Plenty of efforts have been made to reduce the use of low-value care (the care that is not expected to provide net benefits for patients) across the world, but measures of low-value care have not been developed in China. This study aims to develop hospital discharge records-based measures of low-value surgical procedures, evaluate their annual use and associated expenditure, and analyze the practice patterns by characterizing its temporal trends and correlations across rates of different low-value procedures within hospitals.Informed by evidence-based lists including Choosing Wisely, we developed 11 measures of low-value surgical procedures. We evaluated the count and proportion of low-value episodes, as well as the proportion of expenditure and medical insurance payouts for these episodes, using hospital discharge records in Sichuan Province, China during a period of 2016–2022. We compared the count and expenditure detected by different versions of these measures, which varied in sensitivity and specificity. We characterized the temporal trends in the rate of low-value surgical procedures and estimated the annual percent change using joint-point regression. Additionally, we calculated the Spearman correlation coefficients between the risk-standardized rates of low-value procedures which were estimated by multilevel models adjusting for case mix across hospitals.r for pairwise, 0.03; CI, −0.02, 0.07).Low-value episodes detected by more specific versions of measures accounted for 3.25% , and constituted 6.03%  and 5.90%  of overall expenditure and medical insurance payouts, respectively. The three figures accounted for 5.90%, 8.41%, and 8.38% in terms of more sensitive versions of measures. Almost half of the low-value procedures (five out of eleven) experienced an increase in rates during the period of 2016–2022, with four of them increasing over 20% per year. There was no significant correlation across risk-standardized rates of different low-value procedures within hospitals , Taikang Yicai Public Health and Epidemic Control Fund, Sichuan Science and Technology Program (2022YFS0052 and 2021YFQ0060), and 10.13039/501100004912Sichuan University (2018hhf-27 and SKSYL201811).The  We searched the literature to identify articles that reported the measures of low-value care. We searched PubMed, Web of Science, and China National Knowledge Infrastructure (CNKI) from inception to December 31, 2022, with no language restrictions, using combinations of the terms: “low-value∗” or “low value∗” or “overuse”. We manually selected relevant articles by reading the abstracts. We found Low-value care measures have been well established based on routinely collected administrative datasets in developed countries, and numerous attempts have been made to decrease the utilization of low-value care. There is currently a lack of measures of low-value care in China.Our study is the first to provide measures of low-value care in China. Based on hospital discharge records from Sichuan Province, China, the study developed 11 measures of low-value surgical procedures. Despite overall low-value practices detected by the 11 developed measures was modest, certain clinical specialties were plagued by widespread low-value practices which imposed heavy economic burdens for the healthcare system. Almost half of the low-value procedures experienced an increase in rates during the period of 2016–2022, with four of them increasing over 20% per year. There was no significant correlation across risk-standardized rates of different low-value procedures within hospitals.The developed measures of low-value care provide a straightforward way to assess the extent of specific inappropriate healthcare services, thus informing policymakers and healthcare providers to identify areas where improvements can be made to reduce waste and improve patient outcomes. Given the pervasive and significant upward trends in rates of low-value practices, it has become increasingly urgent to reduce such practices. Interventions in reducing low-value practices in China would be procedure-specific as practice patterns of low-value care varied by procedures and common drivers of low-value practices may not exist.Low-value care, which is defined as the care that is not expected to provide net benefits for patients, induces unnecessary costs and threatens patients’ safety.Measures of low-value care provide a straightforward way to assess the extent of specific inappropriate healthcare services, thus informing policymakers and healthcare providers to identify areas where improvements can be made to reduce waste and improve patient outcomes. On the other hand, indirect methods of measuring healthcare efficiency, such as estimating total excessive expenditure (or say unnecessary costs) by comparing risk-adjusted expenditure per patient across geographical areas,Based on evidence-based lists and peer-reviewed literature, we developed algorithms to measure low-value surgical procedures. These algorithms are able to be applied to routinely collected administrative hospital discharge records in China. We employed hospital discharge records from 2016 to 2022 in Sichuan Province, China to assess the annual use of these low-value procedures and their associated expenditure, which were detected using two alternative versions of measure (more specific and more sensitive versions). In addition, we determined the practice pattern by characterizing temporal trends in rates of low-value procedures and examining correlations across risk-standardized rates of different low-value procedures within hospitals. To maintain consistency with prior studies, the term “measures of low-value care” will be consistently employed throughout the text to refer to the algorithms used for the identification of low-value care.We based our empirical analysis on Sichuan, a medium-developed province in Mainland China with 2435 hospitals and more than 83 million residents in 2020.do not do recommendations,D recommendations,i.The recommendations should be relevant to inpatient settings. The procedures that typically occur in outpatient setting were excluded.ii.The recommendations should be identifiable with reliability using variables that are present in the hospital discharge records. Recommendations related to screening, testing, and prescribing were excluded.iii.There should be relevant and specific ICD-9-CM3 codes available for the procedures. Procedures without identifiable code like Marshall Marchetti Krantz procedure were excluded.We based our measures on procedures that have been recommended as low value by Choosing Wisely US,For each selected low-value surgical procedure, we developed an operational definition that is expressed in terms of the following metrics captured in the hospital discharge records: age, sex, ICD-9-CM3, and ICD-10. It should be noted that the value of healthcare frequently varies depending on the clinical context in which services are delivered, and administrative data may not capture all relevant situations due to limited metrics. As a result, our measures of low-value procedures would encounter a tradeoff between sensitivity and specificity.patient-indication measure. By defining the qualified episodes in this manner, we are able to interpret the proportion of low-value episodes as the proportion of low-value medical decisions.Low-value episodes specifically pertain to cases where patients undergo low-value procedures, which were captured by the definition of low-value episodes in To determine the expenditure associated with low-value care, we used the total expenditure during an episode. While this method may include additional costs that are not directly induced by low-value care, it allows us to measure the cost of physicians' low-value treatment decisions. Patients typically receive a bundle of treatment procedures, including preoperative exams, anesthesia, and postoperative care, and this approach enables us to bundle the total costs associated with a specific treatment decision. Consequently, the costs of low-value care in this study can be interpreted as the costs of low-value treatment decisions, and the proportion of low-value expenditure can be interpreted as the percentage of cost generated by low-value medical decisions.Due to the COVID-19 pandemic, China has experienced a nationwide lockdown during a period of January 1, 2020–March 31, 2020. As a result, we excluded the data for this period when calculating the annual counts of low-value procedures and their associated expenditures.To characterize the practice pattern of low-value care, we characterized the temporal trends in the rate of low-value surgical procedures and estimated the average annual percent change (AAPC) using joint-point regression. We also calculated the spearman correlation coefficients between the risk-standardized rates of different low-value procedures which were estimated by multilevel models adjusting for case mix across hospitals. We used narrow definitions to measure low-value procedures to fit the data, as these definitions cover all procedures, unlike broad definitions. As the lockdown could primarily affected patients' healthcare-seeking behavior more than physicians’ practice patterns, we utilized data from 2016 to 2022 for both analyses, without excluding the lockdown period. Additionally, we conducted sensitivity analyses by excluding the data from the lockdown period to assess the robustness of our findings.During a specific year, the rate of a particular low-value procedure was calculated by dividing the number of low-value episodes by the number of qualified episodes for that procedure. The AAPC for each procedure from 2016 to 2022 was estimated by the logarithmic Poisson joint-point regression.predicted individual rates of low-value care by the total of expected individual rates of low-value care for a given hospital and then multiplying this ratio by the unadjusted average rate of low-value care across all hospitals during our study period. The total of predicted individual rates of low-value care was obtained by adding up the probability of each episode where patients receiving low-value care, which was predicted using the hospital random effect, patients' covariates, and year fixed effect, while the total of expected individual rates was estimated using the patients' covariates, and year fixed effect. By adjusting for differences in case mix between hospitals, risk-standardized rates facilitate meaningful comparisons of low-value care rates over hospitals. Patients' covariates in the two-level logistic regression include patients' demographical variables , patients’ clinical variables . The definition of each covariate is presented in To estimate the correlations across procedure-specific risk-standardized rates within hospitals, we first developed a two-level  logistic regression for each procedure. The model incorporated the patients' demographical and clinical characteristics, an indicator variable for the year. We then used the methodology developed by the Centers for Medicare & Medicaid Serviceslme 4 package.The annual percent change was estimated using Joint-Point software version 5.0, developed by the US National Cancer Institution. The multilevel models were developed in R version 4.1.2, using the The study was approved by the Ethics Committee of West China Fourth Hospital and West China School of Public Health, Sichuan University (Grant number: Gwll2022064).The funders had no involvement in the study design, data collection, data analysis, interpretation, or writing.We identified a total of 10,831 annual low-value episodes for 11 measures based on narrow definitions of low-value surgical procedures, accounting for 3.25% of the total qualified episodes in Sichuan Province. For the five low-value procedures applicable to broad definitions, the number of low-value episodes accounted for 5.90% of the corresponding qualified episodes. The proportion of low-value episodes among the 11 procedures exhibited significant variation, ranging from 0.11% of spinal fusion to 71.66% of surgical management of vesicoureteral-reflux in terms of narrow definitions.According to our narrow definitions, low-value episodes accounted for 30.06 million USD in annual expenditure, which is 6.03% of the total expenditure on qualified episodes. Medical insurance payouts for low-value episodes amounted to 23.17 million USD, or 5.90% of the total medical insurance payouts for qualified episodes. When using broad definitions, the two types of costs represented 8.41% of the overall expenditure and 8.38% of the medical insurance expenses. Consistent with the proportion of low-value episodes, the proportion of low-value expenditure and the proportion of low-value medical insurance payouts varied extensively across different procedures. P-value of AAPC less than 0.05. For four out of the five low-value procedures, the rates increased over 20% per year during 2016–2022, showing significant upward trends. The most dramatic increase in low-value procedure rates was observed in spinal fusion, with a staggering 72.1% annual increase. In comparison, only three procedures exhibited downward trends in low-value procedure rates, with relatively slower rates of change (approximately 10% per year) compared to the increasing procedures. The rates of the remaining three low-value procedures showed no statistically significant change during 2016–2022. The results of annual percent change (APC) for segment periods can be found in Sensitivity analyses conducted on data excluding the lockdown period  showed remarkably similar results for temporal trends and correlation patterns .In this study, we have developed 11 measures of low-value surgical procedures based on recommendations from evidenced-lists including Choosing Wisely. These measures have been custom-tailored for application in hospital discharge records within the inpatient setting in China. Recognizing the inherent variability in healthcare value across different clinical contexts and the limitations of administrative data in capturing all relevant situations, we established two alternative measures for particular low-value procedures to account for the uncertainty, namely narrow measures  and broad measures . Using over 123 million episodes from hospital discharge records in Sichuan Province, China, during a period of 2016–2022, we found that the annual use of low-value surgical procedures accounted for a relatively small proportion (3.16% detected by narrow measures and 5.90% by broad measures), and constituted a modest portion of overall expenditure (4.59% and 8.41% by narrow and broad measures) and medical insurance payouts (4.91% and 8.38% by narrow and broad measures). In addition, we found that almost half of the low-value procedures (five out of eleven) experienced an increase in rates during the period of 2016–2022, with four of them increasing over 20% per year. By examining correlations across risk-standardized rates of low-value procedures within hospitals, we found the physicians’ practice patterns of low-value care varied by procedures. 70% of the pairwise correlations showed no statistical significance, and the mean Spearman correlation coefficients was 0.03 .,Similar with previous studies,,,Our study, like most studies on measuring low-value care,patient-indication measure to define the qualified episodes. Our findings revealed that the proportion of low-value procedures, either assessed using narrow or broad measures, is relatively low. As a result, it constitutes only a modest portion of overall expenditure and medical insurance payouts. However, the proportion varied significantly by procedures, and certain clinical specialties were plagued with widespread low-value practices, which imposed heavy economic burdens for the healthcare system. For example, hysterectomy, a widely performed gynecologic surgery worldwide with a prevalence of more than 1/1000,,Using the developed measures, we evaluated the proportion of low-value surgical procedures, as well as the proportion of low-value expenditure and medical insurance payouts. Physicians typically dominate the process of healthcare delivery due to the information asymmetry between physicians and patients, thus the occurrence of low-value care is heavily influenced by physicians’ treatment preferences . To capture the proportion of low-value medical decisions induced by physicians and the corresponding expenditure, we employed ,Comparison with studies that reported prevalence rates of low-value care is limited by several factors, particularly the differences in the low-value services examined as well as the alternative definitions of denominators . Most of the studiesInterestingly, during the period of 2016–2022, while half of the low-value procedures experienced an increase in rates, three exhibited downward trends, and their rates unequivocally started declining from 2018. The decrease in the rate of low-value ERCP since 2018 could be attributed to the update of the clinical guideline in 2018,Given that almost half of the low-value procedures showed upward trends, de-implementation of low-value practices is becoming a pressing matter in China. We could ideally estimate the national annual counts of low-value episodes by multiplying our results by 16.8, which is the ratio of the national population to the population in our study area. Based on our eleven developed measures, this suggests that more than 180 thousand low-value episodes occur annually in China, and even a 1% increase would result in thousands of new low-value episodes. Special attention could be directed towards low-value vertebroplasty/kyphoplasty, as their occurrence rate has reached up to 35.98% and is growing at a rate of 8.5% per year.r for pairwise, 0.03; CI, −0.02, 0.07). The findings imply that practice patterns of low-value care differ across procedures, as indicated by the significant variation in proportion of low-value procedures. There might be two underlying reasons for the results. First, since different physicians typically have distinct practice patterns,,,Analyzing the correlation between the rates of different low-value procedures within hospitals helps reveal the association between practice patterns across procedures. On average, no significant correlation was detected in the study (mean Some limitations should be noted in this study. Most notably, we only assessed 11 measures of low-value surgical procedures due to the limited clinical information available in hospital discharge records. It is possible that these measures may not be the most relevant to the healthcare system in China. In addition, we were not able to analyze low-value prescribing or testing, even though the large number of recommendations related to these low-value services suggests that they are of great concern. Future studies could base their measures on more informative database, such as insurance claims, to bridge these gaps. Furthermore, despite that we have followed Schwartz et al.In conclusion, we developed 11 measures for low-value surgical procedures using China's hospital discharge records in this study. Despite overall low-value practices detected by the 11 developed measures was modest, certain clinical specialties were plagued by widespread low-value practices which imposed heavy economic burdens for the Chinese healthcare system. Given the pervasive and significant upward trends in rates of low-value practices, it has become increasingly urgent to reduce such practices. Interventions in reducing low-value practices in China would be procedure-specific as practice patterns of low-value care varied by procedures and common drivers of low-value practices may not exist.T. Lan and J. Pan conceptualized the study. J. Pan and K. Tan collected the data. T. Lan and L. Chen reviewed the recommendations in the evidence-based lists. T. Lan developed the analysis method. T. Lan implemented the data analysis. T. Lan drafted the manuscript. Y. Hu, J. Wang, K. Tan and J. Pan revised the manuscript. All authors read and approved the final manuscript.The hospital discharge records that support the findings of this study are available from the Health Commission of Sichuan Province, but restrictions apply to the availability of these data. Data can be made available from the authors upon reasonable request and with permission of the Health Commission of Sichuan Province.The authors declare that they have no competing interests.


Cybersecurity is a critical issue in today’s internet world. Classical security systems, such as firewalls based on signature detection, cannot detect today’s sophisticated zero-day attacks. Machine learning (ML) based solutions are more attractive for their capabilities of detecting anomaly traffic from benign traffic, but to develop an ML-based anomaly detection system, we need meaningful or realistic network datasets to train the detection engine. There are many public network datasets for ML applications. Still, they have limitations, such as the data creation process and the lack of diverse attack scenarios or background traffic. To create a good detection engine, we need a realistic dataset with various attack scenarios and various types of background traffic, such as HTTPs, streaming, and SMTP traffic. In this work, we have developed realistic network data or datasets considering various attack scenarios and diverse background/benign traffic. Furthermore, considering the importance of distributed denial of service (DDoS) attacks, we have compared the performance of detecting anomaly traffic of some classical supervised and our prior developed unsupervised ML algorithms based on the convolutional neural network (CNN) and pseudo auto-encoder (AE) architecture based on the created datasets. The results show that the performance of the CNN-Pseudo-AE is comparable to that of many classical supervised algorithms. Hence, the CNN-Pseudo-AE algorithm is promising in actual implementation. Internet Crime Report 2022 [Cyber-attacks are critical problems in today’s internet-based interconnected systems. Most connected systems, such as financial institutions, transportation systems, smart grids, and manufacturing processes, are vulnerable to cyberterrorism. The attacks could jeopardize an organization’s operations, safety, privacy, and stability. Online fraud has many forms, such as computer intrusions (hacking), economic espionage (theft of trade secrets), online extortion, identity theft, and internet-facilitated crimes. Recently, cyber-attackers hacked numerous interconnected systems, stole information and money, and perpetrated crimes. According to the US Federal Bureau of Investigation (FBI)’s ort 2022 , the losort 2022 , the topAn intrusion detection and protection system (IDPS) is critical to any network infrastructure. There are two types of IDPS. One is host-based, and the other is a network-based system. A network-based IDPS is a system that usually sits at the boundary of the internal and external networks (there are various ways to implement it), and it is the primary system to detect and block cyber-attacks. A classical network-based IDPS works based on the attack signatures but is useless for zero-day and sophisticated attacks. These IDPSs are network vendors’ firewalls (software or independent system). Due to the inability to detect sophisticated cyber-attacks, vendors like Palo Alto developed the first-ever smart firewall based on machine learning (ML) algorithms in 2020 . ML comeTo overcome various limitations of the public network datasets, we have developed various network raw data or datasets in this work. We have extended our work , consideIn , we measDevelopment of raw data or datasets considering realistic cyber-attacks for network intrusion detection applications.Publication of data/datasets online for the research community .Identification of the CNN-Pseudo-AE unsupervised algorithm as a good candidate for actual implementation for anomaly traffic detection.The current work has produced the following contributions:The remainder of the article is organized as follows. Size of the dataset;Lack of diverse traffic;Lack of diverse attacks;Unrealistic scenarios;Synthetic development process;Produce traffic by simulation;Network topology.The cyber-attack patterns change often, and detecting a zero-day attack using firewalls based on the signatures is impossible. Because of the inability to detect sophisticated attacks, network vendors are working to develop firewalls with machine learning (ML) techniques. For attack or anomaly traffic detection, ML requires training and testing of a system based on network traffic. Most vendors use proprietary datasets for their development, but the research community uses open/public datasets for their research activities. Various public datasets are available for developing network intrusion detection systems (IDS), and many datasets are unsuitable due to proprietary issues. Many datasets are generated by removing various network traffic features . Other datasets are created using synthetic methods that do not include realistic attack scenarios, which are not generated via complete protocol stacks. We identify that the Canadian Institute of Cybersecurity’s CICIDS2017/19 dataset  is the mNetwork vulnerability comes in two ways concerning private and public networks. In a classical network architecture, a network behind security, such as firewalls, is considered a private or internal network, and anything outside the firewalls is a public network. An enterprise network is vulnerable to both internal and external attacks. Internal threats are linked to employees who carry out attacks by exploiting their inherent privileges. An enterprise network is more prone to external attacks due to human factors such as misconfigurations, users in the phishing trap, weak IT management, etc. In this article, we focus on external attacks since most cyber-attacks originate from remote sources. A persistent attack is more critical compared to a non-persistent attack. Persistent attackers obtain network access, remain undetected, and even remove their tracks and presence. At the same time, the non-persistence attack is a one-time attack and tries to steal information. Cyber-attackers use various techniques to attack. Some attacks are nothing but gathering information in the beginning. Some common techniques for gathering information regarding the hardware, network configurations, and user information are reconnaissance, port scanning, and ping sweeping. Attackers exploit the network vulnerability to steal information by creating backdoors and obtaining escalate privileges, deny services and crash services, and even modify or alter information via scripting or privileged access. Zippy Falco et al.  defined The communication-passive-class attack model is about gathering system information/reconnaissance process for plotting cyber-attacks by discovering network vulnerability. The process does not harm the targeted network, but the discoveries of the vulnerability inspire the attackers to perform communication-active/host/application-class-based attacks. Communication-active-class attacks damage the network services. Such attacks are denial of service (DOS) or distributed denial of service (DDoS). These attacks can crash any service by over-utilizing the service process and exhausting the bandwidth usage. Host and application-class-based attacks are more sophisticated. These attacks are basically malware-driven attacks. Attackers exploit the system or application loopholes to create backdoors and, in the process, obtain privileged access to the systems. These attacks are more dangerous since through this process, attackers can steal information and can engage in various criminal activities, such as asking for ransom. This is why an enterprise must audit its network vulnerabilities often, such as by penetration testing.ML-based solutions need a dataset for training and prediction. A good dataset is a primary need for ML-based solutions. A good dataset could be defined for network intrusion or anomaly traffic detection by considering large data size; diverse benign and attack traffic based on open systems interconnection (OSI) layers 3, 4, and 7; and diverse traffic protocols. The dataset must be developed based on realistic network scenarios where traffic follows the standard protocol stacks. The primary challenge is the unavailability of labeled traffic datasets for various network activities and attack cases. Existing datasets have various limitations, and some are outdated, as mentioned in Emulate benign and attack scenarios and generate traffic.Capture raw traffic.Study raw traffic and feature extraction.Develop/Process dataset.Analyze datasets with ML/DLFollowing A.Probing Dataset (Communication Passive Class):Probing is a communication-passive-class attack. Assuming we have pre-knowledge of the public IP addresses of the victim networks, we used NMAP commands to probe the system/server information along with HTTP background traffic from CF20. Probing was performed to external IP addresses 100.1.1.20-30. B.DDoS Dataset (Communication Active Class)DDoS is a communication-active-class attack. There are two main types of DDoS attacks: volumetric DDoS (VDDoS) and protocol DDoS (PDDos) attacks, based on OSI layers 3 and 4, respectively. Volumetric DDoS attacks are based on ICMP attacks, whereas PDDoS attacks are based on the UDP and TCP protocols. We have completed both types of attacks, using CF20 from the attack network to the DMZ network, bypassing the firewall to create up to 1 Gbps attacks and capturing the bi-directional traffic from the DMZ switch using TCPdump or the Wireshark tool. The captured file size was from 10 to 17 GB in pcap format.We generate VDDoS attacks based on ICMP flooding. This attack is based on open systems interconnection (OSI) layer 3. ICMP is generally used for messaging between two devices. When the number of ICMP-based requests is in the millions, the process saturates the network bandwidth and wastes computing power. This process is simple but can quickly bring down perimeter devices or saturate bandwidth. ICMP has various types. In the dataset, we included ICMP Type 0 (echo reply), 3 (destination unreachable), 8 (source host isolated), and 11 (destination network unreachable for type of service) floods.We generate PDDoS attacks using the OSI layer 4 protocols, UDP and TCP. UDP-based attacks or UDP packet floods saturate network bandwidth and bring down network services. The attacks include a rapid succession of UDP datagrams (garbage payload) with spoofed IPs to a server using different ports . They force the server to respond to ICMP traffic. Both ingress and egress bandwidth are saturated with this attack. We have considered a UDP flood to port 53 (domain name service (DNS)) to bring down the DNS service. The TCP-based attacks are SYN Flood, ACK Flood, and RST Flood. These attacks exhaust the states and cripple a server’s ability to respond to legitimate requests. The SYN flood saturates and prevents the completion of the TCP three-way handshake between the client and the server. The target server’s communications port becomes a half-open state. In this work, we have used ports 25, 80, and 443 for the SYN flood. The TCP ACK flood occupies the server with the flooded junk data and blocks the server from providing service to other users by slowing down or crashing the target using junk data. The targeted server uses so much computing power to respond to each ACK packet received that it cannot serve legitimate users. We have used ports 25, 80, and 443 for the ACK flood. The TCP-RST flood saturates the network bandwidth and resources on stateful devices. When millions of RST packets are sent continuously, the process can bring down the stateful defense. This can create unexpected issues and behavior. We have used ports 25, 80, and 443 for the RST flood. In this work, we also used XMAS Tree Flood, a PDDoS type of attack, using the CF20 emulator. A Christmas tree attack sends a large number of packets with all the options turned on so that any protocol can be used. This kind of packet requires more processing by end devices and routers. If a large number of these packets are sent, they keeps the receivers busy in processing, and it is nearly impossible for it to perform other tasks. Thus, the receivers deny the legitimate traffic.We also used benign traffic to develop the DDoS dataset. Benign traffic included HTTP1.0, HTTP1.1, HTTPs, FTP, SSH-Login, Telnet, SMTP, POP3, RTSP, SIP, BitTorrent, Exchange, CIFS/SMB, IMAP4, SSH, and Facebook traffic. C.Host & Application ClassThe host class includes the unauthorized access from remote to local (R2L) machine attacks and unauthorized access to root (U2R), whereas the application class includes the R2L attacks only. Cyberattacks have now become very sophisticated, and it is challenging to distinguish the R2L from the U2R and vice versa. These attacks look like regular/benign traffic, but they take control of the host systems/applications by gaining access. It is challenging to produce actual traffic characteristics of these attacks. Considering this fact, we investigated the Host and Application attack classes using the real cyberattacks that occurred from 2019 to 2022. To emulate these attacks, we modified our testbed, given in The following attacks were emulated using CF20 in our sandbox environment.Major cybersecurity attacks that occurred during January–June 2022Major cybersecurity attacks that occurred during January–December 2021Major cybersecurity attacks that occurred during January–December 2020Major cybersecurity attacks that occurred during January–December 2019Major malware attacks that occurred during January–June 2022Major malware attacks that occurred during January–December 2021The attacks were performed in two different categories. They were (i) general cyberattacks and (ii) malware attacks. Attack profiles were built to send realistic, stateful attack and malware traffic using Cyberflood’s TestCloud. The TestCloud includes an attack library that is automatically updated, providing thousands of the latest, most relevant attacks to choose from. Remote code executionUnauthenticated attackCross-site scriptingArbitrary code execution in the context of the applicationExploitation by remote code execution in the security context of the systemBuffer overflow attackAuthentication bypass attackUnrestricted file upload and remote code executionWordPress contact form entries plugin stored cross-site scriptingBuffer overflow attack in Oracle MySQL NDB cluster componentSQL injectionArbitrary code execution on the target systemExecution of arbitrary script code in the target user’s browserCrafted request to the target serverStack-based buffer overflowHeap buffer overflowOut-of-bounds read and writeDenial of service by terminating the server due to an unhandled exceptionArbitrary command execution attackSome of the attack techniques used for attack scenarios are given below:Backdoor that targets the Windows platformRansomware that targets the Windows platformWorm that targets Linux operating systemsTrojan malwareTrojan—password stealerBackdoor.MSIL.Crysan TrojansData exfiltrationTrojan—downloaderMalicious_confidence_90%Backdoor.DCRatSome of the malware types used for attack scenarios are given below:D.Benign Mixed TrafficTo develop a dataset for benign traffic, we have emulated network traffic using DNS, HTTP, IMAP4, POP3, SMTP protocols, and RDP, SNMP, SSH, Syslog, uTorrent, and Exchange applications to create benign mixed traffic. We emulated Netflix and Facebook traffic using CF20 for streaming and social networking traffic. The traffic is captured in pcap format. A traffic request is sent from 10.0.0.0 to 172.16.0.0 on the server network. Our work has produced various data/datasets considering realistic traffic and attack scenarios. In this sub-section, we have focused on DDoS attacks to compare the performance of some standard SL models and our developed UL model. Zombie attackers can create millions of requests (DDoS attacks) to any service, exhaust the bandwidth, and increase response time for legitimate requests. The attack can crash any network service. Often, it is complicated to separate attack traffic and legitimate traffic since the attack requests also follow the TCP/IP protocol stack. A conventional firewall is not capable of identifying this kind of attack. ML can be used to identify DDoS attacks based on traffic attributes. ML-based systems can be trained on the DDoS traffic and used to identify anomaly traffic. In this work, we compare the performance of detecting DDoS traffic between the classical supervised ML  and our The PVAMU DDoS2020 dataset has over 4.5 million traffic flows . This toIn our previous work , we usedSince the attacking nature changes with time, it is challenging to create an up-to-date labeled dataset to be used with supervised ML algorithms, and thus it is more desirable to use unsupervised ML algorithms to identify anomalous traffic. In our previous work , we deveIn In this work, we developed raw data/datasets for various realistic cyber-attack scenarios that include probing attacks, DDoS attacks, remote to local , and user to root  privileges) attacks. The attacks were created considering real traffic using Spirent’s CyberFlood CF20 traffic emulator to overcome the various limitations in public datasets. Our developed data contains diverse cyber-attacks and background traffic. Machine-learning-based solutions need suitable datasets to improve training and testing accuracy. The newly developed network data will benefit the further development of intrusion detection systems (IDS) using machine learning algorithms. Further, we compared the performance of anomaly traffic detection of some standard classical supervised ML and our unsupervised CNN-Pseudo-Autoencoder (AE) model. We showed that the performance of our developed CNN-Pseudo-AE model is very close to that of the supervised models. The accuracy of the CNN-Pseudo-AE model is about 99.3%. Because of its very high accuracy, with fine-tuning, this model can be considered for actual implementation.


Computer vision and deep learning have the potential to improve medical artificial intelligence (AI) by assisting in diagnosis, prediction, and prognosis. However, the application of deep learning to medical image analysis is challenging due to limited data availability and imbalanced data. While model performance is undoubtedly essential for medical image analysis, model trust is equally important. To address these challenges, we propose TRUDLMIA, a trustworthy deep learning framework for medical image analysis, which leverages image features learned through self-supervised learning and utilizes a novel surrogate loss function to build trustworthy models with optimal performance. The framework is validated on three benchmark data sets for detecting pneumonia, COVID-19, and melanoma, and the created models prove to be highly competitive, even outperforming those designed specifically for the tasks. Furthermore, we conduct ablation studies, cross-validation, and result visualization and demonstrate the contribution of proposed modules to both model performance (up to  Medical imaging plays a key role in dealing with health problems on a daily basis. To analyze large volumes of imaging data, computer-assisted diagnosis has shown great potential through computer vision and deep learning ,2. In paLimited data. Datasets for medical imaging, especially for novel diseases, are typically small compared to natural image datasets, making model training more difficult. As such, medical feature learning is commonly conducted through transfer learning [learning , with lalearning ,5. ContrClass imbalance. Aside from small datasets, another issue for medical imaging is class imbalance, where there are significantly more negative (benign) data samples than positive  ones. Thus, models are heavily biased toward the majority negative class and exhibit poor predictive performance for the minority positive class, which if misdiagnosed, leads to serious consequences. This issue is especially serious for classifiers trained with traditional loss functions like stochastic gradient descent [ descent ; existin descent ,8,9 that descent . This is descent .Low trustworthiness. In the context of medical artificial intelligence (AI), the trustworthiness of predictions is important to both patients and clinicians. A prevalent problem is that deep neural networks optimized with the standard CE loss function tend to be overly cautious for the minority class, while being overconfident for the majority class [ty class . This prty class , where tTRUDLMIA, a trustworthy deep learning framework for medical image analysis. Both supervised and self-supervised learning are combined for effective medical image feature learning and a novel surrogate AUC margin loss function is adopted to build high-performing and high-trust models. Weaknesses of each module are supplemented by other modules to reduce limitations such as high computational resource requirements for self-supervised learning and training-time instability with AUC margin loss. Moreover, the framework adopts a model-agnostic modular design for generalization capabilities.To address the aforementioned issues, we propose We propose a general deep learning framework for medical image analysis which can be used to build high-performing and high-trust models;We demonstrate that models built using the proposed framework outperform existing ones on benchmark tasks. While the main focus of this work is COVID-19 diagnosis, we verify the generalization ability of our framework by achieving excellent results on pneumonia and melanoma classification tasks, outperforming models specifically designed for those tasks;We study the effect of AUC maximization with margin loss on model trustworthiness, with the goal of addressing under/over-confidence caused by class imbalance.In summary, our contributions and findings are three-fold:In computer vision, self-supervised learning (SSL) has gained increasing popularity for learning visual representations. Overall, the SSL approaches can be categorized into generative or discriminative. Momentum Contrast (MoCo)  and SimCContrastive learning methods have been shown to be effective in medical contexts. In , the MoCSelf-supervised learning with MoCo has also been used for tackling pandemics. In , the autSimCLR has also been previously applied in medical contexts. In , the autAddressing the issue of imbalanced data in machine learning is of significant importance to ensure unbiased and accurate predictions. Many traditional methods exist for the purpose of tackling this issue. One popular approach is oversampling, which involves artificially inflating the minority class by replicating instances or introducing synthetically generated examples, a technique demonstrated by methods such as the Synthetic Minority Over-sampling Technique (SMOTE) . ConversIn medical image analysis, AUC has been widely utilized for performance evaluation. The recent proposal of deep AUC maximization , a novelModel trustworthiness quantification is a relatively new and under-developed area compared to other deep learning performance metrics, such as robustness , efficieBecause of the proven advantage of SSL pre-training and AUC maximization, our proposed approach extends previous work  to combiThe TRUDLMIA framework comprises three modules: (i) generic learning through large-scale supervised pre-training using natural images (ImageNet ), (ii) aThe design of the proposed framework is based on the following considerations. Conducting supervised pre-training (module (i)) first leads to more efficient SSL training as the latter can be very costly. For module (ii), we study two main-stream SSL approaches, namely SimCLR and MoCo, on their performance and trustworthiness as pre-training for our framework. In the comparison below and in ablation studies, we mainly use SimCLR, as we found that it performed better than MoCo for the given task  models, i.e., ResNet  and DensDifferent datasets are used for pre-training and fine-tuning TRUDLMIA modules. In supervised pre-training (module i), the deep CNN models are pre-trained on the ImageNet  dataset.For downstream tasks (module iii), the COVIDx dataset , a smallx, an answer y with respect to a model M, such that z representing the correct answer to x, we use y given by model M matches the correct answer z. Likewise, we use y given by the model does not match the correct answer. We also define the confidence of M in an answer y to question x as question–answer trust of an answer y given by model M of a question x, with knowledge of the correct answer z, is defined asWe compute trust scores for models based on the methods introduced in . Given atrust score according to the question–answer method introduced above. We use To put the approach into practical usage with our models, we first calculate an optimal threshold value by maximizing the F1-score on the validation split. Data samples are passed through the model and outputs lower than the threshold are predicted to be negative, while outputs above the threshold are predicted to be positive. Model outputs are then normalized, such that negative predictions are scaled between 0 and 0.5 and positive predictions are scaled between 0.5 and 1. This allows us to express model confidence, which is then used to compute a During fine-tuning, validation is conducted on a randomly sampled validation split . In order to construct the validation set, we adopt a balanced random sampling strategy. This approach guarantees a random selection of instances while maintaining an equal representation from each class. This procedure aligns the validation set’s distribution with that of the test set, thereby facilitating a more precise estimation of model performance during the validation phase.For each training session, we save the following models: best validation accuracy, best validation AUC score, best validation F1 score, lowest validation loss, and last epoch. However, when comparing different model architectures, learning methods, or loss functions, we strictly use models saved under the “best validation F1 score” criteria for the sake of consistency. This criterion combines both precision and recall, which are important metrics commonly used in the medical context.We implemented the proposed TRUDLMIA framework for validation on three distinct medical datasets: COVIDx 8B, RSNA Pneumonia Challenge, and SIIM-ISIC Melanoma Classification.Our top-performing model on the COVIDx dataset, COVID-CXR-SSL, showcased exceptional performance and trust score . The pre-trained ResNet model is also used as a backbone architecture for training with the SimCLR architecture followed by fine-tuning it using the CE loss function (model “SL + SSL”). Furthermore, the SimCLR model is also fine-tuned using the AUC maximization loss function (model “SL + SSL + AUC”). Both the “SL + SSL” and “SL + SSL + AUC” models are fine-tuned for 200 epochs. To verify the ablation study, we repeat it on the RSNA Pneumonia Challenge dataset , which can be fixed by lung segmentation . The addThe presented framework does have certain limitations tied to the intrinsic characteristics of the selected methods. For instance, the high computational demand associated with self-supervised pre-training poses challenges when applied to domain-specific images with restricted computational resources. AUC maximization-based loss functions are also sensitive to model changes , which cHowever, modules of the TRUDLMIA framework are designed to supplement each other, minimizing the effect of these limitations. The reason for incorporating a supervised learning component was to decrease the necessity for extensive self-supervised learning, reducing the computational load required. The SL + SSL pre-training modules also reduce the amount of AUC maximization-based fine-tuning, minimizing the training instability.Another potential weak point lies in the innovative nature of deep AUC maximization as a surrogate loss function. While deep AUC maximization is appropriate for the problem at hand due to its effectiveness in dealing with class imbalance, its novelty means that its properties have not yet been extensively studied. During training, we observed validation loss curves that were relatively unpredictable compared to the loss curves of traditional. Although this does not inherently undermine the method, the unpredictability of these loss curves reduces interpretability and might complicate the identification of issues arising during the training/validation phases.Despite the limitations discussed, the empirical results underscore the substantial practical benefits and broad applicability of our framework. Even when compared with networks that have been expressly designed for the same tasks, our TRUDLMIA framework demonstrates superior performance. Moreover, even marginal improvements bear significance in the realm of medicine, potentially leading to direct enhancements in patient care and quality of life.In computer-aided diagnosis, deep learning has demonstrated great potential when integrated with medical imaging; however, the adoption thereof is challenged by limited training data, imbalanced data sets, and low trust in models. To combat these issues, we propose TRUDLMIA, a modular deep learning framework that can produce trustworthy and high-performing models for medical image analysis.The framework comprises three modules: large-scale supervised learning (SL) and self-supervised learning (SSL) for pre-training, as well as transfer learning-based fine-tuning with AUC margin loss. Large-scaled supervised pre-training reduces the data-hungriness and computational load of later modules. Self-supervised learning continues to address data-hungriness through efficient feature learning and reducing the need for labeled data. Finally, transfer learning with AUC margin loss prioritizes the minority positive class, dealing with the class imbalance issue. The modules also work to supplement each other’s weaknesses. Supervised pre-training reduces the high computational load of self-supervised learning, and the SL + SSL pre-training modules provide a strong backbone for fine-tuning with AUC margin loss, improving stability.Through highly successful assessments on a benchmark dataset, we show the effectiveness of the TRUDLMIA framework for medical image analysis. Using the COVIDx dataset, models trained through the framework surpass traditional supervised models, including the state-of-the-art COVID-Net CXR-3. Using the RSNA Pneumonia Challenge dataset, the trained model achieved top performance when compared to other notable models. On the SIIM-ISIC Melanoma Classification Challenge, TRUDLMIA achieves competitive performance even when competing with models that have advantages through external data, data augmentations, and ensembling. Our top-performing high-trust models for these tasks will be made publicly available.t-SNE visualizations [Moreover, ablation studies are conducted and verified to show the improvement of our framework with the addition of each module, showing significant improvements in precision, sensitivity, and trust score. We also demonstrate the stability of the framework by adding cross-validation to the fine-tuning phase, which results in a performance range that can be used to evaluate the framework methodology in general instead of single models, which may be high-performing due to lucky randomization during training. Our cross-validation experiment shows stable high-performance results, demonstrating the robustness of the framework in terms of data variability and partitioning . Finallyizations  show theizations  visualizThe proposed framework is by no means perfect, but we hope the TRUDLMIA can contribute to the fight against pandemics, including post-acute COVID syndrome (long COVID) ,50, and


Forty-three countries have now implemented best-practice policies for tackling trans fat in food, with 2.8 billion people protected globally.Despite substantial progress, however, this still leaves 5 billion worldwide at risk from trans fat’s devastating health impacts with the global goal for its total elimination in 2023 remaining unattainable at this time.Industrially produced trans fat  is commonly found in packaged foods, baked goods, cooking oils and spreads. Trans fat intake is responsible for up to 500 000 premature deaths from coronary heart disease each year around the world.“Trans fat has no known benefit, and huge health risks that incur huge costs for health systems,” said WHO Director-General, Dr Tedros Adhanom Ghebreyesus. “By contrast, eliminating trans fat is cost effective and has enormous benefits for health. Put simply, trans fat is a toxic chemical that kills, and should have no place in food. It’s time to get rid of it once and for all.”Currently, 9 of the 16 countries with the highest estimated proportion of coronary heart disease deaths caused by trans fat intake do not have a best-practice policy. They are Australia, Azerbaijan, Bhutan, Ecuador, Egypt, Iran (Islamic Republic of), Nepal, Pakistan and Republic of Korea.Best-practices in trans fat elimination policies follow specific criteria established by WHO and limit industrially produced trans fat in all settings. There are two best-practice policy alternatives: 1) mandatory national limit of 2 grams of industrially produced trans fat per 100 grams of total fat in all foods; and 2) mandatory national ban on the production or use of partially hydrogenated oils (a major source of trans fat) as an ingredient in all foods.“Progress in eliminating trans fat is at risk of stalling, and trans fat continues to kill people,” said Dr Tom Frieden, President and CEO of Resolve to Save Lives. “Every government can stop these preventable deaths by passing a best-practice policy now. The days of trans fat killing people are numbered – but governments must act to end this preventable tragedy.”While most trans fat elimination policies to date have been implemented in higher-income countries (largely in the Americas and in Europe), an increasing number of middle-income countries are implementing or adopting these policies, including Argentina, Bangladesh, India, Paraguay, Philippines and Ukraine. Best-practice policies are also being considered in Mexico, Nigeria and Sri Lanka in 2023. If passed, Nigeria would be the second and most populous country in Africa to put a best-practice trans fat elimination policy in place. No low-income countries have yet adopted a best-practice policy to eliminate trans fat.In 2023, WHO recommends that countries focus on these four areas: adopting best-practice policy, monitoring and surveillance, healthy oil replacements and advocacy. WHO guidance has been developed to help countries make rapid advances in these areas.WHO also encourages food manufacturers to eliminate industrially produced trans fat from their products, aligning to the commitment made by the International Food and Beverage Alliance (IFBA). Major suppliers of oils and fats are asked to remove industrially produced trans fat from the products sold to food manufacturers globally.The report, called Countdown to 2023 WHO Report on global trans fat elimination 2022, is an annual status report published by WHO in collaboration with Resolve to Save Lives, to track progress towards the goal of trans fat elimination in 2023.https://www.who.int/news/item/23-01-2023-five-billion-people-unprotected-from-trans-fat-leading-to-heart-diseaseAvailable from:


Flying cars, essentially vertical takeoff and landing aircraft (VTOL), are an emerging, disruptive technology that is expected to reshape future transportation. VTOLs can be powered by battery electric, fuel cell, or internal combustion engine, which point to entirely different needs for industry expertise, research & development, supply chain, and infrastructure supports. A pre-analysis of the propulsion technology competition is crucial to avoid potential wrong directions of research, investment, and policy making efforts. In this study, we comprehensively examined the cost competitiveness of the three propulsion technologies. Here we show that battery electric has already become the lowest-cost option for below-200-km VTOL applications, covering intra-city and short-range inter-city travels. This cost advantage can be robustly strengthened in the long term under various technology development scenarios. Battery energy density improvement is the key to reducing cost. In particular, a 600 Wh/kg battery energy density provides battery electric with all-range cost advantage, and promises high return in business. Fuel cell and internal combustion engine, under certain technology development scenarios, can obtain cost advantage in long-range applications, but face intense competition from ground transportation such as high-speed rail. The findings suggest a battery-electric-prioritized VTOL development strategy, and the necessity of developing VTOL-customized high-energy-density batteries. Flying cars lie at the heart of future transportation. We comprehensively examined the cost competitiveness of the three major competing propulsion technologies for VTOLs, which are battery electric, fuel cell, or internal combustion engine. The findings suggest a battery-electric-prioritized VTOL development strategy, and the necessity of developing VTOL-customized high-energy-density batteries. This sends strong messages to researchers, entrepreneurs, policy makers, and the general public, that future research, investment and policy making efforts in VTOL development should be mainly directed at battery electric, rather than fuel cell and internal combustion engine.Flying cars, essentially vertical takeoff and landing aircraft (VTOL), are an emerging, disruptive technology that is expected to substantially reshape future transportation. VTOLs provide high-speed point-to-point transportation services that are faster than any other competing transport mode . For exaDespite the widely recognized potential for their commercialization, there is less consensus on the optimal propulsion technology for VTOLs. In recent years, battery-electric (BE) propulsion systems for VTOLs are gaining interest , much liTotal cost of ownership (TCO) analysis is an effective method for investigating the economic performance of competing technologies. It calculates all costs occurring in the life cycle of research objects  and is widely employed in forming technology roadmaps in the transport sector . By usinAs mentioned in the Materials and methods section, this study calculates the TCO of VTOLs under different technologies and applications using (1) the VTOL technological model that simulates flying processes and estimates component sizes, and (2) the cost model that systematically calculates all aspects of VTOL operation costs. We first observed the competition among different propulsion technologies by using TCO estimates. For this, six competing technologies were defined based on propulsion technology  and the fuel type used (fossil energy [FE] based and renewable energy [RE] based). FE includes natural gas-powered electricity, natural gas-derived hydrogen, and gasoline for BE, FC, and ICE, respectively. RE includes renewable-based electricity, renewable-based green hydrogen, and sustainable aviation fuel for BE, FC, and ICE, respectively. Besides the assumed FE and RE scenarios, the results under more realistic electricity energy scenarios (e.g. the US grid) are provided in Figure Figure The 2025 scenario represents technology competition under near-term, predictable technology improvements. Significant changes can be expected under this scenario due to improvements in battery energy density and reductions in RE cost. As indicated by the blue color in Fig. In the 2030 scenario, further technology improvements are expected. Accordingly, BE-RE (blue color) consolidates its TCO advantage in short- and medium-range transport and further expands its advantage into the long-range application domain, replacing the role of ICE-FE entirely.Fully developed powertrain and energy technologies are assumed in the 2050 scenario. Here, FC-RE emerges as a TCO-competitive technology in the long-range transport domain, covering the typical application of air vans, as indicated by the green color in Fig. The sensitivity of TCO-advantageous technology results to the changes in battery life, VTOL life, FE energy price, RE energy price, etc., is provided in Figures In the baseline scenario with reference technology improvements, both battery and fuel cell technologies are undergoing rapid technology changes, making technology projection extremely difficult and, under some circumstances, misleading. While high confidence can be attached to the current and near-term analyses (2020 and 2025 scenarios), there is high uncertainty in the long-term analysis (2050 scenario), especially when considering that the prevalence of BE-RE VTOL only rests on a marginal TCO advantage over other competing technologies, as indicated by the light shading in Fig. Figure In the context of advanced development for both battery and fuel cell technologies Fig. B, the coAs shown in Figs. As another part of VTOL manufacturing cost, powertrain cost (light blue color) imposes a far smaller impact than the VBM cost. Powertrain costs account for less than 1% of their respective total costs for most applications for FC and ICE technologies. The powertrain cost of BE plays a similar role under short-range and low-capacity applications. However, the powertrain cost of BE increases significantly for long-range and high-capacity applications, primarily due to the large battery capacity required to sustain the flight. For example, for air vans, the powertrain cost of BE is $0.01/passenger-km in 2025 and $0.007/passenger-km in 2050, about 10 times higher than for FC and ICE. This explains why BE shows a lower TCO competitiveness in such applications.Energy cost (orange color) is the key factor in determining the market competitiveness of emerging technologies in road transport , 24. It Piloting and infrastructure costs (yellow and dark green colors) are relatively insensitive to VTOL technology development. For example, for short-range air taxis, these two cost components add up to approximately $0.25/passenger-km to $0.30/passenger-km for BE, FC, and ICE in 2020, accounting for approximately 14–45% of total costs. In the long run, piloting and infrastructure costs gradually become the dominant share of TCO due to the substantial reductions in VBM, maintenance, and energy costs. In 2050, these two cost components account for around 60% of the TCO for short-range air taxis, implying substantial changes in the relative importance of these costs.This analysis also shows the sensitivity of the TCO estimates to the changes in VTOL technical parameters, including cruising speed, cruising altitude, and energy reserve amount. TCO estimates tend to be sensitive to parameter changes under immature BE and FC technologies. The upper bounds of the TCO uncertainty range of BE and FC short-range air taxis in 2020 are $0.76/passenger-km (35% higher than baseline estimate) and $2.48/passenger-km (18% higher than baseline estimate), respectively. In contrast, the change is only 0.2% for ICE. This uncertainty can be mitigated with increased technical maturity. In 2050, the TCO uncertainty ranges for both BE and FC are within 2% of their respective baseline estimates.Notably, VTOL technical parameters are highly related to regulations. In this research, the baseline assumptions of VTOL technical parameters are mainly based on current Federal Aviation Administration regulations see . The senThe sensitivity of TCO estimates to a wider range of parameter changes is provided in Figures Although VTOL has a wide range of applications, these applications are not equally important in commercial terms. Thus, it is crucial to identify the commercially prioritized applications so as to further differentiate the relative importance of the propulsion technologies. Thus, in this study, we estimated the passenger fares required to achieve the target return on investment (ROI) so as to evaluate the commercialization priorities of VTOL applications Fig. . An ROI The long-range applications in the near term are only economically viable with ICE propulsion technology, with passenger fares of $2.20/passenger-km for long-range air taxis and $1.08/passenger-km for air vans required to reach 10% ROI. In the long term, BE, FC, and ICE show similar profitability. In order to achieve 10% ROI, the required passenger fare for BE-RE air vans in 2050 is $0.50/passenger-km, 83% higher than that for business class high-speed rail . The devAs expected, an improved load factor—an increase in the number of passengers that can be carried per unit flight—reduces the passenger fare required to reach the same ROI. When the load factor of air taxis is increased from 50%  to 75% , the passenger fares required to meet 10% ROI in 2025 are lowered to $1.05/passenger-km for BE-RE short-range air taxis and $1.34/passenger-km for BE-RE long-range air taxis. These reductions are more than 30% lower than the fares required with the baseline load factor. In addition, these fares are even lower than the required passenger fare for private flying cars ($1.36/passenger-km), which provides air taxis with priority for commercialization. In the early stage of VTOL commercialization, the reduction in required passenger fare through improved load factor is important, since it lowers the economic bar for consumer participation and could induce an earlier commercial breakthrough.While an ROI of 10% is sufficiently profitable for conventional transport business, VTOLs can achieve a much higher ROI with passenger fares set at reasonable levels, especially under short-range applications. The ROI of BE VTOLs in 2030 can reach 13% for private flying cars, 10% for short-range air taxis, and 67% for airport shuttles when the passenger fare is set at 2.5 times the fare of economy ride-sharing, which is acceptable from the consumer perspective considering the substantial improvement in commuting efficiency . The valOur comprehensive TCO analysis of VTOLs shows that both the development of VTOL business as a whole and the internal competition among different propulsion technologies rely critically on technology development and application scenario. Below we discuss some of the most important implications.In this analysis, we demonstrated the high-return potential for short-range VTOL applications. We confirmed the potential of VTOLs to reshape future short-range transport, particularly intra-city and short-range inter-city transport. BE is a compelling propulsion technology with a TCO advantage for short-range applications under most of the scenarios in this study. FC and ICE are preferred for long-range VTOL applications, although they face competition from high-speed rail and show neither service nor economic advantages. Therefore, our findings show that BE will be the dominant propulsion technology for future VTOL applications providing short-range services, while FC and ICE could be used for long-range VTOL applications.As per the results of the TCO analysis, battery technology is an essential aspect of future BE VTOL development and determines the evolution of technology competition. Currently, battery technology development is mainly driven by the large-scale deployment in on-road vehicles. Central to the development of on-road vehicle batteries is a low battery cost that can make the price of electric vehicles acceptable to consumers. However, battery energy density is a relatively lower priority for on-road vehicles, as demonstrated by the prevalence of lithium iron phosphate batteries. In the case of VTOLs, the tradeoff among battery attributes can develop in a different way. Although safety is the main priority , batteryThe economic feasibility of VTOLs, especially in the near term, can be increased by reducing VBM cost. On the one hand, the VBM cost is highly sensitive to VTOL weight due to the utilization of high-cost materials such as titanium alloy , which aUnmanned autonomous driving could also boost VTOL development. As shown in the analysis, piloting cost is expected to become the largest share of operating cost in the long term. Thus, adopting advanced unmanned autonomous driving systems for VTOLs, which would eliminate the need for pilots in most cases (except for rough weather conditions), could significantly reduce piloting costs. In addition, eliminating the pilot need leaves another seat available for passenger transport, bringing additional profits to operators. An additional passenger seat could substantially increase passenger capacity (e.g. a one-third increase for a four-seater air taxi), and thus, eliminating the pilot need is particularly important for low-capacity VTOL operation.High-capacity applications could be considered a priority for VTOL commercialization due to the low passenger fare required to sustain a profit. In 2021, airlines worldwide carried more than 2.2 billion passengers , 33, mosHigh profitability of high-capacity applications demonstrates the great potential of VTOLs for large-scale deployment. In addition, additional congestion in VTOL transportation is less likely to occur with the large-scale deployment of VTOLs. On the one hand, there is more room for VTOL operation as VTOLs can fly at different altitudes without road network constraints in a three-dimension transportation system. On the other hand, VTOLs are expected to get more regulatory supports on their operation, with VTOL development strategies being proposed by many countries such as China, considering that a three-dimension transportation system could improve the transportation efficiency of road traffic .Low-capacity VTOL applications could further enhance privacy and comfort, serving as a one-of-a-kind transport service that none of the existing transport modes can provide. Intra-city or short-range inter-city trips could be suitable for air taxis, considering that airlines hardly run such short-range routes, high-speed rails lack privacy, and on-road taxis and buses have much lower transport efficiency. This provides an opportunity for a high-fare, differentiated-service strategy. Ensuring high load factors is also essential for these low-capacity applications. In addition, user behavior strategies, such as encouraging passengers to carpool, can be considered to lower fares.For VTOLs, a flight can be divided into five sub-phases: hovering, climbing, cruising, descending, and landing. While hovering and landing, VTOLs take off and land vertically. While climbing and descending, VTOLs fly to or descend from the cruising altitude at the angle of attack (AOA). While cruising, VTOLs fly horizontally with a certain cruising speed at the cruising altitude. Based on each sub-phase's kinematic and dynamic characteristics, the power demand in each sub-phase can be obtained Eqs. .Phover, Pclimb, and Pcruise are the power demand of VTOLs while hovering/landing, climbing/descending, and cruising, respectively (kW);where mTO is the maximum takeoff mass (kg);ηhover, ηclimb, and ηcruise are the propulsion system efficiencies while hovering/landing, climbing/descending, and cruising, respectively;δ is the disk loading (N/m2);ρ is the air density (kg/m3);ROC is the rate of climb (m/s);L/Dclimb and L/Dcruise are the lift-to-drag ratios while climbing/descending and cruising, respectively;α is the AOA (degrees);g is the acceleration due to gravity (m/s2);V is the cruising speed (m/s).Total energy consumption is the sum of the energy consumption in each sub-phase, which is obtained by multiplying the power demand with the duration of time in each sub-phase Eq. . For hovEc and Er are total energy consumption and the energy reserve, respectively (MJ);where Pi is the power demand of sub-phase i (kW);ti and tadd are the duration of time in sub-phase i and the additional cruising time, respectively (s);di is the flying distance in sub-phase i (m);Vi is the velocity in sub-phase i (m/s).In this study, the components of structure, energy storage device, and drive system are sized to meet the requirements of range, payload, and power capacities, as calculated in the flying process modeling part. For BE VTOLs, batteries serve as the energy storage device, providing electricity for electric motors to drive the tilt-rotors. For FC VTOLs, fuel cells convert hydrogen stored in tanks into electricity to drive electric motors and tilt-rotors. For ICE VTOLs, fuel from fuel tanks is combusted in internal combustion engines to generate kinetic energy, which is transmitted through mechanical transmission systems to drive tilt-rotors.The takeoff mass of VTOLs is divided into four parts: structural mass, payload mass, energy storage device mass, and drive system mass Eq. . To meetmES is the energy storage device mass (kg);where mDS is the drive system mass (kg);mS is the structural mass (kg);mP is the payload (kg);ρenergy is the energy density of energy storage devices or fuels (Wh/kg or MJ/kg);ρpower is the power density of the drive system (kW/kg);RatioST is the ratio of the mass of structure for VTOL body frame to the takeoff mass;RatioSB is the ratio of the mass of the structure for battery protection to the battery mass.For operators, the TCO of VTOLs consists of VBM; powertrain; energy; piloting; infrastructure; taxes, fees, and insurance (TFI); maintenance costs; and the salvage value. For every year in a VTOL's life, the yearly cost  is discounted to the present value using the discount rate Eq. . The TCOCjt is the cost of item j that occurs in year t ($);where PYCt is the present value of the yearly cost in year t ($);DR is the discount rate;TCO is the total cost of ownership ($/passenger-km);VL is the VTOL life (yr);NP is the number of passengers ;RatioP is the load factor;T is the yearly operation time (h).The VBM cost, which occurs in the first year of VTOL life, is calculated using an empirical formula Eq.  15). Th. Th15). t ($);where CVBM−B and CVBM−V are base and variable VBM costs ($ and $/kg);PDS is the power of the drive system (kW);UCES is the energy storage device cost ($/kg or $/kWh);UCDS is the drive system cost ($/kW);Q is the battery capacity (kWh);pE is the energy price ;R is the range traveled per mission (km);RatioDE is the dead-end ratio;RatioTFI is the ratio of the TFI cost to the direct operation cost;RatioM is the ratio of the fixed maintenance cost to the VBM cost;UCVM is the variable maintenance cost ($/h);RatioSV is the ratio of the salvage value to the VBM cost.The ROI is calculated by dividing the average yearly profit by the total cost Eq. . The yeaROI is the return on investment;where PYPt is the present value of yearly profit ($);pP is the passenger fare ($/passenger-km).The infrastructure cost consists of vertiport construction, vertiport operation, refueling facility construction, and refueling facility operation costs Eq. . VertipoWhere t ($);VPL and RFL are vertiport and refueling facility life, respectively (yr);RatioRFM is the ratio of refueling facility maintenance cost to refueling facility construction cost;NLBR is the number of staff;UCLBR is the unit labor cost ($/h).This study estimates energy price by using a levelized cost approach, which is obtained by dividing the present value of total cost by energy production volume Eq. . The totpk is the price of energy k ;where t ($);PVk is the annual production volume of energy k .In this study, the sensitivity of the TCO estimates to changes in VTOL technical parameters  and cost parameters  is analyzed using Monte Carlo simulation. The sensitivity of the TCO estimates to changes in cruising speed, cruising altitude, and energy reserve amount is used in Figs. pgad019_Supplementary_DataClick here for additional data file.


Borderline personality disorder (BPD) is a severe mental disorder frequently seen in individuals with recurrent self-harm behaviour. To what extent there are distinguishing characteristics between self-harming adolescents who meet the criteria for a full diagnosis of BPD, a sub-threshold number of BPD criteria and those who don’t have BPD, with respect to clinical characteristics, is still uncertain and could have important clinical implications.Data from 103 adolescents with recurrent self-harm behaviour recruited from child and adolescent psychiatric outpatient clinics were collected through clinical interviews and self-reports. Bivariate analyses comparing participants with or without a diagnosis of BPD were performed. Group differences based on the number of BPD criteria fulfilled  were tested and regression analyses performed.Adolescents with a diagnosis of BPD (28.2%) had significantly higher numbers of co-morbid DSM-5 disorders, suicide attempts and self-harm methods. They also reported significantly higher levels of suicidal ideation, depression, anxiety and impulsivity, compared with adolescents without BPD. Adolescents with sub-threshold BPD (20.4%) place themselves in the intermediate position between participants with full-syndrome BPD and participants with few-if-any BPD, in terms of these symptoms. Higher levels of emotional regulation difficulties and a lower level of global functioning were significantly associated with fulfilling a higher number of BPD criteria.Adolescents with recurrent self-harm who meet diagnostic criteria for a full-syndrome BPD or sub-threshold BPD seem to have difficulties within the same spectrum. They seem dimensionally, but not categorically, different with respect to the severity of their difficulties. These adolescents need interventions aimed at their dysfunctional self-harm behaviour, emotional regulation difficulties and BPD symptoms at an earlier, rather than at a later stage of symptom development. Borderline personality disorder (BPD) is a severe mental health disorder frequently seen in individuals with recurrent suicide attempts and non-suicidal self-injury (NSSI); these are both behaviours subsumed under the broader term ‘self-harm’ . BPD is Although recurrent self-harming behaviour is a frequently observed feature of BPD in adolescence , previouThere is accumulating evidence that BPD often has it’s onset in the period between puberty and late teens and that many of the problems associated with a full-syndrome BPD emerge early in the course of the disorder, and that diagnosis and treatment are rarely offered at such an early stage . This isIn recent years, research has shown that early symptoms of BPD in adolescents are unlikely to disappear without intervention, suggesting that clinicians should systematically screen for these symptoms . The pheTo the best of our knowledge, there is still a limited amount of studies in clinical adolescent populations with recurrent self-harm behaviour and BPD features, and especially studies that focus on differences between sub-threshold BPD and full-syndrome BPD difficulties. Although more research has recently emerged finding that BPD starts early and that it is possible to diagnose BPD in adolescence, these are mainly studies from adult samples, and studies that focus on differences between having a diagnosis of BPD or not. In adolescent outpatient clinics, most clinicians will at some point meet children and adolescents with recurrent self-harm behaviour and BPD features, and they need relevant and updated knowledge about the population they will encounter. Thus, there is a need of studies from clinical adolescent samples, with recurrent self-harm behaviour and with varying degrees of BPD difficulties, which could have important implications for treatment planning.In the current study we adopted a 3-point dimensional convention , and utilized the regular DSM-5 algorithm [ “intentional poisoning or self-injury, regardless of intention to die” [Participants were 103 adolescents (age 12—18) recruited from a child and adolescent psychiatric outpatient clinic at Oslo University Hospital, Norway. Inclusion criteria were recurrent self-harm behaviour (two or more episodes) with the last episode within the past 6 months. Exclusion criteria were intellectual disability or insufficient Norwegian language skills to participate in the study. Potential participants were screened for self-harm behaviour defined as to die” . Non-sui to die” , while s to die” . The adoSuicide Attempt Self-Injury Interview (SASII); a robust and comprehensive instrument with good psychometric properties [Schedule for Affective Disorders and Schizophrenia for School-Age Children-Present and Lifetime Version, 2016 (K-SADS-PL) [Childhood Interview for Borderline Personality Disorder (CI-BPD), [n = 13) was performed by two independent raters, resulting in a kappa value of 0.82 for the BPD diagnosis, which is generally considered a satisfactory level of reliability [Information on frequency of non-suicidal self-injury episodes (NSSI), suicide attempts and self-harm methods in use, age of self-harm onset and duration of self-harm behaviour, was collected with the operties , 29. DSMSADS-PL)  whereas CI-BPD), ; a semi-CI-BPD), . The 9 ciability . Testingiability .Children’s Global Assessment Scale (CGAS) [MADRS [The level of global psychosocial functioning was assessed by the e (CGAS)  with a s) [MADRS , whereas) [MADRS .Borderline Symptom List (BSL-23), [Difficulties in Emotion Regulation Scale (DERS), [Barratt Impulsiveness Scale (BIS-15), [Suicidal Ideation Questionnaire (SIQ-Jr) [Hopkins Symptom Checklist (HSCL) [Borderline symptoms were assessed through the 23-item self-report BSL-23), , tapping (DERS), , whereasBIS-15), . Level o(SIQ-Jr) . Level ot (HSCL) .Means and standard deviations are given for normally distributed variables, while median and inter-quartile ranges are given for variables with non-normal distribution. As is commonly observed in clinical studies of self-harm, number of NSSI episodes and suicide attempts are not normally distributed and had several extreme outliers, hence these variables were winsorized at the 90 percentile to test whether this would significantly change the mean and variance , which ip-value less than 0.05 were selected for inclusion in these analyses. The selection of variables was further supported by the correlation matrix and collinearity tests (data not shown) indicating no severe signs of multi-collinearity. All tests were two-sided, and the significance level was set to 0.05. Analyses were performed with SPSS 27.0.First, bivariate analyses were performed based on whether the participants met the criteria for a diagnosis of BPD or not, and differences between the two groups were tested using chi-square for categorical variables and independent-sample t-tests for continuous variables. Effect sizes with Cohen's d for sample means and Phi for bivariate statistics were calculated. Second, based on the number of fulfilled BPD diagnostic criteria, participants were analysed in the following three subgroups: 1) participants with few-if-any BPD (fulfilling 0–2 BPD criteria), 2) participants with sub-threshold BPD (fulfilling 3–4 BPD criteria) and 3) participants with full-syndrome BPD (fulfilling 5–9 BPD criteria). One-way ANOVA with post-hoc test for multiple comparisons and Bonferroni correction was performed to compare the effect of the number of BPD criteria met on the selected variables. Finally, logistic and linear regression analyses were performed, and variables with A total of 103 adolescents with a mean age of 15.9 years (SD = 1.47), the majority (86.4%) identifying as female, participated in the study. There were no significant differences in age of self-harm onset or age between groups. Participants received a mean number of 3.2 (range = 0–8) DSM-5 diagnoses with mood disorders and anxiety disorders being the most prevalent , anxiety symptoms, suicide ideation or borderline symptoms measured by BSL-23.Finally, in the logistic regression analysis Table  a higherIn this study of adolescents with recurrent self-harm behaviour, we aimed to examine the extent to which there are differences between adolescents with or without a BPD diagnosis, and between adolescents who fulfilled different numbers of BPD criteria , with respect to self-harm behaviour and related clinical characteristics. The three main findings of this study were that, a) the largest number of significant differences were found between adolescents with a diagnosis of BPD and adolescents without a BPD diagnosis, b) when comparing subgroups of adolescents with different number of fulfilled BPD-criteria, adolescents with a sub-threshold BPD (3–4 criteria) appeared as an intermediate group between adolescents with a full-syndrome BPD (5 or more) and adolescents with few-if-any BPD criteria (2 or less), and c) higher levels of emotion regulation difficulties and a lower level of global functioning were significantly and multivariately associated with a higher number of BPD criteria fulfilled.Differences between adolescents with or without a diagnosis of BPD.Adolescents with a diagnosis of BPD displayed significantly more signs of mental health problems compared with adolescents without BPD, such as a significantly higher number of comorbid DSM-5 disorders and a significantly lower level of global functioning. This corresponds well with earlier clinical studies suggesting that adolescents with BPD have more psychiatric comorbidity and are more severely impaired , compareAlso, in the current study, adolescents with a diagnosis of BPD reported a higher number of previous suicide attempts and a higher level of suicide ideation. Furthermore, they had used a higher number of self-harm methods, and had a significantly longer duration of self-harm behaviour than adolescents without BPD. These findings suggest that, even within a clinical sample of adolescents where all participants had recurrent self-harm behaviour, participants meeting criteria for a BPD diagnosis have significantly more severe suicidal and self-harming behaviour.Differences between adolescents with full-syndrome BPD, sub-threshold BPD and few-if-any BPD criteria.Adolescents with sub-threshold BPD reported significantly lower levels of emotion regulation difficulties, suicide ideation and anxiety symptoms compared with adolescents with full-syndrome BPD, but they had significantly more comorbid DSM-5 diagnoses and higher levels of suicide ideation than adolescents with few-if-any BPD criteria. Thus, adolescents with sub-threshold BPD place themselves in the intermediate position between participants with full-syndrome BPD and participants with few-if-any BPD criteria, in terms of these symptoms suggesting. This suggests that adolescents with sub-threshold BPD and full-syndrome BPD do not belong to categorically different groups, but rather dimensionally different sub-groups. The same can be said about the relationship between the sub-threshold group and the few-if any group, where only levels of impulsivity and global functioning were significantly different. However, it is important to keep in mind that all participants in this study sample had reported history of recurrent self-harm behaviour, signifying a high level of psychological distress. We don't know whether a group of adolescents without recurrent self-harm, but with few-if-any BPD criteria, would have been equally impaired.Interestingly, we found no significant differences between adolescents with sub-threshold BPD and full-syndrome BPD in terms of number of NSSI episodes, number of suicide attempts or number of self-harm methods. We don't know yet exactly what roles self-harming behaviour may play in the pathogenesis of BPD. Hypothetically, an addictive pattern of self-harm as a strategy to regulate emotions may reinforce latent vulnerabilities among more sensitive and emotionally reactive adolescents and pave the way for development of other BPD related pathology. In this case, early detection of recurrent self-harm behaviour and a reduction of the duration of untreated self-harm could increase our ability to prevent, or even perhaps reverse, a process of developing a full BPD diagnosis through offering adequate treatment at an earlier stage. Our study cannot address this question empirically, and we are not aware of any research that has investigated this hypothesis. At the current state of knowledge, we would regard it important to target the group of adolescents with sub-threshold BPD that is very similar to full-syndrome BPD in many areas, for further study. However, we do not know how much of the sub-threshold symptoms will naturally persist or progress to full-syndrome BPD and how much will fade and disappear. Nor do we currently know how decisive it will be for the prognosis if these adolescents receive BPD-specific treatment, and only follow-up studies of this patient group will be able to answer this.There is still a limited amount of studies in clinical adolescent self-harm populations that focus on differences between sub-threshold BPD and full-syndrome BPD difficulties. Thus, our study adds to the current knowledge concerning distinguishing characteristics between adolescents with recurrent self-harm behaviour and different number of BPD criteria fulfilled, and we believe this is important knowledge for clinicians regarding treatment planning for these patients.In our multivariate analyses, we found that emotional dysregulation and global functioning were strongly predictive of BPD in this material. These findings are in accordance with previous studies , 19 and Strengths of this study are that it is based on a clinical sample of adolescents with recurrent self-harm behaviour and that structured diagnostic interviews were used to assess all DSM-5 diagnostic criteria which enables reliable comparisons between studies. Interview-based assessments of BPD were checked for interrater reliability with satisfactory results. Among the limitations, are the study's cross-sectional design precluding longitudinal or causal conclusions. Also, the limited number of males (13.6%) included in the sample preventing us from generalizing these findings to males in clinical populations. Our findings, however, may be generalized to other similar clinical adolescent populations with recurrent self-harm behaviour. Future studies with more diverse samples and with a longitudinal design would improve confidence in the results, as well as generalizability to a broader clinical population.Consistent findings are that adolescents with recurrent self-harm behaviour with a diagnosis of BPD show a greater total burden of symptoms compared with the adolescents without BPD. Adolescents with sub-threshold BPD compared with full-syndrome BPD show difficulties much in the same areas, and seem dimensionally, but not categorically different with respect to the severity of their difficulties. Consequently, clinicians need to be aware that adolescents with recurrent self-harm behaviour may also have difficulties consistent within the BPD spectrum, and that these adolescents need interventions aimed at the recurrent self-harm behaviour and BPD specific difficulties at an earlier, rather than a later stage of symptom development.


With the development of sequencing techniques, the discovery of new proteins significantly exceeds the human capacity and resources for experimentally characterizing protein functions. Localization, EC numbers, and GO terms with the structure-based Cutoff Scanning Matrix (LEGO-CSM) is a comprehensive web-based resource that fills this gap by leveraging the well-established and robust graph-based signatures to supervised learning models using both protein sequence and structure information to accurately model protein function in terms of Subcellular Localization, Enzyme Commission (EC) numbers, and Gene Ontology (GO) terms.We show our models perform as well as or better than alternative approaches, achieving area under the receiver operating characteristic curve of up to 0.93 for subcellular localization, up to 0.93 for EC, and up to 0.81 for GO terms on independent blind tests.https://biosig.lab.uq.edu.au/lego_csm. In addition, all datasets used to train and test LEGO-CSM’s models can be downloaded at https://biosig.lab.uq.edu.au/lego_csm/data.LEGO-CSM’s web server is freely available at  Proteins are versatile macromolecules that orchestrate most biological processes (BPs). Advances in next-generation sequencing techniques generated an explosion in the number of novel sequencing data available with potential for biomedical and pharmaceutical applications, including a better understanding of links between disease and evolutionary processes , and MF, respectively, using only sequence information . A compaStructural information was incorporated into the feature set to build predictive models for EC and GO terms and achieved compatible performance , even haWe compared LEGO-CSM subcellular localization with three alternative methods  using a Our model for EC prediction was compared with four state-of-the-art methods currently available : ECPred,Fmax  subcellular localization, (2) enzymatic function, and (3) gene ontology terms, which leverage both protein sequence and structure information. To ensure its effectiveness, we compared LEGO-CSM to alternative methods and demonstrated our models performed as well as or better than state-of-the-art approaches. LEGO-CSM is freely available as an easy-to-use web-based platform at btad402_Supplementary_DataClick here for additional data file.