The Influence of Air Pollution Concentrations on Solar Irradiance Forecasting Using CNN-LSTM-mRMR Feature Extraction

Maintaining a steady power supply requires accurate forecasting of solar irradiance,since clean energy resources do not provide steady power.The existing forecasting studies have examined the limited effects of weather conditions on solar radiation such as temperature and precipitation utilizing convolutional neural network(CNN),but no comprehensive study has been conducted on concentrations of air pollutants along with weather conditions.This paper proposes a hybrid approach based on deep learning,expanding the feature set by adding new air pollution concentrations,and ranking these features to select and reduce their size to improve efficiency.In order to improve the accuracy of feature selection,a maximum-dependency and minimum-redundancy(mRMR)criterion is applied to the constructed feature space to identify and rank the features.The combination of air pollution data with weather conditions data has enabled the prediction of solar irradiance with a higher accuracy.An evaluation of the proposed approach is conducted in Istanbul over 12 months for 43791 discrete times,with the main purpose of analyzing air data,including particular matter(PM10 and PM25),carbon monoxide(CO),nitric oxide(NOX),nitrogen dioxide(NO_(2)),ozone(O₃),sulfur dioxide(SO_(2))using a CNN,a long short-term memory network(LSTM),and MRMR feature extraction.Compared with the benchmark models with root mean square error(RMSE)results of 76.2,60.3,41.3,32.4,there is a significant improvement with the RMSE result of 5.536.This hybrid model presented here offers high prediction accuracy,a wider feature set,and a novel approach based on air concentrations combined with weather conditions for solar irradiance prediction.

Epithelioid malignant peripheral nerve sheath tumor of the bladder and concomitant urothelial carcinoma: A case report

BACKGROUND Epithelioid malignant peripheral nerve sheath tumor(EMPNST)of the bladder is a rare entity with devastating features.These tumors are thought to originate from malignant transformation of pre-existing schwannomas of pelvic autonomic nerve plexuses,and unlike the conventional malignant peripheral nerve sheath tumor(MPNST),are not associated with neurofibromatosis.The tumor has dis-tinctive morphological,immunohistochemical and molecular features.Addi-tionally,it tends to be more aggressive and have a higher mortality.This is the first case that presents with a synchronous urothelial carcinoma of the bladder and the epithelioid variant of MPNST in the literature.It’s also the second re-ported case of EMPNST originating from the bladder wall.CASE SUMMARY In this case report,we present the detailed clinical course of a 71-year-old patient with EMPNST of the bladder alongside a literature review.CONCLUSION During the management of EMPNST cases,offering aggressive treatment moda-lities to the patient,such as radical cystectomy,is appropriate for the best chance to contain the disease,regardless of the tumor stage and the extent of local disease at initial diagnosis.

Relational Turkish Text Classification Using Distant Supervised Entities and Relations

Text classification,by automatically categorizing texts,is one of the foundational elements of natural language processing applications.This study investigates how text classification performance can be improved through the integration of entity-relation information obtained from the Wikidata(Wikipedia database)database and BERTbased pre-trained Named Entity Recognition(NER)models.Focusing on a significant challenge in the field of natural language processing(NLP),the research evaluates the potential of using entity and relational information to extract deeper meaning from texts.The adopted methodology encompasses a comprehensive approach that includes text preprocessing,entity detection,and the integration of relational information.Experiments conducted on text datasets in both Turkish and English assess the performance of various classification algorithms,such as Support Vector Machine,Logistic Regression,Deep Neural Network,and Convolutional Neural Network.The results indicate that the integration of entity-relation information can significantly enhance algorithmperformance in text classification tasks and offer new perspectives for information extraction and semantic analysis in NLP applications.Contributions of this work include the utilization of distant supervised entity-relation information in Turkish text classification,the development of a Turkish relational text classification approach,and the creation of a relational database.By demonstrating potential performance improvements through the integration of distant supervised entity-relation information into Turkish text classification,this research aims to support the effectiveness of text-based artificial intelligence(AI)tools.Additionally,it makes significant contributions to the development ofmultilingual text classification systems by adding deeper meaning to text content,thereby providing a valuable addition to current NLP studies and setting an important reference point for future research.

Prediction of Damping Capacity Demand in Seismic Base Isolators via Machine Learning

Base isolators used in buildings provide both a good acceleration reduction and structural vibration control structures.The base isolators may lose their damping capacity over time due to environmental or dynamic effects.This deterioration of them requires the determination of the maintenance and repair needs and is important for the long-termisolator life.In this study,an artificial intelligence prediction model has been developed to determine the damage and maintenance-repair requirements of isolators as a result of environmental effects and dynamic factors over time.With the developed model,the required damping capacity of the isolator structure was estimated and compared with the previously placed isolator capacity,and the decrease in the damping property was tried to be determined.For this purpose,a data set was created by collecting the behavior of structures with single degrees of freedom(SDOF),different stiffness,damping ratio and natural period isolated from the foundation under far fault earthquakes.The data is divided into 5 different damping classes varying between 10%and 50%.Machine learning model was trained in damping classes with the data on the structure’s response to random seismic vibrations.As a result of the isolator behavior under randomly selected earthquakes,the recorded motion and structural acceleration of the structure against any seismic vibration were examined,and the decrease in the damping capacity was estimated on a class basis.The performance loss of the isolators,which are separated according to their damping properties,has been tried to be determined,and the reductions in the amounts to be taken into account have been determined by class.In the developed prediction model,using various supervised machine learning classification algorithms,the classification algorithm providing the highest precision for the model has been decided.When the results are examined,it has been determined that the damping of the isolator structure with the machine learning method is predicted successfully at a level exceeding 96%,and it is an effective method in deciding whether there is a decrease in the damping capacity.

Thinning effects on biomass and element concentrations of roots in adjacent hornbeam and oak stands in Istanbul,Turkey

Background:Thinning is a commonly used treatment in forest management which affects the tree root systems.The effects of thinning on element concentrations and seasonal change of roots were evaluated in adjacent oak(Quercus frainetto Ten.)and hornbeam(Carpinus betulus L.)stands according to the different root diameter classes.Method:Two replicated control and thinning plots(50m×50 m)were set for each species(hornbeam and oak).Thinning treatments(November 2010)reduced 50%of the basal area in both oak and hornbeam stands.Roots were assessed by seasonal collection over 2 years(from October 2010 to October 2012).The roots were then sorted into diameter classes of 0–2mm(fine roots),2–5mm(small roots)and>5mm(coarse roots).C,N,P,K,Ca,Na,Mg,S,Mn,Fe,Al,Zn,Pb,Ni,Cu and Cd were analyzed.Results:Except coarse roots,the highest root biomasses were determined in April-2011 in all plots.Fine-root biomass in oak was found significantly higher in control plots.In contrast to the oak,the fine-root biomass in the thinned hornbeam plots was higher than in the controls.The small-root biomass did not significantly differ between the thinned and the control plots in both oak and hornbeam stands.However,the coarse-root biomass showed significant differences between the control(1989 g∙m^(−2))and thinned plots(1060 g∙m^(−2))in oak,while no difference was detected in hornbeam.The concentrations of C,Al,Pb,Cd,Ni,Zn,Mn,Na,K,Mg and P in the fine roots of oak were significantly higher in the thinned plots.However,the concentration of Pb,Cd and Fe in the fine roots was significantly higher in the thinned plots of hornbeam.Significant differences were observed between the species for all elements in the fine roots except for C,N and P.In particular,elements in the fine roots tended to increase in July in the oak.In the hornbeam,all element concentrations in the fine roots(except C,N,and S)in the thinned plots showed a tendency to increase in April.The concentrations of Pb,Ni,Al,Fe,Cu,Ca,Na,K,Mg and P in the hornbeam control plots increased during the April 2011 period.Conclusion:The results indicated that thinning effects on temporal changes and concentrations of elements in the roots could be attributed to species-specific characteristics.

Mirai Botnet Attack Detection in Low-Scale Network Traffic

The Internet of Things(IoT)has aided in the development of new products and services.Due to the heterogeneity of IoT items and networks,traditional techniques cannot identify network risks.Rule-based solutions make it challenging to secure and manage IoT devices and services due to their diversity.While the use of artificial intelligence eliminates the need to define rules,the training and retraining processes require additional processing power.This study proposes a methodology for analyzing constrained devices in IoT environments.We examined the relationship between different sized samples from the Kitsune dataset to simulate the Mirai attack on IoT devices.The training and retraining stages for the Mirai attack were also evaluated for accuracy.Various approaches are evaluated in smaller sample sizes to minimize training time on low-resource devices.Cross-validation was used to avoid overfitting classification methods during the learning process.We used the Bootstrapping technique to generate 1000,10000,and 100000 samples to examine the performance metrics of different-sized variations of the dataset.In this study,we demonstrated that a sample size of 10000 is sufficient for 99,56%accuracy and learning in the detection of Mirai attacks in IoT devices.

Sustainability of lime stabilized road subgrade in mountainous regions of Türkiye

In this paper the use of lime stabilized subgrade for low volume roads in two regions with high mountains and different frost penetration conditions in Türkiye was investigated in terms of design,performance,and cost.Pavements on unstabilized and stabilized subgrade were designed for two regions(Izmir and Van),covering all climate variations.The resilient modulus of the lime stabilized subgrade with different soil pulverization levels for non-freezing and freezing conditions were taken from a previous laboratory study.Frost effects were considered in pavement design using two different approaches,including limited subgrade frost penetration method and reduced subgrade strength method.Detailed application and evaluation were performed for all steps.Lime stabilized subgrades significantly reduced the thickness of base courses,and the benefit of lime stabilization was highly dependent on soil pulverization level.A detailed cost analysis on the unstabilized and stabilized cases found that the use of lime stabilization in the subgrade provided significant initial cost savings.Comparative analysis by using the AASHTO(1993)method and KENPAVE software,and quantity effect of soil pulverization level on the performance of low volume roads from a service life perspective,show that subgrade resilient modulus can be estimated.It is also possible to make correct performance estimation in the field.The results of the study show that lime stabilization is a good solution for low volume roads in the mountainous regions of Türkiye.

Aluminum Toxicity:A Case Study on Tobacco(Nicotiana tabacum L.)

Aluminum is an abundant metal in the earth’s crust that turns out to be toxic in acidic environments.Many plants are affected by the presence of aluminum at the whole plant level,at the organ level,and at the cellular level.Tobacco as a cash crop(Nicotiana tabacum L.)is a widely cultivated plant worldwide and is also a good model organism for research.Although there are many articles on Al-phytotoxicity in the literature,reviews on a single species that are economically and scientifically important are limited.In this article,we not only provide the biology associated with tobacco Al-toxicity,but also some essential information regarding the effects of this metal on other plant species(even animals).This review provides information on aluminum localization and uptake process by different staining techniques,as well as the effects of its toxicity at different compartment levels and the physiological consequences derived from them.In addition,molecular studies in recent years have reported specific responses to Al toxicity,such as overexpression of various protective proteins.Besides,this review discusses data on various organelle-based responses,cell death,and other mechanisms,data on tobacco plants and other kingdoms relevant to these studies.

低功率超声强化方铅矿-乙基黄原酸钾浮选体系

研究在乙基黄原酸钾(KEX)存在的条件下利用超声提高方铅矿可浮性的可能性。为此,除表面化学研究外,还进行微浮选实验,包括测量不同超声功率水平和调节时间时的Zeta电势、接触角和气泡-颗粒粘附时间。研究结果表明,当超声功率为30 W、调节时间为2 min时浮选回收率最大,为77.5%。另外,使用超声后获得更负的Zeta电势、更大的接触角和更短的气泡-颗粒粘附时间,表明超声作用使方铅矿的疏水性增加。

Mental health of parents of children with autism spectrum disorder during COVID-19 pandemic: A systematic review

BACKGROUND Children diagnosed with autism spectrum disorder(ASD)may have great difficulties when their routines change,and this may affect the psychological wellbeing of their parents.For this reason,it is important to examine studies that address the mental health of parents in order to adapt to the coronavirus disease 2019(COVID-19)pandemic.AIM To determine the mental health status of parents with children diagnosed with ASD in the COVID-19 pandemic.METHODS The study,which is a systematic review,was conducted between December 15,2020 and December 30,2020 by scanning articles in English.The Scopus,Science Direct,PubMed,Cochrane,Web of Science,and Google Scholar databases were used for scanning.The keywords COVID-19 AND(“autism”OR“autistic”OR“autism spectrum disorder”)AND parent AND(“mental health”OR“anxiety”OR“stress”)were used in the search process.The inclusion criteria in the study were findings regarding the mental health of parents with children diagnosed with ASD in the COVID-19 pandemic,addressing their anxiety and stress situations,being a research article,and accessing the full text of the article.RESULTS In the study,a total of 6389 articles were reached,and the full texts of 173 articles were evaluated for eligibility.After the articles excluded by the full-text search were eliminated,12 studies involving 7105 parents were included in the analysis.The findings obtained from the articles containing data on mental health in the COVID-19 pandemic of parents with children with autism spectrum disorder were discussed in three groups.These were findings on the experiences of parents with children with ASD in the COVID-19 pandemic regarding the areas where parents with children with ASD need support in the COVID-19 pandemic and methods of coping with the COVID-19 pandemic for parents with children with ASD.In the systematic review,it was determined that the anxiety and stress of the parents increased,they needed more support compared to the pre-pandemic period,and they had difficulty coping.CONCLUSION In this systematic review,it was concluded that the COVID-19 pandemic negatively affected the mental health of the parents of children with ASD.

草酸水溶液中天青石转化为酸性草酸锶水合物的动力学（英文）

H2C2O4水溶液中SrSO4转化为酸性草酸锶水合物(H[Sr(C2O4)1.5(H2O)])的反应为连串反应。在连串反应的第一步, SrSO4与H2C2O4反应,转化为赝晶SrC2O4·H2O。第二步, SrC2O4·H2O与H2C2O4反应,生成H[Sr(C2O4)1.5(H2O)]。如果溶液达到H[Sr(C2O4)1.5(H2O)]的饱和浓度,当反应混合物冷却至室温时,Sr(HC2O4)(C2O4)0.5·H2O发生结晶。根据溶解的S和Sr的量计算SrSO4的转化率发现,对于H2C2O4浓度大致相同的溶液,反应初始阶段的反应速率随着温度的升高而增大;在低H2C2O4浓度溶液中,一定时间之后反应进行缓慢,并且由于固体颗粒表面形成SrC2O4·H2O保护层而停止;在一定温度下,SrSO4的转化率随着H2C2O4浓度的增加而增大。利用收缩核模型得到每一步骤的动力学方程。

Investigation of the Effect of Using Different Fly Ash on the Mechanical Properties in Cemented Paste Backfill

In the cemented paste backfill(CPB)method,which can also be used for fortification purposes in mines,different additive materials with pozzolanic properties can be employed as substitutes instead of cement that is the main binder.One of the most popular pozzolanic materials that can be employed instead of cement is fly ash,which is thermal power plant tailings.But the compositions of fly ash and tailings used in high amounts in the CPB method,as well as the chemical structures that these materials form by interacting with the cement binder,affect the mechanical properties of the material depending on time.In this study,fly ash with 4 different chemical compositions(TFA,SFA,YFA,and CFA)was used as a cement substitute in CPB.By substituting fly ash with different chemical compositions in different proportions,CPB samples were created and their strength was elucidated according to 28,56,and 90-day curing times.The results of the study revealed that TFA with the highest CaO/SiO_(2) and SO_(3) ratios remained stable at the strength values of 6 MPa(total 9% binder)and 10 MPa(total 11% binder)in the long term.However,CFA with the lowest CaO/SiO_(2),SO_(3),and the highest SiO_(2)+Al_(2)O_(3)+Fe_(2)O_(3) ratios resulted in the greatest strength increase at a 20%substitution rate(11% of the total binder).Nevertheless,it was found that the SFA,which is in Class F,increased its strength in the early period based on the CaO rate.

Association between human leukocyte antigen gene polymorphisms and multiple EPIYA-C repeats in gastrointestinal disorders

BACKGROUND Polymorphisms of human leukocyte antigen(HLA)genes are suggested to increase the risk of gastric cancer(GC).AIM To investigate the HLA allele frequencies of patients with GC relative to a control group in terms of CagA+multiple(≥2)EPIYA-C repeats.METHODS The patient group comprised 94 patients[44 GC and 50 duodenal ulcer(DU)patients],and the control group comprised 86 individuals[(50 non-ulcer dyspepsia patients and 36 people with asymptomatic Helicobacter pylori(H.pylori)].Polymerase chain reaction was performed for the amplification of the H.pylori cagA gene and typing of EPIYA motifs.HLA sequence-specific oligonucleotide(SSO)typing was performed using Lifecodes SSO typing kits(HLA-A,HLA-B HLA-C,HLA-DRB1,and HLA-DQA1-B1 kits).RESULTS The comparison of GC cases in terms of CagA+multiple(≥2)EPIYA-C repeats showed that only the HLA-DQB1*06 allele[odds ratio(OR):0.37,P=0.036]was significantly lower,but significance was lost after correction(Pc=0.1845).The HLA-DQA1*01 allele had a high ratio in GC cases with multiple EPIYA-C repeats,but this was not significant in the univariate analysis.We compared allele frequencies in the DU cases alone and in GC and DU cases together using the same criterion,and none of the HLA alleles were significantly associated with GC or DU.Also,none of the alleles were detected as independent risk factors after the multivariate analysis.On the other hand,in a multivariate logistic regression with no discriminative criterion,HLA-DQA1*01(OR=1.848),HLA-DQB1*06(OR=1.821)and HLA-A*02(OR=1.579)alleles were detected as independent risk factors for GC and DU.CONCLUSION None of the HLA alleles were detected as independent risk factors in terms of CagA+multiple EPIYA-C repeats.However,HLA-DQA1*01,HLA-DQB1*0601,and HLA-A*2 were independent risk factors with no criterion in the multivariate analysis.We suggest that the association of these alleles with gastric malignancies is not specifically related to cagA and multiple EPIYA C repeats.

Relationship between Exposure to Low Dose of x-ray and DNA Hypomethylation in Solid Tumors and Hematological Malignancies

X-rays are used in diagnostic and interventional radiology, and for treatment of certain benign and malignant diseases. Although these procedures utilize only low doses of radiation, the long term effects of exposure to these radiations are not known.

Two smoking-related lesions in the same pulmonary lobe of squamous cell carcinoma and pulmonary Langerhans cell histiocytosis:A case report

BACKGROUND Pulmonary Langerhans cell histiocytosis(PLCH)is a rare cystic lung disease usually affecting young adults.It is predicted that PLCH is a lung tumor precursor associated with dysfunction of the myeloid dendritic cells in the lung.CASE SUMMARY A 70-year-old male patient presented with chronic cough and sputum.He had symptoms for 5 years and described shortness of breath on exertion for the previous 3 years.He had a 60 packs/year smoking history.Computerized tomography of the thorax revealed an 11-mm nodule in the right lung lower lobe superior segment and a 7-mm nodule in the right lung lower lobe poster basal segment.Those two nodules were resected by means of right thoracoscopic surgery.Pathological evaluation revealed a squamous cell carcinoma and PLCH.CONCLUSION Coexistent squamous cell carcinoma and PLCH suggest possible association between PLCH and lung cancer.

Litter decomposition in pure and mixed Quercus and Fagus stands as influenced by arthropods in Belgrad Forest,Turkey

Arthropod assemblages and decomposition rates were compared in the litter of pure and mixed Quercus(Quercus petraea L.)and Fagus(Fagus orientalis L.)stands.Litter was sampled on a monthly basis for 1 year and stored in litterbags of different mesh sizes.The experimental objective was to test the effect of mesh size on litter decomposition,decomposition rates of litter,and diversity of the invertebrate fauna between the two types of stands.Decomposition rates were measured by filling three fiber litterbags of different mesh sizes with pure Quercus litter(3 g)left in the pure Quercus stand,and litterbags with Fagus litter(3 g)were left in the pure Fagus stand.Mixed litter samples were prepared by mixing of equal amounts of each litter in the same litterbag and leaving them in the mixed stand.The residual mass of litter from the pure Quercus stand was significantly lower in fine-and coarse-mesh bags than in the medium-mesh bags in pure the Fagus stand.Carbon and nitrogen levels in the pure Quercus litter were significantly different among the mesh sizes at the end of the incubation period.Macroarthropods from 27 taxa were collected from pitfall traps every month.Their relative numbers differed significantly between the pure and mixed-stand litter samples.Litter-dwelling Isotomidae(Collembola)and Mesostigmata(Acarina),and soil-dwelling Mesostigmata were the most numerous in the mixed stand.It is significant that the abundance of macroarthropods contributed to the mass loss of litter in both the medium and coarse mesh sizes in the mixed stand,but did not significantly affect the mass of litter in the pure stands.In the mixed stand,there was a negative correlation between litter mass loss and total number of microarthropods in all mesh sizes.Mixed-stand litter decomposed more slowly than pure-stand litter.

Comparative evaluation of boron distribution from ulexite,colemanite and DOT rods in Scots pine wood

This study evaluated boron diff usion from rods made of raw boron minerals,ulexite and colemanite with low water solubility,in comparison with di-sodium octaborate tetrahydrate(DOT).Scots pine(Pinus sylvestris L.)sapwood and heartwood blocks were conditioned to target moisture contents of 30%,60%,and 90%.The rods were inserted into the blocks through treatment holes and boron diff usion was observed at three assay zones across the blocks after 7,30,60 and 90-day-incubation at room temperature.Ethylene glycol was also inserted into the holes to improve boron diff usion.Boron levels increased with increased wood moisture content.With some exceptions,boron in the assay zones did not tend to follow consistent amount gradients with distance from the treatment hole.Boron levels from ulexite rods were higher than those from colemanite rods,with DOT rods with the highest diff usion rates as a result of higher water solubility of DOT than ulexite and colemanite.The results suggest that ulexite-based rods may be useful inthe presence of ethylene glycol in sapwood when wood is at high moisture content for extended periods.

A New Segmentation Framework for Arabic Handwritten Text Using Machine Learning Techniques

The writer identification(WI)of handwritten Arabic text is now of great concern to intelligence agencies following the recent attacks perpetrated by known Middle East terrorist organizations.It is also a useful instrument for the digitalization and attribution of old text to other authors of historic studies,including old national and religious archives.In this study,we proposed a new affective segmentation model by modifying an artificial neural network model and making it suitable for the binarization stage based on blocks.This modified method is combined with a new effective rotation model to achieve an accurate segmentation through the analysis of the histogram of binary images.Also,propose a new framework for correct text rotation that will help us to establish a segmentation method that can facilitate the extraction of text from its background.Image projections and the radon transform are used and improved using machine learning based on a co-occurrence matrix to produce binary images.The training stage involves taking a number of images for model training.These images are selected randomly with different angles to generate four classes(0–90,90–180,180–270,and 270–360).The proposed segmentation approach achieves a high accuracy of 98.18%.The study ultimately provides two major contributions that are ranked from top to bottom according to the degree of importance.The proposed method can be further developed as a new application and used in the recognition of handwritten Arabic text from small documents regardless of logical combinations and sentence construction.

Effects of Selected Printing Parameters on the Fire Properties of 3D-Printed Neat Polylactic Acid(PLA)and Wood/PLA Composites

The effects of selected printing parameters on the fire properties of additively produced composites from neat polylactic acid(PLA)and wood/PLA filaments were investigated.The reaction to fire of the 3D-printed specimens was tested according to the ISO 5660-1 cone calorimeter test method.The results showed that the properties of the specimens when exposed to fire were significantly affected by the incorporation of wood flour into the PLA filament.It was also interesting that PLA specimens had much better reactions to fire than the wood/PLA specimens.Time to ignition was found to be much longer in the 3D-printed PLA specimens.Although the maximal heat release rate was a little higher in the PLA than the wood/PLA specimens,the duration of HRR was longer for the wood/PLA specimens.The initial mass of the specimens was smaller in the wood/PLA composites,but during the radiant heat exposure the mass typically decreased slower than in the PLA specimens.

Machine Learning and Classical Forecasting Methods Based Decision Support Systems for COVID-19

From late 2019 to the present day,the coronavirus outbreak tragically affected the whole world and killed tens of thousands of people.Many countries have taken very stringent measures to alleviate the effects of the coronavirus disease 2019(COVID-19)and are still being implemented.In this study,various machine learning techniques are implemented to predict possible confirmed cases and mortality numbers for the future.According to these models,we have tried to shed light on the future in terms of possible measures to be taken or updating the current measures.Support Vector Machines(SVM),Holt-Winters,Prophet,and Long-Short Term Memory(LSTM)forecasting models are applied to the novel COVID-19 dataset.According to the results,the Prophet model gives the lowest Root Mean Squared Error(RMSE)score compared to the other three models.Besides,according to this model,a projection for the future COVID-19 predictions of Turkey has been drawn and aimed to shape the current measures against the coronavirus.