Search Results (7,234)

The rise in Blockchain-based digital assets has transformed the financial ecosystems, which has also created complex governance and taxation challenges. The pseudonymous and cross-border nature of crypto transactions undermines traditional tax enforcement, leaving regulators such as the South African Revenue Service (SARS) reliant on voluntary disclosures with limited verification mechanisms, while existing Blockchain forensic tools and regulatory technologies (RegTechs) have advanced in anti-money laundering and institutional compliance, their integration into issues related to taxpayer compliance and locally adapted solutions remains underdeveloped. Therefore, this study conducts a state-of-the-art review of Blockchain forensics, RegTech innovations, and crypto tax frameworks to identify gaps in the crypto tax compliance space. Then, this study builds on these insights and proposes a conceptual model that integrates digital forensics, cost basis automation aligned with SARS rules, wallet interaction mapping, and non-fungible tokens (NFTs) as verifiable audit anchors. The contributions of this study are threefold: theoretically, which reconceptualise the adoption of Blockchain forensics as a proactive compliance mechanism; practically, it conceptualises a locally adapted proof-of-concept for diverse transaction types, including DeFi and NFTs; and lastly, innovatively, which introduces NFTs to enhance auditability, trust, and transparency in digital tax compliance. Full article

Heavy metals are widespread environmental contaminants from natural and anthropogenic sources, posing risks to human health and ecosystems. In urban areas, levels are elevated due to industrial activity, traffic emissions, and building materials. Camden, New Jersey, a city with a history of industry and illegal dumping, faces increased risk due to aging sewer and stormwater systems. These systems frequently flood neighborhoods and parks, heightening residents’ exposure to heavy metals. Despite this, few studies have examined metal distribution in Camden, particularly during storm events. This study analyzes stormwater metal concentrations across residential and commercial areas to assess contamination levels, potential sources, and land use associations. Stormwater samples were collected from 33 flooded street locations after four storm events in summer 2023, along with samples from a flooded residential basement during three storms. All were analyzed for total lead, cadmium, and arsenic using inductively coupled plasma–mass spectrometry (ICP-MS, (Department of Chemistry and Biochemistry, Rowan University, Glassboro, NJ, USA)). Concentration data were visualized using geographic information system (GIS)-based mapping in relation to land use, socioeconomic, and public health factors. In Camden’s stormwater, lead levels (1–1164 µg L⁻¹) were notably higher than those of cadmium (0.1–3.3 µg L⁻¹) and arsenic (0.2–8.6 µg L⁻¹), which were relatively low. Concentrations varied citywide, with localized hot spots shaped by environmental and socio-economic factors. Principal component analysis indicates lead and cadmium likely originate from shared sources, mainly industries and illegal dumping. Notably, indoor stormwater samples showed higher heavy metal concentrations than outdoor street samples, indicating greater exposure risks in flooded homes. These findings highlight the spatial variability and complex sources of heavy metal contamination in stormwater, underscoring the need for targeted interventions in vulnerable communities. Full article

This study examines the relationship between national open educational resource (OER) policies and Sustainable Development Goal 4 (SDG4) outcomes across 187 countries between 2015 and 2024, with particular attention to the moderating role of artificial intelligence (AI) readiness. Despite widespread optimism about digital technologies as catalysts for universal education, systematic evidence linking formal OER policy frameworks to measurable improvements in educational access and completion remains limited. The analysis employs fixed effects and difference-in-differences estimation strategies using an unbalanced panel dataset comprising 435 country-year observations. The research investigates how OER policies associate with primary completion rates and out-of-school rates while testing whether these relationships depend on countries’ technological and institutional capacity for advanced technology deployment. The findings reveal that AI readiness demonstrates consistent positive associations with educational outcomes, with a ten-point increase in the readiness index corresponding to approximately 0.46 percentage point improvements in primary completion rates and 0.31 percentage point reductions in out-of-school rates across fixed effects specifications. The difference-in-differences analysis indicates that OER-adopting countries experienced completion rate increases averaging 0.52 percentage points relative to non-adopting countries in the post-2020 period, though this estimate remains statistically imprecise (p equals 0.440), preventing definitive causal conclusions. Interaction effects between policies and readiness yield consistently positive coefficients across specifications, but these associations similarly fail to achieve conventional significance thresholds given sample size constraints and limited within-country variation. While the directional patterns align with theoretical expectations that policy effectiveness depends on digital capacity, the evidence should be characterized as suggestive rather than conclusive. These findings represent preliminary assessment of policies in early implementation stages. Most frameworks were adopted between 2019 and 2022, providing observation windows of two to five years before data collection ended in 2024. This timeline proves insufficient for educational system transformations to fully materialize in aggregate indicators, as primary education cycles span six to eight years and implementation processes operate gradually through sequential stages of content development, teacher training, and institutional adaptation. The analysis captures policy impacts during formation rather than at equilibrium, establishing baseline patterns that require extended longitudinal observation for definitive evaluation. High-income countries demonstrate interaction coefficients between policies and readiness that approach marginal statistical significance (p less than 0.10), while low-income subsamples show coefficients near zero with wide confidence intervals. These patterns suggest that OER frameworks function as complementary interventions whose effectiveness depends critically on enabling infrastructure including digital connectivity, governance quality, technical workforce capacity, and innovation ecosystems. The results carry important implications for how countries sequence educational technology reforms and how international development organizations design technical assistance programs. The evidence cautions against uniform policy recommendations across diverse contexts, indicating that countries at different stages of digital development require fundamentally different strategies that coordinate policy adoption with foundational capacity building. However, the modest short-term effects and statistical imprecision observed here should not be interpreted as evidence of policy ineffectiveness, but rather as confirmation that immediate transformation is unlikely given implementation complexities and temporal constraints. The study contributes systematic cross-national evidence on aggregate policy associations while highlighting the conditional nature of educational technology effectiveness and establishing the need for continued longitudinal research as policies mature beyond the early implementation phase captured in this analysis. Full article

Climate change and sea-level change (SLC) are intensifying flooding in U.S. coastal communities, with disproportionate impacts on Black and minority neighborhoods that face displacement, economic hardship, and heightened health risks. In Norfolk, Virginia, sea levels are projected to rise by at least 0.91 m (3 ft) by 2100, placing underserved neighborhoods such as Oakleaf Forest at particular risk. This study investigates the compounded impacts of flooding at both the building and urban scales, situating the work within the framework of the UN Sustainable Development Goals (UN SDGs). A mixed-method, community-based approach was employed, integrating literature review, field observations, and community engagement to identify flooding hotspots, document lived experiences, and determine preferences for adaptation strategies. Community participants contributed actively through mapping sessions and meetings, providing feedback on adaptation strategies to ensure that the process was collaborative, place-based, and context-specific. Preliminary findings highlight recurring flood-related vulnerabilities and the need for interventions that address both environmental and social dimensions of resilience. The study proposes multi-scale, nature-based solutions (NbS) to mitigate flooding, restore ecological functions, and enhance community capacity for adaptation. Ultimately, this work underscores the importance of coupling technical strategies with participatory processes to strengthen resilience and advance climate justice in vulnerable coastal neighborhoods. Full article

Cognitive diagnosis serves as a key component in personalized intelligent education, designed to accurately evaluate students’ knowledge states by analyzing their historical response data. It offers fundamental support for various educational applications such as adaptive learning and exercise recommendation. However, when leveraging student data, existing diagnostic models often incorporate sensitive attributes like family economic background and geographic location, which may lead to bias and unfairness. To address this issue, this paper introduces a Fairness-Aware Cognitive Diagnosis model (FACD) based on counterfactual graph representation learning. The approach builds student-centered causal subgraphs and integrates a graph variational autoencoder with adversarial learning to mitigate the influence of sensitive attributes on node representations. It further employs both central-node and neighbor-node perturbation strategies to generate counterfactual samples. A Siamese network is utilized to enforce representation consistency across different counterfactual scenarios, thereby deriving fair student contextual embeddings. Experimental results on the PISA 2015 dataset show that FACD outperforms conventional cognitive diagnosis models and their fairness-aware variants in terms of ACC, AUC, and RMSE. Ablation studies confirm the effectiveness and synergistic nature of each module. This work provides a viable pathway toward more reliable and equitable cognitive diagnosis systems. Full article

Missing seismic data in reflection seismology, which frequently arises from a variety of operational and natural limitations, immediately impairs the quality of ensuing imaging and calls into question the validity of geological interpretation. Traditional techniques for reconstructing seismic data frequently rely significantly on parameter choices and prior assumptions. Even while these methods work well for partially missing traces, reconstructing whole shot gather is still a difficult task that has not been thoroughly studied. Data-driven approaches that summarize and generalize patterns from massive amounts of data have become more and more common in seismic data reconstruction research in recent years. This work builds on earlier research by proposing an enhanced technique that can recreate whole shot gathers as well as partially missing traces. During model training, we first implement a Moveout-window selective slicing method for reconstructing missing traces. By creating training datasets inside a high signal-to-noise ratio (SNR) window, this method improves the model’s capacity for learning. Additionally, a technique is presented for the receiver domain reconstruction of missing shot data. A dual-domain reconstruction method is used to successfully recover the seismic data in order to handle situations where there is simultaneous missing data in both domains. Full article

This work presents an advanced electro-physical model for hydrogenated amorphous silicon (a-Si:H) Junction Field Effect Transistors (JFETs) to enable the design of devices with energy-efficient analog interface building blocks for Lab-on-Chip (LoC) systems. The presence of this device can support monolithic integration with thin-film sensors and circuit-level design through a validated compact formulation. The model accurately describes the behavior of a-Si:H JFETs addressing key physical phenomena, such as the channel thickness dependence on the gate-source voltage when the channel approaches full depletion. A comprehensive framework was developed, integrating experimental data and mathematical refinements to ensure robust predictions of JFET performance across operating regimes, including the transition toward full depletion and the associated current-limiting behavior. The model was validated through a broad set of fabricated devices, demonstrating excellent agreement with experimental data in both the linear and saturation regions. Specifically, the validation was carried out at 25 °C on 15 fabricated JFET configurations (12 nominally identical devices per configuration), using the mean characteristics of 9 devices with standard-deviation error bars. In the investigated bias range, the devices operate in a sub-µA regime (up to several hundred nA), which naturally supports µW-level dissipation for low-power interfaces. This work provides a compact, experimentally validated modeling basis for the design and optimization of a-Si:H JFET-based LoC front-end/readout circuits within technology-constrained and energy-efficient operating conditions. Full article

The clinical management of major depressive disorder is constrained by a trial-and-error approach. The clinical management of major depressive disorder is constrained by a trial-and-error approach. While computational methods have focused on static binary classification (e.g., responder vs. non-responder), they ignore the dynamic nature of recovery. Building upon the recently proposed prognostic theory of treatment response, this article presents a methodological framework for its operationalisation. We define a multi-modal data architecture for the theory’s core constructs—the Patient State Vector (PSV), Therapeutic Impulse Function (TIF), and Predicted Recovery Trajectory (PRT)—transforming them from abstract concepts into specified computational inputs. To model the asynchronous interactions between these components, we specify a Time-Aware Long Short-Term Memory (LSTM) architecture, providing explicit mathematical formulations for time-decay gates to handle irregular clinical sampling. Furthermore, we outline a synthetic validation protocol to benchmark this dynamic approach against static baselines. By integrating these technical specifications with a translational pipeline for Explainable AI (XAI) and ethical governance, this paper provides the necessary blueprint to transition psychiatry from theoretical prognosis to empirical forecasting. Full article

Maintaining optimal humidity in livestock buildings during winter is a major challenge in cold climate regions due to the conflict between moisture-removing ventilation and the need for heat preservation. To address this issue, a novel condensation dehumidification system is proposed that utilizes the natural low temperature of cold winters. An integrated energy consumption model, coupling moisture and thermal balances, was developed to evaluate room temperature drop, dehumidification rate (DR), and the internal circulation coefficient of performance (IC-COP). The model was calibrated and validated with experimental data comprising over 150 operational cycles under varied operation conditions, including initial temperature differences (ranging from −20 to −5 °C), air flow rates (0.6–1.5 m/s), refrigerant flow rates (3–7 L/min), and high-humidity conditions (>90% RH). Correlation analysis showed that higher indoor humidity improved both DR and IC-COP. Four machine learning models—Extreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine (LightGBM), Random Forest (RF), and Multilayer Perceptron (MLP)—were developed and compared with a stacking ensemble learning model. Results demonstrated that the stacking model achieved superior prediction accuracy, with the best R² reaching 0.908, significantly outperforming individual models. This work provides an energy-saving dehumidification solution for enclosed livestock housing and a case study on the application of machine learning for energy performance prediction and optimization in agricultural environmental control. Full article

Historic timber buildings rely heavily on naturally aged wood. However, the influence of long-term environmental exposure on the thermal behavior and fire performance of such structural members remains insufficiently understood. This study evaluates the effect of natural aging on heat transfer, charring development, and the phase-change interval of free water in larch wood (Larix decidua). Medium-scale radiant panel tests were conducted on fresh and naturally aged timber beams. Internal temperatures were recorded at multiple depths and analyzed using derivative-based T-history methods. The temperature profiles of aged and fresh larch were highly comparable, exhibiting a strong correlation (R² = 0.89). Aged wood, characterized by a slightly higher density, showed shallower thermal gradients and a marginally lower average charring rate (0.63 mm·min⁻¹) compared with fresh wood (0.65 mm·min⁻¹). In both materials, the charring rate decreased with depth. The phase-change interval of free water differed markedly: fresh wood showed water evaporation between 107.8–142.1 °C, whereas aged wood exhibited an earlier and narrower interval (93.6–116.3 °C), indicating facilitated dehydration due to microstructural degradation. Overall, natural aging did not significantly impair fire-relevant thermal properties, suggesting that aged larch retains charring resistance comparable to that of fresh wood and can reliably perform in passive fire protection applications for heritage structures. Full article

Alkali-activated materials (AAMs) or geopolymers have been considered for many years as a sustainable substitution for the traditional ordinary Portland cement (OPC) binder. However, their production needs energy consumption and creates carbon emissions. Since construction and demolition waste (CDW) can become precursors for manufacturing alkali-activated materials, their use as substitutes for traditional AAM (such as metakaolin, blast furnace slag, and fly ash) can solve both the problem of their disposal and the problem of sustainability. Furthermore, CDW can also be used as aggregate replacement, avoiding the exploitation of natural river sand and gravel. A new circular economy could be created based on CDW recycling, creating a new eco-friendly building practice. Unfortunately, this process is quite difficult owing to several variables that should be taken into consideration, such as the possibility of separating and sorting the CDW, the great variability of CDW composition, the cost of the mechanical and thermal treatment, the different parameters that compose an alkali-activated mix-design, and public opinion still being skeptical about the use of recycled materials in the construction sector. This review tries to describe all these aspects, summarizing the results of the most interesting studies performed on this subject. Today, thanks to a comprehensive protocol, the use of building information modeling (BIM) software and machine learning models, a large-scale reuse of CDW in the building industry appears more feasible. Full article

To address the low efficiency and stress-inducing nature of traditional manual weighing for dairy cows, this study proposes a semantically guided 3D reconstruction and body weight estimation method for dairy cows. First, a dual-viewpoint Kinect V2 camera synchronous acquisition system captures top-view and side-view point cloud data from 150 calves and 150 lactating cows. Subsequently, the CSS-PointNet++ network model was designed. Building upon PointNet++, it incorporates Convolutional Block Attention Module (CBAM) and Attention-Weighted Hybrid Pooling Module (AHPM) to achieve precise semantic segmentation of the torso and limbs in the side-view point cloud. Based on this, point cloud registration algorithms were applied to align the dual-view point clouds. Missing parts were mirrored and completed using semantic information to achieve 3D reconstruction. Finally, a body weight estimation model was established based on volume and surface area through surface reconstruction. Experiments demonstrate that CSS-PointNet++ achieves an Overall Accuracy (OA) of 98.35% and a mean Intersection over Union (mIoU) of 95.61% in semantic segmentation tasks, representing improvements of 2.2% and 4.65% over PointNet++, respectively. In the weight estimation phase, the BP neural network (BPNN) delivers optimal performance: For the calf group, the Mean Absolute Error (MAE) was 1.8409 kg, Root Mean Square Error (RMSE) was 2.4895 kg, Mean Relative Error (MRE) was 1.49%, and Coefficient of Determination (R²) was 0.9204; for the lactating cows group, MAE was 12.5784 kg, RMSE was 14.4537 kg, MRE was 1.75%, and R² was 0.8628. This method enables 3D reconstruction and body weight estimation of cows during walking, providing an efficient and precise body weight monitoring solution for precision farming. Full article

In modern construction, natural materials with a low carbon footprint and full recyclability are becoming increasingly important. A typical group here is products made from solid wood, including glued wood, plywood, and wood-based composites. With their many advantages, however, they all burden the environment with the costs of production processes, as well as the need to use harmful chemicals (adhesives and impregnants). Solid wood is devoid of these disadvantages; however, it is often treated as a rather archaic material. One of the arguments here is its low durability compared to, e.g., glued wood. The article discusses the durability of solid wood using the example of a group of wooden churches preserved in Poland, in Upper Silesia. Some of these buildings are over five hundred years old, making them a reliable source of information about the durability of the material from which they were built. A total of 85 churches, at least 200 years old, were analyzed, evaluating the technical state of the main load-bearing elements of their structures. In view of the number of facilities and the inability to conduct tests in most of them, the assessment was limited to a visual inspection of the technical condition, carried out by an experienced building expert. The assessment estimated the area of corrosion damage, probed its depth, and measured the depth of cracks. The relationship between their technical condition and the environmental conditions in which they were used was described and discussed. In this way, both the threats to the durability of solid wood and the ways to keep it in good condition for hundreds of years were identified, refuting the thesis that solid wood is a material with low durability. Its use in structural elements therefore supports efficient resource management and contributes to sustainable construction, especially in small and medium-sized buildings. Full article

Background/Objectives: Considering that the kinematics of the temporomandibular joints (TMJs) is concomitant with head movements and that temporomandibular joint disorders (TMDs) are frequently associated with neck pain in clinics but seldom or never investigated, the aim of this study was to develop a reliable in vivo measurement protocol of the simultaneous amplitudes of the mandible and of the skull. The development of such a protocol is part of a project to build an accurate kinematic assessment tool for clinicians in the orofacial field who treat patients suffering from TMD. Methods: Mouth opening, laterotrusion and protrusion movements for three different positions of the head (neutral, slouched and military) on 12 asymptomatic voluntary subjects (5 men and 7 women, mean 33.6 yo +/− 11.1) were recorded using 20 markers palpated and taped and 14 optoelectronic cameras. The acquisition frequency was set at 150 hertz. The inter- and intra-examiner reliability of marker palpation in mm was calculated using standard deviation (SD), mean difference (MD) and standard error (SE). Amplitudes of movement according to axes defined by the International Society of Biomechanics (ISB) are given for the mandible and skull segments. The propagation of error on the amplitudes was calculated with the root mean square propagation error (RMSPE) in degrees. Repeated-measures ANOVA or Friedman tests were used to assess the influence of the position of the head on the amplitudes of the jaw. Power analysis of the sample size was estimated with Cohen’s f3 size effect test. Steady-state plots (SSPs) and normalized motion graphs between the skull and the mandible motion were performed to study the coordination of their maximum amplitude over time. Results: The protocol demonstrated good intra-examiner reliability (1.5 < MD < 5.8; 2.6 < SD < 7.8; 2.0 < SE < 3.8), good inter-examiner reproducibility (0.2 < MD < 4.0; 3.5 < SD < 4.6; 2.0 < SE < 2.5) and small error propagation (0.0 < RMSPE intra < 2.8; 0.0 < RMSPE inter < 1.0). The amplitudes of the jaw and head found during the three types of movements correspond to the values reported in the literature. Head positions did not appear to significantly influence the amplitudes of jaw movements, which could be explained by the power estimation of our sample (Type II error β = 0.692). The participation of head movements in those of the jaw, for all motions and in all positions, was demonstrated and discussed in detail. Conclusions: The accuracy, test–retest reliability, and intra-individual variability of the TMJ kinematic analysis, including head movements, was ensured. The small sample size and the absence of standardized head positions for the subjects limit the scope of the intra- and inter-group analysis results. Given the natural biological and complex coordination of jaw–head movement, the authors consider its evaluation useful in clinical intervention and would like to further develop the present protocol. The next step should be to test the feasibility of its clinical application with a larger group of asymptomatic subjects compared to patients suffering from TMD. Full article

Hydrogen is widely regarded as a promising clean energy carrier, and blending hydrogen into existing natural gas pipelines is considered a cost-effective and practical pathway for large-scale deployment. Supplying hydrogen-enriched natural gas to buildings requires careful consideration of the safe operation of pipelines and appliances without introducing new risks. In this study, on-site demonstrations and experimental tests were conducted in two high-rise buildings in Weifang to evaluate the impact of hydrogen addition on high-rise building natural gas distribution systems. The results indicate that hydrogen blending up to 20% by volume does not cause stratification in building risers and leads only to a relatively minor increase in additional pressure, approximately 0.56 Pa/m for every 10% increase in hydrogen addition. While hydrogen addition may increase leakage primarily in aging indoor gas systems, gas meter leakage rates under a 10% hydrogen blend remain below 3 mL/h, satisfying safety requirements. In addition, in-service domestic gas alarms remain effective under hydrogen ratios of 0–20%, with average response times of approximately 19–20 s. These findings help clarify the safety performance of hydrogen-blended natural gas in high-rise building distribution systems and provide practical adjustment measures to support future hydrogen injection projects. Full article