) confirmed moderate distributional divergence, while chi-square testing (χ² = 847.23, p < 0.001) and Cramér’s V (0.312, indicating a medium-to-large effect) demonstrated statistically significant and substantively meaningful differences. These findings extend CASA theory by demonstrating that AI recommendation agents develop a characteristic “AI communication signature” distinguishable from human persuasion patterns. We propose an integrated Dual-Agent Persuasion Proposition—synthesizing CASA, ELM, and Source Credibility perspectives—suggesting that AI and human recommenders serve complementary functions across different stages of the consumer decision journey—a proposition whose predictions regarding sequential persuasive effectiveness and consumer processing routes await experimental validation. These findings carry implications for AI content strategy optimization, platform design, and emerging regulatory frameworks for AI-generated content labeling. Full article

Organizations communicate across many channels, yet official websites remain a controlled, authoritative space where firms articulate identity and strategy. This study examines how Croatia’s top enterprises (n = 100) describe themselves on their websites and which emotional tones they use to signal strategic intent. Our goal is to identify recurring strategic signifiers and map distinct sentiment profiles in corporate narratives. We compiled company descriptions from official sites; texts were originally in Croatian and machine-translated into English, and all analysis was conducted on the English corpus. Using lexicon-based sentiment methods (AFINN, Bing, NRC), we quantified polarity and discrete emotions, aggregated scores at the firm level, and applied k-means clustering to normalized emotion vectors. Results show a consistent emphasis on mission–vision–values language and a dominance of positive emotions—especially trust and anticipation. We interpret, based on cluster exemplars, that higher trust/anticipation tones can function as soft governance cues, while transparency about negatives characterizes an issue-addressing regime without eroding overall positivity. Cluster analysis reveals three stable profiles: optimistic consumer-oriented narratives, transparent issue-addressing messaging, and low-affect technical descriptions. We conclude that sentiment profiling offers a practical audit tool for aligning website copy with stakeholder expectations and governance communication, supporting benchmarking, and future tests linking narrative tone to investor behavior and firm performance. Full article

Centralized agricultural data platforms raise concerns about ownership, provenance, and vendor lock-in, motivating decentralized alternatives. This study evaluates BigchainDB as a blockchain-database hybrid for owner-controlled precision agriculture data sharing. We address three research questions: (1) functional feasibility for data integrity, access control, and heterogeneous sensor integration; (2) integration patterns bridging IoT ingestion with blockchain consensus; and (3) operational trade-offs versus centralized alternatives. A proof-of-concept implementation comprising a sensor simulator, FastAPI middleware, and three-node BigchainDB cluster demonstrates end-to-end data flow with cryptographic provenance. Key contributions include the following: identification of three integration patterns (message queue buffering for high-throughput ingestion, hierarchical asset modeling, and dual-key access control); comparative analysis against five blockchain-database alternatives; and characterization of deployment complexity. Results show BigchainDB satisfies the functional requirements for data integrity and access control, while requiring increased operational overhead compared to single-node databases. The architecture is viable when multi-party governance outweighs operational simplicity, though production deployments require further scalability validation, including detailed performance benchmarking. Full article

(1) Background: The incidence and impact of misleading information on public opinion in the field of nutrition and food science, focusing on the mechanisms of dissemination and their potential consequences, are increasingly being explored in academia. It is therefore essential to highlight the importance of studying discourse to understand the contexts and motivations behind the persistent circulation of hoaxes. For this reason, this research compiles and analyses the news content on food fake news published in the web repository of Spanish information verifiers Newtral and Maldita Alimentación. (2) Method: The period analysed extends from the launches of both platforms (2018 and 2021, respectively) to 2024, examining a total of 564 news items using computerised analysis software. (3) Results: The results show three thematic clusters related to the information refuted by Newtral and five clusters belonging to Maldita Alimentación. The findings of this research are consistent with a prevalence of concern for public health; the risk of disease due to poor food management; the role of authorities, especially in the European context; the supervision of food quality and the protection of public health; and the debunking of messages about food properties without scientific evidence. (4) Conclusions: The article highlights the importance of implementing strategies that foster trust in information sources, such as fact-checkers, and encourage the scientific dissemination of food-related content in an accessible manner. Full article

Community detection in graphs can be viewed as the estimation of a partition map that remains stable under admissible perturbations of graph topology and node attributes. While modern graph neural networks (GNNs) achieve strong empirical accuracy, they often exhibit severe assignment drift under minor perturbations, leading to illusory community structures. In this work, we propose DISPEL-GNN, a stability-aware graph learning framework that integrates spectral operator regularization, Bayesian uncertainty modeling, and risk-aware dynamic attention for perturbation-bounded community detection. The model explicitly constrains graph operators through uniform spectral norm bounds, high-frequency energy suppression, and commutator alignment while dynamically modulating message passing based on node-level spectral risk and epistemic uncertainty. We further formalize instability via assignment of drift functional and establish perturbation bounds linking drift to operator norms and spectral gaps, complemented by a PAC-Bayesian generalization guarantee. Extensive experiments on real-world benchmarks including Cora, Citeseer, Pubmed, Cora-Full, and DBLP demonstrate that DISPEL-GNN consistently reduces assignment drift by 18–35% under feature noise and edge perturbations while improving clustering quality with up to +3.0 NMI and +0.04 ARI compared to strong baselines such as GAT and Bayesian GNNs. The normalized mutual information (NMI), adjusted Rand index (ARI), and PAC-Bayesian (PAC) constraints serve as evaluative and theoretical instruments in this study. Additional studies on synthetic graphs with controlled spectral gaps confirm that the proposed method maintains stable community assignments in low-gap regimes where classical spectral and GNN-based methods degrade sharply. These results establish DISPEL-GNN as a mathematically grounded and practically effective framework for robust and interpretable community detection. A metric-wise dominance analysis shows that DISPEL-GNN achieves metric-wise dominance across most accuracy and robustness criteria, with minor tradeoffs in modularity on selected datasets. These results indicate that explicitly modeling stability and uncertainty provides a principled pathway toward reliable and interpretable community detection in noisy graph environments. Full article