Establishment and implementation of safety check project for invasive procedures outside the operating room

Objective:This study aimed to describe the implementation of the surgical safety check policy and the surgical safety checklist for invasive procedures outside the operating room(OR)and evaluate its effectiveness.Methods:In 2017,to improve the safety of patients who underwent invasive procedures outside of the OR,the hospital quality and safety committee established the surgery safety check committee responsible for developing a new working plan,revise the surgery safety check policy,surgery safety check Keywords:Invasive procedures outside the operating room Safety management Surgical safety checklist Patient safety form,and provide training to the related staff,evaluated their competency,and implemented the updated surgical safety check policy and checklist.The study compared the data of pre-implementation(Apr to Sep 2017)and two post-implementation phases(Apr to Sep 2018,Apr to Sep 2019).It also evaluated the number of completed surgery safety checklist,correct signature,and correct timing of signature.Results:The results showed an increase in the completion rate of the safety checklist after the program implementation from 41.7%(521/1,249)to 90.4%(3,572/3,950),the correct rates of signature from 41.9%(218/521)to 99.0%(4,423/4,465),and the correct timing rates of signature from 34.4%(179/521)to 98.5%(4,401/4,465),with statistical significance(P<0.01).Conclusion:Implementing the updated surgery safety check significantly is a necessary and effective measure to ensure patient safety for those who underwent invasive procedures outside the OR.Implementing surgical safety checks roused up the clinical staff's compliance in performing safety checks,and enhanced team collaboration and communication.

Fuzzy Difference Equations in Diagnoses of Glaucoma from Retinal Images Using Deep Learning

The intuitive fuzzy set has found important application in decision-making and machine learning.To enrich and utilize the intuitive fuzzy set,this study designed and developed a deep neural network-based glaucoma eye detection using fuzzy difference equations in the domain where the retinal images converge.Retinal image detections are categorized as normal eye recognition,suspected glaucomatous eye recognition,and glaucomatous eye recognition.Fuzzy degrees associated with weighted values are calculated to determine the level of concentration between the fuzzy partition and the retinal images.The proposed model was used to diagnose glaucoma using retinal images and involved utilizing the Convolutional Neural Network(CNN)and deep learning to identify the fuzzy weighted regularization between images.This methodology was used to clarify the input images and make them adequate for the process of glaucoma detection.The objective of this study was to propose a novel approach to the early diagnosis of glaucoma using the Fuzzy Expert System(FES)and Fuzzy differential equation(FDE).The intensities of the different regions in the images and their respective peak levels were determined.Once the peak regions were identified,the recurrence relationships among those peaks were then measured.Image partitioning was done due to varying degrees of similar and dissimilar concentrations in the image.Similar and dissimilar concentration levels and spatial frequency generated a threshold image from the combined fuzzy matrix and FDE.This distinguished between a normal and abnormal eye condition,thus detecting patients with glaucomatous eyes.

Robust Facial Biometric Authentication System Using Pupillary Light Reflex for Liveness Detection of Facial Images

Pupil dynamics are the important characteristics of face spoofing detection.The face recognition system is one of the most used biometrics for authenticating individual identity.The main threats to the facial recognition system are different types of presentation attacks like print attacks,3D mask attacks,replay attacks,etc.The proposed model uses pupil characteristics for liveness detection during the authentication process.The pupillary light reflex is an involuntary reaction controlling the pupil’s diameter at different light intensities.The proposed framework consists of two-phase methodologies.In the first phase,the pupil’s diameter is calculated by applying stimulus(light)in one eye of the subject and calculating the constriction of the pupil size on both eyes in different video frames.The above measurement is converted into feature space using Kohn and Clynes model-defined parameters.The Support Vector Machine is used to classify legitimate subjects when the diameter change is normal(or when the eye is alive)or illegitimate subjects when there is no change or abnormal oscillations of pupil behavior due to the presence of printed photograph,video,or 3D mask of the subject in front of the camera.In the second phase,we perform the facial recognition process.Scale-invariant feature transform(SIFT)is used to find the features from the facial images,with each feature having a size of a 128-dimensional vector.These features are scale,rotation,and orientation invariant and are used for recognizing facial images.The brute force matching algorithm is used for matching features of two different images.The threshold value we considered is 0.08 for good matches.To analyze the performance of the framework,we tested our model in two Face antispoofing datasets named Replay attack datasets and CASIA-SURF datasets,which were used because they contain the videos of the subjects in each sample having three modalities(RGB,IR,Depth).The CASIA-SURF datasets showed an 89.9%Equal Error Rate,while the Replay Attack datasets showed a 92.1%Equal Error Rate.

BHJO: A Novel Hybrid Metaheuristic Algorithm Combining the Beluga Whale, Honey Badger, and Jellyfish Search Optimizers for Solving Engineering Design Problems

Hybridizing metaheuristic algorithms involves synergistically combining different optimization techniques to effectively address complex and challenging optimization problems.This approach aims to leverage the strengths of multiple algorithms,enhancing solution quality,convergence speed,and robustness,thereby offering a more versatile and efficient means of solving intricate real-world optimization tasks.In this paper,we introduce a hybrid algorithm that amalgamates three distinct metaheuristics:the Beluga Whale Optimization(BWO),the Honey Badger Algorithm(HBA),and the Jellyfish Search(JS)optimizer.The proposed hybrid algorithm will be referred to as BHJO.Through this fusion,the BHJO algorithm aims to leverage the strengths of each optimizer.Before this hybridization,we thoroughly examined the exploration and exploitation capabilities of the BWO,HBA,and JS metaheuristics,as well as their ability to strike a balance between exploration and exploitation.This meticulous analysis allowed us to identify the pros and cons of each algorithm,enabling us to combine them in a novel hybrid approach that capitalizes on their respective strengths for enhanced optimization performance.In addition,the BHJO algorithm incorporates Opposition-Based Learning(OBL)to harness the advantages offered by this technique,leveraging its diverse exploration,accelerated convergence,and improved solution quality to enhance the overall performance and effectiveness of the hybrid algorithm.Moreover,the performance of the BHJO algorithm was evaluated across a range of both unconstrained and constrained optimization problems,providing a comprehensive assessment of its efficacy and applicability in diverse problem domains.Similarly,the BHJO algorithm was subjected to a comparative analysis with several renowned algorithms,where mean and standard deviation values were utilized as evaluation metrics.This rigorous comparison aimed to assess the performance of the BHJOalgorithmabout its counterparts,shedding light on its effectiveness and reliability in solving optimization problems.Finally,the obtained numerical statistics underwent rigorous analysis using the Friedman post hoc Dunn’s test.The resulting numerical values revealed the BHJO algorithm’s competitiveness in tackling intricate optimization problems,affirming its capability to deliver favorable outcomes in challenging scenarios.

Prediction of Geopolymer Concrete Compressive Strength Using Convolutional Neural Networks

Geopolymer concrete emerges as a promising avenue for sustainable development and offers an effective solution to environmental problems.Its attributes as a non-toxic,low-carbon,and economical substitute for conventional cement concrete,coupled with its elevated compressive strength and reduced shrinkage properties,position it as a pivotal material for diverse applications spanning from architectural structures to transportation infrastructure.In this context,this study sets out the task of using machine learning(ML)algorithms to increase the accuracy and interpretability of predicting the compressive strength of geopolymer concrete in the civil engineering field.To achieve this goal,a new approach using convolutional neural networks(CNNs)has been adopted.This study focuses on creating a comprehensive dataset consisting of compositional and strength parameters of 162 geopolymer concrete mixes,all containing Class F fly ash.The selection of optimal input parameters is guided by two distinct criteria.The first criterion leverages insights garnered from previous research on the influence of individual features on compressive strength.The second criterion scrutinizes the impact of these features within the model’s predictive framework.Key to enhancing the CNN model’s performance is the meticulous determination of the optimal hyperparameters.Through a systematic trial-and-error process,the study ascertains the ideal number of epochs for data division and the optimal value of k for k-fold cross-validation—a technique vital to the model’s robustness.The model’s predictive prowess is rigorously assessed via a suite of performance metrics and comprehensive score analyses.Furthermore,the model’s adaptability is gauged by integrating a secondary dataset into its predictive framework,facilitating a comparative evaluation against conventional prediction methods.To unravel the intricacies of the CNN model’s learning trajectory,a loss plot is deployed to elucidate its learning rate.The study culminates in compelling findings that underscore the CNN model’s accurate prediction of geopolymer concrete compressive strength.To maximize the dataset’s potential,the application of bivariate plots unveils nuanced trends and interactions among variables,fortifying the consistency with earlier research.Evidenced by promising prediction accuracy,the study’s outcomes hold significant promise in guiding the development of innovative geopolymer concrete formulations,thereby reinforcing its role as an eco-conscious and robust construction material.The findings prove that the CNN model accurately estimated geopolymer concrete’s compressive strength.The results show that the prediction accuracy is promising and can be used for the development of new geopolymer concrete mixes.The outcomes not only underscore the significance of leveraging technology for sustainable construction practices but also pave the way for innovation and efficiency in the field of civil engineering.

Effects of Seasonal and Temperature Variations on the Association between Nitrogen Dioxide Exposure and First-Aid Incidence for Neurological Diseases in Shenzhen,China

Neurological disorders,including headaches(tension-type headaches,medication-overuse headaches,and migraines)and dementias that include Alzheimer’s disease,are among the most prevalent and debilitating global conditions.In 2016,these disorders affected 276 million people worldwide and were the second leading cause of death that year[1].This highlights the urgent need for effective prevention,treatment,and support strategies.The etiology of neurological disorders is multifaceted and involves genetic,environmental,physiological,and social factors[2].

Effectiveness of traditional chinese medicine nursing intervention on constipation and anxiety in chemotherapy

Background:In a study conducted from March to September 2021,124 cancer patients undergoing chemotherapy at our hospital were divided into two groups.The control group received routine inpatient nursing care,while the observation group received Traditional Chinese Medicine(TCM)nursing interventions in addition to routine care.Data analysis was conducted to compare the incidence of clinical adverse reactions,constipation scores,and changes in anxiety levels between the two groups.The results showed that the observation group,receiving TCM nursing interventions,had lower incidence of clinical adverse reactions and lower constipation scores compared to the control group.Additionally,anxiety levels were found to decrease significantly in the observation group post-intervention.These findings suggest that incorporating TCM nursing interventions in the care of cancer patients undergoing chemotherapy may help in reducing the occurrence of adverse reactions,alleviating constipation,and managing anxiety levels.Further research is needed to explore the full potential of integrating TCM into conventional nursing care for cancer patients.Methods:Following interventions,both groups experienced varying degrees of clinical adverse reactions,with the observation group demonstrating a significantly lower total incidence(29.03%)compared to the control group.This disparity was statistically significant(P<0.05).Furthermore,improvements were observed in defecation time(0.53±0.18)points and defecation frequency(1.17±0.25)points post-intervention.These findings suggest that the intervention had a positive impact on reducing adverse reactions and improving defecation patterns.Results:In a recent study,researchers found that individuals in the observation group experienced lower levels of difficulty with defecation and had a more regular defecation form compared to those in the control group.The results showed a significant difference in defecation difficulty and form,with the observation group scoring lower in both aspects.Interestingly,there was no significant difference in anxiety levels between the two groups prior to the intervention.However,after the intervention,both groups experienced a decrease in anxiety levels,with the observation group showing a greater reduction compared to the control group.This suggests that the intervention had a positive impact on reducing anxiety levels,particularly in the observation group,where anxiety scores were significantly lower.These findings highlight the possible benefits of certain interventions in improving both physical and psychological well-being.Conclusion:TCM nursing interventions have shown to be beneficial in reducing anxiety and improving constipation symptoms in cancer patients.These methods not only enhance the quality of life for patients but also offer a promising approach in clinical cancer treatment.The efficacy of TCM nursing highlights its value and encourages further promotion and application in future cancer care strategies.TCM nursing helps cancer patients undergoing chemotherapy with constipation and anxiety.

Marine Predators Algorithm with Deep Learning-Based Leukemia Cancer Classification on Medical Images

In blood or bone marrow,leukemia is a form of cancer.A person with leukemia has an expansion of white blood cells(WBCs).It primarily affects children and rarely affects adults.Treatment depends on the type of leukemia and the extent to which cancer has established throughout the body.Identifying leukemia in the initial stage is vital to providing timely patient care.Medical image-analysis-related approaches grant safer,quicker,and less costly solutions while ignoring the difficulties of these invasive processes.It can be simple to generalize Computer vision(CV)-based and image-processing techniques and eradicate human error.Many researchers have implemented computer-aided diagnosticmethods andmachine learning(ML)for laboratory image analysis,hopefully overcoming the limitations of late leukemia detection and determining its subgroups.This study establishes a Marine Predators Algorithm with Deep Learning Leukemia Cancer Classification(MPADL-LCC)algorithm onMedical Images.The projectedMPADL-LCC system uses a bilateral filtering(BF)technique to pre-process medical images.The MPADL-LCC system uses Faster SqueezeNet withMarine Predators Algorithm(MPA)as a hyperparameter optimizer for feature extraction.Lastly,the denoising autoencoder(DAE)methodology can be executed to accurately detect and classify leukemia cancer.The hyperparameter tuning process using MPA helps enhance leukemia cancer classification performance.Simulation results are compared with other recent approaches concerning various measurements and the MPADL-LCC algorithm exhibits the best results over other recent approaches.

Self-perceived burden and influencing factors in patients with cervical cancer administered with radiotherapy

BACKGROUND Cervical cancer is the fourth commonest malignancy in women around the world.It represents the second most commonly diagnosed cancer in South East Asian women,and an important cancer death cause in women of developing nations.Data collected in 2018 revealed 5690000 cervical cancer cases worldwide,85%of which occurred in developing countries.AIM To assess self-perceived burden(SPB)and related influencing factors in cervical cancer patients undergoing radiotherapy.METHODS Patients were prospectively included by convenient sampling at The Fifth Affiliated Hospital of Sun Yat-Sen University,China between March 2018 and March 2019.The survey was completed using a self-designed general information questionnaire,the SPB scale for cancer patients,and the self-care self-efficacy scale,Strategies Used by People to Promote Health,which were delivered to patients with cervical cancer undergoing radiotherapy.Measurement data are expressed as the mean±SD.Enumeration data are expressed as frequencies or percentages.Caregivers were the spouse,offspring,and other in 46.4,40.9,and 12.7%,respectively,and the majority were male(59.1%).As for pathological type,90 and 20 cases had squamous and adenocarcinoma/adenosquamous carcinomas,respectively.Stage IV disease was found in 12(10.9%)patients.RESULTS A total of 115 questionnaires were released,and five patients were excluded for too long evaluation time(n=2)and the inability to confirm the questionnaire contents(n=3).Finally,a total of 110 questionnaires were collected.They were aged 31-79 years,with the 40-59 age group being most represented(65.4%of all cases).Most patients were married(91.8%)and an overwhelming number had no religion(92.7%).Total SPB score was 43.13±16.65.SPB was associated with the place of residence,monthly family income,payment method,transfer status,the presence of radiotherapy complications,and the presence of pain(P<0.05).The SPB and self-care self-efficacy were negatively correlated(P<0.01).In multivariate analysis,self-care self-efficacy,place of residence,monthly family income,payment method,degree of radiation dermatitis,and radiation proctitis were influencing factors of SPB(P<0.05).CONCLUSION Patients with cervical cancer undergoing radiotherapy often have SPB.Self-care self-efficacy scale,place of residence,monthly family income,payment method,and radiation dermatitis and proctitis are factors independently influencing SPB.

广域保护中基于能量守恒原理的母线及输电线差动保护(英文)

提出一种基于能量守恒原理的纵联差动保护,对线路两端或母线各端的有功功率进行比较,若各端功率的和超出整定值,则判定为区内故障。在典型400 kV输电系统模型上,运用MATLAB仿真软件对所提出的原理进行了仿真验证。仿真结果表明,该方法比传统电流比率差动保护更加可靠且计算量小,因而可缩短动作时间。该方法能检测所有故障类型,适应于超高压远距离输电线路。

Reduction behavior and kinetics of vanadium–titanium sinters under high potential oxygen enriched pulverized coal injection

In this work, the reduction behavior of vanadium–titanium sinters was studied under five different sets of conditions of pulverized coal injection with oxygen enrichment. The modified random pore model was established to analyze the reduction kinetics. The results show that the reduction rate of sinters was accelerated by an increase of CO and H2contents. Meanwhile, with the increase in CO and H2contents, the increasing range of the medium reduction index (MRE) of sinters decreased. The increasing oxygen enrichment ratio played a diminishing role in improving the reduction behavior of the sinters. The reducing process kinetic parameters were solved using the modified random role model. The results indicated that, with increasing oxygen enrichment, the contents of CO and H2in the reducing gas increased. The reduction activation energy of the sinters decreased to between 20.4 and 23.2 kJ/mol. © 2017, The Author(s).

Experimental and computational chemical studies on the cationic furanylnicotinamidines as novel corrosion inhibitors in aqueous solutions

The corrosion inhibition action of three newly synthesized furanylnicotinamidine derivatives namely: 6-[5-{4(dimethylamino)phenyl}furan-2-yl]nicotinamidine(MA-1256), 6-[5-(4-chlorophenyl)furan-2-yl]nicotinamidine(MA-1266), and 6-[5-{4-(dimethylamino)phenyl}furan-2-yl]nicotinonitrile(MA-1250) on carbon steel(C-steel) was investigated in 1.0 mol·L-1 HCl solution by weight loss(WL), potentiodynamic polarization(PP), electrochemical impedance spectroscopy(EIS), and electrochemical frequency modulation(EFM)techniques. Morphological analysis was performed on the uninhibited and inhibited C-steel using atomic force microscope(AFM) and Infrared Spectroscopy(ATR-IR) methods. The effect of temperature was studied and discussed. Inspection of experimental results revealed that the inhibition efficiency(IE) increases with the incremental addition of inhibitors and with elevating the temperature of the acid media. The adsorption of furanylnicotinamidine derivatives on C-steel follows Temkin’s isotherm. PP studies indicated that the investigated compounds act as mixed-type inhibitors and showed that p-dimethylaminophenyl furanylnicotinamidine derivative(MA-1256) was the most efficient inhibitor among the other studied derivatives with IE reached(95%)at 21 × 10-6 mol·L-1. MA-1266 is highly soluble in aqueous solution and has non-toxicity profile with LC50 N 37 mg·L-1. Thus, MA-1266 can be a promising green corrosion inhibitor candidate with IE N 91% at 21× 10-6 mol·L-1. The experiments were coupled with computational chemical theories such as quantum chemical and molecular dynamic methods. The experimental results were in good agreement with the computational outputs.

Advance Artificial Intelligence Technique for Designing Double T-Shaped Monopole Antenna

Machine learning(ML)has taken the world by a tornado with its prevalent applications in automating ordinary tasks and using turbulent insights throughout scientific research and design strolls.ML is a massive area within artificial intelligence(AI)that focuses on obtaining valuable information out of data,explaining why ML has often been related to stats and data science.An advanced meta-heuristic optimization algorithm is proposed in this work for the optimization problem of antenna architecture design.The algorithm is designed,depending on the hybrid between the Sine Cosine Algorithm(SCA)and the Grey Wolf Optimizer(GWO),to train neural networkbased Multilayer Perceptron(MLP).The proposed optimization algorithm is a practical,versatile,and trustworthy platform to recognize the design parameters in an optimal way for an endorsement double T-shaped monopole antenna.The proposed algorithm likewise shows a comparative and statistical analysis by different curves in addition to the ANOVA and T-Test.It offers the superiority and validation stability evaluation of the predicted results to verify the procedures’accuracy.

The synergistic effect of honey and cinnamon against Streptococcus mutans bacteria

Objective: To investigate the effect of Iranian honey, cinnamon and their combination against Streptococcus mutans bacteria.Methods: Nine experimental solutions were examined in this study, including two types of honey(pasteurized and sterilized), two types of cinnamon extract(dissolved in distilled water or dimethyl sulfoxide) and five different mixtures of cinnamon in honey(prepared by admixing 1%–5% w/w of cinnamon extract into 99%–95% w/w of honey, respectively).Meanwhile, each of mentioned agent was considered as the first solution while it was diluted into seven serially two-fold dilutions(from 1:2 to 1:128 v/v).Therefore, eight different concentrations of each agent were tested.The antibacterial tests were performed through blood agar well diffusion method, and the minimum inhibitory concentration(MIC) was determined.Ultimately, the data were subjected to statistical analysis incorporating Two-way ANOVA and Bonferroni post hoc tests(a = 0.01).Results: The highest zone of inhibition was recorded for the mixtures of honey and cinnamon while all the subgroups containing 95%–99% v/v of honey were in the same range(P < 0.01).The MIC for both honey solutions were obtained as 500 mg/mL whereas it was 50 mg/m L for both cinnamon solutions.Moreover, the MIC related to all honey/cinnamon mixtures were 200 mg/mL.Conclusions: A profound synergistic effect of honey and cinnamon was observed against Streptococcus mutans while there was no significant difference among extracts containing 99%–95% v/v of honey admixing with 1%–5% v/v of cinnamon, respectively.

Graph Transformer for Communities Detection in Social Networks

Graphs are used in various disciplines such as telecommunication,biological networks,as well as social networks.In large-scale networks,it is challenging to detect the communities by learning the distinct properties of the graph.As deep learning hasmade contributions in a variety of domains,we try to use deep learning techniques to mine the knowledge from large-scale graph networks.In this paper,we aim to provide a strategy for detecting communities using deep autoencoders and obtain generic neural attention to graphs.The advantages of neural attention are widely seen in the field of NLP and computer vision,which has low computational complexity for large-scale graphs.The contributions of the paper are summarized as follows.Firstly,a transformer is utilized to downsample the first-order proximities of the graph into a latent space,which can result in the structural properties and eventually assist in detecting the communities.Secondly,the fine-tuning task is conducted by tuning variant hyperparameters cautiously,which is applied to multiple social networks(Facebook and Twitch).Furthermore,the objective function(crossentropy)is tuned by L0 regularization.Lastly,the reconstructed model forms communities that present the relationship between the groups.The proposed robust model provides good generalization and is applicable to obtaining not only the community structures in social networks but also the node classification.The proposed graph-transformer shows advanced performance on the social networks with the average NMIs of 0.67±0.04,0.198±0.02,0.228±0.02,and 0.68±0.03 on Wikipedia crocodiles,Github Developers,Twitch England,and Facebook Page-Page networks,respectively.

An Optimized Ensemble Model for Prediction the Bandwidth of Metamaterial Antenna

Metamaterial Antenna is a special class of antennas that uses metamaterial to enhance their performance.Antenna size affects the quality factor and the radiation loss of the antenna.Metamaterial antennas can overcome the limitation of bandwidth for small antennas.Machine learning(ML)model is recently applied to predict antenna parameters.ML can be used as an alternative approach to the trial-and-error process of finding proper parameters of the simulated antenna.The accuracy of the prediction depends mainly on the selected model.Ensemble models combine two or more base models to produce a better-enhanced model.In this paper,a weighted average ensemble model is proposed to predict the bandwidth of the Metamaterial Antenna.Two base models are used namely:Multilayer Perceptron(MLP)and Support Vector Machines(SVM).To calculate the weights for each model,an optimization algorithm is used to find the optimal weights of the ensemble.Dynamic Group-Based Cooperative Optimizer(DGCO)is employed to search for optimal weight for the base models.The proposed model is compared with three based models and the average ensemble model.The results show that the proposed model is better than other models and can predict antenna bandwidth efficiently.

A Lightweight Sentiment Analysis Method

The emergence of big data leads to an increasing demand for data processing methods.As the most influential media for Chinese domestic movie ratings,Douban contains a huge amount of data and one can understand users'perspectives towards these movies by analyzing these data.In this article,we study movie's critics from the Douban website,perform sentiment analysis on the data obtained by crawling,and visualize the results with a word cloud.We propose a lightweight sentiment analysis method which is free from heavy training and visualize the results in a more conceivable way.

Enhancing Parkinson’s Disease Diagnosis Accuracy Through Speech Signal Algorithm Modeling

Parkinson’s disease(PD),one of whose symptoms is dysphonia,is a prevalent neurodegenerative disease.The use of outdated diagnosis techniques,which yield inaccurate and unreliable results,continues to represent an obstacle in early-stage detection and diagnosis for clinical professionals in the medical field.To solve this issue,the study proposes using machine learning and deep learning models to analyze processed speech signals of patients’voice recordings.Datasets of these processed speech signals were obtained and experimented on by random forest and logistic regression classifiers.Results were highly successful,with 90%accuracy produced by the random forest classifier and 81.5%by the logistic regression classifier.Furthermore,a deep neural network was implemented to investigate if such variation in method could add to the findings.It proved to be effective,as the neural network yielded an accuracy of nearly 92%.Such results suggest that it is possible to accurately diagnose early-stage PD through merely testing patients’voices.This research calls for a revolutionary diagnostic approach in decision support systems,and is the first step in a market-wide implementation of healthcare software dedicated to the aid of clinicians in early diagnosis of PD.

Calcitonin and vitamin D3 have high therapeutic potential for improving diabetic mandibular growth

The goal of this study was to assess the effect of the intermittent combination of an antiresorptive agent （calcitonin） and an anabolic agent （vitamin D3） on treating the detrimental effects of Type 1 diabetes mellitus （DM） on mandibular bone formation and growth. Forty 3-week-old male Wistar rats were divided into four groups： the control group （normal rats）, the control C＋D group （normal rats injected with calcitonin and vitamin D3）, the diabetic C＋D group （diabetic rats injected with calcitonin and vitamin D3） and the diabetic group （uncontrolled diabetic rats）. An experimental DM condition was induced in the male Wistar rats in the diabetic and diabetic C＋ D groups using a single dose of 60 mg.kg-1 body weight of streptozotocin. Calcitonin and vitamin D3 were simultaneously injected in the rats of the control C＋D and diabetic C＋D groups. All rats were killed after 4 weeks, and the right mandibles were evaluated by micro-computed tomography and histomorphometric analysis. Diabetic rats showed a significant deterioration in bone quality and bone formation （diabetic group）. By contrast, with the injection of calcitonin and vitamin D3, both bone parameters and bone formation significantly improved （diabetic C＋ D group） （P 〈 0.05）. These findings suggest that these two hormones might potentially improve various bone properties.

Design of hybrid damping controller based on multi-target gravitational search optimization algorithm in a multi-machine power system with high penetration of PV park

A mathematical approach was proposed to investigate the impact of high penetration of large-scale photovoltaic park(LPP) on small-signal stability of a power network and design of hybrid controller for these units.A systematic procedure was performed to obtain the complete model of a multi-machine power network including LPP.For damping of oscillations focusing on inter-area oscillatory modes,a hybrid controller for LPP was proposed.The performance of the suggested controller was tested using a 16-machine 5-area network.The results indicate that the proposed hybrid controller for LPP provides sufficient damping to the low-frequency modes of power system for a wide range of operating conditions.The method presented in this work effectively indentifies the impact of increased PV penetration and its controller on dynamic performance of multi-machine power network containing LPP.Simulation results demonstrate that the model presented can be used in designing of essential controllers for LPP.