Open Science(OS)and Research has reached mixed maturity levels in Finland.The meaning of the national project in the ecosystem of Finnish universities of applied sciences(UAS)is to enhance and elaborate OS and Open Ed...Open Science(OS)and Research has reached mixed maturity levels in Finland.The meaning of the national project in the ecosystem of Finnish universities of applied sciences(UAS)is to enhance and elaborate OS and Open Education(OE)activities.Future actions were defined based on a survey and interviews carried out in the Finnish UAS sector during 2018 and 2019.The aim of both data collections was to evaluate the current status and attitudes towards open Research,Development,and Innovation(RDI)among staff members.Another purpose was to define the need for internal support services concerning open RDI and OE and to identify knowledge gaps.The results revealed several gaps in understanding OS and OE initiatives.Real-life actions were mostly vague,and the respondents experienced the need for support.On the other hand,the attitudes towards open RDI were positive,and the issue aroused questions and reflections.This study revealed gaps in knowledge and actions in Finnish UAS sectors.These results have been the basis of development actions such as joint workshops,educational webinars,and common instructions.The future plan includes the establishment of an experts’network for supporting open RDI and Education.展开更多
In this study 70 male students were participated to determine the thyroid disorder through ultrasonography. Thyroid scan was done in 70 students prospectively with no indicative of thyroid disease (age of 19 - 23 yrs)...In this study 70 male students were participated to determine the thyroid disorder through ultrasonography. Thyroid scan was done in 70 students prospectively with no indicative of thyroid disease (age of 19 - 23 yrs). Thyroid scan for students who participated the study took place in the department of Faculty of Applied Medical Sciences, King Abdulaziz University by using an IU 22 Philips ultrasound machine with a 5 - 12 MHz linear transducer. Among the total number of the subjects, 26% was found with abnormal ultrasound findings, 17% of them with cystic nodule, while solid and mixed nodule represented 4% for each. The high rate of abnormal findings of thyroid gland in the study suggested that screening using ultrasound scan was useful in detecting early thyroid disorders.展开更多
The University of Applied Sciences in Vienna has offered university degree programs in the field of construction for more than twenty years and has thus gained great expertise in developing its curriculum. Founded in ...The University of Applied Sciences in Vienna has offered university degree programs in the field of construction for more than twenty years and has thus gained great expertise in developing its curriculum. Founded in 1996, the department of Building and Design consists of six university degree programs. A major strength of the department is the possibility to adapt to recent challenges in a timely manner. As shown in Figure 1, in the winter term 2008/2009, the master’s degree program, Sustainability in the Construction Industry, was held for the first time;it was transformed into the master’s degree program, Architecture-Green Building, in 2016. In 2013/14 the bachelor’s degree program, Architecture-Green Building, started with the first students graduating in 2016. For ten years the department has focused on sustainability within the building, planning and designing processes.展开更多
The Financial Technology(FinTech)sector has witnessed rapid growth,resulting in increasingly complex and high-volume digital transactions.Although this expansion improves efficiency and accessibility,it also introduce...The Financial Technology(FinTech)sector has witnessed rapid growth,resulting in increasingly complex and high-volume digital transactions.Although this expansion improves efficiency and accessibility,it also introduces significant vulnerabilities,including fraud,money laundering,and market manipulation.Traditional anomaly detection techniques often fail to capture the relational and dynamic characteristics of financial data.Graph Neural Networks(GNNs),capable of modeling intricate interdependencies among entities,have emerged as a powerful framework for detecting subtle and sophisticated anomalies.However,the high-dimensionality and inherent noise of FinTech datasets demand robust feature selection strategies to improve model scalability,performance,and interpretability.This paper presents a comprehensive survey of GNN-based approaches for anomaly detection in FinTech,with an emphasis on the synergistic role of feature selection.We examine the theoretical foundations of GNNs,review state-of-the-art feature selection techniques,analyze their integration with GNNs,and categorize prevalent anomaly types in FinTech applications.In addition,we discuss practical implementation challenges,highlight representative case studies,and propose future research directions to advance the field of graph-based anomaly detection in financial systems.展开更多
A novel siphon-based divide-and-conquer(SbDaC)policy is presented in this paper for the synthesis of Petri net(PN)based liveness-enforcing supervisors(LES)for flexible manufacturing systems(FMS)prone to deadlocks or l...A novel siphon-based divide-and-conquer(SbDaC)policy is presented in this paper for the synthesis of Petri net(PN)based liveness-enforcing supervisors(LES)for flexible manufacturing systems(FMS)prone to deadlocks or livelocks.The proposed method takes an uncontrolled and bounded PN model(UPNM)of the FMS.Firstly,the reduced PNM(RPNM)is obtained from the UPNM by using PN reduction rules to reduce the computation burden.Then,the set of strict minimal siphons(SMSs)of the RPNM is computed.Next,the complementary set of SMSs is computed from the set of SMSs.By the union of these two sets,the superset of SMSs is computed.Finally,the set of subnets of the RPNM is obtained by applying the PN reduction rules to the superset of SMSs.All these subnets suffer from deadlocks.These subnets are then ordered from the smallest one to the largest one based on a criterion.To enforce liveness on these subnets,a set of control places(CPs)is computed starting from the smallest subnet to the largest one.Once all subnets are live,this process provides the LES,consisting of a set of CPs to be used for the UPNM.The live controlled PN model(CPNM)is constructed by merging the LES with the UPNM.The SbDaC policy is applicable to all classes of PNs related to FMS prone to deadlocks or livelocks.Several FMS examples are considered from the literature to highlight the applicability of the SbDaC policy.In particular,three examples are utilized to emphasize the importance,applicability and effectiveness of the SbDaC policy to realistic FMS with very large state spaces.展开更多
Network attacks have become a critical issue in the internet security domain.Artificial intelligence technology-based detection methodologies have attracted attention;however,recent studies have struggled to adapt to ...Network attacks have become a critical issue in the internet security domain.Artificial intelligence technology-based detection methodologies have attracted attention;however,recent studies have struggled to adapt to changing attack patterns and complex network environments.In addition,it is difficult to explain the detection results logically using artificial intelligence.We propose a method for classifying network attacks using graph models to explain the detection results.First,we reconstruct the network packet data into a graphical structure.We then use a graph model to predict network attacks using edge classification.To explain the prediction results,we observed numerical changes by randomly masking and calculating the importance of neighbors,allowing us to extract significant subgraphs.Our experiments on six public datasets demonstrate superior performance with an average F1-score of 0.960 and accuracy of 0.964,outperforming traditional machine learning and other graph models.The visual representation of the extracted subgraphs highlights the neighboring nodes that have the greatest impact on the results,thus explaining detection.In conclusion,this study demonstrates that graph-based models are suitable for network attack detection in complex environments,and the importance of graph neighbors can be calculated to efficiently analyze the results.This approach can contribute to real-world network security analyses and provide a new direction in the field.展开更多
Challenges in the prevention and treatment of mild cognitive impairment associated with Alzheimer's disease:Increased life expectancy due to advancements in medical care has given rise to an aging population,accom...Challenges in the prevention and treatment of mild cognitive impairment associated with Alzheimer's disease:Increased life expectancy due to advancements in medical care has given rise to an aging population,accompanied by a surge in the incidence of incurable neurodegenerative diseases(NDDs).These diseases primarily affect the cognitive and behavioral functions of older adults by impacting brain activity.Mild cognitive impairment(MCI)is a neurodegenerative condition that affects a significant portion of the population.展开更多
Heart disease remains a leading cause of mortality worldwide,emphasizing the urgent need for reliable and interpretable predictive models to support early diagnosis and timely intervention.However,existing Deep Learni...Heart disease remains a leading cause of mortality worldwide,emphasizing the urgent need for reliable and interpretable predictive models to support early diagnosis and timely intervention.However,existing Deep Learning(DL)approaches often face several limitations,including inefficient feature extraction,class imbalance,suboptimal classification performance,and limited interpretability,which collectively hinder their deployment in clinical settings.To address these challenges,we propose a novel DL framework for heart disease prediction that integrates a comprehensive preprocessing pipeline with an advanced classification architecture.The preprocessing stage involves label encoding and feature scaling.To address the issue of class imbalance inherent in the personal key indicators of the heart disease dataset,the localized random affine shadowsampling technique is employed,which enhances minority class representation while minimizing overfitting.At the core of the framework lies the Deep Residual Network(DeepResNet),which employs hierarchical residual transformations to facilitate efficient feature extraction and capture complex,non-linear relationships in the data.Experimental results demonstrate that the proposed model significantly outperforms existing techniques,achieving improvements of 3.26%in accuracy,3.16%in area under the receiver operating characteristics,1.09%in recall,and 1.07%in F1-score.Furthermore,robustness is validated using 10-fold crossvalidation,confirming the model’s generalizability across diverse data distributions.Moreover,model interpretability is ensured through the integration of Shapley additive explanations and local interpretable model-agnostic explanations,offering valuable insights into the contribution of individual features to model predictions.Overall,the proposed DL framework presents a robust,interpretable,and clinically applicable solution for heart disease prediction.展开更多
Background:Epidemiological studies have confirmed that longer exposure to insecticides like cypermethrin(CYP)significantly increases the risk of male reproductive toxicity.Crocus sativus L.has been recognized due to i...Background:Epidemiological studies have confirmed that longer exposure to insecticides like cypermethrin(CYP)significantly increases the risk of male reproductive toxicity.Crocus sativus L.has been recognized due to its therapeutic properties,but its exact role and molecular mechanisms in treatment of reproductive dysfunction remain unclear.Methods:During this study,36 rats were randomly divided into six groups(n=6):control,CYP-induced(60 mg/kg),standard(leuprolide 3 mg/kg)and three treatment groups receiving aqueous,ethanolic,and oil extracts(50 mg/kg or 20 mL/kg)for post-toxicity induction.Results:The finding represented that exposure of CYP significantly increased oxidative stress,disrupted testicular architecture,and markedly reduced testosterone levels(P<0.05).Importantly,Crocus sativus L.treatment alleviated these changes by increasing the expression of Nrf2(nuclear factor erythroid 2-related factor 2),restoring the activity of antioxidant enzymes,and enhancing testicular histomorphology.Surprisingly,molecular docking established a high binding affinity of Crocus sativus L.phytoconstituents such as gallic acid,cinnamic acid and quercetin to the Nrf2-Keap1 complex.It is worth noting that,Crocus sativus L.exhibited a high level of protection against reproductive toxicity caused by CYP in male rats,which was mediated by the activation of Nrf2 pathway,reduction of oxidative damage,and favorable ADMET characteristics.Conclusion:Notably,this research provides a more valid,safe,and effective method of developing new drugs for reproductive disorders,however,further investigation is needed to support the research findings and implement it in clinical practice.展开更多
Neuronal plasticity,the brain's ability to adapt structurally and functionally,is essential for learning,memory,and recovery from injuries.In neurodegenerative diseases such as Alzheimer's disease and Parkinso...Neuronal plasticity,the brain's ability to adapt structurally and functionally,is essential for learning,memory,and recovery from injuries.In neurodegenerative diseases such as Alzheimer's disease and Parkinson's disease,this plasticity is disrupted,leading to cognitive and motor deficits.This review explores the mechanisms of neuronal plasticity and its effect on Alzheimer's disease and Parkinson's disease.Alzheimer's disease features amyloid-beta plaques and tau tangles that impair synaptic function,while Parkinson's disease involves the loss of dopaminergic neurons affecting motor control.Enhancing neuronal plasticity offers therapeutic potential for these diseases.A systematic literature review was conducted using databases such as PubMed,Scopus,and Google Scholar,focusing on studies of neuronal plasticity in Alzheimer's disease and Parkinson's disease.Data synthesis identified key themes such as synaptic mechanisms,neurogenesis,and therapeutic strategies,linking molecular insights to clinical applications.Results highlight that targeting synaptic plasticity mechanisms,such as long-term potentiation and long-term depression,shows promise.Neurotrophic factors,advanced imaging techniques,and molecular tools(e.g.,clustered regularly interspaced short palindromic repeats and optogenetics)are crucial in understanding and enhancing plasticity.Current therapies,including dopamine replacement,deep brain stimulation,and lifestyle interventions,demonstrate the potential to alleviate symptoms and improve outcomes.In conclusion,enhancing neuronal plasticity through targeted therapies holds significant promise for treating neurodegenerative diseases.Future research should integrate multidisciplinary approaches to fully harness the therapeutic potential of neuronal plasticity in Alzheimer's disease and Parkinson's disease.展开更多
Recommending personalized travel routes from sparse,implicit feedback poses a significant challenge,as conventional systems often struggle with information overload and fail to capture the complex,sequential nature of...Recommending personalized travel routes from sparse,implicit feedback poses a significant challenge,as conventional systems often struggle with information overload and fail to capture the complex,sequential nature of user preferences.To address this,we propose a Conditional Generative Adversarial Network(CGAN)that generates diverse and highly relevant itineraries.Our approach begins by constructing a conditional vector that encapsulates a user’s profile.This vector uniquely fuses embeddings from a Heterogeneous Information Network(HIN)to model complex user-place-route relationships,a Recurrent Neural Network(RNN)to capture sequential path dynamics,and Neural Collaborative Filtering(NCF)to incorporate collaborative signals from the wider user base.This comprehensive condition,further enhanced with features representing user interaction confidence and uncertainty,steers a CGAN stabilized by spectral normalization to generate high-fidelity latent route representations,effectively mitigating the data sparsity problem.Recommendations are then formulated using an Anchor-and-Expand algorithm,which selects relevant starting Points of Interest(POI)based on user history,then expands routes through latent similarity matching and geographic coherence optimization,culminating in Traveling Salesman Problem(TSP)-based route optimization for practical travel distances.Experiments on a real-world check-in dataset validate our model’s unique generative capability,achieving F1 scores ranging from 0.163 to 0.305,and near-zero pairs−F1 scores between 0.002 and 0.022.These results confirm the model’s success in generating novel travel routes by recommending new locations and sequences rather than replicating users’past itineraries.This work provides a robust solution for personalized travel planning,capable of generating novel and compelling routes for both new and existing users by learning from collective travel intelligence.展开更多
In this paper, a theory on the determination of the diffusion coefficient of excess minority carriers in the base of a silicon solar cell is presented. The diffusion coefficient expression has been established and is ...In this paper, a theory on the determination of the diffusion coefficient of excess minority carriers in the base of a silicon solar cell is presented. The diffusion coefficient expression has been established and is related to both frequency modulation and applied magnetic field;the study is then carried out using the impedance spectroscopy method and Bode diagrams. From the diffusion coefficient, we deduced the diffusion length and the minority carriers’ mobility. Electric parameters were derived from the diffusion coefficient equivalent circuits.展开更多
We restudy the master-equation approach applied to aggregation in a one-dimensional freeway, where the decay transition probabilities for the jump processes are reconstructed based on a car-following model. According ...We restudy the master-equation approach applied to aggregation in a one-dimensional freeway, where the decay transition probabilities for the jump processes are reconstructed based on a car-following model. According to the reconstructed transition probabilities, the clustering behaviours and the stochastic properties of the master equation in a one-lane freeway traffic model are investigated in detail The numerical results show that the size of the clusters initially below the critical size of the unstable cluster and initially above that of the unstable cluster all enter the same stable state, which also accords with the nucleation theory and is known from the result in earlier work. Moreover, we have obtained more reasonable parameters of the master equation based on some results of cellular automata models.展开更多
In order to take into account the uncertainties linked to the variables in the evaluation of the statistical properties of structural response, a reliability approach with probabilistic aspect was considered. This is ...In order to take into account the uncertainties linked to the variables in the evaluation of the statistical properties of structural response, a reliability approach with probabilistic aspect was considered. This is called the Probabilistic Transformation Method (PTM). This method is readily applicable when the function between the input and the output of the system is explicit. However, the situation is much more involved when it is necessary to perform the evaluation of implicit function between the input and the output of the system through numerical models. In this work, we propose a technique that combines Finite Element Analysis (FEA) and Probabilistic Transformation Method (PTM) to evaluate the Probability Density Function (PDF) of response where the function between the input and the output of the system is implicit. This technique is based on the numerical simulations of the Finite Element Analysis (FEA) and the Probabilistic Transformation Method (PTM) using an interface between Finite Element software and Matlab. Some problems of structures are treated in order to prove the applicability of the proposed technique. Moreover, the obtained results are compared to those obtained by the reference method of Monte Carlo. A second aim of this work is to develop an algorithm of global optimization using the local method SQP, because of its effectiveness and its rapidity of convergence. For this reason, we have combined the method SQP with the Multi start method. This developed algorithm is tested on test functions comparing with other methods such as the method of Particle Swarm Optimization (PSO). In order to test the applicability of the proposed approach, a structure is optimized under reliability constraints.展开更多
This study sets the objective to involve undergraduate students in the evaluation of radiologic sciences and medical imaging technology programmes in Sudanese universities. Based on the analysis of survey results in w...This study sets the objective to involve undergraduate students in the evaluation of radiologic sciences and medical imaging technology programmes in Sudanese universities. Based on the analysis of survey results in which the participants (BSc students undertaking radiologic sciences and medical imaging technology programmes at university level) are asked to answer both closed and open-ended questions, the study seeks to reveal the participants’ perceptions and introspections about the radiologic sciences and medical imaging technology programmes in Sudan. It also attempts to explore the participants’ suggestions and recommendations as to enhance the quality of these programmes with an eye to helping syllabus designers to improve these programmes, thereby bettering healthcare services for the larger good to the community. A brief cross-sectional survey is completed by a total of 105 radiologic sciences and medical imaging technology students, i.e. 39 (37.1%) third-year students and 66 (62.9%) fourth-year students. The majority of participants is satisfied with the programmes, indicating that they are up-to-date and ran abreast with the latest developments in the field. Very few suggest that the programmes should be reviewed for revision, implying that there is room for improvement. Some participants recommend that more training hours in modern imaging modalities (e.g. MRI, CT and U/S) should be introduced. Only one participant recommends the introduction of advanced training centres.展开更多
Analysis of agricultural production systems of hydroponic tomato in Morelos state of Mexico, through a systematic approach, called systems development of life cycle （SDLC） was performed by comparing this with the me...Analysis of agricultural production systems of hydroponic tomato in Morelos state of Mexico, through a systematic approach, called systems development of life cycle （SDLC） was performed by comparing this with the methodology known as life cycle assessment （LCA）. This permits to analyze the differences in approaches of all these methodologies to propose improvements in the current system, which can allow an improved assessment of the environmental quality of agricultural products, which often is subject to confusion. That due to measurement parameters are not generally accepted by society, producers and consumers, may ensure that the process is fully sustainable and is considered quite as a green technology processes towards an ecological benefit and therefore for the humanity.展开更多
Background: In the Kingdom of Saudi Arabia (KSA), recent studies revealed increasing consumption of animal products and refined foods in the diet at the expense of vegetables and fruits. These dietary changes were acc...Background: In the Kingdom of Saudi Arabia (KSA), recent studies revealed increasing consumption of animal products and refined foods in the diet at the expense of vegetables and fruits. These dietary changes were accused of increasing the prevalence of both overweight and obesity observed among Saudi children, adolescences and adults in the last few decades. Objectives: The present study was a cross sectional study aimed at exploring the BMI distribution among students of college of Sciences and Arts for girls Khamis Mushayt Campus 1 at King Khalid University (KKU) and investigated how Dietary habits of students female affected body mass index (BMI). Methods: A total of 240 female students aged 18 - 22 years were about 25% from total students randomly chosen from college of Arts and Science Campus 1 at Khamis Mushayt, King Khalid University, KSA for the present study. A self-reported questionnaire about the student’s dietary habits was conducted and their body mass index (BMI) was measured. Data were analyzed using SPSS statistical software and Chi-square test conducted for variables. Results: About half of the students (47%) were within normal weight, 27.4% were under weight, 16% were overweight and 9.6% were categorized obese. The majority of the students reported eating meals with their family while more than half of sample (58.7%) had eating meals during watching TV. It was worth mentioning that 84.5% of students reported eating snacks such as chocolate and chips 3 or more times per week. There were no significant differences between BMI category and dietary habits. Conclusion and Recommendations: There were no significant difference between body mass index category and dietary habits. Increasing educational programs introduced healthy dietary concepts to improve the dietary habits of female students.展开更多
The interaction between metabolic dysfunction and inflammation is central to the development of neurodegenerative diseases such as Alzheimer’s disease and Parkinson’s disease.Obesity-related conditions like type 2 d...The interaction between metabolic dysfunction and inflammation is central to the development of neurodegenerative diseases such as Alzheimer’s disease and Parkinson’s disease.Obesity-related conditions like type 2 diabetes and non-alcoholic fatty liver disease exacerbate this relationship.Peripheral lipid accumulation,particularly in the liver,initiates a cascade of inflammatory processes that extend to the brain,influencing critical metabolic regulatory regions.Ceramide and palmitate,key lipid components,along with lipid transporters lipocalin-2 and apolipoprotein E,contribute to neuroinflammation by disrupting blood–brain barrier integrity and promoting gliosis.Peripheral insulin resistance further exacerbates brain insulin resistance and neuroinflammation.Preclinical interventions targeting peripheral lipid metabolism and insulin signaling pathways have shown promise in reducing neuroinflammation in animal models.However,translating these findings to clinical practice requires further investigation into human subjects.In conclusion,metabolic dysfunction,peripheral inflammation,and insulin resistance are integral to neuroinflammation and neurodegeneration.Understanding these complex mechanisms holds potential for identifying novel therapeutic targets and improving outcomes for neurodegenerative diseases.展开更多
As new-generation intelligent technologies rapidly evolve,enhancing artificial intelligence(AI)education has become a global consensus,and improving AI literacy is a key focus in higher education.To address the lack o...As new-generation intelligent technologies rapidly evolve,enhancing artificial intelligence(AI)education has become a global consensus,and improving AI literacy is a key focus in higher education.To address the lack of relevant knowledge among non-computer science students,the complexity of the material,which leads to low interest and high difficulty in learning,this paper proposes a three-pronged teaching design model:“BOPPPS model+large language models(LLMs)+mind maps with 3w2h”.This model aims to assist teachers in designing practical teaching cases and engaging,interactive activities,and provides examples of its application to help teachers better teach AI and improve the AI literacy of non-computer science students.展开更多
Melamine sponge is a major concern for oil-water separation due to its lightweight,high porosity(>99%),cost-effectiveness,impressive mechanical properties,and chemical/thermal stability.However,its amphiphilic natu...Melamine sponge is a major concern for oil-water separation due to its lightweight,high porosity(>99%),cost-effectiveness,impressive mechanical properties,and chemical/thermal stability.However,its amphiphilic nature hinders selective oil absorption in water.Recent strategies to enhance hydrophobicity are reviewed,including synthetic methods and materials,with comprehensive explanations of the mechanisms driven by surface energy and roughness.Key performance indicators for MS in oil-water separation,including adsorption capacity,wettability,stability,emulsion separation,reversible wettability switching,flame retardancy,mechanical properties,and recyclability,are thoroughly discussed.In conclusion,this review provides insights into the future potential and direction of functional melamine sponges in oil-water separation.展开更多
基金based on the work done in the “open RDI, learning, and the innovation ecosystem of Finnish UAS” projectco-funded by the Ministry of Education and Culture of Finland
文摘Open Science(OS)and Research has reached mixed maturity levels in Finland.The meaning of the national project in the ecosystem of Finnish universities of applied sciences(UAS)is to enhance and elaborate OS and Open Education(OE)activities.Future actions were defined based on a survey and interviews carried out in the Finnish UAS sector during 2018 and 2019.The aim of both data collections was to evaluate the current status and attitudes towards open Research,Development,and Innovation(RDI)among staff members.Another purpose was to define the need for internal support services concerning open RDI and OE and to identify knowledge gaps.The results revealed several gaps in understanding OS and OE initiatives.Real-life actions were mostly vague,and the respondents experienced the need for support.On the other hand,the attitudes towards open RDI were positive,and the issue aroused questions and reflections.This study revealed gaps in knowledge and actions in Finnish UAS sectors.These results have been the basis of development actions such as joint workshops,educational webinars,and common instructions.The future plan includes the establishment of an experts’network for supporting open RDI and Education.
文摘In this study 70 male students were participated to determine the thyroid disorder through ultrasonography. Thyroid scan was done in 70 students prospectively with no indicative of thyroid disease (age of 19 - 23 yrs). Thyroid scan for students who participated the study took place in the department of Faculty of Applied Medical Sciences, King Abdulaziz University by using an IU 22 Philips ultrasound machine with a 5 - 12 MHz linear transducer. Among the total number of the subjects, 26% was found with abnormal ultrasound findings, 17% of them with cystic nodule, while solid and mixed nodule represented 4% for each. The high rate of abnormal findings of thyroid gland in the study suggested that screening using ultrasound scan was useful in detecting early thyroid disorders.
文摘The University of Applied Sciences in Vienna has offered university degree programs in the field of construction for more than twenty years and has thus gained great expertise in developing its curriculum. Founded in 1996, the department of Building and Design consists of six university degree programs. A major strength of the department is the possibility to adapt to recent challenges in a timely manner. As shown in Figure 1, in the winter term 2008/2009, the master’s degree program, Sustainability in the Construction Industry, was held for the first time;it was transformed into the master’s degree program, Architecture-Green Building, in 2016. In 2013/14 the bachelor’s degree program, Architecture-Green Building, started with the first students graduating in 2016. For ten years the department has focused on sustainability within the building, planning and designing processes.
基金supported by Ho Chi Minh City Open University,Vietnam under grant number E2024.02.1CD and Suan Sunandha Rajabhat University,Thailand.
文摘The Financial Technology(FinTech)sector has witnessed rapid growth,resulting in increasingly complex and high-volume digital transactions.Although this expansion improves efficiency and accessibility,it also introduces significant vulnerabilities,including fraud,money laundering,and market manipulation.Traditional anomaly detection techniques often fail to capture the relational and dynamic characteristics of financial data.Graph Neural Networks(GNNs),capable of modeling intricate interdependencies among entities,have emerged as a powerful framework for detecting subtle and sophisticated anomalies.However,the high-dimensionality and inherent noise of FinTech datasets demand robust feature selection strategies to improve model scalability,performance,and interpretability.This paper presents a comprehensive survey of GNN-based approaches for anomaly detection in FinTech,with an emphasis on the synergistic role of feature selection.We examine the theoretical foundations of GNNs,review state-of-the-art feature selection techniques,analyze their integration with GNNs,and categorize prevalent anomaly types in FinTech applications.In addition,we discuss practical implementation challenges,highlight representative case studies,and propose future research directions to advance the field of graph-based anomaly detection in financial systems.
基金The authors extend their appreciation to King Saud University,Saudi Arabia for funding this work through the Ongoing Research Funding Program(ORF-2025-704),King Saud University,Riyadh,Saudi Arabia.
文摘A novel siphon-based divide-and-conquer(SbDaC)policy is presented in this paper for the synthesis of Petri net(PN)based liveness-enforcing supervisors(LES)for flexible manufacturing systems(FMS)prone to deadlocks or livelocks.The proposed method takes an uncontrolled and bounded PN model(UPNM)of the FMS.Firstly,the reduced PNM(RPNM)is obtained from the UPNM by using PN reduction rules to reduce the computation burden.Then,the set of strict minimal siphons(SMSs)of the RPNM is computed.Next,the complementary set of SMSs is computed from the set of SMSs.By the union of these two sets,the superset of SMSs is computed.Finally,the set of subnets of the RPNM is obtained by applying the PN reduction rules to the superset of SMSs.All these subnets suffer from deadlocks.These subnets are then ordered from the smallest one to the largest one based on a criterion.To enforce liveness on these subnets,a set of control places(CPs)is computed starting from the smallest subnet to the largest one.Once all subnets are live,this process provides the LES,consisting of a set of CPs to be used for the UPNM.The live controlled PN model(CPNM)is constructed by merging the LES with the UPNM.The SbDaC policy is applicable to all classes of PNs related to FMS prone to deadlocks or livelocks.Several FMS examples are considered from the literature to highlight the applicability of the SbDaC policy.In particular,three examples are utilized to emphasize the importance,applicability and effectiveness of the SbDaC policy to realistic FMS with very large state spaces.
基金supported by the MSIT(Ministry of Science and ICT),Republic of Korea,under the ICAN(ICT Challenge and Advanced Network of HRD)support program(IITP-2025-RS-2023-00259497)supervised by the IITP(Institute for Information&Communications Technology Planning&Evaluation)and was supported by Institute of Information&Communications Technology Planning&Evaluation(IITP)grant funded by the Republic of Korea government(MSIT)(No.IITP-2025-RS-2023-00254129+1 种基金Graduate School of Metaverse Convergence(Sungkyunkwan University))was supported by the Basic Science Research Program of the National Research Foundation(NRF)funded by the Republic of Korean government(MSIT)(No.RS-2024-00346737).
文摘Network attacks have become a critical issue in the internet security domain.Artificial intelligence technology-based detection methodologies have attracted attention;however,recent studies have struggled to adapt to changing attack patterns and complex network environments.In addition,it is difficult to explain the detection results logically using artificial intelligence.We propose a method for classifying network attacks using graph models to explain the detection results.First,we reconstruct the network packet data into a graphical structure.We then use a graph model to predict network attacks using edge classification.To explain the prediction results,we observed numerical changes by randomly masking and calculating the importance of neighbors,allowing us to extract significant subgraphs.Our experiments on six public datasets demonstrate superior performance with an average F1-score of 0.960 and accuracy of 0.964,outperforming traditional machine learning and other graph models.The visual representation of the extracted subgraphs highlights the neighboring nodes that have the greatest impact on the results,thus explaining detection.In conclusion,this study demonstrates that graph-based models are suitable for network attack detection in complex environments,and the importance of graph neighbors can be calculated to efficiently analyze the results.This approach can contribute to real-world network security analyses and provide a new direction in the field.
基金supported by The Basic Science Research Program through the National Research Foundation of Korea(NRF)funded by the Ministry of Education(RS-2023-00244901)(to RB)。
文摘Challenges in the prevention and treatment of mild cognitive impairment associated with Alzheimer's disease:Increased life expectancy due to advancements in medical care has given rise to an aging population,accompanied by a surge in the incidence of incurable neurodegenerative diseases(NDDs).These diseases primarily affect the cognitive and behavioral functions of older adults by impacting brain activity.Mild cognitive impairment(MCI)is a neurodegenerative condition that affects a significant portion of the population.
基金funded by Ongoing Research Funding Program for Project number(ORF-2025-648),King Saud University,Riyadh,Saudi Arabia.
文摘Heart disease remains a leading cause of mortality worldwide,emphasizing the urgent need for reliable and interpretable predictive models to support early diagnosis and timely intervention.However,existing Deep Learning(DL)approaches often face several limitations,including inefficient feature extraction,class imbalance,suboptimal classification performance,and limited interpretability,which collectively hinder their deployment in clinical settings.To address these challenges,we propose a novel DL framework for heart disease prediction that integrates a comprehensive preprocessing pipeline with an advanced classification architecture.The preprocessing stage involves label encoding and feature scaling.To address the issue of class imbalance inherent in the personal key indicators of the heart disease dataset,the localized random affine shadowsampling technique is employed,which enhances minority class representation while minimizing overfitting.At the core of the framework lies the Deep Residual Network(DeepResNet),which employs hierarchical residual transformations to facilitate efficient feature extraction and capture complex,non-linear relationships in the data.Experimental results demonstrate that the proposed model significantly outperforms existing techniques,achieving improvements of 3.26%in accuracy,3.16%in area under the receiver operating characteristics,1.09%in recall,and 1.07%in F1-score.Furthermore,robustness is validated using 10-fold crossvalidation,confirming the model’s generalizability across diverse data distributions.Moreover,model interpretability is ensured through the integration of Shapley additive explanations and local interpretable model-agnostic explanations,offering valuable insights into the contribution of individual features to model predictions.Overall,the proposed DL framework presents a robust,interpretable,and clinically applicable solution for heart disease prediction.
文摘Background:Epidemiological studies have confirmed that longer exposure to insecticides like cypermethrin(CYP)significantly increases the risk of male reproductive toxicity.Crocus sativus L.has been recognized due to its therapeutic properties,but its exact role and molecular mechanisms in treatment of reproductive dysfunction remain unclear.Methods:During this study,36 rats were randomly divided into six groups(n=6):control,CYP-induced(60 mg/kg),standard(leuprolide 3 mg/kg)and three treatment groups receiving aqueous,ethanolic,and oil extracts(50 mg/kg or 20 mL/kg)for post-toxicity induction.Results:The finding represented that exposure of CYP significantly increased oxidative stress,disrupted testicular architecture,and markedly reduced testosterone levels(P<0.05).Importantly,Crocus sativus L.treatment alleviated these changes by increasing the expression of Nrf2(nuclear factor erythroid 2-related factor 2),restoring the activity of antioxidant enzymes,and enhancing testicular histomorphology.Surprisingly,molecular docking established a high binding affinity of Crocus sativus L.phytoconstituents such as gallic acid,cinnamic acid and quercetin to the Nrf2-Keap1 complex.It is worth noting that,Crocus sativus L.exhibited a high level of protection against reproductive toxicity caused by CYP in male rats,which was mediated by the activation of Nrf2 pathway,reduction of oxidative damage,and favorable ADMET characteristics.Conclusion:Notably,this research provides a more valid,safe,and effective method of developing new drugs for reproductive disorders,however,further investigation is needed to support the research findings and implement it in clinical practice.
基金financially supported by King Abdulaziz University,Deanship of Scientific Research(DSR)。
文摘Neuronal plasticity,the brain's ability to adapt structurally and functionally,is essential for learning,memory,and recovery from injuries.In neurodegenerative diseases such as Alzheimer's disease and Parkinson's disease,this plasticity is disrupted,leading to cognitive and motor deficits.This review explores the mechanisms of neuronal plasticity and its effect on Alzheimer's disease and Parkinson's disease.Alzheimer's disease features amyloid-beta plaques and tau tangles that impair synaptic function,while Parkinson's disease involves the loss of dopaminergic neurons affecting motor control.Enhancing neuronal plasticity offers therapeutic potential for these diseases.A systematic literature review was conducted using databases such as PubMed,Scopus,and Google Scholar,focusing on studies of neuronal plasticity in Alzheimer's disease and Parkinson's disease.Data synthesis identified key themes such as synaptic mechanisms,neurogenesis,and therapeutic strategies,linking molecular insights to clinical applications.Results highlight that targeting synaptic plasticity mechanisms,such as long-term potentiation and long-term depression,shows promise.Neurotrophic factors,advanced imaging techniques,and molecular tools(e.g.,clustered regularly interspaced short palindromic repeats and optogenetics)are crucial in understanding and enhancing plasticity.Current therapies,including dopamine replacement,deep brain stimulation,and lifestyle interventions,demonstrate the potential to alleviate symptoms and improve outcomes.In conclusion,enhancing neuronal plasticity through targeted therapies holds significant promise for treating neurodegenerative diseases.Future research should integrate multidisciplinary approaches to fully harness the therapeutic potential of neuronal plasticity in Alzheimer's disease and Parkinson's disease.
基金supported by the Chung-Ang University Research Grants in 2023.Alsothe work is supported by the ELLIIT Excellence Center at Linköping–Lund in Information Technology in Sweden.
文摘Recommending personalized travel routes from sparse,implicit feedback poses a significant challenge,as conventional systems often struggle with information overload and fail to capture the complex,sequential nature of user preferences.To address this,we propose a Conditional Generative Adversarial Network(CGAN)that generates diverse and highly relevant itineraries.Our approach begins by constructing a conditional vector that encapsulates a user’s profile.This vector uniquely fuses embeddings from a Heterogeneous Information Network(HIN)to model complex user-place-route relationships,a Recurrent Neural Network(RNN)to capture sequential path dynamics,and Neural Collaborative Filtering(NCF)to incorporate collaborative signals from the wider user base.This comprehensive condition,further enhanced with features representing user interaction confidence and uncertainty,steers a CGAN stabilized by spectral normalization to generate high-fidelity latent route representations,effectively mitigating the data sparsity problem.Recommendations are then formulated using an Anchor-and-Expand algorithm,which selects relevant starting Points of Interest(POI)based on user history,then expands routes through latent similarity matching and geographic coherence optimization,culminating in Traveling Salesman Problem(TSP)-based route optimization for practical travel distances.Experiments on a real-world check-in dataset validate our model’s unique generative capability,achieving F1 scores ranging from 0.163 to 0.305,and near-zero pairs−F1 scores between 0.002 and 0.022.These results confirm the model’s success in generating novel travel routes by recommending new locations and sequences rather than replicating users’past itineraries.This work provides a robust solution for personalized travel planning,capable of generating novel and compelling routes for both new and existing users by learning from collective travel intelligence.
文摘In this paper, a theory on the determination of the diffusion coefficient of excess minority carriers in the base of a silicon solar cell is presented. The diffusion coefficient expression has been established and is related to both frequency modulation and applied magnetic field;the study is then carried out using the impedance spectroscopy method and Bode diagrams. From the diffusion coefficient, we deduced the diffusion length and the minority carriers’ mobility. Electric parameters were derived from the diffusion coefficient equivalent circuits.
基金Project supported by the National Natural Science Foundation of China (Grant Nos 10435080 and 60674011)exoteric project Foundation of State Key Laboratory of Rail Traffic Control and Safety (Beijing Jiaotong University)
文摘We restudy the master-equation approach applied to aggregation in a one-dimensional freeway, where the decay transition probabilities for the jump processes are reconstructed based on a car-following model. According to the reconstructed transition probabilities, the clustering behaviours and the stochastic properties of the master equation in a one-lane freeway traffic model are investigated in detail The numerical results show that the size of the clusters initially below the critical size of the unstable cluster and initially above that of the unstable cluster all enter the same stable state, which also accords with the nucleation theory and is known from the result in earlier work. Moreover, we have obtained more reasonable parameters of the master equation based on some results of cellular automata models.
文摘In order to take into account the uncertainties linked to the variables in the evaluation of the statistical properties of structural response, a reliability approach with probabilistic aspect was considered. This is called the Probabilistic Transformation Method (PTM). This method is readily applicable when the function between the input and the output of the system is explicit. However, the situation is much more involved when it is necessary to perform the evaluation of implicit function between the input and the output of the system through numerical models. In this work, we propose a technique that combines Finite Element Analysis (FEA) and Probabilistic Transformation Method (PTM) to evaluate the Probability Density Function (PDF) of response where the function between the input and the output of the system is implicit. This technique is based on the numerical simulations of the Finite Element Analysis (FEA) and the Probabilistic Transformation Method (PTM) using an interface between Finite Element software and Matlab. Some problems of structures are treated in order to prove the applicability of the proposed technique. Moreover, the obtained results are compared to those obtained by the reference method of Monte Carlo. A second aim of this work is to develop an algorithm of global optimization using the local method SQP, because of its effectiveness and its rapidity of convergence. For this reason, we have combined the method SQP with the Multi start method. This developed algorithm is tested on test functions comparing with other methods such as the method of Particle Swarm Optimization (PSO). In order to test the applicability of the proposed approach, a structure is optimized under reliability constraints.
文摘This study sets the objective to involve undergraduate students in the evaluation of radiologic sciences and medical imaging technology programmes in Sudanese universities. Based on the analysis of survey results in which the participants (BSc students undertaking radiologic sciences and medical imaging technology programmes at university level) are asked to answer both closed and open-ended questions, the study seeks to reveal the participants’ perceptions and introspections about the radiologic sciences and medical imaging technology programmes in Sudan. It also attempts to explore the participants’ suggestions and recommendations as to enhance the quality of these programmes with an eye to helping syllabus designers to improve these programmes, thereby bettering healthcare services for the larger good to the community. A brief cross-sectional survey is completed by a total of 105 radiologic sciences and medical imaging technology students, i.e. 39 (37.1%) third-year students and 66 (62.9%) fourth-year students. The majority of participants is satisfied with the programmes, indicating that they are up-to-date and ran abreast with the latest developments in the field. Very few suggest that the programmes should be reviewed for revision, implying that there is room for improvement. Some participants recommend that more training hours in modern imaging modalities (e.g. MRI, CT and U/S) should be introduced. Only one participant recommends the introduction of advanced training centres.
文摘Analysis of agricultural production systems of hydroponic tomato in Morelos state of Mexico, through a systematic approach, called systems development of life cycle （SDLC） was performed by comparing this with the methodology known as life cycle assessment （LCA）. This permits to analyze the differences in approaches of all these methodologies to propose improvements in the current system, which can allow an improved assessment of the environmental quality of agricultural products, which often is subject to confusion. That due to measurement parameters are not generally accepted by society, producers and consumers, may ensure that the process is fully sustainable and is considered quite as a green technology processes towards an ecological benefit and therefore for the humanity.
文摘Background: In the Kingdom of Saudi Arabia (KSA), recent studies revealed increasing consumption of animal products and refined foods in the diet at the expense of vegetables and fruits. These dietary changes were accused of increasing the prevalence of both overweight and obesity observed among Saudi children, adolescences and adults in the last few decades. Objectives: The present study was a cross sectional study aimed at exploring the BMI distribution among students of college of Sciences and Arts for girls Khamis Mushayt Campus 1 at King Khalid University (KKU) and investigated how Dietary habits of students female affected body mass index (BMI). Methods: A total of 240 female students aged 18 - 22 years were about 25% from total students randomly chosen from college of Arts and Science Campus 1 at Khamis Mushayt, King Khalid University, KSA for the present study. A self-reported questionnaire about the student’s dietary habits was conducted and their body mass index (BMI) was measured. Data were analyzed using SPSS statistical software and Chi-square test conducted for variables. Results: About half of the students (47%) were within normal weight, 27.4% were under weight, 16% were overweight and 9.6% were categorized obese. The majority of the students reported eating meals with their family while more than half of sample (58.7%) had eating meals during watching TV. It was worth mentioning that 84.5% of students reported eating snacks such as chocolate and chips 3 or more times per week. There were no significant differences between BMI category and dietary habits. Conclusion and Recommendations: There were no significant difference between body mass index category and dietary habits. Increasing educational programs introduced healthy dietary concepts to improve the dietary habits of female students.
基金supported by a Presidential Postdoctoral Fellowship (021229-00001) from Nanyang Technological University,Singapore (to JZ)a Lee Kong Chian School of Medicine Dean’s Postdoctoral Fellowship (021207-00001) from NTU Singaporea Mistletoe Research Fellowship (022522-00001) from the Momental Foundaton,USA (to CHL)
文摘The interaction between metabolic dysfunction and inflammation is central to the development of neurodegenerative diseases such as Alzheimer’s disease and Parkinson’s disease.Obesity-related conditions like type 2 diabetes and non-alcoholic fatty liver disease exacerbate this relationship.Peripheral lipid accumulation,particularly in the liver,initiates a cascade of inflammatory processes that extend to the brain,influencing critical metabolic regulatory regions.Ceramide and palmitate,key lipid components,along with lipid transporters lipocalin-2 and apolipoprotein E,contribute to neuroinflammation by disrupting blood–brain barrier integrity and promoting gliosis.Peripheral insulin resistance further exacerbates brain insulin resistance and neuroinflammation.Preclinical interventions targeting peripheral lipid metabolism and insulin signaling pathways have shown promise in reducing neuroinflammation in animal models.However,translating these findings to clinical practice requires further investigation into human subjects.In conclusion,metabolic dysfunction,peripheral inflammation,and insulin resistance are integral to neuroinflammation and neurodegeneration.Understanding these complex mechanisms holds potential for identifying novel therapeutic targets and improving outcomes for neurodegenerative diseases.
文摘As new-generation intelligent technologies rapidly evolve,enhancing artificial intelligence(AI)education has become a global consensus,and improving AI literacy is a key focus in higher education.To address the lack of relevant knowledge among non-computer science students,the complexity of the material,which leads to low interest and high difficulty in learning,this paper proposes a three-pronged teaching design model:“BOPPPS model+large language models(LLMs)+mind maps with 3w2h”.This model aims to assist teachers in designing practical teaching cases and engaging,interactive activities,and provides examples of its application to help teachers better teach AI and improve the AI literacy of non-computer science students.
基金supported by the National Natural Science Foundation of China(Nos.52372093 and 52102145)the Key R&D Program of Shaanxi Province(Nos.2023GXLH-045 and 2022SF-168)+4 种基金the Xi’an Programs for Science and Technology Plan(Nos.2020KJRC0090 and 21XJZZ0045)the Opening Project of Shanxi Key Laboratory of Advanced Manufacturing Technology(No.XJZZ202001)the Xi’an Municipal Bureau of Science and Technology(No.21XJZZ0054)the Open Foundation of Key Laboratory of Auxiliary Chemistry and Technology for Chemical Industry,Ministry of Education,Shaanxi University of Science and Technology(No.KFKT2021-01)the Shaanxi Collaborative Innovation Center of Industrial Auxiliary Chemistry and Technology,Shaanxi University of Science and Technology(No.KFKT2021-01).
文摘Melamine sponge is a major concern for oil-water separation due to its lightweight,high porosity(>99%),cost-effectiveness,impressive mechanical properties,and chemical/thermal stability.However,its amphiphilic nature hinders selective oil absorption in water.Recent strategies to enhance hydrophobicity are reviewed,including synthetic methods and materials,with comprehensive explanations of the mechanisms driven by surface energy and roughness.Key performance indicators for MS in oil-water separation,including adsorption capacity,wettability,stability,emulsion separation,reversible wettability switching,flame retardancy,mechanical properties,and recyclability,are thoroughly discussed.In conclusion,this review provides insights into the future potential and direction of functional melamine sponges in oil-water separation.
