Abstract: Background: The continued evolution of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) created ongoing challenges for molecular diagnostics and variant surveillance. Reliable assays capable of maintaining diagnostic sensitivity across emerging variants while providing rapid variant information remain essential for clinical management and public health monitoring. This study evaluated the performance of the GXT96 X3 extraction kit in combination with the FluoroType® SARS-CoV-2 varID Q version 1.0 assay (Hain LifeScience SA (Pty) Ltd, South Africa) for the detection, semi-quantitative assessment, and variant characterization of SARS-CoV-2. Methods: A total of 220 samples were evaluated, including residual nasopharyngeal clinical specimens (n = 183), reference materials, and cultured SARS-CoV-2 virus dilutions. Residual specimens collected during multiple COVID-19 waves in South Africa (wild type, Beta, Delta, and Omicron) were compared against standard-of-care (SOC) molecular assays used for routine diagnosis. RNA extraction was performed using the automated GXT96 X3 platform, followed by amplification on the FluoroCycler® XT using the FluoroType® SARS-CoV-2 varID Q assay targeting RdRp and N genes, with additional spike gene mutation detection for variant identification. Diagnostic accuracy, agreement (Cohen’s kappa), precision, linearity, and limit of detection (LoD) were assessed. Results: The assay demonstrated a sensitivity of 98.4% (95% CI: 94.2–99.8) and specificity of 100% (95% CI: 95.9–100.0) compared with SOC assays, with an overall agreement of κ = 0.981. Precision analysis showed acceptable reproducibility with standard deviation ≤1.49 and coefficient of variation ≤3.83%. Regression analysis demonstrated strong linearity across dilution series (R² = 0.9882 for RdRp and 0.994 for N genes). The LoD was ≤100 copies/mL for the RdRp gene and 250 copies/mL for the N gene. Variant-associated spike mutations detected by the assay corresponded broadly with epidemiological wave patterns observed in South Africa. Conclusions: The GXT96 X3 extraction platform combined with the FluoroType® SARS-CoV-2 varID Q assay demonstrated high diagnostic accuracy, reproducibility, and reliable SARS-CoV-2 detection across a range of viral loads. The assay additionally provides rapid mutation-based variant information, supporting its utility for routine diagnostic testing and complementary variant surveillance.

Abstract: Castration-resistant prostate cancer (CRPC) survives androgen deprivation, a mechanism widely attributed to autonomous de novo steroidogenesis. Despite the clinical deployment of CYP17A1 inhibitors, the metabolic fidelity of the models underpinning this "tumor-as-gonad" dogma remains controversial. Here, integrating high-resolution liquid chromatography-mass spectrometry with transcriptomics across diverse prostate cancer models, we demonstrate that malignant cell lines universally lack autonomous steroidogenic capacity due to the transcriptional silencing of CYP17A1. Instead, these models operate as high-efficiency precursor "converters" by upregulating HSD3B1 and AKR1C3. Clinical stratification of 844 Prostate Adenocarcinoma patients corroborated this precursor-dependent phenotype. We identify a critical divergence: AR-High tumors rely on oxidative phosphorylation, whereas the transition to an AR-Low state is marked by extensive lineage plasticity. Strikingly, a neuroendocrine plasticity score inversely correlates with AR flux and independently predicts clinical progression (HR=2.41, p=0.024). Our findings redefine CRPC metabolism, dictating a therapeutic shift toward targeting downstream precursor conversion and adaptive lineage plasticity.

Abstract: Chemical recycling of polyolefins is essential to mitigate plastic waste accumulation and promote circular economy strategies. Among the various chemical recycling pathways, catalytic pyrolysis, tandem catalyst systems, ethenolysis, hydrocracking, and hydrogenolysis have emerged as promising approaches for converting polyolefin waste into valuable hydrocarbons, including gaseous, liquid, and solid products. This review provides a survey of recent research on these methodologies, with a particular focus on the production of light gaseous hydrocarbons (C1–C4), bypassing the intermediate pyrolysis oil stage, which is often associated with contaminants and increased processing costs. The novelty of the present work lies in its emphasis on gaseous fractions, in contrast to most existing studies that primarily target oil recovery. Aspects such as catalyst selection, reaction conditions, and product distribution are analyzed. Additionally, the current Technology Readiness Level (TRL) of the studied processes, their relative advantages, limitations, and perspectives for industrial applications are discussed. The analysis highlights catalytic pyrolysis with zeolites as the most mature and scalable technological alternative for manufacture of light compounds directly from polyolefin waste, while tandem catalyst systems and ethenolysis constitute promising but still emerging alternatives for targeted gas production.

Abstract: Agroecology systems integrate social and ecological principles into agricultural practices. Current assessments do not adequately consider animal welfare. This study introduces a new agroecology assessment tool by adding livestock sustainability and animal welfare criteria based on the Five Domains. Through a cross-sectional survey of 14 case studies, we examine how livestock sustainability and animal welfare are integrated into agroecological systems. Surveyors gathered data from farms in Kenya (10), Thailand (1), Italy (1), Vietnam (1), and Mexico (1). Results indicate that certain management practices within agroecological systems, specifically import of feed edible for human consumption, impact sustainability and painful beak trimming and stressful transport, negatively impact animal welfare. These findings highlight the need to strengthen agroecological assessment methods by including sustainability and animal welfare indicators. Doing so can help drive food-system change that improves health, reduces disease risk, and enhances animals’ ecological and social contributions. The paper concludes that better policy and knowledge are essential to improving the wellbeing of both animals and farmers in agroecological systems. The integrated tool could help researchers and farmer organizations improve animal welfare on agroecological farms across different contexts. Better animal welfare could also support the wider adoption and scaling of livestock integration in agroecology.

Abstract: Background: Medical thoracoscopy (MT) represents the gold standard for undiagnosed pleural effusions, traditionally performed in the presence of pleural fluid. Recent technical advances have enabled MT in "dry space" conditions (minimal or absent pleural effusion), raising questions about comparative diagnostic efficacy and safety profiles. Objective: This literature review aims to evaluate diagnostic yield and complication rates between traditional MT performed in patients with current pleural effusion and dry medical thoracoscopy (DMT). Results: MT demonstrates diagnostic sensitivity ranged from 80% to 96.3% and specificity close to 100% for malignant pleural disease and diagnostic accuracy is 99.1% for tuberculous pleuritis. DMT using ultrasound guidance achieves comparable diagnostic yield, with recent studies reporting optimal success rates in pleural access and tissue sampling, and diagnostic sensitivity for malignancy up to 100%. Major complication rates are comparable between MT and DMT, with no significant differences in overall adverse events. Mortality rates remain exceptionally low (≤0.1%) for both approaches. Conclusions: MT remains a highly effective diagnostic tool for pleural diseases. DMT represents a valid and safe alternative in patients without significant pleural effusion, offering comparable diagnostic yield. Although technically more demanding, DMT expands diagnostic possibilities in selected clinical scenarios.

Abstract: Background/Objectives: Lung cancer survival depends on early detection; however, in the Philippines, high radiologist workloads and the anatomical complexity of chest X-rays (CXRs) contribute to missed pulmonary nodules and false-negative diagnoses. This study aims to develop an enhanced deep learning model to improve nodule classification and localization sensitivity. Methods: We propose RNNet-MST, an extension of ResNet-50 that incorporates Multi-Scale Transformer blocks for global context modeling and a custom spatial attention mechanism for attention-based weak localization of disease-relevant regions. The model was trained and evaluated on the NODE21 chest X-ray dataset and compared with a baseline ResNet-50 using classification metrics, with attention maps used for weak localization analysis. Results: RNNet-MST demonstrated improved performance across evaluated metrics relative to the baseline model. Nodule Recall increased from 86.18% to 93.09% (+6.91%), reducing false negatives. Test Accuracy reached 95.16% (+0.51%), and the Nodule F1-Score improved to 91.40% (+1.50%), indicating better detection of small and subtle nodules. Conclusions: The integration of multi-scale transformer features improved classification sensitivity, while the attention mechanism provided weak localization cues that aligned more closely with annotated nodule regions than the baseline. RNNet-MST shows potential as a diagnostic support tool, warranting further validation on larger and more diverse clinical datasets to reduce perceptual errors and facilitate early lung cancer detection in resource-constrained settings.

Abstract: To address the issues of manual operation dependency and low efficiency in tunnel fire research combining computational fluid dynamics (CFD)with deep learning, this paper proposes a multi-agent collaborative framework based on large language models to automate the entire process of inverting fire source characteristics. The framework decomposes the traditional workflow into four specialized agents, namely physical modeling, data governance, model training, and evaluation analysis, which collaboratively execute end-to-end tasks from CFD scenario generation to model deployment. The results demonstrate that the CNN-LSTM model performs optimally. Under a 6 second observation window and 10 meter sensor spacing, the average R² reaches 0.942, representing a 2% improvement over the baseline LSTM model, while the RMSE is reduced by 28.8%. Under sparse deployment with 30 meter spacing, the average R² remains as high as 0.917, validating the effectiveness of integrating spatial feature extraction with temporal modeling. This work provides an efficient technological pathway for intelligent tunnel fire identification and advances the research paradigm from manual optimization to multi-agent system optimization.

Abstract: Modern residential toilets pose a significant biomechanical challenge for older adults with diminished muscle strength, as standard seat heights necessitate excessive joint range of motion (ROM) and compensatory upper-limb reliance. This study evaluated the biomechanical efficacy of a biomimetic Stand-assist Toilet Seat designed to facili-tate sit-to-stand (STS) transitions through a proactive curvilinear trajectory. Thirty community-dwelling older adults were stratified into high-, moderate-, and low-functioning groups based on 30-second Chair Stand Test normative data. A mul-ti-modal assessment framework was employed, integrating MediaPipe-based AI pose estimation for joint kinematics and instrumented armrests with high-precision load cells for kinetic analysis. The results demonstrated that the biomimetic seat signifi-cantly optimized movement efficiency, evidenced by a robust reduction in hip and knee ROM with a large effect size (η²p > .70, p< .001). Kinetic data further revealed sub-stantial upper-limb unloading, with significant decreases in peak arm-support force (Fmax,p=.001, η²p =.35) and cumulative impulse (Iarm,p< .001, η²p =.42). While no signifi-cant interaction was found, a clinical trend (η²p =.17) suggested that low-functioning individuals derived the greatest mechanical advantage from the device. By actively guiding the user’s center of mass toward a biomechanically advantageous "power zone," the biomimetic trajectory minimizes compensatory trunk flexion and armrest reliance. These findings provide evidence-based insights into the role of trajecto-ry-informed assistive technology in enhancing toileting safety and functional inde-pendence for the aging population, particularly those exhibiting signs of possible sar-copenia.

Abstract: Objectives: To assess the prevalence of depressive symptoms and examine their associations with tuberculosis-related knowledge, attitudes, and practices (KAP) among patients with multidrug-resistant tuberculosis (MDR-TB). Methods: A cross-sectional study was conducted among 528 MDR-TB patients. Depressive symptoms were measured using the Patient Health Questionnaire-9 (PHQ-9), with a score of ≥10 indicating clinically relevant depression. KAP domains were assessed using a structured scoring system. Associations were analyzed using Spearman correlation and multivariable logistic regression. Results: The mean PHQ-9 score was 5.32 ± 4.35, and 14.96% of participants (n = 79) had clinically relevant depressive symptoms. Among them, 57 had moderate, 17 had moderately severe, and 5 had severe symptoms. Multivariable analysis showed that higher attitude scores were associated with lower odds of depression (aOR = 0.936; 95% CI: 0.886–0.990; p = 0.02). Higher practice scores were also strongly associated with reduced depression risk (aOR = 0.837; 95% CI: 0.778–0.901; p < 0.001). Knowledge score was not independently associated with depression (p = 0.622). Conclusions: Depressive symptoms are common among MDR-TB patients and are more strongly linked to attitudes and practices than to knowledge alone. These findings highlight the importance of integrating mental health screening and behavioral support into MDR-TB management programs to improve comprehensive patient care.

Abstract: Students who stall in the final stage of their degree rarely do so because they lack technical skill. More often, confidence erodes under sustained uncertainty, motivation shifts from intrinsic engagement to anxious compliance, and the demands of organizing months of research exceed what willpower alone can sustain. This study examines those emotional and motivational dynamics directly, treating research self-efficacy and intrinsic motivation not as background variables but as the affective-motivational core of thesis performance. Using partial least squares structural equation modeling (PLS-SEM) grounded in self-determination theory and social cognitive theory, we tested an integrative model with data from 396 undergraduate students actively completing theses at public and private universities in the northern region of Peru. Four enabling factors — methodological competencies, intrinsic motivation, tutorial support, and resources and conditions — were linked to thesis quality and process efficiency through two mediating mechanisms: research self-efficacy (the confidence to face methodological difficulty without retreating) and project management (the behavioral self-regulation that converts motivation into organized work). Resources and conditions showed the strongest associations in the model, with the largest effects on both project management (β = 0.533) and research self-efficacy (β = 0.418). Self-efficacy, in turn, was the primary predictor of thesis quality (β = 0.518), while project management and quality together drove process efficiency. The model explained 70.5% of variance in thesis quality and 81.4% in process efficiency. These pa

Abstract: Dental pain due to dentine hypersensitivity or pulpitis is characterized by short or lasting episodes of pain triggered by normally innocuous stimuli originating from exposed dentine. Both represent the most frequent pain of the orofacial region. Transient receptor potential (TRP) superfamily of ion channels participates in the detection of different modalities of sensibility in the mammalian sensory teeth system, i.e., trigeminal neurons and odontoblasts. In particular, some members of the melastatin family (TRPM) serve as molecular thermal sensors, and temperature is one of the most potent stimuli in triggering dentine hypersensitivity. Here we review and update the information about the distribution of TRPM channels in the trigeminal ganglion and dental pulp cells, especially odontoblast, in humans and animal models. In addition to the well know sensory roles of TRPM, other functions such as in development and mineralization of teeth are considered.

Abstract: Plastic packaging waste has emerged as a critical environmental challenge due to its persistence, low degradation rates, and increasing accumulation in terrestrial and marine ecosystems. Conventional petroleum-based plastics dominate packaging applications because of their durability and low cost; however, their environmental impacts have prompted urgent demand for sustainable alternatives. Bio-based and compostable packaging materials offer promising solutions by utilizing renewable resources and enabling environmentally benign end-of-life pathways. This paper examines the development of bio-based and compostable packaging alternatives aimed at reducing plastic waste. Through a systematic review of material innovations, processing technologies, and life-cycle considerations, the study evaluates the performance, environmental benefits, and limitations of emerging bio-based packaging solutions. The findings indicate that materials such as polylactic acid, polyhydroxyalkanoates, starch-based composites, and cellulose-derived packaging can significantly reduce fossil resource dependency and plastic pollution when supported by appropriate infrastructure. The paper concludes that while bio-based and compostable packaging presents strong environmental potential, successful large-scale adoption requires integrated design strategies, composting infrastructure, and supportive policy frameworks.

Abstract: Distributed modern software platforms spanning microservices, serverless functions, and edge computing face unprecedented security threats from stealthy adversaries exploiting encrypted data flows and behavioural camouflage. Conventional defences require decryption for analysis, exposing sensitive information in untrusted cloud environments. This paper proposes an innovative framework integrating homomorphic encryption (HE) with automated threat hunting to enable privacy-preserving threat detection at scale. Using levelled BFV schemes from OpenFHE, we perform computations directly on ciphertexts for anomaly scoring and behavioural profiling, while our hunting engine employs graph neural networks and isolation forests to hypothesize and pursue attacker patterns across distributed logs without plaintext exposure.The architecture deploys as Kubernetes-native operators, processing 10,000 encrypted events per second with 92% detection accuracy on MITRE-emulated scenarios, outperforming traditional UEBA by 35% in F1 score and reducing analysis latency from hours to seconds. Evaluations on AWS EKS clusters demonstrate sub-200ms query times for homomorphic aggregations, with noise management via bootstrapping optimizations. Case studies in fintech pipelines reveal thwarted supply-chain compromises and insider data exfiltration’s. By revolutionizing secure computation in dynamic ecosystems, our solution bridges cryptography and AI-driven hunting, offering deployable resilience against evolving threats while complying with GDPR and zero-trust mandates. Future work extends to fully homomorphic deep learning for adaptive adversary modelling.

Abstract: Avian influenza is a critical zoonotic infection threatening both the poultry industry and global public health. While traditional intramuscular vaccines elicit systemic im-munity, they often fail to provide robust local protection at mucosal surfaces. There is thus significant interest in the development of mucosal avian influenza vaccines administered via the intranasal route. However, in humans, this approach is significantly hampered by the availability of safe and effective adjuvants. This study investigated the immunogenic-ity of a modified recombinant influenza A/H5 hemagglutinin (rHA/H5-modif) formulated with Novochizol, a novel chitosan-derived delivery system, administered intranasally to laboratory animals. Our results demonstrate that mucosal immunization with the rHA/H5-modif/Novochizol complex induces potent humoral (IgG and IgA) and cell-mediated immune responses. Crucially, the formulation provided 100% survival in mice following a lethal challenge with highly pathogenic avian influenza A/H5. These findings position the rHA/H5-modif/Novochizol complex as a promising candidate for next-generation mucosal vaccines, in particular against highly pathogenic avian influen-za A/H5 subtype.

Abstract: Several lipid-management guidelines now favor non-fasting lipid measurements for cardiovascular risk assessment. In parallel, this review evaluated the potential clinical utility of non-fasting glucose measures for disease diagnosis and risk prediction. Postprandial plasma glucose measured 4–7.9 hours after a meal (PPG_4–7.9h) shows relative stability within this window and appears to be a promising marker for diagnosing diabetes and predicting mortality from cardiovascular disease (CVD) and cancer. Similarly, 2-hour plasma glucose during an oral glucose tolerance test performed 4–7.9 hours after a meal (2-h PGOGTT_4–7.9h) demonstrates diagnostic and prognostic value, particularly for diabetes and cardiovascular mortality. Notably, the diagnostic and predictive performance of these non-fasting measures is not inferior to that of traditional fasting glucose assessments. Mechanistically, postprandial hyperglycemia may contribute to CVD through increased oxidative stress and inflammation, endothelial dysfunction, and promotion of atherogenesis and thrombogenesis. It may also increase cancer risk via oxidative stress, inflammation, and insulin-mediated cellular proliferation. In addition, it may enhance lipogenesis to form membrane lipids supporting tumor growth. Further research is required to establish the clinical applicability, optimal thresholds, and generalizability of these non-fasting glucose measures.

Abstract: Decentralized applications (DApps) represent a paradigm shift in software architecture, leveraging blockchain technology and distributed consensus mechanisms to eliminate single points of failure and centralized control. As the adoption of DApps accelerates across sectors such as finance, supply chain, healthcare, and governance, ensuring their functional correctness and behavioral reliability has become a critical engineering challenge. Unlike traditional software, DApps operate in adversarial, permissionless environments where smart contracts execute autonomously and immutably on distributed nodes, making post-deployment correction extremely costly or impossible. This review systematically examines the landscape of functional testing methodologies tailored for decentralized applications, analyzing their suitability, limitations, and practical applicability in modern DApp development workflows. We survey research spanning smart contract verification, consensus protocol testing, oracle interaction validation, cross-chain interoperability testing, and user-layer functional testing of Web3 interfaces. The review identifies four dominant testing paradigms: (1) unit testing of smart contract functions, (2) integration testing of DApp components, (3) property-based testing using formal specifications, and (4) end-to-end simulation on testnets. Through comparative analysis across 13 seminal studies, we evaluate each approach along dimensions of automation feasibility, coverage depth, gas efficiency awareness, and scalability to complex DApp ecosystems. Our findings indicate that while static analysis and symbolic execution tools such as Mythril, Slither, and Manticore offer strong vulnerability detection, they address security properties more than functional correctness. Conversely, framework-based testing tools like Hardhat, Truffle, and Foundry provide adequate unit-level coverage but struggle with cross-contract orchestration and event-driven logic verification. A critical gap exists in testing oracle-dependent and DAO governance workflows. This review concludes with a synthesis of best practices, open research challenges, and a directional roadmap for developing holistic functional testing frameworks suited to the evolving complexity of decentralized systems.

Abstract: Blockchain networks now underpin mission-critical services in finance, healthcare, supply-chain logistics, and digital governance, yet production deployments continue to suffer severe resilience failures ranging from Byzantine consensus violations to cross-chain bridge exploits that have collectively caused losses exceeding $2 billion. The root cause is a critical tooling gap: ex- isting frameworks such as BlockBench and Hyperledger Caliper evaluate only crash-fault performance and provide neither ad- versarial fault modelling nor automated remediation guidance, leaving operators without a rigorous means of holistic resilience assessment prior to deployment.This paper presents the Blockchain Resilience Analysis System (BRAS), a five-layer, platform-agnostic framework that unifies real-time network topology monitoring, multi-class adversarial fault injection, composite resilience scoring, closed-loop adaptive consensus reconfiguration, and structured reporting within a single repeatable pipeline. BRAS introduces the Resilience Index (RI), a mathematically grounded composite metric that aggre- gates four sub-dimensions—network connectivity, throughput stability, mean-time-to-recovery (MTTR), and Byzantine fault tolerance ratio—into a single interpretable score calibrated to operator-defined service-level objectives. An Adaptive Reconfigu- ration Module (ARM) monitors the RI stream and autonomously adjusts consensus timeout parameters and peer-connection poli- cies when the RI drops below a configurable threshold, closing the feedback loop between fault detection and remediation without manual intervention.Experimental evaluation on a 20-node Hyperledger Fabric testnet and a 15-node Ethereum Proof-of-Authority network demonstrates that BRAS achieves a 34% reduction in MTTR under simulated eclipse attacks and reduces false-positive fault detections by 28% relative to threshold-only monitoring base- lines. The RI metric exhibits strong correlation (r = 0.91, p < 0.001) with independently measured system availability across 50 fault campaigns, validating its predictive utility. BRAS is the first framework to simultaneously address network-layer, consensus-layer, and application-layer resilience threats under a unified, vendor-agnostic architecture, offering both a rigor- ous theoretical foundation and a deployable implementation blueprint for blockchain resilience engineering.

Abstract: Open, well-documented datasets are essential for the reproducible development of vision systems for urban utility management. This Data Descriptor presents a curated RGB object-detection benchmark of four classes associated with electrical distribution and street-level utility assets: Inspection Chamber, Overhead-to-Underground Transition, General Protection Box, and Transformer Substation. The public release contains 997 valid image-label pairs partitioned into 698 training, 150 validation, and 149 test images. Images were acquired during 2019 in multiple localities across Spain, predominantly with a mobile phone and, in occasional cases, using Google Maps as a complementary visual source, and were manually annotated with LabelImg before export to YOLO format. During curation, four invalid image-label pairs were removed because at least one YOLO bounding box exceeded the normalized image domain. The benchmark contains 1,939 object instances, with marked class imbalance: General Protection Box accounts for 50.2% of objects whereas Transformer Substation represents 4.7%. Images are heterogeneous in size and viewpoint, ranging from 90 × 170 to 4160 × 4032 pixels, with a median resolution of 619 × 544 pixels and a median of two annotated objects per image. The public GitHub release is organized into images/, labels/, and metadata/ directories; metadata stores split definitions, classes.txt, data.yaml, inventory information, annotation schema documentation, and diagnostic summary figures. Beyond detector benchmarking, the dataset can support scalable mapping of visible distribution-grid assets, with potential value for smart-city digital twins and data-informed EV charging deployment.

Abstract: The input–output model is an important analytical tool in regional economics because it represents economic sectors and their productive interconnections. This study proposes a new analytical framework based on two multipliers—one for production and another for value added—from which a Value Conversion Rate (VCR) is derived. The VCR measures the efficiency with which changes in output are converted into value added. Based on these indicators, a quadrant-based graphical structure is developed that combines the intensity of the production multiplier with the VCR. This structure highlights their possible combinations and indicates whether the propagation of production effectively translates into value-added generation, offering a new perspective for interpreting productive structures. By integrating propagation capacity with the efficiency of value creation, the VCR framework provides analytical support for economic diagnostics and for policies aimed at sustainable economic and social development. The approach is illustrated using the 2019 input–output matrix of the state of Rio Grande do Sul, Brazil.

Abstract: Conventional tests of Bell’s inequality rely on entangled photon pairs. Here, we replace entangled pairs with two independent photons of orthogonal polarization, and demonstrate that Bell’s inequality is still violated. Given the inherent local realism of independent photons, this experiment proves that Bell’s inequality cannot falsify the local realism of photons. We thus conjecture that the violation of Bell’s inequality by entangled photon pairs originates from their orthogonal polarizations, rather than the breakdown of local realism. To interpret this unexpected violation with independent photons, we further substitute the two photons with two monochromatic light beams, and calculate the transmittance correlation through polarizers via Malus’s law and Karl Pearson’s correlation formula. We show that this correlation also defies Bell’s inequality. Retracing the derivation of Bell’s inequality reveals its validity is restricted to binary events, which accounts for the observed violation with light beams. Finally, we propose a thought experiment involving gradual attenuation of light intensity down to the single-photon regime, and hypothesize that single-photon transmission through a polarizer does not constitute a binary event. This hypothesis provides a unified interpretation for both our experimental findings and all canonical Bell inequality tests reported to date.