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Item type:Publication, Autonomous Solar-Powered Smart Sensing Node: Integrating TinyML and Hybrid LoRaWAN/Wi-Fi Connectivity for Sustainable Precision Agriculture(MDPI AG, 2026-08-03) - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Near-Bent Boolean Functions Are Insufficient for Correlation-Robust Hashing: A Spectral Obstruction and an Information-Theoretic FrontierOblivious Transfer (OT) extension, in particular, the construction of Ishai, Kilian, Nissim, and Petrank (CRYPTO 2003) requires a hash function H that is correlation-robust(CR). All practical instantiations model H as a random oracle or an ideal cipher, leaving CR with no quantifiable reduction to a structural property of the deployed hash. It is natural to ask whether the most nonlinear balanced Boolean functions available on an odd number of variables, the near-bent functions of the Maiorana–McFarland (MM) class, furnish an algebraic, standard-model CR candidate. We prove that they do not, and we identify precisely why. First, we keep a correct spectral fact: a balanced (Formula presented.) is (Formula presented.) -CR if and only if (Formula presented.), reducing CR to an autocorrelation bound. Against this criterion we establish three obstructions: (i) The MM-doubling family (Formula presented.) on (Formula presented.) variables has autocorrelation supported only on the directions (Formula presented.), where it equals (Formula presented.) with (Formula presented.) ; hence (Formula presented.), a factor (Formula presented.) above the value one would need, and an exhaustive search over all balanced members for (Formula presented.) returns the maximal (Formula presented.) in every case. (ii) Near-bentness controls the Walsh maximum (nonlinearity), not autocorrelation: every near-bent function satisfies (Formula presented.), so (Formula presented.) and no near-bent function is even approximately CR. (iii) A deterministic (Formula presented.) admits the support bound (Formula presented.), so statistical multi-output CR is impossible for (Formula presented.) and in particular at the IKNP regime (Formula presented.). Together, these results close the near-bent route to standard-model CR and clarify which design objective (low absolute indicator, not high nonlinearity) and which parameter regime ( (Formula presented.) ) a viable algebraic candidate would have to target. © 2026 by the author. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Hierarchical Multimodal Sleep Staging with Optimized EEG, EOG, and PPG Features for Wearable Applications(MDPI AG, 2026-08-16); ; Visconti, PaoloAutomatic sleep staging is fundamental for diagnosing sleep disorders and enabling long-term sleep monitoring with wearable devices. Although Deep Learning has significantly improved classification performance, balancing accuracy with computational efficiency remains challenging, particularly for resource-constrained systems. This paper proposes a lightweight two-stage Deep Learning framework for five-class sleep staging based on optimized multimodal physiological features extracted from electroencephalogram (EEG), electrooculogram (EOG), and photoplethysmography (PPG) signals. The framework is trained and tested using the Bitbrain Open Access Sleep (BOAS) database, considering a 31-subject dataset partitioned into training (24 subjects) and independent test (7 subjects) sets. Feature selection is performed using the minimum Redundancy Maximum Relevance (mRMR) algorithm, followed by Principal Component Analysis (PCA) for EEG and EOG features, while respiratory and cardiac features derived from PPG are directly incorporated into the multimodal representation. A hierarchical Long Short-Term Memory (LSTM) architecture first classifies sleep into Wake, REM, and NREM, then further distinguishes the N1, N2, and N3 stages. On an independent test set, the classifier achieves 88.2% five-class accuracy on the multimodal feature set (EEG + EOG + PPG) with a model size of 3.14 MB, and 87.1% accuracy on the EEG-only feature set using only 2.95 MB of memory. Leave-One-Subject-Out (LOSO) cross-validation yields 86.9% accuracy, supporting subject-independent generalization. Inference latency ranged from 2.55 ms (EEG-only) to 4.02 ms (multimodal), with measured energy per inference of 2.88–10.9 mJ across feature sets. Additional validation on the RichSleep and ISRUC datasets demonstrates robustness across different recording conditions, achieving mean accuracies of 78.2% and 77.3%, respectively. The proposed framework provides a favorable trade-off among classification performance, complexity, and memory footprint, suggesting its potential suitability for wearable and edge-based sleep-monitoring systems. © 2026 by the authors. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Delay-consistent adaptive incentive governance for circular beverage packaging systems: distinguishing structural and algorithmic limits in PET and returnable glass recovery in Mexico(Elsevier BV, 2026-09)Madrid Rico, Angel EduardoMexico's 2030 target of 80% PET-container recovery under the ANNEP agreement remains 16 percentage points above the current national recovery rate, with returnable glass already operating near its infrastructure asymptote. Whether adaptive incentive governance can close this gap depends on distinguishing the mechanism sources of policy performance: the adaptive feedback timing itself, the average incentive intensity at which policies operate, or the underlying behavioral response of the recovery system. This paper investigates the distinction using a delay-consistent adaptive proportional–integral controller embedded in a system-dynamics closed-loop supply chain calibrated against four independent empirical sources for the Mexican beverage-container context. Four policies are compared in a 216-cell balanced factorial design: a no-incentive baseline, a constant deposit-refund, an adaptive proportional–integral controller, and a quarterly threshold rule. A new stratified matched-budget identification experiment decomposes policy performance into three analytically distinct levels — algorithmic effects, operating-point effects, and structural constraints — by comparing the adaptive controller against an equivalent-intensity fixed benchmark across baseline, demand-dip, and demand-whiplash regimes. The matched-budget analysis indicates that the adaptive controller and the equivalent-intensity fixed policy converge to essentially identical steady-state return rates (approximately 0.759) under all three demand regimes; the previously observed adaptive advantage is attributable to operating-point effects rather than to feedback timing. This identification supports interpreting the 2.1-percentage-point gap between the observed ceiling and the 0.78 composite target as structural rather than algorithmic in origin: the ceiling reflects the saturating Michaelis–Menten behavioral response of the recovery system, not a limitation of the control algorithm. Closing this gap requires policy instruments capable of expanding the behavioral elasticity envelope itself — reverse-vending densification, extended producer responsibility, informal-sector formalization, and consumer information campaigns — rather than stronger deposit-refund incentives alone. © 2026 The Author. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Nurses’ knowledge of pressure injuries: the influence of training and experience in Colombian hospitals(Spanish Publishers Associate, 2025-01-01) - Some of the metrics are blocked by yourconsent settings
Item type:Publication, A Multi-Camera Temporal Fusion for False Alarm Suppression in Edge-Based Weapon Surveillance(Institute of Electrical and Electronics Engineers (IEEE), 2026); - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Early-Warning Margins for Preamble-Directed Jamming in LoRa: A Dechirp-Domain Sensitivity Framework with Severity-Graded Composite-Channel Stress Testing(MDPI AG, 2026-09-27); ; ; Mex-Perera, CarlosPhysical-layer jamming directed at the LoRa preamble may become detectable before substantial degradation of the acquisition structure is evident at the receiver. The practically relevant question is therefore not only whether an attack can eventually be detected, but how much jammer-power headroom a monitor provides before the selected preamble-degradation onset is reached. This paper introduces a detection-theoretic evaluation framework that expresses this pre-degradation warning capability in decibels. Three quantities are defined: the detection sensitivity floor, i.e., the smallest relative jammer level yielding a detection probability of 0.9 at a false-alarm probability of 10−2; the preamble-based link-degradation onset, i.e., the level at which the preamble symbol error rate increases by five percentage points relative to its jamming-free baseline; and the resulting early-warning margin separating the two thresholds. The resulting early-warning margin is a power-domain measure of jammer-power headroom and should not be interpreted as elapsed warning time. The framework is evaluated over 13 isolated impairment configurations, corresponding to 221 jammer-level operating points, and complemented by a three-step severity-graded composite-channel ladder that jointly combines noise, multipath, co-channel interference, and synchronization impairments, for more than 5×105 packet-level evaluations. Alongside a convolutional autoencoder and a one-class support vector machine using the same three-channel in-phase, quadrature, and magnitude preamble representation, we evaluate a training-light dechirp-domain peak-to-mean ratio statistic and a simple score-level fusion rule; under independent threshold calibration, the fusion rule preserves the separability of its constituents but, under fading, interference, and synchronization impairments, does not recover the fixed-operating-point sensitivity of the dechirp statistic. Under strong co-channel interference, the proposed scalar statistic reaches the target detection probability at −8.5 dB, whereas the autoencoder does not reach the target detection probability anywhere within the evaluated sweep, corresponding to a sensitivity advantage of more than 14 dB and showing that the observed loss of autoencoder sensitivity is detector-dependent rather than caused by an absence of discriminative information in the received preamble. No detector dominates across all isolated impairments: the dechirp statistic is particularly effective under co-channel interference and synchronization offsets, whereas the autoencoder remains advantageous under severe multipath fading. Some learned-detector margins become negative under moderate additive noise and under severe multipath fading, and the composite-channel ladder shows a progressive degradation of detection sensitivity as joint impairment severity increases. These results provide a detector-agnostic, physically interpretable yardstick for evaluating the pre-degradation sensitivity of LoRa physical-layer jamming monitors. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Technoaxia: Towards an ethics of invisible mediation in the digital ageThis article proposes Technoaxia as a new ethical framework for navigating the moral complexity of the digital age. Drawing on the Platonic myth of the Ring of Gyges as an allegory for the power of technological invisibility, and on the media–ecological tradition from Marshall McLuhan to Neil Postman, the article argues that existing ethical frameworks – virtue ethics, deontology and utilitarianism – are insufficient because they were not designed to address effects that are structural, unconscious and mediated. Technoaxia, understood as an ethics of the invisible mediation of technology, focuses precisely on this gap: the systematic study of how media and technologies reshape human values below the threshold of conscious awareness and the cultivation of reflexive agency in response. The article critically engages with the literature on AI ethics, identifies its limitations and applies the Technoaxia framework to the concrete case of generative AI in education, demonstrating how the framework differs from and advances beyond prior proposals. The article concludes with the foundational principles of Technoaxia and a call for interdisciplinary implementation across education, design and governance. © 2026 Intellect Ltd. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, State of EmergencyNayib Bukele’s presidency has transformed El Salvador into an electoral autocracy by systematically dismantling democratic institutions, subverting the constitution, and consolidating power under his New Ideas party. In response, grassroots collectives of victims’ families engage in quiet, poignant acts of defiance, preserving memory and demanding accountability within a highly restrictive civic space. © 2026 by The Regents of the University of California - Some of the metrics are blocked by yourconsent settings
Item type:Publication, The Need for Ethics in Operations Research(Springer Science and Business Media LLC, 2026-08-25); ; This paper presents a bibliometric and narrative literature review examining how ethics has been addressed within the Operations Research (OR) field. Through a Scopus-based search of 74 papers spanning 1980–2026, the study identifies publication patterns, influential authors, and thematic developments in ethics-related OR research. Findings reveal that explicit normative engagement remains relatively limited within core OR journals, yet ethical considerations increasingly emerge through sustainability, healthcare, and social decision-making applications. The analysis does not permit causal explanations of disciplinary culture, training traditions, or editorial practices; instead, it frames these questions for further systemic investigation. By situating empirical patterns within broader debates on methodological reflexivity and systemic intervention, this study contributes to ongoing discussions about integrating ethics more explicitly into OR theory and practice. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2026.