Loss of volume in midface can result in an aged, wasted appearance. Osseous and fat atrophy with aging may further contribute to the loss of soft tissue support and midface ptosis. In the aging of periorbital area and...Loss of volume in midface can result in an aged, wasted appearance. Osseous and fat atrophy with aging may further contribute to the loss of soft tissue support and midface ptosis. In the aging of periorbital area and midface, fat atrophy occurs mostly in the suborbicularis oculi fat (SOOF) area. The authors proposed that injection of hyaluronic acid (HA) filler to support the SOOF area could counteract the aging sign due to fat atrophy, restore volume loss and achieve a more youthful appearance. The authors described the treatment of 10 female patients who received CHAP<sup></sup><sup>®</sup>-particle hyaluronic acid (CHAP<sup>®</sup>-HA) injections for cheek augmentation, using single-point deep injection technique at midface in close proximity to SOOF area. Such approach provides satisfactory cheek augmentation results without significant complications. The authors discussed a rationale for their choice of dermal filler and provided an injection technique for restoring volume in the midface region with CHAP<sup>®</sup>-HA. Such technique is relatively quick to perform, have little down time, and result in a high rate of patient satisfaction.展开更多
The recent developments in smart cities pose major security issues for the Internet of Things(IoT)devices.These security issues directly result from inappropriate security management protocols and their implementation...The recent developments in smart cities pose major security issues for the Internet of Things(IoT)devices.These security issues directly result from inappropriate security management protocols and their implementation by IoT gadget developers.Cyber-attackers take advantage of such gadgets’vulnerabilities through various attacks such as injection and Distributed Denial of Service(DDoS)attacks.In this background,Intrusion Detection(ID)is the only way to identify the attacks and mitigate their damage.The recent advancements in Machine Learning(ML)and Deep Learning(DL)models are useful in effectively classifying cyber-attacks.The current research paper introduces a new Coot Optimization Algorithm with a Deep Learning-based False Data Injection Attack Recognition(COADL-FDIAR)model for the IoT environment.The presented COADL-FDIAR technique aims to identify false data injection attacks in the IoT environment.To accomplish this,the COADL-FDIAR model initially preprocesses the input data and selects the features with the help of the Chi-square test.To detect and classify false data injection attacks,the Stacked Long Short-Term Memory(SLSTM)model is exploited in this study.Finally,the COA algorithm effectively adjusts the SLTSM model’s hyperparameters effectively and accomplishes a superior recognition efficiency.The proposed COADL-FDIAR model was experimentally validated using a standard dataset,and the outcomes were scrutinized under distinct aspects.The comparative analysis results assured the superior performance of the proposed COADL-FDIAR model over other recent approaches with a maximum accuracy of 98.84%.展开更多
E-commerce,online ticketing,online banking,and other web-based applications that handle sensitive data,such as passwords,payment information,and financial information,are widely used.Various web developers may have va...E-commerce,online ticketing,online banking,and other web-based applications that handle sensitive data,such as passwords,payment information,and financial information,are widely used.Various web developers may have varying levels of understanding when it comes to securing an online application.Structured Query language SQL injection and cross-site scripting are the two vulnerabilities defined by the OpenWeb Application Security Project(OWASP)for its 2017 Top Ten List Cross Site Scripting(XSS).An attacker can exploit these two flaws and launch malicious web-based actions as a result of these flaws.Many published articles focused on these attacks’binary classification.This article described a novel deep-learning approach for detecting SQL injection and XSS attacks.The datasets for SQL injection and XSS payloads are combined into a single dataset.The dataset is labeledmanually into three labels,each representing a kind of attack.This work implements some pre-processing algorithms,including Porter stemming,one-hot encoding,and the word-embedding method to convert a word’s text into a vector.Our model used bidirectional long short-term memory(BiLSTM)to extract features automatically,train,and test the payload dataset.The payloads were classified into three types by BiLSTM:XSS,SQL injection attacks,and normal.The outcomes demonstrated excellent performance in classifying payloads into XSS attacks,injection attacks,and non-malicious payloads.BiLSTM’s high performance was demonstrated by its accuracy of 99.26%.展开更多
High-resolution ultrasound (HRU) imaging is a useful tool to study hyaluronic acid (HA) filler injection in the face. It is noninvasive, quick, well-tolerated, and can provide in vivo and dynamic information. The form...High-resolution ultrasound (HRU) imaging is a useful tool to study hyaluronic acid (HA) filler injection in the face. It is noninvasive, quick, well-tolerated, and can provide in vivo and dynamic information. The formations of pools or pearls in HA fillers could be observed real time during injection. The plane of injection could be determined accurately, and there were no specimen manipulation artifacts. It was observed that HA gel fillers with differing production technologies showed distinct spread and distribution patterns in the periocular tissues on HRU examination. The authors used HRU to assess deep injections of CHAP-Hyaluronic Acid (CHAP-HA) fillers for midface lift. 10 patients who underwent bilateral midface deep injections using CHAP-HA filler were examined with HRU before and immediately after treatment, and in 2 weeks and one month later. The CHAP-HA appeared as hypoechoic densities within the preperiosteal plane in HRU. CHAP-HA adopted variable morphology within the tissue depending on individual tissue densities and the compliance of the tissues in the plane of injection. CHAP-HA was unidentifiable with surrounding tissue after one month in 13 of the 20 injection sites. HRU allows in vivo study of CHAP-HA injection behavior and could be a tool for further studies of HA-tissue reactions.展开更多
From August 21, 2000 to October 20, 2000,a fluid injection-induced seismicity experiment has been carried out in the KTB (German Continental Deep Drilling Program). The KTB seismic network recorded more than 2 700 eve...From August 21, 2000 to October 20, 2000,a fluid injection-induced seismicity experiment has been carried out in the KTB (German Continental Deep Drilling Program). The KTB seismic network recorded more than 2 700 events. Among them 237 events were of high signal-to-noise ratio, and were processed and accurately located. When the events were located, non KTB events were weeded out by Wadatis method. The standard deviation, mean and median were obtained by Jackknife's technique, and finally the events were accurately located by Gei-gers method so that the mean error is about 0.1 km. No earthquakes with focal depth greater than 9.3 km, which is nearly at the bottom of the hole, were detected. One of the explanation is that at such depths the stress levels may not close to the rocks frictional strength so that failure could not be induced by the relatively small perturbation in pore pressure. Or at these depths there may be no permeable, well-oriented faults. This depth may be in close proximity to the bottom of the hole to the brittle-ductile transition, even in this relatively stable interior of the in-teraplate. This phenomenon is explained by the experimental results and geothermal data from the superdeep bore-hole.展开更多
Late at night on 17 June 2019,a magnitude 6.0 earthquake struck Shuanghe Town and its surrounding area in Changning County,Sichuan,China,becoming the largest earthquake recorded within the southern Sichuan Basin.A ser...Late at night on 17 June 2019,a magnitude 6.0 earthquake struck Shuanghe Town and its surrounding area in Changning County,Sichuan,China,becoming the largest earthquake recorded within the southern Sichuan Basin.A series of earthquakes with magnitudes up to 5.6 occurred during a short period after the mainshock,and we thus refer to these earthquakes as the Changning M6 earthquake sequence(or swarm).The mainshock was located very close to a salt mine,into which for^3 decades fresh water had been extensively injected through several wells at a depth of 2.7–3 km.It was also near(within^15 km)the epicenter of the 18 December 2018 M5.7 Xingwen earthquake,which is thought to have been induced by shale gas hydraulic fracturing(HF),prompting questions about the possible involvement of industrial activities in the M6 sequence.Following previous studies,this paper focuses on the relationship between injection and seismicity in the Shuanghe salt field and its adjacent Shangluo shale gas block.Except for a period of serious water loss after the start of cross-well injection in 2005–2006,the frequency of earthquakes shows a slightly increasing tendency.Overall,there is a good correlation between the event rate in the Shuanghe area and the loss of injected water.More than 400 M≥3 earthquakes,including 40 M≥4 and 5 M≥5 events,had been observed by the end of August 2019.Meanwhile,in the Shangluo area,seismicity has increased during drilling and HF operations(mostly in vertical wells)since about 2009,and dramatically since the end of 2014,coincident with the start of systematic HF in the area.The event rate shows a progressively increasing background with some fluctuations,paralleling the increase in HF operations.More than 700 M≥3 earthquakes,including 10 M≥4 and 3 M≥5 in spatially and temporally clustered seismic events,are correlated closely with active fracturing platforms.Well-resolved centroid moment tensor results for M≥4 earthquakes were shown to occur at very shallow depths around shale formations with active HF,in agreement with some of the clusters,which occurred within the coverage area of temporary or new permanent monitoring stations and thus have been precisely located.After the Xingwen M5.7 earthquake,seismic activity in the salt well area increased significantly.The Xingwen earthquake may have created a unidirectional rupture to the NNW,with an end point close to the NW-trending fault of the Shuanghe earthquake.Thus,a fault in the Changning anticline might have terminated the fault rupture of the Xingwen earthquake,possibly giving the Xingwen earthquake a role in promoting the Changning M6 event.展开更多
Carbon Capture and Storage(CCS)is one of the effective means to deal with global warming,and saline aquifer storage is considered to be the most promising storage method.Junggar Basin,located in the northern part of X...Carbon Capture and Storage(CCS)is one of the effective means to deal with global warming,and saline aquifer storage is considered to be the most promising storage method.Junggar Basin,located in the northern part of Xinjiang and with a large distribution area of saline aquifer,is an effective carbon storage site.Based on well logging data and 2D seismic data,a 3D heterogeneous geological model of the Cretaceous Donggou Formation reservoir near D7 well was constructed,and dynamic simulations under two scenarios of single-well injection and multi-well injection were carried out to explore the storage potential and CO2 storage mechanism of deep saline aquifer with real geological conditions in this study.The results show that within 100 km^(2)of the saline aquifer of Donggou Formation in the vicinity of D7 well,the theoretical static CO_(2)storage is 71.967×106 tons(P50)①,and the maximum dynamic CO_(2)storage is 145.295×106 tons(Case2).The heterogeneity of saline aquifer has a great influence on the spatial distribution of CO_(2)in the reservoir.The multi-well injection scenario is conducive to the efficient utilization of reservoir space and safer for storage.Based on the results from theoretical static calculation and the dynamic simulation,the effective coefficient of CO_(2)storage in deep saline aquifer in the eastern part of Xinjiang is recommended to be 4.9%.This study can be applied to the engineering practice of CO_(2)sequestration in the deep saline aquifer in Xinjiang.展开更多
The Internet of Things (IoT) paradigm enables end users to accessnetworking services amongst diverse kinds of electronic devices. IoT securitymechanism is a technology that concentrates on safeguarding the devicesand ...The Internet of Things (IoT) paradigm enables end users to accessnetworking services amongst diverse kinds of electronic devices. IoT securitymechanism is a technology that concentrates on safeguarding the devicesand networks connected in the IoT environment. In recent years, False DataInjection Attacks (FDIAs) have gained considerable interest in the IoT environment.Cybercriminals compromise the devices connected to the networkand inject the data. Such attacks on the IoT environment can result in a considerableloss and interrupt normal activities among the IoT network devices.The FDI attacks have been effectively overcome so far by conventional threatdetection techniques. The current research article develops a Hybrid DeepLearning to Combat Sophisticated False Data Injection Attacks detection(HDL-FDIAD) for the IoT environment. The presented HDL-FDIAD modelmajorly recognizes the presence of FDI attacks in the IoT environment.The HDL-FDIAD model exploits the Equilibrium Optimizer-based FeatureSelection (EO-FS) technique to select the optimal subset of the features.Moreover, the Long Short Term Memory with Recurrent Neural Network(LSTM-RNN) model is also utilized for the purpose of classification. At last,the Bayesian Optimization (BO) algorithm is employed as a hyperparameteroptimizer in this study. To validate the enhanced performance of the HDLFDIADmodel, a wide range of simulations was conducted, and the resultswere investigated in detail. A comparative study was conducted between theproposed model and the existing models. The outcomes revealed that theproposed HDL-FDIAD model is superior to other models.展开更多
文摘Loss of volume in midface can result in an aged, wasted appearance. Osseous and fat atrophy with aging may further contribute to the loss of soft tissue support and midface ptosis. In the aging of periorbital area and midface, fat atrophy occurs mostly in the suborbicularis oculi fat (SOOF) area. The authors proposed that injection of hyaluronic acid (HA) filler to support the SOOF area could counteract the aging sign due to fat atrophy, restore volume loss and achieve a more youthful appearance. The authors described the treatment of 10 female patients who received CHAP<sup></sup><sup>®</sup>-particle hyaluronic acid (CHAP<sup>®</sup>-HA) injections for cheek augmentation, using single-point deep injection technique at midface in close proximity to SOOF area. Such approach provides satisfactory cheek augmentation results without significant complications. The authors discussed a rationale for their choice of dermal filler and provided an injection technique for restoring volume in the midface region with CHAP<sup>®</sup>-HA. Such technique is relatively quick to perform, have little down time, and result in a high rate of patient satisfaction.
基金This research was supported by the Universiti Sains Malaysia(USM)and the ministry of Higher Education Malaysia through Fundamental Research GrantScheme(FRGS-Grant No:FRGS/1/2020/TK0/USM/02/1).
文摘The recent developments in smart cities pose major security issues for the Internet of Things(IoT)devices.These security issues directly result from inappropriate security management protocols and their implementation by IoT gadget developers.Cyber-attackers take advantage of such gadgets’vulnerabilities through various attacks such as injection and Distributed Denial of Service(DDoS)attacks.In this background,Intrusion Detection(ID)is the only way to identify the attacks and mitigate their damage.The recent advancements in Machine Learning(ML)and Deep Learning(DL)models are useful in effectively classifying cyber-attacks.The current research paper introduces a new Coot Optimization Algorithm with a Deep Learning-based False Data Injection Attack Recognition(COADL-FDIAR)model for the IoT environment.The presented COADL-FDIAR technique aims to identify false data injection attacks in the IoT environment.To accomplish this,the COADL-FDIAR model initially preprocesses the input data and selects the features with the help of the Chi-square test.To detect and classify false data injection attacks,the Stacked Long Short-Term Memory(SLSTM)model is exploited in this study.Finally,the COA algorithm effectively adjusts the SLTSM model’s hyperparameters effectively and accomplishes a superior recognition efficiency.The proposed COADL-FDIAR model was experimentally validated using a standard dataset,and the outcomes were scrutinized under distinct aspects.The comparative analysis results assured the superior performance of the proposed COADL-FDIAR model over other recent approaches with a maximum accuracy of 98.84%.
基金funded byResearchers Supporting Project Number(RSP2023R476)King Saud University,Riyadh,Saudi Arabia。
文摘E-commerce,online ticketing,online banking,and other web-based applications that handle sensitive data,such as passwords,payment information,and financial information,are widely used.Various web developers may have varying levels of understanding when it comes to securing an online application.Structured Query language SQL injection and cross-site scripting are the two vulnerabilities defined by the OpenWeb Application Security Project(OWASP)for its 2017 Top Ten List Cross Site Scripting(XSS).An attacker can exploit these two flaws and launch malicious web-based actions as a result of these flaws.Many published articles focused on these attacks’binary classification.This article described a novel deep-learning approach for detecting SQL injection and XSS attacks.The datasets for SQL injection and XSS payloads are combined into a single dataset.The dataset is labeledmanually into three labels,each representing a kind of attack.This work implements some pre-processing algorithms,including Porter stemming,one-hot encoding,and the word-embedding method to convert a word’s text into a vector.Our model used bidirectional long short-term memory(BiLSTM)to extract features automatically,train,and test the payload dataset.The payloads were classified into three types by BiLSTM:XSS,SQL injection attacks,and normal.The outcomes demonstrated excellent performance in classifying payloads into XSS attacks,injection attacks,and non-malicious payloads.BiLSTM’s high performance was demonstrated by its accuracy of 99.26%.
文摘High-resolution ultrasound (HRU) imaging is a useful tool to study hyaluronic acid (HA) filler injection in the face. It is noninvasive, quick, well-tolerated, and can provide in vivo and dynamic information. The formations of pools or pearls in HA fillers could be observed real time during injection. The plane of injection could be determined accurately, and there were no specimen manipulation artifacts. It was observed that HA gel fillers with differing production technologies showed distinct spread and distribution patterns in the periocular tissues on HRU examination. The authors used HRU to assess deep injections of CHAP-Hyaluronic Acid (CHAP-HA) fillers for midface lift. 10 patients who underwent bilateral midface deep injections using CHAP-HA filler were examined with HRU before and immediately after treatment, and in 2 weeks and one month later. The CHAP-HA appeared as hypoechoic densities within the preperiosteal plane in HRU. CHAP-HA adopted variable morphology within the tissue depending on individual tissue densities and the compliance of the tissues in the plane of injection. CHAP-HA was unidentifiable with surrounding tissue after one month in 13 of the 20 injection sites. HRU allows in vivo study of CHAP-HA injection behavior and could be a tool for further studies of HA-tissue reactions.
文摘From August 21, 2000 to October 20, 2000,a fluid injection-induced seismicity experiment has been carried out in the KTB (German Continental Deep Drilling Program). The KTB seismic network recorded more than 2 700 events. Among them 237 events were of high signal-to-noise ratio, and were processed and accurately located. When the events were located, non KTB events were weeded out by Wadatis method. The standard deviation, mean and median were obtained by Jackknife's technique, and finally the events were accurately located by Gei-gers method so that the mean error is about 0.1 km. No earthquakes with focal depth greater than 9.3 km, which is nearly at the bottom of the hole, were detected. One of the explanation is that at such depths the stress levels may not close to the rocks frictional strength so that failure could not be induced by the relatively small perturbation in pore pressure. Or at these depths there may be no permeable, well-oriented faults. This depth may be in close proximity to the bottom of the hole to the brittle-ductile transition, even in this relatively stable interior of the in-teraplate. This phenomenon is explained by the experimental results and geothermal data from the superdeep bore-hole.
基金the State Scholarship Fund of China (No. 201804190004)
文摘Late at night on 17 June 2019,a magnitude 6.0 earthquake struck Shuanghe Town and its surrounding area in Changning County,Sichuan,China,becoming the largest earthquake recorded within the southern Sichuan Basin.A series of earthquakes with magnitudes up to 5.6 occurred during a short period after the mainshock,and we thus refer to these earthquakes as the Changning M6 earthquake sequence(or swarm).The mainshock was located very close to a salt mine,into which for^3 decades fresh water had been extensively injected through several wells at a depth of 2.7–3 km.It was also near(within^15 km)the epicenter of the 18 December 2018 M5.7 Xingwen earthquake,which is thought to have been induced by shale gas hydraulic fracturing(HF),prompting questions about the possible involvement of industrial activities in the M6 sequence.Following previous studies,this paper focuses on the relationship between injection and seismicity in the Shuanghe salt field and its adjacent Shangluo shale gas block.Except for a period of serious water loss after the start of cross-well injection in 2005–2006,the frequency of earthquakes shows a slightly increasing tendency.Overall,there is a good correlation between the event rate in the Shuanghe area and the loss of injected water.More than 400 M≥3 earthquakes,including 40 M≥4 and 5 M≥5 events,had been observed by the end of August 2019.Meanwhile,in the Shangluo area,seismicity has increased during drilling and HF operations(mostly in vertical wells)since about 2009,and dramatically since the end of 2014,coincident with the start of systematic HF in the area.The event rate shows a progressively increasing background with some fluctuations,paralleling the increase in HF operations.More than 700 M≥3 earthquakes,including 10 M≥4 and 3 M≥5 in spatially and temporally clustered seismic events,are correlated closely with active fracturing platforms.Well-resolved centroid moment tensor results for M≥4 earthquakes were shown to occur at very shallow depths around shale formations with active HF,in agreement with some of the clusters,which occurred within the coverage area of temporary or new permanent monitoring stations and thus have been precisely located.After the Xingwen M5.7 earthquake,seismic activity in the salt well area increased significantly.The Xingwen earthquake may have created a unidirectional rupture to the NNW,with an end point close to the NW-trending fault of the Shuanghe earthquake.Thus,a fault in the Changning anticline might have terminated the fault rupture of the Xingwen earthquake,possibly giving the Xingwen earthquake a role in promoting the Changning M6 event.
基金This work was supported by the National Natural Science Foundation of China(NSFC,Grant No.41702284,41602272)National key R&D program of China(Grant No.2019YFE0100100)+2 种基金the Na-tural Science Foundation of Hubei Province,China(Grant No.2019CFB451)and the Open Fund of Hubei Key Laboratory for Efficient Utilization and Agglomeration of Metallurgic Mineral Resources(Grant No.2020zy003)This work was also par-tially supported by the China Australia Geological Storage of CO_(2)project(CAGS),and the China Geo-logical Survey project(Grant No.DD20160307).
文摘Carbon Capture and Storage(CCS)is one of the effective means to deal with global warming,and saline aquifer storage is considered to be the most promising storage method.Junggar Basin,located in the northern part of Xinjiang and with a large distribution area of saline aquifer,is an effective carbon storage site.Based on well logging data and 2D seismic data,a 3D heterogeneous geological model of the Cretaceous Donggou Formation reservoir near D7 well was constructed,and dynamic simulations under two scenarios of single-well injection and multi-well injection were carried out to explore the storage potential and CO2 storage mechanism of deep saline aquifer with real geological conditions in this study.The results show that within 100 km^(2)of the saline aquifer of Donggou Formation in the vicinity of D7 well,the theoretical static CO_(2)storage is 71.967×106 tons(P50)①,and the maximum dynamic CO_(2)storage is 145.295×106 tons(Case2).The heterogeneity of saline aquifer has a great influence on the spatial distribution of CO_(2)in the reservoir.The multi-well injection scenario is conducive to the efficient utilization of reservoir space and safer for storage.Based on the results from theoretical static calculation and the dynamic simulation,the effective coefficient of CO_(2)storage in deep saline aquifer in the eastern part of Xinjiang is recommended to be 4.9%.This study can be applied to the engineering practice of CO_(2)sequestration in the deep saline aquifer in Xinjiang.
文摘The Internet of Things (IoT) paradigm enables end users to accessnetworking services amongst diverse kinds of electronic devices. IoT securitymechanism is a technology that concentrates on safeguarding the devicesand networks connected in the IoT environment. In recent years, False DataInjection Attacks (FDIAs) have gained considerable interest in the IoT environment.Cybercriminals compromise the devices connected to the networkand inject the data. Such attacks on the IoT environment can result in a considerableloss and interrupt normal activities among the IoT network devices.The FDI attacks have been effectively overcome so far by conventional threatdetection techniques. The current research article develops a Hybrid DeepLearning to Combat Sophisticated False Data Injection Attacks detection(HDL-FDIAD) for the IoT environment. The presented HDL-FDIAD modelmajorly recognizes the presence of FDI attacks in the IoT environment.The HDL-FDIAD model exploits the Equilibrium Optimizer-based FeatureSelection (EO-FS) technique to select the optimal subset of the features.Moreover, the Long Short Term Memory with Recurrent Neural Network(LSTM-RNN) model is also utilized for the purpose of classification. At last,the Bayesian Optimization (BO) algorithm is employed as a hyperparameteroptimizer in this study. To validate the enhanced performance of the HDLFDIADmodel, a wide range of simulations was conducted, and the resultswere investigated in detail. A comparative study was conducted between theproposed model and the existing models. The outcomes revealed that theproposed HDL-FDIAD model is superior to other models.
