Search Results (286)

The maintenance of power distribution lines is critically challenged by vegetation encroachment, posing significant risks to the reliability and safety of power utilities. Traditional manual inspection methods are resource-intensive and lack the precision required for effective and proactive maintenance. This paper presents an automated, accurate, and efficient approach to vegetation management near power lines by leveraging advancements in LiDAR as a remote sensing technology and deep learning algorithms. The RandLA-Net model is employed for semantic segmentation of large-scale point clouds to accurately identify vegetation, poles, and power lines. A comprehensive sensitivity analysis is conducted to optimize the model’s hyperparameters, enhancing segmentation accuracy. Post-processing techniques, including clustering and rule-based thresholding, are applied to refine the semantic segmentation results. Proximity detection is applied using spatial queries based on a KDTree structure to assess potential risks of vegetation near power lines. Furthermore, a digital twin of the power distribution network and surrounding trees is developed by integrating 3D object registration and surface generation, enriching it with semantic attributes and incorporating it into City Information Modeling (CIM) systems. This framework demonstrates the potential of remote sensing data integration for efficient environmental monitoring in urban infrastructure. The results of the case study on the Toronto-3D dataset demonstrate the computational efficiency and accuracy of the proposed method, presenting a promising solution for power utilities in proactive vegetation management and infrastructure planning. The optimized full 9-class model achieved an overall accuracy of 96.90% and IoU scores of 97.05% for vegetation, 88.09% for power lines, and 82.33% for poles, supporting comprehensive digital twin creation. An auxiliary 4-class model further improved targeted performance, with IoUs of 99.55% for vegetation, 88.79% for poles, and 87.18% for power lines. Full article

Deep neural networks (DNNs) require efficient hardware accelerators due to the high cost of vector–matrix multiplication operations. Computing-in-memory (CIM) architectures address this challenge by performing computations directly within memory arrays, reducing data movement and improving energy efficiency. This paper introduces a novel analog-domain CIM architecture that enables flexible organization of weights across both rows and columns of the CIM array. A pipelining scheme is also proposed to decouple the multiply-and-accumulate and analog-to-digital conversion operations, thereby enhancing throughput. The proposed architecture is compared with existing approaches in terms of latency, area, energy consumption, and utilization. The comparison emphasizes architectural principles while deliberately avoiding implementation-specific details. Full article

The increasing demand for high-performance and low-power hardware in artificial intelligence (AI) applications—such as speech recognition, facial recognition, and object detection—has driven the exploration of advanced memory designs. Convolutional neural networks (CNNs) and deep neural networks (DNNs) require intensive computational resources, leading to significant challenges in terms of memory access time and power consumption. Compute-in-Memory (CIM) architectures have emerged as an alternative by executing computations directly within memory arrays, thereby reducing the expensive data transfer between memory and processor units. In this work, we present a 6T SRAM-based CIM architecture implemented using FinFET technology, aiming to reduce both power consumption and access delay. We explore and simulate three different SRAM cell structures—PLNA (P-Latch N-Access), NLPA (N-Latch P-Access), and SE (Single-Ended)—to assess their suitability for CIM operations. Compared to a reference 10T XNOR-based CIM design, our results show that the proposed structures achieve an average power consumption approximately 70% lower, along with significant delay reduction, without compromising functional integrity. A comparative analysis is presented to highlight the trade-offs between the three configurations, providing insights into their potential applications in low-power AI accelerator design. Full article

This study aims to evaluate the Ban Ko Groundwater Development Project in Li District, Lamphun Province, which seeks to address PM2.5 pollution from forest fires through rural economic development. The Social Return on Investment (SROI) approach was applied to assess the project’s social return ratio (SRR), revealing that the intervention lacked cost-effectiveness and did not yield sufficient social or economic returns on investment. Decision Tree analysis indicated that economic benefits significantly influenced positive behavioral change toward environmental conservation; however, the magnitude of this change was insufficient to generate substantial environmental improvements. Furthermore, the application of the Collective Interest Model (CIM) revealed that several social factors including personal pro-environmental tendencies, perceived group efficacy, civic responsibility, economic incentives, education, and age contributed to individuals’ decisions to engage in environmental problem-solving. These findings suggest that future economic development efforts must be integrated with social dimensions to foster sustainable environmental solutions in rural contexts. Full article

Amid China’s rural–urban integration and rural revitalization, the Centralized Residence of Farmers Policy (CRFP) emerges as a pivotal tool to optimize rural spatial structure and land-use efficiency, yet its implementation risks—particularly risk coupling effects—remain underexplored. This study addresses this gap by constructing a holistic risk assessment framework and empirically examining CRFP in L Town, Shanghai; it employs a multi-method approach, integrating the Delphi method, Analytic Hierarchy Process (AHP), and Cumulative Impact Model (CIM) to develop and validate a comprehensive risk assessment framework. This framework evaluates five key dimensions: policy content, implementation subjects, resource guarantees, target groups, and environmental adaptation. Empirical analysis of relocated farming households in L town reveals that the overall risk level of CRFP implementation falls within the moderate-risk range. Key identified risk factors identified include public opinion control, clarity of implementation standards, communication feedback accessibility, reliability of information resources, and effectiveness of implementation strategies. Based on these findings, the study proposes several risk mitigation strategies: aligning policies with local realities to promote high-quality social development, fostering collaborative digital governance through multi-stakeholder engagement, ensuring law-based policy formulation with transparent and supervised processes, enhancing public input through effective interest communication mechanisms, improving information dissemination with inclusive public participation, and adopting flexible implementation strategies. This research addresses fragmentation issues in the existing literature with a unified indicator system and provides actionable solutions that offer significant theoretical and practical value for advancing rural revitalization in the context of urban–rural integration. Full article

Public transport systems play a crucial role in the development of large cities. Bus network design to optimize passenger flow coverage in a global metropolis is a challenging task. As an essential part of bus travel planning, considering the bus transfer factor in the existing extremely complex and extensive public bus network usually leads to a optimization problem characterized by high-dimensionality and non-linearity. While classical computers struggle to deal with this kind of problems, quantum computers shed new light into this field. The coherent Ising machine (CIM), a specialized optical quantum computer using a photonic dissipative architecture, has shown its remarkable computational power in combinatorial optimization problems. We construct the classical model and the quadratic unconstrained binary optimization (QUBO) model of the bus route optimization problem, and solve it using a classical computer and CIM, respectively. Our experimental results demonstrate the significant acceleration capability of CIM over classical computers in finding the optimal or near-optimal solutions, albeit subject to the hardware limitations of the 100-qubit CIM. Full article

Measurement while drilling technology (MWD) has emerged as a pivotal approach for geological exploration. However, the accuracy of existing geological recognition models remains limited, primarily due to data fluctuations that result in high overlap rates and reduced reliability of drilling parameters. This study takes torque data as an example and analyzes the frequency distribution laws of torque responses across rock with varying strengths. A quantitative model of the frequency distribution characteristic interval is established, and a rock information prediction approach based on frequency distribution characteristics is proposed. The results indicate that torque frequency distributions for homogeneous rock exhibit a unimodal pattern, whereas those for composite rocks display multimodal characteristics. The boundaries of the frequency distribution characteristic intervals are mathematically defined as CIS = T_p|_{(dF/dT) = 0} ± σ and CIM = x_li ± 0.5∆x_i. The strength prediction model constructed using torque within the characteristic interval achieves an average accuracy of 85.3%. Furthermore, the frequency of torque within the characteristic interval enables the estimation of rock stratum thickness. This research contributes to enhancing the accuracy of rock information identification. Full article

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

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