Search Results (440)

Urban flooding represents a growing global concern, especially in areas with rapid urbanization, unregulated urban sprawl and climate change conditions. Conventional flood modeling approaches do not effectively capture the complex dynamics between natural watershed behavior and urban infrastructure; they typically simulate these domains in isolation. This study introduces the Watershed-BIM methodology, a three-dimensional simulation framework that integrates Building and City Information Modeling (BIM/CIM), Geographic Information Systems (GIS), Flood Risk Assessment (FRA), and Flood Risk Management (FRM) into a single framework. Autodesk InfraWorks 2024, Civil 3D 2024, and RiverFlow2D v8.14 software are incorporated in the development. The methodology enhances interoperability and prediction accuracy by bridging hydrological processes with detailed urban-scale data. The framework was tested on a real-world flash flood event in Mandra, Greece, an area frequently exposed to extreme rainfall and runoff events. A specific comparison with observed flood characteristics indicates improved accuracy in comparison to other hydrological analyses (e.g., by HEC-RAS simulation). Beyond flood depth, the model offers additional insights into flow direction, duration, and localized water accumulation around buildings and infrastructure. In this context, integrated tools such as Watershed-BIM stand out as essential instruments for translating complex flood dynamics into actionable, city-scale resilience planning. Full article

A large number of cities have created semantic 3D city models, but these models are rarely used as input data for simulations, such as noise and flooding, in the urban planning process. Reasons for this are that many simulations require detailed land cover (LC) and elevation data that are often not included in the 3D city models, and that there is no linkage between the elevation and land cover data. In this study, we design, implement and evaluate methods to handle LC and elevation data in a 3D city model. The LC data is stored in 2.5D or 3D in the CityGML modules Transportation, Vegetation, WaterBody, CityFurniture and LandUse, and a complete 3D LC partition is created by combining data from these modules. The entire workflow is demonstrated in the paper: creating 2D LC data, extending CityGML, creating 2.5D/3D data from the 2D LC data, dividing the LC data into CityGML modules, storing it in a database (3DCityDB) and finally visualizing the data in Unreal Engine. The study is part of the 3CIM project where a national profile of CityGML for Sweden is created as an Application Domain Extension (ADE), but the result is generally applicable for CityGML implementations. Full article

Background: Four of the six ESKAPE pathogens are responsible for a majority of antimicrobial resistance (AMR)-related deaths worldwide. Identifying the pathogens that evade antibiotic treatments more efficiently than others can help diagnose pathogens requiring more attention. The study was thus designed to evaluate the biofilm and resistance properties of five ESKAPE pathogens comparatively. A total of 165 clinical isolates of 5 ESKAPE pathogen species (E. faecium, S. aureus, K. pneumoniae, A. baumannii, and P. aerurginosa) were collected from a tertiary hospital in Bangladesh. Methodology: Following secondary identification, antibiotic susceptibility was determined by the disc diffusion method and minimum inhibitory concentration. The biofilm formation was determined by the microtiter plate biofilm formation assay. The biofilm-forming genes were screened by PCR. Detection of carbapenemase and Metallo-β-lactamase was performed by the modified carbapenem inactivation method (mCIM) and the EDTA-modified carbapenem inactivation method (eCIM) test, respectively. Results: Among Gram-positive isolates, E. faecium exhibited higher multi-drug resistance (MDR) rates (90%) compared to S. aureus (10%). In Gram-negative isolates, A. baumannii and K. pneumoniae showed elevated resistance to carbapenems (74.29% and 45.71%, respectively), cephalosporins, and β-lactam inhibitors, while P. aeruginosa demonstrated relatively lower resistance. Colistin resistance was highest in K. pneumoniae (42.86%). Biofilm formation was prevalent, with 88.5% of isolates forming biofilms, including 15.8% strong biofilm producers. Notably, K. pneumoniae and A. baumannii exhibited higher biofilm-forming capabilities compared to P. aeruginosa. A significant correlation was observed between biofilm formation and resistance to carbapenems, cephalosporins, and piperacillin/tazobactam (p < 0.05), suggesting a potential role of biofilms in disseminating resistance to these antibiotics. Carbapenemase production was detected in 23.8% of Gram-negative isolates, with K. pneumoniae showing the highest prevalence (34.3%). Additionally, 45.8% of carbapenemase producers expressed Metallo-β-lactamases (MBLs). Among S. aureus isolates, 46.7% carried the mecA gene, confirming methicillin resistance (MRSA), while 20% of E. faecium isolates exhibited vancomycin resistance, primarily mediated by the vanB gene. Conclusions: These findings can help pinpoint the pathogens of significant threat. Full article

Stripe rust, caused by Puccinia striiformis f. sp. tritici (Pst), represents a major global threat to wheat (Triticum aestivum. L). Planting varieties with adult-plant resistance (APR) is an effective approach for long-term management of this disease. The Chinese winter wheat variety Lantian 25 exhibits moderate-to-high APR against stripe rust under field conditions. To investigate the genetic basis of APR in Lantian 25, a set of 219 F₆ recombinant inbred lines (RILs) was created from a cross between Lantian 25 (resistant parent) and Huixianhong (susceptible parent). These RILs were assessed for maximum disease severity (MDS) in Pixian of Sichuan and Qingshui of Gansu over the 2020–2021 and 2021–2022 growing seasons, resulting in data from four different environments. Genotyping was performed on these lines and their parents using the wheat Illumina 50K single-nucleotide polymorphism (SNP) arrays. Composite interval mapping (CIM) identified six quantitative trait loci (QTL), named QYr.gaas-2BS, QYr.gaas-2BL, QYr.gaas-2DS, QYr.gaas-2DL, QYr.gaas-3BS and QYr.gaas-4BL, which were consistently found across two or more environments and explained 4.8–12.0% of the phenotypic variation. Of these, QYr.gaas-2BL, QYr.gaas-2DS, and QYr.gaas-3BS overlapped with previous studies, whereas QYr.gaas-2BS, QYr.gaas-2DS, and QYr.gaas-4BL might be novel. All the resistance alleles for these QTL originated from Lantian 25. Furthermore, four kompetitive allele-specific PCR (KASP) markers, Kasp_2BS_YR (QYr.gaas-2BS), Kasp_2BL_YR (QYr.gaas-2BL), Kasp_2DS_YR (QYr.gaas-2DS) and Kasp_2DL_YR (QYr.gaas-2DL), were developed and validated in 110 wheat diverse accessions. Additionally, we identified seven candidate genes linked to stripe rust resistance, including disease resistance protein RGA2, serine/threonine-protein kinase, F-box family proteins, leucine-rich repeat family proteins, and E3 ubiquitin-protein ligases. These QTL, along with their associated KASP markers, hold promise for enhancing stripe rust resistance in wheat breeding programs. Full article

In the field of mobile crowdsensing (MCS), a large number of tasks rely on the participation of ordinary mobile device users for data collection and processing. This model has shown great potential for applications in environmental monitoring, traffic management, public safety, and other areas. However, the enthusiasm of participants and the quality of uploaded data directly affect the reliability and practical value of the sensing results. Therefore, the design of incentive mechanisms has become a core issue in driving the healthy operation of MCS. The existing research, when optimizing long-term utility rewards for participants, has often failed to fully consider dynamic changes in trustworthiness. It has typically relied on historical data from a single point in time, overlooking the long-term dependencies in the time series, which results in suboptimal decision-making and limits the overall efficiency and fairness of sensing tasks. To address this issue, a credibility-aware incentive mechanism based on long short-term memory and proximal policy optimization (CIM-LP) is proposed. The mechanism employs a Markov decision process (MDP) model to describe the decision-making process of the participants. Without access to global information, an incentive model combining long short-term memory (LSTM) networks and proximal policy optimization (PPO), collectively referred to as LSTM-PPO, is utilized to formulate the most reasonable and effective sensing duration strategy for each participant, aiming to maximize the utility reward. After task completion, the participants’ credibility is dynamically updated by evaluating the quality of the uploaded data, which then adjusts their utility rewards for the next phase. Simulation results based on real datasets show that compared with several existing incentive algorithms, the CIM-LP mechanism increases the average utility of the participants by 6.56% to 112.76% and the task completion rate by 16.25% to 128.71%, demonstrating its significant advantages in improving data quality and task completion efficiency. Full article

The Muschelkalk sedimentary cycle in the northwestern region of the Iberian Range (central Spain) lies within a transitional area between the Iberian and Hesperia type Triassic domains. To improve the understanding of its paleopalynological record, fifty samples were analyzed from ten stratigraphic sections corresponding to the Tramacastilla Dolostones Formation (TD Fm.), Cuesta del Castillo Sandstones and Siltstones Formation (CCSS Fm.), and Royuela Dolostones, Marls and Limestones Formation (RDML Fm.). Despite previous studies in the area, palynological data remain scarce or insufficiently detailed, highlighting the need for a systematic reassessment. Based on the identified palynological assemblages, the succession is assigned to an age spanning from the Fassanian to the Longobardian, with a possible extension into the base of the Julian (early Carnian). The results confirm that the siliciclastic unit (CCSS) represents a lateral facies change with respect to the carbonate formations of the upper Muschelkalk (TD and RDML). From a paleoecological perspective, the assemblages indicate a warm and predominantly dry environment, dominated by xerophytic conifers, although evidence of more humid local environments, such as marshes or coastal plains, is also observed. Full article

Artificial intelligence (AI) has become ubiquitous in modern computing systems, from high-performance data centers to resource-constrained edge devices. As AI applications continue to expand into mobile and IoT domains, the need for energy-efficient neural network implementations has become increasingly critical. To meet this requirement of energy-efficient computing, this work presents a BWNN (binary-weighted neural network) architecture implemented using FeRAM (Ferroelectric RAM)-based synaptic arrays. By leveraging the non-volatile nature and low-power computing of FeRAM-based CIM (computing in memory), the proposed CIM architecture indicates significant reductions in both dynamic and standby power consumption. Simulation results in this paper demonstrate that scaling the ferroelectric capacitor size can reduce dynamic power by up to 6.5%, while eliminating DRAM-like refresh cycles allows standby power to drop by over 258× under typical conditions. Furthermore, the combination of binary weight quantization and in-memory computing enables energy-efficient inference without significant loss in recognition accuracy, as validated using MNIST datasets. Compared to prior CIM architectures of SRAM-CIM, DRAM-CIM, and STT-MRAM-CIM, the proposed FeRAM-CIM exhibits superior energy efficiency, achieving 230–580 TOPS/W in a 45 nm process. These results highlight the potential of FeRAM-based BWNNs as a compelling solution for edge-AI and IoT applications where energy constraints are critical. Full article

Background/objectives: Proteus mirabilis is a frequent causative agent of urinary and wound infections in both community and hospital settings. It develops resistance to expanded-spectrum cephalosporins (ESCs) due to the production of extended-spectrum β-lactamases (ESBLs) or plasmid-mediated AmpC β-lactamases (p-AmpCs). Recently, carbapenem-resistant isolates of P. mirabilis emerged due to the production of carbapenemases, mostly belonging to Ambler classes B and D. Here, we report an outbreak of infections due to carbapenem-resistant P. mirabilis that were observed in a psychiatric hospital in Zagreb, Croatia. The characteristics of ESBL and carbapenemase-producing P. mirabilis isolates, associated with an outbreak, were analyzed. Materials and methods: The antibiotic susceptibility testing was performed by the disk-diffusion and broth dilution methods. The double-disk synergy test (DDST) and inhibitor-based test with clavulanic and phenylboronic acid were applied to screen for ESBLs and p-AmpCs, respectively. Carbapenemases were screened by the modified Hodge test (MHT), while carbapenem hydrolysis was investigated by the carbapenem inactivation method (CIM) and EDTA-carbapenem-inactivation method (eCIM). The nature of the ESBLs, carbapenemases, and fluoroquinolone-resistance determinants was investigated by PCR. Plasmids were characterized by PCR-based replicon typing (PBRT). Selected isolates were subjected to molecular characterization of the resistome by an Inter-Array Genotyping Kit CarbaResisit and whole-genome sequencing (WGS). Results: In total, 20 isolates were collected and analyzed. All isolates exhibited resistance to amoxicillin alone and when combined with clavulanic acid, cefuroxime, cefotaxime, ceftriaxone, cefepime, imipenem, ceftazidime–avibactam, ceftolozane–tazobactam, gentamicin, amikacin, and ciprofloxacin. There was uniform susceptibility to ertapenem, meropenem, and cefiderocol. The DDST and combined disk test with clavulanic acid were positive, indicating the production of an ESBL. The MHT was negative in all except one isolate, while the CIM showed moderate sensitivity, but only with imipenem as the indicator disk. Furthermore, eCIM tested positive in all of the CIM-positive isolates, consistent with a metallo-β-lactamase (MBL). PCR and sequencing of the selected amplicons identified VIM-1 and VIM-4. The Inter-Array Genotyping Kit CarbaResist and WGS identified β-lactam resistance genes bla_VIM, bla_CTX-M-15, and bla_TEM genes; aminoglycoside resistance genes aac(3)-IId, aph(6)-Id, aph(3″)-Ib, aadA1, armA, and aac(6′)-IIc; as well as resistance genes for sulphonamides sul1 and sul2, trimethoprim dfr1, chloramphenicol cat, and tetracycline tet(J). Conclusions: This study revealed an epidemic spread of carbapenemase-producing P. mirabilis in two wards in a psychiatric hospital. Due to the extensively resistant phenotype (XDR), therapeutic options were limited. This is the first report of carbapenemase-producing P. mirabilis in Croatia. Full article

Myeloproliferative neoplasms (MPNs) are chronic blood cancers characterized by overproduction of blood cells, leading to increased thrombotic and ischemic risk. Patients frequently experience symptoms including fatigue, abdominal discomfort, and complications from thrombotic events, which significantly impact the quality of life (QoL). Many patients inquire about complementary and integrative medicine (CIM) approaches, including nutritional interventions and supplements, creating opportunities for healthcare providers to engage in meaningful discussions guided by the principle of safety. This review examines the current evidence for integrative approaches in MPN management, focusing on nutrition, microbiota, supplements, mind–body techniques, and acupuncture. We analyze the available data on anti-inflammatory interventions, QoL improvement strategies, and treatment tolerance enhancement. The review provides clinicians with evidence-based guidance for safely integrating complementary therapeutic approaches with conventional MPN treatment. This integrative approach represents an opportunity to develop more comprehensive and personalized therapeutic paradigms in hematology while ensuring that complementary interventions serve as adjuncts to evidence-based medical treatment. Full article

Japanese sake, a traditional alcoholic beverage, contains several organic acids that may contribute to its sour taste. To identify these, a precolumn derivatization reagent, benzyl 5-(2-aminoethyl)-3-methyl-4-oxoimidazolidine-1-carboxylate (CIM-C₂-NH₂), developed for labeling carboxyl groups, was synthesized and applied to liquid chromatography–tandem mass spectrometry (LC-MS/MS) analysis of organic acids in six commercial sake samples. The majority primarily contained lactic acid (LA), and dicarboxylic acids, such as succinic acid (SA), malic acid (MA), and citramalic acid (CMA). The organic acid concentrations and compositions in the sake differed among brands. Notably, both l- and d-forms of LA were detected in all samples, while only d-CMA was present. To estimate the total acidic content, neutralization titration with sodium hydroxide was performed. In four of the six samples, titration results closely matched LC-MS/MS data, suggesting that l-LA, d-LA, SA, MA, and d-CMA were the primary contributors for the sour taste in these sakes. The discrepancy between titration and LC-MS/MS data for the other samples was attributed to the presence of other organic acids, which will be investigated in future studies. Full article

Urban regeneration, as a key strategy for promoting sustainable development of urban areas, requires innovative digital technologies to address increasingly complex urban challenges in its implementation. With the fast advancement of digital technologies such as artificial intelligence (AI), Internet of Things (IoT), and big data, these technologies have extensively penetrated various dimensions of urban regeneration, from planning and design to implementation and post-operation management, providing new possibilities for improving urban regeneration efficiency and quality. However, the existing literature lacks a systematic evaluation of technology application patterns across different project scales and phases, comprehensive analysis of stakeholder–technology interactions, and quantitative assessment of technology distribution throughout the urban regeneration lifecycle. This research gap limits the in-depth understanding of how digital technologies can better support urban regeneration practices. This study aims to identify and quantify digital technology application patterns across urban regeneration stages, scales, and stakeholder configurations through systematic analysis of 56 high-quality articles from the Scopus and Web of Science databases. Using a mixed-methods approach combining a systematic literature review, bibliometric analysis, and meta-analysis, we categorized seven major digital technology types and analyzed their distribution patterns. Key findings reveal distinct temporal patterns: GIS and BIM/CIM technologies dominate in the pre-urban regeneration (Pre-UR) stage (10% and 12% application proportions, respectively). GIS applications increase significantly to 14% in post-urban regeneration (Post-UR) stage, while AI technology remains underutilized across all phases (2% in Pre-UR, decreasing to 1% in Post-UR). Meta-analysis reveals scale-dependent technology adoption patterns, with different technologies showing varying effectiveness at building-level, district-level, and city-level implementations. Research challenges include stakeholder digital divides, scale-dependent adoption barriers, and phase-specific implementation gaps. This study constructs a multi-dimensional analytical framework for digital technology support in urban regeneration, providing quantitative evidence for optimizing technology selection strategies. The framework offers practical guidance for policymakers and practitioners in developing context-appropriate digital technology deployment strategies for urban regeneration projects. Full article

Re-identification (ReID) has been widely applied in person and vehicle recognition tasks. This study extends its application to a novel domain: insect (silkworm) recognition. However, unlike person or vehicle ReID, silkworm ReID presents unique challenges, such as the high similarity between individuals, arbitrary poses, and significant background noise. To address these challenges, we propose a multi-view network for silkworm ReID, called WormNet, which is built upon an innovative strategy termed extraction purification extraction interaction. Specifically, we introduce a multi-order feature extraction module that captures a wide range of fine-grained features by utilizing convolutional kernels of varying sizes and parallel cardinality, effectively mitigating issues of high individual similarity and diverse poses. Next, a feature mask module (FMM) is employed to purify the features in the spatial domain, thereby reducing the impact of background interference. To further enhance the data representation capabilities of the network, we propose a channel interaction module (CIM), which combines an efficient channel attention network with global response normalization (GRN) in parallel to recalibrate features, enabling the network to learn crucial information at both the local and global scales. Additionally, we introduce a new silkworm ReID dataset for network training and evaluation. The experimental results demonstrate that WormNet achieves an mAP value of 54.8% and a rank-1 value of 91.4% on the dataset, surpassing both state-of-the-art and related networks. This study offers a valuable reference for ReID in insects and other organisms. Full article

Acinetobacter baumannii is an opportunistic pathogen and a major cause of nosocomial infections worldwide. This study aimed to isolate and characterize phages with lytic activity against multidrug-resistant A. baumannii strains to enable antibacterial alternatives. Eight phages (AKO8a, PS118, B612, MCR, IDQ7, 89P13, CRL20, and CIM23) were isolated and subjected to genomic, phylogenetic, and functional analyses. Antibacterial activity was assessed in vitro against A. baumannii strain AbAK04 by measuring optical density over 17 h at multiplicities of infection (MOIs) of 0.1, 1, and 10, using a repeated-measures design with time as a crossed factor and MOI as a nested factor. Tukey’s post-hoc test identified significant bacterial growth reductions of 57–72% (p < 0.001). Specifically, phages PS118 and 89P13 reduced growth by 71% at MOI 10; CIM23, B612, and CRL20 achieved 68% reduction at MOI 1; and MCR reduced growth by 64% at MOIs 0.1 and 1. Notably, lytic phage MCR encodes a glycosyl hydrolase family 58 (GH58) enzyme, potentially contributing to its antibacterial activity. Genomic analyses confirmed absence of virulence and antibiotic resistance genes, with all phages classified as novel species within the Kagunavirus genus. These findings support the use of these phages as promising candidates for in vivo evaluation. Full article

Computing-in-memory (CIM) with emerging non-volatile resistive memory devices has demonstrated remarkable performance in data-intensive applications, such as neural networks and machine learning. A crosspoint memory array enables naturally parallel computation of matrix–vector multiplication (MVM) in the analog domain, offering significant advantages in terms of speed, energy efficiency, and computational density. However, the intrinsic device non-ideality residing in analog conductance state distorts the MVM precision and limits the application to high-precision scenarios, e.g., scientific computing. Yet, a theoretical framework for guiding reliable computing-in-memory designs has been lacking. In this work, we develop an analytical model describing the constraints on bit precision and row parallelism for reliable MVM operations. By leveraging the concept of capacity from information theory, the impact of non-ideality on computational precision is quantitively analyzed. This work offers a theoretical guidance for optimizing the quantized margins, providing valuable insights for future research and practical implementation of reliable CIM. Full article

This paper proposes a Channel-Aware Speech Network (CAs-Net) for low-resource speech recognition tasks, aiming to improve recognition performance for languages such as Uyghur under complex noisy conditions. The proposed model consists of two key components: (1) the Channel Rotation Module (CIM), which reconstructs each frame’s channel vector into a spatial structure and applies a rotation operation to explicitly model the local structural relationships within the channel dimension, thereby enhancing the encoder’s contextual modeling capability; and (2) the Multi-Scale Depthwise Convolution Module (MSDCM), integrated within the Transformer framework, which leverages multi-branch depthwise separable convolutions and a lightweight self-attention mechanism to jointly capture multi-scale temporal patterns, thus improving the model’s perception of compact articulation and complex rhythmic structures. Experiments conducted on a real Uyghur speech recognition dataset demonstrate that CAs-Net achieves the best performance across multiple subsets, with an average Word Error Rate (WER) of 5.72%, significantly outperforming existing approaches. These results validate the robustness and effectiveness of the proposed model under low-resource and noisy conditions. Full article