José Ricardo Gómez-Rodríguez

Urban LoRaWAN deployments are engineering systems whose connectivity, energy use, reliability, and operating cost depend on informed gateway-placement and configuration decisions. Although LoRa is widely adopted for smart-city and industrial Internet of Things (IoT) applications, the methods used to construct gateway coverage analyses remain fragmented across propagation modeling, field measurement, simulation, optimization, and emerging data-driven approaches. This technical survey critically synthesizes the representative literature on methodologies for LoRa gateway coverage analysis in urban environments. Its contributions are threefold: it organizes the evidence around performance evaluation, configuration and optimization, localization, applications, communication technologies, security, and comparison with alternative LPWANs; it maps those methods onto a system-level workflow linking inputs, inference, decisions, and validation; and it identifies reporting and validation conditions needed for transferable engineering evidence. The cited literature prominently represents empirical measurements and simulation models, whereas integrated intelligent-system workflows that combine urban-context data, real-time measurements, predictive models, and decision support remain underdeveloped. Inconsistent environmental assumptions, incomplete reporting of validation settings, and nonuniform performance metrics limit transferability between cities. This survey therefore formulates a system-level design framework and research agenda centered on digital representations of the radio environment, hybrid physics- and data-driven models, adaptive configuration, and measurable validation of reliability, energy efficiency, and cost. These findings will support researchers and engineering practitioners in designing trustworthy, scalable, and adaptive IoT connectivity systems for urban environments.

Open innovation makes IT investment decisions dependent on knowledge, technologies, and evidence that cross organizational boundaries. Yet these decisions are evaluated through disconnected logics: technical teams assess maturity; executives evaluate cost, strategic value, and flexibility; and artificial intelligence (AI) systems extract evidence or rank alternatives. Their separation creates a translation problem: maturity judgments enter financial models as unexamined assumptions, estimates remain detached from changing technical evidence, and algorithmic recommendations become difficult to contest. This conceptual paper develops the AI-Augmented IT Decision Architecture (AIDA), a closed-loop decision-support architecture connecting an evidence fabric, dynamic maturity assessment, probabilistic valuation, explainable synthesis, and accountable governance. Its design is grounded in a secondary conceptual synthesis of a 39-study core drawn from three previously assembled evidence streams and two post-search updates published in 2026; it is neither a new systematic review nor an empirical validation. AIDA treats evidence bundles, readiness-risk profiles, value-option sets, decision cases, commitment packages, and outcome records as boundary objects coordinating internal and external actors. Twelve propositions specify expected relationships among traceability, uncertainty propagation, option-aware staging, explanation, role separation, feedback, and decision outcomes. The article also contributes a reusable constructed benchmark model: a generative-AI investment case, a fully parameterized 100,000-iteration Monte Carlo implementation, reproducibility files, and a comparative protocol that researchers can reuse, challenge, or extend to evaluate AIDA and alternative approaches. The benchmark demonstrates internal analytical behavior under declared assumptions rather than organizational superiority, while explicit escalation rules distinguish full AIDA from AIDA-Lite. Decision quality is expected to arise not from a single superior method, but from disciplined translation among distributed evidence, economic consequences, and accountable strategic judgment, particularly in open innovation ecosystems where evidence, incentives, and complementary assets span organizational boundaries.

Packet delay variation (jitter) affects the reliability and perceived quality of real-time IP services, but its models and metrics remain fragmented across layers and technologies. This structured survey synthesizes research published from 1990 through August 2026 in three methodological families: stochastic and analytical foundations, simulation and empirical approaches, and cross-layer application-aware methods. The evidence shows a progression from single-queue formulations to end-to-end, heavy-tailed, self-similar, and data-driven models. A reproducible illustration using 1440 RIPE Atlas measurement windows demonstrates why metric choice matters: the 99th percentile of absolute consecutive RTT differences was 8.17 ms, excess-over-minimum RTT reached 52.36 ms, and an RFC 3550-style recursive estimator peaked at only 6.14 ms. The main unresolved problems are unified modeling across heterogeneous wired–wireless paths, comparable high-resolution measurement, cross-layer QoE prediction, reproducibility, and timing-channel security. Priority directions are distribution-aware reporting, open benchmark traces, hybrid analytical–learning models, and stable closed-loop control for 5G/6G, TSN/DetNet, SDN/NFV, and edge networks.

Small-scale clay-brick yards require molding equipment that increases output while remaining mobile, locally serviceable, and compatible with intermittent material supply. This article documents the functional architecture and exploratory operational performance of the mobile 12-cavity hydraulic clay-brick molding machine (ML12). Its design contribution is the integration of a dimensioned wheeled steel chassis, seated paired-lever controls, a translating feed hopper/distributor, a 12-cavity mold, two vertical hydraulic actuators, and a water-spray cleaning subsystem in a four-stage operating cycle. A retrospective concept-appraisal matrix compares this architecture with fixed automated and mobile manual concepts; equal weighting and one-at-a-time ±25% weight variations preserve the ML12’s highest internal score, without establishing stakeholder preference or empirical superiority. The evidence base also comprises sequential daily production logs: ten days of traditional manual molding followed by ten days of ML12-assisted molding. Mean gross green-brick output was 720 ± 86 bricks/day in the traditional period and 1495 ± 16 bricks/day in the ML12 period; corresponding descriptive throughputs were 86.5 and 186.9 bricks/h. A rejection-rate sensitivity analysis shows that, if traditional production had no rejects, ML12 conforming output would equal the traditional gross mean at a 51.8% ML12 rejection rate; this quantity boundary is not an estimate of quality or economic break-even. A preliminary linear-static finite-element case for a reconstructed frame returned a maximum von Mises stress of 112.3 MPa, 1.82 mm resultant displacement, and a minimum elastic safety factor of 2.23 on the reported medium mesh; the result is limited to the specified 1.0 kN load case and is not structural certification of the complete machine. Because the operational comparison was non-randomized and did not control staffing, operators, clay batch, moisture, weather, energy use, or rejection rate, the observed difference cannot be attributed exclusively to the machine. The results establish the machine architecture, an operational signal, and a bounded preliminary frame response, but not brick quality, ergonomic benefit, full structural safety, environmental benefit, or commercial return.

The increasing complexity of information technology (IT) systems requires reference frameworks that connect governance, service management, and quality assurance. This scoping review maps 109 core sources published between 1996 and June 2026 to characterize evidence on IT reference frameworks across organizational and sectoral contexts; records from 2026 are treated as partial-year data and are not interpreted as a complete annual trend. The review was structured using the Population–Concept–Context (PCC) framework and reported in alignment with PRISMA-ScR guidance. Peer-reviewed studies and selected grey literature sources were charted to identify research trends, framework families, implementation patterns, integration interfaces, evaluation practices, and evidence gaps. Dominant families included ISO/TQM-oriented quality systems, COBIT and ITIL/IT service management (ITSM), enterprise architecture, sectoral quality assurance, cybersecurity, and emerging AI/data-governance approaches. The findings indicate that integration is concentrated around recurring interfaces among strategic governance, service operation, quality and assurance controls, enterprise architecture, and evidence feedback. However, the mapped evidence remains methodologically uneven: conceptual frameworks, case studies, and reviews dominate, while longitudinal validation and comparable implementation metrics are limited. Emerging AI-related work is treated as an early research direction rather than as mature evidence of effectiveness.

Background: Hardware-software co-design for resource-constrained systems requires evaluation methods that integrate multiple quality attributes rather than isolated performance indicators. Objectives: This scoping review maps composite evaluation approaches used to assess architecture-relevant software or system artifacts, including software architectures where applicable, on resource-constrained hardware, while distinguishing explicitly aggregated quantitative composite metrics from qualitative and user-centric forms of evaluation. Methods: Using a PRISMA-ScR-informed reporting structure, the review analyzes a curated corpus of publications to identify foundational quality attributes, evaluation mechanisms, target hardware platforms, and architectural trade-off methods. Title and abstract screening, full-text eligibility assessment, and data charting were conducted by two investigators, with a third investigator available to resolve discrepancies when necessary. Results: Within the reviewed corpus, a descriptive shift is observed from single-attribute optimization toward broader multi-attribute evaluation, including explicit objective functions, benchmark-guided system-level modeling, structured qualitative trade-off assessment, and Quality-of-Experience (QoE)-oriented evaluation. These mechanisms are reported across FPGA-based accelerators, ARM-based edge devices, low-end IoT frameworks, heterogeneous intelligent devices, and XR platforms. However, explicitly aggregated architectural-level composite metrics remain rare, and representative studies such as FLASH, HW-FlowQ, Monarch, and ILLIXR illustrate related but distinct evaluation forms, rather than a single unified metric family. Conclusions: The reviewed corpus indicates that composite evaluation approaches support architecture-relevant reasoning under hardware constraints, although further work is needed to improve reproducibility, operational consistency, and cross-domain validation.

In safety-critical domains such as aviation, autonomous driving, and defense, operators must process complex spatial and radar data under severe time pressure. Traditional two-dimensional interfaces often force a “head-down” posture, increasing cognitive workload and impairing situational awareness. Extended reality and multimodal interfaces—incorporating gesture, voice, and haptic feedback—offer a promising paradigm to mitigate these limitations by enabling natural, egocentric data visualization. This scoping review systematically maps the empirical evidence on immersive and multimodal interfaces designed for radar and spatial data visualization in critical operational environments. Following the Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews guidelines, a systematic search was conducted across five major databases for articles published between 2015 and 2025. Out of 538 unique records screened, 54 studies met the eligibility criteria and underwent structured data charting. The findings reveal a technological ecosystem heavily dominated by augmented reality and virtual reality, supplemented by non-extended reality multimodal baselines (n = 8) to evaluate sensory load distribution. While subjective metrics such as the NASA Task Load Index (n = 17, 31.4%) dominate current evaluation practices, there is a notable scarcity of objective real-time physiological biosensors (n = 7, 13%). Crucially, the synthesized data challenges uncritical technological optimism: while multimodal extended reality effectively mitigates visual bottlenecks, certain modalities like mid-air gestures frequently induce physical fatigue and a documented speed–accuracy trade-off. To fully realize the potential of immersive decision support systems, future research must prioritize standardized, ecologically valid evaluation frameworks and explore artificial intelligence-driven adaptive interfaces capable of dynamically modulating information density based on operator workload.

Technological advancements have revolutionized photogrammetry, with the implementation of unmanned aerial vehicles for capturing images from different angles and the ease of obtaining sensor position information at the time of capture. This study evaluates the accuracy of direct georeferencing via Networked Transport of Radio Technical Commission for Maritime Services Via Internet Protocol, in the orthomosaic as a photogrammetric product in a large urban area with steep and highly variable topography, comparing it with the coordinates of nine checkpoints obtained with GNSS equipment connected to the National Active Geodetic Network, managed by the National Institute of Statistics and Geography of Mexico. An orthomosaic of the historic center of Zacatecas was obtained with a resolution of 2.70 cm/pixel. The orthomosaic coordinates, compared to those of the GNSS equipment, show a root mean square error (RMSE) of 0.78 m in the horizontal coordinates and an RMSE of 1.22 m in the vertical coordinates. Previous studies prove the efficiency of the Continuously Operating Reference Station module and network with other aircraft; this study determines that this is true for large areas with high coverage and quality in the internet network, but with rugged topography, the results are not accurate.