BackgroundThree-Dimensional Distance and Coverage Mapping (DM and CM) generated through weightbearing CT (WBCT) can aid in understanding the complex articular relationship in hallux valgus (HV). Prior studies have demonstrated that subluxation can occur at the hallux metatarsophalangeal (MTP) and metatarsosesamoid articulation with greater deformities. Little is known however about these parameters at the level of the tarsometatarsal (TMT) joint, particularly as it relates to the concept of TMT instability as a cause for HV. The purpose of this study is to evaluate DM and CM characteristics at the first TMT joint through WBCT in a cohort of HV patients relative to controls.MethodsTwenty-nine feet (14 left and 15 right) from 16 individuals (avg age of 56.6 ± 6.2 yrs) underwent WBCT. Twelve feet were clinically diagnosed with HV, without clinical evidence of TMT instability, and formed the experimental group, while the remaining 17 feet, absent of deformity, served as the control group. For distance maps, spatial relationship of the joint was assessed by sampling the surface of one bone and calculating the shortest distance to the opposing bone. A limit of 4 mm was set to ensure the analysis focuses on relevant anatomical gaps. For coverage maps, the region was defined along the longitudinal axis of the 1st metatarsal, with 10% of each end defined to be in contact with the opposing bone. The nearest distance was calculated for this region, with regions of 5 mm or less defined as covered and >5 mm defined as uncovered. Sagittal TMT 1 angle was calculated as a marker of TMT instability. Welch's t-test and Mann-Whitney U were used for statistical analysis.ResultsHallux valgus patients demonstrated significantly increased coverage along the dorsal lateral and plantar lateral quadrant of the MTP joint relative to controls (28.59%, P=.002 and 14.47%, P=.007, respectively) and decreased coverage along the dorsal medial and plantar medial quadrant (-42.63%, P=.002 and -46.69%, P<.001, respectively). No differences in distance mapping at the MTP joint or contact and distance mapping at the TMT joint were observed between groups (Table 1). Sagittal TMT 1 angle was higher in the HV group (1.36° vs 0.6°, P=.02).DiscussionThere is notable discrepancy in coverage at the MTP joint in HV patients relative to those without HV, consistent with subluxation of the hallux MTP joint in HV. Contrary to our hypothesis, no discernable difference was noted at the TMT joint with respect to DM and CM. While sagittal plane angulation was higher in HV, it did not result in appreciable changes in contact and distance mapping at the TMT joint.Level of EvidenceIII: Retrospective case control.

Geographic disparities in hepatitis B vaccine coverage across Africa: Implications for targeted interventions and 2030 goals.

This study develops and applies a Climate–Water–Health (CWH) Nexus Index to compare multi-dimensional risk trajectories across six African Least Developed Countries, namely, Chad, Democratic Republic of Congo, Lesotho, Madagascar, Niger, and Togo, each representing major climatic regions. Using decadal averages for 2000–2009 and 2010–2020, the study constructs three sub-indices—Climate Risk Index, Water Insecurity Index, and Health Burden Index—and then aggregates them into a composite CWH index. Indicators are harmonized via min–max normalization, and water and health measures are expressed per 100,000 population to ensure cross-country comparability under differing population sizes. The results of the study indicate substantial heterogeneity in both levels and drivers of nexus risk. The CWH risk decreased in most countries from the 2000s to the 2010s, while relative positions shifted as improvements occurred unevenly across dimensions. Sensitivity analysis with equal and dimension-focused weights confirms that core country groupings and extremes are robust to plausible weighting schemes. External consistency checks show a strong negative Pearson correlation between the standard CWH and the Human Development Index in both decades, indicating that higher human development is associated with lower Nexus risk. The proposed framework is transparent, scalable, and suitable for extension to broader African coverage and subnational mapping.

Efficient RFID spatial exploration in dynamic warehouse environments is challenging due to occlusions, sensing geometry constraints, and the weak coupling between information acquisition and navigation decisions. Many existing inventory robots treat RFID sensing as a passive data source during exploration, without explicitly optimizing sensing pose or prioritizing inventory-driven frontiers, which can result in incomplete coverage and redundant traversal. This paper presents ArmTenna, an articulated mobile robotic platform that formulates RFID inventory exploration as an active perception problem. The system integrates dual 4-DOF robotic arms carrying directional UHF RFID antennas and a 2-DOF neck-mounted RGB-D camera, enabling adaptive interrogation of candidate regions. We propose a multi-modal frontier exploration framework that combines newly detected EPC tags, average RSSI values, and vision-based product detections into a composite utility function for goal selection. By embedding articulated antenna control directly into the frontier evaluation loop, the robot tightly couples sensing geometry with exploration decisions. Experimental validation with 150 tagged items across three separated warehouse zones shows that ArmTenna achieves up to 97% map coverage, compared to 72% for a baseline platform, while reducing missed-tag regions. These results demonstrate that integrating active sensing pose control with multi-modal frontier evaluation provides an effective and scalable solution for RFID-driven warehouse inventory automation.

Access to mobile cell phone service is an important factor in our response to natural hazards. Mobile phones are crucial tools for facilitating communication between those affected by a disaster and first responders or family members. However, damage to physical network components and power outages can lead to cell phone service outages during and after a natural disaster. To better understand the risk of cell phone service outages, we propose a novel framework to estimate areas within a community most likely to lose cell phone service during a natural hazard. Using only publicly available data on cell network infrastructure, we generate a nominal coverage map with a genetic algorithm, expanding on prior attempts to model cell networks under hazards, which have used entirely synthetic networks with fixed coverage polygons. We apply a hazard event to the generated network using Monte Carlo simulation and measure the resulting outages across our simulated scenarios. We demonstrate this framework using an example of Ann Arbor, MI (USA) under high wind scenarios. This work improves on prior attempts to model cell phone network vulnerability by incorporating real tower and antenna data and by explicitly modeling overlapping service areas rather than using stylized hexagon service polygons. This framework can help decision makers prioritize vulnerable areas for backup power and temporary cell service hotspots during and after hazard events.

High-resolution maps of vaccination coverage are valuable for uncovering heterogeneities in coverage to inform vaccine delivery strategies. Coverage maps stratified by age can reveal additional heterogeneities in the timeliness of vaccination and critical immunity gaps among birth cohorts. Here, we propose a spatially varying coefficient model relying on a Bayesian approach for age-structured mapping of vaccination coverage using geolocated individual level household survey and geospatial covariate data. Our flexible modelling framework includes parameterizations capturing spatial (non-)stationarity in differences in coverage between age groups, as well as a modification to allow coverage mapping for single age points through the inclusion of a smoother over age. The proposed models are fitted using the INLA-SPDE approach implemented in the inlabru package in R. We choose between competing model parameterizations by examining their out-of-sample predictive performance via cross-validation and using Bayesian model choice criteria. The methodology is applied to age-structured mapping of measles vaccination coverage in Cote d'Ivoire using the 2021 Demographic and Health Survey. Our results reveal a significant delay in measles vaccination in the first year of life and substantial spatial differences in coverage by age, highlighting the need for targeted interventions to achieve equity and attain vaccine-derived immunity goals.

The increasing digitization of healthcare has led to vast amounts of clinical data, much of which remains underutilized for research. While Health Level Seven (HL7) Fast Healthcare Interoperability Resources (FHIR) improves interoperability in clinical care, it's primarily designed for real-time data exchange to support diagnosis and treatment, rather than for secondary use of health data. As a result, transforming FHIR data into standardized models such as the Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM) remains a challenge. This study employs TermX, an open-source terminology and data interoperability platform designed to enhance health data interoperability and support knowledge management. This allowed us to create bidirectional transformation rules between FHIR and OMOP CDM. Using the Design Science methodology, we developed and validated a set of standardized transformation rules that support bidirectional mapping of vital signs data between FHIR and OMOP CDM. In these transformations we used synthetical FHIR JSON data, focusing on five main resources-Observation, Patient, Encounter, Organization, and Practitioner. The focus of this work is primarily on methodological mapping rather than processing real-world datasets; the evaluation concentrates on mapping coverage, i.e., the proportion of FHIR elements that can be reliably transformed into OMOP CDM structures and vice versa. The resulting rules achieved 74% mapping coverage from FHIR to OMOP CDM tables, with unmapped elements primarily related to structural discrepancies. Mapping from OMOP CDM to FHIR reached approximately 23% coverage, capturing mostly values that were previously mapped from FHIR to OMOP CDM. These percentages reflect variations in the standards' structure and granularity. The application of TermX shows the feasibility of reusable, standards-based transformations that support the secondary use of real-world clinical data for medical research and analysis. By addressing key technical and semantic interoperability challenges, this work contributes to advancing digital health interoperability and supports the objectives of the European Health Data Space.

Abstract. Velocity models of the shallow subsurface (a few hundred meters) are important in near-surface characterization, improving seismic mapping resolution at depth, and constraining deeper geological models. It is therefore interesting to retrieve them from deep seismic exploration data. We compute the near-surface shear wave velocity model in the vicinity of South Deep Gold Mine, using surface waves present in the small-offset 2D and 3D seismic reflection data acquired between 2022 and 2023 at the mine for research, mine planning, and development purposes. The obtained near-surface model is then used to (1) characterize the near-surface, and (2) better constrain the interpretation of possible water preferential flow-pathways (faults, fracture zones, and dykes) mapped at mining levels, that enable the migration of water from overlying aquifer systems (&lt; 0.5 km depth) to the mining levels (∼ 3 km depth). The analysis is carried-out on reflection seismic data acquired for deep mineral exploration, where the acquisition parameters were not optimized for surface wave techniques and the reciprocity principle is used to improve the data density, coverage, and near-surface mapping resolution. The lithostructural information retrieved from the produced pseudo-2D and 3D shear wave velocity models is consistent with information obtained from available surface borehole data and published records in the study area. To investigate the structural linkage between the deep mining levels and shallow groundwater aquifers, we integrated the near-surface shear wave velocity model produced from the small-offset 2D and 3D reflection seismic data with the large-offset 2003 3D reflection seismic data, and geological structures derived from underground mapping, and exploration drilling. The shear wave velocity models help define the faults, fractures, and dykes that compartmentalize the near-surface groundwater aquifer systems. The large-offset legacy 2003 3D seismic data, underground mapping, and exploration drilling provide a better definition of the orebody and its offsets (e.g., faults) at the mining level. The integrated data show that several geological structures (e.g., faults and dykes), defined by legacy seismic data, underground drilling, and mapping, cross-cut the mining levels at ∼ 3 km depth and intersect the near-surface aquifers, thus making these structures possible preferential flow-pathways for water migration to the deep mining levels. The results of the interpretation illustrate the advantages of integrating shallow and deep subsurface information to constrain the timing of geological events and mitigate the risks associated with water ingress to the mining levels. The final model produced can be used for future mine development, improving safety and production, and for the extension of the Life of Mine (LoM).

Despite over two decades of Community-Directed Treatment with Ivermectin (CDTI), onchocerciasis transmission persists in localized pockets in Ghana, particularly in the Kwanware-Ottou community within the Wenchi Health District. This study trialled a scalable approach to identifying context-specific barriers and solutions for improving CDTI effectiveness. A mixed-methods approach was employed, including Geographical Information System mapping, community consultation, census and treatment coverage evaluation, and qualitative assessments. These informed the participatory development of an Action Plan, which was implemented and evaluated across three sub-districts. Key challenges identified and addressed included poor data quality, high population mobility, remote settlements with accessibility issues, limited awareness, and inadequate number and deployment of community drug distributors. As a result, therapeutic coverage increased from 70.8% to 88.2. Seven out of eight communities with pre-intervention coverage below the recommended 65% threshold not only achieved but exceeded this target. Ultimately, all communities met the coverage goal. The intervention also improved data accuracy and quality, community engagement, and adherence to directly observed treatment, while addressing systemic gaps in CDTI delivery. This study demonstrates that a coordinated, locally adapted stimulus package can significantly enhance CDTI performance in areas of persistent onchocerciasis transmission. The approach presents a scalable model for similar endemic settings and aligns with the World Health Organization's 2021-2030 Roadmap for the elimination of Neglected Tropical Diseases.

Efficient autonomous exploration in unknown environments is a core challenge for Unmanned Aerial Vehicle (UAV) applications in unstructured settings. The primary challenges are exploration speed, coverage efficiency, and the autonomous, efficient, and obstacle-/threat-avoiding global guidance of UAV under local observational information. This paper proposes an autonomous exploration method driven by simultaneous incremental map prediction and the fusion of global frontier information to enhance the exploration efficiency of UAVs in unknown unstructured environments. Based on generative deep learning, we introduce an incremental map prediction method for 3D unstructured mountainous terrain, enabling the simultaneous acquisition of map predictions and their uncertainty estimates. Map prediction and trajectory planning are conducted concurrently: by utilizing the simultaneously predicted 3D map and its confidence (i.e., the uncertainty estimates), an overlap analysis is conducted between the flyable areas in the predicted map and the high-confidence regions. Dynamic guidance subspaces are generated by extracting global frontier points, within which shortest-time optimization is adopted for trajectory planning to maximize information gain and coverage per step. Experimental results demonstrate that compared to classical methods, our proposed approach achieves significant performance improvements in key metrics, including map coverage rate, total exploration time, and average path length.

Onthophagus binodis is a coprophagous scarab beetle native to southern Africa. This species and many others in the tribe Onthophagini have been introduced to farms across multiple continents in the context of cattle pasture management efforts. The ecosystem services provided by this species, along with the amenability of comparative developmental and evolutionary studies in this clade, contribute to its role as an emerging insect model system. Here, we present sex-specific chromosomal-level genome assemblies for O. binodis generated from a combination of PacBio long reads and HiC chromatin conformation sequencing. The completeness of the 950.5 Mb female assembly and the 880.5 Mb male assembly is indicated by a contig length N50 of at least 58.6 Mb. BUSCO single-copy and duplicated completeness scores were 99.0% and 0.9% for the female assembly and 97.4% and 2.1% for the male assembly. Gene modeling identified at least 15,403 gene models in each genome with an average transcript length of 1.6 kb. Comparative analyses with other Onthophagini genomes indicated a dramatic expansion of repetitive sequences, which now comprise over 75% of this species' genome and have driven the expansion of overall genome size to nearly twice that of close relatives. We combined the best-assembled chromosome-scale scaffolds from each sex to generate a hybrid reference assembly for this species. Comparative genomic analyses show that the nine autosomes and the X chromosome identified here in O. binodis are likely conserved throughout Onthophagini. Our sex-specific sequencing approach allowed us to identify putative Y chromosome sequences in the male assembly via coverage mapping and k-mer abundance comparisons. These genomes will be of great value to the scientific community as resources for studying insect genome evolution, development, and ecology.

Autonomous unmanned aerial vehicles (UAVs) offer cost-effective and flexible solutions for a wide range of real-world applications, particularly in hazardous and time-critical environments. Their ability to navigate autonomously, communicate rapidly, and avoid collisions makes UAVs well suited for emergency response scenarios. However, real-time path planning in dynamic and unpredictable environments remains a major challenge, especially in confined tunnel infrastructures where accidents may trigger fires, smoke propagation, debris, and rapid environmental changes. In such conditions, conventional preplanned or model-based navigation approaches often fail due to limited visibility, narrow passages, and the absence of reliable localization signals. To address these challenges, this work proposes an end-to-end emergency response framework for tunnel accidents based on Multi-Agent Reinforcement Learning (MARL). Each UAV operates as an independent learning agent using an Independent Q-Learning paradigm, enabling real-time decision-making under limited computational resources. To mitigate premature convergence and local optima during exploration, Grey Wolf Optimization (GWO) is integrated as a policy-guidance mechanism within the reinforcement learning (RL) framework. A customized reward function is designed to prioritize victim discovery, penalize unsafe behavior, and explicitly discourage redundant exploration among agents. The proposed approach is evaluated using a frontier-based exploration simulator under both single-agent and multi-agent settings with multiple goals. Extensive simulation results demonstrate that the proposed framework achieves faster goal discovery, improved map coverage, and reduced rescue time compared to state-of-the-art GWO-based exploration and random search algorithms. These results highlight the effectiveness of lightweight MARL-based coordination for autonomous UAV-assisted tunnel emergency response.

Abstract Banking institutions face dynamic information security (IS) challenges, requiring a balance between stringent confidentiality and privacy mandates and the operational demands of digital banking. Recent research has contributed significantly to this domain through three key strands: (i) the development and validation of an ISO/NIST-aligned framework for assessing confidentiality and privacy in bank security policies, (ii) a systematic review of IS policy risks, benefits, and emerging trends across U.S. and global banking sectors, and (iii) the proposal of an integrated cyber-risk management framework tailored for online banking environments [1-3]. Building on these foundations, this paper introduces a unified approach that bridges policy evaluation with technical risk assessment and treatment [4, 5]. The proposed model integrates multiple layers: policy conformance checks against ISO 27001 and NIST SP 800-series standards, threat modeling using STRIDE and TVRA methodologies, vulnerability classification aligned with OWASP and CWE taxonomies, and iterative risk scoring and treatment cycles. This holistic design addresses the persistent gap between “written policy” and operational security controls in digital channels. Empirical findings—such as variability in confidentiality and privacy readiness among banking institutions and the influence of regulatory and cultural factors on compliance—inform the model’s architecture and adoption strategies [6]. Implementation guidance includes structured steps, governance checkpoints, and measurement artifacts such as maturity indices and control coverage maps. These tools enable banks to progress from policy alignment toward demonstrable control effectiveness and, ultimately, from static compliance to continuous assurance. By linking governance frameworks with technical safeguards, this approach enhances resilience against evolving cyber threats while ensuring regulatory conformity and customer trust.

Abstract Banking institutions face dynamic information security (IS) challenges, requiring a balance between stringent confidentiality and privacy mandates and the operational demands of digital banking. Recent research has contributed significantly to this domain through three key strands: (i) the development and validation of an ISO/NIST-aligned framework for assessing confidentiality and privacy in bank security policies, (ii) a systematic review of IS policy risks, benefits, and emerging trends across U.S. and global banking sectors, and (iii) the proposal of an integrated cyber-risk management framework tailored for online banking environments [1-3]. Building on these foundations, this paper introduces a unified approach that bridges policy evaluation with technical risk assessment and treatment [4, 5]. The proposed model integrates multiple layers: policy conformance checks against ISO 27001 and NIST SP 800-series standards, threat modeling using STRIDE and TVRA methodologies, vulnerability classification aligned with OWASP and CWE taxonomies, and iterative risk scoring and treatment cycles. This holistic design addresses the persistent gap between “written policy” and operational security controls in digital channels. Empirical findings—such as variability in confidentiality and privacy readiness among banking institutions and the influence of regulatory and cultural factors on compliance—inform the model’s architecture and adoption strategies [6]. Implementation guidance includes structured steps, governance checkpoints, and measurement artifacts such as maturity indices and control coverage maps. These tools enable banks to progress from policy alignment toward demonstrable control effectiveness and, ultimately, from static compliance to continuous assurance. By linking governance frameworks with technical safeguards, this approach enhances resilience against evolving cyber threats while ensuring regulatory conformity and customer trust.

Abstract Background Bacterial genotyping can support outbreak investigations including skin and soft tissue infections point source outbreaks caused by Mycobacterium abscessus. This study presents an innovative approach that combines Nanopore long-read and Illumina short-read whole-genome sequencing (WGS) data using a novel bioinformatic pipeline to identify related M. abscessus isolates.Core-genome SNP phylogeny of Montreal M. abscessus isolatesThe core genome was estimated from open reading frames of the assembled outbreak isolates along with 220 additional isolates from the island of Montreal. M. abscessus subsp. massiliense isolates cultured from the skin infection outbreak patients exhibit high relatedness compared to the Montreal M. abscessus catalog. Methods Physicians and public health authorities identified a potential skin and soft tissue infection outbreak in Montreal, Canada. Six isolates from four patients were retrieved and DNA was sequenced using both Illumina short-read and Oxford Nanopore long-read WGS platforms.Minhash k-mer distances to reference M. abscessus type strains were used to confirm taxonomic identity of clinical isolates. Complete genomes for each isolate were assembled using long-reads and polished using short-read data. Single nucleotide polymorphism (SNP) distances between clinical isolates and the putative earliest outbreak case were used to examine their relatedness. Outbreak isolates were also compared to 220 contemporary Montreal M. abscessus genomes using core-genome (cg) SNPs to assess their relatedness and clustering among locally circulating strains. Results All outbreak isolates were most closely related to M. abscessus subsp. massilliense. Short reads from the outbreak isolates showed 100% mapping coverage to the complete assembly of the earliest outbreak isolate. All outbreak isolates were found to be nearly identical, differing by only 0-2 SNPs. In comparison, using the more distantly related subspecies reference exaggerated SNP distances between isolates (32-50 SNPs), and between outbreak isolates and the reference (26612-26644 SNPs). The cgSNP phylogenetic tree shows the outbreak isolates are distinct to other Montreal M. abscessus genomes and to the M. abscessus subspecies reference genomes. Conclusion Our findings indicate that this improved molecular approach leveraging both short and long read sequence data to produce complete genome assemblies of M. abscessus. This helped confirm the relatedness of epidemiologically linked cases and assess their bacterial isolates’ molecular clustering within an extended catalog of locally circulating strains. Disclosures All Authors: No reported disclosures

Business operations increasingly depend on digital workflows, hybrid infrastructures, and third-party ecosystems, making cybersecurity incidents a direct business continuity and governance problem rather than solely a technical concern. This paper proposes an integrated cyber defense and defense-to-response decision framework for organizations seeking to reduce exposure to external attacks and unauthorized access while improving incident detection, containment, and recovery. The framework aligns governance and control selection with NIST Cybersecurity Framework (CSF) 2.0, operational incident response considerations with NIST SP 800-61 Revision 3, control requirements with ISO/IEC 27001:2022, prioritized safeguards with CIS Controls v8.1, and adversary-behavior mapping with the MITRE ATT&amp;CK Enterprise Matrix. We define an evaluation model that combines 1) coverage mapping across prevent-detect-respond-recover functions, 2) multi-criteria decision analysis (MCDA) for cost, complexity, and risk reduction trade-offs, and 3) a playbook-oriented response design for high-frequency attack paths relevant to business environments. A worked comparative example demonstrates how three strategy bundles (traditional perimeter controls, defense-in-depth with SIEM, and a Zero Trust + EDR + SOAR approach) can be ranked using weighted criteria and incident lifecycle metrics. The paper concludes with an implementation roadmap and measurement plan to convert the framework into an evidence-based program that supports executive decision-making and continuous improvement.

Advanced Driver Assistance Systems (ADAS) often rely on camera based traffic sign recognition to enforce speed limits, but vision alone can fail due to occlusion, adverse weather, poor visibility, or even missing signs. This paper proposes a complementary approach using smartphone based map data and enhanced localization (via Bluetooth and Wi-Fi) to predict upcoming speed limit transitions earlier and more reliably than onboard cameras. We present a comprehensive literature review of camera based speed sign recognition limitations, analyze the coverage and accuracy of digital speed limit maps (Google, Apple, HERE, OpenStreetMap), and evaluate how Bluetooth Low Energy (BLE) beacons and Wi-Fi fingerprinting can refine vehicle positioning in challenging environments. We design a predictive model that computes time to speed limit change using map segment metadata, current vehicle dynamics, and localization uncertainty. A conflict detection and resolution framework is outlined to cross verify map predictions with camera readings, assign confidence scores, and correct errors in real time. Finally, we propose an experimental field evaluation across multiple mapping providers (urban, rural, highway, tunnels, and con- struction zones) to benchmark prediction accuracy, lead time gains, localization effects, and failure modes. The results indicate that integrating map based speed limit data with enhanced localization and sensor fusion can significantly improve speed limit awareness, providing earlier warnings (several seconds ahead of camera detection) and greater reliability in diverse conditions. We discuss system architecture, algorithm pseudo code, a comparative performance table of mapping platforms, and consider practical deployment issues (data freshness, con- nectivity, privacy, and regulatory compliance).

This paper investigates access point (AP) cooperation cluster formation in user-centric cell-free massive MIMO (CF-mMIMO) communication systems characterized by fronthaul links with capacity restrictions. Specifically, we consider an open radio access network (O-RAN) architecture that, although it favors the deployment of ultra-dense networks, is constrained in the number of APs that can be active simultaneously. In this context, we propose an innovative framework termed pixel-based CF-mMIMO, which enables efficient control of both the AP activation and cooperation cluster formation. Recognizing the parallels with pixel-based reconfigurable antennas, the proposed framework allows dynamic reconfiguration of the network coverage map with reasonably low computational cost. The high scalability and performance of the framework are mainly supported by a learning model based on graph neural networks (GNNs) that effectively exploits the existing graph-like structures in CF-mMIMO systems. Extensive simulation experiments demonstrate that the proposed approach achieves competitive spectral efficiency (SE) in challenging scenarios with dense AP deployments and numerous user equipments (UEs).

The growing requests for extremely fast indoor wireless connectivity have introduced significant challenges in designing next‐generation communication systems to build Internet of things (IoT)–enabled fully connected smart environments, particularly at higher frequency bands such as millimeter wave (mmWave/MMW). Accurate channel modeling is critical for optimizing these systems, especially in indoor environments where reflections, diffractions, and penetration losses considerably impact signal propagation. This study presents a detailed channel modeling approach using ray tracing techniques to characterize mmWave signal behavior in complex indoor scenarios. To accurately capture essential parameters including path loss, delay spread, and angular spread, the approach simulates signal interactions with environmental elements (e.g., walls, floors, and furniture) by leveraging three‐dimensional (3D) building models. The study provides a deeper understanding of line‐of‐sight (LOS) and non‐line‐of‐sight (NLOS) propagation. Furthermore, it comprehensively compares the propagation characteristics of various frequency bands, ranging from sub‐6 GHz (e.g., 2.4 and 6 GHz) to mmWave (e.g., 28, 60, and 100 GHz), thereby highlighting their distinct behaviors under identical indoor conditions and user trajectories. Using ray tracing, channel impulse responses and path loss metrics are extracted, and coverage map of received power is proposed for each position. Results demonstrate that mmWave bands experience higher path losses than sub‐6 GHz frequencies and are significantly affected by shadowing and blockage. This study not only validates the accuracy of the ray tracing model against empirical data but also demonstrates its utility in designing robust mmWave communication systems, optimizing network deployments, and enhancing beamforming strategies for future 5G and 6G networks.

In wireless networks, radio-frequency coverage maps (RF maps) are critical for tasks such as capacity planning, coverage estimation, and localization. Traditional methods for obtaining these maps, including site surveys and ray-tracing simulations, are either labor-intensive or computationally expensive, particularly at high frequencies. Generative AI offers a promising alternative for RF map synthesis and data augmentation. However, supervised generative approaches are often infeasible due to the lack of labeled training data, while unsupervised methods typically lack control over the generation process, limiting their practical utility. To overcome these challenges, we propose ARCS (Adversarial RF Map Categorization and Synthesis), a novel generative adversarial network (GAN)-based framework for unsupervised RF map categorization and synthesis. ARCS leverages the principles of information maximizing GAN (InfoGAN) to learn the latent structure of RF maps in an unsupervised manner during training and, through manipulation of the learned and interpretable latent codes, enables controlled generation of RF maps across floor-plan regions and Tx locations during inference. To improve training stability and synthesis quality, we integrate a gradient penalty-based Wasserstein GAN objective function along with a customized gradient-based loss function. Extensive experiments on both experimental and simulated datasets show that ARCS generates high-quality RF maps, associates them with discrete regions of the floor plan, and provides fine-grained control of Tx location within each region. Compared with a UNet-based conditional GAN and a conditional diffusion model, ARCS attains the best scores across structural similarity index metric (SSIM), PSNR, MAPE, RMSE, cosine similarity (CS), and Jensen–Shannon divergence (JSD). Moreover, ARCS is extremely fast, requiring a few milliseconds per synthetic map, compared to over 14 seconds with ray-tracing.