Behavioral activation (BA) is a structured psychotherapeutic approach for the treatment of major depressive disorder (MDD), which aims at increasing the engagement in activities that might bring enjoyment and meaning to patients’ lives. Although a growing body of evidence supports the effectiveness of BA, enhancing the motivation and activity level of depressed patients is often challenging. In the present study, we explored the effectiveness of a brief BA treatment supported by virtual reality (VR) to facilitate the visualization and anticipation of four pleasurable activities that we tried to re-introduce in the patients’ daily routine. To do so, we conducted a single-case experimental design with multiple baselines in a sample of patients with moderate to severe depressive symptoms. Three overlap analyses across participants and across behaviors were conducted to calculate the rate of improvement of each patient after the delivery of the intervention. Across the three overlap indices, the participants generally showed moderate-to-large improvements in the level of daily activity, as well as in the time spent planning and/or engaging in one or more activities scheduled during the intervention. Furthermore, most patients also reported a moderate-to-large reduction in daily depressive symptoms and improved mood. Overall, the promising results of the present study suggest that the proposed VR-based BA intervention might represent a valid approach to behaviorally activate depressed patients. The barriers and future lines of research of this innovative field are discussed.  Major depressive disorder (MDD) is a debilitating condition affecting 4.4% of the general adult population . MDD is Among other symptoms, depressed patients have been shown to experience loss of interest and pleasure in daily activities, which often results in the avoidance of pleasant situations and, therefore, in high social isolation. In this vein, negatively biased expectations towards the future have been suggested as a key feature of depressive future-oriented cognition . DepressSo far, a wide range of evidence-based treatments have been developed to target MDD symptoms. Among others, behavioral activation (BA) is a structured psychotherapeutic intervention that aims to increase the patients’ engagement in adaptive daily activities while decreasing the engagement in behaviors that potentially maintain or prolong depressive symptoms . The maiOver the past decades, a growing body of evidence has supported the effectiveness of BA for the treatment of MDD , and stuVirtual reality (VR) has been extensively used for the treatment of several psychological conditions ,21,22 anThe aim of this study was to test the effectiveness of a four-session, VR-assisted BA protocol for individuals with moderate-to-severe depressive symptoms. The novelty of this brief intervention relies on the use of VR to provide patients with a virtual spatial reference in which to visualize and anticipate personally pleasurable and rewarding activities to be reintroduced in their daily routines. Consistent with the ample evidence showing the effectiveness of BA to activate depressed patients ,13,14,15In the present study, we conducted a single-case multiple baseline experimental design , a type Consistent with the guidelines , three dclinicaltrials.gov (accessed on 14 January 2022) database (NCT05138744). Informed consent was obtained from all participants.The present study was approved by the ethics committee of the Jaume I University (number: CD/103/2021). The study was registered in the To be eligible for the study, participants had to meet the following criteria: being aged between 18 and 65 years, scoring more than 10 at the Patient Health Questionnaire-9 (PHQ-9) ,45  , such asThe study was conducted at the Psychological Care Center of the Jaume I University (Spain). In total, eight participants meeting the inclusion criteria were recruited to take part in the study. One participant completed 45% of the daily assessments retrospectively and was therefore excluded from the analyses. The final sample included seven participants . According to the baseline PHQ-9 scores and existing cut-offs , three pAll participants met the criteria for a MDD, as assessed with the MINI. Participant 4 and participant 7 were receiving a pharmacological treatment for depression at the time of the study. However, as the medication had not been introduced or changed over the last year, both participants were included in the study. The protocol consisted of four VR-based BA sessions, which were delivered twice a week over two weeks. Each session lasted between 30 and 45 min. Before the beginning of the study, participants were asked to select four activities from a predetermined list during a face-to-face meeting see , which tThe use of VR was included in the protocol to provide participants with a virtual spatial reference in which to place themselves while visualizing and virtually experiencing the chosen activities. With this aim in mind, we used the Google Earth VR application for Oculus Rift, which allows participants to travel to the place associated with a selected activity and experience the sense of “being there” from a first-person perspective. Participants were asked to identify a specific place associated with the activity to be performed in VR . The virtual scenario was entered by means of a head-mounted display  connected to a laptop . The setup also included two sensors and two hand-controllers, which enabled participants to move and explore the environment.Each session was structured as follows. First, the participants were invited to wear the head-mounted display to start the VR experience. They were guided through the experience by means of a narrative see , which wBehavioral outcomes: Using a Visual Analogue Scale (VAS) ranging from 0  to 100 (extremely), participants were asked to rate their daily activity level (“To what extent have you engaged in activities and be activated today?”) and the rate of savoring during the performed activities . In addition, the short form  of the BAffective outcomes: Similar to previous studies , particiSpecific activity-related outcomes: For each of the four activities included in the protocol, participants were asked to rate their level of motivation on a VAS ranging from 0  to 100 (extremely) (“To what extent do you feel motivated to perform this activity?”). In the daily assessment, participants were also asked to report the time spent  practicing or planning each activity .Participants were recruited at the Jaume I University through poster advertisements offering a VR-based brief training for depression. The individuals interested in the study were sent a web-based survey to complete the PHQ-9 and the PANAS. If inclusion criteria were met, participants were invited to attend the laboratory to further explore the exclusion criteria through the administration of the MINI. If all the criteria were satisfied, participants were asked to sign the informed consent and invited to join the study. During this first face-to-face session, participants were also guided through the selection of the personalized activities to be included in the protocol see . The parThe participants were provided with an email to get in contact with one of the researchers of the team in case of technical problems or doubts about the protocol. When missing data on two consecutive daily assessments were detected, the participants were contacted and were reminded about the importance of completing the diary. All the participants attended the four BA sessions, so no dropout was observed during the study.At the end of the study, the participants were invited to attend the laboratory for a final debriefing session.The present study is a SCED with multiple baselines across participants  and across behaviors .To test the hypotheses related to the behavioral and affective outcomes, we performed several data overlap methods that have been widely used in the SCED literature , includiTo test our hypotheses regarding the specific effect of the intervention on each of the four chosen activities, NAP analyses across behaviors were performed. More specifically, each session was considered as a replication of the intervention targeting a different behavior. In other words, each participant had four different baselines, representing the four activities selected at the beginning of the study. For each participant, NAP analyses for each of the four activities were performed by comparing baseline-to-post-treatment changes of the specific activity-related daily measures.First, we performed analyses to explore whether the intervention produced a significant change in terms of both behavioral and affective measures. As shown in Regarding the affective measures see , the NAPWe therefore explored whether the intervention produced a significant change in the variables assessing each of the activities included in the protocol .All participants showed a moderate-to-large improvement in the time spent performing or planning at least one of the four activities. More specifically, five out of seven participants significantly increased the daily time spent performing one or more of the selected activities. One participant reported a significant increase in the time spent practicing three of the four activities, two participants significantly increased the time spent practicing two activities, and one participant showed a significant increase in the time spent practicing one activity. Importantly, the two participants who did not report a significant increase in the time spent practicing the chosen activities (participant 1 and participant 4) did show a large-to-moderate improvement in the time spent planning four and two activities, respectively.Regarding motivation, only four out of seven participants reported increased rates of motivation to engage in one or more of the activities planned during the intervention.So far, a growing body of research has shown the efficacy of BA for the treatment of depression. In the present study, we used the Google Earth VR application to introduce the use of VR into a brief BA-based protocol and provide participants with a virtual spatial reference in which to visualize and experience four personalized activities to be re-introduced in their daily routines. All the participants showed a significant clinical gain on at least one of the two variables assessing daily activation level, thus suggesting the short-term effectiveness of the protocol to behaviorally activate the participants. Furthermore, most participants reported increased daily rates of savoring. In other words, the VR intervention was not only associated with an adaptive behavioral change, but also with a significant improvement in the quality and enjoyment of the daily activities in more than half participants. Importantly, these findings and those of the remaining outcomes were generally consistent regardless to the overlap index used, with the exception of one participant . These behavioral findings are further strengthened by the secondary analyses designed to explore the behavioral changes in the specific activities included in each participant’s protocol. Indeed, all participants reported a moderate-to-large improvement in the time spent planning and/or practicing one or more activities scheduled during the intervention. The fact that not all the participants significantly increased the engagement in at least one activity should not be considered as a negative outcome. Indeed, the goal of BA interventions is not to abruptly re-introduce an activity into a patient’s daily routine. Rather, to gradually involve the patient in the steps needed to engage in a specific behavior, following his/her personal needs and level of confidence . ConsideBesides, significant improvements in terms of mood and depressive symptoms were also observed in most patients taking part in the study, and results were generally coherent irrespective to the index used. Importantly, one participant (participant 4) did not show any significant gain in any of the affective measures according to the NAP and PEM analyses. This might be explained by the fact that this participant had the lowest average baseline daily mood  and the lowest average baseline PHQ2 score  from the whole sample , thus being approximately 1 SD below the sample average scores. In other words, his baseline was defined by a relatively high average daily mood and very low average depressive symptoms. Accordingly, there might have been less room for clinical improvement, consistent with past research . It is iIn addition to the findings on mood and depression for patient 4, we also did not observe the expected results in terms of motivation. This might be explained by the design of the study. It might be the case that, although behavioral activation did occur after the administration of the treatment, the enhancement of the patients’ motivation would have required more time and practice. A more intense training over a longer period of time might have been more effective to also increase patients’ motivation.Overall, the encouraging findings of the present study suggest that our VR-based BA intervention might represent an effective tool to target depressive symptoms, at least in the short term. Unlike traditional BA treatments, the main novelty relies on the use of VR. First, the use of VR has been shown to increase patients’ engagement and motivation towards a treatment which, in turn, might enhance its effectiveness and reduce dropout rates ,65. EvenThe present study is not free of limitations. First, the sample was mainly composed of female undergraduate students. Future studies are needed to replicate the present findings in a more heterogeneous sample. Additionally, although the use of VR can represent an added value, the specific use of the Google Earth VR application might have limited the possibilities offered by our intervention. Despite the high level of personalization, patients were asked to select their personally significant activities from a predetermined list, which was created based on previous studies and, most importantly, on the feasibility to virtually visualize the activity through the Google Earth VR application . Third, our sample was mainly composed of individuals suffering from moderate to moderate-severe depressive symptoms, and whether our protocol could be similarly effective in patients with more severe symptoms should be further explored. Finally, further studies are needed to disentangle the role of VR in a BA intervention. This study does not allow conclusion of whether the use of VR as compared to visual imagery to anticipate a future positive activity can actually increase the motivation and likelihood to engage in that situation. In this sense, future studies are needed that focus on the specific added value of VR-based BA protocol as compared to a traditional BA intervention, as well as studies that replicate our findings using a larger number of participants and a more robust experimental design.


Cyberattacks are increasing in both number and severity for private, corporate, and governmental bodies. To prevent these attacks, many intrusion detection systems and intrusion prevention systems provide computer security by monitoring network packets and auditing system records. However, most of these systems only monitor network packets rather than the computer itself, so physical intrusion is also an important security issue. Furthermore, with the rapid progress of the Internet of Things (IoT) technology, security problems of IoT devices are also increasing. Many IoT devices can be disassembled for decompilation, resulting in the theft of sensitive data. To prevent this, physical intrusion detection systems of the IoT should be considered. We here propose a physical security system that can protect data from unauthorized access when the computer chassis is opened or tampered with. Sensor switches monitor the chassis status at all times and upload event logs to a cloud server for remote monitoring. If the system finds that the computer has an abnormal condition, it takes protective measures and notifies the administrator. This system can be extended to IoT devices to protect their data from theft. By 2019, according to PurpleSec  designs IDS, such as Snort  and OSSEWe here simulate physical intrusion detection by a computer with sensor switches, which has future application to IoT devices. We design a physically secure protection system (PSPS) that protects sensitive data and gives an alert when someone attempts to open the chassis or tamper with the computer. This can help prevent data from being stolen or reverse engineered. We describe the PSPS methodology in At run-time, PSPS starts with a computer with sensor switches broken by an attacker, which is classified into physical intrusion types. PSPS then extracts the intrusion behavior received from sensor switches and shows an alert message on the website and sends an email and text message to the administrator. Our prototype PSPS sends an alert message to the administrator directly, or the alert message returned by PSPS can be used as input to traditional IDS to handle different types of intrusion .The balance of this work is organized as follows. This section first introduces the basic concept of computer security in Computer security  is a criVulnerabilities are defects in computer system security that threaten the confidentiality, integrity, availability, and access control of the system or its application data. Most vulnerabilities can be found in the common vulnerabilities and exposures (CVE) database . VulneraIn computer security, a security policy is to reduce possible harm as much as possible by eliminating or preventing threats, or to generate security reports to reduce threats . CounterMost research focuses only on network security, but hardware is also a notable security issue. It may be considered more secure to use devices and methods such as computer chassis intrusion detection, dongles, trusted platform modules (TPM), USB ports disabled, mobile-enabled access, and drive locks. The main focus of this section is computer chassis intrusion detection.(1)Network intrusion detection systems (NIDS);(2)Host-based intrusion detection systems (HIDS),An intrusion detection system (IDS) is software that can monitor systems or a network for malicious activity or policy violations . If an IThese two can identify the attempts to break the security objectives, such as confidentiality, integrity, availability, and nonrepudiation . We focuAn NIDS detects network intrusion activity through a system using data mining. (1)Confirms whether the hacker has successfully intruded;(2)Monitors the activity of a specific host system;(3)Detects malicious activity in real time;(4)Does not require additional hardware equipment.HIDS usually only observe and audit system log files under the Windows NT environment when the monitoring system logs in a UNIX environment for malicious behavior. The abilities of HIDS follow:(1)Low flexibility: All hosts may be installed with different operating platforms, and each operating system has different audit log files, so various HIDS must be installed for different hosts.(2)Restricted monitoring and scanning: HIDS can only see the network packet information received through the host.(3)Additional system resources: System resources of the monitored host are consumed during monitoring, which restricts performance of the host itself.The drawbacks of HIDS are:The main purpose of HIDS is to add security for vulnerable software. In contrast, the main purpose of IDS is to detect intrusion activity and send suspicious records to administrators as alarms. An intrusion prevention system (IPS) is a computer network security device that monitors network packages or network devices. It can adjust, interrupt, or isolate some abnormal network packages in real time. IPS can be described as an extension of IDS since it not only detects suspicious activity, but also takes protective measures. The primary objects of physical security consideration are various hardware devices, equipment room environment, and other material carriers that can also be understood as hardware security. Hardware facilities are the basic conditions for carrying and implementing information system functions, so physical security is also the most direct and primitive attack target. If the server hardware has already fallen into the hands of the attacker, even a strict firewall policy and highly complex operation passwords are made useless. Any access to hardware can lead to security risks and become targets of attacks, for example, if the computer chassis is not closed, backup power is lacking, BIOS has not set a password, or there are disasters such as earthquake, fire, or flood.This section describes an existing IDS in Snort is a network intrusion detection and prevention system (NIDS and NIPS) written in C language. It was created in 1998 by Marty Roesch and is now a well-known open-source program. The state-of-the-art in IDS research is represented in the Institute of Electrical and Electronics Engineers (IEEE) Study on Network Intrusion Detection System (Snort) , which oThe current addresses an aspect of intrusion detection that is not a direct focus of the network packets. We also consider physical attacks where the computer chassis has been tampered with.OSSEC is an open-source HIDS  that monWe focus on the prevention of the intrusion by eliminating all sensitive data when the chassis is opened. Preventing servers from intrusion by malicious network packets has long been part of intrusion prevention systems (IPS) research. Zhang et al. (2004) describe the design of an IPS-based on SNMP .In general, an IPS prevents the server from malicious intrusion by using blacklists to their IP. In contrast, we show how to protect sensitive data when the chassis has been tampered with and to prevent intrusion.This section describes the architecture of the proposed design. Our goal is to propose a physically secure protection system that helps companies to prevent their sensitive data on the server from being stolen by breaking the computer or opening the chassis. Our system is to prevent outsiders or people who are not familiar with the system from stealing sensitive data. To acquire the convenience and lower the operating threshold, we build a website to monitor server status and control the system state.There are four states in this system: initial, operating, maintenance, and locked. The five functions to change the four states are: initialize, maintain, lock, unlock, and protect. The state diagram is shown in The initial state is first in the system, so there are neither data nor programs to monitor and protect the server. The administrator needs to check the system and input the password on the website to initialize the system, starting the operating state, as shown in The operating state is second in the system, where the system protects the server and sensitive data. If the server is tampered with or the chassis is opened, the system monitor detects it and locks the system , and senDuring the operating state, if the administrator wants to upgrade programs or pause the monitor, the administrator inputs the password on the website, changing the system into the maintenance state . WithoutIn the locked state, the monitor continues protecting the server. All sensitive data or programs are removed. The administrator checks the server status to fix the problem, and then inputs the password on the website to unlock the system. After unlocking the system , the sysIn the maintenance state, the system is paused. The administrator can upgrade programs or maintain the server in this state. After maintenance, the administrator should input the password on the website to restart the system and protect the server .This section shows the implementation of the system in detail. (1)Visual C++ 2019;(2)Java 1.8.0;(3)HTML, CSS, JavaScript.We develop this system with six modules: PSPS IntrusionManager, PSPS Defense, PSPS Synchronize, PSPS RecoveryManager, SPS IDSWeb, and PSPS IDSServer. We use the following three programming languages:(1)Microsoft Visual Studio Community 2019;(2)Eclipse IDE for Enterprise Java Developers 2020-03;(3)MySQL Server 8.0;(4)Connector ODBC 8.0;(5)Apache Tomcat 8.5;(6)Hei-diSQL 11.0;(7)FileZilla Server;(8)SyncBack.To develop the system, we use the following compilers and software:The experimental machine is an Advantech DPX-S1435(19) running on Windows 10 Enterprise with the following hardware settings: Intel i7-4770TE 2.3 GHz CPU, 8 GB RAM, and Intel(R) HD Graphics 4600) .There are eight intrusion line inputs in the machine, and we use three sensor switches plugged into three of intrusion line inputs, including Sensor 0, Sensor 6, and Sensor 7 . We can Each sensor switch has a turn-on status and turn-off status, yielding eight status combinations . Sensor If the machine is shut down, the sensor switch status is recorded in the EEPROM . This caPSPS IntrusionManager is responsible for detecting the sensor status. If it finds that a sensor status is unsafe, it calls the PSPS Defense to protect the sensitive data and also sends an alert message and email to the administrator. PSPS IntrusionManager first calls the EEPROM function to check the sensor status in the EEPROM, and then calls the sensor function to detect the sensor status every second. It saves all the sensor status in the local database and calls PSPS Synchronize. The administrator can modify the seconds, frequency, file location, and other parameters through the INI file.SMSto API  sends thPSPS Defense is responsible for stopping programs, clearing the database, and removing files to prevent the sensitive data from being accessed. We design an API so the program can shut down normally to avoid data corruption due to forced termination. The administrator can modify the program location, folder location, database, and other parameters through the INI file.PSPS Synchronize uploads the sensor status and the system state to PSPS IDSServer and clears uploaded data in the local database in order to remotely monitor the status of the server. The administrator can modify the seconds, frequency, database, and other parameters through the INI file.Important data are backed up to another computer through SyncBackFree , which iPSPS RecoveryManager changes the system state when instructions and the password are input. For example, if the server is in the initial state, the initialize instruction and the password can be input to change the system state from initial state to operation state.PSPS IDSWeb shows the sensor status and system state on the website, and the password can be input to call PSPS RecoveryManager and change the system state . This wePSPS IDSServer is a cloud server. It can receive the sensor status and system state uploaded from the local database to monitor the system state from any location.When the server is operating normally, the system state is as shown in When PSPS finds a suspected intrusion, it triggers PSPS Defense to protect the data .After defining the purpose of the experiment, this section evaluates the proposed system security using two test cases. Results of the test cases are then discussed.The main purpose is to determine whether this system can discover that the computer’s chassis has been opened, and then clear all the sensitive data and programs. Two test cases are used to evaluate the system.Here, an unauthorized person opened the chassis when the server was running . The IntAn unauthorized person first shut down the computer and then opened the chassis. When the server restarted, the IntrusionManager module detected the intrusion with the EEPROM, then stopped the server and cleared sensitive data . AlthougAnother situation was considered in which the chassis was opened and the intruder removed the hard disk drive to access sensitive data on the hard disk drive. For this, BitLocker protected the disk by providing encryption for entire volumes, so the intruder could read only data in the test machine.In these test cases, the system enters the locked state. To unlock the system, the administrator must first fix the intruded status of the server, and then input the password in PSPS IDSWeb. After the system confirms that the server is safe, it can switch into the normal operating state .We next show the system state in the PSPS IDSWeb. We can monitor each sensor switch status . PSPS IDWhen the system is not running, PSPS IDSWeb is in the initial state, waiting for the administrator to initialize the system . First, After the system is initialized by the administrator, the system switches to the operating state , and PSPWhen the system detects an intrusion, it switches to the locked state, while the IntrusionManager module is still protecting the server .After the administrator unlocks the system or wants to upgrade the programs, the system enters the maintenance state, at which time the IntrusionManager module stops protecting the server .When PSPS IntrusionManager detects the intrusion behavior, it notifies the administrator via email and SMS. We use Java Email API and SMSto to provide these two functions. We package them into two runnable jar files, so that we can easily use these functions.The proposed PSPS system is compared with two well-known IDS systems: HIDS-OSSEC and NIDS-SNORT, as shown in In this work, we propose a security system to protect against physical computer intrusion, in contrast to previous papers that focus on network intrusion. We use Advantech DPX-S1435 to perform several experiments. Through the three sensor switches, this system can detect most of the behaviors used to enter the computer and has effective corresponding measures to prevent the theft of sensitive data. We also present two test cases to verify the system operation and show how to monitor the system state with PSPS IDSWeb. Additionally, we provide a comparison with two well-known IDS systems: HIDS-OSSEC and NIDS-SNORT. Finally, we include two more sections for deeper discussion and future work.PSPS prevents unauthorized persons from accessing system data. However, insiders or persons familiar with the system architecture can disassemble the EEPROM, sensors, and hard disk to avoid PSPS detection. Overcoming this is an important future work.(1)Build a website for PSPS IDSServer to monitor several systems;(2)Encrypt the Ini file to enhance security;(3)Use a smart card to encrypt and decrypt the password in PSPS RecoveryManager;(4)Apply this system to IoT devices;(5)Provide more protection for the EEPROM and sensor switches.In the future, the system can be enhanced as follows:


As the first animal to be domesticated, the dog is of great interest and significance to human society . Neverthet al. report a high-quality de novo grey wolf (Canis lupus) genome and a dhole  genome, respectively, and build a map of dog SVs through whole-genome alignments. Comparative analysis reveals that the dog genome has accumulated the LINEs and SINEs, providing basic genetic materials for domestication. In-depth analysis of the genomes explores the evolutionary tracks for energy metabolism, neurological processes and the immune system. In particular, one dog-specific insertion fully covered a novel copy of gene AKR1B1 that is involved in de novo fatty acids synthesis and carbonyls detoxification. Transcriptomic analysis showed that the new copy is expressed in the small intestine at a high level.Wang This study indeed demonstrates that SVs are of great importance and worth paying more attention to during domestication in the genomic era. It also revealed that a change in dietary composition in dog domestication has sculptured not only on the genetic basis of starch-rich diets, but also lipid biosynthesis and carbonyls detoxification of the agricultural revolution. It is therefore a significant step forward in the genetic hunt for the evolution of complex traits.


The reliable and cost-effective condition monitoring of the bearings installed in water pumps is a real challenge in the industry. This paper presents a novel strong feature selection and extraction algorithm (SFSEA) to extract fault-related features from the instantaneous power spectrum (IPS). The three features extracted from the IPS using the SFSEA are fed to an extreme gradient boosting (XBG) classifier to reliably detect and classify the minor bearing faults. The experiments performed on a lab-scale test setup demonstrated classification accuracy up to 100%, which is better than the previously reported fault classification accuracies and indicates the effectiveness of the proposed method. Induction motors have a simple, cost-effective design and they are easy to manufacture. These motors are designed to operate in tough environmental conditions. This feature makes them reliable for complex industry operations ,2. The sThe condition monitoring of the motor is performed through the acquisition of the data from various sensors, such as vibration sensors , stator current sensors, voltage sensors, magnetic flux sensors, temperature sensors and noise sensors . The namThe raw data obtained through sensors require further analysis to extract meaningful information about the machine’s status . VariousA lot of research has been conducted in the past on the diagnosis of holes or dents in the bearing raceways and it has been shown that a small-sized hole (diameter < 1 mm) can produce tinny amplitudes; it is thus a great challenge to reliably diagnose these small faults ,28,29,30In the current decade, the use of artificial intelligence (AI) has been significantly increased for the reliable diagnostics and classification of machine faults . AI techtype 1 fault is a hole of diameter 0.5 mm and a type 2 fault is a scratch of width 0.5 mm, depth 0.5 mm and length 5 mm. These dimensions were selected to allow a fair comparison of the proposed method with the benchmark paper [The harmonic identification using the IPA technique. The IPA provides an advantage over conventional MCA by providing three harmonics related to fault analysis while the MCA provides only two fault-related harmonics. This extra fault harmonic helps to enhance the reliability and accuracy of the condition monitoring system.The design of an SFSEA for the feature extraction from the IPA data. The SFSEA extracts strong features by eliminating those features whose amplitudes are dominated by environment noise.The development of an XGB classifier to classify the bearing faults using the features extracted through the SFSEA.The aim of this study was to diagnose bearing faults of minor sizes using non-invasive instantaneous power analysis (IPA) as a signal processing tool, SFSEA as a feature extraction tool and XGB as a machine learning tool. Two types of faults were simulated in the bearing: a rk paper , as thisThe rest of the paper is organized as follows. X1 represents the harmonic at the rotational frequency and X2, X3 represent two sidebands around 100 Hz.The instantaneous power spectrum was plotted in LabVIEW through the measured voltage and current signals. The key reason to use the instantaneous power spectrum is that it gives three harmonics related to the fault. One harmonic is located at the shaft rotational frequency and two harmonics are located around the fundamental frequency. The traditional MCA technique provides only two harmonics for the fault analysis. The locations of the fault-related features in the spectrum are shown in The frequency spectrum is plotted using an instantaneous power algorithm developed in LabVIEW;The fault frequencies are identified using the mathematical model of the instantaneous power;The healthy bearing amplitudes are collected and saved as baseline data;The faulty bearing amplitudes are collected and compared with the baseline data to measure the amplitude difference (AD) ;If the AD is zero, then it is an indication of a healthy bearing;If the AD is greater than zero, then it is an indication of the presence of a fault;Signature with an AD is greater than zero are finally compared with the statistical threshold to eliminate the impact of the environment noise. Those signatures which are greater than the threshold are selected as the strong features and are fed to the XGB for the fault classification.After identifying the fault features in the instantaneous spectrum, the strong features selection and extraction algorithm was deployed in the following steps:A flow chart of the SFSEA deployment is shown in In general, there are two types of machine learning algorithms: supervised and unsupervised. The target variables (TVs) in supervised algorithms are those that may be predicted from a known set of independent variables. These variables are used to construct the function and input maps to get the correct result and accomplish the target. They go through a training process to improve their accuracy. The training procedure is repeated before the model reaches optimal accuracy. Unsupervised algorithms, on the other hand, do not have a TV and therefore they use the clustering strategy. The literature indicates that supervised learning is adopted for machine fault classification. The SVM, k-NN and CNN algorithms are the algorithms famous in condition monitoring applications.The SVM algorithm is primarily used for classification and regression problems. The margin and hyperplane are the fundamental parameters of the SVM algorithm. The margin identifies the dataset vectors while the hyperplane divides the datasets and executes the classification operation. The SVM algorithm classifies data by determining the best hyperplane for maximizing the margin width between various classes. The overlapping issue among various classes can be minimized by increasing the margin width. In general, there are two forms of margins: soft margins and hard margins. In the present study, a soft margin was used since the bearing fault diagnosis was a nonlinear classification problem.Three parameters can affect the accuracy of the SVM algorithm. These parameters are the kernel function, the threshold function and the cost parameter (C). The kernel function’s primary purpose is to map the input at high-dimensional features, allowing for nonlinear classification. The gamma parameter influences data classification in the RBF kernel. The role of the threshold function is to develop the feature recognition ability. The cost parameter balances the tradeoff between a smooth boundary state determination and classification of the training points. When a large value is used as the cost parameter, low bias and high variance may be obtained. The gamma parameter sets the sequence contrast in relation to the expense function. Due to overfitting, the cost and gamma parameters should not be too large, and they should not be too poor because of underfitting issues. These parameters may be fine-tuned by choosing the right ranges of programming.The training features are mostly used to determine the best hyperplane. Cross-validation, re-sampling and matrix scan techniques aid in the automated selection of cost and gamma parameter values during the diagnosis. In machine learning, cross-validation assists in the preparation of the algorithm utilizing the right hyper-parameters. Re-sampling is a collection of techniques for reconstructing reference data sets, such as preparation and analyzing datasets. Grid quest is an iterative approach for finding the right parameter value for a machine learning algorithm.The k-NN algorithm is a non-parametric and robust learning algorithm that can be used to solve both classification and regression problems. Rather than studying the discriminative function, this algorithm memorizes the testing datasets. By memorizing the training sets, instance-based learning aids in the avoidance of errors. The non-parametric in the model is not set in advance and differs depending on the sample size. The drawbacks of k-NN include its huge memory footprint, long prediction period and excessive sensitivity to irrelevant functionality.k-NN uses the k-nearest training samples around the test results to conduct classification of the data. k-NN is largely based on two factors: (1) a distance metric for measuring the distance between two points and (2) the value of “k” for defining the number of neighbors. The form of the judgment boundary is defined by the value of k. If one raises the value of k in the neighbor selection, the boundary becomes smoother. Small k values produce a hard boundary state technically, but they also result in a more stable match with low bias and large variance.The standard versions of deep learning are denoising auto-encoders, deep conviction networks and convolutional neural networks (CNNs). CNNs are often used in the medical field but seldom used for motor condition monitoring applications. CNNs are often referred to as a form of machine learning, but they belong to the AI subclass. Both controlled and unsupervised programs may be executed on the network. To put it another way, the network is made up of several layers, each of which contains secret layers that are used to train the input. A CNN was chosen from among the different architectures because of advantages such as shift-variance, weight sharing, a high accuracy rate and data encoding.Type 1 fault was a hole of diameter 0.5 mm and a Type 2 fault was a scratch of width 0.5 mm, depth 0.5 mm and length 5 mm. The data from the sensors were fed to the LabVIEW software through the National Instruments NI PXIe 1082 interface. A photo of the laboratory-scale test setup is shown in A current sensor (SCT-00-15) and a voltage sensor were used to collect the data from the 2 HP induction motor. The bearing used in this study was a 6202-2z bearing. It had eight balls located between the outer and inner rings. Two types of faults were simulated in the bearing: a Plots of the IPA signals for no load (NL), medium load (ML) and full load (FL), for a normal bearing and defective bearing (type 1 and type 2), are shown in The amplitude differences  classifier was used as a machine learning algorithm to classify nine different classes of machine health conditions. The XGB classifier is known for its fast execution speed and high classification performance in several domains ,38. TherTo identify the strength of each feature in classifying the machine health, seven experiments were conducted, as shown in The findings of the proposed XGB classifier using different experiments are presented in These findings are quite encouraging and show the strength of the proposed SFSEA for feature extraction and of the XGB-based machine learning model in classifying the nine different machine health conditions. The analysis further emphasizes the fact that even a single feature (Y2) is sufficient to achieve 100% performance in classifying all the different health conditions.The performances of the developed SFSEA and XGB algorithm were compared with other published papers. The study  was chosThe accurate and cost-effective fault detection of centrifugal water pumps has gained great importance in modern industry and this paper therefore presented an intelligent condition monitoring system for the fault diagnosis of water pump bearings. A mathematical framework to identify the fault harmonics in the IPS data was established, and then a novel SFSEA was developed to extract the features from the IPS spectrum. Three fault features identified in the spectrum were extracted and compared with the baseline data to verify the amplitude difference. The fault feature amplitudes were compared with the threshold to remove the impact of noise. An XGB classifier was developed to classify nine health conditions of the water pump and it was found that the proposed method achieved 100% classification accuracy. The significance of the developed method is evident from the performance comparison with other state-of-the-art techniques, namely the SVM, k-NN and CNN algorithms.


The fracture behavior of polymeric materials has been widely studied in recent years, both experimentally and numerically. Different numerical approaches have been considered in the study of crack propagation processes, from continuum-based numerical formulations to discrete models, many of the latter being limited in the selection of the Poisson’s coefficient of the considered material. In this work, we present a numerical and experimental analysis of the crack propagation process of polymethylmethacrylate beams with central and eccentric notches subjected to quasi-static three-point bending tests. The developed discrete numerical model consists of a regular triangular lattice model based on axial and normal interaction springs, accounting for nearest-neighbor interactions. The proposed model allows solving the above mentioned limitation in the selection of Poisson’s coefficient, incorporating a fracture criterion defined by a bilinear law with softening that includes the fracture energy in the formulation and allows considering a progressive damage. One of the main objectives of this work is to show the capacity of this lattice to simulate quasi-static fracture problems. The obtained results show that the proposed lattice model is capable of providing results close to the experimental ones in terms of crack pattern, peak load and initial stiffening. Polymeric materials have been widely used in automotive, aerospace and many other industries during the last decades because of its outstanding mechanical properties, exhibiting a proper compromise between their impact strength and low density and cost. Moreover, thermoplastic polymers have also shown its potential to be used as substitute for metals in a wide range of technical procedures ,2, and sThe fracture behavior of polymeric materials has been extensively studied in last years, in either polycarbonate (PC) ,12,13, pDue to this growing interest, many studies have addressed the analysis of the crack propagation direction in PMMA, both from an experimental and numerical point of view. Traditionally, numerical models used to study crack propagation have been based on continuum-based numerical formulations, including the Finite Element Method (FEM), or meshless methods . Its majWith the clear objective of reducing the aforementioned high computational cost of finite element models, discrete models have been presented as an alternative in which it is not necessary to define the basis function  on reference elements. Discrete models are those formed by a series of individual points linked through linear elements, so that the total system is shaped like a mesh or grid. The way in which the interaction elements are defined is what distinguishes each discrete model. Included in this category, it is important to mention: molecular dynamics models ,37,38,39The above-mentioned lattice models present restrictions on the selection of the Poisson ratio, being this one of the main obstacles to modeling and analyzing a wide range of materials with this type of models. In some LM, the restrictions are related to problems of instability caused by obtaining negative stiffness for certain values of the Poisson coefficient ,45, whilIn this work, a 2D lattice model based on Born elastic potential is presented and validated with experimental results carried out on PMMA pre-notched specimens under quasi-static conditions. This model allows to solve the mentioned limitation in the selection of the Poisson’s coefficient, incorporating also a fracture criterion defined by a bilinear model with softening that includes the fracture energy in the formulation and allows to consider a progressive damage. It is important to highlight that vast majority of discrete models present in scientific literature employ fracture criteria based in models without considering the progressive damage of the material, due to the extra difficulty that represents relating the model parameters to the fracture energy. The authors have already proved in previous works ,47,48 thThis paper is organized as follows: In In this work, both characterization and fracture tests have been carried out, in order to analyze the fracture behaviour of PMMA. The mechanical properties, used as inputs in the numerical implementation, are obtained from uni-axial tension and fracture toughness tests following the standard ASTM D 50545 . The devThe true stress-true strain curves obtained from the uniaxial tension test are represented in E and the Poisson ratio The fracture energy Once the characterization of the material has been performed, three-point-bending tests have also been carried out, using PMMA notched beams submitted to quasi-static loading conditions. As in the case of those characterization tests, these three-point-bending tests were performed on a computer-controlled INSTRON 8516 universal testing machine under displacement-control mode at normal conditions of pressure and temperature.Three different initial notch lengths  is stable for the entire range of values of Poisson’s ratio.On the other hand, the discretization method fully developed in  yields tα see ,(5)u¨i=M the mass matrix, The equations of the model are assembled by enforcing the second Newton’s law at every node. This procedure results in the subsequent system of equationsMu¨+F(t)−i is calculated as [lated as ,56,57,58i and j node tensors.Thus, the strain tensor which defines each interaction D, for a bilineal problem with linear softening law, is given by [D can assume values between 0 to 1 (The damage variable given by (8)D(γ¯)In order to define the constitutive model, we applied the same methodology that the classical formulation of the Element Deletion Method (EDM) implemented in FEM . Figure In discrete models as well as in EDM, it was necessary to define a relation between the surface energy of a crack passing through parallel elements, and the energy associated to the constitutive model . This enHence, we needed to equalize the energy dissipated due to the failure of a real portion of material, with the energy associated to the stress–strain law shown in :(10)GfhiIn a uni-axial deformation process Therefore, we can calculate the critical strain n ij see , given bIn EDM implemented in FEM no information about the orientation of the crack surface generally is included . InsteadK as the relation between From Equation (13) it is possible to define a coefficient In this way, we considered the objectivity by modifying the constitutive law as a function of cell size.The numerical model was developed through Matlab software, defining all nodes positions and connectivities, reproducing the specimen geometry shown in The external load applied on the beam was modelled as an imposed constant velocity of v verify that the maximum kinetic energy is lower than the In order to guarantee the quasi-static experimental load condition, the adopted velocity To accomplish the validation of the proposed lattice model for fracture behavior of PMMA, the experimental measurement of applied force versus displacement was compared with the reaction force predicted by the numerical simulations. The initial stiffness, peak loads and crack propagation paths were analyzed and consequent relative errors between experimental and numerical results were calculated.With regard to the crack propagation patterns, The obtained results show that the numerical model was able to capture the crack pattern in the initial fracture process. The difference between the numerical model and experimental results increased when the crack progressed. However, the experimental results showed dispersion increased with the crack advance.Moreover, in order to present a deeper analysis of the results provided by the proposed model, the initial crack inclination angle a, s, B and d are the geometrical parameters defined in F is the applied load, and Munz and Fett  defined ted from . Table 5As stated before,  authors ,63,64,65The angle is presented as a function of the dimensionless parameter Note that this parameter is 0 for a pure mode II, and 1 for a pure mode I. Furthermore, in The experimental results showed a maximum dispersion of the inclination angle of about 20 authors . Moreove authors . The expTherefore, in view of the results shown, it can be concluded that the lattice model was able to adequately predict the initial stiffness and peak loads, with average percent errors lower than 8%, and the crack propagation patterns and initial angles on pre-notched PMMA beams subjected to quasi-static three-point bending tests.This paper presents experimental and numerical analysis of the quasi-static fracture behaviour for PMMA, by means of three-point bending tests on specimens with different initial notch lengths and notch eccentricities.A numerical model consisting in a 2D lattice model based on the Born potential has been developed and validated. The implemented model considers a bilinear constitutive model, with a linear softening law, considering a progressive damage in the material and allowing to overcome the limitation in the selection of the Poisson’s coefficient, present in some other discrete models in the scientific literature.The validation of the proposed model has been carried out in terms of initial stiffness, peak load, crack propagation patterns and initial crack inclination angle obtained in the conducted three-point-bending tests. The numerical results provide an average error of about 5.6% in the value of initial stiffness, and an average error of about 7.7% in the value of peak load, for all the tested specimens, showing a good agreement with the experimental results. With regard to the crack propagation patterns, the numerical model is able to capture them in the initial fracture process, while the difference between numerical and experimental results increases as the crack progresses, being also the dispersion obtained in the experimental results so much greater at these stages. Finally, with regard to the values of the initial crack propagation angle, the values obtained with the proposed model have a good correlation with the MTS criterion presented by Erdogan and Sih, being the experimental data obtained in this work slightly lower than the analytical predictions.It can be concluded that in view of the results shown, we have developed a lattice model which allows to select any value of Poisson’s coefficient and is able to predict the initial stiffness, peak load, crack propagation pattern and initial crack inclination angle of PMMA specimens with different initial notch lengths and notch eccentricities subjected to three-point bending tests under quasi-static load conditions.Future works are required to include a parametric study with the proposed model. In this way, we can study an upper range of the dimensionless parameter


With the rapid development of detection technology, CT imaging technology has been widely used in the early clinical diagnosis of lung nodules. However, accurate assessment of the nature of the nodule remains a challenging task due to the subjective nature of the radiologist. With the increasing amount of publicly available lung image data, it has become possible to use convolutional neural networks for benign and malignant classification of lung nodules. However, as the network depth increases, network training methods based on gradient descent usually lead to gradient dispersion. Therefore, we propose a novel deep convolutional network approach to classify the benignity and malignancy of lung nodules. Firstly, we segmented, extracted, and performed zero-phase component analysis whitening on images of lung nodules. Then, a multilayer perceptron was introduced into the structure to construct a deep convolutional network. Finally, the minibatch stochastic gradient descent method with a momentum coefficient is used to fine-tune the deep convolutional network to avoid the gradient dispersion. The 750 lung nodules in the lung image database are used for experimental verification. Classification accuracy of the proposed method can reach 96.0%. The experimental results show that the proposed method can provide an objective and efficient aid to solve the problem of classifying benign and malignant lung nodules in medical images. Lung cancer is one of the most common cancers in the world. Compared with other cancers, there are no obvious symptoms at an early stage. Early detection of lung cancer in its nodular form, screening, classification, and medical management have been demonstrated to be extremely helpful and effective in decreasing lung cancer mortality . TherefoAt present, the classification of benign and malignant lung nodules in the CAD system is mainly performed by extracting the underlying features of the CT image of lung nodules, such as the shape, position, texture, and density, through machine learning methods . MoreoveIn recent years, with the rapid development of deep learning, many studies have also demonstrated that convolutional neural networks (CNN) can be well applied to the field of medical images –10. ThisGuided by the above studies and observations, we propose a novel deep convolutional network learning (DCN) method to obtain better performance in classifying benign and malignant lung nodules. The central idea is as follows: firstly, segment the lung nodule region from the lung CT images to obtain the lung nodule images and perform zero-phase component analysis (ZCA) whitening on the image data so that all features in the images have the same variance and low feature-to-feature correlation. Then, add a multilayer perceptron layer after each convolutional layer of the constructed DCN to achieve cross-channel information interaction and integration. Finally, obtain the second derivative information of the error function directly without calculating the Hessian matrix, and introduce a momentum coefficient based on this information to improve the convergence of the network. The key contributions are summarized as follows: (1) the use of ZCA whitening to process the input data can reduce the correlation between image pixels and thus eliminate redundant information; (2) by introducing multiple perceptron layers after each convolutional layer can further enhance the expression capability of the network; (3) the use of small batch random gradient descent with additional momentum coefficients to train the deep network can effectively avoid gradient dispersion and enhance the generalization capability of the network.To use DCN to learn the features of CT images of lung nodules and enhance the representation of the model by introducing multilayer perceptron, which in turn improves the classification accuracy of lung nodule images. In addition, a momentum coefficient is introduced to improve the convergence in the minibatch stochastic gradient descent (MB-SGD) method to train the deep network model. The structural diagram of the lung nodule benign and malignant classification system based on DCN feature extraction is shown in To study early cancer detection in high-risk populations, the National Cancer Institute (NCI) published the Lung Image Database Consortium (LIDC) by collecting medical image files of the lung and corresponding lesion annotation of diagnostic results . The LIDI={I(1), ⋯, Ii)( in the image collection Q={q(1), q(2), ⋯, qd), =eig(C) is obtained, and then scaling the input data using the feature factors:ξ is the whitening factor. To avoid unstable values or data overflow due to eigenvalues diag(D) close to 0, ξ can be taken as a very small positive number. Based on this, ZCA whitening is performed using equation , ⋯, xi) denotes the nonlinear activation function ReLU. ∗ is the convolution operator. Due to the data distribution of the input, image will change after convolution operation, which leads to the internal covariance shift problem [Let the data of the lung nodule image after ZCA whitening be  problem . Therefo problem ; that is follows:xj′l=f∑j∈β is the downsampling coefficient. Similar to the convolutional layer, on the pooling layer we also normalize the feature map by introducing a BN layer, and its position is generally placed between the activation function and the pooling operation so that the forward-conducting pooling calculation equation  is the gradient function at moment t − 1. From the QuickProp theory proposed by Fahlman [µ is introduced to overcome the aforementioned drawback. Equation  for the construction of the proposed deep network model on a workstation with a Win10 system, i9 processor, and 64G RAM. We choose stratified 10-fold cross-validation as a rigorous validation model . All datμ directly affects the location of the minimum cost function, which in turn affects the classification accuracy of the lung nodule images. For this reason, it is necessary to discuss the value of μ. According to the description in the training setup, ten experiments with 10 different values of μ were conducted to select the best value. We change the value of μ from 0 to 1 at intervals of 0.1. μ values on model classification performance. It can be seen that the classification error rate of lung nodule images gradually decreased as the value of μ increases. When μ is 0.9, the model can obtain the lowest classification error rate.In this paper, we need to modify the proposed network parameters repeatedly by using the method of MB-SGD and finally make the result of the loss function reach the minimum value. For the MB-SGD method with a momentum coefficient, the value of the momentum coefficient In this section, we conduct a series of experiments to find the most suitable sample configuration scheme. According to the data distribution method in the training set, a subset (75 samples) is randomly selected as the testing set. To verify the impact of the training set size on the generalization abilities of the model, we gradually increase the size of the training samples. As can be seen from the result in Gradient descent algorithm is the most commonly used optimization method in machine learning, and current networks are trained using different gradient descent algorithms for network training. Therefore, a good optimizer design in practical applications is of great importance to avoid the gradient dispersion of the deep network. In this section, to test the speed and stability of the proposed MB-SGD method with momentum coefficients during the training process, we plot the RL curves and analyze the effectiveness of the proposed algorithm by comparing it with the traditional MB-SGD method. The mean square error RL curve is a smoothing sequence with minimum mean square error, and its shape shows that with the increase of the number of iterations, the CNN model training process predicts the error. On the other hand, it also represents the speed and stability of the network convergence. In this experiment, the minibatch size is 50 and the training samples are 500. After training the sample data once, the weights are iteratively updated 10 times, and after performing 120 epochs of training, the weights are iteratively updated 1200 times. The experimental results are shown in One of the most important advantages of deep learning is the ability to automatically learn relevant features from the original image. To further evaluate the effectiveness of the proposed method, classical deep learning models such as CNN, DBN, and SAE were used to extract lung nodule features and perform classification experiments under the same dataset segmentation. To highlight the performance of our proposed method, we compared our results with those of the literature designed for lung nodule classification. As shown in As one of the most popular research directions in the field of machine learning, deep learning can learn advanced features of data and has more powerful nonlinear representation capabilities. In this study, we propose a novel DCN learning method for the benign and malignant classification of lung nodules. The main advantages are as follows: (1) perform ZCA whitening processing on the extracted lung nodule images, which can effectively eliminate redundant information between pixels; (2) combine the multilayer perceptron layer with CNN to construct a DCN model capable of learning strongly robust features, thus improving the feature representation power of the network to a greater extent; (3) the MB-SGD method with a momentum coefficient is used to train the deep network, which effectively avoids the local optimum and gradient dispersion phenomena. The experimental results on the LIDC dataset show that the proposed DCN learning method has high classification accuracy for benign and malignant classification of lung nodules.


Overcrowding has become a serious and growing issue globally, which represents a serious impediment to healthcare utilization. To estimate the rate of ED visits attributable to the outbreak and guide the planning of strategies for managing ED access or after the outbreak of transmittable respiratory diseases. This observational study was based on a retrospective review of the epidemiological and clinical records of patients aged > 75 years who visited the Foundation IRCCS Policlinic San Matteo during the first wave of COVID-19 outbreak . The analysis methods included estimation of the changes in the epidemiological and clinical data from the annual baseline data after the start of the COVID-19 pandemic. Outcome measures and analysis: Primary objective is the evaluation of ED admission rate change and ED overcrowding. Secondary objectives are the evaluation of modes of ED access by reason and triage code, access types, clinical outcomes . During the pandemic, ED crowding increased dramatically, although the overall number of patients decreased, in the face of a percentage increase in those with high-acuity conditions, because of changes in patient management that have prolonged length of stay (LOS) and increased rates of access block. Overcrowding during the COVID-19 pandemic can be attributed to the Access Block. Access Block solutions are hence required to prevent a recurrence of crowding to any new viral wave or new epidemic in the future.The geriatric population constitutes a large slice of the population of Western countries and a class of fragile patients, with greater deaths due to COVID-19. The patterns of healthcare utilization change during pandemic disease outbreaks. Identifying the patterns of changes of this particular fragile subpopulation is important for future preparedness and response. Overcrowding in the emergency department (ED) can occur because of the volume of patients waiting to be seen, delays in patient assessment or treatment in the ED, or impediments to leaving the ED once the treatment has been completed. The online version contains supplementary material available at 10.1007/s11739-021-02732-w. Coronavirus disease 2019 (COVID-19) is an acute respiratory infectious disease that is caused by the novel coronavirus severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). SARS-CoV-2 is dissimilar from other coronaviruses that usually spread among humans and cause the common cold. The first confirmed case of pneumonia caused by this novel coronavirus was reported at the end of 2019 . COVID-19 is particularly pathogenic in humans and associated with high mortality rates. A large percent of patients develop severe disease and experience poor outcomes, especially the elderly [Chronic obstructive pulmonary disease (COPD) is usually prevalent among the elderly population, which has a high impact on the quality of life, morbidity, and mortality . VirusesThe elderly often suffers from chronic diseases requiring multi-drug therapy, regular checks for their pathologies, and presents with exacerbations requiring access to EDs. The patterns of healthcare utilization by the elderly change during infectious disease outbreaks. Identifying the patterns of changes is important for future preparedness and response. It seems particularly important to observe this change in an elderly subpopulation, because they represent a fragile population, need frequent access to ED for exacerbations of chronic diseases or for new acute events, and are affected more severely by COVID-19. The effects of infectious disease epidemic on healthcare utilization depend on the characteristics of the infection.Thus, epidemics can have major effects on the healthcare system, including overcrowding.Overcrowding in the ED can occur because of an increase in the volume of patients waiting to be seen (input), delays in patient assessment or treatment in the ED (throughput), or impediments to leaving the ED once treatment has been completed (output) . ED overED crowding has been extensively discussed for several decades, with various suggestions about interventions to reduce the ED crowding –13. PresAlthough the causative agent and the mode of transmission of COVID-19 have been examined in detail, the effects of the epidemic on the availability of emergency services and ED overcrowding remains under-evaluated. We performed a large retrospective observational study by comparing the demographic and clinical data of patients after the COVID-19 wave with data for patients who visited the ED in the corresponding period in the past 2 years, as well as the period preceding the outbreak. We believe that overcrowding has increased, as measured using throughput and output indices.The specific hypotheses made were as follows:The number of attenders among the elderly at the ED decreased regardless of the age groups  and sex after the COVID-19 outbreak;Throughput (such as the length of ED stay) and output crowding indices  were changed due to the COVID-19 outbreak;Regarding the secondary objectives:The modes of ED access , the codes for priority of medical examination, and the exit codes for severity changes after the outbreak reflected more serious illnesses and patients requiring high-intensity care;Marked reduction in some access types (such as access for minor trauma and minor signs and symptoms) was accompanied by a homogeneous reduction in other access types;Clinical outcomes, such as admission and mortality rates, were changed due to the outbreak (as were the output crowding indices).regarding the primary objectives:The final objectives of this study were to estimate the rate of ED visits attributable to the outbreak and guide the planning of strategies for managing ED access or after the outbreak of transmittable respiratory diseases.This observational study was based on a retrospective review of the epidemiological and clinical records of patients aged > 75 years visiting the Foundation IRCCS Policlinic San Matteo during the first wave of COVID-19 outbreak . Data were extracted using the PiEsse software. The methods of evaluation included the estimation of the changes in the epidemiological and clinical data from the annual baseline data after the start of the COVID-19 pandemic.Data were provided directly by the San Matteo Hospital Foundation, which maintains files on all services provided by its ED. An ad hoc query was performed to obtain the data of interest. The first names and surnames of the patients were replaced with anonymous codes to ensure proper blinding to the patients’ identities. At the time of ED admission, the patients provided their informed consent for the processing of their data for medical and research purposes.The primary outcome was aimed at assessing the changes in the use of emergency resources after the COVID-19 outbreak in terms of the ED visits in an elderly subpopulation and the assessment of ED overcrowding through the use of crowding indices such as the length of ED stay, total access block time, and the rate of access block. Meanwhile, the key secondary aim was to define the characteristics of the population that visited our ED during the pandemic, including the gender, age, and method of ED access. Other examined outcomes included the causes of ED visits during the pandemic; the clinical outcomes such as admission and mortality rates.All elderly patients (aged ≥ 75 years) who visited the ED during the study periods were eligible for inclusion. Ophthalmological and gynecological emergencies, relating to specialist ERs, were excluded.For each patient, the demographic data (gender and age), vital parameters , signs and symptoms, the waiting time, LOS in the ED, mode of ED access, priority codes for medical examination, exit codes for severity, total access block time, and the rate of access block were collected. All medical records were accurately viewed and evaluated, and all computed tomography data were thoroughly reviewed. All collected data were stored on a spreadsheet using the Microsoft Excel program and, subsequently, used for statistical analyses.The pandemic group in this study consisted of 1911 consecutive patients who accessed the ED between February 20 and May 1, 2020. As the control period, the sum of timespans from January 1, 2018 to May 1, 2018 from January 1, 2019 to May 1, 2019 and from January 1, 2020 to February 20, 2020  were used.Input crowding indices: waiting times, the number of patients visiting the ED, and disease severity and complexity , the number of people who left without being seen (LWBS).Throughput crowding indices: LOSOutput crowding indices: the mean number or percent of admissions, patients in the ED (number or percent), access block and boarding (the mean number or percent of patients who experienced it), and access block or boarding times .Several indices have been proposed to measure crowding , 16; theThese indices have already been widely validated in the past studies , 18.The “waiting time” can be defined as the total time from the time of initial registration/triage to first being seen by a doctor. The “process time” is the time from the medical contact in ED to the medical decision . The overall LOS in the ED is the time from the arrival at triage or registration until discharge or transfer to another ward. This variable reflects the total patient experience, including care and waiting. Access block can be defined as a > 8-h duration in the ED from the time of presentation to admission . The tot2 test. Moreover, the test of proportions was used to examine the differences in ED mortality between the two periods. The significance level was set at alpha 0.05 , and all tests were two tailed.Statistical analyses were conducted using appropriate logistic multivariate regression models to test the association between time variables, while accounting for crowding and the pandemic period. Continuous variables were described as mean and interquartile range, while qualitative variables were expressed as the number of observations and appropriate proportions. Comparisons between the two groups of continuous variables were made with the non-parametric Mann–Whitney test, according to their non-normal distributions, while associations between the qualitative variables were studied with χn 20200114609).The analyzes were conducted with the STATA software . The study was submitted to the ethics committee , considering aggregate data for both genders. We also recorded a reduction of approximately 25% in the total and daily access when compared with the corresponding periods of 2018 and 2019; the latter witnessed an increase in the number of ED visits related to seasonal influenza (p < 0.001).During the pandemic, we observed a substantial decline of approximately 25% in the volume of patients visiting the ED when compared with the corresponding periods of 2018 and 2019  compared to high-intensity care .In our study, the difference in per day visits number observed during the pandemic period was higher for low-intensity care . During the pandemic, a reduction in female prevalence has been observed  has been recorded  and minor trauma. The incidence of major trauma decreased slightly without reaching statistical significance. Conversely, access for fever and respiratory symptoms decreased only a little in number, but increased in percent. The same trend was noted for visits related to neurological symptoms Table .Table 5Sp < 0.001; Table p < 0.001; Table p < 0.001) for previous corresponding periods .During the pandemic, geriatric patients showed worse percent exit codes and hospitalization rates p < 0.00; Table 1This study evaluated the changes in the utilization of emergency care throughout the first wave of COVID-19 pandemic by geriatric patients in a single ED based in Lombardy, a region in the northeastern Italy and one of the most severely affected COVID-19 areas. There have been radical changes in the manner in which emergency care is accessed in this area. We recorded a sharp reduction in the number of autonomously transported patients and an increase in those transported to the ED by the territorial emergency service.The patterns of medical service used during infectious disease outbreaks can vary based on the characteristics of the infection, including the infectivity and lethality of the disease. In the 2003 SARS epidemic in Taiwan, the medical service utilization declined owing to the perceived risk of nosocomial transmissions . HoweverThus, epidemics can have different effects on patient behavior depending on the mortality rates and the emotional impact of the epidemic itself on the population. On one hand, epidemics with high-mortality rates are likely to reduce the demands on the health system, but increase the number of patients with serious diseases. On the other hand, epidemics with lower mortality rates and lower emotional effects can result in greater healthcare utilization , 27.Both SARS and H1N1 influenza caused global panics, but the patterns of healthcare utilization during these outbreaks were totally opposite. Healthcare utilization decreased during the 2003 SARS epidemic because of concerns over nosocomial infection, whereas it increased explosively during the 2009 H1N1 influenza pandemic because of the excessive fear of influenza itself . During Geriatric patients are particularly vulnerable to this virus and, often, already suffer from pulmonary and cardiovascular comorbidities. However, only a few studies have investigated the use of emergency services by the elderly population in various pandemics.There have been numerous appeals by the civil authorities to the elderly population to reduce their social activities as much as possible, avoiding all those events that are not essential. The high number of mortalities associated with the COVID-19 pandemic has spurred civil authorities to implement measures to contain the virus. Accordingly, “Red zones” have been created, which are areas with restrictions on citizens’ movements, business closures, and working from home, when possible. It is believed that newscasts that constantly updated the spread and mortality of COVID-19 likely promoted apprehension among the population.As observed in past studies that examined changes in healthcare utilization according to disease severity, the reduction in emergency care utilization was most prominent for low-acuity conditions  , 31, 32.More precisely, there was only a slight reduction in the absolute number of patients with red code and yellow code, with an increase in their percentage representativeness. That there has been an increase in more serious codes is in line with what emerged from a maxi-emergency simulation conducted on Italian territory by our study group. The same also highlighted how the geriatric population presented greater management criticality in cases of maxi-emergency . It is oLast but not least a selection of ED visits—independent of patients' choice or fears—was probably performed for several milder cases by GPs and Prehospital Emergency Care.During the ongoing pandemic, there has been a net reduction in some reasons for ED visits, such as for minor trauma or minor medical issues, which confirms the reduction in the number of low-acuity visits. The percent of patients with febrile symptoms at home was much higher during the pandemic period.Patients decided to employ medical care after considering the risks and benefits. During an infectious disease epidemic, patients use medical care when they believe that the benefits of healthcare utilization exceeds the risk of infection , 25. WheRegarding visits for high-acuity conditions, the mild reduction in access for major trauma can be explained by the fact that our teaching hospital no longer served as the hub for major trauma during the pandemic period. This reduction may also be attributed to the least occurrence of car accidents for the lockdown declared by the government. The fact that the reduction was not statistically significant could be due to the fact that, in any case, the elderly can report major traumas even for minor dynamics and with domestic accidents. The rate of visits for serious conditions did not decline in the same manner. As observed for chest pain and neurological symptoms in this study, the reduction in ED use for high-acuity diseases was expected to be minor during the infectious disease outbreaks. When fears of an epidemic spreads and ED visits decrease, preparations for serious conditions must be focused, so that patients with severe diseases do not face barriers to emergency care. This point also underlines the need to consider “clean” or low-risk infectious pathways for the most serious reasons for ED visits of geriatric patients.Crowding of EDs has been reported as an issue for several decades now. We found that the input factors play a modest/ambivalent role in crowding in this pandemic situation, whereas throughput and output factors more accurately reflect crowding and the work performed by healthcare providers in this pandemic. The input factors include the number of patients who visit the ED and the severity of diseases, including the risk of infection . The inpOur study revealed that the pandemic has placed a tremendous burden globally, which has unprecedentedly raised the need for intensive care beds , leadingThus, increased rates of boarding and access block were noted to affect all patients during the pandemic, including those who were COVID-19 negative, despite the strong effort to add, during the emergency peak, almost 300 beds for COVID-19 patients, 65 of which were dedicated to providing intensive care.Several past researchers and research communities have developed measures to prevent ED crowding and provide appropriate care for patients receiving emergency care. Interventions were categorized into input, throughput, and output controls , 11–14. Until date, emergency preparedness for outbreaks of transmittable respiratory illness has scarcely focused on preventing overcrowding and protecting the staff and patients. Instead, the focus has been on preparing emergency quarantine areas and isolating the admission rooms. Overcrowding provides favorable conditions for transmission among patients in the ED through respiratory droplets, and prior researches have recommended infection control measures, such as case management, isolation, and planning for complex emergencies –16, 31.To improve the practice of boarding patients, the ACEP established a task force to develop a list of low-cost, high-impact solutions –16, 31. This measure has been applied in our hospital by creating ad hoc, high-intensity holding area communicating with the emergency room by utilizing medical and nursing staff from the intensive care unit (ICU) and infectious diseases department. Therefore, nurses and doctors from other departments were also employed . Although this was an effective response, the need for effective solutions for reducing the access block must be reiterated. For instance, in 2003, Asplin et al.  advocateWe recorded that the rates of more serious exit codes and the need for hospitalization were approximately two-fold greater during the study period than otherwise in the control periods. This observation signifies the major impact of the current pandemic on the existing healthcare system  as well This study had several limitations. First, this analysis was based on the assumption that no meaningful changes in the factors affecting ED utilization other than the COVID-19 pandemic occurred during the study period, which could not be verified. Second, this study only included patients who had visited the ED. The health outcomes of other patients remain uninvestigated. Although the ED visits for the most severe conditions did not decrease, the global reduction in emergency care utilization may have led to complications other than short-term mortality . For exaThe study identified a reduction in the number of ED visits during the first wave of COVID-19 pandemic, irrespective of the age and gender of the patients, especially for low-acuity conditions. However, geriatric patients who visited the ED more frequently were hemodynamically unstable; moreover, they more commonly exhibited abnormal vital signs and they more frequently required high-intensity care and hospitalization. During the pandemic, ED crowding showed a dramatic increment, primarily because of the increased number of visits by patients with high-acuity conditions, changes in the patient management system with prolonged LOS, and an increase in the rates of boarding and access block.Supplementary file1 (DOCX 15 KB)Supplementary file2 (DOCX 13 KB)Below is the link to the electronic supplementary material.