Abstract: Background: Primary healthcare (PHC) is essential for depression management. However, system navigation remains under-explored in the lived experiences of service recipients in non-Western aging contexts. Objective: To explore experiences, perceptions, and barriers among Thai older adults with depression and family caregivers navigating the PHC depression surveillance network. Methods: Guided by COREQ, this qualitative study spanned six geographic regions in Thailand. Using purposive sampling within a multiphase framework, semi-structured face-to-face interviews were conducted with 60 older adults diagnosed with depression and 12 family caregivers (N = 72). Data were transcribed verbatim and analyzed using qualitative content analysis supported by componential analysis principles. Results: Navigation unfolded across three interconnected domains: (1) Recognizing Emotional Distress: Older adults framed symptoms as physical ailments or normal aging, whereas caregivers identified behavioral withdrawal and functional decline. (2) Interacting with PHC Services: Initial screening by Village Health Volunteers and nurses via 11Q tools enabled system entry, but interactions were constrained by limited consultation time, stigma, and fragmented communication. (3) Sustaining Care Continuity: Referral disconnects between subdistrict health-promoting hospitals and community hospitals’ psychiatric units shifted the burden onto caregivers to maintain treatment adherence without adequate psychosocial support. Conclusions: Navigating primary depression care involves multidimensional challenges. Strengthening community-based care requires transforming routine screening into continuous, person- and family-centered surveillance, streamlining referral pathways, and formally empowering family caregivers as care partners.

Abstract: Machine learning is increasingly applied to midstream oil and gas pipelines, yet reported performance rarely establishes readiness for field use. This narrative review organizes peer-reviewed and industry sources from 2015–2025 using a five-level operational maturity framework (sensing and detection, diagnosis, prediction, decision and optimization, and human-supervised closed-loop automation), defined by a rubric of decision authority, operator role, and the validation evidence each level requires. A second axis records where each study’s performance was demonstrated, from simulation to closed-loop use. Applications include leak and anomaly detection, defect and corrosion assessment, hard spots, pump cavitation, predictive maintenance, failure-risk modeling, scheduling, and digital twins. Cross-classifying the coded sources on both axes shows that validation realism does not keep pace with claimed operational responsibility. Peer-reviewed evaluations are concentrated at Levels 1–3 and rely largely on laboratory, simulated, or retrospective data; the only operational Level 4 example is a practitioner case, and no coded source demonstrates Level 5 closed-loop control. The corpus thus shows a validation gap at the lower levels and an evidence-coverage gap at the upper ones; beyond the performance shown in controlled evaluations, validation, transferability, and governance remain major unresolved barriers to deployment. A ranked agenda led by graduated field validation follows.

Abstract: Urban science has learned to measure cities through the flows that cross them: capital, labour, information, and air traffic. It has paid far less attention to what the name of a city does when a person speaks it about themselves. "I live in London", "I am flying in from Milan", and "I am going to Burlington" are not neutral statements of location; each converts a place into a public claim about the speaker's wealth, mobility, taste, and future. This study theorizes that conversion as toponymic capital: the convertible symbolic value that accrues to a person through the city names attached to their residence, origin, destination, and transit. Drawing on Bourdieusian field theory, Peircean and post-Silversteinian semiotics, mobilities research, and the postcolonial critique of world-city hierarchies, the study conducts a comparative interpretive semiotic analysis of 24 publicly documented instances spanning Africa, Europe, North America, Latin America, the Gulf, South Asia, and East Asia, sourced from vernacular lexicons, platform practices, popular culture, and institutional instruments. Four findings follow. City names operate through a positional grammar in which destination and transit outrank residence; their value is fixed by the audience's reference frame rather than by the city alone; their polarity reverses under identifiable conditions that enable toponymic arbitrage; and their value converts into marriage prospects, credit, employment, and visa access. The study presents a toponymic status grammar as a measurable construct and sets out its implications for city branding, urban rankings, and the study of spatial inequality.

Abstract: While nighttime-light (NTL) data are increasingly used for disaster assessment, different indicator formulations may represent distinct dimensions of disruption and are rarely validated before cross-area comparison and spatial modeling. This study aimed to establish whether alternative NTL indicators derived from the same observations measure the same dimension of disruption, and then to identify the spatial correlates of flood-induced activity disruption and early recovery using the indicators that proved valid. This study examined county-level flood disruption and early recovery during the 2023 Haihe River Basin flood using daily Visible Infrared Imaging Radiometer Suite observations across the Beijing–Tianjin–Hebei region. Three disruption indicators were evaluated against official disaster records, and the retained indicators alongside an early recovery ratio (ERR) were subsequently analyzed using multiscale geographically weighted regression. Spatial disruption extent (SDE) and aggregated disruption magnitude (ADM) strongly discriminated documented flood impacts (area under the curve [AUC] = 0.931 and 0.865, respectively), whereas affected-area disruption intensity performed poorly (AUC = 0.623) and was excluded from spatial modeling. After controlling for baseline luminance and screening spatial associations for robustness, young-child population ratio was positively associated with SDE, mean slope (MS) was negatively associated with SDE and ADM, and cumulative precipitation was negatively associated with ADM. MS was the only retained correlate of early recovery, with steeper terrain associated with lower ERR regionwide. These findings show that NTL indicators derived from the same observations measure distinct dimensions of disaster response and are not interchangeable; each should be interpreted based on the dimension it measures in NTL-based disaster assessment.

Abstract: For \( q≥1 \) and \( gcd(a,q)=1 \) we study the restricted weighted Goldbach sum \( R_{a,q}(N)=\sum_{p_{1}+p_{2}=N,\; p_{1}\equiv a\,(q)} (logp_{1})(logp_{2}) \). We show that an odd prime divisor of \( q \) forces \( R_{a,q}(N)=O_{q}(logN) \) on a set of even \( N \) of positive density, so that a main term of size \( ≍N/φ(q) \) can hold on almost all \( N \) only for \( q=2^{k} \). For \( q=2^{k} \) we state the mean-square estimate \( \sum_{N\leq X} |R_{a,q}(N)-M_{a,q}(N)|^{2}{\ll }_{A,q}X^{3}(logX)^{-A} \) for every \( A>0 \), with \( M_{a,q}(N)=2C_{2}\varphi (q)^{-1}\mathfrak{S}(N)N \), and derive from it that the exceptional set \( \{N\leq X:R_{a,q}(N)=0\} \) has size \( {\ll }_{A,q}X(logX)^{-A} \) and that \( |R_{a,q}(N)-M_{a,q}(N)|\leq N(logN)^{-3} \) for all but \( O_{A,q}(X(logX)^{-A}) \) even \( N≤X \). A ternary anchoring transfer and an unconditional quaternary existence statement via Helfgott’s theorem follow. Part II proves an unconditional limit law: for every fixed integer \( h≠0 \), the odd part \( {\mathfrak{S}}_{0}(n)=\prod_{\mathrm{\ell }\mid n,\mathrm{\ell }\gt 2} (\mathrm{\ell }-1)/(\mathrm{\ell }-2) \) of the Hardy–Littlewood singular series, evaluated at the shifted primes \( n=p+h \), converges in distribution to an explicit random Euler product \( Y_{h}=\prod_{\mathrm{\ell }\gt 2,\mathrm{\ell }\nmid h} a_{\mathrm{\ell }}^{B_{\mathrm{\ell }}} \) with independent Bernoulli factors \( \mathbb{P}(B_{\mathrm{\ell }}=1)=1/(\mathrm{\ell }-1) \); we establish convergence of all integer moments, an entire Mellin transform of order at most one, non-atomicity, unbounded support, a superpolynomial upper tail, and strict amplification relative to generic integers. Part III applies this law to the amplitude of the restricted main term along \( N=p+h \) and, under a stated Hardy–Littlewood-type hypothesis, to the additive richness \( R(p) \) of the prime-generation DAG. Part IV records, as computational findings with stated ranges, an inverse correlation \( corr(R,H)≈-0.7935 \) between richness and hub count over the first 5000 primes, of which conditioning on \( {\mathfrak{S}}_{0}(p+1) \) explains about 8%. All numerical constants are enclosed by truncation of the relevant Euler products at P={10}^{7} with an explicit tail bound. The paper does not prove Goldbach’s conjecture or finiteness of the exceptional set.

Abstract: This study presents a turbineless hybrid electric-hydrogen propulsion system developed and tested under static conditions. Two electric ducted fans (EDFs) provided the baseline thrust and combustion airflow, while hydrogen was generated on demand in an alkaline aluminum-water reactor and burned downstream of the fans. Hydrogen production was regulated by changing aluminum-rod immersion depth and measured directly at each operating condition with a hydrogen-calibrated mass-flow meter; aluminum consumption was additionally recorded to characterize the effective hydrogen yield. Static performance was evaluated at ten immersion-depth conditions using 76 independent paired EDF-only and hybrid runs per condition. At the highest tested hydrogen flow, thrust increased from 8.49 ± 0.15 to 9.94 ± 0.21 N, a 17.1% increase over the matched EDF-only baseline. Hydrogen production varied approximately linearly with exposed aluminum surface area (R² = 0.997), while marginal thrust gain decreased as hydrogen flow increased. The momentum-based combustion-to-jet conversion indicator remained between approximately 8.0 and 9.4%, with its highest observed values over a region of intermediate hydrogen flow rates. Chamber-pressure measurements provided an independent consistency check of the thrust increment. Supplementary cold-flow measurements provided a total two-fan electrical reference of approximately 1.33 kW. An endpoint exergy assessment estimated a lower jet-kinetic-power fraction of the combined electrical and hydrogen chemical inputs at higher hydrogen flow.

Abstract: Flight-control laws are assessed by their frequency response, stability margins, gust rejection and behaviour under failure; connectome-derived controllers have so far been shown mainly in time-domain demonstrations. We applied these tests to a spiking model of the fruit fly's visual yaw pathway, a 20,556-neuron FlyWire subcircuit from the T4/T5 motion detectors to the descending neuron DNa02, with the aircraft's yaw rate imposed directly as the firing rate of artificial T4/T5 sources, and compared it with rewired copies and a classical yaw damper. Under sinusoidal yaw, the right-minus-left DNa02 firing rate followed the yaw rate with coherence 0.98 at 1 Hz and with the stabilizing sign. It ranked first against 19 degree-preserving rewirings (median 0.31; p = 0.05) and narrowly first against 19 cell-type-preserving rewirings, which leave every input to the HS, VS, H2 and DNa02 cells unchanged. Closed around the rudder of a simulated six-degree-of-freedom Cessna 172 in moderate turbulence and tuned under the same margin rule as the yaw damper, it reduced RMS yaw rate by 19%, against 57% for the damper. Of 11 wirings it had the lowest RMS yaw rate and the lowest RMS sideslip on both aircraft models. The three rewired controllers that came closest on yaw rate in the Cessna model were saturated relays flying skidding turns. No lesion-study flight departed, so the pre-specified departure test could not separate the wirings; under random neuron loss the real wiring's mean RMS yaw rate rose toward the bare airframe's but not above it, partly because the loss often silenced its one active readout neuron, and silencing its six HS cells returned it to the bare airframe's yaw rate. Flight-control tests can therefore measure what a connectome's wiring contributes to a controller, and they expose failure modes, such as relay-like skidding, that a single performance score hides.

Abstract: Yoga pose recognition and posture assessment have gained attention with advances in computer vision and machine learning. However, computationally intensive models and cloud-dependent processing can limit deployment on low-cost, resource-constrained devices. Tiny Machine Learning (TinyML) offers a promising approach by enabling on-device inference with limited memory, processing power, and energy consumption. This paper reviews existing research on yoga pose recognition, human pose estimation, lightweight neural networks, and embedded machine learning. A conceptual framework is proposed that integrates data acquisition, preprocessing, pose feature extraction, lightweight model inference, posture assessment, and feedback generation. Both camera-based and wearable sensor-based approaches are considered, highlighting their advantages and limitations for embedded deployment. Model optimization techniques, including quantization and pruning, are discussed as potential methods for reducing memory and computational requirements. The paper also examines challenges related to dataset diversity, model generalization, sensor reliability, privacy, and the accuracy of posture feedback. As this study presents a literature review and conceptual framework, no new implementation or experimental performance results are claimed. Future research should evaluate the proposed framework on physical embedded devices using recognition accuracy, memory usage, inference latency, and energy consumption. This work provides a foundation for developing efficient, privacy-conscious, and accessible yoga monitoring systems using TinyML.

Abstract: Detection of the early phases of cognitive dysfunction is critical for identifying individuals at risk of neurodegeneration before irreversible damage occurs. High levels of Coenzyme Q₁₀ (CoQ₁₀) in plasma are associated with high cognitive and executive capacities. Maintenance of endothelial function is also an important factor in the prevention of neurodegeneration and dementia. Therefore, this study aimed to determine plasma markers of endothelial health that, in conjunction with CoQ₁₀, can effectively serve as predictors of cognitive dysfunction in a cohort of 45 elderly women (78.36 ± 8.41 years) from Seville, Spain. Utilizing a cross-sectional design, we assessed cognitive and executive capacities via the widely used MMSE-30 and FAB-E scales. Physiological health was evaluated through plasma biomarkers (including GDF-15, GFAP, BDNF, P-selectin, NfL, and p-Tau¹⁸¹) and the determination of CoQ₁₀ levels. Furthermore, the study also analyzed exosome levels by measuring CD63, and their content in DNA and mtDNA. Our results indicate that low plasma CoQ₁₀, elevated P-selectin (P-sel), and reduced exosomal DNA content can serve as early warning signs of neurodegeneration, appearing before traditional markers like GFAP, NfL, or p-Tau181. Our results indicate a link between CoQ₁₀ levels, endothelial damage, and cognitive dysfunction risk.

Abstract: Streptococcus pneumoniae is a major bacterial pathogen and the leading cause of community acquired pneumonia. Polyamines, particularly spermidine, play a key role pneumococcal virulence. However, spermidine biosynthesis in S. pneumoniae remains incompletely characterized. We predicted that the S. pneumoniae aspartate semialdehyde dehydrogenase (also known as carboxyspermidine dehydrogenase or CASDH) would use agmatine as its polyamine substrate rather than putrescine, as was previously expected. S. pneumoniae CASDH substrate specificity was evaluated using steady-state kinetic NADPH oxidation assays in the presence of agmatine and putrescine (as well as diaminopropane, cadaverine, N-carbamoylputrescine, and spermidine). CASDH displayed the greatest catalytic efficiency toward agmatine with a k_cat/K_M of 1.0 x 10⁶ M^-1s^-1 , approximately 3,200-fold greater than putrescine and indicating that the S. pneumoniae enzyme is a carboxyaminopropylagmatine dehydrogenase or CAPADH and not a carboxyspermidine dehydrogenase or CASDH. To confirm the residues involved in agmatine specificity we solved a 1.19 Å X-ray crystal structure of CAPADH with agmatine and NADPH bound providing evidence that a conserved Asn-Glu-Phe-Glu motif coordinates agmatine. These results require a reconsideration of the biosynthetic steps leading to spermidine production in S. pneumoniae and provide the first structural evidence for substrate specificity in the aspartate semialdehyde dehydrogenase enzyme class.

Abstract: Background: Severe COVID-19 is driven by a complex interplay between systemic hyperinflammation, cellular immune dysregulation, and activation of coagulation cascades. Case Presentation: We report the case of a 52-year-old man with a history of rheumatoid arthritis and psoriasis managed with chronic etanercept therapy, who was hospitalized with rapidly progressive COVID-19. Longitudinal monitoring of C-reactive protein (CRP), interleukin-6 (IL-6), soluble interleukin-2 receptor (sIL-2R), neutrophil-to-lymphocyte ratio (NLR), D-dimer, and fibrinogen demonstrated a transient suppression of select acute-phase reactants followed by profound cellular immune dysregulation and escalating thrombo-inflammatory activity. Despite multimodal management—including remdesivir, convalescent plasma, invasive mechanical ventilation, and veno-venous extracorporeal membrane oxygenation (ECMO)—the patient suffered fatal cardiac arrest. Conclusions: This case highlights the clinical utility of longitudinal, multiparametric biomarker profiling in severe COVID-19 and underscores the critical importance of timely immunomodulatory interventions prior to irreversible immunothrombotic damage.

Abstract: Generative artificial intelligence (GenAI) is increasingly embedded in students’ learning and knowledge-work routines, yet perceived productivity gains do not necessarily arise from technology acceptance alone. This study develops and empirically examines an integrated Technology Acceptance Model (TAM) and Self-Regulated Learning (SRL) framework for explaining learning productivity among Google/Gemini users in Bangladesh. The study dataset contains 400 respondent IDs, of which 385 records contain complete questionnaire responses and a valid attention-check response; 15 rows are blank. The analysis therefore uses 385 usable cases and conducts an additional sensitivity analysis on 302 respondents classified as regular or occasional AI-feature users. Perceived ease of use (PEOU), perceived usefulness (PU), SRL and learning productivity (LP) were operationalised using three five-point Likert items each. Transparent composite-score path analysis with 5,000 bootstrap resamples was used because the available analytical environment did not contain a dedicated CFA/PLS-SEM implementation; consequently, the reported measurement diagnostics are explicitly treated as pre-publication validation evidence rather than as a claim of completed covariance-based CFA or PLS-SEM. PEOU positively predicted PU (β = .278, p < .001), while both PEOU (β = .191, p < .001) and PU (β = .218, p < .001) predicted SRL. Learning productivity was predicted by PEOU (β = .140, p = .004), PU (β = .256, p < .001) and SRL (β = .202, p < .001), with R² = .188. Bootstrap estimates also supported indirect pathways from PEOU to LP through PU and SRL, including a serial PEOU → PU → SRL → LP effect. The results suggest that the productivity value of GenAI is partly behavioural: users benefit not only because the technology is perceived as useful and easy to operate, but also because those perceptions are associated with planning, monitoring and addressing learning gaps. The study contributes an emerging-market, mechanism-oriented account of GenAI-enabled learning productivity and identifies measurement and sampling issues that should be resolved in subsequent validation research.

Abstract: Let G be a finite simple graph, let \( m = (m_v)_{v∈V(G)} \) with \( m_v ≥ 1 \), and form \( G^m \) by attaching \( m_v \) pendant vertices at each \( v \). For the weighted adjacency matrix \( A_φ(G^m) \) induced by a real symmetric degree-dependent edge weight φ, a generalized Fiedler-type decomposition gives M − n forced zero eigenvalues and \( χ_{A_φ(G^m)}(λ) = λ^{M−n} det(λ²I_n − λB_φ − R_φ) \).Here \( n = |V(G)| \), \( M = ∑_v m_v, B_φ \) is the core block formed from the core-vertex degrees in the thorny graph, and \( R_φ \) is diagonal with squared pendant-fiber couplings. The decomposition gives an arbitrary-core nullity bound, exact nullity and inertia for nonzero pendant couplings, Cauchy interlacing with \( B_φ \), and a 2n-dimensional compression independent of M. For fixed r ≥ 1, m, and φ, the nonzero weighted spectrum of a uniform attachment to an r-regular core determines the core adjacency spectrum whenever the induced core-edge weight is nonzero. On connected walk-regular cores, let exceptional vertices receive m + q rather than m pendant vertices. For independent exceptional sets S, T with |S| = |T| ≥ 2, all moments below twice the smaller minimum separation agree for every real symmetric φ. If the separations differ, then at the first possible mixed order the moment of the smaller-separation placement minus that of the larger-separation placement is an explicit nonnegative multiple of a sum of squared geodesic counts. If the ordinary–ordinary core-edge weight is nonzero and its square differs from that of the exceptional–ordinary core-edge weight, the spectrum determines the minimum separation. On distance-regular cores, it also determines the closest-pair count. Both the ordinary Sombor and Randić weights satisfy this condition.

Abstract: Retractions show how a discipline deals with its own errors, yet the construction literature has not been examined from this perspective. This study analyzed the complete Retraction Watch database distributed through Crossref (72,389 records) and extracted 1385 records assigned to the architecture, engineering, and construction (AEC) sector, defined by the subject categories of the database and a manually compiled list of journals and conference series. Publication channel, reasons for retraction, time to retraction, and geography were examined. Retractions in construction do not form a single phenomenon. Conference papers account for 41.2% of the set; they are retracted quickly (median 35 days), in entire volumes, and 77.1% of their notices give no specific reason. Journal articles are retracted after a median of 529 days, and their notices state specific reasons, including paper mills (19.8%) and computer-generated content (14.5%). A title search for building information modeling (BIM) returned 52 records concentrated in a single publisher and a single country of affiliation. Systematic reviews in construction should verify the status of conference papers, the channel most affected by retractions, while notices without a justification prevent the discipline from distinguishing error from misconduct and learning from its own retractions.

Abstract: Large language models (LLMs) are increasingly used in decision-support systems because of their ability to interpret complex queries, synthesize information, and generate contextually relevant responses. However, their use in reliability sensitive settings remains challenging because LLMs can produce incorrect or unsupported outputs while expressing a high degree of linguistic confidence. This paper presents a confidence aware explainable AI framework for improving the reliability of LLM-based decision support. The proposed approach combines model confidence, semantic consistency across multiple stochastic generations, and explanation support to construct a composite reliability score. A selective decision module then uses this score to determine whether a generated response should be accepted, flagged for review, or withheld through abstention. The framework is evaluated across factual, commonsense, and multi-step reasoning tasks using multiple open-weight LLMs. Performance is assessed in terms of answer accuracy, expected calibration error, failure-detection AUROC, selective accuracy, and response coverage. The evaluation investigates whether combining complementary reliability signals provides a more informative assessment of response correctness than self-reported confidence, model confidence, or semantic consistency alone. The proposed framework provides a practical reliability layer for LLM-based decision-support systems by jointly communicating confidence, explanation support, and an actionable response decision, thereby helping distinguish sufficiently reliable outputs from those requiring further verification or abstention.

Abstract: Hallucinations in large language models (LLMs) remain a critical challenge for deploying these systems in high-stakes domains. We introduce Calib-Abstain, a framework that combines token-level uncertainty estimation with multi-path consistency checking to generate calibrated confidence scores. When confidence falls below a learned threshold, the system triggers tiered mitigation actions: retrieval augmentation, query reformulation, or abstention with explanation. Our approach employs temperature scaling and isotonic regression to minimize Expected Calibration Error (ECE). Experiments on mathematical reasoning (GSM8K, MATH), code generation (HumanEval), and general knowledge tasks (MMLU) demonstrate that Calib-Abstain reduces hallucination rates among answered queries by 24--31% while maintaining 80--88% coverage, with ECE improvements of 0.08--0.12 across datasets.

Abstract: Survivors of sexual violence carry a disproportionate burden of age-related morbidity, including cardiovascular disease, type 2 diabetes and inflammatory conditions, raising the possibility that sexual trauma accelerates the biology of aging rather than acting solely through psychological distress or health behaviors. This narrative review synthesizes translational, epidemiological and biomarker evidence across five mechanistic domains. Epigenetically, posttraumatic stress disorder (PTSD) is associated with accelerated DNA-methylation age, most consistently on the mortality-calibrated GrimAge clock, whereas meta-analytic evidence for cumulative childhood adversity on first- and second-generation clocks is weak. At the cellular level, severe childhood sexual abuse is associated with shorter leukocyte telomeres in several, though not all, studies, and PTSD with telomere attrition, T-cell senescence and oxidative DNA damage. Endocrinologically, childhood sexual abuse produces enduring hypothalamic-pituitary-adrenal axis sensitization and earlier pubertal maturation in girls. Inflammatory evidence is the most robust: childhood maltreatment prospectively predicts adult C-reactive protein, and PTSD is consistently associated with elevated pro-inflammatory cytokines. Metabolically, PTSD is associated with a high prevalence of metabolic syndrome, and childhood sexual abuse with obesity and type 2 diabetes in women. Glucocorticoid-FKBP5-NF-κB signaling and allostatic load emerge as bridges between domains, and we propose an integrated six-step model. Causal inference remains limited by retrospective trauma assessment, confounding by co-occurring adversity and scarce trauma-type-specific analyses. Priorities include prospective, sexual-trauma-specific multi-omic studies and trials testing whether trauma-focused, anti-inflammatory or metabolic interventions can slow the pace of biological aging in survivors.

Abstract: The concept of automotive components, centred on manufacturing output, no longer captures the software, data and computing-power assets that determine advantage in software-defined electric vehicles. Taking China as its setting, this paper asks what a component is once value is generated jointly by parts, code, information and computation. It argues that the vehicle-to-parts ratio has become unusable: the 2025 revision of sub-national vehicle production accounting broke the comparability of city-level data, and components are accounted for as a single manufacturing aggregate in which intangible value is invisible. The ratio therefore remains valid as a measure of localisation but fails as a diagnostic of technological autonomy. The paper develops a four-dimensional framework reclassifying automotive value into hardware, software, data and computing-power carriers, specifies their dependency relations, and operationalises a collaboration ecosystem index with disclosed proxy indicators and an explicit coding rubric. Applied to four Chinese clusters and to Guangzhou, it identifies four coordination modes whose profiles exhibit asymmetric complementarity rather than convergence on one optimum. OEM–supplier coordination shifts from buyer–seller transactions to symbiotic partnership, from local sourcing to network coordination, from hardware to capability procurement and from cost to innovation orientation, with Tier 0.5 integrators as the most consequential structural change.

Abstract: Automated detection of visual indicator targets associated with selected phenological stages can support image-based monitoring of greenhouse cucumber. However, female flowers and young fruits are often small, partially occluded, and visually similar to adjacent developmental indicators, which complicates their detection in greenhouse scenes. In this study, YOLO11n-Cucumber was developed from YOLO11n for detecting seedling plants, female flowers, young fruits, and marketable fruits. The model incorporates an MDGC-GhostNetV3 backbone for feature extraction, a C2PSA-SCSA module for feature enhancement, and an MSF-CIoU loss for bounding-box regression. Experiments were conducted on a greenhouse cucumber dataset constructed in this study, comprising 3,849 images and 8,791 annotated instances. On the internal test set, YOLO11n-Cucumber achieved a precision of 86.4%, a recall of 84.2%, an mAP@0.5 of 86.8%, and an mAP@0.5:0.95 of 61.2%. Compared with YOLO11n, mAP@0.5 and mAP@0.5:0.95 increased by 2.3 and 4.1 percentage points, respectively, while floating-point operations (FLOPs) decreased from 6.4 G to 5.0 G. This reduction in FLOPs was accompanied by increases in the parameter count, from 2.6 M to 3.2 M, and in model size, from 5.6 MB to 6.8 MB. These results indicate that YOLO11n-Cucumber provides an improved accuracy–FLOPs trade-off under the greenhouse image-acquisition conditions of this study. Its generalizability across cultivars, greenhouse types, devices, and environmental conditions requires further evaluation.

Abstract: Vapor-responsive phosphorescent materials are desirable for sensing, yet reversible switching in Cu(I) complexes remains challenging. Here, a series of ionic and neutral Cu(I)–Imphen–diphosphine complexes were designed and synthesized to enable reversible phosphorescence switching upon exposure to NH₃ and HCl vapors. The complexes were characterized by IR, NMR, X-ray single-crystal diffraction, and ESI-MS. Photophysical studies revealed that absorption and emission are governed by both ligand deprotonation and solvent effects: in dichloromethane, ionic and neutral complexes show markedly different spectral profiles, whereas in DMSO, strong solvation and hydrogen-bond acceptance smooth the spectral differences between ionic and neutral complexes. Notably, when deposited on silica gel TLC plates, complexes 1a and 1b display reversible, visually detectable phosphorescence switching upon alternating exposure to NH₃ and HCl vapors. This response is attributed to reversible protonation/deprotonation, which modulates high-frequency vibrational quenching channels and the extent of π-conjugation, with additional influence from the confined silica gel microenvironment and heat-assisted volatilization of DMSO-d₆ during fuming. This work provides a robust foundation for developing Cu(I)-based materials for reversible acid–base vapor phosphorescent sensing.