Stand-alone Program Research Articles

Deep Learning CNN Framework for Detection and Classification of Internet Worms

A worm is a standalone program, which is self-replicating malware that distributes itself to other computers and networks. An Internet worm can spread across the network and infect millions of computers in truly little time and the damages caused from such attacks are considered extremely high. In addition, these worms also affect the network packet and its performance, where the packets are analyzed by the signature-based intrusion detection system (IDS) and the network performance is analyzed by the NetFlow based IDS. Hence, this article proposes a joint detection of both the signature based and NetFlow based Internet worms using deep learning convolution neural network (DLCNN) with respect to various attacks and it can also prevent the suspicious actions of attackers (cyber-criminals). Additionally, it provides the security for users’ data maintenance, countermeasures, and controls the spreading of the internet worms. The effectiveness of proposed DLCNN model is evaluated using both packet capture (PCAP) and KDD-CUP-99 datasets. Finally, various quality metrics are employed to disclose the superiority of proposed DLCNN model as compared existing machine learning, and back propagated neural network models.

Effects of exercise in non-treatment seeking adults with alcohol use disorder: A three-armed randomized controlled trial (FitForChange)

BackgroundMost individuals with alcohol use disorder (AUD) do not seek treatment. Stigma and the desire to self-manage the problem are likely explanations. Exercise is an emerging treatment option but studies in non-treatment seeking individuals are lacking. We compared the effects of aerobic exercise, yoga, and treatment as usual (phone-based support) on alcohol consumption in non-treatment seeking adults with AUD. MethodsThree-group parallel, single blind, randomized controlled trial. 140 physically inactive adults aged 18–75 diagnosed with AUD were included in this community-based trial. Participants were randomized to either aerobic exercise (n = 49), yoga (n = 46) or treatment as usual (n = 45) for 12-weeks. The primary study outcome was weekly alcohol consumption at week 13 (Timeline Follow-back). ResultsA significant decrease in weekly alcohol consumption was seen in all three groups: aerobic exercise (mean ∆ = − 5.0, 95% C = − 10.3, − 3.5), yoga group (mean ∆ = − 6.9, 95% CI = − 10.3, − 3.5) and TAU (mean ∆ = − 6.6, 95% CI = − 8.8, − 4.4). The between group changes were not statistically significant at follow-up. Per-protocol analyzes showed that the mean number of drinks per week reduced more in both TAU (mean ∆ = − 7.1, 95% CI = − 10.6, − 3.7) and yoga (mean ∆ = − 8.7, 95% CI = − 13.2, − 4.1) compared to aerobic exercise (mean ∆ = − 1.7, 95% CI = − 4.4, 1. 0), [F(2, 55) = 4.9, p = 0.011]. ConclusionsParticipation in a 12-week stand-alone exercise program was associated with clinically meaningful reductions in alcohol consumption comparable to usual care (phone counseling) by an alcohol treatment specialist.

A cognitive behavioural therapy smartphone app for adolescent depression and anxiety: co-design of ClearlyMe

Abstract Adolescence is associated with heightened vulnerability to symptoms of depression and anxiety. In-person and computerised cognitive behavioural therapy (CBT) are effective treatment options, yet uptake and engagement remain low. Smartphone delivery of CBT offers an alternative, highly accessible method of delivering CBT. However, there is no freely available CBT smartphone application (app) specifically designed to reduce depression and anxiety symptoms in adolescents. The aim of this study was to design a new CBT smartphone app (ClearlyMe) that targets depressive and anxiety symptoms in adolescents. We engaged in a rigorous co-design process with adolescents (n=36), parents (n=15), and mental health professionals (n=32). Co-design involved: (1) discovery of users’ needs, views and preferences by conducting focus groups, (2) defining app features through ideation workshops and user consultations, (3) designing therapeutic CBT content and visual features, and (4) testing prototypes. Users were involved at every step and the process was iterative, with findings carried forward to ensure continued refinement of concepts and features. We found a preference for vibrant, cheerful colours and illustrations and non-endorsement of gamification and chatbots, which contrasted with findings from other studies. Preferences were largely consistent between the three user groups. However, adolescents preferred an app that could be used autonomously without professional support, whereas mental health professionals desired a product for use as a therapy adjunct to support CBT skill development. The importance of co-design, and particularly the inclusion of all stakeholders throughout the entire co-design process, is discussed in relation to the design of ClearlyMe. Key learning aims (1) To understand the co-design process that underpins the development of a new CBT smartphone app for youth with elevated symptoms of depression and anxiety. (2) To understand adolescent, parent and mental health professionals’ key preferences regarding the features and functionality of a CBT smartphone app for adolescents with elevated symptoms of depression and anxiety. (3) To understand how ClearlyMe has been designed as both a therapy adjunct and stand-alone program, and how it can be incorporated into day-to-day clinical practice.

Impact of yoga therapy in improving perceived stress, depression, and quality of life in elderly population: A randomized controlled trial

Introduction: Geriatric population is vulnerable to physical and emotional discommodes that requires expert care from a holistic perspective. Depression, stress, anxiety, etc., are the common problems faced by the elderly. Holistic interventions such as yoga are reckoned to be a pillar in alleviating these issues; however, most of the studies in this arena are conducted on elderly who resides in shelter homes. Materials and Methods: Ninety-six participants were randomized to a yoga or waitlisted control group (n = 48 each). Yoga group received a set of yogic practices for 12 weeks. Assessments were done using Perceived Stress Scale (PSS), Geriatric Depression Scale (GDS), and older people quality of life (OPQOL). Results: Yoga group has shown statistically significant reduction in PSS (P < 0.001), GDS (P = 0.001), and improvement in the selected components of OPQOL such as social relationship (P = 0.014), neighborhood (P = 0.001), psychological well-being (P = 0.001), financial circumstances (P = 0.001), and OPQOL – total (P = 0.001). Discussion: The results indicate that yoga can successfully be implemented in the elderly population on an outpatient basis and can produce clinically beneficial effects. Nurses, physicians, and other therapists should consider integrating yoga in the elderly. Conclusion: The results of this study are encouraging to recommend yoga as a stand-alone mind-body rehabilitation program for older adults.

Anxiety and Associated Factors with Online Learning in the COVID-19 Pandemic among the EFL Students at Higher Institution in Indonesia

Online learning is defined as learning that takes place partially or entirely over the Internet which excludes purely print-based correspondence education, broadcast television or radio, videoconferencing, videocassettes, and stand-alone educational software programs that do not have a significant internet-based instructional component (Means et al., 2009). It is a crosssectional, observational study. The objective of the present study was to investigate the level of anxiety in online learning in Covid-19 pandemic among the English students from different study programs at the Faculty of Languages and Literature, State University of Makassar in 2020/2021 academic year. There were 71 respondents in this study, 64 or 79% female and 17 or 21% male with a mean age of 21.5 (SD= 2.44949). They are undergraduate students and graduate students of State University of Makassar, Indonesia. Anxiety scale 18 questionnaires adapted from MacIntyre &amp; Gardner (1994) were given for assessment of anxiety. The study concludes that: 1) the students feel anxious during English tests online when they have not prepared their time well before the test, 2) they feel anxious if English online class is not well organized, 3) they may know the proper English expression but when they are nervous it just won't come out, and 4) they get upset when they know how to communicate something in English in online class but they just cannot verbalize what they mean. The associated factors with the online learning as other evidences of this study are: 1) immature preparations, 2) unorganized class, 3) nervous, 4) get upset.

Optimizing the hit finding algorithm for liquid argon TPC neutrino detectors using parallel architectures

Neutrinos are particles that interact rarely, so identifying them requires large detectors which produce lots of data. Processing this data with the computing power available is becoming even more difficult as the detectors increase in size to reach their physics goals. Liquid argon time projection chamber (LArTPC) neutrino experiments are expected to grow in the next decade to have 100 times more wires than in currently operating experiments, and modernization of LArTPC reconstruction code, including parallelization both at data- and instruction-level, will help to mitigate this challenge. The LArTPC hit finding algorithm is used across multiple experiments through a common software framework. In this paper we discuss a parallel implementation of this algorithm. Using a standalone setup we find speedup factors of two times from vectorization and 30–100 times from multi-threading on Intel architectures. The new version has been incorporated back into the framework so that it can be used by experiments. On a serial execution, the integrated version is about 10 times faster than the previous one and, once parallelization is enabled, further speedups comparable to the standalone program are achieved.

A Meta-Analysis of the Current State of Evidence of the Incredible Years Teacher-Classroom Management Program.

This meta-analysis evaluated the current state of evidence and identified potential treatment moderators of the Incredible Years Teacher Classroom Management (IYTCM) program used to reduce externalizing and internalizing behaviors in school-aged children. Inclusion criteria involved published studies between 1984–2018 and examining the effects of IYTCM as a standalone program on teacher and/or child behavioral outcomes. We identified and narratively summarized potential moderators, which included the severity of child behavior, dosage, study design, and reporting methods. Overall, effect sizes revealed IYTCM had moderate positive effects on teachers and small positive effects on children. Narrative summaries indicated larger effect sizes in higher dosage studies and higher risk children. The results align with previous systematic reviews on the Incredible Years Parent Training (IYPT) program but this is the first study to look at the teacher training program. Overall, IYTCM seems to be an effective intervention; however, what components of this program work best, for whom, and under what conditions require further empirical investigation.

Implementing Response to Intervention (RtI) in a Juvenile Detention Center Using Action Research

While proponents claimed Response to Intervention (RtI) improved student learning and prevented failure, there was an absence of research in effectiveness. Applying action research within a case study, there was an investigation into the process of reforming and improving RtI within a short-term juvenile detention center in the Midwest of the United States for students in grades 5-12. Using the conceptual framework of adaptive leadership, there was an analysis of policies and procedures, observations, interviews, and student work. RtI as a stand-alone program revealed many teachers lacked evidence-based instructional methods and alternative teachers lacked content knowledge, making implementation difficult. Within the action research method, role ambiguity caused problems with fidelity, with the need to infuse strategic leadership with action research when teachers’ sense of self and professional were challenged.

Progress toward automated methyl assignments for methyl-TROSY applications

Methyl-TROSY spectroscopy has extended the reach of solution-state NMR to supra-molecular machineries over 100kDa in size. Methyl groups are ideal probes for studying structure, dynamics, and protein-protein interactions in quasi-physiological conditions with atomic resolution. Successful implementation of the methodology requires accurate methyl chemical shift assignment, and the task still poses a significant challenge in the field. In this work, we outline the current state of technology for methyl labeling, data collection, data analysis, and nuclear Overhauser effect (NOE)-based automated methyl assignment approaches. We present MAGIC-Act and MAGIC-View, two Python extensions developed as part of the popular NMRFAM-Sparky package, and MAGIC-Net a standalone structure-based network analysis program. MAGIC-Act conducts statistically driven amino acid typing, Leu/Val pairing guided by 3D HMBC-HMQC, and NOESY cross-peak symmetry checking. MAGIC-Net provides model-based NOE statistics to aid in selection of a methyl labeling scheme. The programs provide a versatile, semi-automated framework for rapid methyl assignment.

Surveying over 100 predictors of intrinsic disorder in proteins

ABSTRACT Introduction Intrinsic disorder prediction field develops, assesses, and deploys computational predictors of disorder in protein sequences and constructs and disseminates databases of these predictions. Over 40 years of research resulted in the release of numerous resources. Areas covered We identify and briefly summarize the most comprehensive to date collection of over 100 disorder predictors. We focus on their predictive models, availability and predictive performance. We categorize and study them from a historical point of view to highlight informative trends. Expert opinion We find a consistent trend of improvements in predictive quality as newer and more advanced predictors are developed. The original focus on machine learning methods has shifted to meta-predictors in early 2010s, followed by a recent transition to deep learning. The use of deep learners will continue in foreseeable future given recent and convincing success of these methods. Moreover, a broad range of resources that facilitate convenient collection of accurate disorder predictions is available to users. They include web servers and standalone programs for disorder prediction, servers that combine prediction of disorder and disorder functions, and large databases of pre-computed predictions. We also point to the need to address the shortage of accurate methods that predict disordered binding regions.

Segmentation of Static and Dynamic Atomic-Resolution Microscopy Data Sets with Unsupervised Machine Learning Using Local Symmetry Descriptors.

We present an unsupervised machine learning approach for segmentation of static and dynamic atomic-resolution microscopy data sets in the form of images and video sequences. In our approach, we first extract local features via symmetry operations. Subsequent dimension reduction and clustering analysis are performed in feature space to assign pattern labels to each pixel. Furthermore, we propose the stride and upsampling scheme as well as separability analysis to speed up the segmentation process of image sequences. We apply our approach to static atomic-resolution scanning transmission electron microscopy images and video sequences. Our code is released as a python module that can be used as a standalone program or as a plugin to other microscopy packages.

Review of commercial software tools for facade acoustics

Environmental noise is an important public health issue in cities worldwide and building facades play an essential role since they occupy a significant amount of vertical surfaces in the urban fabric. Facade acoustics deals with the interaction of sound and the building envelope, and specialists in the field use digital tools to perform calculations and predictive analysis required to fulfil project goals or meet construction regulations. This review aims to give an overview of the different commercial software tools that can be relevant in facade acoustics, pointing out their general information such as their developer, their type of software and license, and the categories within facade acoustics where these tools can be applied. The tools are classified in five categories: Environmental noise modelling, sound insulation, noise mapping, auralization and 3D geometry manipulation. Design tools for acoustics that cannot address any aspect of facades specifically are excluded. A total of 17 software tools are reviewed including 13 standalone programs, three plugins and one online tool.

Teaching with a PURPOSE: An NGN approach to clinical instruction and evaluation

A clinical teaching and evaluation tool was created to improve clinical judgment and diagnostic reasoning for Practical Nursing (PN) and Associate Degree in Nursing (ADN) students in preparation for the Next-Generation National Council Licensure Examination (NCLEX). Implementation of the PURPOSE tool offers a structured approach for nursing students to obtain and retain necessary clinical information in a variety of populations and settings. In addition, the PURPOSE tool provides all levels of faculty a consistent method to contribute to clinical evaluation. Integration of the PURPOSE tool at a large community college in the Southeast resulted in a 100% NCLEX pass rate for new stand-alone program PN graduates.

Abstract 11203: Providing Transcatheter Aortic Valve Replacement Therapies to VA Patients Through a Public-Private Partnership

Introduction: Despite the success of Transcatheter Aortic Valve Replacement (TAVR) in improving morbidity and mortality among patients with aortic stenosis, few stand-alone programs currently exist within the VA system. Many VA centers have developed transfer arrangements with partner institutions to provide TAVR for their patients. There is minimal data for VA patients in such programs. We compared the outcomes of VA patients to non-VA patients undergoing TAVR at a partner institution. Methods: We performed a single-center retrospective study on all patients undergoing TAVR between March 2018 and June 2020. All procedures were performed at an academic medical center (AMC) in Chicago where VA patients were referred for TAVR. Primary outcomes included composite MACE (death from any cause, MI, CVA). Secondary outcomes included length of stay, bleeding events, AKI, change in ejection fraction, CHF exacerbations, and device-related complications. We assessed outcomes at hospital discharge, 6-month and 12-months. Results: There was a total of 115 patients (AMC: 56, VA: 59). Patient demographics between the two groups were significant for the VA group being 57.6% Caucasian and 96.6% male while the AMC group was 33.9% Caucasian and 56.6% male (for both p&lt;0.01). There was no in-hospital mortality in either group. Total length of stay (p=0.6), ICU length of stay (p=0.4), procedure time (p=0.4) and baseline ejection fraction (p=0.8) were similar between both groups. At 6 months, the VA patients experienced a trend towards a higher incidence of composite MACE (VA: 11.9%, AMC: 5.4%, p=0.2). None of the secondary outcomes reached statistical significance. Ejection fraction and CHF exacerbations were similar between groups (p=0.9 and p=0.59). At 12 months there was no statistical difference in primary or secondary outcomes. Similar ejection fractions and CHF exacerbations at 12 months were also recorded. Conclusions: No difference between procedural, 6-month or 12-month outcomes between VA patients and non-VA patients undergoing TAVR were observed. Public-private partnerships can provide state of the art therapies to VA patients in an efficient and safe manner.

A runtime alterable epidemic model with genetic drift, waning immunity and vaccinations.

We present methods for building a Java Runtime-Alterable-Model Platform (RAMP) of complex dynamical systems. We illustrate our methods by building a multivariant SEIR (epidemic) RAMP. Underlying our RAMP is an individual-based model that includes adaptive contact rates, pathogen genetic drift, waning and cross-immunity. Besides allowing parameter values, process descriptions and scriptable runtime drivers to be easily modified during simulations, our RAMP can used within R-Studio and other computational platforms. Process descriptions that can be runtime altered within our SEIR RAMP include pathogen variant-dependent host shedding, environmental persistence, host transmission and within-host pathogen mutation and replication. They also include adaptive social distancing and adaptive application of vaccination rates and variant-valency of vaccines. We present simulation results using parameter values and process descriptions relevant to the current COVID-19 pandemic. Our results suggest that if waning immunity outpaces vaccination rates, then vaccination rollouts may fail to contain the most transmissible variants, particularly if vaccine valencies are not adapted to deal with escape mutations. Our SEIR RAMP is designed for easy use by others. More generally, our RAMP concept facilitates construction of highly flexible complex systems models of all types, which can then be easily shared as stand-alone application programs.

Resource Brokering: Efforts to Assist Patients With Housing, Transportation, and Economic Needs in Primary Care Settings.

Clinicians and policy makers are exploring the role of primary care in improving patients' social conditions, yet little research examines strategies used in clinical settings to assist patients with social needs. Study used semistructured interviews with leaders and frontline staff at 29 diverse health care organizations with active programs used to address patients' social needs. Interviews focused on how organizations develop and implement case management-style programs to assist patients with social needs including staffing, assistance intensity, and use of referrals to community-based organizations (CBOs). Organizations used case management programs to assist patients with social needs through referrals to CBOs and regular follow-up with patients. About one-half incorporated care for social needs into established case management programs and the remaining described standalone programs developed specifically to address social needs independent of clinical needs. Referrals were the foundation for assistance and included preprinted resource lists, patient-tailored lists, and warm handoffs to the CBOs. While all organizations referred patients to CBOs, some also provided more intense services such as assistance completing patients' applications for services or conducting home visits. Organizations described 4 operational challenges in addressing patients' social needs: (1) effectively engaging CBOs; (2) obtaining buy-in from clinical staff; (3) considering patients' perspectives; and (4) ensuring program sustainability. As the US health care sector faces pressure to improve quality while managing costs, many health care organizations will likely develop or rely on case management approaches to address patients' social conditions. Health care organizations may require support to address the key operational challenges.Visual abstract.

BLaDE: A Basic Lambda Dynamics Engine for GPU-Accelerated Molecular Dynamics Free Energy Calculations.

There is an accelerating interest in practical applications of alchemical free energy methods to problems in protein design, constant pH simulations, and especially computer-aided drug design. In the present paper, we describe a basic lambda dynamics engine (BLaDE) that enables alchemical free energy simulations, including multisite λ dynamics (MSλD) simulations, on graphical processor units (GPUs). We find that BLaDE is 5 to 8 times faster than the current GPU implementation of MSλD-based free energy calculations in CHARMM. We also demonstrate that BLaDE running standard molecular dynamics attains a performance competitive with and sometimes exceeding that of the highly optimized OpenMM GPU code. BLaDE is available as a standalone program and through an API in CHARMM.

ProPIP: a tool for progressive multiple sequence alignment with Poisson Indel Process

BackgroundCurrent alignment tools typically lack an explicit model of indel evolution, leading to artificially short inferred alignments (i.e., over-alignment) due to inconsistencies between the indel history and the phylogeny relating the input sequences.ResultsWe present a new progressive multiple sequence alignment tool ProPIP. The process of insertions and deletions is described using an explicit evolutionary model—the Poisson Indel Process or PIP. The method is based on dynamic programming and is implemented in a frequentist framework. The source code can be compiled on Linux, macOS and Microsoft Windows platforms. The algorithm is implemented in C++ as standalone program. The source code is freely available on GitHub at https://github.com/acg-team/ProPIP and is distributed under the terms of the GNU GPL v3 license.ConclusionsThe use of an explicit indel evolution model allows to avoid over-alignment, to infer gaps in a phylogenetically consistent way and to make inferences about the rates of insertions and deletions. Instead of the arbitrary gap penalties, the parameters used by ProPIP are the insertion and deletion rates, which have biological interpretation and are contextualized in a probabilistic environment. As a result, indel rate settings may be optimised in order to infer phylogenetically meaningful gap patterns.

Cops, Clinicians, or Both? Collaborative Approaches to Responding to Behavioral Health Emergencies.

How a community responds to behavioral health emergencies is both a public health issue and social justice issue. Individuals experiencing a behavioral health crisis often receive inadequate care in emergency departments (EDs), boarding for hours or days while waiting for treatment. Such crises also account for a quarter of police shootings and >2 million jail bookings per year. Racism and implicit bias magnify these problems for people of color. Growing support for reform provides an unprecedented opportunity for meaningful change, but solutions to this complex issue will require comprehensive systemic approaches. As communities grapple with behavioral health emergencies, the question is not just whether law enforcement should respond to behavioral health emergencies but how to reduce unnecessary law enforcement contact and, if law enforcement is responding, when, how, and with what support. This policy article reviews best practices for law enforcement crisis responses, outlines the components of a comprehensive continuum-of-crisis care model that provides alternatives to law enforcement involvement and ED use, and offers strategies for collaboration and alignment between law enforcement and clinicians toward common goals. Finally, policy considerations regarding stakeholder engagement, financing, data management, legal statutes, and health equity are presented to assist communities interested in taking steps to build these needed solutions.

HIV Testing Strategies for Health Departments to End the Epidemic in the U.S.

An important goal of the Ending the HIV Epidemic in the U.S. initiative is the timely diagnosis of all people with HIV as early as possible after infection. To end the HIV epidemic, health departments were encouraged to propose new and innovative HIV testing strategies and improve the reach of existing programs. These activities were divided into 3 core strategies: expansion of routine screening in healthcare settings, locally tailored HIV testing initiatives in nonhealthcare settings, and specific efforts to increase the frequency of testing for individuals with increased potential for acquiring HIV. Because HIV testing is such a crucial part of the core activities of the Centers for Disease Control and Prevention's HIV prevention programs, there are many examples of evidence-based programs and best practices for HIV testing in both clinical and nonclinical settings. This article reviews the evidence base for these strategies and some of the activities proposed under the Diagnose pillar to achieve the goal of diagnosing all HIV infections as early as possible. All other Ending the HIV Epidemic in the U.S. activities start with an awareness of HIV status, which is actually the indicator for which most health departments are closest to the proposed targets. There are both proven and emerging approaches to increasing HIV screening and increasing the frequency of HIV screening available. The Ending the HIV Epidemic in the U.S. initiative provides the motivation, the resources, and a coordinated plan to bring them to scale.