Search Results (360)

We introduce TriHex-Cluster, a hierarchical overlapping clustering framework built on the self-similar geometry of the triangular lattice (6-regular planar graph). The primary algorithm is regime C (greedy 2-packing followed by Voronoi completion), a practical hierarchical clustering method producing disjoint clusters with the Voronoi-contact graph $G_{\mathrm{Vor}}^{(k+1)}$ as the next-level graph and aggregation complexity $O(nlogn)$ (embedding cost excluded). On regular triangular domains with near-perfect packings, regime C achieves $n^{(k+1)}\le n^{\left(k\right)}/7+O\left(\sqrt{n^{\left(k\right)}}\right)$ per level; the measured depth on finite data is $K^{*}=\lceil {log}_{7}n\rceil \pm 1$. Two variants complete the framework: regime A (full-overlap edge-induced, ${\mathcal{C}}^{\left(k\right)}=V^{\left(k\right)}$) adds native overlap semantics by preserving the EI meta-graph 6-regularity without reducing the vertex count; regime B (deterministic index-7 sublattice, ${\mathcal{C}}^{\left(k\right)}={\Lambda }_k$ with $\mathbf{a}=2-\omega$) is a theoretical construction establishing an exact sublattice density ratio of 7 per level on the infinite lattice $\mathbb{T}$, and exact termination in $K={log}_{7}n$ levels on finite periodic domains with $n=7^K$. Unconditional results: EI 6-regularity in regime A; perfect star-tiling and exact index-7 structure in regime B; strict hierarchy via Voronoi-completed clusters in regime C; tile-shape alternation proven at levels 1–2 (hexagonal, then triangular-like) and conjectured, with numerical verification, beyond; $h_{\mathrm{Ward}}$ (as an unnormalised SSE) strictly admissible and $h_{max}$ weakly admissible. Aggregation complexity, embedding excluded: $O(nlogn)$ in regime C, $O\left(n\right)$ in regime B, $O(n·K_{max})$ in regime A. We provide a fully reproducible reference implementation (trihex2, MIT-licensed) with extensive parameter sweeps on UCI benchmarks, synthetic Gaussians, non-convex shapes, and overlapping distributions. The genuine contributions of the framework are the multi-scale hierarchical structure with provable geometric guarantees and, in regime A, native overlap semantics that no hard-clustering baseline can provide. A central empirical finding concerns the embedding: an ablation isolating the 2D-lattice projection shows it to be the main bottleneck, and a lattice-free variant that runs the same combinatorial core directly on a k-nearest-neighbour graph in the original feature space—with no embedding and no quantisation—removes the projection entirely and improves accuracy on six of seven pilot datasets. With a frozen, fully unsupervised meta-selection rule (graph-geodesic arbitration between a convex-consensus and a graph-min-cut candidate, no per-dataset tuning), this variant reaches ARI $0.871$ on moons and $1.000$ on circles, where k-means, HAC, and GMM all collapse to $\le 0.43$ and $\le 0.00$, respectively. On a 73-dataset benchmark (23 real UCI, 50 synthetic, all loaded with validated class labels), TriHex is the most frequently best method on the synthetic panel ($46\%$ win rate) and close behind GMM overall ($34\%$ versus $36\%$), while having the lowest mean ARI—the signature of a specialist: it dominates on non-convex structure (rings, spirals, manifolds) and is outperformed on convex tabular data, where we make no claim of superiority. We also report a genuine robustness limitation: with the default configuration, TriHex fails on Cancer (ARI $0.042$, essentially uncorrelated with the ground truth) because the default lattice over-fragments a two-class problem; competitive performance requires a dataset-appropriate configuration, and we report this explicitly rather than only the best configurations. On overlapping Gaussians, regime A detects the boundary points that the data-generating process itself classifies as ambiguous with precision $1.00$ at heavy overlap ($\delta =0.5$); the detector over-flags as the clusters separate (precision falls to $0.43$ at $\delta =3.0$), so its usefulness is confined to the strong-overlap regime. Within that regime, it provides a measurable capability unavailable to hard-clustering baselines. Full article

Frozen speech-to-LLM systems train an adapter between a frozen audio encoder and a text LLM. We test whether adapters preserve sentence stress beyond transcripts and whether the LLM uses it. In a five-seed Qwen3-8B/WavLM baseline, transcript alignment lowers linear adapter Probe-K below the text-only $K_T$ baseline (0.211 vs. 0.290). The R1.8 configuration improves MLP-2 Probe-K from 0.245 to 0.306 (paired $p=0.012$) and passes three controls, but because warmup and augmentation also change, the gain is not attributed to ${\mathcal{L}}_{\mathrm{cf}}$ alone; its crossing of the linear $K_T$ floor is neither statistically established nor capacity-matched. Response Probe-G remains 0.512 in both cohorts; single-seed LoRA and styled-teacher pilots do not improve it. A post hoc trace through all 36 Qwen blocks finds persistent absolute stress decodability in speech-slot states, but no reliable R1.8-over-R0 advantage at any state and no speech-conditioned answer margin; an explicit text tag instead yields a $+6.40$-nat margin. Attention differences depend on slot-length normalization, and late left/system concentration is shared across modalities rather than speech-specific. Thus, the tested system remains an end-to-end negative: adapter decodability does not imply causal response use. Scope is limited to sentence stress, mostly synthetic voices, one encoder, and one LLM. Full article

Adenylate cyclase 5 (Adcy5) generates cyclic adenosine monophosphate (cAMP) downstream of G protein-coupled receptor signaling, yet its role in vertebrate pigmentation remains incompletely understood. Here, we generated a CRISPR/Cas9-mediated adcy5 knockout line in Nile tilapia (Oreochromis niloticus) to investigate its function in chromatophore biology. Loss of adcy5 resulted in a pronounced disruption of body coloration, characterized by the absence of vertical black bars and a global reduction in pigmentation. Despite this, chromatophore number was largely unaffected, indicating that Adcy5 is not required for pigment cell specification but is essential for functional pigmentation. At the cellular level, pigment granules in melanophores and erythrophores failed to undergo dispersion and instead remained constitutively aggregated, revealing a primary defect in intracellular pigment granule transport. Consistently, adcy5 mutants exhibited reduced expression of key melanogenesis-associated genes, including mitfa, tyrb, tyrp1a, and tyrp1b, accompanied by decreased melanin content across multiple tissues. Pharmacological activation of cAMP signaling partially rescued the pigment dispersion defect, whereas stimulation of upstream α-MSH signaling produced only limited effects, placing Adcy5 upstream of intracellular cAMP production within the pigment regulatory cascade. Importantly, we further demonstrate that Adcy5 is required for erythrophore pigment granule dispersion, extending its functional role beyond melanophore biology. Together, these findings identify Adcy5 as a conserved regulator integrating cAMP-dependent pigment synthesis and granule transport across multiple chromatophore types in teleost fish. Full article

The transition to Beyond fifth generation of wireless networks (B5G) and sixth generation of wireless networks (6G) exposes the severe interference and coverage limitations of conventional cell-centric architectures. To overcome these bottlenecks, this paper presents a scalable four-layer radio network planning framework that jointly optimizes the deployment of distributed active antenna arrays and passive reconfigurable intelligent surfaces (RISs). The proposed framework integrates a digital twin (DT) loop within an Open-RAN (O-RAN) architecture, employing multi-agent deep reinforcement learning (MADRL) and fractional programming (FP) for real-time joint active and passive beamforming optimization. Extensive Monte Carlo simulations in a dense urban environment demonstrate a 45% increase in spectral efficiency, a 30% reduction in uplink interference, and an 84% reduction in coverage holes compared to legacy 5G networks. Ultimately, these results provide network operators with a cost-effective, standards-compliant blueprint to extend non-line-of-sight (NLOS) coverage by 40% without incurring the prohibitive capital expenditure (CAPEX) of dense active hardware deployments. Furthermore, the proposed architecture demonstrates a competitive 10–15% margin of improvement in spectral efficiency over recent state-of-the-art DRL-based RIS frameworks. Full article

Background/Objectives: Front-of-pack labelling systems such as Nutri-Score are promoted as public health tools to guide consumers towards healthier food choices. However, their capacity to accurately signal sugar content in child-targeted foods—a key determinant of dental caries risk—remains poorly characterised. This study aimed to evaluate whether Nutri-Score category reliably reflects sugar content in pre-packaged foods marketed to children, and to discuss the implications for paediatric oral health. Methods: A cross-sectional observational study analysed the nutritional labels of 100 pre-packaged foods directed at the paediatric population, all displaying a Nutri-Score label, selected from three major supermarket chains in Valencia, Spain. Products were grouped into eight predefined food categories. Sugar content (g/100 g or 100 mL), Nutri-Score category (A–E), and ordinal position of sugar in the ingredients list were recorded. Global association between Nutri-Score grade and sugar content was evaluated using Spearman’s rank correlation coefficient; category-level analyses used the Kruskal–Wallis or Mann–Whitney U test as appropriate. Results: A moderate statistically significant positive correlation was found between Nutri-Score grade and sugar content across all 100 products (ρ = 0.515, p < 0.001). In the exploratory category-level analyses, suggestive differences were observed in biscuits (H = 8.72, p = 0.033), milks and milk drinks (H = 9.02, p = 0.029), and desserts (H = 8.31, p = 0.016); none of these p-values survived Bonferroni correction for multiple comparisons (adjusted α = 0.006). Notably, some products rated A and B contained high sugar levels: one A-rated breakfast cereal reached 22.4 g/100 g, and the highest B-rated product (a flavoured milk drink) contained 22.1 g/100 g. No significant association was detected in cereals, breads, dairy products, juices, or frozen foods. Conclusions: Nutri-Score demonstrated limited discriminatory ability to identify high-sugar child-targeted foods consistently across food categories. These findings support recommending that paediatric dental practitioners advise caregivers to evaluate sugar content and ingredients lists beyond front-of-pack grading. Further regulatory refinement of the algorithm to specifically weight added sugar exposure in child-targeted products may be warranted. Full article

This work presents new data and several age–depth reconstructions based on distinct approaches to improve previously published age models for sedimentary sequences collected on the Iberian Margin middle shelf: PO287 6-1B, 2G (Porto); PO287 26-1B, 3G, and D13902 (Lisbon); and POPEI VC2B (Algarve). The new age models are constructed using radiocarbon dates calibrated with the IntCal20 calibration curve and smooth-spline regressions from the CLAM (non-Bayesian) and Bacon (Bayesian) models. A comparison of the age–depth models generated by the different approaches shows that, beyond the distinct solutions produced by CLAM and Bacon for each site, the differences between the two methods are inconsistent across sites. Furthermore, neither model yielded reliable results for the discontinuous sedimentary sequence with reworked older sediments and a hiatus. In this specific case, the “classical” linear regression (best-fit) approach appears to yield the results that enable intercomparison across all sequences. This exercise confirms the record of the 1755 earthquake and tsunami in the Lisbon core splice and a potential record of the same event in the Eastern Algarve, although the collected data indicate a more distal or lower-energy wave deposit. Full article

Messenger RNA (mRNA) vaccines represent a transformative platform in vaccinology, with applications extending beyond SARS-CoV-2 to other infectious diseases and cancer immunotherapy. However, patients with solid tumors receiving active anticancer treatment were largely underrepresented in pivotal vaccination trials, limiting understanding of vaccine-induced immunity in this population. In this prospective exploratory study, we assessed humoral and cellular immune responses after two doses of SARS-CoV-2 mRNA vaccines in 39 patients with solid tumors undergoing active treatment. Blood samples were collected before vaccination and approximately two months after the second vaccine dose, prior to the next treatment cycle. Anti-spike IgG, neutralizing antibodies, receptor-binding domain (RBD) levels, interleukin-6 (IL-6), hematological parameters, immune cell subsets, T-cell differentiation, and early apoptosis in αβ and γδ T-cell subsets were analyzed. Vaccination induced a robust humoral response, with high post-vaccination anti-spike IgG levels (median 988.69 BAU/mL), 97.44% seropositivity, 96.88% true seroconversion among baseline IgG−/NAb− patients, and strong neutralizing antibody activity (median 85.73%). Hematological parameters and IL-6 levels remained broadly stable, suggesting no detectable increase in systemic inflammation during the study period. Cellular analyses identified a reduction in peripheral CD19+ B-cell frequencies and decreased early apoptosis, particularly in CD8+ T cells and CD3+CD56+ NKT-like cells. Although changes in T-cell frequencies and differentiation profiles were also observed, these findings were attenuated after exclusion of participants with possible prior SARS-CoV-2 exposure and should be interpreted as exploratory. Overall, these results show that patients with solid tumors receiving active treatment can mount robust humoral responses to SARS-CoV-2 mRNA vaccination and suggest measurable post-vaccination changes in lymphocyte dynamics, including reduced early T-cell apoptosis. Full article

Background/Objectives: Previous publications have primarily examined the individual roles of nutrients during fetal development. However, growing evidence suggests that one-carbon (1C) metabolism nutrients, including choline and key B vitamins, act synergistically within interconnected metabolic pathways that modulate epigenetic regulation and may have implications for the health of future generations. Methods: This narrative integrative review examined evidence relating to the roles of choline and B vitamins (B1, B2, B6, folate (B9) and B12) in fetal development and offspring health. Peer-reviewed literature was identified through PubMed, Science Direct and Semantic Scholar. Results: Current evidence indicates that periconceptional and maternal intake and status of 1C metabolism nutrients are associated with DNA methylation processes involved in developmental programming and the risk of non-communicable diseases (NCDs) in childhood and adulthood. Habitual intakes of several 1C metabolism nutrients are frequently below recommended levels during pregnancy and lactation, particularly for choline and folate. Inadequate intakes, each contributing differently to 1C metabolism, may disrupt 1C metabolic pathways and alter DNA methylation patterns during critical windows of fetal programming. Homocysteine metabolism is intricately linked to 1C metabolism and is modulated by choline and B vitamins. Collectively, these pathways have potential implications for the health of the next generation, including effects on growth, neural tube closure, brain development and increased susceptibility to diseases later in life, e.g., cardiovascular disease, diabetes, obesity and other chronic conditions. Adequate maternal intakes of choline and B vitamins may help mitigate the ‘early life origin’ of certain NCDs by promoting healthy neurodevelopment, reducing inflammation, and regulating central metabolic pathways. Conclusions: Greater awareness of the roles and importance of 1C metabolism nutrients, including choline and key B vitamins (B1, B2, B6, folate and B12), during the early life course is warranted. Furthermore, there is also a need for organizations and policy makers to formalize intake recommendations for 1C metabolism nutrients beyond the individualized simplicity of folate/folic acid, and to extend this to include other methyl-donor nutrients with epigenetic effects, such as choline and key B vitamins, given their interconnected roles in 1C metabolism and fetal development. Full article

This paper presents a novel optimal control-based beamforming framework for phased antenna arrays, targeting advanced wireless communication and radar applications, including 5G systems. Unlike conventional beamforming techniques, such as Fourier-based methods and adaptive algorithms (e.g., LMS and RLS), the proposed approach formulates the beam synthesis problem as a discrete-time optimal control problem. The antenna array is modeled using a state-space representation, and a quadratic cost function is introduced to jointly minimize the deviation from a desired radiation pattern and the excitation power. The optimal excitation weights are derived using the Linear Quadratic Regulator (LQR) framework by solving the discrete-time algebraic Riccati equation. This formulation enables an effective trade-off between sidelobe suppression, main lobe accuracy, and power efficiency. Simulation results demonstrate that the proposed method achieves a well-focused main beam, significantly reduced sidelobe levels, and improved directivity compared to conventional approaches. Furthermore, the framework offers robustness and computational efficiency, making it a promising candidate for future FPGA and embedded implementations. Overall, the proposed optimal control-based beamforming approach provides a flexible, robust, and computationally efficient solution for next-generation antenna systems in 5G, beyond-5G (B5G), and radar applications. Full article

A reconfigurable intelligent surface (RIS) is a promising technology for beyond-fifth-generation (B5G) and sixth-generation (6G) wireless communications, but its passive reflection and two-hop double-fading structure make cascaded channel estimation challenging. Conventional convolutional neural network (CNN) estimators require many trainable parameters, while a single shallow parameterized quantum circuit (PQC) may have limited feature representation. Deep quantum circuits can also suffer from noise and barren-plateau effects on noisy intermediate-scale quantum (NISQ) devices. To address these issues, this paper proposes a multi-filter quantum convolutional neural network (MF-QCNN) for cascaded channel estimation in RIS-assisted multi-user uplink systems. The proposed model uses multiple independent shallow PQC filters in parallel, concatenates their measured features, and estimates the cascaded channel through a compact classical dense head, with the total trainable-parameter count scaling as $182F+696$ for F parallel filters. Simulation results, compared with a single-filter quantum convolutional neural network (QCNN), CNN, and multilayer perceptron (MLP) baselines, show that at a signal-to-noise ratio (SNR) of 20 dB, the 3-filter MF-QCNN reduces the normalized mean squared error (NMSE) by approximately 22.9, 8.1, and 4.6 dB relative to the single-filter QCNN, CNN, and MLP baselines, respectively, while using only about 19.3% of the CNN trainable parameters. Under zero-forcing (ZF) precoding, it achieves the highest achievable sum rate among the learning-based estimators; at SNR = 30 dB, it improves the achievable sum rate by approximately 17.4% and 12.8% over the CNN and MLP baselines, respectively. These simulation results suggest that the parallel shallow-PQC design can serve as a compact quantum-aided estimator for RIS channel estimation and may provide a useful basis for future studies on AI-native transceiver design in B5G/6G networks. Full article

The autism spectrum disorder-associated SCN2A, ANK2, and SHANK-family genes encode molecularly distinct proteins that converge functionally on dendritic integration. Recent work established that ankyrin-B, encoded by ANK2, acts as an obligate dendritic scaffold for NaV1.2, encoded by SCN2A, in neocortical pyramidal neurons. Loss of this module mislocalizes dendritic NaV1.2, reduces dendritic Na⁺ influx, weakens backpropagating action potentials, and impairs synaptic maturation and long-term potentiation. SHANK proteins organize a complementary postsynaptic receptor scaffold within dendritic spines, coupling N-methyl-D-aspartate (NMDA), α-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid (AMPA), and metabotropic glutamate receptor (e.g., mGluR5) signaling to the actin cytoskeleton through layered PSD-95/GKAP/Homer interactions. Disruption of this scaffold can destabilize excitatory transmission, spine morphology, and plasticity. We propose that these dendritic shaft and spine-associated modules jointly regulate dendritic input–output gain and that their disruption may contribute to autism spectrum disorder by destabilizing, rather than uniformly shifting, excitatory integration across cortico-subcortical circuits relevant to sensory reactivity, behavioral flexibility, and social-valence processing. Here, we review the cortical evidence for this layered dendritic convergence and evaluate its potential relevance beyond the cortex. We assess the striatum, thalamus, and amygdala as subcortical sites where related dendritic scaffolding mechanisms may operate. The striatum provides the strongest current test case, with established roles for both NaV1.2 and SHANK3 in medium spiny neuron physiology and corticostriatal connectivity. Thalamic and amygdalar extensions are supported mainly by SHANK-related circuit and channelopathy data but lack direct evidence for ANK2–SCN2A involvement. The framework is experimentally testable: conditional Ank2 deletion in striatal, thalamic, and amygdalar cell types; dendritic Na⁺/Ca²⁺ imaging across Scn2a, Ank2, and Shank3 models; adult rescue experiments; and genetic-interaction designs would determine whether ankyrin-B supports dendritic excitability beyond the cortex and whether these genes converge on, rather than merely parallel, dendritic input–output gain. Validation in human subcortical tissue would then establish whether this dendritic scaffolding logic represents a shared point of convergence through which genetically distinct autism spectrum disorder-risk variants alter circuit function. Full article

Momordica charantia L. is a medicinal plant rich in bioactive compounds, including steroidal glycosides, flavonoids, phenolics, triterpenoids, saponins, and polysaccharides, which exhibit antidiabetic, antioxidant, anti-inflammatory, hepatoprotective, and anticancer activities. This review summarizes its nutritional and phytochemical composition, green extraction technologies, molecular mechanisms, and industrial applications based on literature from Google Scholar, PubMed, Scopus, Web of Science, ScienceDirect, and other scientific databases. Ultrasound-assisted extraction is an efficient and eco-friendly method that provides higher recovery of bioactive compounds from M. charantia and improved bioavailability compared with enzyme-assisted, microwave-assisted, and conventional methods. The phytochemicals of M. charantia regulate oxidative stress, inflammation, lipid peroxidation, and glucose homeostasis. Studies show that its antidiabetic effects involve improved insulin sensitivity, enhanced glucose uptake, and inhibition of carbohydrate-digesting enzymes. These compounds also exhibit antioxidant activity through free radical scavenging and anti-inflammatory effects via inhibition of the NF-κB and MAPK pathways. M. charantia further demonstrates anticancer activity by inducing apoptosis, causing cell-cycle arrest, and downregulating proliferation pathways in several cancer cell lines, including MCF-7, HCT-116, HepG2, A549, and PANC-1. Beyond medicinal uses, it is applied in the food industry as a functional ingredient in products such as yogurt, cookies, pickles, bread, juice, oil, and beverages. Overall, M. charantia shows strong potential for therapeutic applications, including functional foods and pharmaceutical formulations targeting diabetes, inflammation, liver diseases, and cancer; however, further studies are needed to confirm its clinical efficacy. Full article

Digital agriculture employs a wide range of sensing, actuation, and analytics technologies to optimize productivity, sustainability, and decision-making in farming operations. However, rural and remote regions face persistent barriers, including limited network coverage and insufficient support for both low- and high-throughput applications, which hinder the deployment of conventional and broadband-intensive Internet of Things solutions. A central challenge is the lack of adequate field-level network infrastructure, with connectivity often unavailable or unreliable. This article presents a comprehensive survey of Broadband-based IoT (B-IoT) as a solution for supporting both low- and high-data-rate digital agriculture applications, including UAVs, computer vision, and extended reality, even in settings without continuous internet connectivity. Using a structured narrative-review approach, this survey synthesizes relevant peer-reviewed and technical literature on B-IoT-enabled digital agriculture and organizes the evidence around communication key performance indicators (KPIs), deployment constraints, and four technology domains: sensing, connectivity, intelligence/compute, and control/application. It examines how technologies such as 5G/6G, dynamic spectrum access, non-terrestrial networks, and edge computing can help address connectivity and infrastructure gaps in underserved agricultural areas. Furthermore, we introduce and analyze the concept of Evolved-Variety Technologies, which combines modified state-of-the-art modules with next-generation networks to create flexible, modular, and scalable system designs adaptable to diverse topographical and operational conditions. Beyond technical evaluations, the article examines economic feasibility, environmental sustainability, and policy implications, emphasizing the need for coordinated roles among governments, telecom providers, and agribusiness stakeholders. Our findings advocate for hybrid telecom architectures that integrate terrestrial and non-terrestrial components, leveraging emerging technologies to reduce the rural–urban digital divide and enable scalable, data-driven agriculture in underserved regions. Full article

Fifth-generation (5G), Beyond 5G (B5G), and sixth-generation (6G) wireless networks, along with the Internet of Things (IoT), are core communication infrastructure in smart cities. Their increased deployments create high-dimensional optimization and resource management challenges. Consequently, researchers have increasingly explored the use of Artificial Intelligence (AI) models for optimizing networks. The Proximal Policy Optimization (PPO) is one such algorithm that optimizes networks. This Systematic Literature Review (SLR) follows the PRISMA 2020 protocol to review 76 studies published between 2023 and 2026 to synthesize recent PPO-based approaches to optimize communication systems. This study examines key PPO variants in major communication domains. It outlines the primary obstacles to real-world deployment and provides a cross-domain classification. According to this study, PPO provides continuous action spaces with good training stability for AI models. Its stable policy-learning capabilities make it suitable for next-generation communication systems. However, sim-to-real transfer, reward design, and multi-agent scalability are a few key challenges encountered. Future directions emphasize robust, deployable PPO frameworks for 6G, IoT, and internet architecture. Full article

Wireless communication above $300\mathrm{GHz}$ requires highly sophisticated analog circuit design due to severe frequency dependent ohmic losses. The complexity of such electronic hardware motivates exploring wireless quantum optical communication approaches even for the 6G “terahertz (THz) range” $\left[300\mathrm{GHz},10\mathrm{THz}\right]$. In this work, the classical radio frequency (RF)-based inner physical layer (PHY) transceiver blocks of channel coded wireless communication systems are replaced by wireless quantum optical transceiver blocks. Short range concepts employing LEDs as transmitters are particularly attractive, owing to their low implementation cost and practical simplicity. In contrast to laser based wireless quantum optical transmission over multipath channels, the quantum mechanical density operator ${\underline{\mathit{\rho }}}_{\mathrm{RX},[{\mathfrak{s}}_i,b_i]}$ and the transition probability $\gamma ({\mathfrak{s}}_i,{\mathfrak{s}}_{i+1})$ required by the quantum data detection must be revised accordingly. Furthermore, the novel interpretation introduced here, in which the extrinsic information is treated as a diversity branch rather than as an estimate of the a priori information, facilitates turbo equalization that still can accomodate varying a priori information. However, due to the limited uncoded transmission performance achievable with such systems, the incorporation of sophisticated channel coding schemes appears imperative. The authors therefore investigate the combination of sophisticated channel coding techniques, such as polar coding, with LED based wireless quantum optical transmission technologies. All numerical results assume a cryogenically cooled receiver front-end (approximately 10 K), yielding thermal noise levels. Operation at room temperature in the 6G THz range $\left[300\mathrm{GHz},10\mathrm{THz}\right]$ would require an average number ${\overline{N}}_{\alpha }$ of thermal noise photon values of approximately 5 to 20, which is beyond the scope of this feasibility study. The results show that the proposed paradigm enables simple, robust, and practically viable wireless quantum optical communication systems with favorable transmission performance. Additional gains are achieved through iterative turbo equalization. The results also suggest that the proposed approach can pave the way toward robust and economically viable future communication solutions. Full article