Chemical synthesis is essential in industries such as petrochemicals, fine chemicals, and pharmaceuticals, driving economic and social development. The increasing demand for new molecules and materials calls for novel...Chemical synthesis is essential in industries such as petrochemicals, fine chemicals, and pharmaceuticals, driving economic and social development. The increasing demand for new molecules and materials calls for novel chemical reactions;however, manual experimental screening is time-consuming. Artificial intelligence (AI) offers a promising solution by leveraging large-scale experimental data to model chemical reactions, although challenges such as the lack of standardization and predictability in chemical synthesis hinder AI applications. Additionally, the multi-scale nature of chemical reactions, along with complex multiphase processes, further complicates the task. Recent advances in microchemical systems, particularly continuous flow methods using microreactors, provide precise control over reaction conditions, enhancing reproducibility and enabling high-throughput experimentation. These systems minimize transport-related inconsistencies and facilitate scalable industrial applications. This review systematically explores recent developments in intelligent synthesis based on microchemical systems, focusing on reaction system design, synthesis robots, closed-loop optimization, and high-throughput experimentation, while identifying key areas for future research.展开更多
Modern industrial environments require uninterrupted machinery operation to maintain productivity standards while ensuring safety and minimizing costs.Conventional maintenance methods,such as reactive maintenance(i.e....Modern industrial environments require uninterrupted machinery operation to maintain productivity standards while ensuring safety and minimizing costs.Conventional maintenance methods,such as reactive maintenance(i.e.,run to failure)or time-based preventive maintenance(i.e.,scheduled servicing),prove ineffective for complex systems with many Internet of Things(IoT)devices and sensors because they fall short in detecting faults at early stages when it is most crucial.This paper presents a predictive maintenance framework based on a hybrid deep learning model that integrates the capabilities of Long Short-Term Memory(LSTM)Networks and Convolutional Neural Networks(CNNs).The framework integrates spatial feature extraction and temporal sequence modeling to accurately classify the health state of industrial equipment into three categories,including Normal,Require Maintenance,and Failed.The framework uses a modular pipeline that includes IoT-enabled data collection along with secure transmission methods to manage cloud storage and provide real-time fault classification.The FD004 subset of the NASA C-MAPSS dataset,containing multivariate sensor readings from aircraft engines,serves as the training and evaluation data for the model.Experimental results show that the LSTM-CNN model outperforms baseline models such as LSTM-SVM and LSTM-RNN,achieving an overall average accuracy of 86.66%,precision of 86.00%,recall of 86.33%,and F1-score of 86.33%.Contrary to the previous LSTM-CNN-based predictive maintenance models that either provide a binary classification or rely on synthetically balanced data,our paper provides a three-class maintenance state(i.e.,Normal,Require Maintenance,and Failed)along with threshold-based labeling that retains the true nature of the degradation.In addition,our work also provides an IoT-to-cloud-based modular architecture for deployment.It offers Computerized Maintenance Management System(CMMS)integration,making our proposed solution not only technically sound but also practical and innovative.The solution achieves real-world industrial deployment readiness through its reliable performance alongside its scalable system design.展开更多
We investigate the null tests of cosmic accelerated expansion by using the baryon acoustic oscillation(BAO)data measured by the dark energy spectroscopic instrument(DESI)and reconstruct the dimensionless Hubble parame...We investigate the null tests of cosmic accelerated expansion by using the baryon acoustic oscillation(BAO)data measured by the dark energy spectroscopic instrument(DESI)and reconstruct the dimensionless Hubble parameter E(z)from the DESI BAO Alcock-Paczynski(AP)data using Gaussian process to perform the null test.We find strong evidence of accelerated expansion from the DESI BAO AP data.By reconstructing the deceleration parameter q(z) from the DESI BAO AP data,we find that accelerated expansion persisted until z■0.7 with a 99.7%confidence level.Additionally,to provide insights into the Hubble tension problem,we propose combining the reconstructed E(z) with D_(H)/r_(d) data to derive a model-independent result r_(d)h=99.8±3.1 Mpc.This result is consistent with measurements from cosmic microwave background(CMB)anisotropies using the ΛCDM model.We also propose a model-independent method for reconstructing the comoving angular diameter distance D_(M)(z) from the distance modulus μ,using SNe Ia data and combining this result with DESI BAO data of D_(M)/r_(d) to constrain the value of r_(d).We find that the value of r_(d),derived from this model-independent method,is smaller than that obtained from CMB measurements,with a significant discrepancy of at least 4.17σ.All the conclusions drawn in this paper are independent of cosmological models and gravitational theories.展开更多
Task scheduling in cloud computing is a multi-objective optimization problem,often involving conflicting objectives such as minimizing execution time,reducing operational cost,and maximizing resource utilization.Howev...Task scheduling in cloud computing is a multi-objective optimization problem,often involving conflicting objectives such as minimizing execution time,reducing operational cost,and maximizing resource utilization.However,traditional approaches frequently rely on single-objective optimization methods which are insufficient for capturing the complexity of such problems.To address this limitation,we introduce MDMOSA(Multi-objective Dwarf Mongoose Optimization with Simulated Annealing),a hybrid that integrates multi-objective optimization for efficient task scheduling in Infrastructure-as-a-Service(IaaS)cloud environments.MDMOSA harmonizes the exploration capabilities of the biologically inspired Dwarf Mongoose Optimization(DMO)with the exploitation strengths of Simulated Annealing(SA),achieving a balanced search process.The algorithm aims to optimize task allocation by reducing makespan and financial cost while improving system resource utilization.We evaluate MDMOSA through extensive simulations using the real-world Google Cloud Jobs(GoCJ)dataset within the CloudSim environment.Comparative analysis against benchmarked algorithms such as SMOACO,MOTSGWO,and MFPAGWO reveals that MDMOSA consistently achieves superior performance in terms of scheduling efficiency,cost-effectiveness,and scalability.These results confirm the potential of MDMOSA as a robust and adaptable solution for resource scheduling in dynamic and heterogeneous cloud computing infrastructures.展开更多
The study of target proteins is crucial for understanding molecular interactions and developing analytical platforms,therapeutic agents and functional tools.Herein,we present a novel nanoplatform activated by near-inf...The study of target proteins is crucial for understanding molecular interactions and developing analytical platforms,therapeutic agents and functional tools.Herein,we present a novel nanoplatform activated by near-infrared(NIR) light for triple-modal proteins study,which enabling target protein labeling,enrichment and visualization.Azido-naphthalimide-coated upconversion nanoparticles(UCNPs) serve as NIR light-responsive nanoplatforms,showing promising applications in studying interactions between various bioactive molecules and proteins in living systems.Under NIR light irradiation,azido-naphthalimides are activated by ultraviolet(UV) and blue light emitted from UCNPs and the resulting amino-naphthalimides intermediate not only crosslink nearby target proteins but also enable imaging performance.We demonstrate that this nanoplatform is capable of selective protein labeling and imaging in complex protein environments,achieving specific labeling and imaging of both intracellular and extracellular proteins in mammalian cells as well as bacteria.Furthermore,in vivo protein labeling has been achieved using this novel NIR light-activatable nanoplatform.This technique will open new avenues for discoveries and mechanistic interrogation in chemical biology.展开更多
Methanol,a crucial C1 intermediate,bridges traditional fossil-based chemical processes with emerging sustainable catalytic technologies by serving as both a versatile hydrogenation product from CO/CO_(2)and an active ...Methanol,a crucial C1 intermediate,bridges traditional fossil-based chemical processes with emerging sustainable catalytic technologies by serving as both a versatile hydrogenation product from CO/CO_(2)and an active intermediate for hydrocarbon synthesis.Despite significant progress in methanol-to-hydrocarbon(MTH)conversion,a comprehensive understanding of reaction mechanisms remains essential to enhance catalyst design and industrial applicability.This review critically synthesizes recent advances in mechanistic insights related to methanol conversion and methanol-mediated catalytic processes.Firstly,we systematically outline key reaction pathways involved in initial carbon–carbon(C–C)bond formation through direct and indirect mechanisms,emphasizing significant breakthroughs from spectroscopic analyses and theoretical calculations.Subsequently,we highlight the autocatalytic characteristics and dual-cycle mechanisms underlying MTH processes,critically evaluating the roles of zeolite structures,pore sizes,topology,and acidity in governing product selectivity and catalyst stability.Additionally,we discuss cutting-edge developments in tandem catalytic systems employing methanol as a pivotal intermediate for CO_(x)hydrogenation,emphasizing the transferable mechanistic principles and catalytic insights.Finally,we identify future research directions,including elucidating precise hydrocarbon pool(HCP)intermediates,optimizing zeolite structures through computational-guided design,and developing robust catalytic systems leveraging advanced characterization methods and artificial intelligence.By integrating multidisciplinary approaches from catalytic science,materials engineering,and reaction engineering,this review provides actionable guidance towards rational design and optimization of advanced catalytic systems for efficient methanol conversion processes.展开更多
Conductive elastomers combining micromechanical sensitivity,lightweight adaptability,and environmental sustainability are critically needed for advanced flexible electronics requiring precise responsiveness and long-t...Conductive elastomers combining micromechanical sensitivity,lightweight adaptability,and environmental sustainability are critically needed for advanced flexible electronics requiring precise responsiveness and long-term wearability;however,the integration of these properties remains a significant challenge.Here,we present a biomass-derived conductive elastomer featuring a rationally engineered dynamic crosslinked network integrated with a tunable microporous architecture.This structural design imparts pronounced micromechanical sensitivity,an ultralow density(~0.25 g cm^(−3)),and superior mechanical compliance for adaptive deformation.Moreover,the unique micro-spring effect derived from the porous architecture ensures exceptional stretchability(>500%elongation at break)and superior resilience,delivering immediate and stable electrical response under both subtle(<1%)and large(>200%)mechanical stimuli.Intrinsic dynamic interactions endow the elastomer with efficient room temperature self-healing and complete recyclability without compromising performance.First-principles simulations clarify the mechanisms behind micropore formation and the resulting functionality.Beyond its facile and mild fabrication process,this work establishes a scalable route toward high-performance,sustainable conductive elastomers tailored for next-generation soft electronics.展开更多
Layered transition metal oxide cathode materials have garnered increasing attention for sodium-ion batteries(SIBs).However,they are plagued by the Jahn-Teller distortion of MnO6,Na^(+)/vacancy ordering,and irreversibl...Layered transition metal oxide cathode materials have garnered increasing attention for sodium-ion batteries(SIBs).However,they are plagued by the Jahn-Teller distortion of MnO6,Na^(+)/vacancy ordering,and irreversible lattice oxygen loss,which collectively lead to capacity fading and voltage decay.Herein,we report a P2-type material,Na_(0.67)Ni_(0.3)Mn_(0.6)Li_(0.09)Sn_(0.01)O_(2)(NNMO-Li0.09Sn0.01),modified with two closed-shell dopants(i.e.,Li^(+)and Sn^(4+)).Benefiting from the unique electronic configurations of closed-shell ions,NNMO-Li0.09Sn0.01 exhibits enhanced structural and electrochemical stability.Specifically,the incorporation of Li^(+)increases the Mn^(4+)/Mn3+ratio,thereby mitigating Jahn-Teller distortion during(de)sodiation process.In addition,Li^(+)disrupts the Ni/Mn ordering in the transition metal layer,suppressing Na^(+)/vacancy ordering.Meanwhile,the introduction of Sn^(4+)forms stronger Sn-O bonds(548 kJ mol-1),thereby enhancing the bonding strength between neighboring transition metal ions and surrounding oxygen atoms,effectively reducing oxygen loss during cycling.NNMO-Li0.09Sn0.01 exhibits significantly improved cycling stability,delivering a specific capacity of 90.3 mAh g^(-1)with 62.9%capacity retention after 50 cycles at 0.1 C(1 C=200 mA g^(-1)),along with 90.3%voltage retention.This substitution strategy based on closed-shell ions offers a viable approach for enhancing the structural stability of wide-voltage layered oxide cathodes.展开更多
The rapid expansion of the Internet of Things(IoT)and Edge Artificial Intelligence(AI)has redefined automation and connectivity acrossmodern networks.However,the heterogeneity and limited resources of IoT devices expo...The rapid expansion of the Internet of Things(IoT)and Edge Artificial Intelligence(AI)has redefined automation and connectivity acrossmodern networks.However,the heterogeneity and limited resources of IoT devices expose them to increasingly sophisticated and persistentmalware attacks.These adaptive and stealthy threats can evade conventional detection,establish remote control,propagate across devices,exfiltrate sensitive data,and compromise network integrity.This study presents a Software-Defined Internet of Things(SD-IoT)control-plane-based,AI-driven framework that integrates Gated Recurrent Units(GRU)and Long Short-TermMemory(LSTM)networks for efficient detection of evolving multi-vector,malware-driven botnet attacks.The proposed CUDA-enabled hybrid deep learning(DL)framework performs centralized real-time detection without adding computational overhead to IoT nodes.A feature selection strategy combining variable clustering,attribute evaluation,one-R attribute evaluation,correlation analysis,and principal component analysis(PCA)enhances detection accuracy and reduces complexity.The framework is rigorously evaluated using the N_BaIoT dataset under k-fold cross-validation.Experimental results achieve 99.96%detection accuracy,a false positive rate(FPR)of 0.0035%,and a detection latency of 0.18 ms,confirming its high efficiency and scalability.The findings demonstrate the framework’s potential as a robust and intelligent security solution for next-generation IoT ecosystems.展开更多
The contrast experiment of different stirring modes,which includes a new type of stirring-injection with the method of pulse and rotation,and the initial one-way stirring method,is done through physical simulation in ...The contrast experiment of different stirring modes,which includes a new type of stirring-injection with the method of pulse and rotation,and the initial one-way stirring method,is done through physical simulation in the laboratory.The stirring methods of pulse and rotation are of two kinds.One is pulsed and rotary stirrer with positive and opposite directions.The other is pulsed and rotary stirrer with rotation-stop-rotation.The results show that the stirring mode of pulse and rotation has better effects than the one-way stirring method.The specific effects are that the mixing time of the melting bath is apparently shortened,the number of grains involved in the liquid surface is increased,and the residence time of air bubble in water is doubled.展开更多
The natural world spent billions of years in solution-finding during evolution, which could benefit Technology. How do we put that in a nutshell? Biological systems are more complex than the most complex current techn...The natural world spent billions of years in solution-finding during evolution, which could benefit Technology. How do we put that in a nutshell? Biological systems are more complex than the most complex current technology. Any given function and effect are simultaneously coordinated and linked with others at many levels of biological organisation-from cell organelle to organism, to population and ecosystem. Technology does not have tools to deal with the complexity and “goal-intendedness” of living systems. But limits for interaction exist on both sides-Biological science itself is also too empirical and not mature enough to provide a solid base for correlating living with technical systems. Moving towards a synthesis, where engineers can utilize the vast amount of available biological data, we suggest using a tool called “Theory of Inventive Problem Solving” (TRIZ) and clarifying some important methodological issues, which have not previously been recognised in bionic engineering: 1) Requirement for more appropriate definitions of “system”, “effect”, “function”,“law” and “rule”. 2) Requirement for understanding or even measuring the degree of contradiction or analogy between functions in biological and artificial and/or non-living engineering system-there is no simple direct correlation between what engineers find useful and what biology does.展开更多
Gas fluidization has an ability to turn static particles to fluid-like dense flow, which allows greatly improved heat transfer among porous powders and highly efficient solid processing to become reality. As the risin...Gas fluidization has an ability to turn static particles to fluid-like dense flow, which allows greatly improved heat transfer among porous powders and highly efficient solid processing to become reality. As the rising star of current scientific research, some nanoparticles can also be fluidized in the form of agglomerates, with sizes ranging from tens to hundreds of microns. Herein, we have reviewed the recent progress on nanomaterial agglomeration and their fluidization behavior, the assisted techniques to enhance the fluidization of nanomaterials,including some mechanical measures, external fields and improved gas injections, as well as their effects on solid fluidization and mixing behaviors. Most of these techniques are effective in breaking large agglomerates and promoting particulate fluidization, meanwhile, the solid mixing is intensified under assisted fluidization. The applications of nanofluidization in nanostructured material production and sustainable chemical industry are further presented. In summary, the fluidization science of multidimensional, multicomponent and multifunctional particles, their multi-phase characterization, and the guideline of fluidized bed coupled process are prerequisites for the sustainable development of fluidized bed based materials, energy and chemical industry.展开更多
The range of coal-mine underground goaf has continuously expanded over time.Caving,fracture,and deformation zones have also changed,thereby inducing coal-mine water inrush and other environmental disasters.In this stu...The range of coal-mine underground goaf has continuously expanded over time.Caving,fracture,and deformation zones have also changed,thereby inducing coal-mine water inrush and other environmental disasters.In this study,4 D seismic monitoring technology that is effective in reservoir development was used to monitor abnormal changes in coal-mine underground goaf to explore the feasibility of the method.Taking a coal mine in Hancheng,Shaanxi as an example,we used the aforementioned technology to dynamically monitor the abnormal changes in the goaf.Based on the 4 D seismic data obtained in the experiment and the abnormal change characteristics of the coal-mine goaf,the method of 4 D seismic data processing in reservoir was improved.A set of 4 D data processing flow for the goaf was established,and the anomalies in the surface elevation and overlying strata velocity caused by collapse were corrected.We have made the following improvements to the method of 4 D seismic data processing in the reservoir:(1)the static correction problem caused by the changes of surface elevation and destruction of the low-velocity layer has been solved through fusion static correction to comb the low-frequency components of elevation statics with the high-frequency components of refraction statics;(2)the problem of overlying strata velocity changes in the goaf caused by collapse has been solved through the velocity consistency method;(3)the problem of reflection event pull-down in the disturbance area has been solved through space-varying moveout correction based on cross-correlation;and(4)amplitude anomalies in the coal seam caused by the goaf have been addressed using the correction method of space-varying amplitude.Results show that the 4 D seismic data processing and interpretation method established in this study is reasonable and effective.展开更多
All-solid-state lithium-sulfur batteries(ASSLSBs)employing sulfide solid electrolytes are one of the most promising next-generation energy storage systems due to their potential for higher energy density and safety.Ho...All-solid-state lithium-sulfur batteries(ASSLSBs)employing sulfide solid electrolytes are one of the most promising next-generation energy storage systems due to their potential for higher energy density and safety.However,scalable fabrication of sheet-type sulfur cathodes with high sulfur loading and excellent performances remains challenging.In this work,sheet-type freestanding sulfur cathodes with high sulfur loading were fabricated by dry electrode technology.The unique fibrous morphologies of polytetrafluoroethylene(PTFE)binders in dry electrodes not only provides excellent mechanical properties but also uncompromised ionic/electronic conductance.Even employed with thickened dry cathodes with high sulfur loading of 2 mg cm^(-2),ASSLSBs still exhibit outstanding rate performance and cycle stability.Moreover,the all-solid-state lithium-sulfur monolayer pouch cells(9.2 m Ah)were also demonstrated and exhibited excellent safety under a harsh test situation.This work verifies the potential of dry electrode technology in the scalable fabrication of thickened sulfur cathodes and will promote the practical applications of ASSLSBs.展开更多
The mechanical performances and water retention characteristics of clays,stabilised by partial substitution of cement with by-products and inclusion of a nanotechnology-based additive called RoadCem(RC),are studied in...The mechanical performances and water retention characteristics of clays,stabilised by partial substitution of cement with by-products and inclusion of a nanotechnology-based additive called RoadCem(RC),are studied in this research.The unconfined compression tests and one-dimensional oedometer swelling were performed after 7 d of curing to understand the influence of addition of 1%of RC material in the stabilised soils with the cement partially replaced by 49%,59%and 69%of ground granulated blast furnace slag(GBBS)or pulverised fuel ash(PFA).The moisture retention capacity of the stabilised clays was also explored using the soil-water retention curve(SWRC)from the measured suctions.Results confirmed an obvious effect of the use of RC with the obtained strength and swell properties of the stabilised clays suitable for road application at 50%replacement of cement.This outcome is associated with the in-depth and penetrating hydration of the cementitious materials by the RC and water which results in the production of needle-like matrix with interlocking filaments e a phenomenon referred to as the‘wrapping’effect.On the other hand,the SWRC used to describe the water holding capacity and corresponding swell mechanism of clays stabilised by a proportion of RC showed a satisfactory response.The moisture retention of the RC-modified clays was initially higher but reduced subsequently as the saturation level increased with decreasing suction.This phenomenon confirmed that clays stabilised by including the RC are water-proof in nature,thus ensuring reduced porosity and suction even at reduced water content.Overall,the stabilised clays with the combination of cement,GGBS and RC showed a better performance compared to those with the PFA included.展开更多
Diabetes mellitus(DM),an increasingly prevalent chronic metabolic disease,is characterised by prolonged hyperglycaemia,which leads to long-term health consequences.Although much effort has been put into understanding ...Diabetes mellitus(DM),an increasingly prevalent chronic metabolic disease,is characterised by prolonged hyperglycaemia,which leads to long-term health consequences.Although much effort has been put into understanding the pathogenesis of diabetic wounds,the underlying mechanisms remain unclear.The advent of single-cell RNA sequencing(scRNAseq)has revolutionised biological research by enabling the identification of novel cell types,the discovery of cellular markers,the analysis of gene expression patterns and the prediction of develop-mental trajectories.This powerful tool allows for an in-depth exploration of pathogenesis at the cellular and molecular levels.In this editorial,we focus on progenitor-based repair strategies for diabetic wound healing as revealed by scRNAseq and highlight the biological behaviour of various healing-related cells and the alteration of signalling pathways in the process of diabetic wound healing.ScRNAseq could not only deepen our understanding of the complex biology of diabetic wounds but also identify and validate new targets for inter-vention,offering hope for improved patient outcomes in the management of this challenging complication of DM.展开更多
The precise managed pressure drilling(MPD)technology is mainly used to deal with the difficulties encountered when oil and gas open hole sections with multiple pressure systems and the strata with narrow safety densit...The precise managed pressure drilling(MPD)technology is mainly used to deal with the difficulties encountered when oil and gas open hole sections with multiple pressure systems and the strata with narrow safety density window are drilled through.If its liner cementing is carried out according to the conventional method,lost circulation is inevitable in the process of cementing while the displacement efficiency of smallclearance liner cementing is satisfied.If the positive and inverse injection technology is adopted,the cementing quality cannot meet the requirements of later well test engineering of ultradeep wells.In this paper,the cementing operation ofØ114.3 mm liner in Well Longgang 70 which was drilled in the Jiange structure of the Sichuan Basin was taken as an example to explore the application of the cementing technology based on the precise MPD and pressure balancing method to the cementing of long open-hole sections(as long as 859 m)with both high and low pressures running through multiple reservoirs.On the one hand,the technical measures were taken specifically to ensure the annulus filling efficiency of slurry and the pressure balance in the whole process of cementing.And on the other hand,the annulus pressure balance was precisely controlled by virtue of precise MPD devices and by injecting heavy weight drilling fluids through central pipes,and thus the wellbore pressure was kept steady in the whole process of cementing in the strata with narrow safety density window.It is indicated thatØ114.3 mm liner cementing in this well is good with qualified pressure tests and no channeling emerges at a funnel during the staged density reduction.It is concluded that this method can enhance the liner cementing quality of complex ultradeep gas wells and improve the wellbore conditions for the later safe well tests of high-pressure gas wells.展开更多
Based on an electrical resistance tomography(ERT) sensor and the data mining technology,a new voidage measurement method is proposed for air-water two-phase flow.The data mining technology used in this work is a least...Based on an electrical resistance tomography(ERT) sensor and the data mining technology,a new voidage measurement method is proposed for air-water two-phase flow.The data mining technology used in this work is a least squares support vector machine(LS-SVM) algorithm together with the feature extraction method,and three feature extraction methods are tested:principal component analysis(PCA),partial least squares(PLS) and independent component analysis(ICA).In the practical voidage measurement process,the flow pattern is firstly identified directly from the conductance values obtained by the ERT sensor.Then,the appropriate voidage measurement model is selected according to the flow pattern identification result.Finally,the voidage is calculated.Experimental results show that the proposed method can measure the voidage effectively,and the measurement accuracy and speed are satisfactory.Compared with the conventional voidage measurement methods based on ERT,the proposed method doesn't need any image reconstruction process,so it has the advantage of good real-time performance.Due to the introduction of flow pattern identification,the influence of flow pattern on the voidage measurement is overcome.Besides,it is demonstrated that the LS-SVM method with PLS feature extraction presents the best measurement performance among the tested methods.展开更多
It is a common observation that whenever patients arrives at the front desk of a hospital,outpatient clinic,or other health-associated centers,they have to first queue up in a line and wait to fill in their registrati...It is a common observation that whenever patients arrives at the front desk of a hospital,outpatient clinic,or other health-associated centers,they have to first queue up in a line and wait to fill in their registration form to get admitted.The long waiting time without any status updates is the most common complaint,concerning health officials.In this paper,UrNext,a location-aware mobile-based solution using Bluetooth low-energy(BLE)technology is presented to solve the problem.Recently,a technology-oriented method,the Internet of Things(IoT),has been gaining popularity in helping to solve some of the healthcare sector’s problems.The implementation of this solution could be illustrated through a simple example of when a patient arrives at a clinic for a consultation.Instead of having to wait in long lines,that patient will be greeted automatically,receive a push notification of an admittance along with an estimated waiting time for a consultation session.This will not only provide the patients with a sense of freedom but would also reduce the uncertainty levels that are generally observed,thus saving both time and money.This work aims to improve the clinics’quality of services,organize queues and minimize waiting times,leading to patients’comfort while reducing the burden on nurses and receptionists.The results demonstrate that the presented system is successful in its performance and helps achieves a plea-sant and conducive clinic visitation process with higher productivity.展开更多
基金supported by the National Natural Science Foundation of China(22378227)Shijiazhuang Science and Technology Bureau(231790163A).
文摘Chemical synthesis is essential in industries such as petrochemicals, fine chemicals, and pharmaceuticals, driving economic and social development. The increasing demand for new molecules and materials calls for novel chemical reactions;however, manual experimental screening is time-consuming. Artificial intelligence (AI) offers a promising solution by leveraging large-scale experimental data to model chemical reactions, although challenges such as the lack of standardization and predictability in chemical synthesis hinder AI applications. Additionally, the multi-scale nature of chemical reactions, along with complex multiphase processes, further complicates the task. Recent advances in microchemical systems, particularly continuous flow methods using microreactors, provide precise control over reaction conditions, enhancing reproducibility and enabling high-throughput experimentation. These systems minimize transport-related inconsistencies and facilitate scalable industrial applications. This review systematically explores recent developments in intelligent synthesis based on microchemical systems, focusing on reaction system design, synthesis robots, closed-loop optimization, and high-throughput experimentation, while identifying key areas for future research.
文摘Modern industrial environments require uninterrupted machinery operation to maintain productivity standards while ensuring safety and minimizing costs.Conventional maintenance methods,such as reactive maintenance(i.e.,run to failure)or time-based preventive maintenance(i.e.,scheduled servicing),prove ineffective for complex systems with many Internet of Things(IoT)devices and sensors because they fall short in detecting faults at early stages when it is most crucial.This paper presents a predictive maintenance framework based on a hybrid deep learning model that integrates the capabilities of Long Short-Term Memory(LSTM)Networks and Convolutional Neural Networks(CNNs).The framework integrates spatial feature extraction and temporal sequence modeling to accurately classify the health state of industrial equipment into three categories,including Normal,Require Maintenance,and Failed.The framework uses a modular pipeline that includes IoT-enabled data collection along with secure transmission methods to manage cloud storage and provide real-time fault classification.The FD004 subset of the NASA C-MAPSS dataset,containing multivariate sensor readings from aircraft engines,serves as the training and evaluation data for the model.Experimental results show that the LSTM-CNN model outperforms baseline models such as LSTM-SVM and LSTM-RNN,achieving an overall average accuracy of 86.66%,precision of 86.00%,recall of 86.33%,and F1-score of 86.33%.Contrary to the previous LSTM-CNN-based predictive maintenance models that either provide a binary classification or rely on synthetically balanced data,our paper provides a three-class maintenance state(i.e.,Normal,Require Maintenance,and Failed)along with threshold-based labeling that retains the true nature of the degradation.In addition,our work also provides an IoT-to-cloud-based modular architecture for deployment.It offers Computerized Maintenance Management System(CMMS)integration,making our proposed solution not only technically sound but also practical and innovative.The solution achieves real-world industrial deployment readiness through its reliable performance alongside its scalable system design.
基金supported in part by the National Key Research and Development Program of China (Grant No.2020YFC2201504)the National Natural Science Foundation of China (Grant Nos.12588101 and 12535002)。
文摘We investigate the null tests of cosmic accelerated expansion by using the baryon acoustic oscillation(BAO)data measured by the dark energy spectroscopic instrument(DESI)and reconstruct the dimensionless Hubble parameter E(z)from the DESI BAO Alcock-Paczynski(AP)data using Gaussian process to perform the null test.We find strong evidence of accelerated expansion from the DESI BAO AP data.By reconstructing the deceleration parameter q(z) from the DESI BAO AP data,we find that accelerated expansion persisted until z■0.7 with a 99.7%confidence level.Additionally,to provide insights into the Hubble tension problem,we propose combining the reconstructed E(z) with D_(H)/r_(d) data to derive a model-independent result r_(d)h=99.8±3.1 Mpc.This result is consistent with measurements from cosmic microwave background(CMB)anisotropies using the ΛCDM model.We also propose a model-independent method for reconstructing the comoving angular diameter distance D_(M)(z) from the distance modulus μ,using SNe Ia data and combining this result with DESI BAO data of D_(M)/r_(d) to constrain the value of r_(d).We find that the value of r_(d),derived from this model-independent method,is smaller than that obtained from CMB measurements,with a significant discrepancy of at least 4.17σ.All the conclusions drawn in this paper are independent of cosmological models and gravitational theories.
文摘Task scheduling in cloud computing is a multi-objective optimization problem,often involving conflicting objectives such as minimizing execution time,reducing operational cost,and maximizing resource utilization.However,traditional approaches frequently rely on single-objective optimization methods which are insufficient for capturing the complexity of such problems.To address this limitation,we introduce MDMOSA(Multi-objective Dwarf Mongoose Optimization with Simulated Annealing),a hybrid that integrates multi-objective optimization for efficient task scheduling in Infrastructure-as-a-Service(IaaS)cloud environments.MDMOSA harmonizes the exploration capabilities of the biologically inspired Dwarf Mongoose Optimization(DMO)with the exploitation strengths of Simulated Annealing(SA),achieving a balanced search process.The algorithm aims to optimize task allocation by reducing makespan and financial cost while improving system resource utilization.We evaluate MDMOSA through extensive simulations using the real-world Google Cloud Jobs(GoCJ)dataset within the CloudSim environment.Comparative analysis against benchmarked algorithms such as SMOACO,MOTSGWO,and MFPAGWO reveals that MDMOSA consistently achieves superior performance in terms of scheduling efficiency,cost-effectiveness,and scalability.These results confirm the potential of MDMOSA as a robust and adaptable solution for resource scheduling in dynamic and heterogeneous cloud computing infrastructures.
基金supported by the National Natural Science Foundation of China (No.22007008)the LiaoNing Revitalization Talents Program (No.XLYC1907021)the Fundamental Research Funds for the Central Universities (Nos.DUT23YG120,DUT19RC(3)009)。
文摘The study of target proteins is crucial for understanding molecular interactions and developing analytical platforms,therapeutic agents and functional tools.Herein,we present a novel nanoplatform activated by near-infrared(NIR) light for triple-modal proteins study,which enabling target protein labeling,enrichment and visualization.Azido-naphthalimide-coated upconversion nanoparticles(UCNPs) serve as NIR light-responsive nanoplatforms,showing promising applications in studying interactions between various bioactive molecules and proteins in living systems.Under NIR light irradiation,azido-naphthalimides are activated by ultraviolet(UV) and blue light emitted from UCNPs and the resulting amino-naphthalimides intermediate not only crosslink nearby target proteins but also enable imaging performance.We demonstrate that this nanoplatform is capable of selective protein labeling and imaging in complex protein environments,achieving specific labeling and imaging of both intracellular and extracellular proteins in mammalian cells as well as bacteria.Furthermore,in vivo protein labeling has been achieved using this novel NIR light-activatable nanoplatform.This technique will open new avenues for discoveries and mechanistic interrogation in chemical biology.
基金the Inner Mongolia Natural Science Foundation(2023ZD05,2025JQ028,2025MS02001)the National Natural Science Foundation of China(22278238,22238004)+3 种基金the National Key Research and Development Program of China(2024YFE0211400)the Major Science and Technology Program of Inner Mongolia Autonomous Region(20212120326)the“Elite Talents Revitalize Inner Mongolia”Initiative–Tier-1 Talent Team(202410)the Ordos Science and Technology Breakthrough(JBGS2024003),and Ordos Laboratory for their financial support.
文摘Methanol,a crucial C1 intermediate,bridges traditional fossil-based chemical processes with emerging sustainable catalytic technologies by serving as both a versatile hydrogenation product from CO/CO_(2)and an active intermediate for hydrocarbon synthesis.Despite significant progress in methanol-to-hydrocarbon(MTH)conversion,a comprehensive understanding of reaction mechanisms remains essential to enhance catalyst design and industrial applicability.This review critically synthesizes recent advances in mechanistic insights related to methanol conversion and methanol-mediated catalytic processes.Firstly,we systematically outline key reaction pathways involved in initial carbon–carbon(C–C)bond formation through direct and indirect mechanisms,emphasizing significant breakthroughs from spectroscopic analyses and theoretical calculations.Subsequently,we highlight the autocatalytic characteristics and dual-cycle mechanisms underlying MTH processes,critically evaluating the roles of zeolite structures,pore sizes,topology,and acidity in governing product selectivity and catalyst stability.Additionally,we discuss cutting-edge developments in tandem catalytic systems employing methanol as a pivotal intermediate for CO_(x)hydrogenation,emphasizing the transferable mechanistic principles and catalytic insights.Finally,we identify future research directions,including elucidating precise hydrocarbon pool(HCP)intermediates,optimizing zeolite structures through computational-guided design,and developing robust catalytic systems leveraging advanced characterization methods and artificial intelligence.By integrating multidisciplinary approaches from catalytic science,materials engineering,and reaction engineering,this review provides actionable guidance towards rational design and optimization of advanced catalytic systems for efficient methanol conversion processes.
基金supported by National Natural Science Foundation of China(No.52103044)Double First-Class Initiative University of Science and Technology of China(KY2400000037)the Young Talent Programme(GG2400007009).
文摘Conductive elastomers combining micromechanical sensitivity,lightweight adaptability,and environmental sustainability are critically needed for advanced flexible electronics requiring precise responsiveness and long-term wearability;however,the integration of these properties remains a significant challenge.Here,we present a biomass-derived conductive elastomer featuring a rationally engineered dynamic crosslinked network integrated with a tunable microporous architecture.This structural design imparts pronounced micromechanical sensitivity,an ultralow density(~0.25 g cm^(−3)),and superior mechanical compliance for adaptive deformation.Moreover,the unique micro-spring effect derived from the porous architecture ensures exceptional stretchability(>500%elongation at break)and superior resilience,delivering immediate and stable electrical response under both subtle(<1%)and large(>200%)mechanical stimuli.Intrinsic dynamic interactions endow the elastomer with efficient room temperature self-healing and complete recyclability without compromising performance.First-principles simulations clarify the mechanisms behind micropore formation and the resulting functionality.Beyond its facile and mild fabrication process,this work establishes a scalable route toward high-performance,sustainable conductive elastomers tailored for next-generation soft electronics.
基金supported by the Ministry of Science and Technology of China(2025YFE0100200)the Natural Science Foundation of Tianjin(24JCJQJC00220 and 24ZXZSSS00310)+3 种基金the National Natural Science Foundation of China(22479080,92372203,and 92372001)the Open Foundation of Shanghai Jiao Tong University Shaoxing Research Institute of Renewable Energy and Molecular Engineering(JDSX2023003)the Fundamental Research Funds for the Central Universities of Nankai University(020-63253167)the"111 Center"(B25010)。
文摘Layered transition metal oxide cathode materials have garnered increasing attention for sodium-ion batteries(SIBs).However,they are plagued by the Jahn-Teller distortion of MnO6,Na^(+)/vacancy ordering,and irreversible lattice oxygen loss,which collectively lead to capacity fading and voltage decay.Herein,we report a P2-type material,Na_(0.67)Ni_(0.3)Mn_(0.6)Li_(0.09)Sn_(0.01)O_(2)(NNMO-Li0.09Sn0.01),modified with two closed-shell dopants(i.e.,Li^(+)and Sn^(4+)).Benefiting from the unique electronic configurations of closed-shell ions,NNMO-Li0.09Sn0.01 exhibits enhanced structural and electrochemical stability.Specifically,the incorporation of Li^(+)increases the Mn^(4+)/Mn3+ratio,thereby mitigating Jahn-Teller distortion during(de)sodiation process.In addition,Li^(+)disrupts the Ni/Mn ordering in the transition metal layer,suppressing Na^(+)/vacancy ordering.Meanwhile,the introduction of Sn^(4+)forms stronger Sn-O bonds(548 kJ mol-1),thereby enhancing the bonding strength between neighboring transition metal ions and surrounding oxygen atoms,effectively reducing oxygen loss during cycling.NNMO-Li0.09Sn0.01 exhibits significantly improved cycling stability,delivering a specific capacity of 90.3 mAh g^(-1)with 62.9%capacity retention after 50 cycles at 0.1 C(1 C=200 mA g^(-1)),along with 90.3%voltage retention.This substitution strategy based on closed-shell ions offers a viable approach for enhancing the structural stability of wide-voltage layered oxide cathodes.
基金supported by Princess Nourah bint Abdulrahman University Researchers Supporting ProjectNumber(PNURSP2025R97),PrincessNourah bint AbdulrahmanUniversity,Riyadh,Saudi Arabia.
文摘The rapid expansion of the Internet of Things(IoT)and Edge Artificial Intelligence(AI)has redefined automation and connectivity acrossmodern networks.However,the heterogeneity and limited resources of IoT devices expose them to increasingly sophisticated and persistentmalware attacks.These adaptive and stealthy threats can evade conventional detection,establish remote control,propagate across devices,exfiltrate sensitive data,and compromise network integrity.This study presents a Software-Defined Internet of Things(SD-IoT)control-plane-based,AI-driven framework that integrates Gated Recurrent Units(GRU)and Long Short-TermMemory(LSTM)networks for efficient detection of evolving multi-vector,malware-driven botnet attacks.The proposed CUDA-enabled hybrid deep learning(DL)framework performs centralized real-time detection without adding computational overhead to IoT nodes.A feature selection strategy combining variable clustering,attribute evaluation,one-R attribute evaluation,correlation analysis,and principal component analysis(PCA)enhances detection accuracy and reduces complexity.The framework is rigorously evaluated using the N_BaIoT dataset under k-fold cross-validation.Experimental results achieve 99.96%detection accuracy,a false positive rate(FPR)of 0.0035%,and a detection latency of 0.18 ms,confirming its high efficiency and scalability.The findings demonstrate the framework’s potential as a robust and intelligent security solution for next-generation IoT ecosystems.
文摘The contrast experiment of different stirring modes,which includes a new type of stirring-injection with the method of pulse and rotation,and the initial one-way stirring method,is done through physical simulation in the laboratory.The stirring methods of pulse and rotation are of two kinds.One is pulsed and rotary stirrer with positive and opposite directions.The other is pulsed and rotary stirrer with rotation-stop-rotation.The results show that the stirring mode of pulse and rotation has better effects than the one-way stirring method.The specific effects are that the mixing time of the melting bath is apparently shortened,the number of grains involved in the liquid surface is increased,and the residence time of air bubble in water is doubled.
文摘The natural world spent billions of years in solution-finding during evolution, which could benefit Technology. How do we put that in a nutshell? Biological systems are more complex than the most complex current technology. Any given function and effect are simultaneously coordinated and linked with others at many levels of biological organisation-from cell organelle to organism, to population and ecosystem. Technology does not have tools to deal with the complexity and “goal-intendedness” of living systems. But limits for interaction exist on both sides-Biological science itself is also too empirical and not mature enough to provide a solid base for correlating living with technical systems. Moving towards a synthesis, where engineers can utilize the vast amount of available biological data, we suggest using a tool called “Theory of Inventive Problem Solving” (TRIZ) and clarifying some important methodological issues, which have not previously been recognised in bionic engineering: 1) Requirement for more appropriate definitions of “system”, “effect”, “function”,“law” and “rule”. 2) Requirement for understanding or even measuring the degree of contradiction or analogy between functions in biological and artificial and/or non-living engineering system-there is no simple direct correlation between what engineers find useful and what biology does.
基金Supported by the National Natural Science Foundation of China(21306102 and21422604)China Postdoctoral Science Foundation(2015M571049)
文摘Gas fluidization has an ability to turn static particles to fluid-like dense flow, which allows greatly improved heat transfer among porous powders and highly efficient solid processing to become reality. As the rising star of current scientific research, some nanoparticles can also be fluidized in the form of agglomerates, with sizes ranging from tens to hundreds of microns. Herein, we have reviewed the recent progress on nanomaterial agglomeration and their fluidization behavior, the assisted techniques to enhance the fluidization of nanomaterials,including some mechanical measures, external fields and improved gas injections, as well as their effects on solid fluidization and mixing behaviors. Most of these techniques are effective in breaking large agglomerates and promoting particulate fluidization, meanwhile, the solid mixing is intensified under assisted fluidization. The applications of nanofluidization in nanostructured material production and sustainable chemical industry are further presented. In summary, the fluidization science of multidimensional, multicomponent and multifunctional particles, their multi-phase characterization, and the guideline of fluidized bed coupled process are prerequisites for the sustainable development of fluidized bed based materials, energy and chemical industry.
基金funded by the National Key Research and Development Program Subject(No.2018YFC0807804)。
文摘The range of coal-mine underground goaf has continuously expanded over time.Caving,fracture,and deformation zones have also changed,thereby inducing coal-mine water inrush and other environmental disasters.In this study,4 D seismic monitoring technology that is effective in reservoir development was used to monitor abnormal changes in coal-mine underground goaf to explore the feasibility of the method.Taking a coal mine in Hancheng,Shaanxi as an example,we used the aforementioned technology to dynamically monitor the abnormal changes in the goaf.Based on the 4 D seismic data obtained in the experiment and the abnormal change characteristics of the coal-mine goaf,the method of 4 D seismic data processing in reservoir was improved.A set of 4 D data processing flow for the goaf was established,and the anomalies in the surface elevation and overlying strata velocity caused by collapse were corrected.We have made the following improvements to the method of 4 D seismic data processing in the reservoir:(1)the static correction problem caused by the changes of surface elevation and destruction of the low-velocity layer has been solved through fusion static correction to comb the low-frequency components of elevation statics with the high-frequency components of refraction statics;(2)the problem of overlying strata velocity changes in the goaf caused by collapse has been solved through the velocity consistency method;(3)the problem of reflection event pull-down in the disturbance area has been solved through space-varying moveout correction based on cross-correlation;and(4)amplitude anomalies in the coal seam caused by the goaf have been addressed using the correction method of space-varying amplitude.Results show that the 4 D seismic data processing and interpretation method established in this study is reasonable and effective.
基金supported by the National Key Research and Development Program of China(2021YFB2500300)the National Natural Science Foundation of China(22075029,22108151,22109084)the China Postdoctoral Science Foundation(2021TQ0164)。
文摘All-solid-state lithium-sulfur batteries(ASSLSBs)employing sulfide solid electrolytes are one of the most promising next-generation energy storage systems due to their potential for higher energy density and safety.However,scalable fabrication of sheet-type sulfur cathodes with high sulfur loading and excellent performances remains challenging.In this work,sheet-type freestanding sulfur cathodes with high sulfur loading were fabricated by dry electrode technology.The unique fibrous morphologies of polytetrafluoroethylene(PTFE)binders in dry electrodes not only provides excellent mechanical properties but also uncompromised ionic/electronic conductance.Even employed with thickened dry cathodes with high sulfur loading of 2 mg cm^(-2),ASSLSBs still exhibit outstanding rate performance and cycle stability.Moreover,the all-solid-state lithium-sulfur monolayer pouch cells(9.2 m Ah)were also demonstrated and exhibited excellent safety under a harsh test situation.This work verifies the potential of dry electrode technology in the scalable fabrication of thickened sulfur cathodes and will promote the practical applications of ASSLSBs.
文摘The mechanical performances and water retention characteristics of clays,stabilised by partial substitution of cement with by-products and inclusion of a nanotechnology-based additive called RoadCem(RC),are studied in this research.The unconfined compression tests and one-dimensional oedometer swelling were performed after 7 d of curing to understand the influence of addition of 1%of RC material in the stabilised soils with the cement partially replaced by 49%,59%and 69%of ground granulated blast furnace slag(GBBS)or pulverised fuel ash(PFA).The moisture retention capacity of the stabilised clays was also explored using the soil-water retention curve(SWRC)from the measured suctions.Results confirmed an obvious effect of the use of RC with the obtained strength and swell properties of the stabilised clays suitable for road application at 50%replacement of cement.This outcome is associated with the in-depth and penetrating hydration of the cementitious materials by the RC and water which results in the production of needle-like matrix with interlocking filaments e a phenomenon referred to as the‘wrapping’effect.On the other hand,the SWRC used to describe the water holding capacity and corresponding swell mechanism of clays stabilised by a proportion of RC showed a satisfactory response.The moisture retention of the RC-modified clays was initially higher but reduced subsequently as the saturation level increased with decreasing suction.This phenomenon confirmed that clays stabilised by including the RC are water-proof in nature,thus ensuring reduced porosity and suction even at reduced water content.Overall,the stabilised clays with the combination of cement,GGBS and RC showed a better performance compared to those with the PFA included.
基金Supported by Shenzhen Science and Technology Program,No.GJHZ20210705142543019Guangdong Basic and Applied Basic Research Foundation,No.2023A1515220074.
文摘Diabetes mellitus(DM),an increasingly prevalent chronic metabolic disease,is characterised by prolonged hyperglycaemia,which leads to long-term health consequences.Although much effort has been put into understanding the pathogenesis of diabetic wounds,the underlying mechanisms remain unclear.The advent of single-cell RNA sequencing(scRNAseq)has revolutionised biological research by enabling the identification of novel cell types,the discovery of cellular markers,the analysis of gene expression patterns and the prediction of develop-mental trajectories.This powerful tool allows for an in-depth exploration of pathogenesis at the cellular and molecular levels.In this editorial,we focus on progenitor-based repair strategies for diabetic wound healing as revealed by scRNAseq and highlight the biological behaviour of various healing-related cells and the alteration of signalling pathways in the process of diabetic wound healing.ScRNAseq could not only deepen our understanding of the complex biology of diabetic wounds but also identify and validate new targets for inter-vention,offering hope for improved patient outcomes in the management of this challenging complication of DM.
文摘The precise managed pressure drilling(MPD)technology is mainly used to deal with the difficulties encountered when oil and gas open hole sections with multiple pressure systems and the strata with narrow safety density window are drilled through.If its liner cementing is carried out according to the conventional method,lost circulation is inevitable in the process of cementing while the displacement efficiency of smallclearance liner cementing is satisfied.If the positive and inverse injection technology is adopted,the cementing quality cannot meet the requirements of later well test engineering of ultradeep wells.In this paper,the cementing operation ofØ114.3 mm liner in Well Longgang 70 which was drilled in the Jiange structure of the Sichuan Basin was taken as an example to explore the application of the cementing technology based on the precise MPD and pressure balancing method to the cementing of long open-hole sections(as long as 859 m)with both high and low pressures running through multiple reservoirs.On the one hand,the technical measures were taken specifically to ensure the annulus filling efficiency of slurry and the pressure balance in the whole process of cementing.And on the other hand,the annulus pressure balance was precisely controlled by virtue of precise MPD devices and by injecting heavy weight drilling fluids through central pipes,and thus the wellbore pressure was kept steady in the whole process of cementing in the strata with narrow safety density window.It is indicated thatØ114.3 mm liner cementing in this well is good with qualified pressure tests and no channeling emerges at a funnel during the staged density reduction.It is concluded that this method can enhance the liner cementing quality of complex ultradeep gas wells and improve the wellbore conditions for the later safe well tests of high-pressure gas wells.
基金Supported by the National Natural Science Foundation of China (60972138)
文摘Based on an electrical resistance tomography(ERT) sensor and the data mining technology,a new voidage measurement method is proposed for air-water two-phase flow.The data mining technology used in this work is a least squares support vector machine(LS-SVM) algorithm together with the feature extraction method,and three feature extraction methods are tested:principal component analysis(PCA),partial least squares(PLS) and independent component analysis(ICA).In the practical voidage measurement process,the flow pattern is firstly identified directly from the conductance values obtained by the ERT sensor.Then,the appropriate voidage measurement model is selected according to the flow pattern identification result.Finally,the voidage is calculated.Experimental results show that the proposed method can measure the voidage effectively,and the measurement accuracy and speed are satisfactory.Compared with the conventional voidage measurement methods based on ERT,the proposed method doesn't need any image reconstruction process,so it has the advantage of good real-time performance.Due to the introduction of flow pattern identification,the influence of flow pattern on the voidage measurement is overcome.Besides,it is demonstrated that the LS-SVM method with PLS feature extraction presents the best measurement performance among the tested methods.
基金The author extends her appreciation to the Deanship of Scientific Research at King Saud University for funding this work through the Undergraduate Research Support Program,Project no.(URSP-3-18-89).
文摘It is a common observation that whenever patients arrives at the front desk of a hospital,outpatient clinic,or other health-associated centers,they have to first queue up in a line and wait to fill in their registration form to get admitted.The long waiting time without any status updates is the most common complaint,concerning health officials.In this paper,UrNext,a location-aware mobile-based solution using Bluetooth low-energy(BLE)technology is presented to solve the problem.Recently,a technology-oriented method,the Internet of Things(IoT),has been gaining popularity in helping to solve some of the healthcare sector’s problems.The implementation of this solution could be illustrated through a simple example of when a patient arrives at a clinic for a consultation.Instead of having to wait in long lines,that patient will be greeted automatically,receive a push notification of an admittance along with an estimated waiting time for a consultation session.This will not only provide the patients with a sense of freedom but would also reduce the uncertainty levels that are generally observed,thus saving both time and money.This work aims to improve the clinics’quality of services,organize queues and minimize waiting times,leading to patients’comfort while reducing the burden on nurses and receptionists.The results demonstrate that the presented system is successful in its performance and helps achieves a plea-sant and conducive clinic visitation process with higher productivity.
