From the view of underground coal mining safety system, it is extremely important to continuous monitoring of coal mines for the prompt detection of fires or related problems inspite of its uncertainty and imprecise c...From the view of underground coal mining safety system, it is extremely important to continuous monitoring of coal mines for the prompt detection of fires or related problems inspite of its uncertainty and imprecise characteristics. Therefore, evaluation and inferring the data perfectly to prevent fire related accidental risk in underground coal mining (UMC) system are very necessary. In the present article, we have proposed a novel type-2 fuzzy logic system (T2FLS) for the prediction of fire intensity and its risk assessment for risk reduction in an underground coal mine. Recently, for the observation of underground coal mines, wireless underground sensor network (WUSN) are being concerned frequently. To implement this technique IT2FLS, main functional components are sensor nodes which are installed in coal mines to accumulate different imprecise environmental data like, temperature, relative humidity, different gas concentrations etc. and these are sent to a base station which is connected to the ground observation system through network. In the present context, a WUSN based fire monitoring system is developed using fuzzy logic approach to enhance the consistency in decision making system to improve the risk chances of fire during coal mining. We have taken Mamdani IT2FLS as fuzzy model on coal mine monitoring data to consider real-time decision making (DM). It is predicted from the simulated results that the recommended system is highly acceptable and amenable in the case of fire hazard safety with compared to the wired and off-line monitoring system for UMC. Legitimacy of the suggested model is prepared using statistical analysis and multiple linear regression analysis.展开更多
In this paper,a two-dimensional axisymmetric thermal model using finite element method(FEM)has been established for predicting the temperature distribution pro-file on the work piece during electro discharge machining...In this paper,a two-dimensional axisymmetric thermal model using finite element method(FEM)has been established for predicting the temperature distribution pro-file on the work piece during electro discharge machining(EDM)and obtained material removal rate(MRR)from the temperature isotherm.For prediction of MRR,the model utilizes some important features viz.size and shape of the heat source(Gaussian heat distribution),thermal properties of workpiece,amount of heat distribution among the dielectric fluid,workpiece and tool,material flushing efficiency and pulse off/on time,etc.ANSYS software was used for developing the thermal model for the single spark operation.For this investigation,AISI 304 stainless steel and tungsten carbide was used as workpiece and electrode material,respectively.A comparison study has been carried out for theoretical and experimental MRR for the effect of each process parameter viz.gap voltage,pulse on time and peak current.The temperature distribution along the radial and depth direction of the workpiece has been reported.The model was validated by comparing the theoretical MRR with the experimental MRR and found a good correlation between them.展开更多
This paper presents an improved,energy-efficient Model Predictive Current Control(MPCC)strategy based on centroid-based virtual voltage vector synthesis for three-phase inverter-fed induction motor drives in electric ...This paper presents an improved,energy-efficient Model Predictive Current Control(MPCC)strategy based on centroid-based virtual voltage vector synthesis for three-phase inverter-fed induction motor drives in electric vehicle(EV)applications.Unlike conventional finite-set MPCC methods that rely on cost function evaluation over discrete switching states,the proposed approach eliminates the need for look-up tables by employing a pre-defined set of virtual vectors.These centroid-based virtual voltage vectors are synthesized by combining two adjacent active vectors and two nonzero voltage vectors in opposite directions adjacent to the sector replacing the traditional switching set.They approximate the reference voltage vector in both magnitude and phase angle,thereby reducing current tracking error through a simplified cost function.The number of candidate vectors is reduced,preserving computational efficiency.Furthermore,the scheme ensures zero average common-mode voltage(CMV)per sampling interval by completely avoiding zero-voltage vectors(ZVVs).The proposed method reduces torque ripple by up to 17%compared to the conventional approach and lowers stator current total harmonic distortion(THD)by 37%,while ensuring evenly distributed switching transitions among inverter legs.This results in reduced switching losses and enhanced drive efficiency-particularly advantageous in EV applications.Experimental validation under the high-speed extra urban driving cycle(EUDC)and low-speed ECE-R15 cycle,including torque ripple and energy consumption analysis,confirms the effectiveness of the approach,achieving an overall efficiency of 83.3%.展开更多
The soil packing,influenced by variations in grain size and the gradation pattern within the soil matrix,plays a crucial role in constituting the mechanical properties of sandy soils.However,previous modeling approach...The soil packing,influenced by variations in grain size and the gradation pattern within the soil matrix,plays a crucial role in constituting the mechanical properties of sandy soils.However,previous modeling approaches have overlooked incorporating the full range of representative parameters to accurately predict the soaked California bearing ratio(CBR_(s))of sandy soils by precisely articulating soil packing in the modeling framework.This study presents an innovative artificial intelligence(AI)-based approach for modeling the CBR_(s)of sandy soils,considering grain size variability meticulously.By synthesizing extensive data from multiple sources,i.e.extensive tailored testing program undertaking multiple tests and extant literature,various modeling techniques including genetic expression programming(GEP),multi-expression programming(MEP),support vector machine(SVM),and multi-linear regression(MLR)are utilized to develop models.The research explores two modeling strategies,namely simplified and composite,with the former incorporating only sieve analysis test parameters,while the latter includes compaction test parameters alongside sieve analysis data.The models'performance is assessed using statistical key performance indicators(KPIs).Results indicate that genetic AI-based algorithms,particularly GEP,outperform SVM and conventional regression techniques,effectively capturing complex relationships between input parameters and CBR_(s).Additionally,the study reveals insights into model performance concerning the number of input parameters,with GEP consistently outperforming other models.External validation and Taylor diagram analysis demonstrate the GEP models'superiority over existing literature models on an independent dataset from the literature.Parametric and sensitivity analyses highlight the intricate relationships between grain sizes and CBR_(s),further emphasizing GEP's efficacy in modeling such complexities.This study contributes to enhancing CBR_(s)modeling accuracy for sandy soils,crucial for pertinent infrastructure design and construction rapidly and cost-effectively.展开更多
High-plastic clays with significant volume change due to moisture variations present critical challenges to civil engineering structures.Limestone calcined clay cement(LC3),an innovative and sustainable hydraulic bind...High-plastic clays with significant volume change due to moisture variations present critical challenges to civil engineering structures.Limestone calcined clay cement(LC3),an innovative and sustainable hydraulic binder,demonstrates significant potential for improving the engineering characteristics of such soils.Nevertheless,the impact of LC3 on the physico-mechanical characteristics of treated soil under a cyclic wet-dry environment remains unclear.This study for the first time investigates LC3's impact on the long-term durability of treated high-plastic clays through comprehensive macro-micro testing including physical,mechanical,mineralogical,and microstructural investigations with an emphasis on wet-dry cycles.The results revealed that LC3 treatment exhibits significant resistance to wet-dry cycles by completely mitigating the swelling potential,and a considerable reduction in plasticity resulting in enhanced workability.The compressibility and shear strength parameters have been significantly improved to several orders of magnitude.However,after six wet-dry cycles,a slight to modest reduction is observed,but overall durability remains superior to untreated soil.Cohesive and structural bonding ratios quantitatively assessed the impact of wet-dry cycles emphasizing the advantage of LC3 treatment.According to mineralogical and microstructural evaluation,the mechanism behind the adverse effects of wet-dry cycles on the compressibility and strength behavior of LC3-treated soil is mainly attributed to:(1)weakening of CSH/C(A)SH and ettringite(AFt)phases by exhibiting lower peak intensities;and(2)larger pore spaces due to repeated wet-dry cycles.These findings highlight LC3's performance in enhancing the long-term behavior and resilience of treated soils in real-world scenarios,providing durable solutions for infrastructure challenges.展开更多
In this Exa byte scale era, data increases at an exponential rate. This is in turn generating a massive amount of metadata in the file system. Hadoop is the most widely used framework to deal with big data. Due to thi...In this Exa byte scale era, data increases at an exponential rate. This is in turn generating a massive amount of metadata in the file system. Hadoop is the most widely used framework to deal with big data. Due to this growth of huge amount of metadata, however, the efficiency of Hadoop is questioned numerous times by many researchers. Therefore, it is essential to create an efficient and scalable metadata management for Hadoop.Hash-based mapping and subtree partitioning are suitable in distributed metadata management schemes. Subtree partitioning does not uniformly distribute workload among the metadata servers, and metadata needs to be migrated to keep the load roughly balanced. Hash-based mapping suffers from a constraint on the locality of metadata, though it uniformly distributes the load among Name Nodes, which are the metadata servers of Hadoop. In this paper, we present a circular metadata management mechanism named dynamic circular metadata splitting(DCMS). DCMS preserves metadata locality using consistent hashing and locality-preserving hashing, keeps replicated metadata for excellent reliability, and dynamically distributes metadata among the Name Nodes to keep load balancing. Name Node is a centralized heart of the Hadoop. Keeping the directory tree of all files, failure of which causes the single point of failure(SPOF). DCMS removes Hadoop's SPOF and provides an efficient and scalable metadata management. The new framework is named ‘Dr. Hadoop' after the name of the authors.展开更多
This paper proposes a new approach for solving the bearings-only target tracking (BoT) problem by introducing a maximum correntropy criterion to the pseudolinear Kalman filter (PLKF). PLKF has been a popular choice fo...This paper proposes a new approach for solving the bearings-only target tracking (BoT) problem by introducing a maximum correntropy criterion to the pseudolinear Kalman filter (PLKF). PLKF has been a popular choice for solving BoT problems owing to the reduced computational complexity. However, the coupling between the measurement vector and pseudolinear noise causes bias in PLKF. To address this issue, a bias-compensated PLKF (BC-PLKF) under the assumption of Gaussian noise was formulated. However, this assumption may not be valid in most practical cases. Therefore, a bias-compensated PLKF with maximum correntropy criterion is introduced, resulting in two new filters: maximum correntropy pseudolinear Kalman filter (MC-PLKF) and maximum correntropy bias-compensated pseudolinear Kalman filter (MC-BC-PLKF). To demonstrate the performance of the proposed estimators, a comparative analysis assuming large outliers in the process and measurement model of 2D BoT is conducted. These large outliers are modeled as non-Gaussian noises with diverse noise distributions that combine Gaussian and Laplacian noises. The simulation results are validated using root mean square error (RMSE), average RMSE (ARMSE), percentage of track loss and bias norm. Compared to PLKF and BC-PLKF, all the proposed maximum correntropy-based filters (MC-PLKF and MC-BC-PLKF) performed with superior estimation accuracy.展开更多
The development of the Internet of Things(IoT)technology is leading to a new era of smart applications such as smart transportation,buildings,and smart homes.Moreover,these applications act as the building blocks of I...The development of the Internet of Things(IoT)technology is leading to a new era of smart applications such as smart transportation,buildings,and smart homes.Moreover,these applications act as the building blocks of IoT-enabled smart cities.The high volume and high velocity of data generated by various smart city applications are sent to flexible and efficient cloud computing resources for processing.However,there is a high computation latency due to the presence of a remote cloud server.Edge computing,which brings the computation close to the data source is introduced to overcome this problem.In an IoT-enabled smart city environment,one of the main concerns is to consume the least amount of energy while executing tasks that satisfy the delay constraint.An efficient resource allocation at the edge is helpful to address this issue.In this paper,an energy and delay minimization problem in a smart city environment is formulated as a bi-objective edge resource allocation problem.First,we presented a three-layer network architecture for IoT-enabled smart cities.Then,we designed a learning automata-based edge resource allocation approach considering the three-layer network architecture to solve the said bi-objective minimization problem.Learning Automata(LA)is a reinforcement-based adaptive decision-maker that helps to find the best task and edge resource mapping.An extensive set of simulations is performed to demonstrate the applicability and effectiveness of the LA-based approach in the IoT-enabled smart city environment.展开更多
Cancer is one of the deadliest diseases in developing countries. In recent years, natural plant-based compounds have been used in the search for drugs to combat numerous diseases, including cancer. In this study, we e...Cancer is one of the deadliest diseases in developing countries. In recent years, natural plant-based compounds have been used in the search for drugs to combat numerous diseases, including cancer. In this study, we evaluate the cytotoxic properties of paanfo tiben 1 and paanfo tiben 2, two traditional herbal formulations from Burkina Faso used in the treatment of cancer in Burkina Faso. To this end, the recipes were infused and freeze-dried. The dry extracts obtained were used to determine total phenolics and flavonoids content, assess antioxidant activity using the DPPH, ABTS and FRAP methods, evaluate anti-inflammatory properties by inhibiting 15-LOX, COX 1 and 2, and assess cytotoxic activity on HeLa cervical cancer and HePG2 liver cancer cell lines using the MTT test. The paanfo tiben 1 recipe showed the highest levels of total phenolics and flavonoids, as well as the best antioxidant activities, with IC50 values of 21.020 ± 0.6 µg/ml and 22.94 ± 0.57 µg/ml for DPPH and ABTS, and 165.15 mM EAA/mg dry extract for FRAP. It also exhibited the best cytotoxic activity with IC50 values of 112.02 ± 0.025 µg/ml on HeLa cells and 80.67 ± 6.08 µg/ml on HepG2 cells. On the other hand, paanfo tiben 2 exhibited the best anti-inflammatory activities through inhibition of 15-LOX and COX 1, with inhibition percentages at 100 µg/ml of 32.523% and 24.717 % respectively. These results could justify the traditional use of these two recipes by traditional health practitioners in the treatment of cancer sufferers in Burkina Faso.展开更多
This paper presents the design of a non-linear controller to prevent an electric power system losing synchronism after a large sudden fault and to achieve good post fault voltage level. By Direct Feedback Linearizatio...This paper presents the design of a non-linear controller to prevent an electric power system losing synchronism after a large sudden fault and to achieve good post fault voltage level. By Direct Feedback Linearization (DFL) technique robust non-linear excitation controller is designed which will achieve stability enhancement and voltage regulation of power system. By utilizing this technique, there is a possibility of selecting various control loops for a particular application problem. This method plays an important role in control system and power system engineering problem where all relevant variables cannot be directly measured. Simulated results carried out on a single machine infinite bus power system model which shows the enhancement of transient stability regardless of the fault and changes in network parameters.展开更多
Mortality in cirrhosis is mostly associated with the development of clinical decompensation,characterized by ascites,hepatic encephalopathy,variceal bleeding,or jaundice.Therefore,it is important to prevent and manage...Mortality in cirrhosis is mostly associated with the development of clinical decompensation,characterized by ascites,hepatic encephalopathy,variceal bleeding,or jaundice.Therefore,it is important to prevent and manage such complications.Traditionally,the pathophysiology of decompensated cirrhosis was explained by the peripheral arterial vasodilation hypothesis,but it is currently understood that decompensation might also be driven by a systemic inflammatory state(the systemic inflammation hypothesis).Considering its oncotic and nononcotic properties,albumin has been thoroughly evaluated in the prevention and management of several of these decompensating events.There are formal evidence-based recommendations from international medical societies proposing that albumin be administered in individuals with cirrhosis undergoing large-volume paracentesis,patients with spontaneous bacterial peritonitis,those with acute kidney injury(even before the etiological diagnosis),and those with hepatorenal syndrome.Moreover,there are a few randomized controlled trials and meta-analyses suggesting a possible role for albumin infusion in patients with cirrhosis and ascites(long-term albumin administration),individuals with hepatic encephalopathy,and those with acute-on-chronic liver failure undergoing modest-volume paracentesis.Further studies are necessary to elucidate whether albumin administration also benefits patients with cirrhosis and other complications,such as individuals with extraperitoneal infections,those hospitalized with decompensated cirrhosis and hypoalbuminemia,and patients with hyponatremia.展开更多
Background: Dialyzable leukocyte extracts (DLE) are heterogeneous mixtures of peptides less than 10 kDa in size that are used as immunomodulatory adjuvants in immune-mediated diseases. TransferonTM is DLE manufactured...Background: Dialyzable leukocyte extracts (DLE) are heterogeneous mixtures of peptides less than 10 kDa in size that are used as immunomodulatory adjuvants in immune-mediated diseases. TransferonTM is DLE manufactured by National Polytechnic Institute (IPN), and is registered by Mexican health-regulatory authorities as an immunomodulatory drug and commercialized nationally. The proposed mechanism of action of TransferonTM is induction of a Th1 immunoregulatory response. Despite that it is widely used, to date there are no reports of adverse events related to the clinical safety of human DLE or TransferonTM. Objective: To assess the safety of TransferonTM in a large group of patients exposed to DLE as adjuvant treatment. Methods: We included in this study 3844 patients from our Clinical Immunology Service at the Unit of External Services and Clinical Research (USEIC), IPN. Analysis was performed from January 2014 to November 2014, searching for clinical adverse events in patients with immune-mediated diseases and treated with TransferonTM as an adjuvant. Results: In this work we observed clinical nonserious adverse events (AE) in 1.9% of patients treated with TransferonTM (MD 1.9, IQR 1.7 - 2.0). AE were 2.8 times more frequently observed in female than in male patients. The most common AE were headache in 15.7%, followed by rash in 11.4%, increased disease-related symptomatology in 10%, rhinorrhea in 7.1%, cough in 5.7%, and fatigue in 5.7% of patients with AE. 63% of adverse event presentation occurred from day 1 to day 4 of treatment with TransferonTM, and mean time resolution of adverse events was 14 days. In 23 cases, the therapy was stopped because of adverse events and no serious adverse events were observed in this study. Conclusion: TransferonTM induced low frequency of nonserious adverse events during adjuvant treatment. Further monitoring is advisable for different age and disease groups of patients.展开更多
Present work encapsulated the friction and wear behaviour of aluminium matrix composites reinforced with different mass fractions of titanium diboride(TiB_(2))particles,synthesized by stir casting.A pin on disc tribot...Present work encapsulated the friction and wear behaviour of aluminium matrix composites reinforced with different mass fractions of titanium diboride(TiB_(2))particles,synthesized by stir casting.A pin on disc tribotester was employed for conducting the dry sliding wear tests of Al2024−TiB_(2)composites.The tests were performed adopting various parameters like load,sliding distance and sliding velocity for investigating the effect of tribological parameters on the prepared composites.Microstructural characterization confirmed uniform dispersion of TiB_(2)particles and good matrix−reinforcement bonding.Results of the experiments revealed that,low friction and wear rates were observed in the developed composites compared to Al2024 alloy,whereas wear rates of both Al2024 alloy and fabricated composites increased with the increase in load,sliding velocity and sliding distance.However,friction coefficient of both Al2024 alloy and fabricated composites reduced with the increase in applied load but rose with the increase in sliding velocity and sliding distance.SEM studies of the worn surfaces and debris depicted that enhancement in wear resistance can be ascribed to finer debris formation.展开更多
Proper quality planning of limestone raw materials is an essential job of maintaining desired feed in cement plant. Rock-type identification is an integrated part of quality planning for limestone mine. In this paper,...Proper quality planning of limestone raw materials is an essential job of maintaining desired feed in cement plant. Rock-type identification is an integrated part of quality planning for limestone mine. In this paper, a computer vision-based rock-type classification algorithm is proposed for fast and reliable identification without human intervention. A laboratory scale vision-based model was developed using probabilistic neural network（PNN） where color histogram features are used as input. The color image histogram-based features that include weighted mean, skewness and kurtosis features are extracted for all three color space red, green, and blue. A total nine features are used as input for the PNN classification model. The smoothing parameter for PNN model is selected judicially to develop an optimal or close to the optimum classification model. The developed PPN is validated using the test data set and results reveal that the proposed vision-based model can perform satisfactorily for classifying limestone rocktypes. Overall the error of mis-classification is below 6%. When compared with other three classification algorithms, it is observed that the proposed method performs substantially better than all three classification algorithms.展开更多
This paper describes a robust support vector regression （SVR） methodology, which can offer superior performance for important process engineering problems. The method incorporates hybrid support vector regression an...This paper describes a robust support vector regression （SVR） methodology, which can offer superior performance for important process engineering problems. The method incorporates hybrid support vector regression and genetic algorithm technique （SVR-GA） for efficient tuning of SVR meta-parameters. The algorithm has been applied for prediction of pressure drop of solid liquid slurry flow. A comparison with selected correlations in the lit- erature showed that the developed SVR correlation noticeably improved the prediction of pressure drop over a wide range of operating conditions, physical properties, and pipe diameters.展开更多
文摘From the view of underground coal mining safety system, it is extremely important to continuous monitoring of coal mines for the prompt detection of fires or related problems inspite of its uncertainty and imprecise characteristics. Therefore, evaluation and inferring the data perfectly to prevent fire related accidental risk in underground coal mining (UMC) system are very necessary. In the present article, we have proposed a novel type-2 fuzzy logic system (T2FLS) for the prediction of fire intensity and its risk assessment for risk reduction in an underground coal mine. Recently, for the observation of underground coal mines, wireless underground sensor network (WUSN) are being concerned frequently. To implement this technique IT2FLS, main functional components are sensor nodes which are installed in coal mines to accumulate different imprecise environmental data like, temperature, relative humidity, different gas concentrations etc. and these are sent to a base station which is connected to the ground observation system through network. In the present context, a WUSN based fire monitoring system is developed using fuzzy logic approach to enhance the consistency in decision making system to improve the risk chances of fire during coal mining. We have taken Mamdani IT2FLS as fuzzy model on coal mine monitoring data to consider real-time decision making (DM). It is predicted from the simulated results that the recommended system is highly acceptable and amenable in the case of fire hazard safety with compared to the wired and off-line monitoring system for UMC. Legitimacy of the suggested model is prepared using statistical analysis and multiple linear regression analysis.
文摘In this paper,a two-dimensional axisymmetric thermal model using finite element method(FEM)has been established for predicting the temperature distribution pro-file on the work piece during electro discharge machining(EDM)and obtained material removal rate(MRR)from the temperature isotherm.For prediction of MRR,the model utilizes some important features viz.size and shape of the heat source(Gaussian heat distribution),thermal properties of workpiece,amount of heat distribution among the dielectric fluid,workpiece and tool,material flushing efficiency and pulse off/on time,etc.ANSYS software was used for developing the thermal model for the single spark operation.For this investigation,AISI 304 stainless steel and tungsten carbide was used as workpiece and electrode material,respectively.A comparison study has been carried out for theoretical and experimental MRR for the effect of each process parameter viz.gap voltage,pulse on time and peak current.The temperature distribution along the radial and depth direction of the workpiece has been reported.The model was validated by comparing the theoretical MRR with the experimental MRR and found a good correlation between them.
文摘This paper presents an improved,energy-efficient Model Predictive Current Control(MPCC)strategy based on centroid-based virtual voltage vector synthesis for three-phase inverter-fed induction motor drives in electric vehicle(EV)applications.Unlike conventional finite-set MPCC methods that rely on cost function evaluation over discrete switching states,the proposed approach eliminates the need for look-up tables by employing a pre-defined set of virtual vectors.These centroid-based virtual voltage vectors are synthesized by combining two adjacent active vectors and two nonzero voltage vectors in opposite directions adjacent to the sector replacing the traditional switching set.They approximate the reference voltage vector in both magnitude and phase angle,thereby reducing current tracking error through a simplified cost function.The number of candidate vectors is reduced,preserving computational efficiency.Furthermore,the scheme ensures zero average common-mode voltage(CMV)per sampling interval by completely avoiding zero-voltage vectors(ZVVs).The proposed method reduces torque ripple by up to 17%compared to the conventional approach and lowers stator current total harmonic distortion(THD)by 37%,while ensuring evenly distributed switching transitions among inverter legs.This results in reduced switching losses and enhanced drive efficiency-particularly advantageous in EV applications.Experimental validation under the high-speed extra urban driving cycle(EUDC)and low-speed ECE-R15 cycle,including torque ripple and energy consumption analysis,confirms the effectiveness of the approach,achieving an overall efficiency of 83.3%.
文摘The soil packing,influenced by variations in grain size and the gradation pattern within the soil matrix,plays a crucial role in constituting the mechanical properties of sandy soils.However,previous modeling approaches have overlooked incorporating the full range of representative parameters to accurately predict the soaked California bearing ratio(CBR_(s))of sandy soils by precisely articulating soil packing in the modeling framework.This study presents an innovative artificial intelligence(AI)-based approach for modeling the CBR_(s)of sandy soils,considering grain size variability meticulously.By synthesizing extensive data from multiple sources,i.e.extensive tailored testing program undertaking multiple tests and extant literature,various modeling techniques including genetic expression programming(GEP),multi-expression programming(MEP),support vector machine(SVM),and multi-linear regression(MLR)are utilized to develop models.The research explores two modeling strategies,namely simplified and composite,with the former incorporating only sieve analysis test parameters,while the latter includes compaction test parameters alongside sieve analysis data.The models'performance is assessed using statistical key performance indicators(KPIs).Results indicate that genetic AI-based algorithms,particularly GEP,outperform SVM and conventional regression techniques,effectively capturing complex relationships between input parameters and CBR_(s).Additionally,the study reveals insights into model performance concerning the number of input parameters,with GEP consistently outperforming other models.External validation and Taylor diagram analysis demonstrate the GEP models'superiority over existing literature models on an independent dataset from the literature.Parametric and sensitivity analyses highlight the intricate relationships between grain sizes and CBR_(s),further emphasizing GEP's efficacy in modeling such complexities.This study contributes to enhancing CBR_(s)modeling accuracy for sandy soils,crucial for pertinent infrastructure design and construction rapidly and cost-effectively.
基金The financial support of the National Natural Science Foundation of China(Grant No.42030714)the National Key R&D Program of China(Grant No.2019YFC1509900)is greatly acknowledged.
文摘High-plastic clays with significant volume change due to moisture variations present critical challenges to civil engineering structures.Limestone calcined clay cement(LC3),an innovative and sustainable hydraulic binder,demonstrates significant potential for improving the engineering characteristics of such soils.Nevertheless,the impact of LC3 on the physico-mechanical characteristics of treated soil under a cyclic wet-dry environment remains unclear.This study for the first time investigates LC3's impact on the long-term durability of treated high-plastic clays through comprehensive macro-micro testing including physical,mechanical,mineralogical,and microstructural investigations with an emphasis on wet-dry cycles.The results revealed that LC3 treatment exhibits significant resistance to wet-dry cycles by completely mitigating the swelling potential,and a considerable reduction in plasticity resulting in enhanced workability.The compressibility and shear strength parameters have been significantly improved to several orders of magnitude.However,after six wet-dry cycles,a slight to modest reduction is observed,but overall durability remains superior to untreated soil.Cohesive and structural bonding ratios quantitatively assessed the impact of wet-dry cycles emphasizing the advantage of LC3 treatment.According to mineralogical and microstructural evaluation,the mechanism behind the adverse effects of wet-dry cycles on the compressibility and strength behavior of LC3-treated soil is mainly attributed to:(1)weakening of CSH/C(A)SH and ettringite(AFt)phases by exhibiting lower peak intensities;and(2)larger pore spaces due to repeated wet-dry cycles.These findings highlight LC3's performance in enhancing the long-term behavior and resilience of treated soils in real-world scenarios,providing durable solutions for infrastructure challenges.
文摘In this Exa byte scale era, data increases at an exponential rate. This is in turn generating a massive amount of metadata in the file system. Hadoop is the most widely used framework to deal with big data. Due to this growth of huge amount of metadata, however, the efficiency of Hadoop is questioned numerous times by many researchers. Therefore, it is essential to create an efficient and scalable metadata management for Hadoop.Hash-based mapping and subtree partitioning are suitable in distributed metadata management schemes. Subtree partitioning does not uniformly distribute workload among the metadata servers, and metadata needs to be migrated to keep the load roughly balanced. Hash-based mapping suffers from a constraint on the locality of metadata, though it uniformly distributes the load among Name Nodes, which are the metadata servers of Hadoop. In this paper, we present a circular metadata management mechanism named dynamic circular metadata splitting(DCMS). DCMS preserves metadata locality using consistent hashing and locality-preserving hashing, keeps replicated metadata for excellent reliability, and dynamically distributes metadata among the Name Nodes to keep load balancing. Name Node is a centralized heart of the Hadoop. Keeping the directory tree of all files, failure of which causes the single point of failure(SPOF). DCMS removes Hadoop's SPOF and provides an efficient and scalable metadata management. The new framework is named ‘Dr. Hadoop' after the name of the authors.
文摘This paper proposes a new approach for solving the bearings-only target tracking (BoT) problem by introducing a maximum correntropy criterion to the pseudolinear Kalman filter (PLKF). PLKF has been a popular choice for solving BoT problems owing to the reduced computational complexity. However, the coupling between the measurement vector and pseudolinear noise causes bias in PLKF. To address this issue, a bias-compensated PLKF (BC-PLKF) under the assumption of Gaussian noise was formulated. However, this assumption may not be valid in most practical cases. Therefore, a bias-compensated PLKF with maximum correntropy criterion is introduced, resulting in two new filters: maximum correntropy pseudolinear Kalman filter (MC-PLKF) and maximum correntropy bias-compensated pseudolinear Kalman filter (MC-BC-PLKF). To demonstrate the performance of the proposed estimators, a comparative analysis assuming large outliers in the process and measurement model of 2D BoT is conducted. These large outliers are modeled as non-Gaussian noises with diverse noise distributions that combine Gaussian and Laplacian noises. The simulation results are validated using root mean square error (RMSE), average RMSE (ARMSE), percentage of track loss and bias norm. Compared to PLKF and BC-PLKF, all the proposed maximum correntropy-based filters (MC-PLKF and MC-BC-PLKF) performed with superior estimation accuracy.
基金supported by the Kempe post-doc fellowship via Project No.SMK21-0061,Sweden.Additional support was provided by the Wallenberg AI,Autonomous Systems and Software Program(WASP)funded by Knut and Alice Wallenberg Foundation.
文摘The development of the Internet of Things(IoT)technology is leading to a new era of smart applications such as smart transportation,buildings,and smart homes.Moreover,these applications act as the building blocks of IoT-enabled smart cities.The high volume and high velocity of data generated by various smart city applications are sent to flexible and efficient cloud computing resources for processing.However,there is a high computation latency due to the presence of a remote cloud server.Edge computing,which brings the computation close to the data source is introduced to overcome this problem.In an IoT-enabled smart city environment,one of the main concerns is to consume the least amount of energy while executing tasks that satisfy the delay constraint.An efficient resource allocation at the edge is helpful to address this issue.In this paper,an energy and delay minimization problem in a smart city environment is formulated as a bi-objective edge resource allocation problem.First,we presented a three-layer network architecture for IoT-enabled smart cities.Then,we designed a learning automata-based edge resource allocation approach considering the three-layer network architecture to solve the said bi-objective minimization problem.Learning Automata(LA)is a reinforcement-based adaptive decision-maker that helps to find the best task and edge resource mapping.An extensive set of simulations is performed to demonstrate the applicability and effectiveness of the LA-based approach in the IoT-enabled smart city environment.
文摘Cancer is one of the deadliest diseases in developing countries. In recent years, natural plant-based compounds have been used in the search for drugs to combat numerous diseases, including cancer. In this study, we evaluate the cytotoxic properties of paanfo tiben 1 and paanfo tiben 2, two traditional herbal formulations from Burkina Faso used in the treatment of cancer in Burkina Faso. To this end, the recipes were infused and freeze-dried. The dry extracts obtained were used to determine total phenolics and flavonoids content, assess antioxidant activity using the DPPH, ABTS and FRAP methods, evaluate anti-inflammatory properties by inhibiting 15-LOX, COX 1 and 2, and assess cytotoxic activity on HeLa cervical cancer and HePG2 liver cancer cell lines using the MTT test. The paanfo tiben 1 recipe showed the highest levels of total phenolics and flavonoids, as well as the best antioxidant activities, with IC50 values of 21.020 ± 0.6 µg/ml and 22.94 ± 0.57 µg/ml for DPPH and ABTS, and 165.15 mM EAA/mg dry extract for FRAP. It also exhibited the best cytotoxic activity with IC50 values of 112.02 ± 0.025 µg/ml on HeLa cells and 80.67 ± 6.08 µg/ml on HepG2 cells. On the other hand, paanfo tiben 2 exhibited the best anti-inflammatory activities through inhibition of 15-LOX and COX 1, with inhibition percentages at 100 µg/ml of 32.523% and 24.717 % respectively. These results could justify the traditional use of these two recipes by traditional health practitioners in the treatment of cancer sufferers in Burkina Faso.
文摘This paper presents the design of a non-linear controller to prevent an electric power system losing synchronism after a large sudden fault and to achieve good post fault voltage level. By Direct Feedback Linearization (DFL) technique robust non-linear excitation controller is designed which will achieve stability enhancement and voltage regulation of power system. By utilizing this technique, there is a possibility of selecting various control loops for a particular application problem. This method plays an important role in control system and power system engineering problem where all relevant variables cannot be directly measured. Simulated results carried out on a single machine infinite bus power system model which shows the enhancement of transient stability regardless of the fault and changes in network parameters.
文摘Mortality in cirrhosis is mostly associated with the development of clinical decompensation,characterized by ascites,hepatic encephalopathy,variceal bleeding,or jaundice.Therefore,it is important to prevent and manage such complications.Traditionally,the pathophysiology of decompensated cirrhosis was explained by the peripheral arterial vasodilation hypothesis,but it is currently understood that decompensation might also be driven by a systemic inflammatory state(the systemic inflammation hypothesis).Considering its oncotic and nononcotic properties,albumin has been thoroughly evaluated in the prevention and management of several of these decompensating events.There are formal evidence-based recommendations from international medical societies proposing that albumin be administered in individuals with cirrhosis undergoing large-volume paracentesis,patients with spontaneous bacterial peritonitis,those with acute kidney injury(even before the etiological diagnosis),and those with hepatorenal syndrome.Moreover,there are a few randomized controlled trials and meta-analyses suggesting a possible role for albumin infusion in patients with cirrhosis and ascites(long-term albumin administration),individuals with hepatic encephalopathy,and those with acute-on-chronic liver failure undergoing modest-volume paracentesis.Further studies are necessary to elucidate whether albumin administration also benefits patients with cirrhosis and other complications,such as individuals with extraperitoneal infections,those hospitalized with decompensated cirrhosis and hypoalbuminemia,and patients with hyponatremia.
文摘Background: Dialyzable leukocyte extracts (DLE) are heterogeneous mixtures of peptides less than 10 kDa in size that are used as immunomodulatory adjuvants in immune-mediated diseases. TransferonTM is DLE manufactured by National Polytechnic Institute (IPN), and is registered by Mexican health-regulatory authorities as an immunomodulatory drug and commercialized nationally. The proposed mechanism of action of TransferonTM is induction of a Th1 immunoregulatory response. Despite that it is widely used, to date there are no reports of adverse events related to the clinical safety of human DLE or TransferonTM. Objective: To assess the safety of TransferonTM in a large group of patients exposed to DLE as adjuvant treatment. Methods: We included in this study 3844 patients from our Clinical Immunology Service at the Unit of External Services and Clinical Research (USEIC), IPN. Analysis was performed from January 2014 to November 2014, searching for clinical adverse events in patients with immune-mediated diseases and treated with TransferonTM as an adjuvant. Results: In this work we observed clinical nonserious adverse events (AE) in 1.9% of patients treated with TransferonTM (MD 1.9, IQR 1.7 - 2.0). AE were 2.8 times more frequently observed in female than in male patients. The most common AE were headache in 15.7%, followed by rash in 11.4%, increased disease-related symptomatology in 10%, rhinorrhea in 7.1%, cough in 5.7%, and fatigue in 5.7% of patients with AE. 63% of adverse event presentation occurred from day 1 to day 4 of treatment with TransferonTM, and mean time resolution of adverse events was 14 days. In 23 cases, the therapy was stopped because of adverse events and no serious adverse events were observed in this study. Conclusion: TransferonTM induced low frequency of nonserious adverse events during adjuvant treatment. Further monitoring is advisable for different age and disease groups of patients.
文摘Present work encapsulated the friction and wear behaviour of aluminium matrix composites reinforced with different mass fractions of titanium diboride(TiB_(2))particles,synthesized by stir casting.A pin on disc tribotester was employed for conducting the dry sliding wear tests of Al2024−TiB_(2)composites.The tests were performed adopting various parameters like load,sliding distance and sliding velocity for investigating the effect of tribological parameters on the prepared composites.Microstructural characterization confirmed uniform dispersion of TiB_(2)particles and good matrix−reinforcement bonding.Results of the experiments revealed that,low friction and wear rates were observed in the developed composites compared to Al2024 alloy,whereas wear rates of both Al2024 alloy and fabricated composites increased with the increase in load,sliding velocity and sliding distance.However,friction coefficient of both Al2024 alloy and fabricated composites reduced with the increase in applied load but rose with the increase in sliding velocity and sliding distance.SEM studies of the worn surfaces and debris depicted that enhancement in wear resistance can be ascribed to finer debris formation.
文摘Proper quality planning of limestone raw materials is an essential job of maintaining desired feed in cement plant. Rock-type identification is an integrated part of quality planning for limestone mine. In this paper, a computer vision-based rock-type classification algorithm is proposed for fast and reliable identification without human intervention. A laboratory scale vision-based model was developed using probabilistic neural network（PNN） where color histogram features are used as input. The color image histogram-based features that include weighted mean, skewness and kurtosis features are extracted for all three color space red, green, and blue. A total nine features are used as input for the PNN classification model. The smoothing parameter for PNN model is selected judicially to develop an optimal or close to the optimum classification model. The developed PPN is validated using the test data set and results reveal that the proposed vision-based model can perform satisfactorily for classifying limestone rocktypes. Overall the error of mis-classification is below 6%. When compared with other three classification algorithms, it is observed that the proposed method performs substantially better than all three classification algorithms.
文摘This paper describes a robust support vector regression （SVR） methodology, which can offer superior performance for important process engineering problems. The method incorporates hybrid support vector regression and genetic algorithm technique （SVR-GA） for efficient tuning of SVR meta-parameters. The algorithm has been applied for prediction of pressure drop of solid liquid slurry flow. A comparison with selected correlations in the lit- erature showed that the developed SVR correlation noticeably improved the prediction of pressure drop over a wide range of operating conditions, physical properties, and pipe diameters.
