Multidisciplinary Digital Publishing Institute (Switzerland)
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Analyzing the “Opposite” Approach in Additions to Historic Buildings Using Visual Attention Tools: Dresden Military History Museum Case
No full textFrom past to present, modern additions have continued to transform historic environments. While some argue that contemporary extensions disrupt the integrity of historic buildings, others suggest that the contrast between past and present creates a meaningful architectural dialog. This debate raises a key question: in contrasting compositions, which architectural elements draw more visual attention, the historic or the modern? To address this, a visual attention-based analytical approach is adopted. In this study, eye-tracking-based visual attention analysis is used to examine how viewers perceive the relationship between historical and contemporary architectural elements. Instead of conventional laboratory-based eye-tracking, artificial intelligence-supported visual attention software developed from eye-tracking datasets is employed. Four tools—3M-VAS, EyeQuant, Attention Insight, and Expoze—were used to generate heat maps, gaze sequence maps, hotspots, focus maps, attention distribution diagrams, and saliency predictions. These visualizations enabled both a qualitative and quantitative comparison of viewer focus. The case study is the Military History Museum in Dresden, Germany, known for its widely debated contemporary addition representing an oppositional design approach. The results illustrate which architectural components are visually prioritized, offering insight into how contrasting architectural languages are cognitively perceived in historic settings
A Hybrid LSTM–Attention Model for Multivariate Time Series Imputation: Evaluation on Environmental Datasets
No full textEnvironmental monitoring systems generate large volumes of multivariate time series data from heterogeneous sensors, including those measuring soil, weather, and air quality parameters. However, sensor malfunctions and transmission failures frequently lead to missing values, compromising the performance of downstream analytical and predictive models. To address this challenge, this study presents a comprehensive and systematic evaluation of previously proposed hybrid architecture that interleaves Long Short-Term Memory (LSTM) layers with a Multi-Head Attention mechanism in a “sandwiched” setting (LSTM–Attention–LSTM) for robust multivariate data imputation in environmental IoT datasets. The first LSTM layer captures short-term temporal dependencies, the attention layer emphasises long-range relationships among correlated features, and the second LSTM layer re-integrates these enriched representations into a coherent temporal sequence. The model is evaluated using multiple environmental datasets of soil temperature, meteorological (precipitation, temperature, wind speed, humidity), and air quality data across missingness levels ranging from 10% to 90%. Performance is compared against baseline methods, including K-Nearest Neighbour (KNN) and Bidirectional Recurrent Imputation for Time Series (BRITS). Across all datasets, the Hybrid model consistently outperforms baseline methods, achieving MAE reductions exceeding 50% and reaching over 80% in several scenarios, along with RMSE reductions of up to approximately 85%, particularly under moderate to high missingness conditions. An ablation study further examines the contribution of each layer to overall model performance. Results demonstrate that the proposed Hybrid model achieves superior accuracy and robustness across datasets, confirming its effectiveness for environmental sensor data imputation under varying missing data conditions
Evaluation of the Impact of Submerged Zone Height on the Performance of Iron-Carbon Bioretention System
No full textTraditional bioretention systems have limited nitrogen and phosphorus removal capacity and insufficient operational stability. To address this issue, this study developed an iron-carbon bioretention system (IB) with varying submerged zone heights. The system’s performance in removing pollutants was systematically evaluated under different rainfall intensities, influent pollutant concentrations, and antecedent drying durations. In addition, the potential nitrification ability (PNA) of the substrate, denitrifying enzyme activity (DEA), and phosphorus species were analyzed to reveal the mechanisms responsible for its efficient nitrogen and phosphorus removal. The results showed that a submerged zone height of 400 mm enabled the IB system to achieve removal rates of 98.05% for NO3−-N and 91.67% for total nitrogen (TN). The removal rates of total phosphorus (TP) and chemical oxygen demand (COD) remained stable at over 91% and 92%, respectively. The submerged zone also created a stable anoxic environment, while the iron-carbon micro-electrolysis process continually consumed dissolved oxygen and provided Fe2+ as an electron donor, enhancing both the denitrification process and chemical phosphorus removal. Furthermore, the IB system demonstrated superior stability when dealing with high hydraulic and pollutant loads, as well as varying dry periods, with the effluent iron concentration maintained at low levels. This study confirms that iron-carbon micro-electrolysis and the incorporation of a submerged zone can significantly enhance the removal performance of bioretention systems, offering a reference for addressing nitrogen and phosphorus pollution in urban stormwater runoff
Measuring Environmental Efficiency of Ports Under Undesirable Outputs and Uncertainty
No full textPorts are the major gateways of cities. Background: Sustainable growth requires ports to prioritize efficiency while balancing economic, social, and environmental goals. There is limited synthesized evidence on the sustainability evaluation of ports, including those of North America. In this paper, we propose a multi-step approach based on fuzzy DEA to evaluate the environmental performance of ports. Methods: In the first step, we identify indicators for environmental performance evaluation. The second step involves application of fuzzy DEA using the identified indicators to measure the environmental efficiency of ports. In the third step, a numerical illustration is provided using open data. The proposed model incorporates undesirable outputs and employs one set of constraints to make a production frontier. Results: The findings show wide differences in performance, ports reach higher scores when they use resources wisely plus keep emissions low, not merely when they expand. Conclusions: The proposed methodology provides a robust and comparable measurement of port environmental efficiency under uncertainty
SemanticHPC: Semantics-Aware, Hardware-Conscious Workflows for Distributed AI Training on HPC Architectures
No full textHigh-Performance Computing (HPC) has become essential for training medium- and large-scale Artificial Intelligence (AI) models, yet two bottlenecks remain under-exploited: the semantic coherence of training data and the interaction between distributed deep learning runtimes and heterogeneous HPC architectures. Existing work tends to optimise multi-node, multi-GPU training in isolation from data semantics or to apply semantic technologies to data curation without considering the constraints of large-scale training on modern clusters. This paper introduces SemanticHPC, an experimental framework that integrates ontology and Resource Description Framework (RDF)-based semantic preprocessing with distributed AI training (Horovod/PyTorch Distributed Data Parallel) and hardware-aware optimisations for Non-Uniform Memory Access (NUMA), multi-GPU and high-speed interconnects. The framework has been evaluated on 1–8 node configurations (4–32 GPUs) on a production-grade cluster. Experiments on a medium-size Open Images V7 workload show that semantic enrichment improves validation accuracy by 3.5–4.4 absolute percentage points while keeping the additional end-to-end overhead below 8% and preserving strong scaling efficiency above 79% on eight nodes. We argue that bringing semantic technologies into the training workflow—rather than treating them as an offline, detached phase—is a promising direction for large-scale AI on HPC systems. We detail an implementation based on standard Python libraries, RDF tooling and widely adopted deep learning runtimes, and we discuss the limitations and practical hurdles that need to be addressed for broader adoption
Coconut Shell-Derived Activated Carbons: Preparation, Physicochemical Properties, and Dye Removal from Water
No full textValorizing coconut shell waste as a renewable lignocellulosic precursor offers a sustainable route to produce high-performance activated carbons for wastewater treatment. In this study, coconut shells were transformed into activated carbons through physical activation (air, CO2, steam) and chemical activation (H3PO4, ZnCl2, KOH), allowing direct comparison of how each method influences porosity and surface chemistry. Among the physically activated samples, steam activation produced the best material, A-ST, with SBET = 738 m2 g−1, Vmi = 0.38 cm3 g−1 and Vme = 0.07 cm3 g−1. KOH activation yielded the top-performing carbon, A-KOH, achieving SBET = 1600 m2 g−1, Vmi = 0.74 cm3 g−1, and Vme = 0.22 cm3 g−1. Adsorption tests with methylene blue, methyl orange, and orange G showed a clear link between physicochemical features and dye uptake. A-ST and A-KOH exhibited the highest capacities due to their wide micro–mesoporosity and favorable surface charge at the adsorption pH. In both cases, methylene blue was most strongly retained, confirming that large aromatic cations benefit from π–π interactions with graphene-like layers and easy micropore access. Overall, the results demonstrate that coconut-shell valorization is maximized when activation enhances both porosity and surface chemistry, enabling the production of tailored sorbents for the efficient removal of organic contaminants
Dunaliella salina-Loaded Diosmetin Carriers Alleviate Oxidative Stress and Inflammation in Cisplatin-Induced Acute Kidney Injury via PI3K/AKT Pathway
No full textBackground: As a widely used chemotherapeutic agent, cisplatin frequently induces acute kidney injury (AKI), which severely compromises patient survival and limits its clinical use. While the natural flavonoid diosmetin (Dio) shows promise in mitigating cisplatin-induced nephrotoxicity, its clinical translation is challenged by poor solubility, low bioavailability, and incompletely elucidated mechanisms. This study aimed to overcome these limitations by developing a novel drug delivery system using the microalgae Dunaliella salina (D. salina, Ds) to load Dio (Ds-Dio), thereby enhancing its efficacy and exploring its therapeutic potential. Methods: We first characterized the physicochemical properties of Ds and Dio, and then Ds-Dio complex was synthesized via co-incubation. Its nephroprotective efficacy and safety were systematically evaluated in a cisplatin-induced mouse AKI model by assessing renal function (serum creatinine, blood urea nitrogen), injury biomarkers, histopathology, body weight, and organ index. The underlying mechanism was predicted by network pharmacology and subsequently validated experimentally. Results: The novel Ds-Dio delivery system has been successfully established. In the AKI model, Ds-Dio significantly improved renal function and exhibited a superior protective effect over Dio alone; this benefit is attributed to the enhanced bioavailability provided by Ds carrier. In addition, Ds-Dio also demonstrated safety performance, with no evidence of toxicity to major organs. Network pharmacology analysis predicted the involvement of PI3K/AKT pathway, which was experimentally verified. Specifically, we confirmed that Ds-Dio alleviates AKI by modulating the PI3K/AKT pathway, resulting in concurrent suppression of NF-κB-mediated inflammation and activation of NRF2-dependent antioxidant responses. Conclusions: This study successfully developed a microalgae-based drug delivery system, Ds-Dio, which significantly enhances the nephroprotective efficacy of Dio against cisplatin-induced AKI. The nephroprotective mechanism is associated with modulation of the PI3K/AKT pathway, resulting in the simultaneous attenuation of oxidative stress and inflammation
Antioxidant and Anti-Inflammatory Constituents from the Roots of Anodendron affine: Inhibition of the fMLP-Induced Superoxide Anion Generation and Molecular Docking Studies
No full textOxidative stress is a key driver of chronic inflammatory diseases. Anodendron affine is a native Formosan plant species in Taiwan that remains largely underexplored phytochemically and bioactivity. To reveal the bioactive constituents and assess its potential as a source of anti-inflammatory antioxidants, we performed bioactivity-guided fractionation and evaluated the inhibition of superoxide anion (O2•−) generation in formyl-L-methionyl-L-leucyl-L-phenylalanine-stimulated human neutrophils. Molecular docking simulations were employed to model interactions with Formyl peptide receptor 1 (FPR1) and the Nicotinamide adenine dinucleotide phosphate (NADPH) oxidase complex, including neutrophil cytosol factor 1 (p47phox) and NADPH oxidase 2 (NOX2), to propose a theoretical mechanism of action. Phytochemical investigation led to the isolation of two new compounds, methyl 4,5-O-diferuloyl-3-methoxyquinate (1) and 16-pregnen-3,12,20-trione (2), together with four known compounds. Notably, 4-hydroxy-3-prenylbenzoic acid (5) exhibited potent inhibitory activity (IC50 = 17.65 ± 0.97 μM), surpassing the activity of the positive control, ibuprofen (IC50 = 27.85 ± 3.56 μM). Docking studies suggested that anodendrosin H (4) and 4-hydroxy-3-prenylbenzoic acid (5) exhibit high predicted binding affinity to p47phox and NOX2. Based on these results, compounds 1, 4, and 5 from A. affine were identified as potential lead candidates for the development of novel anti-inflammatory therapeutics
Presence of Trace Metals in the Biological Samples of Prostate Cancer Patients: A Systematic Review of Case-Control Studies
No full textBackground/Objective: Prostate cancer (PCa) is the leading cause of death in the ageing male population across the globe, and trace metals have garnered much attention due to their sometimes-dual role in cellular mechanisms, as such contribute to the development and progression of prostate cancer. Methods: This review consolidates the results of case-control studies that investigated the concentrations of certain trace metals—Arsenic (As), cadmium (Cd), mercury (Hg), manganese (Mn), nickel (Ni), lead (Pb), selenium (Se), and zinc (Zn) in various biological samples. Results: There are decreased concentrations of Se and Zn and increased Cd concentrations in samples of PCa patients when compared to healthy controls. As, Hg, Ni, and Pb concentrations have proven to be insignificant. Conclusions: There are other variables to consider and limitations that need to be investigated in studies of this nature; however, the results have been consistent in that increased exposure to toxic metals such as Cd, along with a deficiency in protective essential nutrients like Zn and Se, tends to produce a prostatic environment
