This works presents the first fully validated and predictive capability to model the V_0-V_(100) probabilistic penetration response of a woven fabric using a yarn-level fabric finite element model. The V_0-V_(100) cur...This works presents the first fully validated and predictive capability to model the V_0-V_(100) probabilistic penetration response of a woven fabric using a yarn-level fabric finite element model. The V_0-V_(100) curve describes the probability of complete fabric penetration as a function of projectile impact velocity. The exemplar case considered in this paper comprises of a single-layer, fully-clamped, plain-weave Kevlar fabric impacted at the center by a 17-gr, 0.22 cal FSP or fragment-simulating projectile. Each warp and fill yarn in the fabric is individually modeled using 3 D finite elements and the virtual fabric microstructure is validated in detail against the experimental fabric microstructure. Material and testing sources of statistical variability including yarn strength and modulus, inter-yarn friction, precise projectile impact location, and projectile rotation are mapped into the finite element model. A series of impact simulations at varying projectile impact velocities is executed using LS-DYNA on the fabric models, with each model comprising unique mappings. The impact velocities together with the outcomes(penetration, nonpenetration) are used to generate the numerical V_0-V_(100) curve which is then validated against the experimental V_0-V_(100) curve. The numerical Vi-Vrdata(impact, residual velocities) is also validated against the experimental Vi-Vrdata. For completeness, this paper also reports the experimental characterization data and its statistical analysis used for model input, viz. the Kevlar yarn tensile strengths, moduli, and inter-yarn friction, and the experimental ballistic test data used for model validation.展开更多
Objective: To provide a decision-making basis for sustainable and effective development of cervical cancer screening.Methods: This cross-sectional study assesses the service capacity to conduct cervical cancer screeni...Objective: To provide a decision-making basis for sustainable and effective development of cervical cancer screening.Methods: This cross-sectional study assesses the service capacity to conduct cervical cancer screening with a sample of 310 medical staff, medical institutions and affiliated township health centers from 20 countylevel/district-level areas in 14 Chinese provinces in 2016.Results: The county-level/district-level institutions were the main prescreening institutions for cervical cancer screening. More medical staff have become engaged in screening, with a significantly higher amounts in urban than in rural areas(P<0.05). The number of human papillomavirus(HPV) testers grew the fastest(by 225% in urban and 125% in rural areas) over the course of the project. HPV testing took less time than cytology to complete the same number of screening tasks in both urban and rural areas. The proportion of mid-level professionals was the highest among the medical staff, 40.0% in urban and 44.7% in rural areas(P=0.406), and most medical staff had a Bachelor’s degree, accounting for 76.3% in urban and 52.0% in rural areas(P<0.001). In urban areas, 75.0% were qualified medical staff, compared with 68.0% in rural areas, among which the lowest proportion was observed for rural cytology inspectors(22.7%). The medical equipment for cervical pathology diagnosis in urban areas was better(P<0.001). HPV testing equipment was relatively adequate(typing test equipment was 70% in urban areas, and non-typing testing equipment was 70% in rural areas).Conclusions: The service capacity of cervical cancer screening is insufficient for the health needs of the Chinese population. HPV testing might be an optimal choice to fill the needs of cervical cancer screening given current Chinese medical health service capacity.展开更多
BACKGROUND The prevalence of non-alcoholic fatty liver(NAFLD)has increased recently.Subjects with NAFLD are known to have higher chance for renal function impairment.Many past studies used traditional multiple linear ...BACKGROUND The prevalence of non-alcoholic fatty liver(NAFLD)has increased recently.Subjects with NAFLD are known to have higher chance for renal function impairment.Many past studies used traditional multiple linear regression(MLR)to identify risk factors for decreased estimated glomerular filtration rate(eGFR).However,medical research is increasingly relying on emerging machine learning(Mach-L)methods.The present study enrolled healthy women to identify factors affecting eGFR in subjects with and without NAFLD(NAFLD+,NAFLD-)and to rank their importance.AIM To uses three different Mach-L methods to identify key impact factors for eGFR in healthy women with and without NAFLD.METHODS A total of 65535 healthy female study participants were enrolled from the Taiwan MJ cohort,accounting for 32 independent variables including demographic,biochemistry and lifestyle parameters(independent variables),while eGFR was used as the dependent variable.Aside from MLR,three Mach-L methods were applied,including stochastic gradient boosting,eXtreme gradient boosting and elastic net.Errors of estimation were used to define method accuracy,where smaller degree of error indicated better model performance.RESULTS Income,albumin,eGFR,High density lipoprotein-Cholesterol,phosphorus,forced expiratory volume in one second(FEV1),and sleep time were all lower in the NAFLD+group,while other factors were all significantly higher except for smoking area.Mach-L had lower estimation errors,thus outperforming MLR.In Model 1,age,uric acid(UA),FEV1,plasma calcium level(Ca),plasma albumin level(Alb)and T-bilirubin were the most important factors in the NAFLD+group,as opposed to age,UA,FEV1,Alb,lactic dehydrogenase(LDH)and Ca for the NAFLD-group.Given the importance percentage was much higher than the 2nd important factor,we built Model 2 by removing age.CONCLUSION The eGFR were lower in the NAFLD+group compared to the NAFLD-group,with age being was the most important impact factor in both groups of healthy Chinese women,followed by LDH,UA,FEV1 and Alb.However,for the NAFLD-group,TSH and SBP were the 5th and 6th most important factors,as opposed to Ca and BF in the NAFLD+group.展开更多
The potential impact of quantum computing on various industries such as finance, healthcare, cryptography, and transportation is significant;therefore, sectors face challenges in understanding where to start because o...The potential impact of quantum computing on various industries such as finance, healthcare, cryptography, and transportation is significant;therefore, sectors face challenges in understanding where to start because of the complex nature of this technology. Starting early to explore what is supposed to be done is crucial for providing sectors with the necessary knowledge, tools, and processes to keep pace with rapid advancements in quantum computing. This article emphasizes the importance of consultancy and governance solutions that aid sectors in preparing for the quantum computing revolution. The article begins by discussing the reasons why sectors need to be prepared for quantum computing and emphasizes the importance of proactive preparation. It illustrates this point by providing a real-world example of a partnership. Subsequently, the article mentioned the benefits of quantum computing readiness, including increased competitiveness, improved security, and structured data. In addition, this article discusses the steps that various sectors can take to achieve quantum readiness, considering the potential risks and opportunities in industries. The proposed solutions for achieving quantum computing readiness include establishing a quantum computing office, contracting with major quantum computing companies, and learning from quantum computing organizations. This article provides the detailed advantages and disadvantages of each of these steps and emphasizes the need to carefully evaluate their potential drawbacks to ensure that they align with the sector’s unique needs, goals, and available resources. Finally, this article proposes various solutions and recommendations for sectors to achieve quantum-computing readiness.展开更多
BACKGROUND The incidence of chronic kidney disease(CKD)has dramatically increased in recent years,with significant impacts on patient mortality rates.Previous studies have identified multiple risk factors for CKD,but ...BACKGROUND The incidence of chronic kidney disease(CKD)has dramatically increased in recent years,with significant impacts on patient mortality rates.Previous studies have identified multiple risk factors for CKD,but they mostly relied on the use of traditional statistical methods such as logistic regression and only focused on a few risk factors.AIM To determine factors that can be used to identify subjects with a low estimated glomerular filtration rate(L-eGFR<60 mL/min per 1.73 m^(2))in a cohort of 1236 Chinese people aged over 65.METHODS Twenty risk factors were divided into three models.Model 1 consisted of demographic and biochemistry data.Model 2 added lifestyle data to Model 1,and Model 3 added inflammatory markers to Model 2.Five machine learning methods were used:Multivariate adaptive regression splines,eXtreme Gradient Boosting,stochastic gradient boosting,Light Gradient Boosting Machine,and Categorical Features+Gradient Boosting.Evaluation criteria included accuracy,sensitivity,specificity,area under the receiver operating characteristic curve(AUC),F-1 score,and balanced accuracy.RESULTS A trend of increasing AUC of each was observed from Model 1 to Model 3 and reached statistical significance.Model 3 selected uric acid as the most important risk factor,followed by age,hemoglobin(Hb),body mass index(BMI),sport hours,and systolic blood pressure(SBP).CONCLUSION Among all the risk factors including demographic,biochemistry,and lifestyle risk factors,along with inflammation markers,UA is the most important risk factor to identify L-eGFR,followed by age,Hb,BMI,sport hours,and SBP in a cohort of elderly Chinese people.展开更多
1 Overview The Three Gorges Project is the largest hydro junction project in the world.A key backbone project for the harnessing and exploitation of the Yangtze River,the Three Gorges Project consists of the pivotal d...1 Overview The Three Gorges Project is the largest hydro junction project in the world.A key backbone project for the harnessing and exploitation of the Yangtze River,the Three Gorges Project consists of the pivotal dam project,a power transmission and transformation project,and a resettlement project(Figure 1).展开更多
Water pollution affects plants and organisms living in these bodies of water; and, in almost all cases the effect is damaging not only to individual species and populations, but also to the natural biological communit...Water pollution affects plants and organisms living in these bodies of water; and, in almost all cases the effect is damaging not only to individual species and populations, but also to the natural biological communities. Genetic algorithm and kernel partial least square （GA-KPLS） and Levenberg- Marquardt artificial neural network （L-M ANN） techniques were used to investigate the correlation between retention time （tR） and descriptors for 150 organic contaminants in natural water and wastewater, which are obtained by gas chromatography coupled to high-resolution time-of-flight mass spectrometry （GC-TOF MS）. The L-M ANN model gave a significantly better performance than the GA-KPLS model. This indicates that L-M ANN can be used as an alternative modeling toot for quantitative structure-retention relationship （QSRR） studies.展开更多
Anews piece from last year about an elderly Chinese man whose life savings were eaten by mice tells us two things; firstly,that the stove,as in 80-yearold Yang Lihong's case,isn't exactly a safe piggy bank.Mor...Anews piece from last year about an elderly Chinese man whose life savings were eaten by mice tells us two things; firstly,that the stove,as in 80-yearold Yang Lihong's case,isn't exactly a safe piggy bank.More importantly,it highlights the norm among Chinese households to stash away cash with the intention of saving for a rainy day.展开更多
Developing an integrated and intelligent approach to securing the ITE(information technology environment)is an emergent and evolving concern for every organization and consumer entity during the last few decades.Major...Developing an integrated and intelligent approach to securing the ITE(information technology environment)is an emergent and evolving concern for every organization and consumer entity during the last few decades.Major topics of concern include“SI”(security intelligence),“D-DA”(data-driven analytics),“PE”(proven expertise),and“R-TD”(real-time defense)capabilities.“DRBTs”(dynamic response behavior types)include“incident response”,“endpoint management”,“threat intelligence”,“network security”,and“fraud protection”.The consumer demand for electricity as essential public access and service is indexed to population growth estimates.Consumer-driven economies continue to add electrical consumption to their grids even though improvements in lower-power consumption and higher design efficiencies are present in new electric-powered products.Dependence on the production of electrical energy has no peer replacement technology and creates a societal vulnerability to targeted public electrical grid interruptions.When access to,or production of,electrical power is interrupted,the result is a“Mass Effect”every consumer feels with equal distribution.Electric grid security falls directly into the levels of authorized,and unauthorized,access via the“IoT”(Internet of Things)concepts,and the“IoM2M”(Internet of Machine-to-Machine)integration.Electrical grid operations that include production and network management augment each other in order to support the demand for electricity every day either in peak or off-peak,thus cybersecurity plays a big role in the protection of such assets at our disposal.With help from AI(artificial intelligence)integrated into the IoT a resilient system can be built to protect the electric grid system nationwide and will be able to detect and preempt Smart Malware attacks.展开更多
As quantum computing transitions from a theoretical domain to a practical technology, many aspects of established practice in software engineering are being faced with new challenges. Quantum Software Engineering has ...As quantum computing transitions from a theoretical domain to a practical technology, many aspects of established practice in software engineering are being faced with new challenges. Quantum Software Engineering has been developed to address the peculiar needs that arise with quantum systems’ dependable, scalable, and fault-tolerant software development. The present paper critically reviews how traditional software engineering methodologies can be reshaped to fit into the quantum field. This also entails providing some critical contributions: frameworks to integrate classical and quantum systems, new error mitigation techniques, and the development of quantum-specific testing and debugging tools. In this respect, best practices have been recommended to ensure that future quantum software can harness the evolving capabilities of quantum hardware with continued performance, reliability, and scalability. The work is supposed to act as a foundational guide for the researcher and developer as quantum computing approaches widespread scientific and industrial adoption.展开更多
Self-learning is one of the most important scientific methods that helps develop sciences, as it derives from the desire and interests of the individual. However, self-learning loses importance if it does not follow t...Self-learning is one of the most important scientific methods that helps develop sciences, as it derives from the desire and interests of the individual. However, self-learning loses importance if it does not follow the scientific methodology for building and organizing information. The case becomes harder if the science is new and few scientific sources are available. Quantum computing is one of the new sciences in computer science and needs the support of specialists to develop it. Quantum computing overlaps with many sciences such as physics, chemistry, and mathematics, so any student in one of the previous disciplines may lose the correct self-learning path to find themselves learning the details of another discipline that does not achieve their goals. This article motivates students and those interested in computer science to begin studying the science of quantum computing and choose the same specialization that suits their interests. The article also provides a roadmap for self-learning steps to protect the learner from losing the correct learning path. I have categorized the stages of learning quantum computing into four steps through which all the essential basics can be learned, provided the goals mentioned in each stage which should be achieved. The learning strategy proposed in this article corresponds with individuals’ self-learning rules. Through my personal experience, the proposed learning strategy has proven its effectiveness in building information in an enjoyable scientific way.展开更多
When we think of forests we usually think of trees,plants and animals.But forests could not exist without fungi,which lie at the base of the biodiversity webs that support much of life on Earth.Most fungi live as bran...When we think of forests we usually think of trees,plants and animals.But forests could not exist without fungi,which lie at the base of the biodiversity webs that support much of life on Earth.Most fungi live as branching,fusing networks of tubular cells known as mycelium which can make up between a third and a half of the living mass of soils.Globally,the total length of fungal mycelium in the top 10cm of soil is more than 450 quadrillion km:about half the width of our galaxy.These networks comprise an ancient life-support system that easily qualifies as one of the wonders of the living world.Despite that,fungi represent a meagre 0.2%of our global conservation priorities.展开更多
基金supported by Teledyne Scientific&Imaging(TS&I),Internal Research and Development(IR&D)and approved for public release under TSI-PP-17-08
文摘This works presents the first fully validated and predictive capability to model the V_0-V_(100) probabilistic penetration response of a woven fabric using a yarn-level fabric finite element model. The V_0-V_(100) curve describes the probability of complete fabric penetration as a function of projectile impact velocity. The exemplar case considered in this paper comprises of a single-layer, fully-clamped, plain-weave Kevlar fabric impacted at the center by a 17-gr, 0.22 cal FSP or fragment-simulating projectile. Each warp and fill yarn in the fabric is individually modeled using 3 D finite elements and the virtual fabric microstructure is validated in detail against the experimental fabric microstructure. Material and testing sources of statistical variability including yarn strength and modulus, inter-yarn friction, precise projectile impact location, and projectile rotation are mapped into the finite element model. A series of impact simulations at varying projectile impact velocities is executed using LS-DYNA on the fabric models, with each model comprising unique mappings. The impact velocities together with the outcomes(penetration, nonpenetration) are used to generate the numerical V_0-V_(100) curve which is then validated against the experimental V_0-V_(100) curve. The numerical Vi-Vrdata(impact, residual velocities) is also validated against the experimental Vi-Vrdata. For completeness, this paper also reports the experimental characterization data and its statistical analysis used for model input, viz. the Kevlar yarn tensile strengths, moduli, and inter-yarn friction, and the experimental ballistic test data used for model validation.
基金supported by the National Health Commission of the People’s Republic of China (formerly the Health and Family Planning Commission of China) (No. 201502004)
文摘Objective: To provide a decision-making basis for sustainable and effective development of cervical cancer screening.Methods: This cross-sectional study assesses the service capacity to conduct cervical cancer screening with a sample of 310 medical staff, medical institutions and affiliated township health centers from 20 countylevel/district-level areas in 14 Chinese provinces in 2016.Results: The county-level/district-level institutions were the main prescreening institutions for cervical cancer screening. More medical staff have become engaged in screening, with a significantly higher amounts in urban than in rural areas(P<0.05). The number of human papillomavirus(HPV) testers grew the fastest(by 225% in urban and 125% in rural areas) over the course of the project. HPV testing took less time than cytology to complete the same number of screening tasks in both urban and rural areas. The proportion of mid-level professionals was the highest among the medical staff, 40.0% in urban and 44.7% in rural areas(P=0.406), and most medical staff had a Bachelor’s degree, accounting for 76.3% in urban and 52.0% in rural areas(P<0.001). In urban areas, 75.0% were qualified medical staff, compared with 68.0% in rural areas, among which the lowest proportion was observed for rural cytology inspectors(22.7%). The medical equipment for cervical pathology diagnosis in urban areas was better(P<0.001). HPV testing equipment was relatively adequate(typing test equipment was 70% in urban areas, and non-typing testing equipment was 70% in rural areas).Conclusions: The service capacity of cervical cancer screening is insufficient for the health needs of the Chinese population. HPV testing might be an optimal choice to fill the needs of cervical cancer screening given current Chinese medical health service capacity.
基金Supported by the Kaohsiung Armed Forces General Hospital.
文摘BACKGROUND The prevalence of non-alcoholic fatty liver(NAFLD)has increased recently.Subjects with NAFLD are known to have higher chance for renal function impairment.Many past studies used traditional multiple linear regression(MLR)to identify risk factors for decreased estimated glomerular filtration rate(eGFR).However,medical research is increasingly relying on emerging machine learning(Mach-L)methods.The present study enrolled healthy women to identify factors affecting eGFR in subjects with and without NAFLD(NAFLD+,NAFLD-)and to rank their importance.AIM To uses three different Mach-L methods to identify key impact factors for eGFR in healthy women with and without NAFLD.METHODS A total of 65535 healthy female study participants were enrolled from the Taiwan MJ cohort,accounting for 32 independent variables including demographic,biochemistry and lifestyle parameters(independent variables),while eGFR was used as the dependent variable.Aside from MLR,three Mach-L methods were applied,including stochastic gradient boosting,eXtreme gradient boosting and elastic net.Errors of estimation were used to define method accuracy,where smaller degree of error indicated better model performance.RESULTS Income,albumin,eGFR,High density lipoprotein-Cholesterol,phosphorus,forced expiratory volume in one second(FEV1),and sleep time were all lower in the NAFLD+group,while other factors were all significantly higher except for smoking area.Mach-L had lower estimation errors,thus outperforming MLR.In Model 1,age,uric acid(UA),FEV1,plasma calcium level(Ca),plasma albumin level(Alb)and T-bilirubin were the most important factors in the NAFLD+group,as opposed to age,UA,FEV1,Alb,lactic dehydrogenase(LDH)and Ca for the NAFLD-group.Given the importance percentage was much higher than the 2nd important factor,we built Model 2 by removing age.CONCLUSION The eGFR were lower in the NAFLD+group compared to the NAFLD-group,with age being was the most important impact factor in both groups of healthy Chinese women,followed by LDH,UA,FEV1 and Alb.However,for the NAFLD-group,TSH and SBP were the 5th and 6th most important factors,as opposed to Ca and BF in the NAFLD+group.
文摘The potential impact of quantum computing on various industries such as finance, healthcare, cryptography, and transportation is significant;therefore, sectors face challenges in understanding where to start because of the complex nature of this technology. Starting early to explore what is supposed to be done is crucial for providing sectors with the necessary knowledge, tools, and processes to keep pace with rapid advancements in quantum computing. This article emphasizes the importance of consultancy and governance solutions that aid sectors in preparing for the quantum computing revolution. The article begins by discussing the reasons why sectors need to be prepared for quantum computing and emphasizes the importance of proactive preparation. It illustrates this point by providing a real-world example of a partnership. Subsequently, the article mentioned the benefits of quantum computing readiness, including increased competitiveness, improved security, and structured data. In addition, this article discusses the steps that various sectors can take to achieve quantum readiness, considering the potential risks and opportunities in industries. The proposed solutions for achieving quantum computing readiness include establishing a quantum computing office, contracting with major quantum computing companies, and learning from quantum computing organizations. This article provides the detailed advantages and disadvantages of each of these steps and emphasizes the need to carefully evaluate their potential drawbacks to ensure that they align with the sector’s unique needs, goals, and available resources. Finally, this article proposes various solutions and recommendations for sectors to achieve quantum-computing readiness.
基金Supported by the Kaohsiung Armed Forces General HospitalThe study protocol was approved by the Institutional Review Board of the Tri-Service General Hospital,National Defense Medical Center(IRB No.:KAFGHIRB 109-46).
文摘BACKGROUND The incidence of chronic kidney disease(CKD)has dramatically increased in recent years,with significant impacts on patient mortality rates.Previous studies have identified multiple risk factors for CKD,but they mostly relied on the use of traditional statistical methods such as logistic regression and only focused on a few risk factors.AIM To determine factors that can be used to identify subjects with a low estimated glomerular filtration rate(L-eGFR<60 mL/min per 1.73 m^(2))in a cohort of 1236 Chinese people aged over 65.METHODS Twenty risk factors were divided into three models.Model 1 consisted of demographic and biochemistry data.Model 2 added lifestyle data to Model 1,and Model 3 added inflammatory markers to Model 2.Five machine learning methods were used:Multivariate adaptive regression splines,eXtreme Gradient Boosting,stochastic gradient boosting,Light Gradient Boosting Machine,and Categorical Features+Gradient Boosting.Evaluation criteria included accuracy,sensitivity,specificity,area under the receiver operating characteristic curve(AUC),F-1 score,and balanced accuracy.RESULTS A trend of increasing AUC of each was observed from Model 1 to Model 3 and reached statistical significance.Model 3 selected uric acid as the most important risk factor,followed by age,hemoglobin(Hb),body mass index(BMI),sport hours,and systolic blood pressure(SBP).CONCLUSION Among all the risk factors including demographic,biochemistry,and lifestyle risk factors,along with inflammation markers,UA is the most important risk factor to identify L-eGFR,followed by age,Hb,BMI,sport hours,and SBP in a cohort of elderly Chinese people.
文摘1 Overview The Three Gorges Project is the largest hydro junction project in the world.A key backbone project for the harnessing and exploitation of the Yangtze River,the Three Gorges Project consists of the pivotal dam project,a power transmission and transformation project,and a resettlement project(Figure 1).
文摘Water pollution affects plants and organisms living in these bodies of water; and, in almost all cases the effect is damaging not only to individual species and populations, but also to the natural biological communities. Genetic algorithm and kernel partial least square （GA-KPLS） and Levenberg- Marquardt artificial neural network （L-M ANN） techniques were used to investigate the correlation between retention time （tR） and descriptors for 150 organic contaminants in natural water and wastewater, which are obtained by gas chromatography coupled to high-resolution time-of-flight mass spectrometry （GC-TOF MS）. The L-M ANN model gave a significantly better performance than the GA-KPLS model. This indicates that L-M ANN can be used as an alternative modeling toot for quantitative structure-retention relationship （QSRR） studies.
文摘Anews piece from last year about an elderly Chinese man whose life savings were eaten by mice tells us two things; firstly,that the stove,as in 80-yearold Yang Lihong's case,isn't exactly a safe piggy bank.More importantly,it highlights the norm among Chinese households to stash away cash with the intention of saving for a rainy day.
文摘Developing an integrated and intelligent approach to securing the ITE(information technology environment)is an emergent and evolving concern for every organization and consumer entity during the last few decades.Major topics of concern include“SI”(security intelligence),“D-DA”(data-driven analytics),“PE”(proven expertise),and“R-TD”(real-time defense)capabilities.“DRBTs”(dynamic response behavior types)include“incident response”,“endpoint management”,“threat intelligence”,“network security”,and“fraud protection”.The consumer demand for electricity as essential public access and service is indexed to population growth estimates.Consumer-driven economies continue to add electrical consumption to their grids even though improvements in lower-power consumption and higher design efficiencies are present in new electric-powered products.Dependence on the production of electrical energy has no peer replacement technology and creates a societal vulnerability to targeted public electrical grid interruptions.When access to,or production of,electrical power is interrupted,the result is a“Mass Effect”every consumer feels with equal distribution.Electric grid security falls directly into the levels of authorized,and unauthorized,access via the“IoT”(Internet of Things)concepts,and the“IoM2M”(Internet of Machine-to-Machine)integration.Electrical grid operations that include production and network management augment each other in order to support the demand for electricity every day either in peak or off-peak,thus cybersecurity plays a big role in the protection of such assets at our disposal.With help from AI(artificial intelligence)integrated into the IoT a resilient system can be built to protect the electric grid system nationwide and will be able to detect and preempt Smart Malware attacks.
文摘As quantum computing transitions from a theoretical domain to a practical technology, many aspects of established practice in software engineering are being faced with new challenges. Quantum Software Engineering has been developed to address the peculiar needs that arise with quantum systems’ dependable, scalable, and fault-tolerant software development. The present paper critically reviews how traditional software engineering methodologies can be reshaped to fit into the quantum field. This also entails providing some critical contributions: frameworks to integrate classical and quantum systems, new error mitigation techniques, and the development of quantum-specific testing and debugging tools. In this respect, best practices have been recommended to ensure that future quantum software can harness the evolving capabilities of quantum hardware with continued performance, reliability, and scalability. The work is supposed to act as a foundational guide for the researcher and developer as quantum computing approaches widespread scientific and industrial adoption.
文摘Self-learning is one of the most important scientific methods that helps develop sciences, as it derives from the desire and interests of the individual. However, self-learning loses importance if it does not follow the scientific methodology for building and organizing information. The case becomes harder if the science is new and few scientific sources are available. Quantum computing is one of the new sciences in computer science and needs the support of specialists to develop it. Quantum computing overlaps with many sciences such as physics, chemistry, and mathematics, so any student in one of the previous disciplines may lose the correct self-learning path to find themselves learning the details of another discipline that does not achieve their goals. This article motivates students and those interested in computer science to begin studying the science of quantum computing and choose the same specialization that suits their interests. The article also provides a roadmap for self-learning steps to protect the learner from losing the correct learning path. I have categorized the stages of learning quantum computing into four steps through which all the essential basics can be learned, provided the goals mentioned in each stage which should be achieved. The learning strategy proposed in this article corresponds with individuals’ self-learning rules. Through my personal experience, the proposed learning strategy has proven its effectiveness in building information in an enjoyable scientific way.
文摘When we think of forests we usually think of trees,plants and animals.But forests could not exist without fungi,which lie at the base of the biodiversity webs that support much of life on Earth.Most fungi live as branching,fusing networks of tubular cells known as mycelium which can make up between a third and a half of the living mass of soils.Globally,the total length of fungal mycelium in the top 10cm of soil is more than 450 quadrillion km:about half the width of our galaxy.These networks comprise an ancient life-support system that easily qualifies as one of the wonders of the living world.Despite that,fungi represent a meagre 0.2%of our global conservation priorities.
