Robust Adaptation Research Articles

Rural Friendship Bench: A qualitative study in Zaka district, Zimbabwe

BackgroundWe piloted the Friendship Bench—an evidence-based, urban-area task sharing intervention for common mental disorders—in rural Zimbabwe. The intervention combines problem solving therapy with income generating activities. This study sought to understand the intervention's implementation in terms of acceptability, feasibility, and sustainability as well as local attitudes towards mental wellbeing in rural Zimbabwe. MethodsUsing four separate semi-structured interview guides, we conducted in-depth interviews (N = 32) with patients (n = 9), village health workers (n = 12), nurses (n = 6), and community leaders (n = 5). We analyzed our data using thematic analysis with a diverse coding team using an integrative deductive-inductive approach. ResultsFive themes emerged: 1) explanatory models for mental illness, 2) clinical workflow and emphasis on documentation, 3) positive feedback about the Friendship Bench, 4) accessibility, and 5) feasibility. ConclusionIn its current format, our intervention was acceptable but neither feasible nor sustainable. Sociocultural context is critical in the development of rural task sharing interventions for mental health. We thus recommend a robust pilot and adaptation phase when scaling task sharing interventions in rural sub-Saharan Africa to elevate community voices, leverage existing social structures, and embed interventions as deeply into communities as possible.

Novel manganese and nitrogen co-doped biochar based on sodium bicarbonate activation for efficient removal of bisphenol A: Mechanism insight and role analysis of manganese and nitrogen by combination of characterizations, experiments and density functional theory calculations

A novel porous manganese and nitrogen co-doped biochar (Mn-N@SBC) was synthesized via one-step pyrolysis, utilizing loofah agricultural waste as the precursor and NaHCO3 as the activator. The behavior of bisphenol A adsorbed on Mn-N@SBC was evaluated using static batch adsorption experiments. Compared to direct manganese-nitrogen co-doping, co-doping based on NaHCO3 activation significantly increased the specific surface area (231 to 1027 m2·g−1) and adsorption capacity (15 to 351 mg·g−1). Wide pH (2–10) and good resistance to cation/anion, humic acid and actual water demonstrated the robust adaptability of Mn-N@SBC to environmental factors. The significantly reduced specific surface area after adsorption, adverse effects of ethanol and phenanthrene on the removal of bisphenol A, and theoretically predicted interaction sites indicated the primary adsorption mechanisms involved pore filling, hydrophobicity, and π-π-electron-donor–acceptor interaction. This work presented an approach to create high-efficiency adsorbents from agricultural waste, offering theoretical and practical guidance for the removal of pollutants.

Motor adaptation in deaf and hearing native signers.

Previous studies have suggested that deafness could lead to deficits in motor skills and other body-related abilities. However, the literature regarding motor skills in deaf adults is scarce and existing studies often included participants with heterogeneous language backgrounds and deafness etiologies, thus making it difficult to delineate the effects of deafness. In this study, we investigated motor learning in deaf native signers and hearing nonsigners. To isolate the effects of deafness and those of acquiring a signed language, we additionally tested a group of hearing native signers. Two well-established paradigms of motor learning were employed, in which participants had to adapt their hand movements to a rotation of the visual feedback (Experiment 1) or to the introduction of a force field (Experiment 2). Proprioceptive estimates were assessed before and after adaptation. Like hearing nonsigners, deaf and hearing signers showed robust adaptation in both motor adaptation paradigms. No significant differences in motor adaptation and memory were observed between deaf signers and hearing nonsigners, as well as between hearing signers and hearing nonsigners. Moreover, no discernible group differences in proprioceptive accuracy were observed. These findings challenge the prevalent notion that deafness leads to deficits in motor skills and other body-related abilities.

Model-level Weight Update Domain Adaptive Dynamic CNN Soft Sensor for Free Calcium Ion Concentration in Cement Clinker

The paramount determinant of clinker quality within the cement clinker production process resides in the precise prediction of F-CAO (free calcium ion) content. Given the intricate nature of cement clinker production, a profound coupling and temporal progression are inherently intertwined between the process data, as working conditions undergo gradual transformations over time. This research suggests a Model-level Weight Update Domain Adaptive Dynamic CNN Soft Sensor for Free Calcium Ion Concentration in Cement Clinker to overcome the aforementioned issues. We introduce a graph-structured two-dimensional methodology aimed at transforming both labeled and unlabeled data within the original process dataset into two-dimensional process data blocks. This innovative approach serves to enhance the spatial and temporal attributes of the labeled samples within the comprehensive sample set. Importantly, this methodology effectively addresses the intricate challenges posed by robust data coupling and the substantial presence of unlabeled samples. In response to the challenge posed by the frequent temporal fluctuations in working conditions, we introduce the Model-level Weight Update Domain Adaptive (MWUDA) approach. MWUDA leverages the hidden layer outputs from both the source and target domains to optimize the weight updates of the fully-connected layer within the CNN model. This strategic weight optimization is aimed at ameliorating the performance degradation observed in static models when confronted with shifting patterns in sample distributions. Subsequently, a limited subset of source domain samples exhibiting analogous regularities to those in the test samples is identified, enabling the acquisition of refined weights through the MWUDA method. Employing this strategy safeguards the precision of model updates throughout the MWUDA computation procedure, ensuring a robust and accurate adaptation process. The effectiveness of the proposed method is verified in an experimental study with real cement production data.

An efficient method for detecting Camellia oleifera fruit under complex orchard environment

Efficient Camellia oleifera fruit detection in complex orchard environments is one of the most crucial technologies for automated picking and is significant for reducing labor costs. At present, Camellia oleifera fruit detection remains challenging due to the influence of fruit trait differences, cultivar differences, orchard environment differences, complex illumination, and occlusion. To address these challenges, a modified YOLOv7 model SDF-YOLO (SKConv-Decoupled Head-Focal EIoU-YOLOv7) has been developed. In SDF-YOLO, the selective kernel convolution (SKConv) is introduced to detect fruits of different sizes resulting from trait difference and occlusion with high performance, which utilizes multiple branches with different kernel sizes to fully extract and fuse the multi-scale information. To fully extract feature information of occlusion fruits, the decoupled head is used as the head network of SDF-YOLO to enhance the generalization performance of SDF-YOLO to cultivar differences and orchard environment differences. In addition, focal efficient intersection over union (Focal EIoU) loss is introduced to focus on high-quality bounding boxes, which can deal with positive and negative sample imbalance problems caused by occlusion and complex illumination. Experiment results demonstrate that SDF-YOLO can effectively detect Camellia oleifera fruits in complex orchard environments. Compared with eight mainstream models shows that SDF-YOLO achieves the highest precision of 94.91 %, recall of 93.01 %, F1 score of 93.95 %, and mAP of 96.65 % with an encouraging mean detection time of 36.57 ms. Moreover, SDF-YOLO exhibits robust adaptability to fruit trait differences, cultivar differences, orchard environment differences, complex illumination, and occlusion. These results indicated that SDF-YOLO is feasible for Camellia oleifera fruit detection in complex orchard environments, which provided a technical foundation for the automated picking of commercial orchards.

Nutrient and salinity stress induced biodiesel production from a green alga, Monoraphidium neglectum

The potential of a green alga as a versatile and sustainable feedstock for biodiesel production was assessed. Molecular identification revealed the alga to be Monoraphidium neglectum. Under diverse growth conditions, including nutrient deficiency and salinity stress, the microalga showed robust adaptability. Under nitrate deficiency (N-, the lipid content peaked (30.67 %), with a notable increase in monounsaturated fatty acids (MUFA), reaching 50.72 % in the stressed sample. Additionally, the highest polyunsaturated fatty acid (PUFA) content (24.55 %) was observed at a salinity level of 0.12 M. At 0.01 M salinity, the highest total carbohydrate content was observed. A significant fatty acid methyl ester (FAME) yield of 85.08 % was obtained under nitrate deficiency (N-) emphasizing its biodiesel production potential. The as-obtained biodiesel exhibited low viscosity, superior low-temperature performance and enhanced oxidation stability rendering the alga as an ideal candidate for eco-friendly, sustainable biodiesel production.

Efficient removal of 17α-ethinylestradiol by algae-based red mud catalyst activated persulfate: Removal mechanism and defect of quenching

The synthesis and optimization of carbon-based materials, derived from various solid waste sources to activate peroxydisulfate (PDS), offer a practical approach that addresses both the recycling of solid waste resources and the removal of recalcitrant organic pollutants. In this study, stacked algae sludge and discarded red mud solid waste were repurposed to synthesize an algae-based red mud catalyst (AR-BC) using the carbon-thermal reduction technique. The obtained AR-BC catalyst exhibited efficient adsorption and degradation capabilities for 17α-ethinylestradiol (EE2). The results showed that AR-BC can adsorb 42.67 % of EE2 at equilibrium, with a maximum capacity of 8.0 mg/g. After the activation of PDS by the AR-BC catalyst, it was observed that EE2 was completely degraded within 20 min. The superior activation ability is mainly attributed to the presence of Fe3O4 and oxygen-containing functional groups in AR-BC. Both free radicals and non-free radicals coexisted in the AR-BC/PDS system, with the former predominantly influencing the EE2 degradation process. And hydroxyl radical was identified as the primary reactive oxygen species. Moreover, AR-BC exhibited robust adaptability and reusability in real water for efficient adsorption and degradation of EE2. This study presents a viable solution by seamlessly integrating resource recycling with pollutant removal, offering an innovation approach.

Discrimination and sensorimotor adaptation of self-produced vowels in cochlear implant users.

Humans rely on auditory feedback to monitor and adjust their speech for clarity. Cochlear implants (CIs) have helped over a million people restore access to auditory feedback, which significantly improves speech production. However, there is substantial variability in outcomes. This study investigates the extent to which CI users can use their auditory feedback to detect self-produced sensory errors and make adjustments to their speech, given the coarse spectral resolution provided by their implants. First, we used an auditory discrimination task to assess the sensitivity of CI users to small differences in formant frequencies of their self-produced vowels. Then, CI users produced words with altered auditory feedback in order to assess sensorimotor adaptation to auditory error. Almost half of the CI users tested can detect small, within-channel differences in their self-produced vowels, and they can utilize this auditory feedback towards speech adaptation. An acoustic hearing control group showed better sensitivity to the shifts in vowels, even in CI-simulated speech, and elicited more robust speech adaptation behavior than the CI users. Nevertheless, this study confirms that CI users can compensate for sensory errors in their speech and supports the idea that sensitivity to these errors may relate to variability in production.

Unsupervised and Semi-Supervised Robust Spherical Space Domain Adaptation.

Adversarial domain adaptation has been an effective approach for learning domain-invariant features by adversarial training. In this paper, we propose a novel adversarial domain adaptation approach defined in the spherical feature space, in which we define spherical classifier for label prediction and spherical domain discriminator for discriminating domain labels. In the spherical feature space, we develop a spherical robust pseudo-label loss to utilize pseudo-labels robustly, which weights the importance of the estimated labels of target domain data by the posterior probability of correct labeling, modeled by the Gaussian-uniform mixture model in the spherical space. Our proposed approach can be generally applied to both unsupervised and semi-supervised domain adaptation settings. In particular, to tackle the semi-supervised domain adaptation setting where a few labeled target domain data are available for training, we propose a novel reweighted adversarial training strategy for effectively reducing the intra-domain discrepancy within the target domain. We also present theoretical analysis for the proposed method based on the domain adaptation theory. Extensive experiments are conducted on multiple benchmarks for object recognition, digit recognition, and face recognition. The results show that our method either surpasses or is competitive compared with the recent methods for both unsupervised and semi-supervised domain adaptation. Ablation studies also confirm the effectiveness of the spherical classifier, spherical discriminator, spherical robust pseudo-label loss, and reweighted adversarial training strategy.

Thauera sp. for efficient nitrate removal in continuous denitrifying moving bed biofilm reactor.

Thauera is the most widely found dominant denitrifying genus in wastewater. In earlier study, MBBR augmented with a specially developed denitrifying five-membered bacterial consortium (DC5) where Thauera was found to be the most abundant and persistent genus. Therefore, to check the functional potential of Thauera in the removal of nitrate-containing wastewater in the present study Thauera sp.V14 one of the member of the consortium DC5 was used as the model organism. Thauera sp.V14 exhibited strong hydrophobicity, auto-aggregation ability, biofilm formation and denitrification ability, which indicated its robust adaptability short colonization and nitrate removal efficiency. Continuous reactor studies with Thauera sp.V14 in 10 L dMBBR showed 91% of denitrification efficiency with an initial nitrate concentration of 620 mg L-1 within 3 h of HRT. Thus, it revealed that Thauera can be employed as an effective microorganism for nitrate removal from wastewater based on its performance in the present studies.

Analysis and design of antithetic proportional-integral-derivative biocontrol-systems with species dilution

The nonlinearity and non-separability of the antithetic PID (aPID) controller have provided greater flexibility in the design of biochemical reaction networks (BCRNs), resulting in significant impacts on biocontrol-systems. Nevertheless, the dilution of control species is disregarded in designs of aPID controllers, which would lead to the failure of inhibition mechanism in the controller and loss of robust perfect adaptation (RPA)—the biological counterpart of robust steady-state tracking. Here, the impact of dilution processes on the structure of aPID is investigated in this study. It is discovered that the proportional and low-pass filters are altered when the dilution processes is present in control species, which increases the coupling between the controller parameters. Moreover, additional integrations for the reference signal and control output generated by control species dilution further leads to the loss of RPA. Subsequently, a novel aPID controller represented by BCRNs, termed quasi-aPID, has been designed to eliminate the detrimental effects of the dilution processes. In an effort to ameliorate the interdependencies among controller parameters, a degradation inhibition mechanism is employed within this controller. Furthermore, this work establishes the limiting relationship between the controller’s reaction rates in order to guarantee RPA, while abstaining from the introduction of supplementary species and biochemical reactions. By using the quasi-aPID controller in both the Escherichia coli gene expression model and the whole-body cholesterol metabolism model, its effectiveness is confirmed. Simulation results demonstrate that, the quasi-aPID exhibits a smaller absolute steady-state error in both models and guarantees the RPA property.

Locomotion control of a rigid-soft coupled snake robot in multiple environments

The versatile motion capability of snake robots offers themselves robust adaptability in varieties of challenging environments where traditional robots may be incapacitated. This study reports a novel flexible snake robot featuring a rigid–flexible coupling structure and multiple motion gaits. To better understand the robot’s behavior, a bending model for the soft actuator is established. Furthermore, a dynamic model is developed to map the relationship between the input air pressure and joint torque, which is the model base for controlling the robot effectively. Based on the wave motion generated by the joint coupling direction function in different planes, multiple motion gait planning methods of the snake-like robot are proposed. In order to evaluate the adaptability and maneuverability of the developed snake robot, extensive experiments were conducted in complex environments. The results demonstrate the robot’s effectiveness in navigating through intricate settings, underscoring its potential for applications in various fields.

Unveiling the Genetic Secrets of Chinese Indigenous Pigs from Guizhou Province: Diversity, Evolution and Candidate Genes Affecting Pig Coat Color.

The local pig breeds in Guizhou possess exceptional meat quality, robust adaptability, and resilience to harsh feeding conditions, making them ideal for producing high-quality pork. With over 10 local pig breeds in the region, we focused on 7 specific breeds: Baixi pigs (BX), Congjiang Xiang pigs (CJX), Guanling pigs (GL), Jianhe White Xiang pigs (JHBX), Jiangkou Luobo pigs (JKLB), Kele pigs (KL), and Qiandong Hua pigs (QDH). Unfortunately, these breeds face threats such as introduced species and inbreeding, resulting in a decline in population size and numbers. To better protect and utilize these breeds, we employed genome-wide single-nucleotide polymorphism (SNP) markers to investigate the population structure, genetic diversity, and selection characteristics of 283 pigs across these seven breeds. Our findings revealed distinct ancestral sources between Chinese and Western pig breeds, as demonstrated by principal component analysis, adjacent tree analysis, and ADMIXTURE analysis. Notably, JHBX exhibited a distant genetic relationship from the other six local pig breeds in Guizhou province, showcasing unique genetic characteristics. While the genetic diversity of the six Chinese native pig populations, excluding JHBX, was generally moderate in Guizhou province, the JHBX population displayed low genetic diversity. Therefore, it is imperative to intensify selection efforts to prevent inbreeding decline in JHBX while further enhancing the protection measures for the other six pig populations. Additionally, we identified candidate genes influencing the size disparity among pigs in Guizhou province through signal selection. Our study outcomes serve as a reference for developing effective conservation and utilization plans for pig breeds in Guizhou province and deepen our understanding of the genetic mechanisms underlying pig body size.

Optimization of multi-dimensional indices for kiwifruit orchard soil moisture content estimation using UAV and ground multi-sensors

Accurately estimating of soil moisture content (SMC) is essential for effective irrigation water management and optimizing plant water productivity. Recent advancements in multi-sensor platforms and ensemble learning (EL) algorithms such as the use of UAV-based multispectral (MS) and thermal infrared (TIR) images, have enabled precise mapping of SMC at the field level. The purpose of this study is to optimize the multi-dimensional (n-D) index set and EL model to achieve accurate inversion mapping of SMC in the 0–60 cm root zone at a kiwifruit orchard during the fruit growth period (May-Sep 2022). The n-D index set was computed using kiwifruit canopy MS and TIR data from UAV along with in-field temperature data (air temperature [Ta], kiwifruit canopy temperature [Tc], soil temperature [Ts], and Ta-Tc, Ta-Ts, Tc-Ts). The primary findings of this study are as follows: (1) The Three-Dimensional Drought Index (TDDI), Temperature Vegetation Drought Index (TVDI), and Crop Water Stress Index (CWSI) demonstrated superior performance in capturing temporal variations of root zone SMC compared to Normalized Difference Vegetation Index (NDVI) and Enhance Vegetation Index (EVI). (2) The optimal Categorical Boosting (Catboost) model with two optimal TDDIs (TDDI22: (Tc-Ts)-EVI10,6,2-CWSI space and TDDI7: Tc-NDVI7,3-CWSI space), exhibited exceptional performance for SMC estimation, with determination coefficient (R2) and root-mean-square error (RMSE) of 0.966 ± 0.010 and 0.046 ± 0.003%, respectively. (3) The planted-by-planted mapping performed well in estimating root zone SMC at different growth stages and under different irrigation treatments, with a correlation coefficient (R) of 0.936 ± 0.074 (p < 0.001). This study demonstrated that the integration of multi-sensor indicators and EL models can improve the accuracy of SMC estimation due to its robust adaptability to various field conditions. Furthermore, the planted-grid-based SMC mapping framework can be considered as a valuable approach for monitoring dynamic SMC, facilitating informed irrigation decisions at the individual planting grid.

A novel exponential model for tool remaining useful life prediction

Implementing proactive maintenance strategies based on condition prediction for cutting tools can reduce expensive, unscheduled maintenance events. This work proposes an novel exponential model to predict the Remaining Useful Life (RUL) of cutting tools. Firstly, a new monitoring indicator named second-order derivative of health index (SDHI) is constructed, and on this basis, a 3σ interval-based first predicting time (FPT) adaptive selection method is proposed to correlate the observable SDHI with the unobservable tool wear rate, automatically determines the abnormal tool wear state without human intervention. Secondly, the integration of the Bayesian inference mechanism with the expectation maximization (EM) algorithm enables the achieving of real-time iterative updates for model parameters. Thirdly, To reduce stochastic errors while predicting the RUL, particle filtering and probability density function (PDF) are applied to handle prediction uncertainty. The experimental findings obtained from the milling experiments demonstrate that the proposed model exhibits robust adaptability to various cutting conditions, thereby leading to enhanced RUL prediction performance.

Autumnal Potassium Induced Modulations in Plant Osmoprotectant Substances, Nutrient Stoichiometry and Precision Sustainable Seedling Cultivation in Parashorea chinensis

Parashorea chinensis, an endemic tree species in China’s tropical rainforests, holds ecological and economic importance. Challenges like low resistance, poor quality, and low survival rates hinder its successful cultivation. This study explores the potential of autumn potassium fertilization on Parashorea seedlings from two provenances (Napo and Tianyang). The treatments included no fertilizer (CK-1), a single application of 160 mg K·plant−1 (CK-2), and various potassium levels K1, K2, K3, K4, K5, and K6 (corresponding to 0, 40, 80, 160, 320, and 640 mg·K·plant−1, respectively) combined with nitrogen (200 mg·plant−1) and phosphorus (80 mg·plant−1) fertilization. The findings indicate that autumn potassium application, in conjunction with nitrogen (N) and phosphorus (P) fertilization, significantly enhances seedling height and biomass in both provenances, resulting in an average increase of 101% and 89% under the K4 treatment compared to CK-1 and CK-2, comparatively. Both Napo and Tianyang provenances exhibited distinct responses in photosynthetic rate (2.70 μmol·m−2·s−1 and 1.97 μmol·m−2·s−1, respectively) and stomatal conductance (0.042 mol·m−2·s−1 and 0.029 mol·m−2·s−1, respectively) to the K4 treatment, which proved most effective. The chlorophyll content was significantly higher for Napo provenance with the K3 treatment (74.31%, 58.99%), while for Tianyang, it was higher with the K4 treatment (41.48%, 17.36%), compared to CK-1 and CK-2, respectively. Antioxidant enzymes activity, osmoregulatory capacity, and malondialdehyde content all exhibited variations with potassium application levels, with the K4 treatment offering significant benefits. In Napo provenance, lignin (199.82 mg·g−1) and cellulose (252.38 mg·g−1) peaked at K4, while Tianyang exhibited variation, higher lignin (184.25 mg·g−1) at K3, and cellulose (257.73 mg·g−1) at K4. Nutrient content analysis demonstrates that the K4 treatment enhances nutrient absorption and storage, increasing total N (21.56 mg·kg−1), P (4.69 mg·kg−1), and K (13.49 mg·kg−1) content. A comprehensive analysis reveals that the K4 treatment yields the highest quality scores (1.87, 1.85) and membership values (0.82, 0.68) for both Napo and Tianyang seedlings, with Napo seedlings outperforming their Tianyang provenance. Thus, treatment K4 underscores the effectiveness of autumn potassium applications for robust seedling cultivation and adaptation, offering valuable insights for sustainable cultivation practices.

Facial image deblurring network for robust illuminance adaptation and key structure restoration

Facial image deblurring is an active research area that aims to restore blurry face images to clear ones. However, this task requires special consideration to restore the detailed elements of facial structures, such as eyes, nose, and mouth. Additionally, facial occlusions and varying illuminance conditions in common environments can degrade the deblurring performance. Previous studies have not accounted for these conditions, necessitating the development of deblurring method that considers these factors. In this paper, we propose a novel approach, called Illuminance-robust Multi-stage DeblurNet with Channel Attention (IMDeCA), which leverages semantic mask and landmark information of the face to restore detailed facial structures. Our approach is robust to various illuminance conditions and facial occlusions. The proposed network comprises a multi-stage structure that extracts facial semantic feature maps, reconstructs clear images, and improves illuminance. We also consider facial landmark information in the loss function to ensure well-restored facial structures even in the presence of facial occlusions. Furthermore, we construct a new facial image dataset, named BIO, which includes Blurred images with various types of Illuminance conditions and facial Occlusions. Through extensive experiments on this dataset, we demonstrate the superior performance of our proposed network, outperforming the latest existing methods.

Validation and cross-cultural adaptation of the six-dimension scale of nursing performance- arabic version

BackgroundNursing performance is a key indicator of patients’ care quality and safety. Most healthcare research tools are available in the English language; however, nurses around the world can employ these tools if rigorously adapted and cross-culturally validated.Aims and objectivesThis study aims to provide a cross-cultural adaptation and validation of the six-dimension scale of nursing performance to be used among Arabic-speaking nurses.DesignThe study employed a descriptive, correlational design with a cross-sectional approach.MethodsA five-step cross-cultural adaptation process was adopted. The scale was administered to 216 Jordanian nurses between January 2022 to April 2022. SPSS and AMOS were used for descriptive and correlation analyses and testing the six-dimension model through structural equation modeling (SEM).ResultsThe current study produced a valid, reliable, culturally adapted Arabic language version of the six-dimension scale of nursing performance. The internal consistency of the tool was supported by a Cronbach Alpha’s value of 0.99. The model’s goodness of fit indices were: CFI = 0.96, RMSEA = 0.048, and CMIN/df = 1.49. The exploratory factor analysis (EFA) of the scale identified three factors with eigenvalues greater than 1.00., explaining 75.22% of the variance. A subsequent EFA, specifying six factors, yielded 79.79% explained variance. All item factor loadings exceeded 0.30, confirming the scale’s robust factor structure.ConclusionsThis study proved that following a robust cross-cultural adaptation process results in a reliable and valid measure of nursing performance to be used among Arabic-speaking nurses. The study supports the dimensionality of nursing performance as evidenced by the SEM results. Therefore, the findings have the potential to considerably enhance studying nursing performance in healthcare fields in Arabic-speaking nurses.Relevance to clinical practiceThe validation and cross-cultural adaptation of the Arabic version of the Six-Dimension Scale of Nursing Performance have direct implications for improving the quality of nursing services, enhancing patient safety, promoting cultural competence, and supporting the professional growth of Arabic-speaking nurses.

Investigating Mechanisms of Robust Perfect Adaptation in Cancer Metabolisms Using Chemical Reaction Network Theory

Perfect adaptation is a ubiquitous phenomenon in biology, characterized by the ability of living cells to maintain chemical levels despite disturbances. This adaptation is said to be robust when it holds even after parameter variations. This property of the system is called Robust Perfect Adaptation (RPA). When certain variables lose this property or undesired chemical species acquire RPA property, it results in disease conditions. Metabolic adaptation in extreme tumor microenvironment is a hallmark of cancer cells and allows them to evade death and develop drug resistance. Hence, it is important to investigate the robustness properties of cancer metabolism using a system-theoretical approach. In this work, we topologically investigate the robust perfect adaptation property of large-scale metabolic reaction networks of three types of cancers using the theory proposed by Hirono, Gupta, and Khammash, 2023 (https://arxiv.org/abs/2307.07444). We enumerate the RPA properties of all biologically relevant reactions in large-scale metabolic models of cancer cells, along with the corresponding healthy cells. Finally, perform a comparative analysis to identify drug targets based on the differences in the RPA properties between healthy and cancer cell metabolisms.

A game theory-based pricing model for ancillary services in electricity markets

Abstract Amidst the ongoing evolution and substantial reforms within the electric power market, the development of an auxiliary service pricing model grounded in game theory emerges as crucial. This study delineates the construction of an electricity auxiliary service pricing model, utilizing a dual mechanism approach: a cooperative game-based electricity price formation mechanism and a Stackelberg game-based time-sharing pricing mechanism. Furthermore, it incorporates demand response technology to conduct a detailed analysis of optimization results and the applicability of the proposed electricity service pricing model. The pricing scheme designated as Scheme 4, derived from the proposed model, demonstrates notable superiority in terms of economic efficiency and environmental sustainability when juxtaposed with three alternative schemes. Specifically, Scheme 4 yields a net profit of $13,267.6, achieves clean energy utilization amounting to 89.67 MWh, and minimizes wind abandonment to 17.35 MWh, outperforming all other considered scenarios in these metrics. Operational analysis reveals that the model's execution time varies between 15 and 50 seconds across different sample sizes, exhibiting minimal fluctuations. Additionally, the Monte Carlo simulations consistently produce values inferior to the objective function value of the developed model, with the discrepancy narrowing from 38 to 20, indicating the model's robust adaptability.