Objective:To evaluate the effectiveness of surgical combination with traditional Chinese medicine dialectical therapy in three phases for the treatment of intertrochanteric fracture of the femur(IFF).Methods:84 patien...Objective:To evaluate the effectiveness of surgical combination with traditional Chinese medicine dialectical therapy in three phases for the treatment of intertrochanteric fracture of the femur(IFF).Methods:84 patients with IFF admitted to the hospital from December 2022 to December 2024 were selected and randomly divided into two groups using a random number table.The combined group received surgery and traditional Chinese medicine dialectical therapy in three phases,while the control group received surgery alone.The total effective rate,fracture healing time,hip function score,and lower extremity function score were compared between the two groups.Results:The total effective rate was higher in the combined group than in the control group(P<0.05).After treatment,the fracture healing time was shorter in the combined group than in the control group,and the hip function and lower extremity function scores were higher in the combined group than in the control group(P<0.05).Conclusion:Surgical combination with traditional Chinese medicine dialectical therapy in three phases can shorten the fracture healing time of IFF patients and restore their hip and lower extremity function,demonstrating significant efficacy.展开更多
Arabic Dialect Identification(DID)is a task in Natural Language Processing(NLP)that involves determining the dialect of a given piece of text in Arabic.The state-of-the-art solutions for DID are built on various deep ...Arabic Dialect Identification(DID)is a task in Natural Language Processing(NLP)that involves determining the dialect of a given piece of text in Arabic.The state-of-the-art solutions for DID are built on various deep neural networks that commonly learn the representation of sentences in response to a given dialect.Despite the effectiveness of these solutions,the performance heavily relies on the amount of labeled examples,which is labor-intensive to atain and may not be readily available in real-world scenarios.To alleviate the burden of labeling data,this paper introduces a novel solution that leverages unlabeled corpora to boost performance on the DID task.Specifically,we design an architecture that enables learning the shared information between labeled and unlabeled texts through a gradient reversal layer.The key idea is to penalize the model for learning source dataset specific features and thus enable it to capture common knowledge regardless of the label.Finally,we evaluate the proposed solution on benchmark datasets for DID.Our extensive experiments show that it performs signifcantly better,especially,with sparse labeled data.By comparing our approach with existing Pre-trained Language Models(PLMs),we achieve a new state-of-the-art performance in the DID field.The code will be available on GitHub upon the paper's acceptance.展开更多
Wearable pressure sensors capable of adhering comfortably to the skin hold great promise in sound detection.However,current intelligent speech assistants based on pressure sensors can only recognize standard languages...Wearable pressure sensors capable of adhering comfortably to the skin hold great promise in sound detection.However,current intelligent speech assistants based on pressure sensors can only recognize standard languages,which hampers effective communication for non-standard language people.Here,we prepare an ultralight Ti_(3)C_(2)T_(x)MXene/chitosan/polyvinylidene difluoride composite aerogel with a detection range of 6.25 Pa-1200 k Pa,rapid response/recovery time,and low hysteresis(13.69%).The wearable aerogel pressure sensor can detect speech information through the throat muscle vibrations without any interference,allowing for accurate recognition of six dialects(96.2%accuracy)and seven different words(96.6%accuracy)with the assistance of convolutional neural networks.This work represents a significant step forward in silent speech recognition for human–machine interaction and physiological signal monitoring.展开更多
Arabic dialect identification is essential in Natural Language Processing(NLP)and forms a critical component of applications such as machine translation,sentiment analysis,and cross-language text generation.The diffic...Arabic dialect identification is essential in Natural Language Processing(NLP)and forms a critical component of applications such as machine translation,sentiment analysis,and cross-language text generation.The difficulties in differentiating between Arabic dialects have garnered more attention in the last 10 years,particularly in social media.These difficulties result from the overlapping vocabulary of the dialects,the fluidity of online language use,and the difficulties in telling apart dialects that are closely related.Managing dialects with limited resources and adjusting to the ever-changing linguistic trends on social media platforms present additional challenges.A strong dialect recognition technique is essential to improving communication technology and cross-cultural understanding in light of the increase in social media usage.To distinguish Arabic dialects on social media,this research suggests a hybrid Deep Learning(DL)approach.The Long Short-Term Memory(LSTM)and Bidirectional Long Short-Term Memory(BiLSTM)architectures make up the model.A new textual dataset that focuses on three main dialects,i.e.,Levantine,Saudi,and Egyptian,is also available.Approximately 11,000 user-generated comments from Twitter are included in this dataset,which has been painstakingly annotated to guarantee accuracy in dialect classification.Transformers,DL models,and basic machine learning classifiers are used to conduct several tests to evaluate the performance of the suggested model.Various methodologies,including TF-IDF,word embedding,and self-attention mechanisms,are used.The suggested model fares better than other models in terms of accuracy,obtaining a remarkable 96.54%,according to the trial results.This study advances the discipline by presenting a new dataset and putting forth a practical model for Arabic dialect identification.This model may prove crucial for future work in sociolinguistic studies and NLP.展开更多
According to the second law of thermodynamics, as currently understood, any given transit of a system along the reversible path proceeds with a total entropy change equal to zero. The fact that this condition is also ...According to the second law of thermodynamics, as currently understood, any given transit of a system along the reversible path proceeds with a total entropy change equal to zero. The fact that this condition is also the identifier of thermodynamic equilibrium, makes each and every point along the reversible path a state of equilibrium, and the reversible path, as expressed by a noted thermodynamic author, “a dense succession of equilibrium states”. The difficulties with these notions are plural. The fact, for example, that systems need to be forced out of equilibrium via the expenditure of work, would make any spontaneous reversible process a consumer of work, this in opposition to common thermodynamic wisdom that makes spontaneous reversible processes the most efficient transformers of work-producing-potential into actual work. The solution to this and other related impasses is provided by Dialectical Thermodynamics via its previously proved notion assigning a negative entropy change to the energy upgrading process represented by the transformation of heat into work. The said solution is here exemplified with the ideal-gas phase isomerization of butane into isobutane.展开更多
This paper explores the methodological aspect of the harmonious philosophy-the yin-yang dialectics-by elaborating on its core concepts,eight fundamental laws,and the theoretical model of the mutual generation and over...This paper explores the methodological aspect of the harmonious philosophy-the yin-yang dialectics-by elaborating on its core concepts,eight fundamental laws,and the theoretical model of the mutual generation and overcoming among the Five Phases(Wuxing).Using the yin-yang dialectics to analyze international relations,the paper proposes pathways and insights to address global challenges,and offers a methodological framework to reduce wars in the international community and guide the world toward peace and harmony.展开更多
文摘Objective:To evaluate the effectiveness of surgical combination with traditional Chinese medicine dialectical therapy in three phases for the treatment of intertrochanteric fracture of the femur(IFF).Methods:84 patients with IFF admitted to the hospital from December 2022 to December 2024 were selected and randomly divided into two groups using a random number table.The combined group received surgery and traditional Chinese medicine dialectical therapy in three phases,while the control group received surgery alone.The total effective rate,fracture healing time,hip function score,and lower extremity function score were compared between the two groups.Results:The total effective rate was higher in the combined group than in the control group(P<0.05).After treatment,the fracture healing time was shorter in the combined group than in the control group,and the hip function and lower extremity function scores were higher in the combined group than in the control group(P<0.05).Conclusion:Surgical combination with traditional Chinese medicine dialectical therapy in three phases can shorten the fracture healing time of IFF patients and restore their hip and lower extremity function,demonstrating significant efficacy.
基金supported by the Deanship of Scientific Research at King Khalid University through Small Groups funding(Project Grant No.RGP1/243/45)The funding was awarded to Dr.Mohammed Abker.And Natural Science Foundation of China under Grant 61901388.
文摘Arabic Dialect Identification(DID)is a task in Natural Language Processing(NLP)that involves determining the dialect of a given piece of text in Arabic.The state-of-the-art solutions for DID are built on various deep neural networks that commonly learn the representation of sentences in response to a given dialect.Despite the effectiveness of these solutions,the performance heavily relies on the amount of labeled examples,which is labor-intensive to atain and may not be readily available in real-world scenarios.To alleviate the burden of labeling data,this paper introduces a novel solution that leverages unlabeled corpora to boost performance on the DID task.Specifically,we design an architecture that enables learning the shared information between labeled and unlabeled texts through a gradient reversal layer.The key idea is to penalize the model for learning source dataset specific features and thus enable it to capture common knowledge regardless of the label.Finally,we evaluate the proposed solution on benchmark datasets for DID.Our extensive experiments show that it performs signifcantly better,especially,with sparse labeled data.By comparing our approach with existing Pre-trained Language Models(PLMs),we achieve a new state-of-the-art performance in the DID field.The code will be available on GitHub upon the paper's acceptance.
基金supported by the National Nature Science Foundation of China(No.62122030,62333008,62371205,52103208)National Key Research and Development Program of China(No.2021YFB3201300)+1 种基金Application and Basic Research of Jilin Province(20130102010 JC)Fundamental Research Funds for the Central Universities,Jilin Provincial Science and Technology Development Program(20230101072JC)。
文摘Wearable pressure sensors capable of adhering comfortably to the skin hold great promise in sound detection.However,current intelligent speech assistants based on pressure sensors can only recognize standard languages,which hampers effective communication for non-standard language people.Here,we prepare an ultralight Ti_(3)C_(2)T_(x)MXene/chitosan/polyvinylidene difluoride composite aerogel with a detection range of 6.25 Pa-1200 k Pa,rapid response/recovery time,and low hysteresis(13.69%).The wearable aerogel pressure sensor can detect speech information through the throat muscle vibrations without any interference,allowing for accurate recognition of six dialects(96.2%accuracy)and seven different words(96.6%accuracy)with the assistance of convolutional neural networks.This work represents a significant step forward in silent speech recognition for human–machine interaction and physiological signal monitoring.
基金the Deanship of Graduate Studies and Scientific Research at Qassim University for financial support(QU-APC-2024-9/1).
文摘Arabic dialect identification is essential in Natural Language Processing(NLP)and forms a critical component of applications such as machine translation,sentiment analysis,and cross-language text generation.The difficulties in differentiating between Arabic dialects have garnered more attention in the last 10 years,particularly in social media.These difficulties result from the overlapping vocabulary of the dialects,the fluidity of online language use,and the difficulties in telling apart dialects that are closely related.Managing dialects with limited resources and adjusting to the ever-changing linguistic trends on social media platforms present additional challenges.A strong dialect recognition technique is essential to improving communication technology and cross-cultural understanding in light of the increase in social media usage.To distinguish Arabic dialects on social media,this research suggests a hybrid Deep Learning(DL)approach.The Long Short-Term Memory(LSTM)and Bidirectional Long Short-Term Memory(BiLSTM)architectures make up the model.A new textual dataset that focuses on three main dialects,i.e.,Levantine,Saudi,and Egyptian,is also available.Approximately 11,000 user-generated comments from Twitter are included in this dataset,which has been painstakingly annotated to guarantee accuracy in dialect classification.Transformers,DL models,and basic machine learning classifiers are used to conduct several tests to evaluate the performance of the suggested model.Various methodologies,including TF-IDF,word embedding,and self-attention mechanisms,are used.The suggested model fares better than other models in terms of accuracy,obtaining a remarkable 96.54%,according to the trial results.This study advances the discipline by presenting a new dataset and putting forth a practical model for Arabic dialect identification.This model may prove crucial for future work in sociolinguistic studies and NLP.
文摘According to the second law of thermodynamics, as currently understood, any given transit of a system along the reversible path proceeds with a total entropy change equal to zero. The fact that this condition is also the identifier of thermodynamic equilibrium, makes each and every point along the reversible path a state of equilibrium, and the reversible path, as expressed by a noted thermodynamic author, “a dense succession of equilibrium states”. The difficulties with these notions are plural. The fact, for example, that systems need to be forced out of equilibrium via the expenditure of work, would make any spontaneous reversible process a consumer of work, this in opposition to common thermodynamic wisdom that makes spontaneous reversible processes the most efficient transformers of work-producing-potential into actual work. The solution to this and other related impasses is provided by Dialectical Thermodynamics via its previously proved notion assigning a negative entropy change to the energy upgrading process represented by the transformation of heat into work. The said solution is here exemplified with the ideal-gas phase isomerization of butane into isobutane.
文摘This paper explores the methodological aspect of the harmonious philosophy-the yin-yang dialectics-by elaborating on its core concepts,eight fundamental laws,and the theoretical model of the mutual generation and overcoming among the Five Phases(Wuxing).Using the yin-yang dialectics to analyze international relations,the paper proposes pathways and insights to address global challenges,and offers a methodological framework to reduce wars in the international community and guide the world toward peace and harmony.
