Processing Methods Research Articles

Quantitative Three-Dimensional Imaging Analysis of HfO2 Nanoparticles in Single Cells via Deep Learning Aided X-ray Nano-Computed Tomography.

It is crucial for understanding mechanisms of drug action to quantify the three-dimensional (3D) drug distribution within a single cell at nanoscale resolution. Yet it remains a great challenge due to limited lateral resolution, detection sensitivities, and reconstruction problems. The preferable method is using X-ray nano-computed tomography (Nano-CT) to observe and analyze drug distribution within cells, but it is time-consuming, requiring specialized expertise, and often subjective, particularly with ultrasmall metal nanoparticles (NPs). Furthermore, the accuracy of batch data analysis through conventional processing methods remains uncertain. In this study, we used radioenhancer ultrasmall HfO2 nanoparticles as a model to develop a modular and automated deep learning aided Nano-CT method for the localization quantitative analysis of ultrasmall metal NPs uptake in cancer cells. We have established an ultrasmall objects segmentation method for 3D Nano-CT images in single cells, which can highly sensitively analyze minute NPs and even ultrasmall NPs in single cells. We also constructed a localization quantitative analysis method, which may accurately segment the intracellularly bioavailable particles from those of the extracellular space and intracellular components and NPs. The high bioavailability of HfO2 NPs in tumor cells from deeper penetration in tumor tissue and higher tumor intracellular uptake provide mechanistic insight into HfO2 NPs as advanced radioenhancers in the combination of quantitative subcellular image analysis with the therapeutic effects of NPs on 3D tumor spheroids and breast cancer. Our findings unveil the substantial uptake rate and subcellular quantification of HfO2 NPs by the human breast cancer cell line (MCF-7). This revelation explicates the notable efficacy and safety profile of HfO2 NPs in tumor treatment. These findings demonstrate that this 3D imaging technique promoted by the deep learning algorithm has the potential to provide localization quantitative information about the 3D distributions of specific molecules at the nanoscale level. This study provides an approach for exploring the subcellular quantitative analysis of NPs in single cells, offering a valuable quantitative imaging tool for minute amounts or ultrasmall NPs.

ICG signal denoising based on ICEEMDAN and PSO-VMD methods.

Impedance cardiography (ICG) plays a crucial role in clinically evaluating cardiac systolic and diastolic functions, along with various other cardiac parameters. However, its accuracy heavily depends on precisely identifying feature points reflecting cardiac function. Moreover, traditional signal processing techniques used to mitigate random noise and breathing artifacts may inadvertently distort the amplitude and temporal characteristics of ICG signals. To address this issue, this study investigates a noise and artifact elimination method based on Improved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (ICEEMDAN) and Particle Swarm Optimization-based Variational Mode Decomposition Algorithm (PSO-VMD). The goal is to preserve the amplitude and temporal features of ICG signals to ensure accurate feature point extraction and computation of associated cardiac parameters. Comparative analysis with signal processing methods employing various wavelet families and Ensemble Empirical Mode Decomposition (EEMD) in ICG signal processing applications reveals that the proposed method achieves superior signal-to-noise ratio (SNR) and lower root-mean-square error (RMSE), while demonstrating enhanced correlation and waveform consistency with the original signal.

A comprehensive perspective on local drivers in community-based health tourism industry development: a qualitative study of Mazandaran Province, Iran

BackgroundHealth tourism is an important component that may influence the direction of development in a region. Mazandarn, Iran, is recognized as a highly sustainable market in the region because of its abundant natural resources, temperate temperature, and strategic geographical location. Therefore, considering the importance of health tourism and its existing potential in Mazandaran, Iran, this study was conducted with the aim of exploring a comprehensive perspective on local drivers in community-based health tourism industry development and factors affecting the attraction of health tourism.MethodsWe conducted this study in Mazandarn, Iran, using a qualitative approach. Participants included a sample of Iranian people, aged 34–54 years, with previous history of health tourism or expert in it. Participants were selected from three different categories of the community: academic professionals in health tourism, managers in health tourism, and health tourists. Data were obtained via semi-structured in-depth interviews and focus group discussions. Inductive qualitative content analysis was used to converge and compare themes through participant data. The interviews kept going until data saturation was achieved.ResultsBased on our findings, we distilled local drivers in community-based health tourism industry development into five main categories and 30 subcategories: (Sharifabadi AM, Ardakani FA. A model for health tourism development using fuzzy TOPSIS and interpretive structural modeling in Yazd province. J Health Adm (JHA). 2014;17:55.) infrastructure and resources; (Hemmati F, Dabbaghi F, Mahmoudi G. Investigating the impact of Information Technology on the status of Health Tourism in Mashhad, Iran. Revista Publicando. 2018;5(15):54–65.) tourist attractions; (Sarabi Asiabar A, Rezapour A, Raei B, Tahernezhad A, Alipour V, Behzadifar M. Economic, Cultural, and Political Requirements for Medical Tourism Development in Iran: Insights from a Fuzzy Analytical Hierarchy Process Method. Med J Islamic Repub Iran. 2022;35:199.) socio-cultural contexts; (Mosadeghrad AM, Sadeghi M. Medical tourism: Reasons for choosing Iran. Payesh (Health Monitor). 2021;20(2):145–66.) economic-financial factors; and (Manna R, Cavallone M, Ciasullo MV, Palumbo R. Beyond the rhetoric of health tourism: shedding light on the reality of health tourism in Italy. Curr Issues Tourism. 2020;23(14):1805–19.) political-communicative factors. The findings of the study showed that, from the participants’ point of view, although there are several strategies, such as the development of public service and tourism infrastructure, increasing tourist attractions, and formulating appropriate policies and procedures for the development of health tourism, they are also faced with many challenges, especially political, economic, and cultural challenges.ConclusionThis study showed that improving infrastructure and resources, promoting tourist attractions, informing socio-cultural contexts, improving economic and financial capacity, and developing political and communicative contexts might increase the attraction of health tourists. The suggested components are not contextually driven, although empirical outcomes may differ based on the level of service offerings in health tourism locations.

The Application of Analytic Hierarchy Process Method in the Evaluation of Educational Quality in Colleges and Universities

Talent quality is the lifeline of colleges and universities. Based on employment guidance, this paper establishes a student quality evaluation and control system including knowledge, moral character, and ability, and uses the Analytic Hierarchy Process (AHP) method to analyze the factors affecting the quality of education in colleges and universities. In addition, some suggestions are provided to improve the quality of students.

Effects of Different Pretreatments on Hot Air Drying Characteristics, Nutrition, and Antioxidant Capacity of Tartary Buckwheat Sprouts

In order to enhance the quality of hot air drying for Tartary buckwheat sprouts and minimize the loss of active substances, this research explored the impact of Color Protection(CP), Osmosis(OM), Blanching (BC), β-cyclodextrin (β-CD), and Ultrasound (US) in conjunction with hot air drying on the color, nutritional value, antioxidant properties, and other attributes of Tartary buckwheat sprouts. The findings revealed that as the drying temperature increased from 50 °C to 70 °C, the drying duration for Tartary buckwheat sprouts decreased across all treatment groups, leading to a higher dehydration rate. Treatments involving CP, US, and BC effectively reduced the drying time of Tartary buckwheat sprouts. Sprouts subjected to CP, β-CD, and US treatments exhibited elevated L* values and decreased a* values and displayed a more vibrant green color. When exposed to a hot air setting of 60 °C, the total flavonoid content in the CP, OM, β-CD, and US groups increased by 8.76%, 6.76%, 12.34%, and 4.25%, respectively, compared to the Control Group (CK). The application of the CP, OM, β-CD, and US treatments enhanced the sprouts’ ability to combat ABTS and DPPH free radicals. Notably, under hot air conditions of 60 °C, the β-CD treatment demonstrated the most effective quality preservation during the hot air drying process for sprouts. This study provides valuable insights into the drying behavior of Tartary buckwheat sprouts and offers guidance for optimizing the drying procedures in industrial settings. Tartary buckwheat sprouts contain a variety of polyphenols and have a high water content. The study of changes in active components such as polyphenols and their alteration mechanisms in Tartary buckwheat sprouts under different processing methods is particularly important for the development of sprout processing.

Artificial intelligence applications in education: Natural language processing in detecting misconceptions

AbstractThis study aims to determine the misconceptions of teacher candidates about the greenhouse effect concept by using Artificial Intelligence (AI) algorithm instead of human experts. The Knowledge Discovery from Data (KDD) process model was preferred in the study where the Analyse, Design, Develop, Implement, Evaluate (ADDIE) instructional design cycle was used. The dataset obtained from 402 teacher candidates was analysed by Natural Language Processing (NLP) methods. Data was classified using Machine Learning (ML), one of the AI tools, and supervised learning algorithms. It was concluded that 175 teacher candidates did not have sufficient knowledge about the concept of greenhouse effect. It was found that the AI algorithm with the highest accuracy rate and used to predict teacher candidates’ misconceptions was Multilayer Perceptron (MLP). Furthermore, through the Enhanced Ensemble Model Architecture developed by researchers, the combination of ML algorithms has achieved the highest accuracy rate. The kappa (κ) value was examined in determining the significant difference between the AI algorithm and the human expert evaluation, and it was found that there was a significant difference, and the strength of agreement was significant according to the research findings. The findings of the current study represent a significant alternative to the prevailing pedagogical approach, which has increasingly come to rely on information technologies in the process of improving conceptual understanding through the detection of conceptual misconceptions. In addition, recommendations were made for future studies.

A study on HAMR read/write channel models for double layer magnetic recording

Abstract Three-dimensional (3D) magnetic recording with multiple recording layers has garnered considerable attention as a next-generation magnetic recording method that uses dual conventional recording layers and a magnetoresistive (MR) head for reading. In this work, to examine signal processing methods for the 3D magnetic read/write (R/W) channel using heat-assisted magnetic recording (HAMR), we have constructed a HAMR R/W channel using a granular media model. Our 3D HAMR R/W channel model, specialized for signal processing development, can account for the Curie temperatures of the recording layers and the temperature distribution of the heat source.

Cortical and Subjective Measures of Individual Noise Tolerance Predict Hearing Outcomes with Varying Noise Reduction Strength

Noise reduction (NR) algorithms are employed in nearly all commercially available hearing aids to attenuate background noise. However, NR processing also involves undesirable speech distortions, leading to variability in hearing outcomes among individuals with different noise tolerance. Leveraging 30 participants with normal hearing engaged in speech-in-noise tasks, the present study examined whether the cortical measure of neural signal-to-noise ratio (SNR)—the amplitude ratio of auditory evoked responses to target speech onset and noise onset—could predict individual variability in NR outcomes with varying strength, thus serving as a reliable indicator of individual noise tolerance. In addition, we also measured subjective ratings of noise tolerance to see if these measures could capture different perspectives on individual noise tolerance. Results indicated a significant correlation between neural SNR and NR outcomes that intensified with increasing strength of NR processing. While subjective ratings of noise tolerance were not correlated with the neural SNR, noise-tolerance ratings could predict outcomes with stronger NR processing and account for additional variance in the regression model, although the effect was limited. Our findings underscore the importance of accurately assessing an individual’s noise tolerance characteristics in predicting perceptual benefits from various NR processing methods and suggest the advantage of incorporating both cortical and subjective measures in the relevant methodologies.

Dynamics of a single cavitation bubble near a cylindrical blind hole

Cavitation bubble dynamics, such as microjets and moving shock waves, are strongly influenced by their surroundings. We investigated the complex bubble behavior during the growth and collapse stages near a blind hole. The simultaneous study, which is a combination of experiment and numerical methods, was performed. The single bubble was induced by a laser focus beam near a blind hole in the cuvette and measured by a high-speed camera with an image processing method. Numerical predictions were conducted for a detailed further investigation of the pressure and velocity fields near and inside the bubble. A fully compressible two-phase Navier-Stokes solver based on the homogeneous flow assumption was used. Both experimental and numerical results showed good agreement with each other. Strong deformation of the bottom bubble surface evolution was observed at low standoffs above the hole entrance. An extremely high-velocity microjet with a mushroom cap-shaped bubble was predicted under high standoffs below the hole entrance. Secondary cavitation in the blind hole was observed experimentally, and we discussed the low-pressure region caused by the interaction among the shock wave, bubble, and blind hole. Consequently, the tendencies of maximum microjet velocity and time-dependent surface pressure in the collapse phase are suggested.

Cutting-Edge Microwave Sensors for Vital Signs Detection and Precise Human Lung Water Level Measurement

In this article, a comprehensive review is presented of recent technological advancements utilizing electromagnetic sensors in the microwave range for detecting human vital signs and lung water levels. With the main objective of improving detection accuracy and system robustness, numerous advancements in front-end architecture, detection techniques, and system-level integration have been reported. The benefits of non-contact vital sign detection have garnered significant interest across a range of applications, including healthcare monitoring and search and rescue operations. Moreover, some integrated circuits and portable systems have lately been shown off. A comparative examination of various system architectures, baseband signal processing methods, system-level integration strategies, and possible applications are included in this article. Going forward, researchers will continue to focus on integrating radar chips to achieve compact form factors and employ advanced signal processing methods to further enhance detection accuracy.

Principles of construction of nanosatellite radar systems based on global navigation satellite system reflectometry

Objectives. The development of radar remote sensing systems based on the reception of signals of navigation satellite systems reflected from the surface enables a constellation of nanosatellites to be deployed, in order to perform radar surveying of the Earth’s surface. The aim of this work is to develop the principles of construction of onboard bistatic remote sensing systems on nanosatellites, in order to assess the energy potential and possibilities for its increase.Methods. The optimal processing method in onboard bistatic radar systems is a development of known analytical methods of optimal processing in monostatic systems. The calculation of the energy potential is based on the experimental data obtained by other authors.Results. The utilization of signals from navigation satellite systems for surface sensing is a promising and developing area. The USA and China have deployed satellite constellations to perform remote sensing using reflected signals of navigation satellites. An algorithm for optimal processing in such systems, which realizes the principle of aperture synthesis, was developed, and the energy potential of bistatic synthetic aperture radar was calculated. In order to achieve this processing, the proposed scheme uses a standard navigation receiver to form reference signals.Conclusions. The application of optimal processing methods in bistatic radar enables a synthetic aperture based on scattered satellite navigation system signals. In order to improve the accuracy of estimates, the signal-to-noise ratio needs to be increased by combining coherent accumulation (aperture synthesis) and incoherent accumulation (aggregating measurements from different spacecraft). The signal processing methods and receiver structure proposed in this work onboard nanosatellites allow aperture synthesis to be achieved with realizable hardware requirements.

Effect of antibacterial masterbatches selection on the crystal structure and properties of nanosilver composite materials

AbstractThe distribution of nanosilver in composite materials with two different matrices is related to the selection of antibacterial masterbatches, which has a profound impact on the antibacterial effectiveness of the composite materials. This study utilized ethylene‐vinyl acetate (EVA) and polypropylene (PP) as base materials to fabricate two types of nanosilver composite materials (C1‐EVA/PP and C2‐EVA/PPP) employing varying methods of antimicrobial masterbatches incorporation. Additionally, EVA‐Ag and PP‐Ag nanosilver composite materials were fabricated via melt blending, along with corresponding neat materials and blends (EVA, PP, and C0‐EVA/PP) for comparison. The study results indicate that the addition of nanosilver did not affect the functional groups of either the neat materials and the EVA/PP blends. SEM analysis revealed that nanosilver was dispersed unevenly in the C1‐EVA/PP substrate, while it was more uniformly distributed in the C2‐EVA/PP substrate. In the antibacterial test, the antibacterial rate of C2‐EVA/PP was higher than that of C1‐EVA/PP (56.0% vs. 40.0%). However, all silver‐containing composite materials did not exhibit antibacterial properties in the agar diffusion test. Additionally, nanosilver exhibited an induced crystallization effect on the PP phase of the C2‐EVA/PP composite material, increasing its Tc2 by 0.8°C. Compared with pure PP, the long period of PP‐Ag increased by 0.7 nm, and its impact resistance improved by 13.8%. By comparison, the long period of EVA‐Ag composite materials increased by only 0.2 nm compared to pure EVA. Both C1‐EVA/PP and C2‐EVA/PP composite materials showed 0.3 nm increase in long period compared to C0‐EVA/PP, with their impact resistance improving by 3.1% and 10.3%, respectively, compared to C0‐EVA/PP. The introduction of nanosilver increased the storage modulus, loss modulus, and apparent viscosity of EVA/PP blends. Optical performance analysis shows that nanosilver increases the internal haze of C1‐EVA/PPP by 60% while reducing the transmittance by 0.3%. The effect of nanosilver on the optical properties of C2‐EVA/PPP composites is relatively small. Therefore, this study provides theoretical guidance for the selection of processing methods for dual‐phase antibacterial materials.Highlights Different antibacterial masterbatch impacts silver distribution in composites. The addition of nanosilver can improve the impact performance of materials. Two process methods were used to prepare low‐silver content composite materials. The antibacterial rate of C2‐EVA/PP is higher than C1‐EVA/PP (56% vs. 40%). This study guides the processing methods for dual‐phase antibacterial materials.

Evaluation of hydrogen embrittlement susceptibility of underwater laser direct metal deposited 316L stainless steel

Underwater laser direct metal deposition (UDMD) shows great application potential in underwater high-performance repair. The susceptibility of underwater repaired components to hydrogen embrittlement (HE) is not extensively researched yet. In this study, HE in 316L stainless steel repaired by UDMD is systematically investigated and compared with in-air direct metal deposited (DMD) 316L and conventionally manufactured (CM) 316L. The experimental results show that the high cooling rates involved in UDMD contribute to a “austenite-ferrite” solidification mode. The cellular structure with a high-density of dislocations increases the tensile strength of as-built UDMD 316L. Thermal desorption spectroscopy analysis reveals that the hydrogen concentrations are similar for 316L manufactured by three processing methods, suggesting that the complex microstructural features by UDMD and DMD do not provide additional H-trapping sites. SSRT results show that the HE susceptibility is in the sequence: CM 316L (30.7%) > UDMD 316L (15.1%) > DMD 316L (9.6%). Compared with CM 316L, the stable cellular structure and higher stability towards martensitic transformation of as-built 316L are beneficial for the HE resistance. In the hydrogen-charged UDMD 316L, the hydrogen-assisted dislocation movement and mechanical twinning formation enhance the localized plasticity and induce cracking. The micron-sized oxide particles and micro-pores further contribute to the fast decohesion of interfaces in the presence of hydrogen. Those combined effects lead to the reductions in tensile strength and elongation of the hydrogen-charged UDMD 316L.

Кластерный анализ в оценке эффективности зернового производства Тюменской области

Abstract. In recent years, there has been stable production of grain industry products in volumes that meet the needs of the Tyumen region and allow the sale of products outside the region. The article is devoted to the study of territorial differentiation of the production of grain and leguminous crops in the Tyumen region. The purpose of the study is to assess the efficiency of grain production in the context of municipalities in the south of the Tyumen region and determine the dependence on the level of fertilizer application. Methods. Statistical methods of data processing (analysis of time series, method of variation, method of correlation-regression analysis), as well as cluster analysis using the Ward method, were used for the study. The scientific novelty lies in the fact that municipalities have been clustered and ranked according to a set of parameters characterizing the development of the grain production industry in the Tyumen region. Results. An assessment was made of the scale of industry development for the period from 2018 to 2022, quantitative and qualitative indicators were considered: the size of sown areas, gross yields, yields of grain and leguminous crops differentiated by municipal areas. An assessment was made of the dynamics of development of the grain production industry, trends of changes over 5 years, parameters of the influence and dependence of the yield of grain and leguminous crops on the level of fertilizer application were identified, clusters were identified that reflect the differentiation of municipal areas in grain production.

Period Enhanced Feature Mode Decomposition and Its Application for Bearing Weak Fault Feature Extraction

Abstract Decomposition methods which can separate the fault components into different modes have been widely applied in bearing fault diagnosis. However, early fault diagnosis is always a challenge for the signal processing methods as well as the traditional decomposition methods due to the heavy noise. Therefore, how to extract the weak fault information from the complicated signal with low SNR is of significance. To overcome this issue, a period-enhanced feature mode decomposition (PEFMD) method is proposed in this paper. Firstly, the initialized filters used for the mode decomposition are adaptively designed according to the spectrum of the original vibration signal. Secondly, time synchronized averaging is used in the iterative process to excavate and identify accurately the weak period components and determine the period of the iterative signal. Finally, the period information can promote the proposed method to decompose the fault component into the hopeful modes by setting correlation kurtosis as the optimation objective and the mode selection. The practicability and superiority of the proposed PEFMD are verified by the simulated and experimented data. Compared with the feature mode decomposition method and variational mode decomposition, the proposed decomposition method shows an obvious performance advantage under low SNR situations.

Evaluation of the mineral and antinutritional composition of bottled and sterilized Tigernut (Cyperus esculentus) milk

Nowadays, beverages are not just considered as thirst quenchers; specific functionality is the major ingredient expected in beverages, which has becomes a lifestyle. In recent years, these initiations have led to newly developed products in the beverages sector. Companies are learning the consumption styles of various individuals and making novel beverages through blends of milk and energy drinks. Tigernut milk is among the most valued plant-based beverages, obtained from the aqueous extract of tigernuts tubers. This study investigated the mineral and antinutritional components of bottled and sterilized tigernut milk. Two varieties of tigernut tuber (Yellow and brown) were processed using three methods (Boiling, soaking and malting), and the milk was bottled and further sterilized. The results obtained showed that Iron recorded the least value at (1.73mg and 2.24mg) for yellow and brown tigernut tubers; alsotigernut tubers were low in some antinutrients except flavonoids. The result for the tigernut milk elucidated that processing and sterilization had little effect on the mineral content of tigernut milk. The study also showed that the pre-processing treatment applied (Soaking, boiling and malting) to the tigernut either increased or caused a decline in the mineral content of the tigernut milk, whereas, processing and sterilization affected the antinutrient content of tigernut milk. All processing methods showed a significant difference (p<0.05.) as there was a decrease in the phytate and oxalate content. The study highlighted the possibility of processing tigernut tubers into milk extract that can be bottled and sterilized for a period of time.

The Role of Artificial Intelligence in Treatment and Diagnosis in Healthcare

Technology, specifically artificial intelligence (AI) is gradually but progressively creeping into the health sector and it’s perhaps the one that has been revolutionised most in diagnosis and treatment. This review brings out discussions on the practices of AI technologies in medical, the pros and the cons. First of all, an endeavour is made to elucidate the meaning of the term AI and its utilization in the field of healthcare. The specific AI techniques are described comprehensively focusing on the machine learning, deep learning, and natural language processing methods to be used in the project The role of multiple types of data in AI includes the EHR, medical images, and genomics data. Self-diagnosis: AI is improving the diagnosis approaches in the radiology and pathology fields and predicting the early-stage disease with better results in most of the cases, and enhancing the identification of genetic diseases. As for treatment, the enhancement of the use of AI has had an impact on issues such as; Prescribing and recommending drugs according to the characteristics of the patients, smart drug administration and management, robotic surgeries and simulations. Discussions are made using concrete and successful implementation of AI in cancer, cardiovascular, neurological and infectious diseases for the purpose of elucidating particular results. This also has to do with the ethical and legal problems like who has the liability to determine in the instance of complicated problems, patients’ information discretion, data privacy, and other legalities. In this article, we briefly mention the prosaic matters of AI, which deals with the engineering aspects of establishing AI such as the aspect of data and the ways and means of checking them and the interdisciplinary character of it. Concerning future developments, additional technologies like AI and connected devices in the field of health care, interdisciplinary at national and international level as well as data sharing is emphasized. Thus. AI has a very great perspective in healthcare, particularly in diagnostics and treatment of diseases due to the probability of increasing the level of accuracy, efficacy, and personalization. Despite these, they are tangible objectives with major challenges and require cooperation between nations with proper handling of Artificial Intelligence to practice clinical medication.

Reinforcement learning for distributed hybrid flowshop scheduling problem with variable task splitting towards mass personalized manufacturing

Mass personalization manufacturing (MPM), an emerging production pattern, aims to improve enterprise profit in modern industries. However, the processing of heterogeneous orders from the consumers complicates such production scheduling problem. In addition, different scale tasks should adopt different splitting strategies in practical manufacturing, which makes the task splitting method more worthy of investigation. Towards MPM, this paper presents a distributed hybrid flowshop scheduling problem with variable task splitting (DHFSP-VTS) to minimize the makespan and total energy consumption simultaneously. Meanwhile, the VTS allows the tasks to be split into different sublots so they can save setup and transfer time. To solve these problems, we present an order modularization processing method that can categorize multiple types of orders into specific generation tasks, and a highly effective reinforcement learning-multiple objective evolutionary algorithm based on decomposition (RL-MOEA/D) is designed. In RL-MOEA/D, there are three features: (1) three initial rules are used for initialization based on the current splitting scheme that can increase the diversity of solutions; (2) the reinforcement learning agent uses the Q-learning mechanism to dynamically select the scheme of task splitting as action; (3) a neighborhood search strategy improves the exploitation ability and expand the solution space. To verify the effectiveness of RL-MOEA/D, the MOEA/Ds based on four splitting schemes and four RL combined meta-heuristics are compared on 18 instances. The results show that RL-MOEA/D can obtain the best optimization and stability of all the other comparison algorithms. Therefore, it’s a new technique to solve DHFSP with large-scale tasks, especially for the problem of MPM.

Research and application of the flatness target curve discrete dynamic programming based on two-dimensional decision making

The flatness target curve (FTC) is a fundamental process model in cold rolling flatness control, which directly affects the quality control effect of the strip. The accurate and efficient setting of the flatness target curve is the key to ensuring continuous rolling and is a common problem faced by various production companies. This paper establishes a discrete dynamic planning (DDP) setting method for FTC with two-dimensional decision-making. To quantitatively analyze the roles of the coefficients in the FTC, the preprocessing process is established using standardized processing methods and statistical analysis methods. The flatness target curve discrete dynamic programming (FTC-DDP) is based on Bellman optimization theory with a dual structure of local and system decision-making. The decision-making functions are established based on the effect of each coefficient on FTC geometric characteristics. The concepts of “curvature factor” and “wedge factor” are innovatively proposed to obtain the decision-making value functions. The regression analysis establishes the solution equations of even number coefficients, odd number coefficients, and gain coefficients, respectively. Roll bending and tilting roll analyze the FTC’s ability to correct flatness defects. Meanwhile, utilizing a 1450 mm five-stand six-roll cold rolling mill, it is verified that FTC-DDP can quickly and accurately set the FTC, which provides an effective solution for obtaining high-precision and high-quality cold-rolled strips. Application results show that FTC-DDP can reduce the difficulty of setting up FTC by more than 70 %, reduce the setup time to milliseconds, and improve the setup accuracy by more than 50 %.

Selection of Best Hospital in Mumbai for Tuberculosis Treatment using AHP Technique

The healthcare sector in India is one of the largest sectors in the Indian economy where private sector dominates. Tuberculosis, commonly known as TB, is seen as one of the major health problems in the Indian health scenario. Treatment for the same has been progressing continuously. Tuberculosis being a bacterial infection requires antibiotics for treatment and prevention. Increasing sophistication of technology has also been playing a huge role in the treatment of tuberculosis. A survey was conducted for three known hospitals in the city of Mumbai, Maharashtra- Fortis Hospital, Apollo Hospital and Nanavati Hospital. A survey was done considering four factors- cost, infrastructure, quality and technology. Reasons for choosing these factors are elaborated upon ahead in the research paper. The data was collected through a primarysource where physical papers were circulated and responses collected were then analysed for the further process. Respondents were surveyed and their opinions on various factors as well as priorities of hospitals based on these factors were collected, analysed and processed for further understanding. By the way of conducting Analytic Hierarchy Process Method, it finds out which of these hospitals in Mumbai is preferred the most for its tuberculosis treatment. Keywords: Healthcare, Tuberculosis, treatment, survey, cost, infrastructure, quality, technology