Search Results (5,849)

Agriculture is a major economic industry that sustains life. Moreover, plant health is a crucial aspect of a highly functional agricultural system. Because stress agents can damage crops and plants, it is important to understand what effect these agents can have and be able to detect this negative impact early in the process. Machine learning technology can help to prevent these undesirable consequences. This research investigates machine learning applications for plant health analysis and classification. Specifically, Residual Networks (ResNet) and Long Short-Term Memory (LSTM) models are utilized to detect and classify plants response to abiotic external stressors. Two types of plants, azalea (shrub) and Chinese tallow (tree), were used in this research study and different concentrations of sodium chloride (NaCL) and acetic acid were used to treat the plants. Data from cameras and soil sensors were analyzed by the machine learning algorithms. The ResNet34 and LSTM models achieved accuracies of 96% and 97.8%, respectively, in classifying plants with good, medium, or bad health status on test data sets. These results demonstrate that machine learning algorithms can be used to accurately detect plant health status as well as healthy and unhealthy plant conditions and thus potentially prevent negative long-term effects in agriculture. Full article

To address the challenges of small target detection in UAV aerial images—such as difficulty in feature extraction, complex background interference, high miss rates, and stringent real-time requirements—this paper proposes an innovative model series named SRTSOD-YOLO, based on YOLO11. The backbone network incorporates a Multi-scale Feature Complementary Aggregation Module (MFCAM), designed to mitigate the loss of small target information as network depth increases. By integrating channel and spatial attention mechanisms with multi-scale convolutional feature extraction, MFCAM effectively locates small objects in the image. Furthermore, we introduce a novel neck architecture termed Gated Activation Convolutional Fusion Pyramid Network (GAC-FPN). This module enhances multi-scale feature fusion by emphasizing salient features while suppressing irrelevant background information. GAC-FPN employs three key strategies: adding a detection head with a small receptive field while removing the original largest one, leveraging large-scale features more effectively, and incorporating gated activation convolutional modules. To tackle the issue of positive-negative sample imbalance, we replace the conventional binary cross-entropy loss with an adaptive threshold focal loss in the detection head, accelerating network convergence. Additionally, to accommodate diverse application scenarios, we develop multiple versions of SRTSOD-YOLO by adjusting the width and depth of the network modules: a nano version (SRTSOD-YOLO-n), small (SRTSOD-YOLO-s), medium (SRTSOD-YOLO-m), and large (SRTSOD-YOLO-l). Experimental results on the VisDrone2019 and UAVDT datasets demonstrate that SRTSOD-YOLO-n improves the mAP@0.5 by 3.1% and 1.2% compared to YOLO11n, while SRTSOD-YOLO-l achieves gains of 7.9% and 3.3% over YOLO11l, respectively. Compared to other state-of-the-art methods, SRTSOD-YOLO-l attains the highest detection accuracy while maintaining real-time performance, underscoring the superiority of the proposed approach. Full article

Background/Objectives: This retrospective study aimed to evaluate the survival and marginal bone loss (MBL) of sloped-shoulder implants placed in the posterior mandible, and to explore the influence of both patient- and implant-related factors. Materials and Methods: All patients treated with sloped-shoulder-profile implants (Astra Tech Implant System, Dentsply Sirona, Bensheim, Germany) in the posterior mandible between 2012 and 2023 at two private clinics were included. Implant survival was analyzed with Kaplan–Meier estimates. MBL was measured from prosthesis delivery (baseline radiograph) to the most recent available radiograph. Outcomes were compared across thresholds of 0, 0.5, and 1.5 mm, which were considered radiographic success criteria. According to the 2017 World Workshop, peri-implantitis was not diagnosed solely based on MBL. Associations with potential risk factors (periodontitis, bruxism, and smoking) were explored. The study was approved by a local ethics committee (PI 106/2023); informed consent was waived due to the retrospective design and anonymization of data. Results: A total of 43 patients with 48 implants were included, with a mean follow-up of 40.1 months. The cumulative survival rate was 93.7%, with all failures occurring before 24 months. Mean MBL at the mesial and distal aspects was 0.27 mm and 0.39 mm, respectively. In 82.2% of implants, MBL remained ≤0.5 mm at a mean follow-up of 44.2 months. No statistically significant associations were found between risk factors such as periodontitis, bruxism, or smoking and implant outcomes. Conclusions: Sloped-shoulder implants in the posterior mandible showed high survival and stable marginal bone levels over the medium term. Their design may simplify treatment in oblique ridges, potentially reducing the need for GBR procedures. Full article

In oil well environments, pressure sensors are often challenged by electromagnetic interference, temperature drift, and corrosive fluids, which reduce their stability and service life. To improve long-term reliability under these conditions, we developed a fiber optic Fabry–Perot (FP) cavity pressure sensor that employs an Inconel 718 diaphragm to provide both high mechanical strength and corrosion resistance. An integrated fiber Bragg grating (FBG) was included to monitor temperature simultaneously, allowing temperature–pressure cross-sensitivity to be decoupled. The sensor was fabricated and tested over a temperature range of 20–100 °C and a pressure range of 0–60 MPa. Experimental characterization showed that the FP cavity length shifted linearly with pressure, with a sensitivity of 377 nm/MPa, while the FBG demonstrated a temperature sensitivity of 0.012 nm/°C. After temperature compensation, the overall pressure measurement accuracy reached 0.5% of the full operating pressure range (0–60 MPa). These results confirm that the combined FP–FBG sensing approach maintained stable performance in harsh downhole conditions, making it suitable for pressure monitoring in shallow and medium-depth reservoirs. The proposed design offers a practical route to extend the operational lifetime of optical sensors in oilfield applications. Full article

Steel structures that support machines and industrial process installations should ideally be flexible, adaptable, and easily reconfigurable. However, in current practice, new profiles are frequently used and discarded whenever layout modifications are required, leading to considerable material waste, increased costs, and environmental burdens. Such practices conflict with the principles of the circular economy, in which reusability is preferable to recycling. This paper presents a life cycle sustainability assessment (life cycle cost, LCC, and life cycle assessment, LCA) applied to six structural typologies: (a) welded IPE profiles, (b) bolted IPE profiles, (c) welded tubular profiles, (d) bolted tubular profiles, (e) clamped IPE profiles with demountable joints, and (f) flanged tubular profiles with demountable joints. The assessment integrates structural calculations with an updatable database of costs, operation times, and service lives, providing a systematic framework for evaluating both economic and environmental performance in medium-load industrial structures (0.5–9.8 kN/m²). Application to nine representative case studies demonstrated that demountable clamped and flanged joints become economically competitive after three life cycles, and after only two life cycles under high-load conditions (9.8 kN/m²). The findings indicate relative cost savings of up to 75% in optimized configurations and carbon-footprint reductions of approximately 50% after three cycles. These results provide quantitative evidence of the long-term advantages of demountable and reconfigurable steel structures. Their capacity for repeated reuse without loss of performance supports sustainable design strategies, reduces environmental impacts, and advances circular economy principles, making them an attractive option for modern industrial facilities subject to frequent modifications. Full article

Larix olgensis is a valuable timber species in northern China, typically propagated through somatic embryogenesis (SE). However, long-term subculture can lead to a loss of embryogenic potential. This study aimed to establish a simple and stable protocol for the cryopreservation and regeneration of L. olgensis embryogenic callus (EC) that preserves its SE potential and regenerative capacity. The slow-freezing method was employed for cryopreservation. A cryopreservation protocol for L. olgensis EC was developed by optimizing the preculture duration and conditions, cryoprotectant composition and thawing temperature. The results showed that optimal outcomes were achieved using a 24 h stepwise preculture on medium containing 0.2 and 0.4 mol∙L⁻¹ sucrose, followed by cryoprotectant treatment with 0.4 mol∙L⁻¹ sucrose, 2.5% (v/v) dimethyl sulfoxide (DMSO) and 10% polyethylene glycol 6000 (PEG₆₀₀₀), and thawing at 37 °C. EC cryopreserved using this protocol achieved a 100% recovery rate. Moreover, the cryopreserved recoverable EC successfully underwent SE, progressing through germination and rooting. Cryopreservation duration (storage duration in liquid nitrogen) did not affect cell viability and proliferation rate, confirming the protocol’s suitability for long-term cryopreservation of L. olgensis EC. This study provides a valuable reference for the cryopreservation and regeneration of L. olgensis EC, with potential applications for other coniferous species. It establishes a robust foundation for the large-scale propagation of conifers. Full article

Strategic mergers and acquisitions (M&A) can support sustainable business models by enabling firms to adapt their capabilities and competitive positions as conditions change. This study examines how target listing status (public vs. private) and acquirer size shape short-term shareholder wealth in Korean SMEs (Small- and medium-sized enterprise), and links announcement reactions to subsequent operating outcomes. Using an event study and multivariate regressions on 155 M&A announcements by KOSDAQ-listed SMEs (Korean Securities Dealers Automated Quotations) (2016–2020), we find that smaller acquirers earn significantly higher announcement-period cumulative abnormal returns (CAR)—i.e., smaller firm size is positively associated with superior market-adjusted performance around M&A events. Although acquisitions of privately held targets and diversifying deals show higher unadjusted means, their effects become statistically insignificant once firm fundamentals and size are controlled for. To connect M&A strategy with business-model sustainability, we operationalize sustainability as the alignment between short-term market expectations (CAR) and realized operating performance over 1–2 years, measured by return on operating cash flow (ROCF); medium-term checks indicate that the short-run “size effect” attenuates, underscoring the role of execution and scale in longer-run outcomes. Overall, the evidence highlights the primacy of firm-specific fundamentals, strategic fit, and integration capacity in guiding M&A decisions that advance both near-term performance and longer-term resilience. The Korean SME setting—marked by concentrated ownership, resource constraints, and a chaebol-influenced market and policy environment—provides a stringent context for these tests. Full article

Parking shortages and high costs in Chinese central business districts (CBDs) remain major urban challenges. Emerging automated vehicles (AVs) are expected to diversify parking options and mitigate these problems. However, AV users’ parking preferences and their influencing factors within existing urban zoning frameworks remain unclear. This study examines Nanjing as a representative case, proposing six distinct AV parking modes. Using survey data from 4644 responses collected from 1634 potential users, we employed nested logit models and random forest algorithms to analyze parking choice behavior. Results indicate that diversified AV parking modes would significantly reduce CBD parking demand. Users with medium- to long-term needs prefer home-parking, while short-term users favor CBD proximity. Key influencing factors include parking service satisfaction, duration, congestion time, AV punctuality, and individual characteristics, with satisfaction attributes showing the greatest impact across all modes. Comparative analysis reveals that random forest algorithms provide superior predictive accuracy for parking mode importance, while nested logit models better explain causal relationships between choices and influencing factors. This study establishes a dual analytical framework combining interpretability and predictive accuracy for urban AV parking research, providing valuable insights for transportation management and future metropolitan studies. Full article

Once an online public opinion emerges, the interweaving of information distortion and public panic makes it difficult for enterprises to accurately grasp the emotional turning point and formulate sustainable marketing strategies. Based on the perspective of information asymmetry, in this paper, we construct a four-agent evolutionary game model involving the central government, local governments, enterprises and netizens. It analyzes the balance of strategies used by different actors in public opinion crises and examines how these strategies drive public panic from three perspectives: content, users and emotions. Finally, the findings are verified through simulation calculations. Our research reveals that when panic sentiment is in the medium range, the central government’s strengthened supervision coexists with enterprises’ deceptive marketing, and the impact of the event is magnified. When panic breaks through the threshold, local governments shift from full disclosure to partial disclosure, while consumers maintain their purchasing confidence and are less likely to be swayed by rumors. Research shows that after a public opinion crisis occurs, only by replacing deception with transparent and genuine content and jointly creating green solutions with consumers can enterprises transform panic into sustainable brand assets and provide a decision-making basis for the long-term development of the enterprise. Full article

Hydrogen is increasingly recognized as a clean energy vector and storage medium, yet its viability and strategic role in the Western Balkans remain underexplored. This study provides the first comprehensive techno-economic, environmental, and strategic evaluation of hydrogen production pathways in Albania. Results show clear trade-offs across options. The levelized cost of hydrogen (LCOH) is estimated at 8.76 €/kg H₂ for grid-connected, 7.75 €/kg H₂ for solar, and 7.66 €/kg H₂ for wind electrolysis—values above EU averages and reliant on lower electricity costs and efficiency gains. In contrast, fossil-based hydrogen via steam methane reforming (SMR) is cheaper at 3.45 €/kg H₂, rising to 4.74 €/kg H₂ with carbon capture and storage (CCS). Environmentally, Life Cycle Assessment (LCA) results show much lower Global Warming Potential (<1 kg CO₂-eq/kg H₂) for renewables compared with ~10.39 kg CO₂-eq/kg H₂ for SMR, reduced to 3.19 kg CO₂-eq/kg H₂ with CCS. However, grid electrolysis dominated by hydropower entails high water-scarcity impacts, highlighting resource trade-offs. Strategically, Albania’s growing solar and wind projects (electricity prices of 24.89–44.88 €/MWh), coupled with existing gas infrastructure and EU integration, provide strong potential. While regulatory gaps and limited expertise remain challenges, competition from solar-plus-storage, regional rivals, and dependence on external financing pose additional risks. In the near term, a transitional phase using SMR + CCS could leverage Albania’s gas assets to scale hydrogen production while renewables mature. Overall, Albania’s hydrogen future hinges on targeted investments, supportive policies, and capacity building aligned with EU Green Deal objectives, with solar-powered electrolysis offering the potential to deliver environmentally sustainable green hydrogen at costs below 5.7 €/kg H₂. Full article

In the long-term evolutionary process, species maintain a natural balance within certain limits through communication. As plants grow and function as producers, root enrichment fosters a dynamic rhizosphere microbiome, which serves not only as a disintegrator within the ecological niche but also as a medium for interaction between the host and the soil environment. The life cycle of fungi within the microbiome alternates between single-cell resting spores and multicellular trophic mycelia. This cycle not only establishes a stable rhizosphere environment but also plays a crucial role in regulating both intra- and interspecific information transmission, significantly impacting the environment and plant health. The rhizosphere microbiome, particularly the fungi it contains, can be harnessed to repair environmental damage and either promote the growth of the plant host or inhibit pathogens. However, the mechanisms underlying these actions remain inadequately understood, hindering the advancement of artificial regulation. Additionally, the variability of influencing factors, along with unstable genes and traits, poses challenges to industrial development. In conclusion, this paper focuses on the fungal components of the rhizosphere microbiome, introduces the mechanisms of communication and current applications, and further analyzes existing bottlenecks and potential solutions. The aim is to provide theoretical support for achieving green, sustainable agriculture through biological means. Full article

Accurate load profile identification is crucial for effective and sustainable power system planning. This study proposes a characterization methodology based on clustering techniques applied to consumption data from medium- and low-voltage users, as well as distribution transformers from an electric utility. Three algorithms—K-means, DBSCAN (Density-Based Spatial Clustering of Applications with Noise), and Gaussian Mixture Models (GMM)—were implemented and compared in terms of their ability to form representative strata using variables such as observation count, projected energy, load factor ($LF$), and characteristic power levels. The methodology includes data cleaning, normalization, dimensionality reduction, and quality metric analysis to ensure cluster consistency. Results were benchmarked against a prior study conducted by Empresa Eléctrica Regional Centro Sur C.A. (EERCS). Among the evaluated algorithms, GMM demonstrated superior performance in modeling irregular consumption patterns and probabilistically assigning observations, resulting in more coherent and representative segmentations. The resulting clusters exhibited an average $LF$ of 58.82%, indicating balanced demand distribution and operational consistency across the groups. Compared to alternative clustering techniques, GMM demonstrated advantages in capturing heterogeneous consumption patterns, adapting to irregular load behaviors, and identifying emerging user segments such as induction-cooking households. These characteristics arise from its probabilistic nature, which provides greater flexibility in cluster formation and robustness in the presence of variability. Therefore, the findings highlight the suitability of GMM for real-world applications where representativeness, efficiency, and cluster stability are essential. The proposed methodology supports improved transformer sizing, more precise technical loss assessments, and better demand forecasting. Periodic application and integration with predictive models and smart grid technologies are recommended to enhance strategic and operational decision-making, ultimately supporting the transition toward smarter and more resilient power distribution systems. Full article

Despite the widespread adoption of network monitoring tools, Managed Service Providers (MSPs), specifically small- and medium-sized enterprises (SMEs), continue to face persistent challenges in achieving predictive, multi-tenant-aware visibility across distributed client networks. Existing monitoring systems lack integrated predictive analytics and edge intelligence. To address this, we propose an AI- and IoT-driven monitoring and visualisation framework that integrates edge IoT nodes (Raspberry Pi Prometheus modules) with machine learning models to enable predictive anomaly detection, proactive alerting, and reduced downtime. This system leverages Prometheus, Grafana, and Mimir for data collection, visualisation, and long-term storage, while incorporating Simple Linear Regression (SLR), K-Means clustering, and Long Short-Term Memory (LSTM) models for anomaly prediction and fault classification. These AI modules are containerised and deployed at the edge or centrally, depending on tenant topology, with predicted risk metrics seamlessly integrated back into Prometheus. A one-month deployment across five MSP clients (500 nodes) demonstrated significant operational benefits, including a 95% reduction in downtime and a 90% reduction in incident resolution time relative to historical baselines. The system ensures secure tenant isolation via VPN tunnels and token-based authentication, while providing GDPR-compliant data handling. Unlike prior monitoring platforms, this work introduces a fully edge-embedded AI inference pipeline, validated through live deployment and operational feedback. Full article

Introduction: This study explores the impact of logistics information technology (LIT) on supply chain relationships and distribution performance in small and medium-sized enterprises (SMEs) using South Africa as a case study. Although digital supply chain solutions are increasingly important, there is limited evidence of SME efficiency in emerging markets using LIT. Methods: This study utilises a survey of 313 SMEs from four South African provinces. Bayesian structural equation modelling (Bayesian SEM) was then used to examine LIT’s effects on distribution performances in terms of timeliness, product availability, and condition. Results: The results show that the adoption of LIT strengthens buyer–seller networks (β = 0.524, CI = [0.434, 0.613]) and improves distribution by enhancing both timeliness performance (β = 0.237, CI = [0.098, 0.372]) and product condition performance (β = 0.175, CI = [0.042, 0.259], β = 0.222, p < 0.001). However, it does not directly enhance product availability performance (β = 0.085, CI = [−0.030, 0.199]), signifying that LIT adoption by itself fails to improve product availability. The results also demonstrate that SME network relationships mediate the connection between LIT adoption and distribution performance metrics. Discussion: This study’s findings contribute to the literature and offer valuable information and guidance to policymakers as they underscore the importance for SMEs to invest in LIT integration and compatibility, as well as inventory optimisation and improved supplier communication to minimise transit time variation. Policymakers should support SMEs’ digital transformation through interventions including funding and training for LIT adoption. This study confirms the essential role of LIT in SME supply chains and illustrates that technology-facilitated relationships enhance distribution performance, which enhances SME competitiveness. Full article

This study investigates the utilisation of AI tools, including Grammarly Free, QuillBot Free, Canva Free Individual, and others, to enhance learning outcomes for 180 s-year telecommunications engineering students at Universidad Politécnica de Madrid. This research incorporates teaching methods like problem-based learning, experiential learning, task-based learning, and content–language integrated learning, with English as the medium of instruction. These tools were strategically used to enhance language skills, foster computational thinking, and promote critical problem-solving. A control group comprising 120 students who did not receive AI support was included in the study for comparative analysis. The control group’s role was essential in evaluating the impact of AI tools on learning outcomes by providing a baseline for comparison. The results indicated that the pilot group, utilising AI tools, demonstrated superior performance compared to the control group in listening comprehension (98.79% vs. 90.22%) and conceptual understanding (95.82% vs. 84.23%). These findings underscore the significance of these skills in enhancing communication and problem-solving abilities within the field of engineering. The assessment of the pilot course’s forum revealed a progression from initially error-prone and brief responses to refined, evidence-based reflections in participants. This evolution in responses significantly contributed to the high success rate of 87% in conducting complex contextual analyses by pilot course participants. Subsequent to these results, a project for educational innovation aims to implement the AI-PBL-CLIL model at Universidad Politécnica de Madrid from 2025 to 2026. Future research should look into adaptive AI systems for personalised learning and study the long-term effects of AI integration in higher education. Furthermore, collaborating with industry partners can significantly enhance the practical application of AI-based methods in engineering education. These strategies facilitate benchmarking against international standards, provide structured support for skill development, and ensure the sustained retention of professional competencies, ultimately elevating the international recognition of Spain’s engineering education. Full article