Software Model Research Articles

Drug discovery and development in the era of artificial intelligence: From machine learning to large language models

Drug Research and Development (R&D) is a complex and difficult process, and current drug R&D faces the challenges of long time span, high investment, and high failure rate. Machine learning, with its powerful learning ability to characterize big data and complex networks, is increasingly effective to improve the efficiency and success rate of drug R&D. Here we review some recent examples of the application of machine learning methods in six areas: disease gene prediction, virtual screening, drug molecule generation, molecular attribute prediction, and prediction of drug combination synergism. We also discuss the advantages of integrative learning in multi-attribute prediction. Integrative models based on base learners constructed from data of different dimensions on the one hand fully utilize the information contained in these data, and on the other hand improve the average prediction performance. Finally, we envision a new paradigm for drug discovery and development: a large language model acts as a central hub to organize public resources into a knowledge base, validating the knowledge with computational software and smaller predictive models, as well as high-throughput automated screening platforms based on organoidal technologies, to speed up development and reduce the differences in efficacy between disease models and humans to improve the success rate of a drug.

Assessing the effect of sample storage time on viral detection using a rapid and cost-effective CTAB-based extraction method

BackgroundCassava leaf samples degrade quickly during storage and transportation from distant areas. Proper sampling and efficient, low-cost storage methods are critical for obtaining sufficient quality DNA and RNA for plant virus epidemiology and improving disease control understanding. This is useful when samples are collected from remote areas far from a laboratory or in developing countries where money and materials for virus diagnostics are scarce.ResultsThe effect of sample storage duration on nucleic acid (N.A.) quality on virus detection was investigated in this study. A simple, rapid, and cost-effective CTAB-based approach (M3) for single N.A. extraction was optimized and tested alongside two existing CTAB-based methods (M1 and M2) for N.A. extraction from fresh and herbarium cassava leaves stored for; 1, 8, 26, and 56 months. The amount and quality of DNA and RNA were determined using Nanodrop 2000 c U.V.–vis Spectrophotometer and agarose gel electrophoreses. The sample degradation rate was estimated using a simple mathematical model in Matlab computational software. The results show no significant difference in mean DNA concentration between M1 and M2 but a significant difference between M3 and the other two methods at p < 0.005. The mean DNA concentration extracted using M3 was higher for 1 and 8 months of leave storage. M3 and M2 produced high concentrations at 26 and 56 months of leave storage. Using a developed scale for quality score, M3 and M2 produced high-quality DNA from fresh samples. All methods produced poor-quality DNA and RNA at 8 and 26 months of leave storage and no visual bands at the age of 56 months. Statistically, there was a significant difference in the mean DNA quality between M1 and M2, but there was no significant difference between M3 and the other two methods at p < 0.005. However, Cassava brown streak virus (CBSV) and Ugandan cassava brown streak virus (UCBSV) were readily detected by RT-PCR from RNA isolated using M3. The quality of DNA declined per storage time at 0.0493 and 0.0521/month, while RNA was 0.0678 and 0.0744/month. Compared to the existing two methods, modified CTAB extracted enough high-quality N.A. in one-third the time of the existing two methods.ConclusionOur method provides cost-effective, quick, and simple processing of fresh and dry samples, which will quicken and guide the decision on when and what type of sample to process for plant disease management and surveillance actions.

An open-source research platform for mechanical ventilation based on Simulink® and STM32 nucleo

Abstract Gain in knowledge in experimental fields of research is mostly achieved using commercial hardware platforms. The linked costs however can limit a broad access by scientists and in the specific case of mechanical ventilation the pandemic demonstrated that further and collective research is essential and necessary. This contribution presents the modular design and implementation of a research platform which is based on STM32 Nucleo boards and Simulink®. The hardware required such as the pneumatic circuit are covered as well as the software architecture in which clinician related settings are processed for control references. The core functionality of mandatory volume controlled (VCV) and pressure controlled ventilation (PCV) while ensuring a positive end-expiratory pressure for the patient ( PEEP ) $(\text{PEEP})$ is evaluated and the foundational software models are published in a repository.

Enhancing complex bioprocess learning through simulation technology and hybrid teaching: A case study in university education.

The utilization of computer simulators in university education is progressively being embraced to offer students a practical exposure to industrial bioprocesses. Bioreactor computer simulators hold various advantages over conventional laboratory experiments, such as cost-effectiveness and enhanced safety. The research objective is to assess the effectiveness of integrating bioreactor computer simulators into hybrid teaching to promote active and collaborative learning experiences and evaluate their impact on student participation and understanding. A hybrid strategy combining synchronous, face-to-face, and online teaching has been implemented to enhance the teaching-learning processes in the Industrial Bioprocesses course for Biochemistry students. The simulation software BIOSTAT®T Yeast was used. This software models the production of ethanol with Saccharomyces cerevisiae through batch cultivation and the determination of the kLa value of a bioreactor. In the first simulation activity, students analyzed the software response based on parameter values input by the instructor, while in the second simulation activity, students autonomously used the computer simulator under the primary oversight of the instructor. The survey results indicate that the pedagogical innovation was positively received and significantly motivating for the students. Comparing student satisfaction surveys between the two simulation activities suggests that fostering student autonomy and engagement through simulation technology can improve satisfaction and learning outcomes in bioprocess education.

Presenting the AI models in predicting the settlement of earth dams using the results of spatiotemporal clustering and k-means algorithm

In this work, the results of instrumentation over 8 years, including the phases of construction, first impounding, and operation, have been used to analyze the location of the Eyvashan Dam settlement. Mohr–Coulomb behavioral model and numerical model of Plaxis 2D software were used to verify the monitoring results. The results demonstrated that settlement of the dam has increased in the dam's core since the beginning of construction, and they eventually stabilized during the operation phase. After the completion of the construction phase, the maximum settlement of the dam core was recorded as 809 mm, which is equivalent to 1.2% of the height of the dam at the middle level. Also, an approach to interpreting the settlement behavior of earth dams has been presented that is based on spatiotemporal clustering. Also, RF, MARS, and GMDH models were created based on a proposed scenario to predict settlement using points located in a cluster. Therefore, the settlement location of the studied dam was determined using the results of the k-means clustering algorithm in the aforementioned AI models. The high accuracy of the results of the proposed method confirms the proper performance of using AI models in predicting and diagnosing the settlement of earthen dams using the results of k-means spatiotemporal clustering algorithm. The evaluation of the models shows that the ENN model is a more suitable and efficient tool in this field and can be useful in monitoring the settlement of earth dams.

Generalized Formal Model-Verifier: A Formal Approach for Verifying Static Models

The field of software modeling has gained significant popularity in the last decades. By capturing the static aspects of the software requirements, model-driven engineering eases the development and maintenance of software. However, additional constraints, such as invariants on model elements, that the solution must conform to may be too complex to include in the structure of the model itself. External solutions are often used to describe static constraints on models, the most prevalent approach being the Object Constraint Language (OCL) and its formal variants. This paper proposes the Generalized Formal Model-Verifier (GFMV), which is a general approach for verifying static constraints on software models. GFMV employs different formal verification methods based on Kripke Structures. Kripke Structures are used to capture the static structure of the model, then the constraints are formalized using a first-order branching-time logic, the Computational Tree Logic (CTL). Finally, the NuSMV model checker is reused to verify whether the constraints formalized in CTL hold on the formal Kripke Structure. When compared to existing solutions, GFMV offers increased generality and formal proof that the constraints hold on the model. The expressive power and runtime-scalability of the approach are evaluated on a real-world example model and OCL invariants cited from literature.

Fostering proactive work behavior: Where to start?

This study underscores the significance of assessing the capabilities of rehabilitation officers in navigating challenges, devising innovative work methods, and successfully executing the rehabilitation process. This is particularly crucial amid the dual challenges of overcapacity and the repercussions of the Covid-19 pandemic, making it an essential area for research. To be specific, it aims to obtain empirical evidence about the influence of proactive personality and supportive supervision on proactive work behavior, as well as the mediating role of Role Breadth Self-efficacy and Change Orientation. This research was conducted on all rehabilitation officers at the Narcotics Penitentiary in Sumatra, totaling 272 respondents. This study employs a quantitative method via a questionnaire using a purposive sampling technique. The data was subsequently examined using the Lisrel 8.70 software and Structural Equation Modeling (SEM). It can be concluded from the results that the rehabilitation officers for narcotics addicts at the Narcotics Penitentiary can create and improve proactive work behavior properly through the influence of proactive personality, supportive supervision, role breadth self-efficacy, and change orientation. The study may suggest new ways of working and generate new ideas to increase initiative, encourage feedback, and voice employee concerns. Furthermore, this research has the potential to pinpoint deficiencies in proactive work behavior, serving as a foundation for designing interventions or training programs. These initiatives aim to enhance the innovative and creative contributions of rehabilitation officers in the rehabilitation process.

A semi-automated software model to support AI ethics compliance assessment of an AI system guided by ethical principles of AI

AbstractCompliance with principles and guidelines for ethical AI has a significant impact on companies engaged in the development of artificial intelligence (AI) systems. Specifically, ethics is a broad concept that continuously evolves over time and across cultural and geographical boundaries. International organisations (IOs), individual states, and private groups, all have an interest in defining the concept of ethics of AI. IOs, as well as regional and national bodies, have issued many decisions on AI ethics. Developing a system that complies with the ethical framework poses a complex challenge for companies, and the consequences of not complying with ethical principles can have severe consequences, making compliance with these requirements a key issue for companies. Furthermore, there is a shortage of technical tools to ensure that such AI systems comply with ethical criteria. The scarcity of ethics compliance checking tools for AI, and the current focus on defining ethical guidelines for AI development, has led us to undertake a proposal consisting in a semi-automated software model to verify the ethical compliance of an AI system’s code. To implement this model, we focus on the following important aspects: (1) a literature review to identify existing ethical compliance systems, (2) a review of principles and guidelines for ethical AI to determine the international and European views regarding AI ethics, and (3) the identification of commonly accepted principles and sub-principles of AI. These elements served to inform (4) our proposal for the design of a semi-automated software for verifying the ethical compliance of AI systems both at design-time (ethics-by-design perspective) and afterwards on the resulting software.

Development of a New Vertical Dynamic Model of a Rail Vehicle for the Analysis of Ride Comfort

The rail vehicle industry wants to produce vehicles with higher speeds, to maintain and increase its market share. However, when the speed of the vehicle increases, it may have an undesirable effect on ride comfort, in terms of ride dynamics. Recent developments towards lighter and faster vehicles make the problem of ride comfort at higher speeds increasingly important. Focusing on the behavior of flexible rather than rigid body behavior should not be neglected when designing long and light car bodies. There are several approaches to incorporate body flexibility in multibody simulations and they have some superiorities and weaknesses. In this study, an efficient and accurate vertical dynamic model for the ride comfort analysis is developed and implemented in a commercial object-oriented modeling (OOM) software Dymola (2015 FD01) which uses the open-source code Modelica. This model includes car body flexibility with the assembling of a rigid body approach. The developed model is compared to a three-dimensional vehicle model in the commercial Vampire software (Pro V5.50) at different velocities. For the vertical ride comfort analysis, the ISO 2631-1 standard was used for both the developed model and the three-dimensional model. The results are presented as acceleration history and awrms—weighted r.m.s (root mean square) of accelerations—as required by the standard. The developed model has shown its feasibility in terms of its efficiency and accuracy for the vertical ride comfort analysis. The accuracy of the model is evidenced by the fact that the car body vibration level at high speeds shows minor differences compared to the results of the Vampire, which is a validated commercial software in the area of rail vehicle dynamics. The approach involving the assembly of rigid bodies is applied for the first time for high-speed trains in dynamical modelling, with flexible car bodies for ride comfort analysis. Furthermore, it can be used for parametrical studies focusing on ride comfort, thereby offering a quite beneficial framework for addressing the challenges of ride comfort analysis in high-speed rail vehicles. Improvements for and analyses of other aspects are also possible, since the optimization and other useful libraries are readily available in Dymola/Modelica.

Analysis of Land Use/Cover Characteristics and Dynamic of Changes in Huai River Basin from 2000 to 2020

Globally, land use is considered as one of the most closely related link between human being and nature. But land use/cover change (LULC) is regarded as the main cause for global environmental change with possible serious impact on human livelihoods and many parts of social and physical environmental aspects. Huai River Basin (HRB) is among the global river-land ecosystems that has been intensively altered by land use change. Unfortunately, the increasing land use change in HRB is not well scientifically addressed and still, related existing problems are not solved putting the basin ecological status and its basic grain production agricultural capacity at risks. This study adopted data image processing software and various mathematical statistical models to analyze the current LULC characteristics and dynamics of change in the study basin. From the analysis that adopted combined scientific techniques of CA-Markov, InVEST models with ArcGIS and IDRISI software, the results indicate that in 20 years of study, area of cultivated land in HRB declined by -4.34%, grassland declined by -21.28%, forest land declined by -3.22% and unused land declined by -18.84% while area of construction land increased by 26.38% and water area increased by 11.62%. Cultivated land was the major land use type in the basin while unused land was the minor land use type. Land use projection trend of HRB in 2035 predicts decline of areas coverage on cultivated land by -6.92%, on forestland by -9.20% and on grassland by -12.29%. But the basin is expected to have area increase on construction land by 30.07%, on water area by 15.03% and on unused land by 18.45%. Pictorial summary for the analysis of land use/cover characteristics and dynamic of changes in HRB is shown below.

Experimental and numerical analysis of the behavior of rehabilitated aluminum structures using chopped strand mat GFRP composite patches

PurposeThis paper comprehensively addresses the influence of chopped strand mat glass fiber-reinforced polymer (GFRP) patch configurations such as geometry, dimensions, position and the number of layers of patches, whether a single or double patch is used and how well debonding the area under the patch improves the strength of the cracked aluminum plates with different crack lengths.Design/methodology/approachSingle-edge cracked aluminum specimens of 150 mm in length and 50 mm in width were tested using the tensile test. The cracked aluminum specimens were then repaired using GFRP patches with various configurations. A three-dimensional (3D) finite element method (FEM) was adopted to simulate the repaired cracked aluminum plates using composite patches to obtain the stress intensity factor (SIF). The numerical modeling and validation of ABAQUS software and the contour integral method for SIF calculations provide a valuable tool for further investigation and design optimization.FindingsThe width of the GFRP patches affected the efficiency of the rehabilitated cracked aluminum plate. Increasing patch width WP from 5 mm to 15 mm increases the peak load by 9.7 and 17.5%, respectively, if compared with the specimen without the patch. The efficiency of the GFRP patch in reducing the SIF increased as the number of layers increased, i.e. the maximum load was enhanced by 5%.Originality/valueThis study assessed repairing metallic structures using the chopped strand mat GFRP. Furthermore, it demonstrated the superiority of rectangular patches over semicircular ones, along with the benefit of using double patches for out-of-plane bending prevention and it emphasizes the detrimental effect of defects in the bonding area between the patch and the cracked component. This underlines the importance of proper surface preparation and bonding techniques for successful repair.Graphical abstract

3D printing of an artificial intelligence-generated patient-specific coronary artery segmentation in a support bath

Accurate segmentation of coronary artery tree and personalized 3D printing from medical images is essential for CAD diagnosis and treatment. The current literature on 3D printing relies solely on generic models created with different software or 3D coronary artery models manually segmented from medical images. Moreover, there are not many studies examining the bioprintability of a 3D model generated by artificial intelligence (AI) segmentation for complex and branched structures. In this study, deep learning algorithms with transfer learning have been employed for accurate segmentation of the coronary artery tree from medical images to generate printable segmentations. We propose a combination of deep learning and 3D printing, which accurately segments and prints complex vascular patterns in coronary arteries. Then, we performed the 3D printing of the AI-generated coronary artery segmentation for the fabrication of bifurcated hollow vascular structure. Our results indicate improved performance of segmentation with the aid of transfer learning with a Dice overlap score of 0.86 on a test set of 10 coronary tomography angiography images. Then, bifurcated regions from 3D models were printed into the Pluronic F-127 support bath using alginate + glucomannan hydrogel. We successfully fabricated the bifurcated coronary artery structures with high length and wall thickness accuracy, however, the outer diameters of the vessels and length of the bifurcation point differ from the 3D models. The extrusion of unnecessary material, primarily observed when the nozzle moves from left to the right vessel during 3D printing, can be mitigated by adjusting the nozzle speed. Moreover, the shape accuracy can also be improved by designing a multi-axis printhead that can change the printing angle in three dimensions. Thus, this study demonstrates the potential of the use of AI-segmented 3D models in the 3D printing of coronary artery structures and, when further improved, can be used for the fabrication of patient-specific vascular implants.

Seismic displacement analysis of slope reinforced by frame prestressed anchor rod structure

Instability of slope induced by earthquake seriously affects casualties and property losses and hinders earthquake rescue, so the study of slope displacement under earthquake is of great significance. For the slope reinforced by frame prestressed anchor rod structure under earthquake, the calculation method of slope displacement and axial force of anchor rod under seismic action were derived by combing Newmark method and limit analysis method. This method proposed in this paper comprehensively considers the amplification effect of earthquake acceleration on the slope surface, the inertial force of reinforce structure and the dynamic change of axial force of anchor rod. The solution program of this method was compiled in MATLAB and its rationality was proved by a numerical model in FLAC3D software. The results show the slope displacement calculated by the method proposed is consistent with the variation law of slope displacement obtained by traditional methods. Regardless of whether the anchoring force is considered or not, the slope yield acceleration obtained by this method is very close to the slope yield acceleration obtained by the Bishop method. The critical sliding surfaces obtained by the two methods are very close, and the sliding points are located at the foot of the slope. Compared with the Bishop method, this method can consider the amplification effect of seismic force on the free surface of the slope and the gravity of the supporting structure. Ignoring the change of the axial force of anchor rod will make the slope displacement larger, while not considering the amplification effect of the seismic acceleration on the slope surface and the gravity of the reinforce structure will make the slope displacement smaller. This method proposed in this paper comprehensively considers the above factors and is more accurate. In the process of landslide mass sliding, the axial force of the anchor rods increase gradually, and the growth rate of the axial force of the lower anchor rods are larger than that of the upper anchor rods. The increase of anchor rod prestress and elastic modulus will reduce the seismic displacement of the slope, and the increase of the dip angle of the anchor rod will increase the seismic displacement of the slope. The dip angle of the anchor rod should be minimized without affecting the grouting effect of the anchor rod.

Efficiency of Financial Interns in CA firm

The efficiency of financial interns in a chartered accountancy (CA) firm is pivotal to its operations and client service. These interns serve as integral members of the team, contributing to tasks ranging from data entry to financial analysis. Their role involves assisting senior staff in auditing, taxation, and advisory services, thereby gaining practical experience crucial for their professional development. Efficient interns exhibit a blend of technical skills, such as proficiency in accounting software and financial modeling, coupled with strong analytical abilities and attention to detail. They play a vital role in ensuring the smooth functioning of day-to-day operations, aiding in meeting deadlines, and delivering accurate reports to clients. Moreover, their presence allows for the delegation of routine tasks, enabling senior staff to focus on strategic planning and client management. In essence, the efficiency of financial interns not only enhances the firm's productivity but also nurtures talent for the future of the accounting profession.

Haematological and Serum Biochemical Responses of Yankasa Rams Supplemented Cinnamon Powder

This experiment was conducted to evaluate the effect of cinnamon supplements on Yankasa Rams' growth performance, nutrient digestibility, haematology and serum biochemical profile. A total number of sixteen (16) Yankasa grower rams with an average live weight of 20kg each was randomly allotted into four dietary treatments comprised of four replicates consisting of four animals per treatment in a completely randomized design (CRD), where data obtained in this study were analyzed using General Linear Model of Statistical Analysis Software and Means were separated using Duncan Multiple Range Test. The haematological variables demonstrated that packed cell volume was significantly (P&lt;0.05) affected by an increased level of cinnamon Significant (P&lt;0.05) differences were also recorded in white blood cells, neutrophils, basophils and haemoglobin while others were not significantly (P&gt;0.05) changed. With the exception of plasma potassium level, there were no significant (P&gt;0.05) differences in all other serum biochemistry with the supplementation of cinnamon. Cinnamon supplementation plays a vital role in improving the haematological profile. No significant considerable changes were observed in the serum biochemical profile as a result of cinnamon supplements in Yankasa rams. It is therefore recommended that cinnamon powder can be incorporated into ruminant animal diet up to 15g/kg without detrimental effect in animal health status. However further studies were encouraged.

Deep learning based continuous integration and continuous delivery software defect prediction with effective optimization strategy

Software defect prediction is one of the most difficult tasks in the IT sector. Continuous Integration and Continuous Delivery (CI/CD) software defect prediction is used in earlier stage, which consumes less amount of time. Various learning algorithms, such as convolutional neural networks and other machine learning algorithms (ML), are employed to forecast the flaws in the software model. In these existing algorithms, some issues are noticed, such as high computational complexity, excessive time consumption, the need for more energy to predict the model and a high-loss function. To address these issues, a novel deep learning (DL)-based CI/CD software defect prediction technique applying an effective optimization strategy is provided to improve the model's efficiency. The numerical data are collected from open source, and the obtained data is initially labeled based on time domain limitations. Initially, the Modified Synthetic Minority Over-Sampling Technique (M-SMOTE) mechanism is applied to balance the data to avoid overfitting problems, and data normalization is performed to rescale and normalize the data properly. After moralization, the optimal set of features is extracted from the data using Focal Bidirectional Encoder Representations from Transformers (F-BERT) to enhance the efficiency of the model. Finally, the software defects are predicted using Bidirectional Long Short-Term Memory (Bi-LSTM) integrated into the convolutional Gated recurrent units (GRU) model (Bi-CGRU) based on collected features. Hybrid Levy Rao (HLR) optimization is used to tune the hyperparameters properly in the classifier model. The proposed model's performance indicators are examined. The proposed model has a 95.32 % accuracy, a 93.3 % recall, a 94.98 % Matthews correlation coefficient, and an F1-score of 91.355 %. The proposed model generates less labeling noise and wait time than existing methods.

Impact of Demographic Profile on Sustainability Learning: A Management Education Students’ Survey

PurposeThe specific objective of the research is to analyze the impact of academic level, year of study, age group and gender on cognitive learning, socio-emotional learning, behavioural learning and overall sustainability learning. MethodologyA sample of 389 management students were considered for the current study. The study incorporates multiple regression technique using SPSS software and structure equation modeling employing AMOS 26 software. For analyzing reliability Cronbach's alpha is used and further the construct validity is established via confirmatory factor analysis and for assessing discriminant validity Heterotrait-Monotrait ratio of correlations (HTMT) is employed. FindingsThe overall sustainability learning which has been calculated from the cognitive, socio-emotional and behavioural learning scores is found to be positively impacted by the demographic variables age group and is negatively impacted by the academic level. Practical implicationsThis study suggests that the educational programmes or initiatives must be specifically designed to induce sustainability learning based on gender and age-groups. Industry specific sustainability initiatives can be included in the curricula of professional educational programmes. Originality/valueThere is a dearth of studies in measuring the impact of age group, gender, level and year of study on attainment of sustainability learning objectives in terms of cognitive, socio-emotional, and behavioural learning on professional education students especially in India.

Research on the effect of water-cooling steel pipe on preventing spontaneous combustion of coal pile and its thermal migration behavior

During the storage and transportation process after mining, coal piles are placed in open environments, making them prone to self-heating and spontaneous combustion due to the nature of coal and factors like natural wind flow. In recent years, there have been frequent spontaneous combustion incidents involving coal piles, posing significant safety risks. To effectively prevent and control spontaneous combustion disasters in open-air coal storage piles, we propose a method involving the arrangement of water-cooling steel pipes within the coal piles. This method applies theories of coal spontaneous combustion mechanisms, porous media heat transfer, and non-isothermal pipeline heat transfer. The multi-physics coupling model of COMSOL numerical simulation software is used to analyze the spontaneous ignition process and prevention effect of open pit coal pile. In the model, the thin material transfer of porous media is taken as the oxygen concentration field, the heat transfer of porous media is taken as the temperature field, and the free and porous media flow is taken as the air seepage velocity field. The simulation results of the spontaneous combustion process in the coal pile indicate that the high-temperature zone of spontaneous combustion is situated within the range of 0.5 ~ 1.5 m inside the wind-facing surface and extends 0.5 m above the ground level. These findings serve as a basis for determining the optimal placement of water-cooling steel pipes within the coal pile. The simulation results of a single water-cooling steel pipe demonstrate a positive correlation between the cooling effect on the coal pile and the water cool flow, and a negative correlation with the water cool temperature. Additionally, the cooling radius of the water-cooling steel pipe is determined by the circumference of the pipe and remains unaffected by the water cool flow. Finally, simulations were conducted to evaluate the cooling effect of multiple rows of steel pipes, and optimal arrangement parameters were determined: a center distance between steel pipes of 1 m and a water cool flow rate of 1500 L/min. As a result, the onset of the self-heating period in the coal pile was delayed by 11 days, and the spontaneous combustion period was extended by 56 days. The arrangement of water-cooling steel pipes in the coal pile has demonstrated significant efficacy in preventing and controlling spontaneous combustion.

Experimental investigation of tungsten–nickel–iron alloy, W95Ni3.5Fe1.5, compared to copper monolithic bullets

Introduction:The demand for improved small arms ammunition has led to exploring advanced materials and manufacturing techniques. This research investigates the machining characteristics of CM and WNF alloy bullets, aiming to enhance ballistic performance and durability.Methods:Bullet profile-making trials were conducted to evaluate the impact of machining parameters such as cutting speed and feed. The study also considered variables including surface roughness, cutting temperature, and hardness, alongside a detailed morphological analysis, The evaluation utilized an orthogonal array and MCDM approach, incorporating the TOPSIS method for decision-making processes.Results:The findings reveal that WNF alloy bullets exhibit 3.01% to 27.95% lower machining temperatures, 24.88%-61.85% reduced surface roughness, and 19.45%-34% higher microhardness compared to CM bullets. Moreover, CM bullets demonstrated higher machining temperatures, resulting in 47.53% increased tool flank wear. WNF bullets showed a 24.89% reduction in crater wear and a 38.23% decrease in compressive residual stress in bullet profiles, indicating superior machining performance.Discussion:The superior machining performance of WNF alloy bullets suggests their potential to improve the ballistic performance and durability of small arms ammunition. The reduced tool wear and favorable machining parameters highlight WNF alloy's advantages for military and defense applications. A ballistic impact analysis using a finite element method (FEM) model in Abaqus software further supports the potential of WNF alloy bullets, providing a solid foundation for future advancements in bullet manufacturing technologies.

Effects of implementing non-nutritive sucking on oral feeding progression and outcomes in preterm infants: A systematic review and meta-analysis.

Preterm infants have imperfect neurological development, uncoordinated sucking-swallowing-breathing, which makes it difficult to realize effective oral feeding after birth. How to help preterm infants achieve complete oral feeding as soon as possible has become an important issue in the management of preterm infants. Non-nutritive sucking (NNS), as a useful oral stimulation, can improve the effect of oral feeding in preterm infants. This review aimed to explore the effect of NNS on oral feeding progression through a meta-analysis. We systematically searched PubMed, CINHAL, Web of Science, Embase, Cochrane databases, China's National Knowledge Infrastructure (CNKI), Wanfang and VIP database from inception to January 20, 2024. Search terms included 'non-nutritive sucking' 'oral feeding' and 'premature.' Eligibility criteria involved randomized controlled studies in English or Chinese. Studies were excluded if they were reviews, case reports, or observational studies from which valid data could not be extracted or outcome indicators were poorly defined. The meta-analysis will utilize Review Manager 5.3 software, employing either random-effects or fixed-effects models based on observed heterogeneity. We calculated the mean difference (MD) and 95% confidence interval (CI) for continuous data, and estimated pooled odds ratios (ORs) for dichotomous data. Sensitivity and publication bias analyses were conducted to ensure robust and reliable findings. We evaluated the methodological quality of randomized controlled trials (RCTs) utilizing the assessment tool provided by the Cochrane Collaboration. A total of 23 randomized controlled trials with 1461 preterm infants were included. The results of the meta-analysis showed that NNS significantly shortened time taken to achieve exclusive oral feeding (MD = -5.37,95%CI = -7.48 to-3.26, p<0.001), length of hospital stay(MD = -4.92, 95% CI = -6.76 to -3.09, p<0.001), time to start oral feeding(MD = -1.41, 95% CI = -2.36 to -0.45, p = 0.004), time to return to birth weight(MD = -1.72, 95% CI = -2.54 to -0.91, p<0.001). Compared to the NNS group, the control group had significant weight gain in preterm infants, including weight of discharge (MD = -61.10, 95% CI = -94.97 to -27.23, p = 0.0004), weight at full oral feeding (MD = -86.21, 95% CI = -134.37 to -38.05, p = 0.0005). In addition, NNS reduced the incidence of feeding intolerance (OR = 0.22, 95% CI = 0.14 to 0.35, p<0.001) in preterm infants. NNS improves oral feeding outcomes in preterm infants and reduces the time to reach full oral feeding and hospitalization length. However, this study was limited by the relatively small sample size of included studies and did not account for potential confounding factors. There was some heterogeneity and bias between studies. More studies are needed in the future to validate the effects on weight gain and growth in preterm infants. Nevertheless, our meta-analysis provides valuable insights, updating existing evidence on NNS for improving oral feeding in preterm infants and promoting evidence-based feeding practices in this population.