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The research reported in this paper concerns the ongoing development of a Knowledge Intensive Sketching (KiS) framework through which designers are supported in foreseeing directly from their paper-based sketches the relevant life-cycle consequences of their ‘component form’ solution concepts. The goal of the KiS framework is to retain the important characteristics of freehand sketching, i.e. pencil and paper, whilst at the same time exploit the benefits of Knowledge Intensive CAD technology for proactively guiding designers in generating life-oriented solutions as from early design.

The COVID-19 pandemic has forced a new reality on the delivery of psychotherapeutic services. Therapists have had to rapidly adapt to telehealth therapy using various video conferencing technologies while working from spaces that were not necessarily designed for delivering therapy sessions. While COVID-19 continues to be present in therapists' lives and shapes how they provide services, answering the question of whether to meet with clients in person again is a decision laden with complexity and ambivalence. In this mixed-method study, surveys with 169 therapists and interviews with 17 therapists were used to determine the factors that currently influence their attitudes toward resuming traditional in-person psychotherapy in light of COVID-19. We focused on the therapists' personal and professional characteristics to assess their plans to continue with telepsychotherapy or return to a traditional psychotherapy setting. Our findings suggest that positive attitudes toward telepsychotherapy, the lack of an effective vaccine, clients' satisfaction with telepsychotherapy, insurance reimbursement for telepsychotherapy, and negative attitudes toward wearing masks during sessions influenced therapists' decisions on whether or not to return to traditional in-person psychotherapy in the age of COVID-19.

Screening, Brief Intervention and Referral to Treatment (SBIRT), is an evidence-based approach to screening and early intervention for those at risk of substance use disorders. With the ongoing health concerns related to COVID-19, there is an increased need for social workers who can competently deliver evidence-based interventions, such as SBIRT, via telehealth. Due to the COVID-19 pandemic, traditional SBIRT training approaches using face-to-face (FTF) instruction and FTF simulated practice may not be a safe or feasible way to develop students' SBIRT- related skills. This study explores 35 social work graduate students' experiences of learning SBIRT skills in a remote learning format and subsequently delivering a SBIRT intervention to a live "client" via a peer-to-peer simulated telehealth session. Overall, students reported that the shift from FTF to remote learning made learning SBIRT skills difficult, and that providing brief intervention and referral was the most difficult step of the simulated SBIRT telehealth intervention. Qualitative feedback indicates that overall, students found the simulated telehealth sessions a valuable learning experience, but also reported that richer educational experiences would have resulted from additional practice opportunities and real time feedback. Implications for future research, simulation-based education and clinical practice are discussed.

Population pharmacokinetic analysis is used to estimate pharmacokinetic parameters and their variability from concentration data. Due to data sparseness issues, available datasets often do not allow the estimation of all parameters of the suitable model. The PRIOR subroutine in NONMEM supports the estimation of some or all parameters with values from previous models, as an alternative to fixing them or adding data to the dataset. From a literature review, the best practices were compiled to provide a practical guidance for the use of the PRIOR subroutine in NONMEM. Thirty-three articles reported the use of the PRIOR subroutine in NONMEM, mostly in special populations. This approach allowed fast, stable and satisfying modelling. The guidance provides general advice on how to select the most appropriate reference model when there are several previous models available, and to implement and weight the selected parameter values in the PRIOR function. On the model built with PRIOR, the similarity of estimates with the ones of the reference model and the sensitivity of the model to the PRIOR values should be checked. Covariates could be implemented a priori (from the reference model) or a posteriori, only on parameters estimated without prior (search for new covariates). Graphic abstract Electronic supplementary material The online version of this article (10.1007/s10928-020-09695-z) contains supplementary material, which is available to authorized users.

In many search environments, searchers are learning about the distribution of offers in the market. I conduct an experiment exploring a broad class of search problems with learning about the distribution of payoffs. My results support the prediction that learning results in declining reservation values, providing evidence that learning may be an explanation for recall. Theory predicts a “one step” reservation value strategy, but many subjects instead choose to set a high reservation value in order to learn about the distribution before adjusting based on their observations. Under-searching in search experiments may stem from a reinforcement heuristic and lack of negative feedback after using sub-optimal strategies. Electronic supplementary material The online version of this article (10.1007/s10683-020-09659-7) contains supplementary material, which is available to authorized users.

Adolescent girls and young women (AGYW) remain underserved and at risk for HIV acquisition in Ethiopia. However, there is significant risk heterogeneity among AGYW with limited consensus on optimal strategies of identifying vulnerable AGYW. This study assessed the utility of venue-based sampling approaches to identify AGYW at increased risk for HIV infection. Venue mapping and time-location-sampling (TLS) methods were used to recruit AGYW from three sub-cities of Addis Ababa, February–June 2018. Interviewer-administered surveys captured socio-demographic and behavioral characteristics. Measures of AGYW vulnerability were assessed geographically and described by venue type. A total of 2468 unique venues were identified, of which 802 (32%) were systematically selected for validation and 371 (46%) were eligible including many sites that would traditionally not be included as venues in need of HIV prevention services. Overall, 800 AGYW were enrolled across 81 sampled venues. AGYW reached were largely out-of-school (n = 599, 75%) with high proportions of AGYW reporting transactional sex (n = 101, 12.6%), food insecurity (n = 165, 20.7%) and migration (n = 565, 70.6%). Taken together, these data suggest the utility of TLS methods in reaching vulnerable, out-of-school AGYW in Addis Ababa, Ethiopia. Electronic supplementary material The online version of this article (10.1007/s10461-019-02537-1) contains supplementary material, which is available to authorized users.

Diseases of viral origin in humans are among the most serious threats to health and the global economy. As recent history has shown the virus has a high pandemic potential, among other reasons, due to its ability to spread by air, hence the identification, investigation, containment, and treatment of viral diseases should be considered of paramount importance. In this sense, the bioinformatics research has focused on finding fast and efficient algorithms that can identify highly toxic antiviral peptides and to serve as a first filter, so that trials in the laboratory are substantially reduced. The work presented here contributes to this effort through the use of an algorithm already published by this team, called polarity index method, which identifies with high efficiency antiviral peptides from the exhaustive analysis of the polar profile, using the linear sequence of the peptide. The test carried out included all peptides in APD2 Database and 60 antiviral peptides identified by Kumar and co-workers (Nucleic Acids Res 40:W199–204, 2012), to build its AVPpred algorithm. The validity of the method was focused on its discriminating capacity so we included the 15 sub-classifications of both Databases. Electronic supplementary material The online version of this article (doi:10.1007/s12013-014-0084-4) contains supplementary material, which is available to authorized users.

The use of wireless sensor networks (WSN) in healthcare applications is growing in a fast pace. Numerous applications such as heart rate monitor, blood pressure monitor and endoscopic capsule are already in use. To address the growing use of sensor technology in this area, a new field known as wireless body area networks (WBAN or simply BAN) has emerged. As most devices and their applications are wireless in nature, security and privacy concerns are among major areas of concern. Due to direct involvement of humans also increases the sensitivity. Whether the data gathered from patients or individuals are obtained with the consent of the person or without it due to the need by the system, misuse or privacy concerns may restrict people from taking advantage of the full benefits from the system. People may not see these devices safe for daily use. There may also possibility of serious social unrest due to the fear that such devices may be used for monitoring and tracking individuals by government agencies or other private organizations. In this paper we discuss these issues and analyze in detail the problems and their possible measures.

In the last few years bureaucratic procedures didn't show a significant reduction in the volume of paper documents created. In order to reduce the huge amount of space for archiving and preserving documents and to speed up the secarh process, a semantic-based dematerialization process should be performed. In this paper we describe a novel system that manages several kind of bureaucratic documents in the e-gov domain, automatically extracts several interesting information and produces a suitable semantic representation that may be considered as the first step towards a full automated document management system. © 2008 Springer Science+Business Media, LLC.

As health systems strive to meet the needs of linguistically diverse patient populations, determining a physician’s non-English language proficiency is becoming increasingly important. However, brief, validated measures are lacking. To determine if any of four self-reported measures of physician Spanish language proficiency are useful measures of fluency in Spanish. Physician self-report of Spanish proficiency was compared to Spanish-speaking patients’ report of their physicians’ language proficiency. 110 Spanish-speaking patients and their 46 physicians in two public hospital clinics with professional interpreters available. Physicians rated their Spanish fluency with four items: one general fluency question, two clinically specific questions, and one question on interpreter use. Patients were asked if their doctor speaks Spanish (“yes/no”). Concordance, sensitivity, specificity, and positive and negative predictive values (PPV, NPV) were calculated for each of the items, and receiver operating (ROC) curves were used to compare performance characteristics. Concordance between physician and patient reports of physician Spanish proficiency ranged from 84 to 91%. The PPV for each of the four items ranged from 91 to 99%, the NPV from 60 to 90%, and the area under their ROC curves from 90 to 95%. The general fluency question gave the best combination of PPV and NPV, and the item on holding sensitive discussions had the highest PPV, 99%. Physicians who reported fluency as “fair” were as likely to have patients report they did not speak Spanish as that they did. Physician self-report of Spanish language proficiency is highly correlated with patient report, except when physicians report “fair” general fluency. In settings where no financial or other incentives are linked to language skills, simple questions may be a useful way to assess physician language proficiency.

Realizing the accurate prediction of data flow is an important and challenging problem in industrial automation. However, due to the diversity of data types, it is difficult for traditional time series prediction models to have good prediction effects on different types of data. To improve the versatility and accuracy of the model, this paper proposes a novel hybrid time-series prediction model based on recursive empirical mode decomposition (REMD) and long short-term memory (LSTM). In REMD-LSTM, we first propose a new REMD to overcome the marginal effects and mode confusion problems in traditional decomposition methods. Then use REMD to decompose the data stream into multiple in intrinsic modal functions (IMF). After that, LSTM is used to predict each IMF subsequence separately and obtain the corresponding prediction results. Finally, the true prediction value of the input data is obtained by accumulating the prediction results of all IMF subsequences. The final experimental results show that the prediction accuracy of our proposed model is improved by more than 20% compared with the LSTM algorithm. In addition, the model has the highest prediction accuracy on all different types of data sets. This fully shows the model proposed in this paper has a greater advantage in prediction accuracy and versatility than the state-of-the-art models. The data used in the experiment can be downloaded from this website: https://github.com/Yang-Yun726/REMD-LSTM .

In recent times, a phishing attack has become one of the most prominent attacks faced by internet users, governments, and service-providing organizations. In a phishing attack, the attacker(s) collects the client's sensitive data (i.e., user account login details, credit/debit card numbers, etc.) by using spoofed emails or fake websites. Phishing websites are common entry points of online social engineering attacks, including numerous frauds on the websites. In such types of attacks, the attacker(s) create website pages by copying the behavior of legitimate websites and sends URL(s) to the targeted victims through spam messages, texts, or social networking. To provide a thorough understanding of phishing attack(s), this paper provides a literature review of Artificial Intelligence (AI) techniques: Machine Learning, Deep Learning, Hybrid Learning, and Scenario-based techniques for phishing attack detection. This paper also presents the comparison of different studies detecting the phishing attack for each AI technique and examines the qualities and shortcomings of these methodologies. Furthermore, this paper provides a comprehensive set of current challenges of phishing attacks and future research direction in this domain.

Artificial intelligence (AI) techniques have grown rapidly in recent years in the context of computing with smart mobile phones that typically allows the devices to function in an intelligent manner. Popular AI techniques include machine learning and deep learning methods, natural language processing, as well as knowledge representation and expert systems, can be used to make the target mobile applications intelligent and more effective. In this paper, we present a comprehensive view on “mobile data science and intelligent apps” in terms of concepts and AI-based modeling that can be used to design and develop intelligent mobile applications for the betterment of human life in their diverse day-to-day situation. This study also includes the concepts and insights of various AI-powered intelligent apps in several application domains, ranging from personalized recommendation to healthcare services, including COVID-19 pandemic management in recent days. Finally, we highlight several research issues and future directions relevant to our analysis in the area of mobile data science and intelligent apps. Overall, this paper aims to serve as a reference point and guidelines for the mobile application developers as well as the researchers in this domain, particularly from the technical point of view.

Funder: University of Cambridge, In this paper we study a class of fast geometric image inpainting methods based on the idea of filling the inpainting domain in successive shells from its boundary inwards. Image pixels are filled by assigning them a color equal to a weighted average of their already filled neighbors. However, there is flexibility in terms of the order in which pixels are filled, the weights used for averaging, and the neighborhood that is averaged over. Varying these degrees of freedom leads to different algorithms, and indeed the literature contains several methods falling into this general class. All of them are very fast, but at the same time all of them leave undesirable artifacts such as “kinking” (bending) or blurring of extrapolated isophotes. Our objective in this paper is to build a theoretical model in order to understand why these artifacts occur and what, if anything, can be done about them. Our model is based on two distinct limits: a continuum limit in which the pixel width h→0 and an asymptotic limit in which h>0 but h≪1. The former will allow us to explain “kinking” artifacts (and what to do about them) while the latter will allow us to understand blur. Both limits are derived based on a connection between the class of algorithms under consideration and stopped random walks. At the same time, we consider a semi-implicit extension in which pixels in a given shell are solved for simultaneously by solving a linear system. We prove (within the continuum limit) that this extension is able to completely eliminate kinking artifacts, which we also prove must always be present in the direct method. Finally, we show that although our results are derived in the context of inpainting, they are in fact abstract results that apply more generally. As an example, we show how our theory can also be applied to a problem in numerical linear algebra.

The soaring development of Web technologies and mobile devices has blurred time-space boundaries of people’s daily activities. Such development together with the life-long learning requirement give birth to a new learning style, micro learning. Micro learning aims to effectively utilize learners’ fragmented time to carry out personalized learning activities through online education resources. The whole workflow of a micro learning system can be separated into three processing stages: micro learning material generation, learning materials annotation and personalized learning materials delivery. Our micro learning framework is firstly introduced in this paper from a higher perspective. Then we will review representative segmentation and annotation strategies in the e-learning domain. As the core part of the micro learning service, we further investigate several the state-of-the-art recommendation strategies, such as soft computing, transfer learning, reinforcement learning, and context-aware techniques. From a research contribution perspective, this paper serves as a basis to depict and understand the challenges in the data sources and data mining for the research of micro learning.

Context: Obfuscation is a common technique used to protect software against malicious reverse engineering. Obfuscators manipulate the source code to make it harder to analyze and more difficult to understand for the attacker. Although different obfuscation algorithms and implementations are available, they have never been directly compared in a large scale study.\ud \ud Aim: This paper aims at evaluating and quantifying the effect of several different obfuscation implementations (both open source and commercial), to help developers and project managers to decide which algorithms to use.\ud \ud Method: In this study we applied 44 obfuscations to 18 subject applications covering a total of 4 millions lines of code. The effectiveness of these source code obfuscations has been measured using 10 code metrics, considering modularity, size and complexity of code.\ud \ud Results: Results show that some of the considered obfuscations are effective in making code metrics change substantially from original to obfuscated code, although this change (called potency of the obfuscation) is different on different metrics. In the paper we recommend which obfuscations to select, given the security requirements of the software to be protected.

Most clinicians have come to imagine that scientific evidence has levels, and at the top of the pyramid are randomized clinical trials (RCTs) and meta-analyses [12]. But we all have read some poorly designed randomized trials, and sometimes a humble case series can substantially influence thought about a technique or implant, particularly if it describes failures of treatment. Nevertheless, as the Centre for Evidence-Based Medicine analogizes [6], if clinical evidence were stored in boxes marked “randomized trial,” “cohort study,” “case series,” and the like, and we needed to answer an important question, we might first open the box stamped “randomized trial” to see if there is anything inside. All the same, there are some important kinds of questions that RCTs and meta-analyses will never answer. Surgical RCTs generally are small to medium-sized, and as such, are tooled to assess efficacy more than safety. Randomized trials have almost no ability to detect rare events, evaluate resource utilization in normal practice, or compare any but the most common complications. Likewise, premature failures of commonly used interventions probably will not be identified first by RCTs, but rather by studies of other designs. To assess real-world and important endpoints like these, we will need to look elsewhere for evidence. Registry studies and analyses drawn from large-system databases represent two robust approaches to answering these kinds of questions. Clinical Orthopaedics and Related Research® is proud to support the excellent, thought-provoking work being done using the world’s registries. We publish it frequently [10, 11], and we have promoted it in Spotlight commentaries and interviews on these pages [13]. CORR® will publish selected proceedings from the most-recent meeting of the International Society of Arthroplasty Registries this summer, and we will cover the important role registries play in clinical research in an editorial in the coming months. We also believe that important, pressing questions can be answered efficiently and accurately using large databases, including the Nationwide Inpatient Sample (NIS), National Surgical Quality Improvement Program (NSQIP), National Hospital Discharge Survey (NHDS), National Trauma Databank (NTDB), and Medicare administrative databases, among others. In fact, there has been something of a bloom of orthopaedic papers from these kinds of sources lately [2–4]. With sample sizes that run from thousands to millions of patients, these studies allow us to tackle questions that likely will never be answered any other way. To make best use of this material, readers need to know just what kinds of questions can be asked of databases, how the same question may elicit different answers from different databases, and how slipshod or carelessly presented work from databases can mislead us. Finally, authors should know how editors for CORR® evaluate this relatively new type of research. We believe studies drawn from these databases best serve clinicians when they focus on what they are uniquely suited to do: Correlate less-common adverse events with modifiable and previously unidentified risk factors [1], identify adjustable provider- or hospital-level variables associated with readmissions or complications [7], and compare resource utilization for common interventions across diverse geographic regions or practice settings [5]. As with any other study design, the more novel the questions (and the more unexpected the answers), the more important these studies are. They do not make such good reading when they tell us what we already know. Readers also should realize that the same question asked of different databases may result in different answers [2, 4]. The most obvious reason for this is that each database surveys a specific population and may gather different data in different ways. Some databases, like the NIS, are inpatient-only samples: The moment a patient leaves the hospital, (s)he no longer is followed. As a result, studies drawn from inpatient-only samples that ask about complications that occur both before and after discharge—such as surgical site infection—will badly underestimate event rates. By contrast, prospective surgical registry-type databases (such as NSQIP) follow patents for 30 days or longer after surgery, and so are better tooled to find problems that occur after discharge. What do they lose in the exchange? Sample size. Prospective registries are smaller. There are other differences as well. Some registries focus on particular types of patients. The NTDB, for instance, includes patients with higher-energy injuries. Clearly, matching the study population to the question being asked is crucial. Additionally, each database uses different data elements. Many databases, including NIS, are built from ICD-9 codes. Since these codes’ primary use is billing, they have lower sensitivity to answer the clinical questions in which most readers are interested [8, 9]. By contrast, other databases—including NSQIP and parts of the NTDB—use chart-abstracted data, which offer more of the details clinicians seek. Readers should also recognize that missing data limits all databases to some degree, and any study drawn from them should comment on the degree to which this might influence its findings. We believe the most important failing of orthopaedic studies from these sources is that these databases generally do not capture enough patient-level diagnostic information or patient-reported outcomes data. Efforts are underway to address many of these limitations. But at present, questions calling for that level of detail probably are better studied using single or multicenter designs that capture more complete patient-level data of this sort. Notwithstanding the shortcomings of large-database studies, we at CORR® believe that they have an important role to play. But since these studies are not always easy reading, there must be a payoff for the reader who toughs it out—at the very least, a finding that changes the way the reader thinks about an important problem or treats his or her patients. Going forward, CORR® will consider a study that draws from a large database if it either presents a genuinely counterintuitive descriptive finding, or provides a specific suggestion to improve clinical care, practice management, or public policy. We will publish papers meeting one or both of those criteria when they are methodologically robust. As with the all of the important studies we publish in CORR®, we will continue to make every attempt to accompany these studies with CORR® Insights commentaries or Editor’s Spotlight features.

The recent COVID-19 outbreak highlighted the requirement for a more sophisticated healthcare system and real-time data analytics in the pandemic mitigation process. Moreover, real-time data plays a crucial role in the detection and alerting process. Combining smart healthcare systems with accurate real-time information about medical service availability, vaccination, and how the pandemic is spreading can directly affect the quality of life and economy. The existing architecture models are become inadequate in handling the pandemic mitigation process using real-time data. The present models are server-centric and controlled by a single party, where the management of confidentiality, integrity, and availability (CIA) of data is doubtful. Therefore, a decentralised user-centric model is necessary, where the CIA of user data is assured. In this paper, we have suggested a decentralized blockchain-based pandemic detection and assistance system (iBlock). The iBlock uses robust technologies like hybrid computing and IPFS to support system functionality. A pseudo-anonymous personal identity is introduced using H-PCS and cryptography for anonymous data sharing. The distributed data management module guarantees data CIA, security, and privacy using cryptography mechanisms. Furthermore, it delivers useful intelligent information in the form of suggestions and alerts to assist the users. Finally, the iBlock reduces stress on healthcare infrastructure and workers by providing accurate predictions and early warnings using AI/ML.

In the century of automation, which is digitized, and more and more technology is used, automatic systems' replacement of old manual systems makes people's lives easier. Nowadays, people have made the Internet an integral part of humans' daily lives unless they are insecure. The Internet of Things (IoT) secures a platform that authorizes devices and sensors to be remotely detected, connected, and controlled over the Internet. Due to the developments in sensor technologies, the production of tiny and low-cost sensors has increased. Many sensors, such as temperature, pressure, vibration, sound, light, can be used in the IoT. As a result of the development of these sensors with new generations, the power of the IoT technology increases, and accordingly, the revolution of IoT applications are developing rapidly. Therefore, their security issues and threats are challenging topics. In this paper, the benefits and open issues, threats, limitations of IoT applications are presented. The assessment shows that the most influential factor for evaluating IoT applications is the cost that is used in 79% of all articles, then the real-time-ness that is used in 64%, and security and error are used in 57% of all reviewed articles.