Miracle-dz/UniFAR-FTUs huggingface dataset:
Printing imperfections – geometric patterns to improve resistances of 3D printed steel plates

Traditional structural steel manufacturing routes typically produce prismatic members comprising of flat plate elements. Under compressive actions, the capacity of these sections is often dominated by plate instability of the lowest buckling mode. The current study proposes the use of innovative additive manufacturing to produce metallic plate elements with predefined imperfection patterns capable of postponing instability via the lowest buckling mode thus providing increased buckling resistances. To this end, a program of numerical modelling has been carried out to explore various combinations of predefined surface waves with different amplitudes in stainless steel square hollow section stub columns of varying slenderness. Their stiffness, strength, and material consumption against the typical flat sided structural steel members with the same nominal dimensions were assessed. The study identifies the possibility for ‘imperfect’ sections to achieve up to a 50% increase in strength for a minimal increase in material consumption. This innovative study highlights the significant potential of new metallic additive manufacturing technologies in achieving unprecedented material efficiency and economic design in the future steel construction industry.
The boson peak and the first sharp diffraction peak in (As2S3)x(GeS2)1–x glasses

The parameters of the boson peak (BP) and the first sharp diffraction peak (FSDP) in (As2S3)x(GeS2)1x glasses measured using high-resolution Raman spectroscopy and high-energy synchrotron X-ray diffraction measurements are examined as a function of x. It has been found that there is no correlation between the positions of BP and FSDP. The BP position shows a nonlinear composition behavior with a maximum at about x = 0.4, whereas the FSDP position changes virtually linearly with x. The intensities of both BP and FSDP show nonlinear composition dependences with the slope changes at x = 0.4, although there is no direct proportionality. Analysis of the partial structure factors for the glasses with x = 0.2, 0.4 and 0.6 obtained in another study has shown that the cation-cation atomic pairs of Ge–Ge, Ge–As and As–As make the largest contribution to FSDP, where the Ge–Ge and Ge–As pairs are dominant.
Thermal conduction modeling of composites by embedding technique

Composite materials are extremely important in several industrial areas and have been thoroughly used to solve many engineering problems. In more recent years, new numerical models and manufacturing processes made interest grow on those materials. This work contributes to the state of the art of composite materials modeling by proposing an embedding technique for thermal problems using the finite element method. The technique relies on rewriting the reinforcement finite element variables according to the composite matrix finite element form functions. By doing so, it is possible to embed the reinforcement element without increasing the total number of degrees of freedom. The embedding method is capable of modeling the phase’s orientation as well as it’s placement. It’s also possible to make use of nonlinear conductive parameters to model nonlinear thermal problems. Numerical examples are described to show the technique’s capability. The numerical results show good agreement with references, as well as reducing the total number of degrees of freedom and being able to model nonlinear material behavior. Such characteristics make the model suitable to perform more complex analysis such as reliability, optimization, multi-physics, among others.
Design and implementation of a novel real‐time unmanned aerial vehicle localization scheme based on received signal strength

To accurately localize unmanned aerial vehicles (UAVs) is one of the key issues to deal with the security threats caused by UAVs. Thus, this article proposes a UAV localization scheme that utilizes the area grid quantization and transmit power statistical calibration techniques, in which the location and transmit power of the UAV are unknown. Firstly, the adaptive discrete particle swarm optimization (ADPSO) algorithm is used to find the global optimal solution to the localization problem. A grid quantization method is proposed to discretize the value space of the traditional PSO algorithm to reduce the estimated localization time. An adaptive factor is also introduced to adjust the value of power for each iteration in the discrete particle swarm optimization (DPSO) algorithm in order to find the global optimal solution efficiently. Secondly, the fixed transmit power range is adopted to calibrate the estimated value of transmit power and the corresponding location. The prototype system comprises four anchor nodes based on a universal software radio peripheral radio frequency (USRP RF). Finally, field experiments are carried out to demonstrate the effectiveness of the proposed localization scheme.
Community composition of ants beneath invasive plant Rosa rugosa

Planting of Japanese rose Rosa rugosa along the Baltic Sea coastline has contributed to the synanthropization of coastal ecosystems. Many ant species inhabiting coastal dunes are rare, have high conservation value and are sensitive to ecosystem disturbances. This short communication presents a study describing the species composition of ground‐nesting ants beneath the invasive plant Rosa rugosa. Twenty‐five paired invaded–native plots were established along the Hel Peninsula in gray and white dunes. We identified a total of 13 species within both the invaded and reference plots, of which 11 species were shared. The difference between reference and invaded plots was tested using permutational multivariate anova based on the Bray–Curtis index dissimilarity matrix. Permutational multivariate anova partitioning showed the greatest component was the residual, and there was no evidence for vegetation type effect. Thus, we did not confirm the negative influence of R. rugosa invasion on the community composition of ground‐nesting ants. Comparing the species composition of invaded and reference plots, we identified a small bias towards a slightly higher frequency of hygrophilous Myrmica species and a higher frequency of the psammophilous ant Lasius psammophilus, which raises the question for future investigations of whether R. rugosa creates nesting microhabitats preferred by hygrophilous ants.
Maternal Stress and Early Neurodevelopment: Exploring the Protective Role of Maternal Growth Mindset

This article has supplementary material on the web site: www.jdbp. org. ABSTRACT: Objective: The purpose of this study was to test associations between maternal stress, maternal mindset, and infant neurodevelopment at 12 months of age. Specifically, we sought to examine the extent to which maternal growth mindsets may serve to attenuate the negative associations between maternal stress and infants' neurodevelopment. Methods: The current exploratory study leverages data from a longitudinal cohort study following mother-infant dyads. Maternal-perceived stress, maternal mindset, and infant electroencephalography (EEG) recordings were collected when infants were 12 months of age. The final analytic sample included 33 dyads. Results: Results revealed no statistically significant main effects of maternal stress or maternal mindset for any of the infant EEG frequency band outcomes. After including interactions between maternal stress and mindset, statistically significant positive interactions were detected for all EEG frequency bands. Simple slope tests revealed significant negative associations between maternal stress and each of the 6 EEG frequency bands for mothers with more fixed-oriented mindsets. Associations between maternal stress and infant EEG outcomes for mothers with more growth-oriented mindsets did not differ from 0. Conclusion: These findings suggest that infants raised by mothers with growth mindsets may be protected against the neurodevelopmental consequences of higher maternal stress.
Automated Detection of Voice Disorder in the Saarbrücken Voice Database: Effects of Pathology Subset and Audio Materials

The Saarbrücken Voice Database contains speech and simultaneous electroglottography recordings of 1002 speakers exhibiting a wide range of voice disorders, together with recordings of 851 controls. Previous studies have used this database to build systems for automated detection of voice disorders and for differential diagnosis. These studies have varied considerably in the subset of pathologies tested, the audio materials analyzed, the cross-validation method used and the performance metric reported. This variation has made it hard to determine the most promising approaches to the problem of detecting voice disorders. In this study we re-implement three recently published systems that have been trained to detect pathology using the SVD and compare their performance on the same pathologies with the same audio materials using a common cross-validation protocol and performance metric. We show that under this approach, there is much less difference in performance across systems than in their original publication. We also show that voice disorder detection on the basis of a short phrase gives similar performance to that based on a sequence of vowels of different pitch. Our evaluation protocol may be useful for future studies on voice disorder detection with the SVD.
Validation of clinical‐grade whole genome sequencing reproduces cytogenetic analysis and identifies mutational landscape in newly‐diagnosed multiple myeloma patients: A pilot study from the 100,000 Genomes Project

Multiple myeloma is characterized by chromosomal abnormalities and genetic variation, which may inform prognosis and guide treatment. This pilot study sought to examine the feasibility of incorporating Whole Genome Sequencing (WGS) alongside the routine laboratory evaluation of 14 patients with newly diagnosed multiple myeloma who had enrolled in the 100,000 Genomes Project. In all 14 cases, WGS data could be obtained in a timely fashion within existing clinical frameworks in a tertiary hospital setting. The data not only replicated standard‐of‐care FISH analysis of chromosomal abnormalities but also provided further chromosomal and molecular genetic insights that may influence patient management.
Topic Modeling of Online Media News Titles during COVID-19 Emergency Response in Indonesia Using the Latent Dirichlet Allocation (LDA) Algorithm

Online media news portals have the advantage of speed in conveying information on any events that occur in society. One way to know what a story is about is from the title. The headline is a headline that introduces the reader's knowledge about the news content to be described. From these headlines, you can search for the main topics or trends that are being discussed. It takes a fast and efficient method to find out what topics are trending in the news. One method that can be used to overcome this problem is topic modeling. Topic modeling is necessary to help users quickly understand recent issues. One of the algorithms in topic modeling is Latent Dirichlet Allocation (LDA). The stages of this research began with data collection, preprocessing, forming n-grams, dictionary representation, weighting, validating the topic model, forming the topic model, and the results of topic modeling. The results of modeling LDA topics in news headlines taken from www.detik. com for 8 months (March-October 2020) during the COVID-19 pandemic showed that the best number of topics produced each month were 3 topics dominated by news topics about corona cases, positive corona, positive COVID, COVID-19 with an accuracy of 0.824 (82.4%). The resulting precision and recall values indicate that the two values are identical, so this is ideal for an information retrieval system.
Revisiting Recall Effects of Filler Particles in German and English

This paper reports on two experiments that partially replicate an experiment by Fraundorf and Watson (2011, J Mem. Lang.) on the recall effect of ﬁller particles. Their subjects listened to three passages of a story, either with or without ﬁller particles, which they had to retell afterwards. They analysed the subjects’ retelling in terms of whether important plot points were remem-bered or not. For their English data, they found that ﬁller particles facilitate the recall of the plot points signiﬁcantly compared to stories that did not include ﬁller particles. As this seems to be a convincing experimental design, we aimed at evaluating this method as a web-based experiment which may, if found to be suitable, easily be applied to other languages. Furthermore, we investigated whether their results are found in German as well (Experiment 1), and evaluated whether ﬁller duration has an effect on recall performance (Experiment 2). Our results could not replicate the ﬁndings of the original study: in fact, the op-posite effect was found for German. In Experiment 1, participants performed better on recall in the ﬂuent condition, while no signiﬁcant results were found for English in Experiment 2.
Evaluating the Quality of Public Services Through Social Media

Public services need to be evaluated regularly to identify areas that need further improvement. Data collection via Twitter is affordable and timely, so it has the potential to be utilized to evaluate the quality of public service. This study utilizes tweets mentioning three service units of the provincial government of Jakarta and applies both sentiment analysis and topic classification to predict a rating/score of public service quality. The research goal is to examine if the evaluation of public services based on social media data is possible. The findings indicate that the use of Twitter has an advantage in terms of sample size and variety of opinions. Tweets can be translated into scores as well. Nonetheless, the representativeness issue and the predominance of complaint tweets can affect the reliability of the results.
Psychometric Evaluation of the Comfort from Companion Animals Scale in a Sexual and Gender Minority Sample

ABSTRACT Human–animal interaction (HAI) research is growing in popularity and methodological rigor; however, there remains a need for psychometrically validated measures and inclusion of broader populations. This study addressed these gaps by reporting on the psychometric properties of the Comfort from Companion Animals Scale (CCAS) in a sample of sexual and gender minority emerging adults. Participants included 138 emerging adults between the ages of 18–21 years (M = 19.33 years, SD = 1.11; 38.4% racial/ethnic minority) who identified as a gender (48.6%) and/or sexual minority (98.6%) and who reported living with a companion animal in the past year. We utilized the following analytic methods: (a) confirmatory factor analyses to compare the unidimensional structure of the CCAS with the two alternative models, (b) multiple group analyses to test measurement invariance across demographic groups, and (c) structural equation models to evaluate construct validity. Preliminary analysis found that the majority of participants did not endorse the two lowest response options. To conduct invariance testing, we eliminated items 3, 5, and 8 from the CCAS and collapsed the lowest response options. The results of the confirmatory factor analysis supported the use of this revised unidimensional model. We found evidence of measurement invariance across gender identity, sexual orientation, and race/ethnicity groups. Construct validity was supported by comparing the CCAS with factors on the Pet Attachment and Life Impact Scale; the positive association between the CCAS and anxiety are discussed in the context of prior research. Overall, our findings highlight the importance of validating HAI measures across samples from diverse backgrounds. We recommend that future studies continue to test the CCAS and other measures of human–animal attachment among diverse samples to delineate which aspects of HAI may be most beneficial in promoting mental health in vulnerable populations.
A Review on Silver Nanoparticles -green Synthesis, Antimicrobial Action and Application in Textiles

ABSTRACT Silver nanoparticles (AgNPs) and green synthesis have hegemony over their counterparts. This paper reviews- green synthesis mechanism, antimicrobial mechanism and, incorporation of AgNPs in textiles. Green synthesis is nontoxic, unlike chemical methods, cost-effective and precise, unlike physical techniques. In green synthesis, the reconversion of biomolecules from NADPH to NADP+releases electrons that reduce silver ions. Harmoniously, the functional groups of biomolecules act as polar-end to formulate steric stabilization. Green synthesized AgNPs are loaded on fabrics through different loading techniques such as pad-dry-cure, immersion, in situ, and others. This review also depicts the feasible mechanisms to explain the antimicrobial action of AgNPs. The antimicrobial activity of AgNPs is adequate for annihilating both gram-positive and gram-negative bacteria. The nanoparticle morphology depends on various constituents such as pH, temperature, concentration, and others. The acidic environment causes a larger nanoparticle size. Typically, the room temperature is enough for green synthesis. Whereas, high concentration of either plant extracts or metal precursors causes large nanoparticles. Hence, various shapes and sizes are possible by consolidating diverse concentrations of plants and metal precursors. Complicated connections may prevail amongst numerous concentrations, pH, temperature, and others with varying phytochemicals.
HTTP DDoS Flooding Attack Mitigation in Software-Defined Networking

SUMMARY HTTP Distributed Denial of Service (DDoS) ﬂooding attack aims to deplete the connection resources of a targeted web server by transmitting a massive amount of HTTP request packets using botnets. This type of attack seriously deteriorates the service quality of the web server by tying up its connection resources and uselessly holds up lots of network resources like link capacity and switching capability. This paper proposes a defense method for mitigating HTTP DDoS ﬂooding attack based on software-deﬁned networking (SDN). It is demonstrated in this paper that the proposed method can e ﬀ ectively defend the web server and preserve network resources against HTTP DDoS ﬂooding attacks.
“Where Do I Start?” Inquiry into K-12 Mainstream Teachers’ Knowledge about Differentiating Instruction for ELLs in One U.S. School District

ABSTRACT The purpose of this paper is to report themes identified in questions K-12 mainstream teachers in one U.S. school district posed about differentiating instruction for English language learners (ELLs) in order to hear directly from teachers about topics they would like to learn more about. Teachers in the district completed a selected-response and open-ended survey to inform future professional development opportunities. Findings suggest that most teachers work with ELLs but are underprepared to teach them. Teachers posed questions about curriculum and assessment; instruction; building community; language difference versus disability; and where to start in teaching ELLs. Conclusions are drawn about how well ELLs are being educated in the district. Implications for teacher professional development and policy are also discussed.
Feedback Control of Melt Pool Area in Selective Laser Melting Additive Manufacturing Process

Selective laser melting (SLM), a metal powder fusion additive manufacturing process, has the potential to manufacture complex components for aerospace and biomedical implants. Large-scale adaptation of these technologies is hampered due to the presence of defects such as porosity and part distortion. Nonuniform melt pool size is a major cause of these defects. The melt pool size changes due to heat from the previous powder bed tracks. In this work, the effect of heat sourced from neighbouring tracks was modelled and feedback control was designed. The objective of control is to regulate the melt pool cross-sectional area rejecting the effect of heat from neighbouring tracks within a layer of the powder bed. The SLM process’s thermal model was developed using the energy balance of lumped melt pool volume. The disturbing heat from neighbouring tracks was modelled as the initial temperature of the melt pool. Combining the thermal model with disturbance model resulted in a nonlinear model describing melt pool evolution. The PID, a classical feedback control approach, was used to minimize the effect of intertrack disturbance on the melt pool area. The controller was tuned for the desired melt pool area in a known environment. Simulation results revealed that the proposed controller regulated the desired melt pool area during the scan of multiple tracks of a powder layer within 16 milliseconds and within a length of 0.04 mm reducing laser power by 10% approximately in five tracks. This reduced the chance of pore formation. Hence, it enhances the quality of components manufactured using the SLM process, reducing defects.
The role of an ant colony optimisation algorithm in solving the major issues of the cloud computing

ABSTRACT There are many issues and problems in cloud computing that researchers try to solve by using different techniques. Most of the cloud challenges are NP-hard problems; therefore, many meta-heuristic techniques have been used for solving these challenges. As a famous and powerful meta-heuristic algorithm, the Ant Colony Optimisation (ACO) algorithm has been recently used for solving many challenges in the cloud. However, in spite of the ACO potency for solving optimisation problems, its application in solving cloud issues in the form of a review article has not been studied so far. Therefore, this paper provides a complete and detailed study of the different types of ACO algorithms for solving the important problems and issues in cloud computing. Also, the number of published papers for various publishers and different years is shown. In this paper, available challenges are classified into different groups, including scheduling, resource allocation, load balancing, consolidation, virtual machine placement, service composition, energy consumption, and replication. Then, some of the selected important techniques from each category by applying the selection process are presented. Besides, this study shows the comparison of the reviewed approaches and also it highlights their principal elements. Finally, it highlights the relevant open issues and some clues to explain the difficulties. The results revealed that there are still some challenges in the cloud environments that the ACO is not applied to solve.
Survey on Deep Learning Based Intrusion Detection System

Development of computer network has changed human lives in many ways. Currently, everyone is connected to each other from everywhere. Information can be accessed easily. This massive development has to be followed by good security system. Intrusion Detection System is important device in network security which capable of monitoring hardware and software in computer network. Many researchers have developed Intrusion Detection System continuously and have faced many challenges, for instance: low detection of accuracy, emergence of new types malicious traffic and error detection rate. Researchers have tried to overcome these problems in many ways, one of them is using Deep Learning which is a branch of Machine Learning for developing Intrusion Detection System and it will be discussed in this paper. Machine Learning itself is a branch of Artificial Intelligence which is growing rapidly in the moment. Several researches have showed that Machine Learning and Deep Learning provide very promising results for developing Intrusion Detection System. This paper will present an overview about Intrusion Detection System in general, Deep Learning model which is often used by researchers, available datasets and challenges which will be faced ahead by researchers