Domain Model Research Articles

A bidirectional domain separation adversarial network based transfer learning method for near-infrared spectra

Traditional calibration transfer (CT) methods usually fail to adapt the source domain model to the target domain because of changes associated with the instrument, detection environment, sample composition, or sample type. Most deep transfer learning (DTL) methods have shown reliable capability in extracting domain-invariant representations. However, factors unique to the spectral domain limit their development and application, including difficulty in obtaining labeled samples, numerous spectral perturbation factors, and the scarcity of spectral databases. To address these challenges, we here introduce the Bidirectional Domain-Separating Adversarial Network (BiDSAN), based on a deep adversarial network transfer architecture, for learning and adapting to distribution/domain differences between sample spectra. BiDSAN incorporates Wasserstein divergence (w-div) and dynamic adversarial factor into the domain critic (discriminator) to accelerate the extraction of spectral shared components. Additionally, the use of the spectral signal distortion ratio as a loss function and the introduction of a bidirectional mechanism further enhanced the training accuracy and speed of spectral models. Calibration analyses were tested on pharmaceutical and two sets of wood datasets, corresponding to three common domain/distribution difference scenarios discussed in the study. Compared to existing CT and DTL methods, the proposed BiDSAN method demonstrated superior spectral signal calibration capability and stability, achieving unsupervised domain adaptation for similar samples and, for the first time, obtaining ideal prediction results in cross-species spectral domain adaptation. Our method represents significant potential for using DTL approaches in spectral domain adaptation.

An agent-based model of elephant crop raid dynamics in the Periyar–Agasthyamalai complex, India

Human–wildlife conflict challenges conservation worldwide, which requires innovative management solutions. We developed a prototype Agent-Based Model (ABM) to simulate interactions between humans and solitary bull Asian elephants in the Periyar–Agasthyamalai complex of the Western Ghats in Kerala, India. The main challenges were the complex behavior of elephants and insufficient movement data from the region. Using literature, expert insights, and field surveys, we created a prototype behavior model that incorporates crop habituation, thermoregulation, and aggression. We designed a four-step calibration method to adapt relocation data from radio-tagged elephants in Indonesia to model elephant movements in the model domain. The ABM’s structure, including the assumptions, submodels, and data usage are detailed following the Overview, Design concepts, Details protocol. The ABM simulates various food availability scenarios to study elephant behavior and environmental impact on space use and conflict patterns. The results indicate that the wet months increase conflict and thermoregulation significantly influences elephant movements and crop raiding. Starvation and crop habituation intensify these patterns. This prototype ABM is an initial model that offers information on the development of a decision support system in wildlife management and will be further enhanced with layers of complexity and subtlety across various dimensions. Access the ABM at https://github.com/quest-lab-iisc/abm-elephant-project.

Evidence for separate backward recall and n-back working memory factors: a large-scale latent variable analysis

ABSTRACT Multiple studies have explored the factor structure of working memory (WM) tasks, yet few have done so controlling for both the domain and category of the memory items in a single study. In the current pre-registered study, we conducted a large-scale latent variable analysis using variant forms of n-back and backward recall tasks to test whether they measured a single underlying construct, or were distinguished by stimuli-, domain-, or paradigm-specific factors. Exploratory analyses investigated how the resulting WM factor(s) were linked to fluid intelligence. Participants (N = 703) completed a fluid reasoning test and multiple n-back and backward recall tasks containing memoranda that varied across (spatial or verbal material) and within (verbal digits or letters) domain, allowing the variance specific to task content and paradigm to be assessed. Two distinct but related backward recall and n-back constructs best captured the data, in comparison to other plausible model constructions (single WM factor, two-factor domain, and three-factor materials models). Common variance associated with WM was a stronger predictor of fluid reasoning than a residual n-back factor, but the backward recall factor predicted fluid reasoning as strongly as the common WM factor. These data emphasise the distinctiveness between backward recall and n-back tasks.

Simple incision and suture modeling on fixed structured grids

This paper presents a simple yet versatile approach to simulate skin incisions and suturing using nonlinear finite element analysis on a fixed structured grid with a spring-based suture model. Incisions and wounds are introduced by element removal and the sutures are discretely modeled by applying linear constraint relations pairwise to groups of nodes. Two numerical examples utilize the closing of an elliptical wound to study the influence of the number of sutures and their location, and two test cases use the Z-plasty transposition flap technique to investigate the versatility of the proposed method and for comparison with state-of-the-art results. The results indicate that the presented method is able to recreate literature results to an acceptable level of accuracy. Further, the method proves versatile due to the ability to freely introduce incisions and wounds onto the fixed grid modeling domain without remeshing. Lastly, the two main advantages of the discrete suture representation are the ability to capture local stress concentrations near the sutures and the full freedom in placing the sutures.

Highly Selective Novel Heme Oxygenase-1 Hits Found by DNA-Encoded Library Machine Learning beyond the DEL Chemical Space.

DNA-encoded library (DEL) technology, especially when combined with machine learning (ML), is a powerful method to discover novel inhibitors. DEL-ML can expand a larger chemical space and boost cost-effectiveness during hit finding. Heme oxygenase-1 (HO-1), a heme-degrading enzyme, is linked to diseases such as cancer and neurodegenerative disorders. The discovery of five series of new scaffold HO-1 hits is reported here, using a DEL-ML workflow, which emphasizes the model's uncertainty quantification and domain of applicability. This model exhibits a strong extrapolation ability, identifying new structures beyond the DEL chemical space. About 37% of predicted molecules showed a binding affinity of K D < 20 μM, with the strongest being 141 nM, amd 14 of those molecules displayed >100-fold selectivity for HO-1 over heme oxygenase-2 (HO-2). These molecules also showed structural novelty compared to existing HO-1 inhibitors. Docking simulations provided insights into possible selectivity rationale.

Competence Profile of the Intra-Hospital Emergency Team Nurse: A Scoping Review

Background: Nurses in intra-hospital emergency teams are required to be competent in responding to situations of clinical deterioration and emergency. This study aimed to map the extent, variety, and nature of the literature on the competences of the intra-hospital emergency teams nurse. Methods: A scoping review based on the principles of the Joanna Briggs Institute. The search of the databases was conducted in July and August 2023, and the search strategy was adjusted. The analysis of the relevance of the articles, data extraction, and synthesis were conducted by two reviewers. Discrepancies were resolved by a third reviewer. The data were extracted using the competency outcomes and performance assessment (COPA) model. Results: Fourteen results were analyzed, and the competences organized by the domains of the COPA model which are considered by some authors to be advanced or specialized are as follows: assessment and intervention, communication, critical thinking, teaching, human caring relationships, management, leadership, and knowledge integration. Other competences, such as self-confidence, ability to remain calm, concentration, quick reaction, and a sense of humor, were mentioned. Conclusions: The results highlighted the competences of the intra-hospital emergency team nurse. This knowledge could contribute to educational programs and shape the competency profile of intra-hospital emergency team nurses.

Prioritising nurses’ and doctors’ health at work: a scoping review of monitoring instruments

ObjectiveNurses’ and doctors’ health at work is crucial for their overall performance and the quality of care they provide. The Jobs Demands Resources (JD-R) model offers a framework for health...

Numerical Simulation of Internal Flow Field in Optimization Model of Gas–Liquid Mixing Device

This article studies the influence of structural parameters of the optimization model for the gas–liquid mixing device of a fire truck (compressed air foam lift fire truck, model JP21/G2, made in China) on the liquid phase volume fraction, static pressure, velocity streamline, and the influence of smaller flow rates on the mixing effect. By using the computational fluid dynamics (CFD) software FLUENT 2021 R2, numerical simulations were conducted on the fluid domain model of the gas–liquid mixing device of the JP21/G2 fire truck. The changes in the mixing effect time dimension, liquid phase volume fraction, static pressure, and velocity streamline inside the gas–liquid mixing device were obtained. The optimal mixer structure combination in practical applications was inferred through orthogonal experiments, and the influence of flow rate on the optimal pipe diameter and shortest mixing distance was obtained through variable flow rate simulation experiments. The numerical simulation results show that the presence of bent pipes in the JP21/G2 real vehicle model hinders the gas–liquid mixing process. A straight pipe section of at least 8 m was added after the bent pipe to ensure the mixing effect. The optimal parameter combination for orthogonal experiments had an accurate value of 50°-50°-220 mm. Under the same pipe diameter, using a larger flow rate can achieve better mixing effects.

How GPs can help young people avoid future self-harm: a qualitative study.

Self-harm is a growing problem in young people. GPs are usually the first point of healthcare contact for young people aged 16-25 years, after self-harm. GPs can experience barriers to supporting young people and behaviour change theory can help to understand these, and the influences on, GP behaviour. To explore the capabilities, opportunities, and motivations (COM-B model of behaviour) of GPs, and their perceived training needs, to help young people aged 16-25 years avoid future self-harm. This was a qualitative study of GPs in England. Semi-structured interviews were conducted with NHS GPs who were purposively sampled. Interviews occurred in 2021. Data were analysed using reflexive thematic analysis and mapped onto the COM-B model domains. The study's patient and public involvement group supported data analysis. Fifteen interviews were completed. Four themes were generated. GPs described mixed capabilities, with many feeling they had the physical and psychological skills to support young people to avoid future self-harm, but some felt doing so was emotionally tiring. GPs identified opportunities to better support young people, such as use of electronic consultation tools, but cited lack of time as a concern. GPs reported motivation to help young people, but this can be influenced by their workload. Unmet training needs around communication, knowledge, and optimising safety were identified. GPs are supported by their practice teams to support young people after self-harm, but a lack of time hinders opportunities to do so. Future effective GP-led interventions may improve GP motivation to support young people after self-harm.

Improvements in precision and accuracy of complex- relative to real-domain linear combination model spectral fitting not necessarily recovered by zero filling.

Although the information obtained from in vivo proton magnetic resonance spectroscopy (1H MRS) presents a complex-valued spectrum, spectral quantification generally employs linear combination model (LCM) fitting using the real spectrum alone. There is currently no known investigation comparing fit results obtained from LCM fitting over the full complex data versus the real data and how these results might be affected by common spectral preprocessing procedure zero filling. Here, we employ linear combination modeling of simulated and measured spectral data to examine two major ideas: first, whether use of the full complex rather than real-only data can provide improvements in quantification by linear combination modeling and, second, to what extent zero filling might influence these improvements. We examine these questions by evaluating the errors of linear combination model fits in the complex versus real domains against three classes of synthetic data: simulated Lorentzian singlets, simulated metabolite spectra excluding the baseline, and simulated metabolite spectra including measured in vivo baselines. We observed that complex fitting provides consistent improvements in fit accuracy and precision across all three data types. While zero filling obviates the accuracy and precision benefit of complex fitting for Lorentzian singlets and metabolite spectra lacking baselines, it does not necessarily do so for complex spectra including measured in vivo baselines. Overall, performing linear combination modeling in the complex domain can improve metabolite quantification accuracy relative to real fits alone. While this benefit can be similarly achieved via zero filling for some spectra with flat baselines, this is not invariably the case for all baseline types exhibited by measured in vivo data.

Dual-drive RUL prediction of gear transmission systems based on dynamic model and unsupervised domain adaption under zero sample

Gear transmission systems are key components in rotating machinery, and its remaining useful life (RUL) prediction can provide adequate leading time for well-timed maintenance. Existing RUL prediction models usually rely on sufficient training data. However, the acquisition of full life cycle dataset of gear transmission system is difficult or impossible. To solve the problem on the difficult startup and poor generalization of prediction model under zero environment, a dual-drive prediction method based on dynamic model of gear transmission system and unsupervised domain adaptation is proposed in this paper. Firstly, a dynamic model of gear transmission system and growth mechanism of local defect is established to generate full-life cycle simulation data. Then, the multi-scale modulation features are extracted based on simulated and measured data. Furthermore, multi-scale temporal convolution operations are introduced into dual-channel unsupervised domain adaptation model. Besides, a compound principle of reverse truncation and forward expansion principle is investigated to determine the first prediction time. Finally, the validity of the proposed model is verified by two kinds of gearbox data. Ablation experiments are carried out to evaluate the contribution of each module in proposed model. In addition, the effectiveness and generalization ability of proposed model are verified when compared with other advanced transfer learning methods.

A five domains assessment of sow welfare in a novel free farrowing system.

The Maternity Ring was developed as a free farrowing alternative to crates that preserved space whilst providing the sow with unrestricted movement. This experiment aimed to apply the Five Domains model to assess sow welfare in the Maternity Ring in comparison with the farrowing crate. Eighty-eight sows were housed in a farrowing crate (FC) and 83 in a Maternity Ring (MR), and measures collected focussed on nutrition, environment, health, behaviour, and mental state outcomes. MR sows consumed less feed than FC sows (total feed intake: 93.8 ± 3.06 kg vs. 111.2 ± 3.13 kg; p < 0.001) but had reduced P2 backfat loss during lactation (0.0 ± 0.11 vs. 1.2 ± 0.11, p < 0.001). Fewer frustrated and pain-related behaviours during farrowing were observed in MR sows (bar biting: FC 3.3 ± 2.12 vs. MR 0.5 ± 0.29 events, p = 0.038, and back leg forward: FC 227 ± 50.7 vs. MR 127 ± 26.4 events, p = 0.019), and a decreased proportion of MR sows had facial injuries after farrowing (10% CI [5, 20] vs. 67% CI [47, 95], p < 0.001). More FC sows had udder damage at weaning (70% CI [48, 97] vs. 10% CI [6, 24], p < 0.001), and their piglets were medicated more frequently when compared to those in MR (51% CI [40, 61] vs. 30% [21, 41], p = 0.008). MR sows tended to have a higher reaction score to piglet processing (MR 2.0 ± 0.38 vs. FC 1.2 ± 0.27, p = 0.094) and had more contact with piglets once the procedure was complete than FC sows (13.5 ± 2.55 vs. 6.9 ± 1.26 events, respectively, p = 0.016). Whilst there was no difference in anticipation of a feeding event (p > 0.05), MR sows displayed a reduced startle response to an aversive noise stimulus at day 18 (FC 2.8 ± 0.35, MR 0.7 ± 0.16, p < 0.001). Using the Five Domains framework, sows housed in the MR during farrowing and lactation have improved welfare than those in FC and can be thought of as being in a positive affective state.

Cross-domain diagnosis for polymer electrolyte membrane fuel cell based on digital twins and transfer learning network✰

Existing research on fault diagnosis for polymer electrolyte membrane fuel cells (PEMFC) has advanced significantly, yet performance is hindered by variations in data distributions and the requirement for extensive fault data. In this study, a cross-domain adaptive health diagnosis method for PEMFC is proposed, integrating the digital twin model and transfer convolutional diagnosis model. A physical-based high-fidelity digital twin model is developed to obtain diverse and high-quality datasets for training diagnosis method. To extract long-term time series features from the data, a temporal convolutional network (TCN) is proposed as a pre-trained diagnosis model for the source domain, with feature extraction layers that can be reused to the transfer learning network. It is demonstrated that the proposed pre-trained model can hold the ability to accurately diagnose the various fuel cell faults, including pressure, drying, flow, and flooding faults, with 99.92 % accuracy, through the effective capture of the long-term dependencies in time series data. Finally, a domain adaptive transfer convolutional network (DATCN) is established to improve the diagnosis accuracy across diverse fuel cells by learning domain-invariant features. The results show that the DATCN model, tested on three different target domain devices with adversarial training using only 10 % normal data, can achieve an average accuracy of 98.5 % (30 % improved over traditional diagnosis models). This proposed method provides an effective solution for accurate cross-domain diagnosis of PEMFC devices, significantly reducing the reliance on extensive fault data.

An Analytical Model for an Anode-Supported Solid Oxide Fuel Cell

This study presents a one-dimensional, non-isothermal, steady-state model to analyze performance of an anode-supported solid oxide fuel cell (SOFC). The modeling domain encompasses components including a NiO-YSZ support and functional layer forming the anode, a YSZ electrolyte, a GDC interlayer, as well as LSCF+GDC and LSCF constituting the cathode. The model distinguishes different types of electrode polarization resistances using experimental impedance data at various temperatures under open circuit voltage (OCV), employing a detailed equivalent circuit model to determine the temperature and partial pressure dependencies of electrode exchange current densities. Moreover, this approach serves as an efficient method for estimating the conductivity of both the anode and cathode. As a result, activation energies and ohmic overpotentials are derived, considering microstructural and geometrical aspects, which allows for accurate quantification of the ohmic resistance associated with ionic conduction.The model incorporates coupled multi-physics elements, such as mass and charge transport, electrochemical reactions, and heat transfer. A notable feature of the model is its consideration of reaction zone layers near the electrolyte, where electrochemical reactions generate electrons, oxide ions, and water vapor. Enhancing prediction accuracy, the model applies multilayer discretization within the electrode and electrolyte to reflect actual material properties and localized conditions more accurately. The model predicts the performance of a single-cell SOFC using pure hydrogen at the anode and varying oxygen concentrations (100%, 50%, 20%, and 10%) at different operating temperatures (700°C, 660°C, 620°C, and 580°C) and design conditions. Simulated results are compared with experimental data, and the impact of various operating and design parameters on SOFC performance is examined. Findings indicate that kinetic and concentration overpotentials in an anode-supported SOFC are lower than ohmic overpotentials, even at higher current densities. Furthermore, ohmic overpotential is identified as the primary contributor to total cell potential loss, highlighting the need for its minimization to improve cell performance.

Factors influencing functional recovery after limb-salvage surgery for lower extremity musculoskeletal tumours: a qualitative study combining patients’ and healthcare professionals’ perspectives

Purpose Survival rates of lower extremity musculoskeletal tumours (LEMTs) have been increasing. However, patients continue to experience functional limitations after LEMT followed by limb-salvage surgery (LSS). This study aimed to identify factors influencing functional recovery after LSS for LEMT. Methods A qualitative study was conducted using semi-structured interviews with a purposive sample of adult patients who underwent LSS for LEMT (n=7) and healthcare professionals (HCPs) with expertise in orthopaedic oncology (n=7). Recruitment continued until data saturation. A combination of deductive and inductive qualitative content analysis was performed to analyse the transcribed data, producing subthemes under the main International Classification of Functioning, disability and health (ICF)-model domains. Results Four themes were described: (1) bodily functions and structures, (2) activities and participation, (3) environmental factors, and (4) expectations. Physical aspects such as larger resection size and complications negatively affected recovery. The importance of communication strategies of tertiary HCPs and the unfamiliarity of the primary physical therapists with the disease, its surgical treatment, and related consequences were emphasised by both the interviewed patients and HCPs. Conclusions Functional recovery after LSS for LEMT is a multifactorial process. To improve patient care, improving (intercollegiate) communication strategies on treatment details and expectations about functional outcomes is warranted. Implications for rehabilitation Healthcare professionals should be aware some patients experience limited functional recovery after limb-salvage surgery (LSS) for lower extremity musculoskeletal tumours. Healthcare professionals should use multiple communication strategies to bridge the gap between the information provided and what the patient recalls. Knowledge exchange and communication between primary and tertiary care should be more extensive. Multidisciplinary consultations and/or case managers are needed to address all aspects of the individual’s functional recovery after LSS.

Feature Model for Pet Health Application Domain using Feature Oriented Domain Analysis

Feature Model for Pet Health Application Domain using Feature Oriented Domain Analysis

Adaptive QSMO-Based Sensorless Drive for IPM Motor with NN-Based Transient Position Error Compensation

In commercial electrical equipment, the popular sensorless drive scheme for the interior permanent magnet synchronous motor, based on the quasi-sliding mode observer (QSMO) and phase-locked loop (PLL), still faces challenges such as position errors and limited applicability across a wide speed range. To address these problems, this paper analyzes the frequency domain model of the QSMO. A QSMO-based parameter adaptation method is proposed to adjust the boundary layer and widen the speed operating range, considering the QSMO bandwidth. A QSMO-based phase lag compensation method is proposed to mitigate steady-state position errors, considering the QSMO phase lag. Then, the PLL model is analyzed to select the estimated speed difference for transient position error compensation. Specifically, a transient position error compensator based on a feedback time delay neural network (FB-TDNN) is proposed. Based on the back propagation learning algorithm, the specific structure and optimal parameters of the FB-TDNN are determined during the offline training process. The proposed parameter adaptation method and two position error compensation methods were validated through simulations in simulated wide-speed operation scenarios, including sudden speed changes. Overall, the proposed scheme fully mitigates steady-state position errors, substantially mitigates transient position errors, and exhibits good stability across a wide speed range.

Surface area and porosity analysis in nanoporous carbons by atomistic pore domain model

We present a new atomistic model for evaluating the surface area and porosity of micro-mesoporous carbons. This method, referred to as the atomistic pore domain model (APDM), advances the adsorption porosity methodology by calculating textural properties of micro-mesoporous carbons without relying on assumptions about pore geometry. A thorough analysis of porosity across eleven porous carbons demonstrates a robust correlation between the surface area accessible to N2 molecules, as computed using APDM and the Brunauer-Emmett-Teller method with Rouquerol criterium. This correlation is observed across a spectrum of nanoporous carbons, ranging from ultramicroporous activated carbon fibers and nanoporous carbon beads to supermicro-mesoporous activated carbons and activated carbon fibers. APDM facilitates the extraction of the information regarding the N2 surface area accessibility and the intrinsic geometric surface area of micro-mesoporous carbons. The N2-to-He surface area accessibility ratio of the investigated porous carbons varies from approximately 57 %–94 %, indicating varying degrees of pore sieving among studied carbon samples. Except for ACF-25 micro-mesoporous activated carbon fiber, the intrinsic geometric surface areas of the studied micro-mesoporous carbons are smaller than the geometrical surface area of a single graphene sheet (2640 m2/g).

Genome-Wide Identification of Glutathione S-Transferase Family from Dendrobium officinale and the Functional Characterization of DoGST5 in Cadmium Tolerance.

Glutathione S-transferases (GSTs) are members of a protein superfamily with diverse physiological functions, including cellular detoxification and protection against oxidative damage. However, there is limited research on GSTs responding to cadmium (Cd) stress. This study classified 46 GST genes in Dendrobium officinale (D. officinale) into nine groups using model construction and domain annotation. Evolutionary analysis revealed nine subfamilies with diverse physical and chemical properties. Prediction of subcellular localization revealed that half of the GST members were located in the cytoplasm. According to the expression analysis of GST family genes responding to Cd stress, DoGST5 responded significantly to Cd stress. Transient expression of DoGST5-GFP in tobacco leaves revealed that DoGST5 was localized in the cytoplasm. DoGST5 overexpression in Arabidopsis enhanced Cd tolerance by reducing Cd-induced H2O2 and O2- levels. These findings demonstrate that DoGST5 plays a critical role in enhancing Cd tolerance by balancing reactive oxygen species (ROS) levels, offering potential applications for improving plant adaptability to heavy metal stress.

Simulating the effects of retreating Urmia Lake and increased evapotranspiration rates on the nearby unconfined aquifer

The unsustainable management of water resources along with climate change impacts have led Urmia Lake to severely shrink since 1995. To quantify the groundwater losses in the surrounding area and to avoid the future worsening of a yet worrying scenario a numerical model is needed. For this purpose, a transient state MODFLOW model (2000–2016) has been calibrated and validated versus hydraulic heads. Good model performances were obtained, allowing to accurately estimate the groundwater budget. MODIS dataset was employed to set up the numerical model to identify temporal and spatial extent of evapotranspiration. Evapotranspiration turned out to play a massive contribution in groundwater outflow showing an increasing trend over time. Several scenarios, considering a different way to implement recharge and evapotranspiration in the modelling routine were assessed. The role of the spatialization of the evapotranspiration extinction depth was investigated by averaging remotely sensed heterogeneous datasets over the model domain. No significant differences in model performance indicators were found. Moreover, a model scenario without human groundwater exploitation and land use change was considered. The results slightly differ from the groundwater budget calculated by the validated model and thus indicating the climate change as the major driver of groundwater depletion in the study area. The implementation of such a model allowed for the assessment of the exchange between the lake and the unconfined aquifer, providing a robust foundation for future sustainable management.