A field study of advanced municipal wastewater treatment technology for artificial groundwater recharge

Field studies were conducted to investigate the advanced treatment of the municipal secondary effluent and a subsequent artificial groundwater recharge at Gaobeidian Wastewater Treatment Plant, Beijing. To improve the secondary effluent quality, the combined process of powdered activated carbon adsorption, flocculation and rapid sand filtration was applied, which could remove about 400 dissolved organic carbon （DOC） and 70% adsorbable organic halogens. The results of liquid size exclusion chromatography indicate that in the adsorption unit the removed organic fraction was mainly low molecular weight compounds. The fractions removed by the flocculation unit were polysaccharides and high molecular weight compounds. The retention of water in summer in the open recharge basins resulted in a growth of algae. Consequently, DOC increased in the polysaccharide and high molecular weight humic substances fraction. The majority of the DOC removal during soil passage took place in the unsaturated area. A limited reduction of DOC was observed in the aquifer zone.

Effect of solution treatment and artificial aging on microstructure and mechanical properties of Al-Cu alloy

In order to achieve good mechanical properties of Al-Cu alloys such as high strength and good toughness,precipitation hardening and artificial aging treatment were applied.As defined by the T6 heat treatment,the standard artificial aging treatment for Al-Cu alloy followed heat treatments of solution treatment at 510-530 ℃ for 2 h,quenching in water at 60 ℃ and then artificial aging at 160-190 ℃ for 2-8 h.The effects of solution treatment and artificial aging on the microstructure and mechanical properties of Al-Cu alloy were studied by optical microscopy（OM）,scanning electron microscopy（SEM）,energy dispersive X-ray spectroscopy（EDS）,transmission electron microscopy（TEM） and tensile test.The results of solution treatment indicate that the mechanical properties of Al-Cu alloy increase and then decrease with the increase of solution temperature.This is because the residual phases dissolve gradually into the matrix,and the fraction of the precipitation and the size of the re-crystallized grain increased.Compared to the solution temperature,the solution holding time has less effect on the microstructure and the mechanical properties of Al-Cu alloy.The artificial aging treatments were conducted at 160-180 ℃ for 2-8 h.The results show that the ultimate tensile strength can be obtained at 180 ℃ for 8 h.Ultimate tensile strength increased with increasing time or temperature.Yield strength was found as the same as the ultimate tensile strength result.

Application of artificial intelligence in the diagnosis and treatment of hepatocellular carcinoma: A review

Although artificial intelligence(AI)was initially developed many years ago,it has experienced spectacular advances over the last 10 years for application in the field of medicine,and is now used for diagnostic,therapeutic and prognostic purposes in almost all fields.Its application in the area of hepatology is especially relevant for the study of hepatocellular carcinoma(HCC),as this is a very common tumor,with particular radiological characteristics that allow its diagnosis without the need for a histological study.However,the interpretation and analysis of the resulting images is not always easy,in addition to which the images vary during the course of the disease,and prognosis and treatment response can be conditioned by multiple factors.The vast amount of data available lend themselves to study and analysis by AI in its various branches,such as deeplearning(DL)and machine learning(ML),which play a fundamental role in decision-making as well as overcoming the constraints involved in human evaluation.ML is a form of AI based on automated learning from a set of previously provided data and training in algorithms to organize and recognize patterns.DL is a more extensive form of learning that attempts to simulate the working of the human brain,using a lot more data and more complex algorithms.This review specifies the type of AI used by the various authors.However,welldesigned prospective studies are needed in order to avoid as far as possible any bias that may later affect the interpretability of the images and thereby limit the acceptance and application of these models in clinical practice.In addition,professionals now need to understand the true usefulness of these techniques,as well as their associated strengths and limitations.

Development of artificial intelligence technology in diagnosis,treatment,and prognosis of colorectal cancer

Artificial intelligence(AI)technology has made leaps and bounds since its invention.AI technology can be subdivided into many technologies such as machine learning and deep learning.The application scope and prospect of different technologies are also totally different.Currently,AI technologies play a pivotal role in the highly complex and wide-ranging medical field,such as medical image recognition,biotechnology,auxiliary diagnosis,drug research and development,and nutrition.Colorectal cancer(CRC)is a common gastrointestinal cancer that has a high mortality,posing a serious threat to human health.Many CRCs are caused by the malignant transformation of colorectal polyps.Therefore,early diagnosis and treatment are crucial to CRC prognosis.The methods of diagnosing CRC are divided into imaging diagnosis,endoscopy,and pathology diagnosis.Treatment methods are divided into endoscopic treatment,surgical treatment,and drug treatment.AI technology is in the weak era and does not have communication capabilities.Therefore,the current AI technology is mainly used for image recognition and auxiliary analysis without in-depth communication with patients.This article reviews the application of AI in the diagnosis,treatment,and prognosis of CRC and provides the prospects for the broader application of AI in CRC.

Harnessing the Power of Artificial Intelligence in Neuromuscular Disease Rehabilitation: A Comprehensive Review and Algorithmic Approach

Neuromuscular diseases present profound challenges to individuals and healthcare systems worldwide, profoundly impacting motor functions. This research provides a comprehensive exploration of how artificial intelligence (AI) technology is revolutionizing rehabilitation for individuals with neuromuscular disorders. Through an extensive review, this paper elucidates a wide array of AI-driven interventions spanning robotic-assisted therapy, virtual reality rehabilitation, and intricately tailored machine learning algorithms. The aim is to delve into the nuanced applications of AI, unlocking its transformative potential in optimizing personalized treatment plans for those grappling with the complexities of neuromuscular diseases. By examining the multifaceted intersection of AI and rehabilitation, this paper not only contributes to our understanding of cutting-edge advancements but also envisions a future where technological innovations play a pivotal role in alleviating the challenges posed by neuromuscular diseases. From employing neural-fuzzy adaptive controllers for precise trajectory tracking amidst uncertainties to utilizing machine learning algorithms for recognizing patient motor intentions and adapting training accordingly, this research encompasses a holistic approach towards harnessing AI for enhanced rehabilitation outcomes. By embracing the synergy between AI and rehabilitation, we pave the way for a future where individuals with neuromuscular disorders can access tailored, effective, and technologically-driven interventions to improve their quality of life and functional independence.

Adoption of Artificial Intelligence for Diagnosis and Treatment of <i>Staphylococcus aureus</i>Infections Disease on Humans

The aim of this paper is to develop an expert system that could aid medical practitioner to effectively diagnose and treat Staphylococcus aureus infections disease on a human. The objective of the research includes to develop an expert system for quick diagnosis and detection of Staphylococcus aureus bacteria on human skin, a system that aids in accurate treatment of staph infectious diseases by doctors, helps in quick decision making in the hospital, improves accuracy in drug prescription, and a system that will bring about computerized storage process, and to enlighten the knowledge workers on how to implement a computer based decision support systems and importance of it in the health care. The research was motivated due to delay in diagnosis and identification of Staphylococcus aureus bacteria and the fast rate at which infectious disease is spreading, delay in treatment of these bacteria, increase of guess work by health practitioners leading to delay in decision making and lack of electronic storage facility in the hospitals. Top down approach was used in the system design of this research while adopting expert system as the methodology and the programming language used was Java and database design used was MySQL. The result after design was a computerized standalone application that assists health practitioners (Doctor’s) in quick identification, diagnosis, prescription and treatment of Staphylococcus aureus bacteria on human skin. The expert system will facilitate quick decision making in the clinic.

Treatment of Tuberculous Pleurisy with Effusion by Artificial Pneumothorax

patients were divided into two groups at random. The patients of two groups were all given standard treatments with anti-tuberculous drugs. Treatment group received artificial pneumothorax to help the cure. Results showed that the frequency and quantity of drawing liquid in the treatment group were obviously less than those in the control group and the duration of the complete liquid absorption was shortened markedly in the treatment group and that total effective rate in treatment group (92.5%) was obviously higher than that of the control group (83.33%). We found that the artificial pneumothorax could raise the intra-pleural pressure by 0.20-0.39 kpa, reduce leakage in parietal pleurae and increase the absorption in visceral layer evidently. As it can isolate the two layers of pleurae from one another by the air in thorax, the incidence of pleurae adhesion can be decreased.

Attaching Organism Community Characteristics of Different Surface Treatments on Artificial Reef Tem-plates Inshore of Yuanzhou Island

In order to understand the biological community characteristics of different surface treatments of ＇artificial reef templates ,which had been placed on the offshore, Yuanzhou island, Daya Bay. Sampling survey was taken in August of 2014. The results showed that 39 species of fouling organisms were collected and iden- tiffed, Ostrea nigromarginata has attached the absolute position of the dominant species ; Through the concrete block surface treatment has a better biofouling effect, and red brick, granite plates and no-surface-treated concrete template attached biomass and abundance of organisms were very low ; Biomass and abundance indices and ecological indices do not show a certain degree of regularity.

Simulation of Low TDS and Biological Units of Fajr Industrial Wastewater Treatment Plant Using Artificial Neural Network and Principal Component Analysis Hybrid Method

Being familiar with characteristics of industrial town effluents from various wastewater treatment units, which have high qualitative and quantitative variations and more uncertainties compared to urban wastewaters, plays very effective role in governing them. With regard to environmental issues, proper operation of wastewater treatment plants is of par- ticular importance that in the case of inappropriate utilization, they will cause serious problems. Processes that exist in environmental systems mostly have two major characteristics: they are dependent on many variables;and there are complex relationships between its components which make them very difficult to analyze. In order to achieve a better and efficient control over the operation of an industrial wastewater treatment plant (WWTP), powerful mathematical tool can be used that is based on recorded data from some basic parameters of wastewater during a period of treatment plant operation. In this study, the treatment plant was divided into two main subsystems including: Low TDS (Total Dissolved Solids) treatment unit and Biological unit (extended aeration). The multilayer perceptron feed forward neural network with a hidden layer and stop training method was used to predict quality parameters of the industrial effluent. Data of this study are related to the Fajr Industrial Wastewater Treatment Plant, located in Mahshahr—Iran that qualita- tive and quantitative characteristics of its units were used for training, calibration and validation of the neural model. Also, Principal Component Analysis (PCA) technique was applied to improve performance of generated models of neural networks. The results of L-TDS unit showed good accuracy of the models in estimating qualitative profile of wastewater but results of biological unit did not have sufficient accuracy to being used. This model facilitates evaluating the performance of each treatment plant units through comparing the results of prediction model with the standard amount of outputs.

Applications of artificial intelligence technology to wastewater treatment fields in China

Current applications of artificial intelligence technology to wastewater treatment in China are summarized. Wastewater treatment plants use expert system mainly in the operation decision-making and fault diagnosis of system operation, use artificial neuron network for system modeling, water quality forecast and soft measure, and use fuzzy control technology for the intelligence control of wastewater treatment process. Finally, the main problems in applying artificial intelligence technology to wastewater treatment in China are analyzed.

Application of artificial intelligence in liver diseases: From diagnosis to treatment

Infectious or noninfectious liver disease has inexorably risen as one of the leading causes of global death and disease burden.There were an estimated 2.14 million liver-related deaths in 2017,representing an 11.4%increase since 2012.Traditional diagnosis and treatment methods have various dilemmas in different causes of liver disease.As a hot research topic in recent years,the application of artificial intelligence(AI)in different fields has attracted extensive attention,and new technologies have brought more ideas for the diagnosis and treatment of some liver diseases.Machine learning(ML)is the core of AI and the basic way to make a computer intelligent.ML technology has many potential uses in hepatology,ranging from exploring new noninvasive means to predict or diagnose different liver diseases to automated image analysis.The application of ML in liver diseases can help clinical staff to diagnose and treat different liver diseases quickly,accurately and scientifically,which is of importance for reducing the incidence and mortality of liver diseases,reducing medical errors,and promoting the development of medicine.This paper reviews the application and prospects of AI in liver diseases,and aims to improve clinicians’awareness of the importance of AI in the diagnosis and treatment of liver diseases.

Surgical treatment of anterior mitral valve prolapse using artificial chordal loop

Objective To summarize clinical experience for treating anterior mitral leaflet prolapse with an artificial chordal loop. Methods From January 2008 to August 2009,pre-measured ePTFE loops were used to treat anterior leaflet prolapse in 8 patients,5 males and 3 females, aged from 28 to 68 (average 56.0±8.9) years. The heart

Prediction of mechanical properties of A357 alloy using artificial neural network

The workpieces of A357 alloy were routinely heat treated to the T6 state in order to gain an adequate mechanical property.The mechanical properties of these workpieces depend mainly on solid-solution temperature,solid-solution time,artificial aging temperature and artificial aging time.An artificial neural network（ANN） model with a back-propagation（BP） algorithm was used to predict mechanical properties of A357 alloy,and the effects of heat treatment processes on mechanical behavior of this alloy were studied.The results show that this BP model is able to predict the mechanical properties with a high accuracy.This model was used to reflect the influence of heat treatments on the mechanical properties of A357 alloy.Isograms of ultimate tensile strength and elongation were drawn in the same picture,which are very helpful to understand the relationship among aging parameters,ultimate tensile strength and elongation.

Application of novel physical picture based on artificial neural networks to predict microstructure evolution of Al-Zn-Mg-Cu alloy during solid solution process

The effects of the solid solution conditions on the microstructure and tensile properties of Al?Zn?Mg?Cu aluminum alloy were investigated by in-situ resistivity measurement, optical microscopy (OM), scanning electron microscopy (SEM), transmission electron microscopy (TEM) and tensile test. A radial basis function artificial neural network (RBF-ANN) model was developed for the analysis and prediction of the electrical resistivity of the tested alloy during the solid solution process. The results show that the model is capable of predicting the electrical resistivity with remarkable success. The correlation coefficient between the predicted results and experimental data is 0.9958 and the relative error is 0.33%. The predicted data were adopted to construct a novel physical picture which was defined as “solution resistivity map”. As revealed by the map, the optimum domain for the solid solution of the tested alloy is in the temperature range of 465?475 °C and solution time range of 50?60 min. In this domain, the solution of second particles and the recrystallization phenomenon will reach equilibrium.

Prediction of Properties in Thermomechanically Treated Cu-Cr-Zr Alloy by an Artificial Neural Network

A supervised artificial neural network (ANN) to model the nonlinear relationship between parameters of thermomechanical treatment processes with respect to hardness and conductivity properties was proposed for Cu-Cr-Zr alloy. The improved model was developed by the Levenberg-Marquardt training algorithm. A basic repository on the domain knowledge of thermomechanical treatment processes is established via sufficient data acquisition by the network. The results showed that the ANN system is an effective way and can be successfully used to predict and analyze the properties of Cu-Cr-Zr alloy.

Application of artificial intelligence in preoperative imaging of hepatocellular carcinoma:Current status and future perspectives

Hepatocellular carcinoma(HCC)is the most common primary malignant liver tumor in China.Preoperative diagnosis of HCC is challenging because of atypical imaging manifestations and the diversity of focal liver lesions.Artificial intelligence(AI),such as machine learning(ML)and deep learning,has recently gained attention for its capability to reveal quantitative information on images.Currently,AI is used throughout the entire radiomics process and plays a critical role in multiple fields of medicine.This review summarizes the applications of AI in various aspects of preoperative imaging of HCC,including segmentation,differential diagnosis,prediction of histo-pathology,early detection of recurrence after curative treatment,and evaluation of treatment response.We also review the limitations of previous studies and discuss future directions for diagnostic imaging of HCC.

Status quo and future prospects of artificial neural network from the perspective of gastroenterologists

Artificial neural networks(ANNs)are one of the primary types of artificial intelligence and have been rapidly developed and used in many fields.In recent years,there has been a sharp increase in research concerning ANNs in gastrointestinal(GI)diseases.This state-of-the-art technique exhibits excellent performance in diagnosis,prognostic prediction,and treatment.Competitions between ANNs and GI experts suggest that efficiency and accuracy might be compatible in virtue of technique advancements.However,the shortcomings of ANNs are not negligible and may induce alterations in many aspects of medical practice.In this review,we introduce basic knowledge about ANNs and summarize the current achievements of ANNs in GI diseases from the perspective of gastroenterologists.Existing limitations and future directions are also proposed to optimize ANN’s clinical potential.In consideration of barriers to interdisciplinary knowledge,sophisticated concepts are discussed using plain words and metaphors to make this review more easily understood by medical practitioners and the general public.

Liver transplantation and artificial liver support in fulminant hepatic failure

INTRODUCTIONFulminant hepatic failure(FHF)is a severe disease with devastating consequences;the incidence is high in China.Before the availability of liver transplantation,the mortality rate was more than 80%[1,2].The advent of liver transplantation revolutionized the outcome of FHF[3,4].However,many patients were unwilling to accept liver transplantation until very late,hence most of them died because of donor shortage and urgency of the disease[5-7],To overcome he problems,we performed orthotopic liver transplantation(OLT)in combination with artificial liver support(ALS) in the treatment of FHF in the past 2 years with satisfactory results.Our experience was reported below.

Artificial intelligence and cholangiocarcinoma:Updates and prospects

Artificial intelligence(AI)is the timeliest field of computer science and attempts to mimic cognitive function of humans to solve problems.In the era of“Big data”,there is an ever-increasing need for AI in all aspects of medicine.Cholangiocarcinoma(CCA)is the second most common primary malignancy of liver that has shown an increase in incidence in the last years.CCA has high mortality as it is diagnosed in later stages that decreases effect of surgery,chemotherapy,and other modalities.With technological advancement there is an immense amount of clinicopathologic,genetic,serologic,histologic,and radiologic data that can be assimilated together by modern AI tools for diagnosis,treatment,and prognosis of CCA.The literature shows that in almost all cases AI models have the capacity to increase accuracy in diagnosis,treatment,and prognosis of CCA.Most studies however are retrospective,and one study failed to show AI benefit in practice.There is immense potential for AI in diagnosis,treatment,and prognosis of CCA however limitations such as relative lack of studies in use by human operators in improvement of survival remains to be seen.

Prediction of Anoxic Sulfide Biooxidation Under Various HRTs Using Artificial Neural Networks

Objective During present investigation the data of a laboratory-scale anoxic sulfide oxidizing （ASO） reactor were used in a neural network system to predict its performance. Methods Five uncorrelated components of the influent wastewater were used as the artificial neural network model input to predict the output of the effluent using back-propagation and general regression algorithms. The best prediction performance is achieved when the data are preprocessed using principal components analysis （PCA） before they are fed to a back propagated neural network. Results Within the range of experimental conditions tested, it was concluded that the ANN model gave predictable results for nitrite removal from wastewater through ASO process. The model did not predict the formation of sulfate to an acceptable manner. Conclusion Apart from experimentation, ANN model can help to simulate the results of such experiments in finding the best optimal choice for ASO based denitrification. Together with wastewater collection and the use of improved treatment systems and new technologies, better control of wastewater treatment plant （WTP） can lead to more effective maneuvers by its operators and, as a consequence, better effluent quality.