Miracle-dz/UniFAR-FTUs huggingface dataset:
A Comparative Study of FP-growth Variations

Summary Finding frequent itemsets in databases is crucial in data mining for purpose of extracting association rules. Many algorithms were developed to find those frequent itemsets. This paper presents a summarization and a comparative study of the available FP-growth algorithm variations produced for mining frequent itemsets showing their capabilities and efficiency in terms of time and memory consumption.
Harmonic Suppression of a Three-Stage 25–31-GHz GaN MMIC Power Amplifier Using Elliptic Low-Pass Filtering Matching Network

In this letter, the modified elliptic low-pass filtering (LPF) matching network (MN) is proposed for harmonic suppression of a wideband gallium nitride (GaN) microwave monolithic integrated circuit (MMIC) power amplifier (PA) at millimeter-wave (mm-wave). To investigate the effectiveness of the method at mm-wave, a three-stage 25–31-GHz GaN MMIC PA is designed using a commercial 150-nm GaN HEMT foundry process, with a mask area of $4.5\times2.6$ mm2. Simulated results show that the second harmonic of the PA can be effectively suppressed by using the elliptic LPF MN. Experimental results show that the realized MMIC PA achieves a saturated power-added efficiency (PAE) higher than 26.1% from 25 to 31 GHz, with a peak PAE of 31.5% at 29 GHz. The saturated gain and output power are more than 20 and 34 dBm within the band, respectively.
Collective Program Social Justice Identity and Perceived Norms on Promoting Student Advocacy

A growing body of research suggests that graduate psychology training programs with a stronger collective social justice identity are likely to provide more support for students’ advocacy engagements than those with discrepant views among members. We conducted response surface analyses (N = 178 PhD students; across 16 counseling psychology programs) to examine whether the degree of congruence and discrepancy in social justice attitudes and perceptions of training program norms (perceived social justice norms among students and faculty) between the individual student and other students in their program would be associated with students’ social justice advocacy intentions and behaviors. Higher congruence in attitudes and perceived norms (among students and faculty) were significantly associated with advocacy intentions. For advocacy behaviors, only congruently higher perceptions of faculty were significantly associated. Discrepant attitudes and perceived norms were not significantly related to students’ advocacy intentions and behaviors. Recommendations for training and research are discussed.
Media representations of young people during pandemic times in Turkey

ABSTRACT This study discusses patterns of media representations of Turkish youth during the COVID-19 pandemic, especially, during the early lockdowns. We focus on media narratives as discursive practices, with the pandemic being the main object of these narratives. Building upon a thematic analysis of mainstream news media, we suggest that discursive representations portray young people along the axis of responsibilities and vulnerabilities. Along this axis, there lie three types of representations: (i) responsibilization of youth to deal with the health crisis, (ii) socio-economic vulnerabilities, and (iii) socio-cultural impoverishment. Viewing through this lens, we argue that representations of youth in news media during pandemic times maintain the recurrent figures of the youth that are conceived through the terms of the ‘myth of youth’ and ‘denied citizenship’ at the same time. To the extent that young people are recognized and praised as instrumental figures of crisis management, their vulnerabilities are partially or completely disregarded. Thus, youth in pandemic times emerges as a figure that is both instrumentalized and obscured.
The Design of Crowd-Funded Products

Crowdfunding is an emerging phenomenon where entrepreneurs publicize their product concepts to raise development funding and collect design feedback directly from potential supporters. Many innovative products have raised a significant amount of crowdfunding. This paper analyzes the crowd-funded products to develop design guidelines for crowdfunding success. A database of 127 samples is collected in two different product categories from two different crowdfunding websites. They are evaluated using a design project assessment scorecard, the Real-Win-Worth framework, which focuses on the state of maturity on various customer, technical and supply chain dimensions. Our analysis identified key RWW factors that characterize successful design for crowd-funded products. For example, success at crowdfunding is attained through clear explanation of how the design operates technically and meets customer needs. Another recommendation is to not emphasize patent protection, for which crowd-funders are less concerned. Also, evidence of a strong startup financial plan is not necessary for crowdfunding success. These key RWW factors provide guidelines for designers and engineers to improve their design and validate their concepts early to improve their chances for success on crowdfunding platforms.Copyright © 2015 by ASME
Real-time Weapon Detection in Videos

Real-time weapon detection in video is a challenging object detection task due to the small size of weapons relative to the image size. Thus, we try to solve the common problem that object detectors deteriorate dramatically as the object becomes smaller. In this manuscript, we aim to detect small-scale non-concealed rifles and handguns. Our contribution in this paper is (i) proposing a scale-invariant object detection framework that is particularly effective with small objects classification, (ii) designing anchor scales based on the effective receptive fields to extend the Single Shot Detection (SSD) model to take an input image of resolution 900*900, and (iii) proposing customized focal loss with hard-mining. Our proposed model achieved a recall rate of 86% (94% on rifles and 74% on handguns) with a false positive rate of 0.07% on a self-collected test set of 33K non-weapon images and 5K weapon images.
Optimal Design of a Parallel Manipulator for Aliquoting of Biomaterials Considering Workspace and Singularity Zones

This article presents the concept of a robotic system for aliquoting of biomaterial, consisting of a serial manipulator in combination with a parallel Delta-like robot. The paper describes a mathematical formulation for approximating the geometric constraints of the parallel robot as a set of solutions to a system of nonlinear inequalities. The analysis of the workspace is carried out, taking into account singularity zones, using a method based on the analysis of the Jacobian matrix of the mechanism and the interference of links. An optimal design procedure is proposed for the dimensional synthesis based on a criterion for maximizing the volume of the workspace, taking into account the ambiguity of the solution of the inverse kinematics. Simulation results are reported and discussed to propose a suitable design solution.
Renewing models for change

Purpose This paper aims to contribute to discussion around the need for improved models for change in a rapidly changing world. It also aims to provide insights for further discussion around how a Nordic management approach can relate to the need for new change ideas. Since Lewin proposed that change requires unfreezing, moving and refreezing, several changes in models have been introduced. Many models include similar factors such as the need for a vision, a clear process and to motivate change. These change factors are investigated in this paper and related to a fast-paced, uncertain and volatile environment Design/methodology/approach Based on a collaborative research approach, engaging with leaders in five organizations, all headquartered in the Nordic countries, this paper examines problems with traditional change models in a contemporary context as well as how these challenges could be handled. Findings This paper concludes that leaders still find value in traditional models but see that these models need to be adapted to include elements of more recent research. This paper suggests tweaks in traditional change factors and ends with a proposition with a renewed model for change. Originality/value The ideas in this paper could be seen as a bridge between traditional and modern models – a bridge that seems to be needed in practice. It draws on action research and close relationships with top management, as they are working with change – potentially giving the study a unique angle on a practical, widespread problem: succeeding with change
Variational Phylodynamic Inference Using Pandemic-scale Data

The ongoing global pandemic has sharply increased the amount of data available to researchers in epidemiology and public health. Unfortunately, few existing analysis tools are capable of exploiting all of the information contained in a pandemic-scale data set, resulting in missed opportunities for improved surveillance and contact tracing. In this paper, we develop the variational Bayesian skyline (VBSKY), a method for fitting Bayesian phylodynamic models to very large pathogen genetic data sets. By combining recent advances in phylodynamic modeling, scalable Bayesian inference and differentiable programming, along with a few tailored heuristics, VBSKY is capable of analyzing thousands of genomes in a few minutes, providing accurate estimates of epidemiologically relevant quantities such as the effective reproduction number and overall sampling effort through time. We illustrate the utility of our method by performing a rapid analysis of a large number of SARS-CoV-2 genomes, and demonstrate that the resulting estimates closely track those derived from alternative sources of public health data.
Computational Investigations of Learning and Synchronization in Cognitive Control

Complex cognition requires binding together of stimulus, action, and other features, across different time scales. Several implementations of such binding have been proposed in the literature, most prominently synaptic binding (learning) and synchronization. Biologically plausible accounts of how these different types of binding interact in the human brain are still lacking. To this end, we adopt a computational approach to investigate the impact of learning and synchronization on both behavioral (reaction time, error rate) and neural (θ power) measures. We train four models varying in their ability to learn and synchronize for an extended period of time on three seminal action control paradigms varying in difficulty. Learning, but not synchronization, proved essential for behavioral improvement. Synchronization however boosts performance of difficult tasks, avoiding the computational pitfalls of catastrophic interference. At the neural level, θ power decreases with practice but increases with task difficulty. Our simulation results bring new insights in how different types of binding interact in different types of tasks, and how this is translated in both behavioral and neural metrics.
Managing fisheries for maximum nutrient yield

Abstract Wild‐caught fish are a bioavailable source of nutritious food that, if managed strategically, could enhance diet quality for billions of people. However, optimising nutrient production from the sea has not been a priority, hindering development of nutrition‐sensitive policies. With fisheries management increasingly effective at rebuilding stocks and regulating sustainable fishing, we can now begin to integrate nutritional outcomes within existing management frameworks. Here, we develop a conceptual foundation for managing fisheries for multispecies Maximum Nutrient Yield (mMNY). We empirically test our approach using size‐based models of North Sea and Baltic Sea fisheries and show that mMNY is predicted by the relative contribution of nutritious species to total catch and their vulnerability to fishing, leading to trade‐offs between catch and specific nutrients. Simulated nutrient yield curves suggest that vitamin D, which is deficient in Northern European diets, was underfished at fishing levels that returned maximum catch weights. Analysis of global catch data shows there is scope for nutrient yields from most of the world's marine fisheries to be enhanced through nutrient‐sensitive fisheries management. With nutrient composition data now widely available, we expect our mMNY framework to motivate development of nutrient‐based reference points in specific contexts, such as data‐limited fisheries. Managing for mMNY alongside policies that promote access to fish could help close nutrient gaps for coastal populations, maximising the contribution of wild‐caught fish to global food and nutrition security.
Detect Megaregional Communities Using Network Science Analytics

Urban science research and the research on megaregions share a common interest in the system of cities and its implications for world urbanization and sustainability. The two lines of inquiry currently remain largely separate efforts. This study aims to bridge urban science and megaregion research by applying network science’s community detection algorithm to explore the spatial pattern of megaregions in the contiguous United States. A network file was constructed consisting of county centroids as nodes, the direct links between each pair of counties as edges, and inter-county commuting flows as the weight to capture spatial interactions. Analyses were carried out at two levels, one at the national level using Gephi and the other for the State of Texas involving NetworkX, an open-source Python programming package to implement a weighted community detection algorithm. Results show the detected communities largely conforming to the qualitative knowledge on megaregions. Despite a number of limitations, the study indicates the great potential of applying network science analytics to improve understanding of the spatial process of megaregions.
Resveratrol and Reproductive Health

Resveratrol (RSV), a plant-derived polyphenol, demonstrates broad-spectrum health benefits, including anti-proliferative, anti-inflammatory, antidiabetic, anti-ischemic and antioxidant effects. The aim of this review is to give an important heads-up regarding the influence of RSV as a phytoestrogen, RSV effects on most common pregnancy-related complications, as well as its impact on the embryogenesis, spermatogenesis, and women’s reproductive health. Considering the important implications of RSV on human reproductive health, this overview could provide a groundwork, encouraging more detailed research at the clinical level.
In the Eye of the Storm: A Quantitative and Qualitative Account of the Impact of the COVID-19 Pandemic on Dutch Home Healthcare

The COVID-19 pandemic has severely affected healthcare delivery across the world. However, little is known about COVID-19’s impact on home healthcare (HHC) services. Our study aimed to: (1) describe the changes in volume and intensity of HHC services and the crisis management policies implemented; (2) understand the responses and the experiences of HHC staff and clients. We conducted an explanatory sequential mixed methods study. First, retrospective client data (N = 43,495) from four Dutch HHC organizations was analyzed. Second, four focus group interviews were conducted for the strategic, tactical, operational, and client levels of the four HHC organizations. Our results showed that both the supply of and demand for Dutch HHC decreased considerably, especially during the first wave (March–June 2020). This was due to factors such as fear of infection, anticipation of a high demand for COVID-19-related care from the hospital sector, and lack of personal protective equipment. The top-down management style initially applied made way for a more bottom-up approach in the second wave (July 2020–January 2021). Experiences vary between levels and waves. HHC organizations need more responsive protocols to prevent such radical scaling-back of HHC in future crises, and interventions to help HHC professionals cope with crisis situations.
The TRISEC framework for optimizing conversational agent design across search, experience and credence service contexts

PurposeService providers increasingly use conversational agents (CAs), such as chatbots, to effectively communicate with customers while managing interaction costs and providing round-the-clock customer service. Yet, the adoption and implementation of such agents in service contexts remains a hit-and-miss, and firms often struggle to balance their CAs implementation complexities and costs with relation to their service objectives, technology design and customer experiences. The purpose of this paper is to provide guidance on optimizing CA design, therefore, the authors develop a conceptual framework, TRISEC, that integrates service logic, technology design and customer experience to examine the implementation of CA solutions in search, experience and credence (SEC) contexts.Design/methodology/approachThe paper draws on service marketing and communications research, combining the service context classification scheme of search, experience and credence and the technology infused service marketing triangle foci (service, technology and customer) in its conceptual development.Findings The authors find that an opportunity exists in recognizing the importance of context when designing CAs and aiming to achieve a balance between service objectives, technology design and customer experiences.Originality/value This study contributes to service management and communications research literature by providing interactive service marketing researchers with the highly generalizable TRISEC framework to aid in optimizing CA design and implementation in interactive customer communication technologies. Furthermore, the study provides an array of future research avenues. From a practical perspective, this study aims at providing managers with a means to optimize CA technology design while maintaining a balance between customer centricity and implementation complexity and costs in different service contexts.
Palliative Sedation—The Last Resort in Case of Difficult Symptom Control: A Narrative Review and Experiences from Palliative Care in Switzerland

Palliative sedation can be considered as “the last resort” in order to treat unbearable, refractory symptoms or suffering in end-of-life patients. The aim is symptom relief and not to induce death as in the case of euthanasia. The treatment might be one of the most challenging therapeutic options in the field of palliative care, involving both ethical and practical issues. Still, studies have shown that it is a safe and valuable treatment and in general does not shorten the life of the patient. Since patients in Switzerland have the legal option of assisted suicide, palliative sedation is an alternative that has become increasingly important. The use of palliative sedation was reported in 17.5% of all patients admitted to palliative care in Switzerland, making the country of those with the highest use of this treatment. The aim of this narrative review is to discuss ethical and practical issues in palliative sedation, with specific focus on experiences from Switzerland. Indications, ethical considerations, drugs of choice and duration are discussed. Decision making should be based on solid guidelines. When used correctly, palliative sedation is an important and useful tool in palliative care in order to provide good symptom relief.
Intelligent fault diagnosis methods of rolling bearing based on SPWVD and AIN

Intelligent fault diagnosis methods based on pattern recognition of vibration spectrogram were studied. Firstly,taking rolling bearing as an example,the GLCM was extracted from SPWVD spectrogram and its statistic characteristics were described. Moreover,a modified AIN algorithm was introduced and used in bearing fault diagnosis. Through optimization of fault antigen sample,the memory antibodies sets were formed and classification was processed by the k-nearest neighbor method. A mass of fault sample were analyzed in the algorithm proposed and the results were compared with those obtained by BPNN. The comparison result indicated that the modified AIN algorithm had better classification ability as well as higher diagnosis accuracy. With intelligent fault diagnosis methods developing,methods based on spectrogram identification should be popularized,its practicability was proved through recognition of bearing fault here.
Differences in Structural Brain Characteristics Between Individuals with Chronic Nonspecific Neck Pain and Asymptomatic Controls: A Case–Control Study

Background Neck pain is a prevalent and costly problem, but its underlying mechanisms are poorly understood. Neuroimaging studies show alterations in brain morphometry in chronic musculoskeletal pain, but reports on neck pain are scarce. Objective This study investigates (1) differences in brain morphometry between individuals with chronic nonspecific neck pain and asymptomatic individuals and (2) associations between brain morphometry and patient-reported outcomes. Methods Sixty-three participants (33 pain, 11 female, mean [SD] age 35 [10] years; 30 control, 12 female, age 35 [11] years) underwent magnetic resonance imaging. Brain regions of interest (ROIs) were determined a priori, outcomes included cortical thickness and volume. Between-group differences were determined using cluster-wise correction for multiple comparisons and analyses of pain-related ROIs. Results Between-group differences in volume were identified in the precentral, frontal, occipital, parietal, temporal, and paracentral cortices. ROI analyses showed that parahippocampal cortical thickness was larger in the neck pain group (p=0.015, 95% CI: −0.27 to −0.03). Moderate to strong associations between volume and thickness of the cingulate cortex, prefrontal cortex, and temporal lobe and neck pain duration, pain intensity, and neck disability were identified (p-values 0.006 to 0.048). Conclusion Alterations in brain morphology that are associated with clinical characteristics inform the mechanisms underlying chronic nonspecific neck pain and may guide the development of more effective treatment approaches.
A Step-by-step Guide on How to Construct quasi-Markov State Models to Study Functional Conformational Changes of Biological Macromolecules

Conformational changes play an important role for many biomolecules to perform their functions. In recent years, Markov State Model (MSM) has become a powerful tool to investigate these functional conformational changes by predicting long time-scale dynamics from many short molecular dynamics (MD) simulations. In MSM, dynamics are modelled by a first-order master equation, in which a biomolecule undergoes Markovian transitions among conformational states at discrete time intervals, called lag time. The lag time has to be sufficiently long to build a Markovian model, but this parameter is often bound by the length of MD simulations available for estimating the frequency of interstate transitions. To address this challenge, we recently employed the generalized master equation (GME) formalism (e.g., the quasi-Markov State Model or qMSM) to encode the non-Markovian dynamics in a time-dependent memory kernel. When applied to study protein dynamics, our qMSM can be built from MD simulations that are an order-of-magnitude shorter than MSM would have required. The construction of qMSM is more complicated than that of MSMs, as time-dependent memory kernels need to be properly extracted from the MD simulation trajectories. Here, we present a step-by-step guide on how to build qMSM from MD simulation datasets, and the materials accompanying this protocol are publicly available on Github: https://github.com/ykhdrew/qMSM_tutorial. We hope this protocol is useful for researchers who want to apply qMSM and study functional conformational changes in biomolecules.