Search Results (74,528)

The invasive red palm weevil (Rhynchophorus ferrugineus, RPW) poses a severe threat to heritage palm collections across the Mediterranean Region, necessitating robust protection strategies. This study addresses the urgent challenge of safeguarding the Spanish Phoenix Collection at Miguel Hernandez University (EPSO) and the Orihuela Palmetum by analyzing the efficacy of applied Integrated Pest Management (IPM) strategies over a ten-year period (2014–2024). Monitoring and treatment protocols included targeted chemical, biological, and cultural controls, and were benchmarked against infestation progression data, climatic trends, and comparative case studies from Murcia, Elche, Nice, and Palermo. Results indicate that a proactive, multi-component IPM strategy, especially when coupled with probabilistic risk models, can significantly improve survival and recovery indicators in Phoenix taxa, although species-specific susceptibility to RPW was a major differentiating factor. Comparative analysis reveals common regional patterns in R. ferrugineus management but underscores the critical need for collection-specific, resource-sensitive protocols for high-value heritage plants, demonstrating that evidence-based best practices and coordinated monitoring are essential for effective heritage palm conservation amid continuous pest pressure. Full article

Immunotherapy has become a cornerstone of cancer treatment in both the early and advanced setting in recent years, leading to the achievement of substantial and durable responses with an excellent safety profile across different tumor types. This demonstrates the high potential of engaging the immune system in the treatment of solid tumors. Consequently, there has been renewed interest in vaccines to enhance therapeutic effects, prevent tumor development, and eliminate or control minimal residual disease. Although therapeutic cancer vaccines have shown potential benefits in certain settings, their results in clinical trials remain highly variable and generally unsatisfactory, depending on tumor site, biology, and vaccine type. Currently, Sipuleucel-T for prostate cancer is the only cell-based vaccine that received FDA approval for the treatment of a solid tumor. Innovative techniques such as personalized neoantigen vaccines and mRNA-based vaccines have shown promising preclinical and early-phase clinical results, supporting their further development. Despite the current evidence of vaccine efficacy in treating solid tumors being derived from only a few clinical trials with relatively small sample sizes, ongoing trials are also exploring innovative approaches aimed at preventing cancer development or enhancing immune responses in combination with other immunotherapeutic agents. In this review, we provide an overview of the clinical results and the current state of vaccine development for cancer treatment, outlining future perspectives on their role in managing patients with cancer. Full article

The exponential growth of the digital economy has transformed data centers into major energy consumers, yet their inflexible power consumption patterns and substantial waste heat generation pose significant challenges to grid stability and carbon neutrality targets. Existing energy management strategies often overlook the deep coupling potential between computing workload flexibility, thermal dynamics, and carbon trading mechanisms, leading to suboptimal resource utilization. To address these issues, this study proposes a collaborative low-carbon economic scheduling strategy for data center microgrids. A multiple-dimensional coupling framework is established, integrating a queuing theory-based model for delay-tolerant workload shifting and a heat-determined computing mechanism for active waste heat recovery (WHR). Furthermore, a mixed-integer linear programming (MILP) model is formulated, incorporating a linearized tiered carbon trading mechanism to facilitate source–load coordination. Simulation results demonstrate that the proposed strategy achieves a dual optimization of economic and environmental benefits, reducing total operating costs by 11.7% while minimizing carbon emissions to 6879 kg compared to baseline scenarios. Additionally, by leveraging temperature aware load migration, the daily weighted power usage effectiveness (PUE) is optimized to 1.2607. These findings quantify the marginal benefits of load flexibility under tiered pricing, providing insights for operators to balance service timeliness and energy efficiency in next generation green computing infrastructure. Full article

Growing pressure for sustainability has intensified the need for small- and medium-sized enterprises (SMEs) to adopt environmental innovation while maintaining competitive performance. This study examines how green innovation strategy, environmental regulations, and green absorptive capacity jointly shape the sustainable performance of Chinese manufacturing SMEs. Drawing on Resource-Based View, Institutional Theory, and Dynamic Capability Theory, we develop and empirically test a model that positions green innovation as a mediating mechanism linking strategic intent and regulatory forces to environmental, social, and economic performance outcomes. Data were collected through a structured survey of 250 SME managers in Jiangsu Province and analyzed using PLS-SEM. Results show that green innovation strategy significantly enhances both green innovation and sustainability performance, and that green innovation partially mediates the effects of both strategy and regulatory pressure on performance. While environmental regulations positively influence green innovation, they do not directly improve sustainability outcomes unless translated into innovation. Furthermore, green absorptive capacity displays a boundary-conditioning role, unexpectedly weakening the strategy–innovation path when knowledge integration exceeds implementation capacity. The findings extend the sustainability-oriented innovation literature by clarifying the indirect nature of regulation–performance linkages and revealing conditions under which absorptive capability accelerates or impedes green transformation. The study offers practical guidance for policymakers seeking to design innovation-enabling regulatory frameworks and for SMEs aiming to balance capability development with strategic focus to advance sustainability transitions. Full article

Considering the nonlinear trends, multi-scale variations, and capacity regeneration phenomena exhibited by battery capacity degradation under real-world conditions, accurately predicting its trajectory remains a critical challenge for ensuring the reliability and safety of electric vehicles. To address this, this study proposes a hybrid prediction framework based on Variational Mode Decomposition and a Transformer–Long Short-Term Memory architecture. Specifically, the proposed Variational Mode Decomposition–Transformer for Time Series–Long Short-Term Memory (VMD–TTS–LSTM) framework first decomposes the capacity sequence using Variational Mode Decomposition. The resulting modal components are then aggregated into high-frequency and low-frequency parts based on their frequency centroids, followed by targeted feature analysis for each part. Subsequently, a simplified Transformer encoder (Transformer for Time Series, TTS) is employed to model high-frequency fluctuations, while a Long Short-Term Memory (LSTM) network captures the long-term degradation trends. Evaluated on charging data from 20 commercial electric vehicles under a long-horizon setting of 20 input steps predicting 100 steps ahead, the proposed method achieves a mean absolute error of 0.9247 and a root mean square error of 1.0151, demonstrating improved accuracy and robustness. The results confirm that the proposed frequency-partitioned, heterogeneous modeling strategy provides a practical and effective solution for battery health prediction and energy management in real-world electric vehicle operation. Full article

Anterior Cruciate Ligament (ACL) injuries are among the most common knee injuries sustained during sport. Following injury, only 65% of patients may return to their previous level of sport. Individuals who have suffered ACL injury are far more likely to develop post-traumatic osteoarthritis of the knee (PTOA). This suggests an unmet need for strategies to help advance return to play, reduce risk of PTOA, and provide additional options for pain management after ACL injury. Extracorporeal shockwave therapy (ESWT) and radial pressure waves (RPW) are non-invasive treatment options that have been shown effective for treatment of a variety of orthopedic injuries. This perspective proposes the use of ESWT and RPW as treatment options during the peri-operative and post-operative management of ACL injuries, with the goal of modifying risk for PTOA. The available literature indicates that ESWT may have chondroprotective effects after ACL injury, and numerous clinical trials demonstrate the effectiveness of ESWT and RPW for orthopedic conditions including tendinopathy or bone marrow edema. Limited data and a lack of consensus on standardized rehabilitation protocols present gaps in the literature and emphasize the need for research leading to evidence-based recommendations for the use of ESWT and RPW to modify risk for the onset of PTOA after ACL injuries. Full article

Timely access to soil moisture conditions in farmland crops is the foundation and key to achieving precise irrigation. Due to their high spatiotemporal resolution, unmanned aerial vehicle (UAV) remote sensing has become an important method for monitoring soil moisture. This study addresses soil moisture retrieval in alfalfa fields across different growth stages. Based on UAV multispectral images, a multi-source feature set was constructed by integrating spectral and texture features. The performance of three machine learning models—random forest regression (RFR), K-nearest neighbors regression (KNN), and XG-Boost—as well as two ensemble learning models, Voting and Stacking, was systematically compared. The results indicate the following: (1) The integrated learning models generally outperform individual machine learning models, with the Voting model performing best across all growth stages, achieving a maximum R² of 0.874 and an RMSE of 0.005; among the machine learning models, the optimal model varies with growth stage, with XG-Boost being the best during the branching and early flowering stages (maximum R² of 0.836), while RFR performs better during the budding stage (R² of 0.790). (2) The fusion of multi-source features significantly improved inversion accuracy. Taking the Voting model as an example, the accuracy of the fused features (R² = 0.874) increased by 0.065 compared to using single-texture features (R² = 0.809), and the RMSE decreased from 0.012 to 0.005. (3) In terms of inversion depth, the optimal inversion depth for the branching stage and budding stage is 40–60 cm, while the optimal depth for the early flowering stage is 20–40 cm. In summary, the method that integrates multi-source feature fusion and ensemble learning significantly improves the accuracy and stability of alfalfa soil moisture inversion, providing an effective technical approach for precise water management of artificial grasslands in arid regions. Full article

This study investigates the integration of audio features with CO2 sensor data to enhance occupancy detection accuracy in naturally ventilated office environments. Accurate occupancy detection is pivotal for smart building energy management, yet CO2-based methods cannot provide fast enough response times and are sensitive to air circulation changes due to internal convection. In this article we propose a combination of CO2 sensors and audio features from MEMS microphones to improve the occupancy detection accuracy and improve the response times. We use a Random Forest classifier and evaluate the results across two scenarios: CO2-only and CO2 combined with audio features. Results show that incorporating the audio features into the occupancy detection algorithms yields a significant increase in detection accuracy and speed, especially when the environment is subject to frequent air circulation changes due to internal convection, like the opening and closing of windows and doors. Combining the CO2 and audio sensing offers a promising, cost-effective approach to occupancy detection in smart buildings, yet more research on advanced audio processing and feature selection is necessary. Full article

The aim of this article is to identify and systematize the principal research directions in sustainable strategic management (SSM) at the intersection of eco-innovation and business performance. Despite the growing prominence of sustainability in management scholarship, systematic understanding of how SSM, eco-innovation, and business performance are connected in the academic literature remains limited. In particular, it is unclear whether this intersection constitutes a coherent research domain or instead reflects a set of loosely related and fragmented lines of inquiry. To address this gap, the study combines bibliometric analysis and science mapping of 181 Scopus-indexed publications (2006–2024) with a PRISMA-guided scoping review of five core papers that explicitly link SSM, eco-innovation, and business performance. VOSviewer was used to identify thematic clusters and structural gaps, including missing or weak linkages between eco-innovation and different dimensions of business performance. Building on these findings, the article proposes a dual-path conceptual model: (1) a mediated path in which eco-innovation functions as a transmission mechanism between SSM and multidimensional business performance, and (2) a direct path linking SSM to business performance without mediation. The model further distinguishes between internal organizational conditions, which predominantly support the direct path, and external business environment factors, which are critical in enabling the mediated path through eco-innovation. The main contributions are as follows: (a) a structured mapping of the SSM–eco-innovation research field and its emerging thematic architecture; and (b) a conceptual model specifying the dual role of eco-innovation in shaping business performance outcomes. The study also outlines implications for theory, managerial practice, and public policy, particularly in terms of how organizations and their environments influence the effectiveness of different strategic sustainability pathways. The proposed framework should be interpreted as an evidence-informed conceptual model derived from bibliometric patterns and focused qualitative synthesis, rather than as a statistically validated causal model. Full article

Grassland degradation, exacerbated by climate change and anthropogenic disturbances, poses a substantial barrier to ecological restoration, largely due to the invasion of toxic weeds. In the degraded grasslands of the Ili River Valley, Xinjiang, Sophora alopecuroides has emerged as the dominant toxic species; yet, its expansion mechanisms and sensitivity to management interventions remain poorly understood. This study utilized a three-year (2023–2025) field experiment to evaluate the impacts of nitrogen addition (N), mowing (M), and their combination (NM) on the stoichiometric characteristics and homeostasis of the plant–soil system. The results demonstrated that while M suppressed aboveground biomass, it facilitated the accumulation of root carbon (RC) and phosphorus (RP). Nitrogen enrichment significantly lowered soil C:N and C:P ratios, thereby alleviating phosphorus limitation. Crucially, the NM treatment effectively counteracted N-induced weed proliferation and mitigated M-induced biomass reductions. Analysis of stoichiometric homeostasis revealed that NM optimized plant adaptive strategies, maintaining strict homeostasis for RC and RP (H > 4) while preserving the sensitivity of the root N:P ratio of S. alopecuroides (RN:P). Structural equation modeling further indicated that soil C:P and N:P indirectly regulated total biomass by modulating the root C:P ratio of S. alopecuroides (RC:P). Consequently, stoichiometric coupling within the plant–soil system is essential for maintaining ecosystem functions. Integrated management (NM) optimizes soil nutrient balance and harnesses compensatory growth to suppress weed expansion, providing a robust scientific framework for the restoration of S. alopecuroides-invaded grasslands. Full article

Urban metro systems are highly sensitive to seismic disturbances, and the ability of metro stations to manage emergencies effectively has become an increasingly important component of urban resilience. This study develops a resilience-oriented evaluation framework that conceptualizes emergency management as a sequential managerial process encompassing preparedness, response, and recovery. A multi-dimensional indicator system was constructed based on the four resilience capacities—absorptive, maintaining, recovery, and adaptive—and operationalized through a multi-stage Data Envelopment Analysis (DEA) model. The framework enables both overall efficiency assessment and stage-specific diagnosis of managerial weaknesses. Methodologically, the study demonstrates how resilience theory can be operationalized into a network efficiency structure suitable for process-level diagnosis rather than aggregate scoring. A case study of a representative metro station demonstrates the applicability of the proposed method. The results reveal that while preparedness practices are relatively mature, notable inefficiencies exist in real-time response and post-event recovery due primarily to managerial factors such as communication reliability, personnel coordination, and restoration planning. Improvement simulations confirm that targeted enhancements in these management processes can substantially increase overall emergency efficiency. The findings highlight that seismic resilience is not solely determined by physical infrastructure but is heavily dependent on managerial effectiveness across the emergency cycle. The proposed framework contributes a process-oriented, data-driven tool for evaluating and improving emergency management performance and offers practical guidance for metro operators seeking to strengthen resilience under earthquake scenarios. Full article

Background: Thoracoamniotic shunting (TAS) is a well-established fetal therapy for severe pleural effusions complicated by hydrops. Although survival in selected cases exceeds 60%, retained or migrated shunts can pose significant postnatal management challenges. Case presentation: We report a neonate with intrathoracic migration of a Somatex^® shunt placed at 26 weeks’ gestation for hydropic pleural effusion. Although initially asymptomatic, the infant developed recurrent pleural effusions requiring multiple readmissions. Thoracoscopic retrieval on day 76 of life allowed safe removal despite dense adhesions, leading to complete clinical resolution. Discussion: Retained thoracoamniotic shunts may remain asymptomatic or cause recurrent effusions, pneumothorax, or other complications. This case highlights the limitations of conservative management in the presence of clinical deterioration and supports timely surgical removal. Standardized criteria for intervention are lacking and urgently needed. Conclusions: In infants with retained TAS, recurrence of effusions or respiratory compromise should prompt active removal. Thoracoscopic retrieval is a safe and effective minimally invasive option. Full article

The increasing resistance of agricultural pests to conventional pesticides necessitates the development of alternative biological control strategies. This study evaluated the acaricidal potential of two Photorhabdus luminescens strains (0805-P2R and the newly isolated 2103-RUVI) against the spider mite Tetranychus truncatus. Culture conditions were optimized using a Taguchi L9(3⁴) design to maximize growth, protease activity, and acaricidal efficacy. The optimized medium for strain 2103-RUVI achieved 90% mortality against T. truncatus at 72 h, compared to 83% for strain 0805-P2R under equivalent conditions. Genomic analysis identified putative phosphoporin PhoE genes exclusively in 2103-RUVI, which may contribute to its enhanced virulence, although this association remains correlative and requires functional validation. Histopathological observations confirmed severe tissue disruption in treated mites. Comparative analysis demonstrated complex, strain-specific relationships among bacterial growth, enzyme activity, and acaricidal effects. These results highlight the potential of the P. luminescens strain 2103-RUVI as an effective biocontrol agent, providing insights for its application in sustainable integrated pest management programs. Full article

The deployment of autonomous robots in construction remains constrained by the complexity and variability of real-world environments. Conventional programming and single-agent approaches lack the adaptability required for dynamic multi-robot operating conditions, underscoring the need for cooperative, learning-based systems. This paper presents an ROS-based modular framework that integrates Multi-Agent Reinforcement Learning (MARL) into a generic 2D simulation and execution pipeline for cooperative mobile robots in construction-oriented digital environments to enable adaptive task allocation and coordinated execution without predefined datasets or manual scheduling. The framework adopts a centralized-training, decentralized-execution (CTDE) scheme based on Multi-Agent Proximal Policy Optimization (MAPPO) and decomposes the system into interchangeable modules for environment modelling, task representation, robot interfaces, and learning, allowing different layouts, task sets, and robot models to be instantiated without redesigning the core architecture. Validation through an ROS-based 2D simulation and real-world experiments using TurtleBot3 robots demonstrated effective task scheduling, adaptive navigation, and cooperative behavior under uncertainty. In simulation, the learned MAPPO policy is benchmarked against non-learning baselines for multi-robot task allocation, and in real-robot experiments, the same policy is evaluated to quantify and discuss the performance gap between simulated and physical execution. Rather than presenting a complete construction-site deployment, this first study focuses on proposing and validating a reusable MARL–ROS framework and digital testbed for multi-robot task allocation in construction-oriented digital environments. The results show that the framework supports effective cooperative task scheduling, adaptive navigation, and logic-consistent behavior, while highlighting practical issues that arise in sim-to-real transfer. Overall, the framework provides a reusable digital foundation and benchmark for studying adaptive and cooperative multi-robot systems in construction-related planning and management contexts. Full article

Unmanned aerial vehicle (UAV)-based multispectral monitoring has become an increasingly important tool for assessing crop vigor and stress under commercial agricultural conditions. However, most UAV-based studies using the normalized difference vegetation index (NDVI) in citrus systems have focused on yield estimation, disease detection, or canopy characterization during active growth phases, while the immediate post-harvest recovery period remains poorly documented. In this study, UAV-derived NDVI products were used to evaluate the canopy response in a commercial ‘Washington Navel’ orange orchard located in La Yarada Los Palos district (Tacna, Peru) following harvest. The study specifically assessed the effect of an on-farm, residue-based organic biostimulant produced from local organic wastes within a circular economy framework. The results indicate that treated plots exhibited a faster and more pronounced recovery of canopy vigor compared to untreated controls during the early post-harvest period. By integrating high-resolution UAV-based multispectral monitoring with a residue-derived biostimulant strategy, this work advances current NDVI-based applications in citrus by shifting the analytical focus from productive stages to post-harvest physiological recovery. The proposed approach provides a scalable and non-invasive framework for evaluating post-harvest canopy dynamics under water-limited, hyper-arid conditions and highlights the potential of locally sourced biostimulants as complementary management tools in precision agriculture systems. Full article