bluuebunny/crossref_metadata_2025 huggingface dataset:
Laser-induced damage of an anti-resonant hollow-core fiber for high-power laser delivery at 1 µm
Authors: Zhu Xinyue , Yu Fei , Wu Dakun , Chen Shufen , Jiang Yi , Hu Lili
Published: June 2022

We demonstrate high-power laser delivery exceeding 1 kilowatt through a 5-meter homemade anti-resonant hollow-core fiber (AR-HCF) at 1-µm wavelength. Laser-induced damage to the fiber coating and jacket glass is experimentally observed respectively for different incident laser powers from a few hundred watts up to nearly 1.5 kilowatts. The cladding microstructure of the AR-HCF is free of damage at the incident end when 80% of the 1.5-kW incident power is coupled in. The deviation of an incident laser beam from the core to the cladding causes no damage but only deterioration of the coupling efficiency. The potential of the AR-HCF for higher-power laser delivery is discussed.
Cascaded Raman fiber lasers with ultrahigh spectral purity
Authors: Deheri Rashmita , Dash Sarthak , Supradeepa V. R. , Balaswamy V.
Published: June 2022

Random distributed feedback (RDFB) cascaded Raman fiber lasers (CRFLs) are simple, wavelength agile, and enable high-power fiber lasers outside emission bandwidths of rare-earth doped fiber lasers. However, the spectral purity, defined as the percentage of total output power in the desired Stokes wavelength band, and relative intensity noise (RIN) of these systems are limited due to the intensity noise of the pump source used for Raman conversion. RIN gets amplified and transferred to Raman Stokes orders which causes incomplete Raman conversion and hence limits the spectral purity. Here, we demonstrate a low-intensity noise (<–100 dBc/Hz from 9 kHz to 10 GHz) CRFL with a record spectral purity of ∼99%, tunable over six Stokes orders, using a very low-intensity noise, narrow linewidth Yb-fiber amplifier as a pump source.
Imaging ultrafast evolution of subwavelength-sized topography using single-probe structured light microscopy
Authors: Xu Jie , Min Changjun , Zhang Yuquan , Ni Jielei , Cao Gengwei , Wei Qianyi , Yang Jianjun , Yuan Xiaocong
Published: June 2022

Imaging ultrafast processes in femtosecond (fs) laser–material interactions such as fs laser ablation is very important to understand the physical mechanisms involved. To achieve this goal with high resolutions in both spatial and temporal domains, a combination of optical pump–probe microscopy and structured illumination microscopy can be a promising approach, but suffers from the multiple-frame method with a phase shift that is inapplicable to irreversible ultrafast processes such as ablation. Here, we propose and build a wide-field single-probe structured light microscopy (SPSLM) to image the ultrafast three-dimensional topography evolution induced by fs lasers, where only a single imaging frame with a single structured probe pulse is required for topography reconstruction, benefiting from Fourier transform profilometry. The second harmonic of the fs laser is used as the structured probe light to improve spatial lateral resolution into the subwavelength region of

∼
478
  
nm

, and the spatial axial and temporal resolutions are estimated to be

∼
22
  
nm

and

∼
256
  
fs

, respectively. With SPSLM, we successfully image the ultrafast topography evolution of a silicon wafer surface impacted by single and multiple fs pulses. The variable formation and evolution of the laser induced periodic surface structures during an ultrashort time are visualized and analyzed. We believe that SPSLM will be a significant approach for revealing and understanding various ultrafast dynamics, especially in fs laser ablation and material science.
Quantifying biochemical reaction rates from static population variability within incompletely observed complex networks
Authors: Wittenstein Timon , Leibovich Nava , Hilfinger Andreas
Published: June 2022

Quantifying biochemical reaction rates within complex cellular processes remains a key challenge of systems biology even as high-throughput single-cell data have become available to characterize snapshots of population variability. That is because complex systems with stochastic and non-linear interactions are difficult to analyze when not all components can be observed simultaneously and systems cannot be followed over time. Instead of using descriptive statistical models, we show that incompletely specified mechanistic models can be used to translate qualitative knowledge of interactions into reaction rate functions from covariability data between pairs of components. This promises to turn a globally intractable problem into a sequence of solvable inference problems to quantify complex interaction networks from incomplete snapshots of their stochastic fluctuations.
Bycatch and discards in the artisanal shrimp trawl fishery in Northern Peru
Authors: Mendo Jaime , Mendo Tania , Gil-Kodaka Patricia , Martina Jimmy , Gómez Iván , Delgado Ruggeri , Fernández Jhenifer , Travezaño Alejandra , Arroyo Ruggeri , Loza Karla , James Mark A.
Published: June 2022

This study analyses the bycatch composition of an artisanal shrimp trawl fleet operating between Cabo Blanco and Máncora in Northern Peru between April 2019 and March 2020. A total of 300 hauls were analysed with respect to target catch and bycatch (consisting of other commercial species, discards, and macroalgae). A total of 277 species were recorded including 111 species of fish, 65 species of molluscs, 51 species of crustaceans, 22 species of algae, 12 species of cnidarian, 9 species of echinoderms, 4 species of Bryozoa and 3 species of polychaeta. Capture per unit effort (CPUE, kg.h-1) was highest for fish, followed by crustaceans, algae and molluscs. The target species Penaeus californiensis coffee shrimp constituted 17.8% of the overall catch,82.2% represented bycatch, and 50.6% represented discards. Coffee shrimps were more abundant in June and November 2019 and in January and February 2020. Highest bycatch CPUE occurred in May, June and December 2019. The most abundant species in the bycatch throughout the study period were sand perch (Diplectrum conceptione, 16% weight of the total catch), the macroalgae caulerpa (Caulerpa filiformis, 13%), sole flounder (Etropus ectenes, 6.4%), Pacific drum (Larimus pacificus, 5.7%), and lumptail searobin (Prionotus stephanophrys, 5.1%). Overall, the contribution of sand perch and flounder, exceeded the weight of coffee shrimp, therefore the interpretation that shrimp is the sole target species needs to be revised. The number of discarded species per month increased towards the spring months with the highest value in November. This study represents the first characterisation of bycatch in the artisanal trawling fishery in the Piura region in northern Peru and reveals a high proportion of bycatch in the fishery but also hints at potential temporal management measures that could be imposed to reduce the levels of bycatch. For example, the months of May and December had the greatest bycatch to shrimp ratios and the fishery could potentially be closed to avoid high bycatch risk, however, longer term information is needed to assess if the trends observed in bycatch are similar over longer periods of time. The species characterisation of bycatch also provides information for the design of modified nets which would target the reduction of small fish present in the catch.
Analyzing COVID-19 disinformation on Twitter using the hashtags #scamdemic and #plandemic: Retrospective study
Authors: Lanier Heather D. , Diaz Marlon I. , Saleh Sameh N. , Lehmann Christoph U. , Medford Richard J.
Published: June 2022

Introduction

The use of social media during the COVID-19 pandemic has led to an "infodemic" of mis- and disinformation with potentially grave consequences. To explore means of counteracting disinformation, we analyzed tweets containing the hashtags #Scamdemic and #Plandemic.

Methods

Using a Twitter scraping tool called twint, we collected 419,269 English-language tweets that contained “#Scamdemic” or “#Plandemic” posted in 2020. Using the Twitter application-programming interface, we extracted the same tweets (by tweet ID) with additional user metadata. We explored descriptive statistics of tweets including their content and user profiles, analyzed sentiments and emotions, performed topic modeling, and determined tweet availability in both datasets.

Results

After removal of retweets, replies, non-English tweets, or duplicate tweets, 40,081 users tweeted 227,067 times using our selected hashtags. The mean weekly sentiment was overall negative for both hashtags. One in five users who used these hashtags were suspended by Twitter by January 2021. Suspended accounts had an average of 610 followers and an average of 6.7 tweets per user, while active users had an average of 472 followers and an average of 5.4 tweets per user. The most frequent tweet topic was “Complaints against mandates introduced during the pandemic” (79,670 tweets), which included complaints against masks, social distancing, and closures.

Discussion

While social media has democratized speech, it also permits users to disseminate potentially unverified or misleading information that endangers people’s lives and public health interventions. Characterizing tweets and users that use hashtags associated with COVID-19 pandemic denial allowed us to understand the extent of misinformation. With the preponderance of inaccessible original tweets, we concluded that posters were in denial of the COVID-19 pandemic and sought to disperse related mis- or disinformation resulting in suspension.

Conclusion

Leveraging 227,067 tweets with the hashtags #scamdemic and #plandemic in 2020, we were able to elucidate important trends in public disinformation about the COVID-19 vaccine.
Commonalities among dental patient-reported outcomes (dPROs)—A Delphi consensus study
Authors: Chanthavisouk Phonsuda , John Mike T. , Paulson Danna , Pattanaik Swaha
Published: June 2022

Improvement of patients’ oral health-related quality of life (OHRQoL) is the main goal of oral health care professionals. However, OHRQoL is not a homogenous construct and how to assess it is challenging because of the large number of currently available instruments. Investigating available instruments and what they have in common would be necessary for consolidation and standardization of these instruments into a smaller set of tools. If the OHRQoL dimensions including Oral Function, Orofacial Pain, Orofacial Appearance, and Psychosocial Impact are the fundamental building blocks of the dental patient’s oral health experience, then these dimensions should be measured by generic multi-item dPROMs. In this study, a panel of 11 international dentists use the Delphi consensus process to determine how well 20 of these instruments measured the four OHRQoL dimensions. All 20 dPROMs questionnaires assessed at least one OHRQoL dimension while all four OHRQoL dimensions were measured by at least one dPROM instrument, i.e., the four OHRQoL dimensions were essential components of the patient’s oral health experience. This shows that the currently available generic multi-item dPROMs have a lot in common, in that they share Oral Function, Orofacial Pain, Orofacial Appearance, and Psychosocial Impact as targeted dimensions. Based on these commonalities, it is plausible and desirable to move towards a single four-dimensional metric to assess oral health impact in all clinical, community-based, and research settings. This step is necessary to advance evidence-based dentistry and value-based oral health care.
What makes a violent mind? The interplay of parental rearing, dark triad personality traits and propensity for violence in a sample of German adolescents
Authors: Yendell Alexander , Clemens Vera , Schuler Julia , Decker Oliver
Published: June 2022

Machiavellianism, narcissism and psychopathy are socially aversive personality traits that are strongly linked to the propensity of violence. A central determinate of aggression and violence is parental rearing. Interestingly, while the origin of the development of Dark Triad is not yet entirely understood, next to genetic and environmental factors, literature points towards an influence of parenting styles to the development of dark traits. Therefore, in a sample of 1366 9th grade students (mean age 14.89,), we assessed the interplay between parental rearing, dark triad traits, observation of violence among peers and their propensity for violence. The sample has a good representativeness on school types. Results reveal a positive association between the experience of parental rejection by both parents and punishment as well as parental control and overprotection and Machiavellianism, narcissism and psychopathy. Parental emotional warmth was associated negatively with Machiavellianism and psychopathy while no significant association with narcissism was seen. In a path model, parental rearing, dark triad traits and observation of violence among peers significantly contributed to the propensity of violence. However, differences between the experienced parenting behaviour of mothers and fathers should be noted. Both rejection and overly harsh punishments by fathers and emotional warmth by mothers have no significant influence on the dark triad. It is interesting that the effects regarding maternal parenting behaviour are stronger overall than the effects regarding paternal parenting behaviour. These results underline the importance of parental rearing on the development of Machiavellianism, narcissism and psychopathy and suggest a significant role of parental rearing and the dark triad traits on propensity for violence in adolescents. Parenting trainings and family interventions may be a promising starting point to prevent antisocial behavior linked to the dark triad and to prevent violent behavior in future generations
Climate policy support as a tool to control others’ (but not own) environmental behavior?
Authors: Kukowski Charlotte A. , Bernecker Katharina , von der Heyde Leoni , Boos Margarete , Brandstätter Veronika
Published: June 2022

Drastic reductions in greenhouse gas emissions are necessary to successfully mitigate climate change. Individual environmental behavior is central to this change. Given that environmental behavior necessitates 1) effortful individual self-control and 2) cooperation by others, public policy may constitute an attractive instrument for regulating one’s own as well as others’ environmental behavior. Framing climate change mitigation as a cooperative self-control problem, we explore the incremental predictive power of self-control and beliefs surrounding others’ cooperation beyond established predictors of policy support in study 1 using machine-learning (N= 610). In study 2, we systematically test and confirm the effects of self-control and beliefs surrounding others’ cooperation (N= 270). Both studies showed that personal importance of climate change mitigation and perceived insufficiency of others’ environmental behavior predict policy support, while there was no strong evidence for a negative association between own-self control success and policy support. These results emerge beyond the effects of established predictors, such as environmental attitudes and beliefs, risk perception (study 1), and social norms (study 2). Results are discussed in terms of leveraging policy as a behavioral enactment constraint to control others’ but not own environmental behavior.
I love the way you love me: Responding to partner’s love language preferences boosts satisfaction in romantic heterosexual couples
Authors: Mostova Olha , Stolarski Maciej , Matthews Gerald
Published: June 2022

Chapman’s Love Languages hypothesis claims that (1) people vary in the ways they prefer to receive and express affection and (2) romantic partners who communicate their feelings congruent with their partner’s preferences experience greater relationship quality. The author proposes five distinct preferences and tendencies for expressing love, including: Acts of Service, Physical Touch, Words of Affirmation, Quality Time and Gifts. In the present study partners (N= 100 heterosexual couples) completed measures assessing their preferences and behavioral tendencies for a) expressions of love and b) reception of signs of affection, for each of the five proposed “love languages”. Relationship satisfaction, sexual satisfaction and empathy were also assessed. The degree of the within-couple mismatch was calculated separately for each individual based on the discrepancies between the person’s felt (preferred) and their partner’s expressed love language. The joint mismatch indicator was a sum of discrepancies across the five love languages. Matching on love languages was associated with both relationship and sexual satisfaction. In particular, people who expressed their affection in the way their partners preferred to receive it, experienced greater satisfaction with their relationships and were more sexually satisfied compared to those who met their partner’s needs to lesser extent. Empathy was expected to be a critical factor for better understanding of and responding to the partner’s needs. Results provided some support for this hypothesis among male but not female participants.
Service user perceptions of smoking cessation in residential substance use treatment
Authors: Swithenbank Zoe , Harrison Rebecca , Porcellato Lorna
Published: June 2022

Introduction

Prevalence of tobacco smoking among adults in substance misuse treatment is much higher than the wider population, yet limited research is available, and residential treatment services have been overlooked as a potential setting for cessation interventions. Exploring the perceptions of service users about smoking cessation in residential rehabilitation is important to gain better understanding of this issue and identify ways to inform future intervention development.

Methods

Ten semi-structured interviews were conducted in the Northwest of England in 2017 with adults (7 male, 3 female) who were currently or had previously been in residential treatment for substance misuse. Five participants were current smokers, three had never smoked, and two were former smokers. Participants were asked about their smoking behaviours, factors relating to smoking and smoking cessation and the relationship between smoking and substance use. All interviews were transcribed and data was analysed thematically.

Results

Study findings highlighted a general consensus amongst participants that residential treatment services offered an ideal opportunity for cessation but there were concerns that doing so might jeopardise recovery. Smoking in substance use treatment services is still the norm and factors such as perceived social and psychological benefits, normative behaviours and lack of perceived risk or prioritisation pose challenges for implementing smoking cessation within this setting, although facilitators such as motivation to change and appropriateness of the setting were also identified.

Conclusions

This study suggests that service users perceive residential treatment services as suitable environments to introduce smoking cessation. To address the needs of adults who smoke and are in recovery from substance use, further research and cooperation from treatment organisations is needed to integrate substance misuse and smoking cessation services. More conclusive evidence on the effectiveness of tackling both issues at the same time is also required.
Distinctive features of the respiratory syncytial virus priming loop compared to other non-segmented negative strand RNA viruses
Authors: Cressey Tessa N. , Shareef Afzaal M. , Kleiner Victoria A. , Noton Sarah L. , Byrne Patrick O. , McLellan Jason S. , Mühlberger Elke , Fearns Rachel
Published: June 2022

De novo initiation by viral RNA-dependent RNA polymerases often requires a polymerase priming residue, located within a priming loop, to stabilize the initiating NTPs. Polymerase structures from three different non-segmented negative strand RNA virus (nsNSV) families revealed putative priming loops in different conformations, and an aromatic priming residue has been identified in the rhabdovirus polymerase. In a previous study of the respiratory syncytial virus (RSV) polymerase, we found that Tyr1276, the L protein aromatic amino acid residue that most closely aligns with the rhabdovirus priming residue, is not required for RNA synthesis but two nearby residues, Pro1261 and Trp1262, were required. In this study, we examined the roles of Pro1261 and Trp1262 in RNA synthesis initiation. Biochemical studies showed that substitution of Pro1261 inhibited RNA synthesis initiation without inhibiting back-priming, indicating a defect in initiation. Biochemical and minigenome experiments showed that the initiation defect incurred by a P1261A substitution could be rescued by factors that would be expected to increase the stability of the initiation complex, specifically increased NTP concentration, manganese, and a more efficient promoter sequence. These findings indicate that Pro1261 of the RSV L protein plays a role in initiation, most likely in stabilizing the initiation complex. However, we found that substitution of the corresponding proline residue in a filovirus polymerase had no effect on RNA synthesis initiation or elongation. These results indicate that despite similarities between the nsNSV polymerases, there are differences in the features required for RNA synthesis initiation.
DNA replication dynamics during erythrocytic schizogony in the malaria parasites Plasmodium falciparum and Plasmodium knowlesi
Authors: McDonald Jennifer , Merrick Catherine J.
Published: June 2022

Malaria parasites are unusual, early-diverging protozoans with non-canonical cell cycles. They do not undergo binary fission, but divide primarily by schizogony. This involves the asynchronous production of multiple nuclei within the same cytoplasm, culminating in a single mass cytokinesis event. The rate and efficiency of parasite reproduction is fundamentally important to malarial disease, which tends to be severe in hosts with high parasite loads. Here, we have studied for the first time the dynamics of schizogony in two human malaria parasite species, Plasmodium falciparum and Plasmodium knowlesi. These differ in their cell-cycle length, the number of progeny produced and the genome composition, among other factors. Comparing them could therefore yield new information about the parameters and limitations of schizogony. We report that the dynamics of schizogony differ significantly between these two species, most strikingly in the gap phases between successive nuclear multiplications, which are longer in P. falciparum and shorter, but more heterogenous, in P. knowlesi. In both species, gaps become longer as schizogony progresses, whereas each period of active DNA replication grows shorter. In both species there is also extreme variability between individual cells, with some schizonts producing many more nuclei than others, and some individual nuclei arresting their DNA replication for many hours while adjacent nuclei continue to replicate. The efficiency of schizogony is probably influenced by a complex set of factors in both the parasite and its host cell.
GARANTISMO, PRINCIPIALISMO E O DILEMA DA CONEXÃO ENTRE DIREITO E MORAL
Authors: Barreto Júnior Williem da Silva , De Cademartori Sérgio Urquhart
Published: June 2022

O presente artigo postula debater o fenômeno histórico do constitucionalismo a partir da análise das teorias principialista e garantista. Pretende, também, discorrer sobre a crítica do garantismo à tese que correlaciona o direito e a moral. Conclui-se pela necessidade de reforço ao paradigma juspositivista em rejeição ao cognitivismo ético, endossado pelo principialismo. Para tanto, utiliza-se a metodologia hipotético-dedutiva, em associação à técnica bibliográfica.
Editorial em Português / Editorial en Español
Authors: Hidalgo López Carelia Rayen , Kitzmann Dione Iara Silveira , Ribeiro Bernard Constantino , Silva Lisiana Lawson Terra da
Published: June 2022

Editoral Bilíngue.
Flipped Teaching A Trend for Students Learning in Higher Education
Authors:   English Department, Faculty of Education, Soran University, Kurdistan Region, Iraq, Ismail Chaqmaqchee Zina A.
Published: June 2022

It is time to shift teaching practice from traditional teacher-centred to novel student- centred which mainly focuses on the digital technology in higher education. Flipped approach can empower student’s self- learning knowledge and help them think critically; however, inductive teaching is essential for academic process at university level. The current study investigates student’s ongoing cognitive skills and the effects of flipped approach on student’s procedure. This research shows students view towards flipped approach during the learning process. The researcher as a teacher has evaluated a flipped classroom with the second and fourth year students in English department, faculty of education, Soran University. The survey was conducted among 104 undergraduate students of the English department. The data were analysed by t-test to find out student’s view on flipped approach. Most students were challenged with flipped process inside and outside the class due to lacking on campus facilities. Thus, the results demonstrated that the students prefer face to face approach in the class although flipped approach has positive effect on students learning. The study proposes that academic staff should be more involved in teaching flipped approach and apply new pedagogy in the leaning process to save time and promote students' critical thinking.
Social Familial Problems During Corona Pandemic A Field Study in Erbil
Authors:   Department of Psychology and Educational Sciences, College of Education, Salahaddin University-Erbil, Kurdistan Region, Iraq, Albarzinjy Yasir B.
Published: June 2022

This study seeks to identify the familial problems during the Covid-19 pandemic in Erbil, devise ways to cope with these problems and identify the nature of the social life of families in Erbil, to discuss the Coronavirus. It also aims to identify the negative and positive impact that the Corona pandemic has had on the domestic survival of society members and to detect the social, economic, and psychological dimensions of the spread of the new Coronavirus and its effects on individuals in society. For each research, there is a methodology. In the current one, the researcher adopted the social survey sampling method, which is the most widely used method in the social sciences and humanities. To this end, a sample consisting of (140) families were selected randomly in the center of Erbil city (KRG/ FI). As for the research tools, in addition to the interview, the researcher designed and distributed a questionnaire. The study has arrived at a number of results, the most important of which are: The field study showed that the family in Erbil encountered social, psychological, and economic pressures during the quarantine and the spread of the Corona pandemic. The pandemic progenerated various social problems ranging from simple to the most complex ones some of which led to divorce. Additionally, family members (children) were exposed to different educational, economic, and social problems due to psychological pressures under the quarantine. The field study also revealed that the family was exposed to fears and psychological anxiety due to undisclosed factors because of a complete halt of life.
The Language of Law; Problems and Solutions A Case of Parliament and The Courts
Authors:   Department of Kurdish Language, College of Basic Education, University of Halabja, Kurdistan Region, Iraq, Qadr Sherko H.
Published: June 2022

Legal language is delicate as well as complex which is under investigated in Kurdistan Region, representing a wide gap in the literature on legal discourse. Moreover, this topic is fundamental and has direct relationship to the populace's life. Any misunderstanding in the legal texts is likely to have direct impact on an individual's life and their freedom. Therefore, exploring the legal language, whereby the terms should be optimally accurate, is highly crucial. The issue is mainly related to the standard and formal language of the state which requires corporate compliance with strict grammatical rules and precise spelling. Adopting the qualitative method, the current paper makes an attempt to find the gaps observed in legal texts and propose solutions. The data were obtained from Kurdistan Parliament, Judiciary Council and the Courts documents. The study consists of two parts and conclusions. The first part which is mostly theoretical focuses on the language of writing, the jargon and the nontechnical language. The second part starts with a brief historical background; then the legal jargon and its characteristics are discussed. At the level of orthography, the relationship between spelling and semantics, the terminologies, and the grammar and its significance in the area are analyzed. The researcher has made use of the Transformational Generative Grammar for the grammatical analysis. The paper ends with conclusions and a list of references.
Modeling and Experimental Studies on Water Spray Cooler for Commercial Photovoltaic Modules
Authors: Ngo Xuan Cuong , Do Nhu Y , Dang Quoc Vuong
Published: June 2022

This paper presents modeling and experimental studies on water spray coolers for commercial photovoltaic modules. This paper has compared the energy yield of four photovoltaic commercial modules that were installed with a fixed tilt angle being equal to the local latitude in central Vietnam, including one photovoltaic module using a water spray cooler and three photovoltaic modules without cooling. Experimental results on sunny days have been shown that the energy yield difference between four PV modules under the same working condition is lower than 1%. In addition, on sunny days when the set working temperature of the water spray cooler is 45 °C, the average improvement efficiency of a photovoltaic module using a water spray cooler compared to three reference photovoltaic modules is 2.64%, 3.83%, and 6.18%, for an average of 4.22%. A simple thermal–electrical model of a photovoltaic module with a water spray cooler has been developed and tested. The normalized root mean square error between simulated and measured results of photovoltaic module power output on a sunny day without cooling and with water spray cooler reached 6.5% and 8.5%, respectively. The obtained results are also demonstrated that the reasonableness of the simple thermal–electrical model of the photovoltaic module with water spray cooler and the feasibility of a cooling system is improved to increase the efficiency of the photovoltaic module. In addition, they can be considered as a basis for new experimental models in the future.
Effect of Storage on The Chemical Quality of Pasteurized Milk with Supplemented Soybean Oil and Phycocyanin
Authors: Nefasa Angela Nitia , Nisa Ega Zahrotun , Christwardana Marcelinus
Published: June 2022

The interaction between the addition of soybean oil and phycocyanin extract on the protein, fat, and solid non-fat content of pasteurized milk after a one-week storage period is highly important to investigate. Soybean oil is used to improve the quality of pasteurized milk, particularly its chemical quality, consisting of fat, protein, and Solid Non-Fat (SNF). Phycocyanin extract provides a source of protein, whereas soybean oil contains healthy fatty acids. Before the fresh milk is pasteurized, soybean oil and phycocyanin are added. The LTLT (Low Temperature Long Time) technique was used to pasteurize the milk at 63 °C for 30 minutes. After the pasteurization procedure was completed, the milk samples were refrigerated for one week at a temperature of 4 °C. Following the storage period, the chemical composition of pasteurized milk is examined. With the addition of soybean oil and phycocyanin extract, the protein content of milk rose, as shown by the findings. The sample T1 with a concentration of 0.45% soybean oil and 0.50% phycocyanin extract had the greatest amount of protein (3.58). The sample T2 with 0.45% soybean oil concentration and phycocyanin extract (1%) had the greatest fat content (6.4%). Adding soybean oil and phycocyanin extract enhanced the total SNF concentration. On the basis of the study conducted, it can be concluded that the addition of soybean oil and phycocyanin extract to pasteurized milk has an influence and interaction on the milk's protein, fat, and SNF content. The addition of soybean oil and phycocyanin extract to pasteurized milk held for one week may boost protein, fat, and SNF concentrations.
PACk
Authors: Wang Yue , Narasayya Vivek , He Yeye , Chaudhuri Surajit
Published: June 2022

The Agglomerative Hierarchical Clustering (AHC) algorithm is widely used in real-world applications. As data volumes continue to grow, efficient scale-out techniques for AHC are becoming increasingly important. In this paper, we propose a Partition-based distributed Agglomerative Hierarchical Clustering (PACk) algorithm using novel distance-based partitioning and distance-aware merging techniques. We have developed an efficient implementation of PACk on Spark. Compared to the state-of-the-art distributed AHC algorithm, PACk achieves 2X to 19X (median=9X) speedup across a variety of synthetic and real-world datasets.
Multivariate correlations discovery in static and streaming data
Authors: Minartz Koen , d'Hondt Jens E. , Papapetrou Odysseas
Published: June 2022

Correlation analysis is an invaluable tool in many domains, for better understanding data and extracting salient insights. Most works to date focus on detecting high
pairwise
correlations. A generalization of this problem with known applications but no known efficient solutions involves the discovery of strong multivariate correlations, i.e., finding vectors (typically in the order of 3 to 5 vectors) that exhibit a strong dependence when considered altogether. In this work we propose algorithms for detecting multivariate correlations in static and streaming data. Our algorithms, which rely on novel theoretical results, support two different correlation measures, and allow for additional constraints. Our extensive experimental evaluation examines the properties of our solution and demonstrates that our algorithms outperform the state-of-the-art, typically by an order of magnitude.
Moneyball
Authors: Poppe Olga , Guo Qun , Lang Willis , Arora Pankaj , Oslake Morgan , Xu Shize , Kalhan Ajay
Published: June 2022

Microsoft Azure SQL Database is among the leading relational database service providers in the cloud. Serverless compute automatically scales resources based on workload demand. When a database becomes idle its resources are reclaimed. When activity returns, resources are resumed. Customers pay only for resources they used. However, scaling is currently merely reactive, not proactive, according to customers' workloads. Therefore, resources may not be immediately available when a customer comes back online after a prolonged idle period. In this work, we focus on reducing this delay in resource availability by predicting the pause/resume patterns and proactively resuming resources for each database. Furthermore, we avoid taking away resources for short idle periods to relieve the back-end from ineffective pause/resume workflows. Results of this study are currently being used worldwide to find the middle ground between quality of service and cost of operation.
PGE
Authors: Cheng Kewei , Li Xian , Xu Yifan Ethan , Dong Xin Luna , Sun Yizhou
Published: June 2022

Although product graphs (PGs) have gained increasing attentions in recent years for their successful applications in product search and recommendations, the extensive power of PGs can be limited by the inevitable involvement of various kinds of errors. Thus, it is critical to validate the correctness of triples in PGs to improve their reliability. Knowledge graph (KG) embedding methods have strong error detection abilities. Yet, existing KG embedding methods may not be directly applicable to a PG due to its distinct characteristics: (1) PG contains rich textual signals, which necessitates a joint exploration of both text information and graph structure; (2) PG contains a large number of attribute triples, in which attribute values are represented by free texts. Since free texts are too flexible to define entities in KGs, traditional way to map entities to their embeddings using ids is no longer appropriate for attribute value representation; (3) Noisy triples in a PG mislead the embedding learning and significantly hurt the performance of error detection. To address the aforementioned challenges, we propose an end-to-end noise-tolerant embedding learning framework, PGE, to jointly leverage both text information and graph structure in PG to learn embeddings for error detection. Experimental results on real-world product graph demonstrate the effectiveness of the proposed framework comparing with the state-of-the-art approaches.
CHEX
Authors: Manne Naga Nithin , Satpati Shilvi , Malik Tanu , Bagchi Amitabha , Gehani Ashish , Chaudhary Amitabh
Published: June 2022

In scientific computing and data science disciplines, it is often necessary to share application workflows and repeat results. Current tools containerize application workflows, and share the resulting container for repeating results. These tools, due to containerization, do improve sharing of results. However, they do not improve the efficiency of replay. In this paper, we present the multiversion replay problem, which arises when multiple versions of an application are containerized, and each version must be replayed to repeat results. To avoid executing each version separately, we develop
CHEX
, which checkpoints program state and determines when it is permissible to reuse program state across versions. It does so using system call-based execution lineage. Our capability to identify common computations across versions enables us to consider optimizing replay using an in-memory cache, based on a checkpoint-restore-switch system. We show the multiversion replay problem is NP-hard, and propose efficient heuristics for it.
CHEX
reduces overall replay time by sharing common computations but avoids storing a large number of checkpoints. We demonstrate that
CHEX
maintains lightweight package sharing, and improves the total time of multiversion replay by 50% on average.
Edge-based local push for personalized PageRank
Authors: Wang Hanzhi , Wei Zhewei , Gan Junhao , Yuan Ye , Du Xiaoyong , Wen Ji-Rong
Published: June 2022

Personalized PageRank (PPR) is a popular node proximity metric in graph mining and network research. A single-source PPR (SSPPR) query asks for the PPR value of each node on the graph. Due to its importance and wide applications, decades of efforts have been devoted to the efficient processing of SSPPR queries. Among existing algorithms,
LocalPush
is a fundamental method for SSPPR queries and serves as a cornerstone for subsequent algorithms. In
LocalPush
, a
push
operation is a crucial primitive operation, which distributes the probability at a node
u
to ALL
u
's neighbors via the corresponding edges. Although this
push
operation works well on
unweighted
graphs, unfortunately, it can be rather inefficient on
weighted
graphs. In particular, on
unbalanced
weighted graphs where only a few of these edges take the majority of the total weight among them, the
push
operation would have to distribute "insignificant" probabilities along those edges which just take the minor weights, resulting in expensive overhead.

To resolve this issue, in this paper, we propose the
EdgePush
algorithm, a novel method for computing SSPPR queries on weighted graphs.
EdgePush
decomposes the aforementioned push operations in
edge-based push
, allowing the algorithm to operate at the edge level granularity. As a result, it can flexibly distribute the probabilities according to edge weights. Furthermore, our
EdgePush
allows a fine-grained termination threshold for each individual edge, leading to a superior complexity over
LocalPush.
Notably, we prove that
EdgePush
improves the theoretical query cost of
LocalPush
by an order of up to
O
(
n
) when the graph's weights are
unbalanced.
Our experimental results demonstrate that
EdgePush
significantly outperforms state-of-the-art baselines in terms of query efficiency on large motif-based and real-world weighted graphs.
Stingy sketch
Authors: Li Haoyu , Chen Qizhi , Zhang Yixin , Yang Tong , Cui Bin
Published: June 2022

Recording the frequency of items in highly skewed data streams is a fundamental and hot problem in recent years. The literature demonstrates that
sketch
is the most promising solution. The typical metrics to measure a sketch are accuracy and speed, but existing sketches make only trade-offs between the two dimensions. Our proposed solution is a new sketch framework called Stingy sketch with two key techniques: Bit-pinching Counter Tree (
BCTree
) and Prophet Queue (
PQueue
) which optimizes both the accuracy and speed. The key idea of
BCTree
is to split a large fixed-size counter into many small nodes of a tree structure, and to use a precise encoding to perform carry-in operations with low processing overhead. The key idea of
PQueue
is to use pipelined prefetch technique to make most memory accesses happen in L2 cache without losing precision. Importantly, the two techniques are cooperative so that Stingy sketch can improve accuracy and speed simultaneously. Extensive experimental results show that Stingy sketch is up to 50% more accurate than the SOTA of accuracy-oriented sketches and is up to 33% faster than the SOTA of speed-oriented sketches.
TGL
Authors: Zhou Hongkuan , Zheng Da , Nisa Israt , Ioannidis Vasileios , Song Xiang , Karypis George
Published: June 2022

Many real world graphs contain time domain information. Temporal Graph Neural Networks capture temporal information as well as structural and contextual information in the generated dynamic node embeddings. Researchers have shown that these embeddings achieve state-of-the-art performance in many different tasks. In this work, we propose TGL, a unified framework for large-scale offline Temporal Graph Neural Network training where users can compose various Temporal Graph Neural Networks with simple configuration files. TGL comprises five main components, a temporal sampler, a mailbox, a node memory module, a memory updater, and a message passing engine. We design a Temporal-CSR data structure and a parallel sampler to efficiently sample temporal neighbors to form training mini-batches. We propose a novel random chunk scheduling technique that mitigates the problem of obsolete node memory when training with a large batch size. To address the limitations of current TGNNs only being evaluated on small-scale datasets, we introduce two large-scale real-world datasets with 0.2 and 1.3 billion temporal edges. We evaluate the performance of TGL on four small-scale datasets with a single GPU and the two large datasets with multiple GPUs for both link prediction and node classification tasks. We compare TGL with the open-sourced code of five methods and show that TGL achieves similar or better accuracy with an average of 13X speedup. Our temporal parallel sampler achieves an average of 173X speedup on a multi-core CPU compared with the baselines. On a 4-GPU machine, TGL can train one epoch of more than one billion temporal edges within 1-10 hours. To the best of our knowledge, this is the first work that proposes a general framework for large-scale Temporal Graph Neural Networks training on multiple GPUs.
Endure
Authors: Huynh Andy , Chaudhari Harshal A. , Terzi Evimaria , Athanassoulis Manos
Published: June 2022

Log-Structured Merge trees (LSM trees) are increasingly used as the storage engines behind several data systems, frequently deployed in the cloud. Similar to other database architectures, LSM trees consider information about the
expected
workload (e.g., reads vs. writes, point vs. range queries) to optimize their performance via tuning. However, operating in a shared infrastructure like the cloud comes with workload
uncertainty
due to the fast-evolving nature of modern applications. Systems with static tuning discount the variability of such hybrid workloads and hence provide an inconsistent and overall suboptimal performance.

To address this problem, we introduce Endure - a new paradigm for tuning LSM trees in the presence of workload uncertainty. Specifically, we focus on the impact of the choice of compaction policies, size ratio, and memory allocation on the overall performance. Endure considers a robust formulation of the throughput maximization problem and recommends a tuning that maximizes the worst-case throughput over the
neighborhood
of each expected workload. Additionally, an uncertainty tuning parameter controls the size of this neighborhood, thereby allowing the output tunings to be conservative or optimistic. Through both model-based and extensive experimental evaluations of Endure in the state-of-the-art LSM-based storage engine, RocksDB, we show that the robust tuning methodology consistently outperforms classical tuning strategies. The robust tunings output by Endure lead up to a 5X improvement in throughput in the presence of uncertainty. On the flip side, Endure tunings have negligible performance loss when the observed workload exactly matches the expected one.
A critical re-evaluation of neural methods for entity alignment
Authors: Leone Manuel , Huber Stefano , Arora Akhil , García-Durán Alberto , West Robert
Published: June 2022

Neural methods have become the de-facto choice for the vast majority of data analysis tasks, and entity alignment (EA) is no exception. Not surprisingly, more than 50 different neural EA methods have been published since 2017. However, surprisingly, an analysis of the differences between neural and non-neural EA methods has been lacking. We bridge this gap by performing an in-depth comparison among five carefully chosen representative state-of-the-art methods from the pre-neural and neural era. We unravel, and consequently mitigate, the inherent deficiencies in the experimental setup utilized for evaluating neural EA methods. To ensure fairness in evaluation, we homogenize the entity matching modules of neural and non-neural methods. Additionally, for the first time, we draw a parallel between EA and record linkage (RL) by empirically showcasing the ability of RL methods to perform EA. Our results indicate that Paris, the state-of-the-art non-neural method, statistically significantly outperforms all the representative state-of-the-art neural methods in terms of both efficacy and efficiency across a wide variety of dataset types and scenarios, and is second only to BERT-INT for a specific scenario of cross-lingual EA. Our findings shed light on the potential problems resulting from an impulsive application of neural methods as a panacea for all data analytics tasks. Overall, our work results in two overarching conclusions: (1) Paris should be used as a baseline in every follow-up work on EA, and (2) neural methods need to be positioned better to showcase their true potential, for which we provide multiple recommendations.
The Use of Norse Loanwords in Late Old English Historical Poems
Authors: Lutz Angelika
Published: June 2022

The use of Norse loanwords in Old English poetry seems to be restricted to historical poems in praise of prominent contemporaries. It is demonstrated that the few Norse loans in these poems neither contribute to the laudatory character of such texts nor serve as new, additional means of stylistic enrichment. Instead, the Norse loans in these late Old English historical poems can be shown to have been used to add factual plausibility to such poems as historical texts. This contrasts with the use of Norse loanwords in Middle English poems.