Natural stones used as floor and wall coverings are exposed to many different abrasive forces,so it is essential to choose suitable materials for wear resistance in terms of the life of the structure.The abrasion resi...Natural stones used as floor and wall coverings are exposed to many different abrasive forces,so it is essential to choose suitable materials for wear resistance in terms of the life of the structure.The abrasion resistance of natural stones can be determined in the laboratory by applying the B?hme abrasion resistance(BAR)test.However,the direct analysis of BAR in the laboratory has disadvantages such as wasting time and energy,experimental errors,and health impacts.To eliminate these disadvantages,the estimation of BAR using artificial neural networks(ANN)was proposed.Different natural stone samples were collected from Türkiye,and uniaxial compressive strength(UCS),flexural strength(FS),water absorption rate(WA),unit volume weight(UW),effective porosity(n),and BAR tests were carried out.The outputs of these tests were gathered and a data set,consisting of a total of 105 data,was randomly divided into two groups:testing and training.In the current study,the success of three different training algorithms of Levenberg-Marquardt(LM),Bayesian regularization(BR),and scaled conjugate gradient(SCG)were compared for BAR prediction of natural stones.Statistical criteria such as coefficient of determination(R~2),mean square error(MSE),mean square error(RMSE),and mean absolute percentage error(MAPE),which are widely used and adopted in the literature,were used to determine predictive validity.The findings of the study indicated that ANN is a valid method for estimating the BAR value.Also,the LM algorithm(R~2=0.9999,MSE=0.0001,RMSE=0.0110,and MAPE=0.0487)in training and the BR algorithm(R~2=0.9896,MSE=0.0589,RMSE=0.2427,and MAPE=1.2327)in testing showed the best prediction performance.It has been observed that the proposed method is quite practical to implement.Using the artificial neural networks method will provide an advantage in similar laborintensive experimental studies.展开更多
Introduction: Hereditary multiple exostosis (HME) is a hereditary disorder characterized by multiple osteochondromas. Clinical symptoms can result from compression of adjacent structures such as peripheral nerves. In ...Introduction: Hereditary multiple exostosis (HME) is a hereditary disorder characterized by multiple osteochondromas. Clinical symptoms can result from compression of adjacent structures such as peripheral nerves. In Indonesia, HME with nerve compression cases have rarely reported. Presentation of Case: An eleven-year-old female with complaining of left knee joint pain and progressive masses in left lower leg since 6 years ago. This complains followed by numbness and difficulty to dorso flexion motion on left ankle joint since four months ago. Physical examination showed of the bony masses was detected at the left lateral upper third lower leg with measuring about six into eight centimeters. Range of motion of left ankle joint patient had difficult to dorso flexion. X-ray imaging viewed demonstrates multiple exostosis appearance involving distal femoral, proximal fibula, proximal tibia and distal fibula bone. MR Imaging revealed cartilage cap of head fibula is thin less 1.5 cm and the axially specimen showed peroneal nerve compression. The patient underwent left head fibula wide resection. Intraoperative findings peripheral nerve peroneal compression and was decompression. Medical rehabilitation for physiotherapy was advised. The results of the follow-up after 2 years, no pain feels and the patient was able to dorso flexion of left ankle joint and no additional bumps in other areas of the body. These lesions may arise from any bone which was pre-formed in the cartilage. Nerve compression syndromes are the neurological complex symptom caused by the mechanical or dynamic compression of a specific single segment. MRI was excellent demonstration of blood vessels compromise and represents choices with peripheral nerves structures and to measuring cartilage cap thickness for criterion of osteochondromas differentiation and exostotic grade. Complete resection was importance of the cartilaginous cap to prevent recurrence. The decompressing the peroneal nerve that pressured by the masses and vascular problems occured. Conclusion: Hereditary multiple exostosis is an inherited disorder characterized by multiple osteochondromas. It is important to monitor all cases of HME especially if the patient complains of pain or growth of an osteochondroma. The surgical excision, with complete resection of the cartilaginous cap of the tumor, is important in preventing recurrence.展开更多
文摘Natural stones used as floor and wall coverings are exposed to many different abrasive forces,so it is essential to choose suitable materials for wear resistance in terms of the life of the structure.The abrasion resistance of natural stones can be determined in the laboratory by applying the B?hme abrasion resistance(BAR)test.However,the direct analysis of BAR in the laboratory has disadvantages such as wasting time and energy,experimental errors,and health impacts.To eliminate these disadvantages,the estimation of BAR using artificial neural networks(ANN)was proposed.Different natural stone samples were collected from Türkiye,and uniaxial compressive strength(UCS),flexural strength(FS),water absorption rate(WA),unit volume weight(UW),effective porosity(n),and BAR tests were carried out.The outputs of these tests were gathered and a data set,consisting of a total of 105 data,was randomly divided into two groups:testing and training.In the current study,the success of three different training algorithms of Levenberg-Marquardt(LM),Bayesian regularization(BR),and scaled conjugate gradient(SCG)were compared for BAR prediction of natural stones.Statistical criteria such as coefficient of determination(R~2),mean square error(MSE),mean square error(RMSE),and mean absolute percentage error(MAPE),which are widely used and adopted in the literature,were used to determine predictive validity.The findings of the study indicated that ANN is a valid method for estimating the BAR value.Also,the LM algorithm(R~2=0.9999,MSE=0.0001,RMSE=0.0110,and MAPE=0.0487)in training and the BR algorithm(R~2=0.9896,MSE=0.0589,RMSE=0.2427,and MAPE=1.2327)in testing showed the best prediction performance.It has been observed that the proposed method is quite practical to implement.Using the artificial neural networks method will provide an advantage in similar laborintensive experimental studies.
文摘Introduction: Hereditary multiple exostosis (HME) is a hereditary disorder characterized by multiple osteochondromas. Clinical symptoms can result from compression of adjacent structures such as peripheral nerves. In Indonesia, HME with nerve compression cases have rarely reported. Presentation of Case: An eleven-year-old female with complaining of left knee joint pain and progressive masses in left lower leg since 6 years ago. This complains followed by numbness and difficulty to dorso flexion motion on left ankle joint since four months ago. Physical examination showed of the bony masses was detected at the left lateral upper third lower leg with measuring about six into eight centimeters. Range of motion of left ankle joint patient had difficult to dorso flexion. X-ray imaging viewed demonstrates multiple exostosis appearance involving distal femoral, proximal fibula, proximal tibia and distal fibula bone. MR Imaging revealed cartilage cap of head fibula is thin less 1.5 cm and the axially specimen showed peroneal nerve compression. The patient underwent left head fibula wide resection. Intraoperative findings peripheral nerve peroneal compression and was decompression. Medical rehabilitation for physiotherapy was advised. The results of the follow-up after 2 years, no pain feels and the patient was able to dorso flexion of left ankle joint and no additional bumps in other areas of the body. These lesions may arise from any bone which was pre-formed in the cartilage. Nerve compression syndromes are the neurological complex symptom caused by the mechanical or dynamic compression of a specific single segment. MRI was excellent demonstration of blood vessels compromise and represents choices with peripheral nerves structures and to measuring cartilage cap thickness for criterion of osteochondromas differentiation and exostotic grade. Complete resection was importance of the cartilaginous cap to prevent recurrence. The decompressing the peroneal nerve that pressured by the masses and vascular problems occured. Conclusion: Hereditary multiple exostosis is an inherited disorder characterized by multiple osteochondromas. It is important to monitor all cases of HME especially if the patient complains of pain or growth of an osteochondroma. The surgical excision, with complete resection of the cartilaginous cap of the tumor, is important in preventing recurrence.
