Practical Applications Research Articles

Particle size is an important factor influencing the effects of biochar return to woodland soils: An evaluation from the perspective of sapling growth and soil microbial carbon processes

Biochar can increase ecosystem carbon sequestration by promoting plant growth and stabilizing soil organic carbon (SOC). Biochar produced from forest waste typically varies in particle size and is frequently applied directly for soil enhancement without pulverization. The effects of different biochar particle sizes on sapling growth, woodland soil properties and microbial carbon processes are unclear. This study used field experiments to compare the effects of different biochar particle sizes on sapling growth and microbial metabolic entropy (qCO2). The impacting mechanisms were explored by examining soil physicochemical properties, enzyme activity, and microbial community structure. The application of forest waste (FW) and small particle biochar (SPBC, particle size<2 mm) did not significantly affect sapling growth. Conversely, middle particle biochar (MPBC, particle size 2–10 mm) and large particle biochar (LPBC, particle size>10 mm) reduced sapling biomass by 20.76% and 38.87%, respectively, compared to SPBC. MPBC and LPBC applications resulted in soil nutrient loss (total nitrogen and available phosphorus), inhibiting sapling growth. After 167 days, qCO2 rankings were as follows: FW (30.37 ± 5.18) (P<0.05)> LPBC (20.91 ± 3.62) > CK (16.21 ± 2.71) > MPBC (15.99 ± 3.54) > SPBC (7.8 ± 0.80) (P < 0.05). The rankings of organic carbon retention rates rankings were as follows: SPBC (85.14%) > LPBC (70.35%) > FW (67.31%) > CK (54.53%) > MPBC (51.96%). SPBC increased biochar-soil-microbe interactions, raised the relative proportion of k/r-strategy bacteria, reduced extracellular cellulase activity thus inhibit qCO2. In conclusion, small particle biochar (<2 mm), compared to larger-particle biochar, improves SOC sequestration without negatively affecting sapling growth. Therefore, particle size should be considered as a management indicator for biochar applications in artificial forest practices.

Biochar-induced changes in soil microbial communities: a comparison of two feedstocks and pyrolysis temperatures

BackgroundThe application of a biochar in agronomical soil offers a dual benefit of improving soil quality and sustainable waste recycling. However, utilizing new organic waste sources requires exploring the biochar’s production conditions and application parameters. Woodchips (W) and bone-meat residues (BM) after mechanical deboning from a poultry slaughterhouse were subjected to pyrolysis at 300 °C and 500 °C and applied to cambisol and luvisol soils at ratios of 2% and 5% (w/w).ResultsInitially, the impact of these biochar amendments on soil prokaryotes was studied over the course of one year. The influence of biochar variants was further studied on prokaryotes and fungi living in the soil, rhizosphere, and roots of Triticum aestivum L., as well as on soil enzymatic activity. Feedstock type, pyrolysis temperature, application dose, and soil type all played significant roles in shaping both soil and endophytic microbial communities. BM treated at a lower pyrolysis temperature of 300 °C increased the relative abundance of Pseudomonadota while causing a substantial decrease in soil microbial diversity. Conversely, BM prepared at 500 °C favored the growth of microbes known for their involvement in various nutrient cycles. The W biochar, especially when pyrolysed at 500 °C, notably affected microbial communities, particularly in acidic cambisol compared to luvisol. In cambisol, biochar treatments had a significant impact on prokaryotic root endophytes of T. aestivum L. Additionally, variations in prokaryotic community structure of the rhizosphere depended on the increasing distance from the root system (2, 4, and 6 mm). The BM biochar enhanced the activity of acid phosphatase, whereas the W biochar increased the activity of enzymes involved in the carbon cycle (β-glucosidase, β-xylosidase, and β-N-acetylglucosaminidase).ConclusionsThese results collectively suggest, that under appropriate production conditions, biochar can exert a positive influence on soil microorganisms, with their response closely tied to the biochar feedstock composition. Such insights are crucial for optimizing biochar application in agricultural practices to enhance soil health.

Iron(III) Monoglycerolate as a New Biocompatible Precursor in the Synthesis of Bioactive Nanocomposite Glycerohydrogels.

Nanocomposite glycerohydrogels based on biocompatible elementcontaining glycerolates are of practicular interest for biomedical applications. Using two biocompatible precursors, silicon and iron glycerolates, a new bioactive nanocomposite silicon‒iron glycerolates hydrogel was obtained by sol-gel method. The composition and structural features of the hydrogel were studied using a complex of modern analytical techniques, including TEM, XRD, and AES. On the example of experimental animals hemostatic activity of the hydrogel was studied, as well as primary toxicological studies were carried out. The composition of dispersed phase and dispersion medium of silicon‒iron glycerolates hydrogel was determined. The structural features of hydrogel were revealed and its structure model was proposed. It was shown that silcon-iron glycerolates hydrogel is nontoxic, and exhibits pronounced hemostatic activity. Silicon-iron glycerolates hydrogel is a potential hemostatic agent for topical application in medical and veterinary practice.

Factors related to preoperative anxiety in older patients with sensory impairment: A cross-sectional study.

To identify the factors related to preoperative anxiety in older adults with sensory impairment. Descriptive quantitative research design. Study participants were 120 older adults admitted to a tertiary hospital in Seoul, South Korea to undergo eye or ear surgery. Data were collected from 11 February to 30 April 2023. The collected data were analysed using descriptive statistics, t-tests, ANOVA, and multiple regression analysis using SPSS 28.0. The study was reported following the STROBE checklist. Preoperative anxiety differed significantly according to gender (p = .002), average monthly household income (p < .001), subjective health status (p = .003), subjective vision loss (p = .004), one-sided or both-sided eye discomfort (p = .002), use of glasses (p = .010), subjective hearing loss (p = .022) and anxiety coping mechanisms (p < .001). The results of a multiple regression analysis on the significant variables identified that gender (β = .178, p = .036) and anxiety coping mechanisms (β = .336, p < .001) had the most significant effect on preoperative anxiety, with an explanatory power of 34.5%. The development and practical application of tailored nursing interventions catering to specific genders and anxiety coping mechanisms would contribute to reducing preoperative anxiety and providing efficient nursing care for older adults with sensory impairment.

The problem of student’s responsibility in the context of digitalization of education

Introduction. The article discusses some socio-psychological problems related to the change in the subjectivity of the student as the main actor of educational practices.Purpose setting. The purpose of this article is to discuss the acute problems of a student’s responsibility in the context of the emerging non-traditional nature of educational communications.Methodology and methods of the study. The discussion is conducted within the framework of the methodological principles of cognitivism and social constructionism, which today determine the vector of research related to the impact of digitalization on educational practices.Results. The thesis is updated that the change in the subjectivity of an individual (as a learning subject) in the context of convergence of AI applications in educational practices occurs in two main planes. The first of them is associated with the deformation (decrease) of student’s level of responsibility for performing educational practices «as specific learning actions». The second is that the determination of reducing responsibility is largely determined by the «moving»of the driver into the «comfort zone». In this zone, there is a «delegation»of choice and decision-making from the individual (subject, student) by AI application.Conclusion. Against the background of issues related to the «reliability of solutions»offered by AI and the lack of «digital literacy», the possibility of self-identification of the student and the level of his responsibility (as a real subject of educational practices) can be clearly questioned and requires methodological and methodical reflection.

Religion, secularism, and postsecularism in Chinese internet literature

Abstract Internet literature emerged in China in the 1990s and became commercialized in the early 2000s, with readers paying an access fee, which is split between literary platforms and their contracted writers, to access popular novels. While the most popular genre is fantasy, a type of imaginative literature devoted to invoking magic beyond the limits of empirical reality and scientific rationality, the primary source of magic for Chinese fantasy is traditional Chinese cosmology and its concrete applications in everyday practices. These ideas and practices, the onto-epistemic-practical foundation of Chinese religion before the twentieth century, were considered superstitious and targeted for elimination in modernizing and secularization processes. However, they have not only survived but taken up a new life in fantasy novels, which depict them as the antecedents of, alternatives to, or more advanced forms than modern scientific endeavors at engineering natural environments and enhancing human bodies. This is why I see online fantasy literature as a palimpsest bearing the traces of Chinese religion, Chinese secularism, and the social conditions of contemporary China. In bearing such traces, this literature exemplifies the postsecular.

Chitosan nanoparticles (CSNPs) conferred salinity tolerance in maize by upregulating E3 ubiquitin-protein ligase, P5CS1, HKT1, NHX1, and PMP3 genes.

This study explored the transcriptional behaviors of several candidate genes in response to the application of CSNPs (50 and 100 mgl-1) in maize seedlings grown under two salinity levels (NaCl of 0.07 and 0.14 gkg-1soil). Employing CSNPs at both concentrations mitigated the inhibitory role of salinity on the leaf and root fresh weights. The application of CSNPs enhanced the transcription of the E3 ubiquitin-protein ligase gene by an average of threefold, contrasted with the salinity controls. The Δ1-pyrroline-5-carboxylate synthetase (P5CS1) gene was upregulated in response to both individual and mixed treatments of CSNPs and salinity. The transcription of the high-affinity K+ transporter (HKT1) gene displayed an upward trend in response to the CSNPs and salinity treatments. The Na+/H+ exchangers (NHX1) gene exhibited a similar trend to that of the HKT1 gene. The utilization of CSNPs was accompanied by an upregulation in the plasma membrane proteolipid 3 (PMP3) gene, contrasted with the salinity controls. The phenylalanine ammonia-lyase (PAL) activity displayed an upward trend in response to the foliar application of CSNPs. The CSNPs at the 100 mgl-1 concentration were more capable of inducing the ascorbate peroxidase enzyme under both salinity conditions than the 50 mgl-1 dose. The simultaneous exposure of maize seedlings to CSNPs and salinity resulted in the drastic upregulation of the catalase activities. This study provides novel insights into the major mechanisms underlying the stress-mitigating effects of CSNPs, thereby providing a suitable platform for their application in sustainable agricultural practices.

Discrimination and precision of Continuous Glucose Monitoring in staging children with presymptomatic type 1 diabetes.

Staging and monitoring of pre-symptomatic type 1 diabetes includes the assessment for dysglycemia. To assess the ability of Continuous Glucose Monitoring (CGM) to differentiate between islet autoantibody-negative controls and early-stage type 1 diabetes and explore whether CGM classifiers predict progression to clinical diabetes. Children and adolescents participating in public health screening for islet autoantibodies in Bavaria, Germany were invited to undergo CGM with Dexcom G6. In total, 118 participated and valid data was obtained from 97 (57 female; median age 10 [range 3-17] years), including 46 with stage 1, 18 with stage 2, and 33 with no islet autoantibodies. Mean glucose during CGM in islet autoantibody-negative controls was high (median, 115.3 mg/dl) and varied substantially (IQR, 106.8-124.4). Eleven (33%) of the controls had more than 10% of glucose values above 140 mg/dl (TA140). Using thresholds corresponding to 100% specificity in controls, differences between controls and stage 1 and stage 2 were obtained for glucose standard deviation, TA140, TA160 and TA180. Elevations in any two of these parameters identified 12 (67%) with stage 2 and 9 (82%) of 11 participants who developed clinical diabetes within one year. However, there was marked variation within groups for all parameters and poor consistency observed in a second CGM performed in 18 participants. This study demonstrated the potential of integrating CGM into staging and monitoring of early-stage type 1 diabetes. However, substantial improvement in the precision of CGM is required for its application in routine monitoring practices.

Методика перетворення кодових послідовностей у відповідності до системи координат модуляційного диска

An analysis of current approaches used in the design of optical recording systems for modulation disks has been conducted. By adapting mathematical models, software algo-rithms were developed to transform code sequences represented in polar, homogeneous, and parametric coordinate systems, and the unique characteristics of each approach in de-veloping a universal method for ensuring accurate and efficient modulation pattern trans-formation were established. It was noted that the polar coordinate system is the most suit-able for practical applications in code sequence transformations. Its ability to effectively represent circular and symmetrical structures, as well as the convenient representation of objects with radial symmetry, provides advantages in the creation of modulation disks. As a result of the study, effective software solutions for automating data processing in the formation of modulation patterns were identified. The implementation of the developed software algorithms for the presented approach involves adapting the code sequence to the current metric within the design of an optical system based on modulation disks. The pre-sented methodology ensures the ability to transform code sequences regardless of the cho-sen coordinate system, which significantly enhances its flexibility and versatility for prac-tical applications.

Genotype-specific responses to in vitro drought stress in myrtle (Myrtus communis L.): integrating machine learning techniques.

Myrtle (Myrtus communis L.), native to the Mediterranean region of Türkiye, is a valuable plant with applications in traditional medicine, pharmaceuticals, and culinary practices. Understanding how myrtle responds to water stress is essential for sustainable cultivation as climate change exacerbates drought conditions. This study investigated the performance of selected myrtle genotypes under in vitro drought stress by employing tissue culture techniques, rooting trials, and acclimatization processes. Genotypes were tested under varying polyethylene glycol (PEG) concentrations (1%, 2%, 4%, and 6%). Machine learning (ML) algorithms, including Gaussian process (GP), support vector machine (SVM), Random Forest (RF), and Extreme Gradient Boosting (XGBoost), were utilized to model and predict micropropagation and rooting efficiency. The research revealed a genotype-dependent response to drought stress. Black-fruited genotypes exhibited higher micropropagation rates compared to white-fruited ones under stress conditions. The application of ML models successfully predicted micropropagation and rooting efficiency, providing insights into genotype performance. The findings suggest that selecting drought-tolerant genotypes is crucial for enhancing myrtle cultivation. The results underscore the importance of genotype selection and optimization of cultivation practices to address climate change impacts. Future research should explore the molecular mechanisms of stress responses to refine breeding strategies and improve resilience in myrtle and similar economically important crops.

Robust adaptive dynamic control of electromagnetically actuated soft-tethered robots for medical intervention

Electromagnetically actuated soft-tethered robots (EASTRs) exhibit significant potential for medical intervention applications due to their compact size and minimal invasiveness. However, their inherent nonlinear behavior and susceptibility to unexpected disturbances present challenges for achieving robust control performance. To address this issue, in this paper, a dynamic model for EASTRs is first established, which encompasses the actuation model of the magnetic tip and the dynamic model of the soft tether. Subsequently, a robust adaptive dynamic controller is designed to compensate for the inaccuracy of the established dynamic model, thus ensuring robust control performance. The proposed approach integrates adaptive laws based on the Lyapunov method to effectively handle time-varying parameters and disturbances. Experimental results present an excellent control accuracy of the proposed scheme in trajectory tracking tasks, showcasing its superior performance compared to passive schemes and classical proportional–derivative control. This research contributes valuable insights into advancing the control capabilities of EASTRs for diverse applications in medical practices.

Comprehensive Graph Gradual Pruning for Sparse Training in Graph Neural Networks.

Graph neural networks (GNNs) tend to suffer from high computation costs due to the exponentially increasing scale of graph data and a large number of model parameters, which restricts their utility in practical applications. To this end, some recent works focus on sparsifying GNNs (including graph structures and model parameters) with the lottery ticket hypothesis (LTH) to reduce inference costs while maintaining performance levels. However, the LTH-based methods suffer from two major drawbacks: 1) they require exhaustive and iterative training of dense models, resulting in an extremely large training computation cost, and 2) they only trim graph structures and model parameters but ignore the node feature dimension, where vast redundancy exists. To overcome the above limitations, we propose a comprehensive graph gradual pruning framework termed CGP. This is achieved by designing a during-training graph pruning paradigm to dynamically prune GNNs within one training process. Unlike LTH-based methods, the proposed CGP approach requires no retraining, which significantly reduces the computation costs. Furthermore, we design a cosparsifying strategy to comprehensively trim all the three core elements of GNNs: graph structures, node features, and model parameters. Next, to refine the pruning operation, we introduce a regrowth process into our CGP framework, to reestablish the pruned but important connections. The proposed CGP is evaluated over a node classification task across six GNN architectures, including shallow models [graph convolutional network (GCN) and graph attention network (GAT)], shallow-but-deep-propagation models [simple graph convolution (SGC) and approximate personalized propagation of neural predictions (APPNP)], and deep models [GCN via initial residual and identity mapping (GCNII) and residual GCN (ResGCN)], on a total of 14 real-world graph datasets, including large-scale graph datasets from the challenging Open Graph Benchmark (OGB). Experiments reveal that the proposed strategy greatly improves both training and inference efficiency while matching or even exceeding the accuracy of the existing methods.

The Moderating Effect of App Trustworthiness and User Attitudes on Intention to Use Adopt Mobile Applications Among Employees in The Oil and Gas Industry

In today’s technologically advanced world, the adoption of mobile applications has become a transformative force across various industries, significantly impacting the way businesses operate and manage their processes. One area where mobile applications are making a remarkable impact is in the realm of Occupational Health and Safety (OHS). The integration of mobile applications in OHS practices has ushered in a new era of efficiency, accessibility, and data-driven decision-making, elevating workplace safety standards to unprecedented heights. Occupational health and safety are paramount concerns for organizations as they strive to create safe and healthy work environments for their employees. Historically, OHS management relied on manual and paper-based processes, which could be time-consuming, error-prone, and challenging to track comprehensively. However, with the advent of mobile technology, organizations now have the opportunity to streamline their OHS protocols, enhance communication, and proactively manage workplace risks, all through the convenience of mobile applications. This research aims to analyze the effects between perceived ease of use and the attitude towards the use of the mobile application. Perceived usefulness of applications on Attitude towards the use of the app, attitude towards the use of the app on intention to use the application. The population of this research is the employees from the oil and gas industry in Malaysia. The theory of the Technology Acceptance Model (TAM) was applied. Perceived danger, perceived utility, perceived ease of use, and perceived intention to use were the antecedents for mobile apps. Employees’ intentions to use mobile applications were shown to be significantly influenced by their satisfaction with the information system and their inventiveness. In this study, devices were categorized as large or small according to their screen size, while respondents were categorized as young (&lt;30 years old) or old (&gt;30 years old) according to their age. A data collection of 545 responses to an online survey with 32 questions was obtained for analysis. The survey was distributed via email to respondents using a Survey among employees. The factors impacting the intention to use mobile applications were investigated using structural equation modeling. We looked at the moderating impact of the app’s trustworthiness on users’ intentions to use/adopt it. The variables’ responses for perceived ease of use, perceived usefulness, attitude towards the use of the App, trust (integrity) of the App, and intention to use/adopt the application varied significantly between the subgroups, according to the results. The study also showed the existence of moderating effects on the intention to use/adopt an app related to the trust integrity of the app. This paper discusses the survey, the findings, and the ramifications of the observations. This research contributes to the current literature by presenting empirical evidence on the importance of trustworthiness and user attitudes in mobile app adoption in the oil and gas industry. It also discusses the broader implications for improving workplace safety through the strategic use of mobile devices in OHS practices.

Improvements to EXSIM in Ground Motion Simulation for Earthquakes Explained by Double‐Corner‐Frequency Source Model

AbstractThe stochastic EXtended finite‐fault ground‐motion SIMulation algorithm (EXSIM) has been widely applied in simulating and predicting broadband strong ground‐motion. However, an increasingly number of researchers have found that EXSIM may overestimate ground‐motions at low frequencies for some large‐magnitude earthquakes and/or thrust earthquakes, for which the far‐field source model has been explained by a double‐corner‐frequency model. Despite controversy, the double‐corner‐frequency model is now being accepted as one of the main categories of the far‐field source model. This study demonstrated the limited applicability of EXSIM to earthquakes explained by the double‐corner‐frequency source model, by presenting the equivalence between motions generated by EXSIM and those generated by EXSIM's point‐source version, SMSIM, which adopts the ω‐square single‐corner‐frequency model. Furthermore, two improvements to EXSIM have been proposed: (a) the incorporation of the asperity‐distributed stress‐drop compound faults model and (b) the hybrid application of EXSIM with the proposed model. The effects of the two improvements have been verified by comparing EXSIM‐generating motions with recorded ground‐motions for the 2013 Mw 6.7 Lushan thrust earthquake. Significantly, consistent simulation accuracy has been achieved across high‐ and low‐frequency bands as well as in far‐ and near‐fields. The consistent accuracy of the improved EXSIM in simulating high‐ and low‐frequency ground motions enables its direct and independent application to broadband ground motion simulations. Moreover, the first validation of this consistent accuracy in both near‐ and far‐field scenarios further enhances its application in earthquake engineering practices.

Virtual Reality in Waste Management: Evaluating Its Impact on Community Classification Behavior

The purpose of this study is to explore the impact of virtual reality (VR) technology on improving community garbage sorting behavior through a questionnaire survey. Targeting residents of varying ages, educational backgrounds, and lengths of residence in the community, the study utilized VR technology for garbage classification education and behavior guidance. This comprehensive method evaluated improvements in individuals' willingness, cognition, and practices regarding garbage classification. We designed a multi-dimensional questionnaire and employed a random sampling method to select residents who met specific classification criteria as survey subjects. The research involved analyzing the influence of VR technology on enhancing garbage classification cognition across different demographics, exploring how VR can shift attitudes and willingness towards garbage classification, and investigating its role in promoting actual sorting behavior and frequency. Methodologically, we implemented an immersive VR experience that simulated various types of garbage and classification scenarios, followed by preand post-intervention surveys to measure changes in cognition and behavior. Additionally, we examined the interrelationship between VR technology and other influencing factors, such as policy advocacy, educational background, and community environment. The findings indicate that VR technology significantly enhances residents' understanding of garbage classification standards and improves classification accuracy. Furthermore, the visual representation of the negative environmental impacts of garbage, depicted through VR, profoundly heightened residents' environmental awareness and willingness to engage in garbage classification. This study confirms the practical application value and significant impact of VR technology in improving community garbage sorting behavior, providing a scientific basis for its further promotion and application in community waste management practices.

Noninvasive Blood Glucose Monitoring Using Spatiotemporal ECG and PPG Feature Fusion and Weight-Based Choquet Integral Multimodel Approach.

change of blood glucose (BG) level stimulates the autonomic nervous system leading to variation in both human's electrocardiogram (ECG) and photoplethysmogram (PPG). In this article, we aimed to construct a novel multimodal framework based on ECG and PPG signal fusion to establish a universal BG monitoring model. This is proposed as a spatiotemporal decision fusion strategy that uses weight-based Choquet integral for BG monitoring. Specifically, the multimodal framework performs three-level fusion. First, ECG and PPG signals are collected and coupled into different pools. Second, the temporal statistical features and spatial morphological features in the ECG and PPG signals are extracted through numerical analysis and residual networks, respectively. Furthermore, the suitable temporal statistical features are determined with three feature selection techniques, and the spatial morphological features are compressed by deep neural networks (DNNs). Lastly, weight-based Choquet integral multimodel fusion is integrated for coupling different BG monitoring algorithms based on the temporal statistical features and spatial morphological features. To verify the feasibility of the model, a total of 103 days of ECG and PPG signals encompassing 21 participants were collected in this article. The BG levels of participants ranged between 2.2 and 21.8 mmol/L. The results obtained show that the proposed model has excellent BG monitoring performance with a root-mean-square error (RMSE) of 1.49 mmol/L, mean absolute relative difference (MARD) of 13.42%, and Zone A + B of 99.49% in tenfold cross-validation. Therefore, we conclude that the proposed fusion approach for BG monitoring has potentials in practical applications of diabetes management.

A framework for human evaluation of large language models in healthcare derived from literature review

With generative artificial intelligence (GenAI), particularly large language models (LLMs), continuing to make inroads in healthcare, assessing LLMs with human evaluations is essential to assuring safety and effectiveness. This study reviews existing literature on human evaluation methodologies for LLMs in healthcare across various medical specialties and addresses factors such as evaluation dimensions, sample types and sizes, selection, and recruitment of evaluators, frameworks and metrics, evaluation process, and statistical analysis type. Our literature review of 142 studies shows gaps in reliability, generalizability, and applicability of current human evaluation practices. To overcome such significant obstacles to healthcare LLM developments and deployments, we propose QUEST, a comprehensive and practical framework for human evaluation of LLMs covering three phases of workflow: Planning, Implementation and Adjudication, and Scoring and Review. QUEST is designed with five proposed evaluation principles: Quality of Information, Understanding and Reasoning, Expression Style and Persona, Safety and Harm, and Trust and Confidence.

Design, Simulation and Functional Testing of a Novel Ankle Exoskeleton with 3DOFs

This paper presents a study on developing a new exoskeleton for ankle joint rehabilitation with three degrees of freedom (3 DOFs). The primary attention is paid to the process of designing and modelling the device aimed at restoring the lost functions of joint mobility. The authors conducted a complex analysis of the functional requirements of the exoskeleton based on research into the potential user’s needs, which allowed for the development of a conceptual model of the proposed device. In this study, a prototype of the exoskeleton is designed using modern additive technologies. The prototype underwent virtual testing in conditions maximally close to reality, which confirmed its effectiveness and comfort of use. The main results of this study indicate the promising potential of the proposed solution for application in rehabilitation practices, especially for patients with ankle joint injuries and diseases.

Ecotoxicological insights into the effects of triflumezopyrim on P. fuscipes fitness, detoxification pathways, and gene expression

The primary objective was to evaluate the toxicity of triflumezopyrim (TFP) on P. fuscipes larvae and adults at lethal and sublethal levels through topical application. Sublethal effects were assessed by examining developmental period, fecundity, life-table parameters, and fitness parameters. Enzymatic and transcriptional analyses were conducted to determine the impact of TFP on P. fuscipes physiology and gene expression. The LC50, LC30, and LC10 of TFP against P. fuscipes larvae and adults were lower than the field-recommended dose (48.75 mg a.i. L−1), indicating direct toxicity and sublethal effects during immature stages. Exposure to LC30 of TFP extended developmental periods for 2nd-instar larvae and pupae, reduced oviposition, larval predation efficiency, and body weight in both sexes. Sublethal concentrations affected antioxidant, detoxification, and energy reservoir enzymes significantly. Transcriptional analysis revealed impacts on insecticide detoxification, resistance, and stress-related genes. KEGG analysis showed glycerolipid metabolism is the most regulated pathway, and UGT2B10 regulated several detoxification-related pathways under TFP stress. These findings prompt reconsideration of the role of TFP in paddy field IPM due to its adverse effects on P. fuscipes, emphasizing the importance of assessing its ecological impacts before widespread application in agricultural practices.

Effect of Tillage Practices and Hydrogel Applications on Yield and Yield Attributing Traits of Maize (Zea mays L.) over the Years

This study investigated the effects of different tillage practices and hydrogel applications on maize yield in sandy loam soils at the Agricultural Research Station, Karimnagar, PJTSAU, Telangana state over a period of four years (2015-2018) during the Kharif season. The experiment was designed as a split plot with four main treatments i.e., Conventional tillage (CT), Conventional tillage with residue mulching (4 t/ha), Zero tillage (ZT) and Zero tillage with residue mulching (4 t/ha). These were combined with three sub-treatments involving hydrogel application i.e., Control (no hydrogel), Hydrogel at 2.5 kg/ha and Hydrogel at 5.0 kg/ha. The study was replicated three times to ensure reliability of the results. The combination of zero tillage with residue mulching yielded the highest maize grain yield (4435 kg/ha). This suggests that this practice is more effective in retaining soil moisture, which is critical under rainfed conditions. The addition of hydrogel, regardless of the rate (2.5 kg/ha or 5.0 kg/ha), did not lead to any significant improvement in maize yield. The use of zero tillage combined with residue mulching is beneficial for maize production in sandy loam soils under rainfed conditions, primarily due to enhanced moisture retention. However, hydrogel application did not provide any additional yield benefits in this context.