Search Results (65)

Automated license plate readers (ALPRs) are often evaluated as discrete police tools, although their public power arises from cross-vendor socio-technical infrastructure. This article examines roadside and mobile sensors, vehicle-attribute classification, cloud archives, commercial databases, real-time crime center integration, interagency access, automated alerts, and police action. Flock Safety supplies the principal documentary case because unusually extensive public records permit system-level tracing; Axon/Fusus, Motorola Vigilant/VehicleManager, and federal access to commercial ALPR data establish the wider vendor-independent boundary. A structured documentary analysis of 59 sources triangulates official records, peer-reviewed research, vendor materials used only for stated functions, and record-based investigations. It integrates boundary critique, control-structure mapping, feedback analysis, constitutional doctrine, a STRIDE-informed threat model, and empirical research on policing effectiveness and surveillance effects through 3 August 2026. The analysis identifies four conditional mechanisms: infrastructure aggregation, authority diffusion, asymmetric feedback, and rights invisibility. The article reformulates the Rights Control Deficit (RCD) as a non-arithmetic profile relation between operational demands and effective governance capacity and applies it to three documented configurations and a clearly labeled normative benchmark. Seven falsifiable propositions specify variables, indicators, suitable methods, and disconfirming conditions for later empirical study. A rights-preserving hybrid-intelligence architecture combines bounded automation with judicial authorization, short retention, sensitive-location protections, immutable audit, availability safeguards, independent review, contestability, sanctions, and credible termination authority. The evidence identifies capabilities, activated pathways, and conditional risks; it does not estimate population prevalence or a universal ALPR-specific causal effect. Meaningful human oversight is an institutional control property, not merely an officer’s presence at an interface. Full article

Predicting CRISPR-Cas9 guide RNA efficiency and off-target activity is a precondition for precise genome editing. Computational models have progressively incorporated chromatin accessibility and epigenetic descriptors into their feature sets, yet synthesising findings from independently published studies—especially when those studies contradict one another—remains an unresolved methodological gap. Large Language Models (LLMs) have been proposed as a route to automate cross-study synthesis, but their utility depends on a constraint that receives less attention than model architecture: how much of the source text actually reaches the model at inference time. Cloud-based models process 48,000-token corpora without hardware limitations, but at the cost of data leaving the local environment and with limited reproducibility across API versions. Local RAG systems avoid the cloud dependency while fragmenting the input, discarding the global context needed to link biological arguments that are distributed across separate papers. We benchmark these strategies using a corpus of four CRISPR-Cas9 efficiency prediction studies and apply the Reduced Interaction Sampling (RIS) engine—a local sparse attention method—to retain the full sequence within the memory envelope of a laboratory server. Preserving that context uncovers three latent inconsistencies. The static epigenetic markers used in DeepCRISPR (CTCF, DNase I) show near-zero Spearman correlations with off-target cleavage ($\rho \le 0.07$), while nucleosome positioning scores from the Block Decomposition Method reach $\rho =0.388$–$0.423$. The sequence-only Apindel model was published in June 2022 without incorporating nucleosome descriptors reported in the concurrent literature. The benchmark review by Konstantakos et al. attributed 10–20% of rank correlation to epigenetics—a figure that reflects the weak feature subset evaluated, not a ceiling on chromatin influence. These discrepancies are invisible when papers are read individually or retrieved as chunks; they become traceable only when the full corpus is processed as a single context window. An independent empirical analysis of 2000 CRISPR-Cas9 off-target cleavage events provides evidence consistent with this pattern: static epigenetic markers yield $\left|\rho \right|\le 0.11$, whereas computed NuPoP Affinity descriptors reach $r=-0.622$ ($p<{10}^{-210}$). On a 30-question cross-study synthesis benchmark (5 independent seeds), baseline accuracy is 53.33%, RAG 60.00%, and RIS (30 seeds, 3% density) 70.00% ($p<0.0001$, t-test vs. RAG, $\sigma =0.00\%$ for all configurations). Full article

Understanding which segments of the economy drive aggregate stock market movements is central to risk management. This study traces how the economic drivers of the Standard & Poor’s 500 Index (S&P 500) changed across two episodes: the 2020 COVID-19 shock and the 2025 technology-led period, using eleven sector indices and nine deep learning architectures. During COVID-19, forecasting power concentrated in Consumer Discretionary, Health Care, and Industrials before reorganizing sharply around Information Technology, consistent with a disruptive break. In 2025, Information Technology and market momentum dominated throughout, with no comparable reorganization, consistent with a gradual adjustment rather than a disruptive shift. This distinction, invisible from accuracy metrics alone (Gated Recurrent Unit: Mean Absolute Percentage Error = 3.41% and 2.16%), shows that information-concentration diagnostics can complement forecast-accuracy and risk-monitoring frameworks. Full article

Credit risk assessment forms a cornerstone of banking risk management and the stability of the wider financial system. Over the past decade, the rapid development of machine learning (ML) techniques has substantially enhanced traditional credit risk assessment methodologies. ML has now emerged as a core technological pillar for the banking sector, strengthening risk identification capabilities, optimising credit decision-making, and advancing financial inclusion. Conventional credit scoring models, dominated by logistic regression (LR) and scorecard approaches, offer inherent strengths in interpretability and regulatory compliance. However, constrained by their linear assumptions, these methods struggle to capture complex non-linear relationships within credit data and deliver insufficient predictive accuracy for the “credit-invisible” population lacking formal credit histories. This paper presents a systematic literature review (SLR) of ML applications in credit risk assessment (CRA), covering publications from January 2016 to May 2026. A total of 894 papers were retrieved from five digital libraries, and following a rigorous multi-stage screening process, 129 studies were selected for final inclusion. Our analysis reveals that tree-based ensemble models and deep learning (DL) architectures predominate in contemporary research in this field. Meanwhile, post hoc explanation methods and machine learning operations (MLOps) are gaining significant traction as solutions to address fairness, transparency, and system maintenance challenges in real-world production environments. We synthesise prevailing methodologies into a unified end-to-end credit risk modelling framework spanning data preprocessing, feature engineering, model training, evaluation, and operational deployment. Through a critical assessment of the advantages, limitations, and inherent trade-offs of existing approaches, this SLR not only identifies current research gaps and future directions for the academic community, but also provides practical guidance for the banking sector to build compliant, fair, and efficient intelligent risk assessment systems. Full article

This paper seeks to illuminate the historical and biological intersections between Druze traditions and the silent architecture of Freemasonry within the Balkan milieu, a region historically defined by a constellation of preeminent scientists, writers, and artists whose legacies were forged within the crucible of Masonic thought. Central to this inquiry is the reconstruction of a familial intellectual lineage and genealogy of the author’s ancestor that maintained, for generations, a quietude of profound exchanges with esoteric custodians in France. These subtle conduits suggest the operation of “invisible networks” bound by an unstated ethical and philosophical cipher. To probe the biological currents beneath these traditions, the study unveils genetic evidence of the K1a5a haplogroup, a Levantine signature found within the domestic sphere. By examining this familial case study, the work suggests that the preservation of “the Secret” is not merely an intellectual endeavor, but a biological inheritance, where genetic markers and initiatory silence converge across centuries. The continuity of these invisible networks is historically evidenced by the 1926 Belgrade Masonic Peace Congress, led by French and German Grand Masters. This Legacy directly connects to its centennial peace declaration scheduled in Belgrade for late Juni 2026, showcasing the enduring role of the region in trans-regional diplomacy. Full article

Chrome extensions are increasingly exploited as an attack surface, yet existing static malware detectors share a critical blind spot: they analyze JavaScript but cannot inspect WebAssembly (Wasm) or reason across the Wasm–JS boundary. We exploit this gap by embedding malicious logic in Wasm modules while confining JavaScript to minimal glue code, rendering the core of each attack invisible to static analysis. Grounded in this collaborative architecture, we implement eight proof-of-concept attack scenarios across six categories—adware, malicious file delivery, forced redirection, keylogging, credential theft, and ransomware—as functioning Manifest V3 extensions. Evaluated against four representative static detectors, none achieves genuine detection: three register the samples as benign or raise no alert, and the fourth flags every sample only through systematic false positives on generated glue code. An analysis of 165,314 live extensions further shows that every permission our attacks require is already in widespread legitimate use, so such extensions would not be distinguishable from benign ones by permission-based screening alone. Full article

Vehicular Named Data Networking (VNDN) inherits the broadcast-oriented forwarding of NDN, which exposes safety messages to position-falsification attacks. Existing detectors rely either on static fuzzy thresholds, which drift as traffic patterns change, or on opaque deep models, which are accurate but uninterpretable to safety auditors. We propose a two-stage detector that combines an Adaptive Fuzzy Membership Tuning (AFMT) pre-filter with an attention-augmented bidirectional LSTM. AFMT is a Mamdani fuzzy classifier whose triangular membership-function parameters are updated online by gradient descent on a prediction-error feedback signal from the downstream BiLSTM, replacing offline-fixed thresholds. The BiLSTM consumes the fuzzy suspicion score as an extra feature and produces interpretable per-time-step attention weights aligned with attack onsets. On a simulator-synthesized VNDN benchmark following the five canonical VeReMi attack types, the detector attains F1-scores between 0.955 and 0.979 (macro-average 0.964), ties the strongest baselines on the hardest Random-Offset attack while achieving the highest ROC-AUC of all models (0.984), and runs in 0.44 ms per sample on a CPU. On a live OMNeT++/Veins/SUMO testbed running the five attacks on the LuST scenario, the detector attains an F1 value of 0.986. A leave-one-feature-out study shows that detection does not hinge on the Kalman plausibility feature, and on the real public VeReMi v1.0 dataset the architecture transfers to four of the five attack types at an F1 near 1.0, while the Constant Offset stays invisible to kinematics-only features, and this quantifies the value of the named-data-plane features. Every number reported here is measured from the running detector. Full article

Background: Preterm premature rupture of membranes (PPROM) before 32 weeks of gestation with prolonged latency is associated with substantial neonatal morbidity, including Dry Lung Syndrome (DLS), pulmonary hypoplasia (PH), bronchopulmonary dysplasia (BPD), and death. Accurate individualized risk stratification remains elusive, as the interacting contributions of amniotic fluid dynamics, inflammatory status, and microbiological burden are inadequately captured by traditional statistical approaches. Methods: We performed a retrospective, exploratory–predictive analysis of 66 pregnancies complicated by second-trimester PPROM with latency exceeding 14 days. Elastic Net and Random Forest models were trained across six clinically defined predictor domains using a multi-stage block modelling strategy. To address the clinically relevant distinction between antenatal and postnatal information, results are reported separately for Model A—comprising exclusively antenatal predictors available during expectant management (gestational age at PPROM, latency, amniotic fluid trajectory, inflammatory status, vaginal microbiome at admission)—and Model B, which additionally incorporates postnatal variables and characterizes the full mechanistic perinatal risk trajectory. Binary and ordinal outcomes included DLS, PH, BPD, intraventricular hemorrhage (IVH), and neonatal death. Pairwise interaction models were additionally computed to identify cross-domain risk constellations. Results: Distinct predictor architectures emerged per outcome. Pulmonary hypoplasia was most strongly associated with temporal features of oligohydramnios—particularly the persistence and timing of SDP < 1 cm—rather than isolated measurements. For DLS, the antenatal model (Model A) achieved AUC 0.776, driven by gestational maturity and inflammatory status; surfactant administration—a postnatal variable reflecting therapeutic response rather than an antenatal risk factor—dominated only the mechanistic Model B. Neonatal death was driven by a combined profile of respiratory support burden, amniotic fluid persistence, and co-morbidity. IVH showed consistently high ordinal predictability (accuracy 0.863), with amniotic fluid dynamics and microbiological burden as leading contributors. BPD remained the least linearly separable endpoint across all configurations. Conclusions: Multi-domain machine learning reveals outcome-specific, cross-domain risk architectures following second-trimester PPROM that are invisible to conventional statistical models. Longitudinal amniotic fluid trajectory is the dominant antenatal determinant of structural pulmonary morbidity, while microbiological burden independently shapes neurological risk. These findings support prospective validation of integrated ML-based risk stratification tools for individualized antenatal counselling in this high-risk population. Full article

Although the goal of “sustainable” urbanism has generated an impressive array of international frameworks and declarations, systemic progress remains elusive. A prior paper by the author identified “lock-in” as a central cause: the economic incentives, professional standards, codes, and institutional feedback structures that reinforce unsustainable patterns of urban development despite stated commitments to reform. This paper advances that diagnosis by asking what sustains the lock-in itself, and what structural intervention can address it at the root. We argue that the answer lies in recognizing a fundamental deficit in the feedback architecture governing urban development—a systematic failure to account for two categories of capital on which human welfare depends: natural and resource capital, whose depletion standard metrics render invisible, and human and value-added capital, including the built public realm and the economies of place that markets systematically undersupply. Standard welfare-economic instruments, including Pigouvian taxes, address this at the level of price signals but are unable to fully resolve it there, because multiple forms of goods—referred to as “polycapital”—are structurally interrelated and resist single scalar remedies. The paper proceeds to advance two complementary conclusions: first, that a generative modeling methodology, capable of encoding the interrelated, multi-scale character of polycapital structures, is a necessary precondition for adequate institutional response, and that pattern language methodology provides this capacity; and second, that new transactional mechanisms going substantially beyond Pigouvian instruments—which we outline—represent a necessary direction and a promising research frontier. Full article

Background: Telehealth is increasingly positioned as a solution for healthcare access among older adults; yet for transgender older adults, its application remains undertheorized, inconsistently implemented, and frequently reductive. Structural barriers, including provider incompetence, administrative misgendering, insurance precarity, and the clinical invisibility of aging transmasculine bodies, shape this population’s relationship to telehealth in ways that existing frameworks have not adequately addressed. Objective: This study examines the structural conditions shaping transmasculine and gender-nonconforming older adults’ engagement with healthcare and telehealth, and centers their visions for transformed, justice-oriented virtual care. Methods: Four semi-structured focus groups (n = 14 transmasculine and gender-nonconforming older adults, ages 40–67) were conducted via Zoom in June 2024 and analyzed using Braun and Clarke’s reflexive thematic analysis. The study was designed according to community-based participatory research (CBPR) principles. This study followed the Consolidated Criteria for Reporting Qualitative Research guidelines to ensure methodological transparency in reporting. Results: Analysis yielded five themes: (1) the provider competency crisis; (2) administrative violence and the architecture of misgendering; (3) insurance, politics, and the precarity of access; (4) the aging transmasculine body as uncharted clinical territory; and (5) participants’ collective vision for relational, community-centered care. Conclusions: We introduce the Campfire Model of Relational Telehealth, a conceptual framework comprising five empirically derived pillars: gathering, warmth, collective knowledge, safety, and accountability. The model argues that telehealth must move beyond transactional encounters toward a relational ecosystem of care grounded in justice, belonging, and structural transformation. We conclude with a call to action for providers, policymakers, and researchers to dismantle structural barriers and advance telehealth that cultivates dignity, belonging, and equity. Full article

Tourism destinations consume vast quantities of energy, water, food, and materials, yet these resource flows remain largely invisible in destination planning practice. The aim of this paper is to develop a conceptual framework that reconceptualises tourism destinations as industrial ecosystems and makes their material and energy flows visible, quantifiable, and amenable to destination-scale planning. Existing frameworks prioritise governance and demand management, leaving the material dimension of sustainability unaddressed. To this end, the paper proposes a multi-scale resource-flow framework grounded in industrial ecology. This is a conceptual framework paper: it develops analytical architecture for destination-scale resource accounting rather than reporting empirical measurements. The framework organises four analytical components—actors, flows, structural configurations, and feedback mechanisms—across macro, meso, and micro scales. Three planning capabilities are advanced: supply-chain-complete environmental accounting, resource hotspot detection, and policy design along the full causal chain from structural arrangement to environmental outcome. Material flow analysis, life cycle assessment, and industrial symbiosis mapping are presented as operational tools, illustrated through reference to high-intensity coastal tourism systems. Industrial symbiosis is positioned as a structural mechanism through which by-product valorisation reduces destination-level resource throughput. The study contributes a bridging framework between governance-oriented tourism planning and the material accounting rigour of industrial ecology, distinguishing it from circular economy models that supply a design principle but no material accounting, from urban metabolism approaches that assume temporally stable flows, and from regenerative development that is values-based rather than quantitative. The framework offers a foundation for more integrated and resource-efficient destination sustainability planning. Full article

In BIM-enabled architectural projects, information systems preserve accepted decisions far more reliably than the rejected and non-selected alternatives that shaped them. Drawings, models, specifications and common data environments record what a project became, while the reasons that eliminated competing options are dispersed across meeting notes and revision logs or lost. This asymmetry weakens design coordination, change management and cross-project knowledge reuse. This article proposes a conceptually derived and analytically evaluated recording artefact for recovering these lost decision traces within the phase-transition band from spatial coordination to technical design. A two-gate evaluation logic separates codified screening from stakeholder-mediated review and decouples the procedural location of rejection from the category family that organises its reason. Three loss types are identified: pre-stakeholder invisible loss, trace/version loss and terminal loss. These are linked to six rejection-category families, four process redirection effects and differentiated memory destinations, with a constraint-bearing layer divided into avoidance and comparative branches. A fillable eight-field decision record template, formalised as a single recording-and-routing procedure, is specified for BIM, common data environment and design review workflows, supported by a query specification. The model is illustrated through a constructed hotel-floor decision node and offers a structured basis for retaining the knowledge carried by rejected, revised and valid but non-selected architectural decisions. Full article

Background: Black Sub-Saharan African immigrants are among the fastest-growing immigrant populations in the United States, and their mental health needs, particularly with respect to depression, remain understudied. Cultural beliefs, linguistic frameworks, and coping practices in this population often diverge from Western psychiatric models, suggesting that conventional approaches may fail to capture how distress is experienced and expressed. Objective: This scoping review mapped literature on how Black Sub-Saharan African immigrant adults in the United States perceive, report, and respond to depression. Methods: Following PRISMA-ScR guidelines, six electronic databases were systematically searched for empirical studies published between 2000 and 2026. Two reviewers independently screened and extracted data using a standardized form. Data were analyzed using a narrative synthesis approach combining deductive thematic categorization across three predefined review domains with inductive identification of subthemes through iterative team discussion and consensus, with sociocultural, religious, linguistic, and structural factors examined as cross-cutting themes. Findings were synthesized narratively across three domains: perceptions of depression, reporting and communication, and responses to depression. Results: A total of 19 studies met the inclusion criteria (7 quantitative, 10 qualitative, 2 mixed methods; total N ≈ 1900), generating 24 themes. Perception themes highlighted cultural non-recognition of depression (12 of 19 studies), absence of equivalent terms in African languages (7 studies), spiritual explanatory models, and profound stigma. Reporting patterns showed predominant somatic symptom expression and very low disclosure to providers (2.6–4.2%), with depression prevalence ranging from 8.1% to 100% and no validated screening instrument identified for this population. Response themes emphasized religion and social support as primary coping strategies, with formal mental health utilization virtually absent due to structural, cultural, and intersectional barriers. Conclusions: Depression among Black Sub-Saharan African immigrants is widely experienced yet rendered invisible through interlocking cultural, linguistic, somatic, and institutional mechanisms, which this review terms an architecture of invisibility, leaving it largely unaddressed by formal mental health systems. The identification of only one intervention study underscores a substantial gap between documenting the burden of depression and advancing evidence-informed solutions. Culturally validated measures, faith-based intervention models, longitudinal designs, and attention to structural determinants are urgently needed. Full article

Accurate detection of early invisible apple bruises is important for post-harvest quality assessment. Although hyperspectral imaging (HSI) provides rich spectral information, its high dimensionality introduces substantial redundancy and weak-signal interference. This study proposes an integrated framework combining waveband optimization and discrete spectral modeling for efficient bruise detection. A Selection-Refined Improved Grey Wolf Optimization (SR-IGWO) algorithm was developed to select 18 bruise-sensitive wavebands from 273 channels (996–2501 nm), achieving a 93.4% reduction in spectral dimensionality. SHAP analysis was further used to interpret the selected bands in relation to biochemical responses associated with bruising. To address the mismatch between conventional CNNs and sparse discrete spectral inputs, a CNN–Transformer hybrid model (DSFormer) was designed using pointwise convolution for band embedding and a Transformer encoder to capture global dependencies. Experimental results across ten independent runs achieved a classification accuracy of 99.11% ± 0.08%, a recall of 96.04% ± 1.08%, and an F1-score of 95.95% ± 0.39% under the tested conditions. Ablation studies suggest that the proposed architecture supports effective detection under sparse spectral conditions. Although validation was limited to a single cultivar and controlled sampling, the proposed framework provides a promising preliminary exploration of reduced hyperspectral data for non-destructive fruit bruise detection. Full article

This study examines the kalif structure, a unique and increasingly invisible component of the rural architecture in the Eastern Black Sea region that is currently under threat of extinction, along with the tradition of kalif-guarding integrated with this structure. Historically constructed to protect agricultural production from wildlife, kalifs are not merely functional shelters but also multi-layered memory objects where collective solidarity and social interaction are reproduced. A qualitative research method was adopted for the study, utilizing literature review, on-site physical documentation, and technical analysis centered on Yücehisar village in the Pazar district of Rize. Within the scope of the research, the material use and construction techniques of kalifs are detailed from an architectural perspective, and these practices are evaluated through the lens of Intangible Cultural Heritage. The findings indicate that the loss of the physical presence of kalifs due to the transition from corn to tea cultivation and rural migration signifies the dissolution of a production-based culture of living. Consequently, the study reveals the critical importance of incorporating the kalif and the act of kalif-guarding into academic literature and cultural memory within the framework of Intangible Cultural Heritage standards to preserve local identity and rural memory. Full article