DC-DC boost power converters play an important role in solar power systems;they step up the input voltage of a solar array for a given set of conditions. This paper presents an overview of the variance boost converter...DC-DC boost power converters play an important role in solar power systems;they step up the input voltage of a solar array for a given set of conditions. This paper presents an overview of the variance boost converter topologies. Each boost converter is evaluated on its capability to operate efficient, size, and cost of implementation. Conventional boost converter and interleaved boost converter are widely used topologies in photovoltaic systems reported;however, they have negative sides of varied efficiency level under changed weather conditions. Therefore, this paper proposes, interleaved boost converter with novel switch adaptive control, to maximise efficiency of standalone photovoltaic system under change of solar power levels, due to illadation condition.展开更多
The efficiency of photovoltaic power generation is affected by the changeable weather conditions. This paper improves the efficiency of a standalone PV system over a wider range of operating conditions by employing no...The efficiency of photovoltaic power generation is affected by the changeable weather conditions. This paper improves the efficiency of a standalone PV system over a wider range of operating conditions by employing novel switch adaptive control to an interleaved boost converter. With various loads, simulation and experimental results show that the interleaved boost converter with novel switch adaptive control offers better performance and higher conversion efficiency under changeable weather conditions.展开更多
Data warehouses (DW) must integrate information from the different areas and sources of an organization in order to extract knowledge relevant to decision-making. The DW development is not an easy task, which is why v...Data warehouses (DW) must integrate information from the different areas and sources of an organization in order to extract knowledge relevant to decision-making. The DW development is not an easy task, which is why various design approaches have been put forward. These approaches can be classified in three different paradigms according to the origin of the information requirements: supply-driven, demand-driven, and hybrids of these. This article compares the methodologies for the multidimensional design of DW through a systematic mapping as research methodology. The study is presented for each paradigm, the main characteristics of the methodologies, their notations and problem areas exhibited in each one of them. The results indicate that there is no follow-up to the complete process of implementing a DW in either an academic or industrial environment;however, there is also no evidence that the attempt is made to address the design and development of a DW by applying and comparing different methodologies existing in the field.展开更多
The evolution of microstructure and microhardness was studied in a commercial tungsten-25%rhenium(mass fraction)(W-25Re)alloy processed by the high pressure torsion(HPT)procedure under a pressure of7.7GPa up to10revol...The evolution of microstructure and microhardness was studied in a commercial tungsten-25%rhenium(mass fraction)(W-25Re)alloy processed by the high pressure torsion(HPT)procedure under a pressure of7.7GPa up to10revolutions at different temperatures.The results show that the samples processed by10revolutions at room temperature could have the smallest grain size at around0.209μm.High saturation hardness(HV^1200)could be achieved after the rapid strengthening stage for samples processed by10revolutions both at room temperature and at573K.Microstructural observation and analysis from Hall-Patch relationship could reveal that grain refinement and grain boundaries strengthening are the main factors of hardening mechanism in W-25Re alloy.It is also demonstrated that sintered W-25Re sample may have brittle phase separation phenomenon after HPT processing.展开更多
As the cutting speed goes higher, the mechanism of chip deformation will be changed significantly, i.e., continuous chip in low cutting speed will shift to serrated chip with shear localization. For the shear localize...As the cutting speed goes higher, the mechanism of chip deformation will be changed significantly, i.e., continuous chip in low cutting speed will shift to serrated chip with shear localization. For the shear localized chip, the parameters used to assess the chip deformation for continuous chip, such as shorten coefficient ξ, shear angle φ and shear strain ε, can not describe the chip deformation correctly or comprehensively. This paper deals with the assessment of chip deformation of shear localization. There are two deformation regions in shear localized chip, one is the chip segment body with relative smaller plastic deformation, another one is the boundary between segments with shear localization, so called shear band. Considering the two distinct deformation regions, two parameters are used to define their deformation respectively. According to the analysis of chip formation process, the equations have been deduced to calculate the shear strains of shear band ε, shear strain of chip segment ε 1 and shear rate so that the shear localized chip deformation can be assessed correctly and comprehensively. By use of this assessment, the chip deformation in machining selenium treated stainless steel (STSS) and common stainless steel at various cutting conditions is investigated. The experiment results obtained by the machining of stainless steel prove that: (1) the shear strain and strain rate increase with the increasing of cutting speed; (2) the shear strain in shear band can be over 10 when cutting speed exceeding 200 m/min for both types of stainless steel, and it is much higher than the strain of chip segment. The difference will be enlarged as the cutting speed increasing; (3) As the comparison, the shear strain for the STSS is a little lower than that for JIS304; (4) The stain rate is extremely high (= 2.5×10 5 1/s ). In range of cutting speed less than 180 m/min, the strain rate for STSS is lower than that for JIS304. However, when the cutting speed is higher than 180 m/min, the strain rate for STSS is higher than that for JIS304.展开更多
The major mortality factor relevant to the intestinal tract is the growth of tumorous cells(polyps)in various parts.More specifically,colonic polyps have a high rate and are recognized as a precursor of colon cancer g...The major mortality factor relevant to the intestinal tract is the growth of tumorous cells(polyps)in various parts.More specifically,colonic polyps have a high rate and are recognized as a precursor of colon cancer growth.Endoscopy is the conventional technique for detecting colon polyps,and considerable research has proved that automated diagnosis of image regions that might have polyps within the colon might be used to help experts for decreasing the polyp miss rate.The automated diagnosis of polyps in a computer-aided diagnosis(CAD)method is implemented using statistical analysis.Nowadays,Deep Learning,particularly throughConvolution Neural networks(CNN),is broadly employed to allowthe extraction of representative features.This manuscript devises a new Northern Goshawk Optimization with Transfer Learning Model for Colonic Polyp Detection and Classification(NGOTL-CPDC)model.The NGOTL-CPDC technique aims to investigate endoscopic images for automated colonic polyp detection.To accomplish this,the NGOTL-CPDC technique comprises of adaptive bilateral filtering(ABF)technique as a noise removal process and image pre-processing step.Besides,the NGOTL-CPDC model applies the Faster SqueezeNet model for feature extraction purposes in which the hyperparameter tuning process is performed using the NGO optimizer.Finally,the fuzzy Hopfield neural network(FHNN)method can be employed for colonic poly detection and classification.A widespread simulation analysis is carried out to ensure the improved outcomes of the NGOTL-CPDC model.The comparison study demonstrates the enhancements of the NGOTL-CPDC model on the colonic polyp classification process on medical test images.展开更多
Parkinson’s disease(PD),characterized by loss of nigrostriatal dopaminergic neurons,is one of the most predominant neurodegenerative diseases affecting the elderly population worldwide.The concept of stem cell therap...Parkinson’s disease(PD),characterized by loss of nigrostriatal dopaminergic neurons,is one of the most predominant neurodegenerative diseases affecting the elderly population worldwide.The concept of stem cell therapy in managing neurodegenerative diseases has evolved over the years and has recently rapidly progressed.Neural stem cells(NSCs)have a few key features,including selfrenewal,proliferation,and multipotency,which make them a promising agent targeting neurodegeneration.It is generally agreed that challenges for NSC-based therapy are present at every stage of the transplantation process,including preoperative cell preparation and quality control,perioperative procedures,and postoperative graft preservation,adherence,and overall therapy success.In this review,we provided a comprehensive,careful,and critical discussion of experimental and clinical data alongside the pros and cons of NSC-based therapy in PD.Given the state-of-the-art accomplishments of stem cell therapy,gene therapy,and nanotechnology,we shed light on the perspective of complementing the advantages of each process by developing nano-stem cell therapy,which is currently a research hotspot.Although various obstacles and challenges remain,nano-stem cell therapy holds promise to cure PD,however,continuous improvement and development from the stage of laboratory experiments to the clinical application are necessary.展开更多
Following the quantum theory-based physical model of the human body,a new interpretation of the traditional Chinese medicine(TCM)principle of"Cunkou reads viscera"is presented.Then,a Gaussian pulse wave mode...Following the quantum theory-based physical model of the human body,a new interpretation of the traditional Chinese medicine(TCM)principle of"Cunkou reads viscera"is presented.Then,a Gaussian pulse wave model as a solution to the Schrodinger equation is shown to accurately describe 19 different pulse shapes,and to quantitatively capture the degree of YinYang attributes of 13 pulse shapes.Furthermore,the model suggests using pulse depth and strength as leading-order quantity and pulse shape as first-order quantity,to characterize the hierarchical resonance between the human body and the environment.The future pulse informatics will focus on determining an individual’s unique quantum human equilibrium state,and diagnose its health state according to the pulse deviation from its equilibrium state,to truly achieve the high level of TCM:"knowing the normal state and reaching the change".展开更多
This review covers extensively the synthesis&surface modification,characterization,and application of magnetic nanoparticles.For biomedical applications,consideration should be given to factors such as design stra...This review covers extensively the synthesis&surface modification,characterization,and application of magnetic nanoparticles.For biomedical applications,consideration should be given to factors such as design strategies,the synthesis process,coating,and surface passivation.The synthesis method regulates post-synthetic change and specific applications in vitro and in vivo imaging/diagnosis and pharmacotherapy/administration.Special insights have been provided on biodistribution,pharmacokinetics,and toxicity in a living system,which is imperative for their wider application in biology.These nanoparticles can be decorated with multiple contrast agents and thus can also be used as a probe for multi-mode imaging or double/triple imaging,for example,MRI-CT,MRI-PET.Similarly loading with different drug molecules/dye/fluorescent molecules and integration with other carriers have found application not only in locating these particles in vivo but simultaneously target drug delivery/hyperthermia inside the body.Studies are underway to collect the potential of these magnetically driven nanoparticles in various scientific fields such as particle interaction,heat conduction,imaging,and magnetism.Surely,this comprehensive data will help in the further development of advanced techniques for theranostics based on high-performance magnetic nanoparticles and will lead this research area in a new sustainable direction.展开更多
In the study,a quantum resonant cavity model based on wave-particle duality was proposed for the explanation of the dynamic processes of essence,vigor,and spirit in the human body in traditional Chinese medicine(TCM)....In the study,a quantum resonant cavity model based on wave-particle duality was proposed for the explanation of the dynamic processes of essence,vigor,and spirit in the human body in traditional Chinese medicine(TCM).It is assumed that there is a macro human order parameter(wave function),and its dynamics are governed by a macro potential field reflecting influences from heaven,earth,and society,and satisfy the generalized Schrodinger equation.This proposed model was applied in the study to interpret basic concepts of human body in TCM,with an aim to unfold the TCM development in the future.展开更多
Recently,human healthcare from body sensor data has gained considerable interest from a wide variety of human-computer communication and pattern analysis research owing to their real-time applications namely smart hea...Recently,human healthcare from body sensor data has gained considerable interest from a wide variety of human-computer communication and pattern analysis research owing to their real-time applications namely smart healthcare systems.Even though there are various forms of utilizing distributed sensors to monitor the behavior of people and vital signs,physical human action recognition(HAR)through body sensors gives useful information about the lifestyle and functionality of an individual.This article concentrates on the design of an Improved Transient Search Optimization with Machine Learning based BehaviorRecognition(ITSOMLBR)technique using body sensor data.The presented ITSOML-BR technique collects data from different body sensors namely electrocardiography(ECG),accelerometer,and magnetometer.In addition,the ITSOML-BR technique extract features like variance,mean,skewness,and standard deviation.Moreover,the presented ITSOML-BR technique executes a micro neural network(MNN)which can be employed for long term healthcare monitoring and classification.Furthermore,the parameters related to the MNN model are optimally selected via the ITSO algorithm.The experimental result analysis of the ITSOML-BR technique is tested on the MHEALTH dataset.The comprehensive comparison study reported a higher result for the ITSOMLBR approach over other existing approaches with maximum accuracy of 99.60%.展开更多
Objective To investigate the human body’s complex system,and classify and characterize the human body’s health states with“a comprehensive integrated method from qualitative to quantitative”.Methods This paper int...Objective To investigate the human body’s complex system,and classify and characterize the human body’s health states with“a comprehensive integrated method from qualitative to quantitative”.Methods This paper introduces the concept of“order parameters”and proposes a method for establishing an order parameter model of gas discharge visualization(GDV)based on the principle of“mastering both permanence and change(MBPC)”.The method involved the fol-lowing three steps.First,average luminous intensity(I)and average area(S)of the GDV im-ages were calculated to construct the phase space,and the score of the health questionnaire was calculated as the health deviation index(H).Second,the k-means++clustering method was employed to identify subclasses with the same health characteristics based on the data samples,and to statistically determine the symptom-specific frequencies of the subclasses.Third,the distance(d)between each sample and the“ideal health state”,which determined in the phase space of each subclass,was calculated as an order parameter describing the health imbalance,and a linear mapping was established between the d and the H.Further,the health implications of GDV signals were explored by analyzing subclass symptom profiles.We also compare the mean square error(MSE)with classification methods based on age,gen-der,and body mass index(BMI)indices to verify that the phase space possesses the ability to portray the health status of the human body.Results This study preliminarily tested the reliability of the order parameter model on data samples provided by 20 participants.Based on the discovered linear law,the current model can use d calculated by measuring the GDV signal to predict H(R^(2)>0.77).Combined with the symptom profiles of the subclasses,we explain the classification basis of the phase space based on the pattern identification.Compared with common classification methods based on age,gender,BMI,etc.,the MSE of phase space-based classification was reduced by an order of magnitude.Conclusion In this study,the GDV order parameter model based on MBPC can identify sub-classes and characterize individual health levels,and explore the TCM health meanings of the GDV signals by using subjective-objective methods,which holds significance for establishing mathematical models from TCM diagnosis principles to interpret human body signals.展开更多
This paper analyzes the combustion characteristics and greenhouse gas emissions from varied heat fluxes with rice husks. In general, rice husks burnt outdoors at a lower temperature range of 300-400 ℃, which cannot a...This paper analyzes the combustion characteristics and greenhouse gas emissions from varied heat fluxes with rice husks. In general, rice husks burnt outdoors at a lower temperature range of 300-400 ℃, which cannot assure complete combustion, thus generating a large volume of toxic air pollutants. A heat flux of 40 kW/m^2, with a cone calorimeter, is the equivalent to the 700 ℃ of an incinerator. The test result shows that the mass reduction rate of the sample at this or at a higher temperature condition was 99.5% or higher, meaning that the sample was almost completely combusted. In this study using rice husks, the amount of carbon dioxide, which is a greenhouse gas, discharged were 1.57-3.61 kg/kg. This is as high as 10 times, than produced in other studies. When the rice husks are burnt outdoors, they are not completely combusted as the combustion temperature remains low, and the rice husk residuals are continuously being combusted in a smoldering phase which creates a large volume of carbon dioxide and carbon monoxide. Therefore, it is recommended to burn rice husks at 700 ℃ or higher to minimize the carbon dioxide and carbon monoxide emissions.展开更多
This paper describes the wastewater treatment of 3 liters per second of capacity of discharges of capacity,from the avocado washed,as well as the production of avocado sauce.Effluents were pretreated with a grease and...This paper describes the wastewater treatment of 3 liters per second of capacity of discharges of capacity,from the avocado washed,as well as the production of avocado sauce.Effluents were pretreated with a grease and oil trap and contained small amounts of oil and grease,dissolved and total solids,organic matter and trace of organic and inorganic compounds from the vitamins and minerals of avocado.The treatment train is located in an small surface and the train is integrated with coagulation in line with alumina in a static mixer,rapid filtration in two steps,double advanced oxidation with ozone and heterocyclic photocatalysis with a nanofilm of zinc oxide as,photocatalyzer and final adsorption of refractory compounds with activated charcoal.The treated wastewater meets the environmental regulation to urban sewerage discharges of the city,according to the results of a Mexican Certified Laboratory.展开更多
This research is an attempt to validate how glu-cose-insulin dynamic mathematical model facilitate to identify the root causes for hypoglycaemia. The purpose is to determine whether increased insulin sensitivity or in...This research is an attempt to validate how glu-cose-insulin dynamic mathematical model facilitate to identify the root causes for hypoglycaemia. The purpose is to determine whether increased insulin sensitivity or increased insulin secretion causes post- prandial hypoglycemic (PPH) response, by linking experimental patient data with dynamic mathematical model. For this purpose two groups, as hypoglycemic Group 1 and non-hypoglycemic Group 2, each of which consists of 10 people, are formed. The oral glucose tolerance test (OGTT) is carried out for each person in the groups by measuring plasma glucose and insulin concentrations at every 30 minutes for a period of 5 hours. To distinguish the actual cause of hypoglycemia, the glucose minimal dynamic model is used. The model is executed in MATLAB platform using patient data and the results showed that insulin secretion is assumed to be the potential root cause for the hypoglycemia.展开更多
Weed management is a crucial aspect of modern agriculture as invasive plants can negatively impact crop yields and profitability.Long-established methods of weed control,such as manual labor and synthetic herbicides,h...Weed management is a crucial aspect of modern agriculture as invasive plants can negatively impact crop yields and profitability.Long-established methods of weed control,such as manual labor and synthetic herbicides,have been widely used but come with their own set of challenges.These methods are often time-consuming,labor-intensive,and pose environmental risks.Herbicides have been the primary method of weed control due to their efficiency and cost-effectiveness.However,over-reliance on herbicides has led to environmental contamination,weed resistance,and potential health hazards.To address these issues,researchers and industry experts are now exploring the integration of machine learning into chemical weed management strategies.As technology advances,there is a growing interest in exploring innovative and sustainable weed management approaches.This review examines the potential of machine learning in chemical weed management.Machine learning offers innovative and sustainable approaches by analyzing large data sets,recognizing patterns,and making accurate predictions.Machine learning models can classify weed species and optimize herbicide usage.Real-time monitoring enables timely intervention,preventing invasive species spread.Integrating machine learning into chemical weed management holds promise for enhancing agricultural practices,reducing herbicide usage and minimizing environmental impact.Validation and refinement of these algorithms are needed for practical application.展开更多
To date, extensive research has been carried out,with considerable success, on the development of highperformance perovskite solar cells(PSCs). Owing to its wide absorption range and remarkable thermal stability, the ...To date, extensive research has been carried out,with considerable success, on the development of highperformance perovskite solar cells(PSCs). Owing to its wide absorption range and remarkable thermal stability, the mixedcation perovskite FAxMA1-xPbI3(formamidinium/methylammonium lead iodide) promises high performance. However, the ratio of the mixed cations in the perovskite film has proved difficult to control with precursor solution. In addition, the FAxMA1-xPbI3 films contain a high percentage of MA+and suffer from serious phase separation and high trap states, resulting in inferior photovoltaic performance. In this study, to suppress phase separation, a post-processing method was developed to partially nucleate before annealing, by treating the as-prepared intermediate phase FAI-Pb I2-DMSO(DMSO: dimethylsulfoxide) with mixed FAI/MAI solution. It was found that in the final perovskite, FA0.92MA0.08 PbI3, defects were substantially reduced because the analogous molecular structure initiated ion exchange in the post-processed thin perovskite films, which advanced partial nucleation. As a result, the increased light harvesting and reduced trap states contributed to the enhancement of open-circuit voltage and short-circuit current. The PSCs produced by the post-processing method presented reliable reproducibility, with a maximum power conversion efficiency of 20.80% and a degradation of ~30% for 80 days in standard atmospheric conditions.展开更多
Wind power is crucial for achieving carbon neutrality,but its output can vary due to local wind conditions.The spatio-temporal behavior of wind power generation connected to the power grid can have a significant impac...Wind power is crucial for achieving carbon neutrality,but its output can vary due to local wind conditions.The spatio-temporal behavior of wind power generation connected to the power grid can have a significant impact on system operations.To assess this impact,the use of long-term reanalysis results of wind data based on a numerical weather prediction(NWP)model is considered valid.However,in Japan,the behavior of on-shore wind power generation is influenced by diverse topographical and meteorological features(TMFs)of the installation site,making it challenging to assess possible operational impacts based solely on power curve-based estimates using a popular conversion equation.In this study,a nonparametric machine learning-based post-processing model that learns the statistical relationship between the TMFs at the target location and the actual wind farm(WF)output was developed to represent the expected per-unit output at each location.Focusing on historical reconstruction results and using this post-processing model to reproduce the real-world WF output behavior created a set of expected wind power generation profiles.The dataset includes hourly long term(1958-2012)wind power generation profiles expected under the WF installation assumptions at various on-shore locations in Japan with a 5 km spatial resolution and is expected to contribute to an accurate understanding of the impact of spatio-temporal wind power behavior.The dataset is publicly accessible at https://doi.org/10.5281/zenodo.11496867(Fujimotoet al.,2024).展开更多
文摘DC-DC boost power converters play an important role in solar power systems;they step up the input voltage of a solar array for a given set of conditions. This paper presents an overview of the variance boost converter topologies. Each boost converter is evaluated on its capability to operate efficient, size, and cost of implementation. Conventional boost converter and interleaved boost converter are widely used topologies in photovoltaic systems reported;however, they have negative sides of varied efficiency level under changed weather conditions. Therefore, this paper proposes, interleaved boost converter with novel switch adaptive control, to maximise efficiency of standalone photovoltaic system under change of solar power levels, due to illadation condition.
文摘The efficiency of photovoltaic power generation is affected by the changeable weather conditions. This paper improves the efficiency of a standalone PV system over a wider range of operating conditions by employing novel switch adaptive control to an interleaved boost converter. With various loads, simulation and experimental results show that the interleaved boost converter with novel switch adaptive control offers better performance and higher conversion efficiency under changeable weather conditions.
文摘Data warehouses (DW) must integrate information from the different areas and sources of an organization in order to extract knowledge relevant to decision-making. The DW development is not an easy task, which is why various design approaches have been put forward. These approaches can be classified in three different paradigms according to the origin of the information requirements: supply-driven, demand-driven, and hybrids of these. This article compares the methodologies for the multidimensional design of DW through a systematic mapping as research methodology. The study is presented for each paradigm, the main characteristics of the methodologies, their notations and problem areas exhibited in each one of them. The results indicate that there is no follow-up to the complete process of implementing a DW in either an academic or industrial environment;however, there is also no evidence that the attempt is made to address the design and development of a DW by applying and comparing different methodologies existing in the field.
基金Project(11402264)supported by the National Natural Science Foundation of ChinaProject(BK20160182)supported by the Natural Science Foundation of Jiangsu Province,ChinaProjects(JUSRP116027,JUSRP51732B)supported by the Fundamental Research Funds from Jiangnan University,China
文摘The evolution of microstructure and microhardness was studied in a commercial tungsten-25%rhenium(mass fraction)(W-25Re)alloy processed by the high pressure torsion(HPT)procedure under a pressure of7.7GPa up to10revolutions at different temperatures.The results show that the samples processed by10revolutions at room temperature could have the smallest grain size at around0.209μm.High saturation hardness(HV^1200)could be achieved after the rapid strengthening stage for samples processed by10revolutions both at room temperature and at573K.Microstructural observation and analysis from Hall-Patch relationship could reveal that grain refinement and grain boundaries strengthening are the main factors of hardening mechanism in W-25Re alloy.It is also demonstrated that sintered W-25Re sample may have brittle phase separation phenomenon after HPT processing.
文摘As the cutting speed goes higher, the mechanism of chip deformation will be changed significantly, i.e., continuous chip in low cutting speed will shift to serrated chip with shear localization. For the shear localized chip, the parameters used to assess the chip deformation for continuous chip, such as shorten coefficient ξ, shear angle φ and shear strain ε, can not describe the chip deformation correctly or comprehensively. This paper deals with the assessment of chip deformation of shear localization. There are two deformation regions in shear localized chip, one is the chip segment body with relative smaller plastic deformation, another one is the boundary between segments with shear localization, so called shear band. Considering the two distinct deformation regions, two parameters are used to define their deformation respectively. According to the analysis of chip formation process, the equations have been deduced to calculate the shear strains of shear band ε, shear strain of chip segment ε 1 and shear rate so that the shear localized chip deformation can be assessed correctly and comprehensively. By use of this assessment, the chip deformation in machining selenium treated stainless steel (STSS) and common stainless steel at various cutting conditions is investigated. The experiment results obtained by the machining of stainless steel prove that: (1) the shear strain and strain rate increase with the increasing of cutting speed; (2) the shear strain in shear band can be over 10 when cutting speed exceeding 200 m/min for both types of stainless steel, and it is much higher than the strain of chip segment. The difference will be enlarged as the cutting speed increasing; (3) As the comparison, the shear strain for the STSS is a little lower than that for JIS304; (4) The stain rate is extremely high (= 2.5×10 5 1/s ). In range of cutting speed less than 180 m/min, the strain rate for STSS is lower than that for JIS304. However, when the cutting speed is higher than 180 m/min, the strain rate for STSS is higher than that for JIS304.
文摘The major mortality factor relevant to the intestinal tract is the growth of tumorous cells(polyps)in various parts.More specifically,colonic polyps have a high rate and are recognized as a precursor of colon cancer growth.Endoscopy is the conventional technique for detecting colon polyps,and considerable research has proved that automated diagnosis of image regions that might have polyps within the colon might be used to help experts for decreasing the polyp miss rate.The automated diagnosis of polyps in a computer-aided diagnosis(CAD)method is implemented using statistical analysis.Nowadays,Deep Learning,particularly throughConvolution Neural networks(CNN),is broadly employed to allowthe extraction of representative features.This manuscript devises a new Northern Goshawk Optimization with Transfer Learning Model for Colonic Polyp Detection and Classification(NGOTL-CPDC)model.The NGOTL-CPDC technique aims to investigate endoscopic images for automated colonic polyp detection.To accomplish this,the NGOTL-CPDC technique comprises of adaptive bilateral filtering(ABF)technique as a noise removal process and image pre-processing step.Besides,the NGOTL-CPDC model applies the Faster SqueezeNet model for feature extraction purposes in which the hyperparameter tuning process is performed using the NGO optimizer.Finally,the fuzzy Hopfield neural network(FHNN)method can be employed for colonic poly detection and classification.A widespread simulation analysis is carried out to ensure the improved outcomes of the NGOTL-CPDC model.The comparison study demonstrates the enhancements of the NGOTL-CPDC model on the colonic polyp classification process on medical test images.
基金Supported by Narodowe Centrum Nauki,No.2021/42/E/NZ7/00246.
文摘Parkinson’s disease(PD),characterized by loss of nigrostriatal dopaminergic neurons,is one of the most predominant neurodegenerative diseases affecting the elderly population worldwide.The concept of stem cell therapy in managing neurodegenerative diseases has evolved over the years and has recently rapidly progressed.Neural stem cells(NSCs)have a few key features,including selfrenewal,proliferation,and multipotency,which make them a promising agent targeting neurodegeneration.It is generally agreed that challenges for NSC-based therapy are present at every stage of the transplantation process,including preoperative cell preparation and quality control,perioperative procedures,and postoperative graft preservation,adherence,and overall therapy success.In this review,we provided a comprehensive,careful,and critical discussion of experimental and clinical data alongside the pros and cons of NSC-based therapy in PD.Given the state-of-the-art accomplishments of stem cell therapy,gene therapy,and nanotechnology,we shed light on the perspective of complementing the advantages of each process by developing nano-stem cell therapy,which is currently a research hotspot.Although various obstacles and challenges remain,nano-stem cell therapy holds promise to cure PD,however,continuous improvement and development from the stage of laboratory experiments to the clinical application are necessary.
基金the ENN Institute of Life Science and Technology for their financial support。
文摘Following the quantum theory-based physical model of the human body,a new interpretation of the traditional Chinese medicine(TCM)principle of"Cunkou reads viscera"is presented.Then,a Gaussian pulse wave model as a solution to the Schrodinger equation is shown to accurately describe 19 different pulse shapes,and to quantitatively capture the degree of YinYang attributes of 13 pulse shapes.Furthermore,the model suggests using pulse depth and strength as leading-order quantity and pulse shape as first-order quantity,to characterize the hierarchical resonance between the human body and the environment.The future pulse informatics will focus on determining an individual’s unique quantum human equilibrium state,and diagnose its health state according to the pulse deviation from its equilibrium state,to truly achieve the high level of TCM:"knowing the normal state and reaching the change".
基金Parveen Kumar acknowledges the department of science and technology(DST)New Delhi for the INSPIRE(Innovation in Science Pursuit for Inspired Research)-Faculty grant.
文摘This review covers extensively the synthesis&surface modification,characterization,and application of magnetic nanoparticles.For biomedical applications,consideration should be given to factors such as design strategies,the synthesis process,coating,and surface passivation.The synthesis method regulates post-synthetic change and specific applications in vitro and in vivo imaging/diagnosis and pharmacotherapy/administration.Special insights have been provided on biodistribution,pharmacokinetics,and toxicity in a living system,which is imperative for their wider application in biology.These nanoparticles can be decorated with multiple contrast agents and thus can also be used as a probe for multi-mode imaging or double/triple imaging,for example,MRI-CT,MRI-PET.Similarly loading with different drug molecules/dye/fluorescent molecules and integration with other carriers have found application not only in locating these particles in vivo but simultaneously target drug delivery/hyperthermia inside the body.Studies are underway to collect the potential of these magnetically driven nanoparticles in various scientific fields such as particle interaction,heat conduction,imaging,and magnetism.Surely,this comprehensive data will help in the further development of advanced techniques for theranostics based on high-performance magnetic nanoparticles and will lead this research area in a new sustainable direction.
基金the ENN Institute of Life Science and Technology for their financial support。
文摘In the study,a quantum resonant cavity model based on wave-particle duality was proposed for the explanation of the dynamic processes of essence,vigor,and spirit in the human body in traditional Chinese medicine(TCM).It is assumed that there is a macro human order parameter(wave function),and its dynamics are governed by a macro potential field reflecting influences from heaven,earth,and society,and satisfy the generalized Schrodinger equation.This proposed model was applied in the study to interpret basic concepts of human body in TCM,with an aim to unfold the TCM development in the future.
文摘Recently,human healthcare from body sensor data has gained considerable interest from a wide variety of human-computer communication and pattern analysis research owing to their real-time applications namely smart healthcare systems.Even though there are various forms of utilizing distributed sensors to monitor the behavior of people and vital signs,physical human action recognition(HAR)through body sensors gives useful information about the lifestyle and functionality of an individual.This article concentrates on the design of an Improved Transient Search Optimization with Machine Learning based BehaviorRecognition(ITSOMLBR)technique using body sensor data.The presented ITSOML-BR technique collects data from different body sensors namely electrocardiography(ECG),accelerometer,and magnetometer.In addition,the ITSOML-BR technique extract features like variance,mean,skewness,and standard deviation.Moreover,the presented ITSOML-BR technique executes a micro neural network(MNN)which can be employed for long term healthcare monitoring and classification.Furthermore,the parameters related to the MNN model are optimally selected via the ITSO algorithm.The experimental result analysis of the ITSOML-BR technique is tested on the MHEALTH dataset.The comprehensive comparison study reported a higher result for the ITSOMLBR approach over other existing approaches with maximum accuracy of 99.60%.
基金Program of Office of Science and Technology Development,Peking University(3124-2021|-L-w6).
文摘Objective To investigate the human body’s complex system,and classify and characterize the human body’s health states with“a comprehensive integrated method from qualitative to quantitative”.Methods This paper introduces the concept of“order parameters”and proposes a method for establishing an order parameter model of gas discharge visualization(GDV)based on the principle of“mastering both permanence and change(MBPC)”.The method involved the fol-lowing three steps.First,average luminous intensity(I)and average area(S)of the GDV im-ages were calculated to construct the phase space,and the score of the health questionnaire was calculated as the health deviation index(H).Second,the k-means++clustering method was employed to identify subclasses with the same health characteristics based on the data samples,and to statistically determine the symptom-specific frequencies of the subclasses.Third,the distance(d)between each sample and the“ideal health state”,which determined in the phase space of each subclass,was calculated as an order parameter describing the health imbalance,and a linear mapping was established between the d and the H.Further,the health implications of GDV signals were explored by analyzing subclass symptom profiles.We also compare the mean square error(MSE)with classification methods based on age,gen-der,and body mass index(BMI)indices to verify that the phase space possesses the ability to portray the health status of the human body.Results This study preliminarily tested the reliability of the order parameter model on data samples provided by 20 participants.Based on the discovered linear law,the current model can use d calculated by measuring the GDV signal to predict H(R^(2)>0.77).Combined with the symptom profiles of the subclasses,we explain the classification basis of the phase space based on the pattern identification.Compared with common classification methods based on age,gender,BMI,etc.,the MSE of phase space-based classification was reduced by an order of magnitude.Conclusion In this study,the GDV order parameter model based on MBPC can identify sub-classes and characterize individual health levels,and explore the TCM health meanings of the GDV signals by using subjective-objective methods,which holds significance for establishing mathematical models from TCM diagnosis principles to interpret human body signals.
文摘This paper analyzes the combustion characteristics and greenhouse gas emissions from varied heat fluxes with rice husks. In general, rice husks burnt outdoors at a lower temperature range of 300-400 ℃, which cannot assure complete combustion, thus generating a large volume of toxic air pollutants. A heat flux of 40 kW/m^2, with a cone calorimeter, is the equivalent to the 700 ℃ of an incinerator. The test result shows that the mass reduction rate of the sample at this or at a higher temperature condition was 99.5% or higher, meaning that the sample was almost completely combusted. In this study using rice husks, the amount of carbon dioxide, which is a greenhouse gas, discharged were 1.57-3.61 kg/kg. This is as high as 10 times, than produced in other studies. When the rice husks are burnt outdoors, they are not completely combusted as the combustion temperature remains low, and the rice husk residuals are continuously being combusted in a smoldering phase which creates a large volume of carbon dioxide and carbon monoxide. Therefore, it is recommended to burn rice husks at 700 ℃ or higher to minimize the carbon dioxide and carbon monoxide emissions.
文摘This paper describes the wastewater treatment of 3 liters per second of capacity of discharges of capacity,from the avocado washed,as well as the production of avocado sauce.Effluents were pretreated with a grease and oil trap and contained small amounts of oil and grease,dissolved and total solids,organic matter and trace of organic and inorganic compounds from the vitamins and minerals of avocado.The treatment train is located in an small surface and the train is integrated with coagulation in line with alumina in a static mixer,rapid filtration in two steps,double advanced oxidation with ozone and heterocyclic photocatalysis with a nanofilm of zinc oxide as,photocatalyzer and final adsorption of refractory compounds with activated charcoal.The treated wastewater meets the environmental regulation to urban sewerage discharges of the city,according to the results of a Mexican Certified Laboratory.
文摘This research is an attempt to validate how glu-cose-insulin dynamic mathematical model facilitate to identify the root causes for hypoglycaemia. The purpose is to determine whether increased insulin sensitivity or increased insulin secretion causes post- prandial hypoglycemic (PPH) response, by linking experimental patient data with dynamic mathematical model. For this purpose two groups, as hypoglycemic Group 1 and non-hypoglycemic Group 2, each of which consists of 10 people, are formed. The oral glucose tolerance test (OGTT) is carried out for each person in the groups by measuring plasma glucose and insulin concentrations at every 30 minutes for a period of 5 hours. To distinguish the actual cause of hypoglycemia, the glucose minimal dynamic model is used. The model is executed in MATLAB platform using patient data and the results showed that insulin secretion is assumed to be the potential root cause for the hypoglycemia.
文摘Weed management is a crucial aspect of modern agriculture as invasive plants can negatively impact crop yields and profitability.Long-established methods of weed control,such as manual labor and synthetic herbicides,have been widely used but come with their own set of challenges.These methods are often time-consuming,labor-intensive,and pose environmental risks.Herbicides have been the primary method of weed control due to their efficiency and cost-effectiveness.However,over-reliance on herbicides has led to environmental contamination,weed resistance,and potential health hazards.To address these issues,researchers and industry experts are now exploring the integration of machine learning into chemical weed management strategies.As technology advances,there is a growing interest in exploring innovative and sustainable weed management approaches.This review examines the potential of machine learning in chemical weed management.Machine learning offers innovative and sustainable approaches by analyzing large data sets,recognizing patterns,and making accurate predictions.Machine learning models can classify weed species and optimize herbicide usage.Real-time monitoring enables timely intervention,preventing invasive species spread.Integrating machine learning into chemical weed management holds promise for enhancing agricultural practices,reducing herbicide usage and minimizing environmental impact.Validation and refinement of these algorithms are needed for practical application.
基金support from the National Key Research and Development Program of China (2016YFA0202401)the 111 Project (B16016)+2 种基金the National Natural Science Foundation of China (51702096 and U1705256)the Fundamental Research Funds for the Central Universities (2018ZD07)Metatest Scan Pro Laser Scanning System
文摘To date, extensive research has been carried out,with considerable success, on the development of highperformance perovskite solar cells(PSCs). Owing to its wide absorption range and remarkable thermal stability, the mixedcation perovskite FAxMA1-xPbI3(formamidinium/methylammonium lead iodide) promises high performance. However, the ratio of the mixed cations in the perovskite film has proved difficult to control with precursor solution. In addition, the FAxMA1-xPbI3 films contain a high percentage of MA+and suffer from serious phase separation and high trap states, resulting in inferior photovoltaic performance. In this study, to suppress phase separation, a post-processing method was developed to partially nucleate before annealing, by treating the as-prepared intermediate phase FAI-Pb I2-DMSO(DMSO: dimethylsulfoxide) with mixed FAI/MAI solution. It was found that in the final perovskite, FA0.92MA0.08 PbI3, defects were substantially reduced because the analogous molecular structure initiated ion exchange in the post-processed thin perovskite films, which advanced partial nucleation. As a result, the increased light harvesting and reduced trap states contributed to the enhancement of open-circuit voltage and short-circuit current. The PSCs produced by the post-processing method presented reliable reproducibility, with a maximum power conversion efficiency of 20.80% and a degradation of ~30% for 80 days in standard atmospheric conditions.
文摘Wind power is crucial for achieving carbon neutrality,but its output can vary due to local wind conditions.The spatio-temporal behavior of wind power generation connected to the power grid can have a significant impact on system operations.To assess this impact,the use of long-term reanalysis results of wind data based on a numerical weather prediction(NWP)model is considered valid.However,in Japan,the behavior of on-shore wind power generation is influenced by diverse topographical and meteorological features(TMFs)of the installation site,making it challenging to assess possible operational impacts based solely on power curve-based estimates using a popular conversion equation.In this study,a nonparametric machine learning-based post-processing model that learns the statistical relationship between the TMFs at the target location and the actual wind farm(WF)output was developed to represent the expected per-unit output at each location.Focusing on historical reconstruction results and using this post-processing model to reproduce the real-world WF output behavior created a set of expected wind power generation profiles.The dataset includes hourly long term(1958-2012)wind power generation profiles expected under the WF installation assumptions at various on-shore locations in Japan with a 5 km spatial resolution and is expected to contribute to an accurate understanding of the impact of spatio-temporal wind power behavior.The dataset is publicly accessible at https://doi.org/10.5281/zenodo.11496867(Fujimotoet al.,2024).
