Search Results (263)

Understanding protein and amino acid deposition in pregnant gilts is important for developing nutritional strategies that meet these demands and enhance reproductive performance. Current models, such as the NRC (2012) gestating sow model, assume a constant proportional protein and amino acid content in tissues throughout pregnancy. However, empirical data suggest that gestational tissue growth and composition change dynamically. In this study, we developed a gestation model that characterizes the dynamic changes in growth, crude protein, and amino acid deposition throughout gestation. Based on a systematized search of published data, mathematical functions were developed to estimate daily protein and amino acid deposition in key tissues, including allantoic and amniotic fluid, uterus, placenta, fetus, mammary gland, and maternal body. Our results suggest that dietary crude protein levels and amino acid profiles should be adjusted to meet metabolic demands, particularly in early gestation, where a potential nutritional deficiency was identified. Additionally, the amino acid profile of deposited protein shifts during late gestation, suggesting a changing demand for specific amino acids. These findings challenge existing models and highlight the need for adaptive dietary strategies that better align with pregnancy’s biological demands. By refining protein and amino acid deposition estimates, this study provides a framework guiding future research on precision feeding, ultimately improving gilt and sow reproductive performance. Full article

This study evaluated the effects of fermented banana agro-waste silage (BAWS) in finishing diets for KHAPS pigs (Duroc × MeiShan hybrid). BAWS was produced via 30 days of anaerobic fermentation of disqualified banana fruit, pseudostem, and wheat bran, doubling crude protein content and generating short-chain fatty acids, as indicated by a satisfactory Flieg’s score. Thirty-six pigs were assigned to control (0%), 5%, or 10% BAWS diets formulated to meet NRC nutritional guidelines. Over a 70-day period, BAWS inclusion caused no detrimental effects on growth performance, carcass traits, or meat quality; a transient decline in early-stage weight gain and feed efficiency occurred in the 10% group, while BAWS-fed pigs demonstrated reduced backfat thickness and increased lean area. Fore gut microbiome analysis revealed reduced Lactobacillus and elevated Clostridium sensu stricto 1, Terrisporobacter, Streptococcus, and Prevotella, suggesting enhanced fiber and carbohydrate fermentation capacity. Predictive COG (clusters of orthologous groups)-based functional profiling showed increased abundance of proteins associated with carbohydrate transport (COG2814, COG0561, COG0765) and stress-response regulation (COG2207). These results support BAWS as a sustainable feed ingredient that maintains production performance and promotes fore gut microbial adaptation, with implications for microbiota-informed nutrition and stress resilience in swine. Full article

A reliable ocean surface current (OSC) estimate is difficult to retrieve from synthetic aperture radar (SAR) data due to the challenge of accurately partitioning the Doppler shifts induced by wind waves and OSC. Recent research on SAR-based OSC retrieval is typically based on the assumption that the SAR Doppler shifts caused by wind waves and OSC are linearly superimposed. However, this assumption may lead to large errors in regions where nonlinear wave–current interactions are significant. To address this issue, we developed a novel deep learning model, OSCNet, for OSC retrieval. The model leverages Sentinel-1 Interferometric Wide (IW) Level 2 Ocean products collected from July 2023 to September 2024, combined with wave data from the European Centre for Medium-Range Weather Forecasts (ECMWF) and geostrophic currents from newly available SWOT Level 3 products. The OSCNet model is optimized by refining input ocean surface physical parameters and introducing a ResNet structure. Moreover, the Normalized Radar Cross-Section (NRCS) is incorporated to account for wave breaking and backscatter effects on Doppler shift estimates. The retrieval performance of the OSCNet model is evaluated using SWOT data. The mean absolute error (MAE) and root mean square error (RMSE) are found to be 0.15 m/s and 0.19 m/s, respectively. This result demonstrates that the OSCNet model enhances the retrieval of OSC from SAR data. Furthermore, a mesoscale eddy detected in the OSC map retrieved by OSCNet is consistent with the collocated sea surface chlorophyll-a observation, demonstrating the capability of the proposed method in capturing the variability of mesoscale eddies. Full article

The potential of black soldier fly larvae (BSFL) and microalgae (MA) in poultry diets has garnered increasing interest due to their high nutritional value and reduced environmental footprint. BSFL represent a sustainable alternative to conventional protein sources such as soybean meal, whereas MA contributes to improved egg quality, particularly through its enrichment with polyunsaturated fatty acids. This study assessed the effects of BSFL and MA inclusion on the growth performance and egg quality of Rhode Island Red (RIR) laying hens. Three diets were formulated: Diet A (10% BSFL), Diet B (10% BSFL + 2% MA), and Diet C (commercial control). The diets were formulated to meet the age-specific nutrient requirements of RIR hens, according to the National Research Council (NRC, 1994) guidelines. A total of 96 four-week-old chicks were randomly allocated to six pens (n = 16 per pen) and provided ad libitum access to feed and water throughout the trial. The results demonstrated that the inclusion of BSFL and MA significantly influenced the growth rate, onset of lay, and egg characteristics. Hens fed Diet B exhibited the highest average weekly body weight gain (0.034 ± 0.001 kg/week); initiated laying at 20 weeks of age, three weeks earlier than hens on Diets B and C; and produced significantly heavier eggs (51.208 ± 0.511 g). Enhanced eggshell quality and yolk pigmentation were also observed. In addition, Diet B enhanced the nutritional profile of the eggs, yielding a higher albumen protein content (76.546 ± 1.382%DM) and lower lipid concentrations (0.451 ± 0.128%DM). These findings underscore the potential of BSFL and MA as functional feed ingredients for improving poultry performance and egg quality in a sustainable production system. Full article

Sea surface wind retrieval methods using synthetic aperture radar (SAR) are generally classified into two categories: the direct inversion method and the variational analysis method (VAM). Traditional VAM retrieves wind fields by integrating background wind information with SAR normalized radar cross-section (NRCS). Recent studies have shown that incorporating SAR Doppler centroid anomaly (DCA) as an additional observation for variational analysis can improve the accuracy of wind speed and direction retrieval. However, this method has yet to be systematically evaluated, particularly with respect to its applicability to Sentinel-1 SAR data. This study presents a comprehensive assessment based on 1803 Sentinel-1 vertical–vertical (VV) polarization level-2 Ocean (OCN) product scenes collocated with in situ measurements from the National Data Buoy Center (NDBC), yielding a total of 2826 matched data pairs. We systematically evaluate the performance of three distinct VAM configurations: VAM1 (J_NRCS), utilizing only NRCS; VAM2 (J_DCA), employing solely DCA; and VAM3 (J_NRCS+DCA), which combines both NRCS and DCA. The results demonstrate that VAM3 (J_NRCS+DCA) achieves the best performance, with the lowest root mean square error (RMSE) of 1.42 m/s for wind speed and 26.00° for wind direction across wind speeds up to 23.2 m/s, outperforming both VAM1 (J_NRCS) and VAM2 (J_DCA). Furthermore, the accuracy of background wind speed is identified as a critical factor affecting VAM performance. After correcting the background wind speed, the RMSE and bias of the retrieved wind speed decreased significantly across all VAMs. The most notable bias reduction was observed at wind speeds exceeding 10 m/s. These findings provide essential theoretical support for the operational application of Sentinel-1 OCN products in sea surface wind retrieval. Full article

Rogue waves are reported to wreck ships and claim lives. The prompt detection of their presence is difficult due to their small footprint and unpredictable emergence. The retrieval of sea surface height via remote sensing techniques provides a viable solution for detecting rogue waves. However, conventional synthetic aperture radar (SAR) techniques are ineffective at retrieving the surface height profile of rogue waves in real time due to nonlinearity between surface height and normalized radar cross-section (NRCS), which is not obvious in the absence of rogue waves. In this work, a cross-track interferometric SAR (XTI-SAR) imaging approach is proposed to detect elusive rogue waves over a wide area, with sea-surface profiles embedding rogue waves simulated using a probability-based model. The performance of the proposed imaging approach is evaluated in terms of errors in the position and height of rogue-wave peaks, the footprint area of rogue waves, and a root-mean-square error (RMSE) of the sea-surface height profile. Different rogue-wave events under different wind speeds are simulated, and the reconstructed height profiles are analyzed to determine the proper ranges of look angle, baseline, and mean-filter size, among other operation variables, in detecting rogue waves. The proposed approach is validated by simulations in detecting a rogue wave at a spatial resolution of 3 m × 3 m and height accuracy of decimeters. Full article

High-precision and high-resolution scattering data play a crucial role in remote sensing applications, including ocean environment monitoring, target recognition, and classification. This paper proposes a deep learning-based model aimed at enhancing and reconstructing the spatial resolution of Sentinel-1 backscatter NRCS (Normalized Radar Cross Section) data for China’s offshore seas, including the Bohai Sea, Yellow Sea, East China Sea, Taiwan Strait, and South China Sea. The proposed model innovatively integrates a Self-Attention Feature Fusion based on the Weighted Channel Concatenation (SAFF-WCC) module, combined with the Global Attention Mechanism (GAM) and High-Order Attention (HOA) modules. The feature fusion module effectively regulates the proportion of each feature during the fusion process through weight allocation, significantly enhancing the effectiveness of multi-feature integration. The experimental results show that the model can effectively enhance the fine structural features of marine targets when the resolution is doubled, though the enhancement effect is slightly diminished when the resolution is quadrupled. For high-resolution data reconstruction, the proposed model demonstrates significant advantages over traditional methods under a scale factor of 2 across four key evaluation metrics, including PSNR, SSIM, MS-SSIM, and MAPE. These results indicate that the proposed deep learning-based model is not only well-suited for scattering data from China’s offshore seas but also provides robust support for subsequent research on ocean target recognition, as well as the compression and transmission of SAR data. Full article

The rising prevalence of mental health disorders, particularly depression, highlights the need for improved approaches in therapeutic interventions. Traditional psychotherapy relies on subjective assessments, which can vary across therapists and sessions, making it challenging to track emotional progression and therapy effectiveness objectively. Leveraging the advancements in Natural Language Processing (NLP) and domain-specific Large Language Models (LLMs), this study introduces nBERT, a fine-tuned Bidirectional Encoder Representations from the Transformers (BERT) model integrated with the NRC Emotion Lexicon, to elevate emotion recognition in psychotherapy transcripts. The goal of this study is to provide a computational framework that aids in identifying emotional patterns, tracking patient-therapist emotional alignment, and assessing therapy outcomes. Addressing the challenge of emotion classification in text-based therapy sessions, where non-verbal cues are absent, nBERT demonstrates its ability to extract nuanced emotional insights from unstructured textual data, providing a data-driven approach to enhance mental health assessments. Trained on a dataset of 2021 psychotherapy transcripts, the model achieves an average precision of 91.53%, significantly outperforming baseline models. This capability not only improves diagnostic accuracy but also supports the customization of therapeutic strategies. By automating the interpretation of complex emotional dynamics in psychotherapy, nBERT exemplifies the transformative potential of NLP and LLMs in revolutionizing mental health care. Beyond psychotherapy, the framework enables broader LLM applications in the life sciences, including personalized medicine and precision healthcare. Full article

In order to improve the acoustic performance of honeycomb sandwich structures, a Helmholtz-type honeycomb sandwich acoustic metamaterial (HHSAM) was proposed. The theoretical and finite element models were established by calculating the acoustic impedance of multiple parallel Helmholtz resonators (HR). By comparing the sound absorption of the single and multiple HR, it was found that the simulation results were basically consistent with the theoretical calculations. The sound absorption and insulation performance of the honeycomb panels, the honeycomb perforated panels, and the HHSAM structure were compared through impedance tube experiments. The results showed that, over a wide frequency range, the acoustic performance of the HHSAM structure was superior to that of the other two structures. Under scattered sound field conditions, the reverberation room results showed that the sound absorption of the HHSAM structure was better than that of the honeycomb panel in the frequency range of 100–5000 Hz. The noise reduction coefficient (NRC) of the honeycomb panel was 0.1, indicating almost no sound absorption effect in engineering. The NRC of the HHSAM structure could reach 0.35. In terms of sound insulation, the HHSAM structure was more prominent in the 400–4000 Hz range than the honeycomb panel. In the frequency range of 500–1600 Hz, the transmission loss of the HHSAM was 5 dB higher than that of the honeycomb panel. Full article

This paper investigates the critical role of land in advancing sustainability, drawing insights from the landmark report by the U.S. National Research Council, Our Common Journey: A Transition Toward Sustainability (hereafter referred to as NRC (1999)), and aligning them with the leverage points perspective on sustainability. Four key problem entries—land as a resource, land use and ecosystem services, land systems, and landscapes or regional scales—are identified as pivotal framings for addressing sustainability challenges, and are further elaborated with practical examples. Regretfully, despite decades of multidisciplinary research progress, land-related approaches remain fragmented. This paper contributes to the existing research by illustrating, for the first of time, how these multidisciplinary research traditions can be integrated cohesively using the four nested realms of sustainability leverage points—rebuilding capital stocks, redirecting interaction flows, reforming governance architectures, and re-/co-piloting sustainability transitions—to achieve nested transformations across varying timeframes. We hope that this hierarchical perspective fosters top-down and bottom-up collaborations among researchers, policymakers, and practitioners to take transformative actions. To leave a legacy of sustainability for future generations, we must act collectively, boldly, and without delay to harness the transformative potential of all land-related approaches. Full article

Actor-network theory (ANT) represents a research paradigm that emerged within science and technology studies by explicitly focusing on the contingency of scientific inventions and the role of non-human actants in the invention course of action. The article adopts an ANT perspective to focus on the invention of Sub-Wavelength Grating (SWG) photonic metamaterials by the members of a research group in the National Research Council (NRC) of Canada. The results are based on unstructured interviews with the key inventor and two domain experts as well as on textual analysis (topic modeling) of the contributions and novelty claims in the corpus of research articles by the NRC group crafting the concept and potential applications of SWGs in the photonics domain. Topic modeling is a type of statistical modeling that uses unsupervised machine learning to identify clusters or groups of similar words within a body of text. It uses semantic structures in texts to understand unstructured data without predefined tags or training data. Adopting topic modeling as a semantic technology allowed the identification of two of the key non-human factors or actants: (a) photonics design and simulations and (b) the fabrication techniques and facilities used to produce the physical prototypes of the photonics devices incorporating the invented SWG waveguiding effect. Using topic modeling as a semantic technology in ANT-inspired research studies focusing on non-human actants provides significant opportunities for future research. Full article

Due to the difficulty in ensuring launch safety under unfavorable launch site conditions, restrictions regarding the selection of launch sites significantly weaken the maneuverability of the unsupported random vertical launch (URVL) mode. In this paper, a nonlinear robust control strategy is proposed to control the missile attitude by actively adjusting the oscillation of the launcher through the hydraulic actuator, enhancing the launching safety and the adaptability of the VMLS to the launching site. Firstly, considering the interaction among the launch canister, adapters, and missile, a 6-DOF dynamic model of the launch system is established, in combination with the dynamics of the hydraulic actuator. Then, in order to facilitate the nonlinear controller design, the seventh-order state-space equation is constructed, according to the dynamic model of the launch system. Subsequently, in view of the problem of “differential explosion” in the backstepping controller design of the seventh-order nonlinear system, a nonlinear dynamic surface control (DSC) framework is proposed. Meanwhile, the time delay estimation (TDE) technique is introduced to suppress the influence of the complex nonlinearities of the launch system on the missile attitude control, and a nonlinear robust control (NRC) is introduced to attenuate the TDE error. Both of these are integrated into the DSC framework, which can achieve asymptotic output tracking. Finally, numerical simulations are conducted to validate the superiority of the proposed control strategy in regards to missile launch response control. Full article

Based on the three-parameter approximate JCAL analytical model (hereinafter referred to as the three-parameter model), this study conducted an in-depth analysis of the effects of porosity, median pore size, and pore size standard deviation on the acoustic performance of porous materials and developed a composite porous material composed of glass fibers and zeolite particles. Experimental results indicate that the pore size distribution significantly affects the acoustic performance of fibrous porous sound-absorbing materials. Specifically, smaller pores lead to better sound absorption at mid–low frequencies, with the optimal sound absorption performance observed when the median pore size is between 60 and 80 μm. Increasing the material density and decreasing the fiber diameter help reduce the internal pore size, thereby improving the material’s sound absorption performance. Additionally, the appropriate addition of zeolite can further optimize the internal pore size and effective sound-absorbing interface, thus enhancing the material’s sound absorption performance. When the material density is 120 kg/m³ and the zeolite substitution rate is around 10%, the material exhibits the best acoustic performance, with a noise reduction coefficient (NRC) reaching 0.65, which is a 10.17% increase compared to the material without zeolite. Comparing the simulation data from the three-parameter model with the actual measurement data shows that the model has excellent predictive performance for the sound absorption coefficient (SAC) of single-fiber porous materials (with an error of approximately 5%). However, for composite porous materials, due to the complex changes in interfaces, there is a certain prediction error (with the maximum error reaching 12.81%), indicating that the model needs further optimization and correction when applied to composite materials. Full article

Empathetic and coherent responses are critical in automated chatbot-facilitated psychotherapy. This study addresses the challenge of enhancing the emotional and contextual understanding of large language models (LLMs) in psychiatric applications. We introduce Emotion-Aware Embedding Fusion, a novel framework integrating hierarchical fusion and attention mechanisms to prioritize semantic and emotional features in therapy transcripts. Our approach combines multiple emotion lexicons, including NRC Emotion Lexicon, VADER, WordNet, and SentiWordNet, with state-of-the-art LLMs such as Flan-T5, Llama 2, DeepSeek-R1, and ChatGPT 4. Therapy session transcripts, comprising over 2000 samples, are segmented into hierarchical levels (word, sentence, and session) using neural networks, while hierarchical fusion combines these features with pooling techniques to refine emotional representations. Attention mechanisms, including multi-head self-attention and cross-attention, further prioritize emotional and contextual features, enabling the temporal modeling of emotional shifts across sessions. The processed embeddings, computed using BERT, GPT-3, and RoBERTa, are stored in the Facebook AI similarity search vector database, which enables efficient similarity search and clustering across dense vector spaces. Upon user queries, relevant segments are retrieved and provided as context to LLMs, enhancing their ability to generate empathetic and contextually relevant responses. The proposed framework is evaluated across multiple practical use cases to demonstrate real-world applicability, including AI-driven therapy chatbots. The system can be integrated into existing mental health platforms to generate personalized responses based on retrieved therapy session data. The experimental results show that our framework enhances empathy, coherence, informativeness, and fluency, surpassing baseline models while improving LLMs’ emotional intelligence and contextual adaptability for psychotherapy. Full article

Resource conflicts constitute a major global issue in areas rich in natural resources. The modeling of factors influencing natural resource conflicts (NRCs), including environmental, health, socio-economic, political, and legal aspects, presents a significant challenge compounded by inadequate data. Quantitative research frequently emphasizes large-scale conflicts. This study presents a novel multilevel approach, SEFLAME-CM—Spatially Explicit Fuzzy Logic-Adapted Model for Conflict Management—for advancing understanding of the relationship between NRCs and drivers under territorial and rebel-based typologies at a community level. SEFLAME-CM is hypothesized to yield a more robust positive correlation between the risk of NRCs and the interacting conflict drivers, provided that the conflict drivers and input variables remain the same. Local knowledge from stakeholders is integrated into spatial decision-making tools to advance sustainable peace initiatives. We compared our model with spatial multi-criteria evaluation for conflict management (SMCE-CM) and spatial statistics. The results from the Moran’s I scatter plots of the overall conflicts of the SEFLAME-CM and SMCE-CM models exhibit substantial values of 0.99 and 0.98, respectively. Territorial resource violence due to environmental drivers increases coast-wards, more than that stemming from rebellion. Weighing fuzzy rules and conflict drivers enables equal comparison. Environmental variables, including proximity to arable land, mangrove ecosystems, polluted water, and oil infrastructures are key factors in NRCs. Conversely, socio-economic and political factors seem to be of lesser importance, contradicting prior research conclusions. In Third World nations, local communities emphasize food security and access to environmental services over local political matters amid competition for resources. The synergistic integration of fuzzy logic analysis and community perception to address sustainable peace while simultaneously connecting environmental and socio-economic factors is SEFLAME-CM’s contribution. This underscores the importance of a holistic approach to resource conflicts in communities and the dissemination of knowledge among specialists and local stakeholders in the sustainable management of resource disputes. The findings can inform national policies and international efforts in addressing the intricate underlying challenges while emphasizing the knowledge and needs of impacted communities. SEFLAME-CM, with improvements, proficiently illustrates the capacity to model intricate real-world issues. Full article