bluuebunny/crossref_metadata_2025 huggingface dataset:
A review of the identification methods and types of collapsible soils
Authors: Opukumo Alfred Wilson , Davie Colin T. , Glendinning Stephanie , Oborie Ebiegberi
Published: February 2022

Collapsible soils have caused infrastructural damages resulting in several economic losses and loss of lives in certain cases. The prerequisite for collapse occurring is an open metastable structure; capable of developing in any soil type given the right placement or aging conditions. Natural and anthropogenic (engineered and non-engineered) collapsible soils exist in many regions of the world. In their unsaturated state, these soils exhibit high enough shear strength and stiffness (provided by inter-particle bonds of either suction, clay, calcium carbonate, or other salts) but upon wetting and/or loading they undergo repacking due to bond softening/weakening. This collapse of the soil structure leads to a rapid volume decrease and consequently associated issues such as rapid differential settlement, ground fissuring and landslides occur causing damages to civil structures, and loss of lives. Despite these threats and the large body of research available in this subject, there is still poor understanding of the process of softening/weakening and the collapse mechanism of certain bond in some collapse elements. The aim of this paper is to provide a state-of-the-art comprehensive review of the different types of collapsible soils, field, and laboratory methods of predicting and measuring their potential to collapse. This understanding is crucial for geotechnical characterisation of soils in order to design safe and economic infrastructures with their long-term serviceability also in focus.
MSI-RPi: Affordable, Portable, and Modular Multispectral Imaging Prototype Suited to Operate in UV, Visible and Mid-Infrared Regions
Authors: Arunachalam Ajay , Andreasson Henrik
Published: February 2022

Digital plant inventory provides critical growth insights, given the associated data quality is good. Stable & high-quality image acquisition is critical for further examination. In this work, we showcase an affordable, portable, and modular spectral camera prototype, designed with open hardware’s and open-source software’s. The image sensors used were color, and infrared Pi micro-camera. The designed prototype presents the advantage as being low-cost and modular with respect to other general commercial market available spectral devices. The micro-size connected sensors make it a compact instrument that can be used for any general spectral acquisition purposes, along with the provision of custom selection of the bands, making the presented prototype design a Plug-nd-Play (PnP) setup that can be used in different wide application areas. The images acquired from our custom-built prototype were back-tested by performing image analysis and qualitative assessments. The image acquisition software, and processing algorithm has been programmed, which is bundled with our developed system. Further, an end-to-end automation script is integrated for the users to readily leverage the services on-demand. The design files, schematics, and all the related materials of the spectral block design is open-sourced with open-hardware license & is made available at https://github.com/ajayarunachalam/Multi-Spectral-Imaging-RaspberryPi-Design. The automated data acquisition scripts & the spectral image analysis done is made available at https://github.com/ajayarunachalam/SI-RPi.
Tomato: Different Leaf Disease Detection Using Transfer Learning Based Network
Authors: Patnayakuni Siva Prasad
Published: February 2022

Plant diseases have a significant effect on crop productivity, financial costs, and output. It is vital to research plant diseases in order to increase agricultural yield. Tomatoes are the world’s most frequently cultivated crop, and they are a staple ingredient in almost every cuisine. After potatoes and sweet potatoes, tomato is the most extensively cultivated vegetable on the planet. India was ranked second in tomato output. Numerous diseases have a detrimental effect on the quantity and quality of the tomato crop. Early disease detection will assist farmers in increasing crop production. The research proposes a transfer learning-based technique for detecting five distinct leaf diseases. AlexNet has been used to detect and classify disease. The simulation results reveal that the method based on transfer learning outperforms the other methods with a classification accuracy of 95.6%.
A Dimensional Consistency Aware Time Domain Analysis of the Generic Fractional Order Biquadratic System
Authors: Banchuin Rawid , Chaisricharoen Roungsan
Published: February 2022

In this research, the time domain analysis of the fractional order biquadratic system with nonzero input and nonzero damping ratio has been performed. Unlike the previous works, the analysis has been generically done with dimensional consistency awareness without referring to any specific physical system where nonzero input and nonzero damping ratio have been allowed. The fractional differential equation of the system has been derived and analytically solved. The physical measurability of the dimensions of the fractional derivative terms which have been defined in Caputo sense, and response with significantly different dynamic from its dimensional consistency ignored counterpart have been obtained due to our dimensional consistency awareness. The resulting solution is applicable to the fractional biquadratic systems of any kind with any physical nature. Based on such solution and numerical simulations, the influence of the fractional order parameter to all major time domain parameters have been studied in detailed. The obtain results provide insight to the fractional order biquadratic system with dimensional consistency awareness in a generic point of view.
DTTV Localization with Fingerprinting Technique and Clean Algorithm Based on Measurement Data
Authors: Promwong Sathaporn , Suwansukho Nattapan
Published: February 2022

Digital terrestrial television (DTTV) technology has been developed and used to broadcast the television program. A numerous applications have been developed for the additional feature used with DTTV. One of these features that can be used which is the localization system with DTTV broadcasting. The advantage of DVB-T2 broadcasting channel for localization technology which very wide coverage area that covered whether outdoor and indoor environment. In the present there are divers methodologies to locate the position of an object or user such as the Global Positioning System (GPS), Cellular Positioning System (CPS) and Wi-Fi Positioning System (WPS). Nowadays there are various application that used for monitoring and controlling such as a water level sensor system, a traffic control system, an intrusion monitoring system etc. that consists of the localization system. The received data can’t be useful without the accuracy location. The mentioned foregoing system still have a limitation in some environment such as the GPS signal is not accessible to some environments, the CPS signal is based on a cell phone tower and the WPS is based on Wi-Fi hotspot. Therefore, the accuracy of localization is decreased. In order to overcome the foregoing limitation of these three systems the complementary remedy the poor coverage is required. The objective of this research is to improve the DVB-T2 propagation channel by a Clean algorithm to eliminate the noise propagation channel for an accuracy of localization system. This technique is very useful for localization analysis in DTTV technology. The distinctive advantage of the DTTV localization is the wide coverage of signal whether an indoor or outdoor environment. Moreover, when the Clean algorithm has been used the noise in propagation channel has been eliminated lead to the accuracy of location receive.
Experimental Evaluation of UWB Transmission Waveform with Body-Shadowing in an Indoor Environment
Authors: Suwan Supakorn , Promwong Sathaporn
Published: February 2022

Wireless radio transmission performance in a realistic environment is a significant issue for designing and evaluating in short-range transmission technologies. Human body-shadowing is a significant propagation effect in an indoor wireless communication network. This paper presents the measurement model of impulse radio transmission with human body-shadowing in an indoor environment with IEEE 802.15.4 multipath impulse parameters. The impulse radio transfer function measurement model for the human body impulse radio transfer function with frequency band cover from 3 GHz to 11 GHz. The optimum system evaluation of impulse radio transmission is due to the human body and antennas. The characteristics of impulse radio transmission loss are using decay factor, log-normal standard, clusters, and ray arrival rate. The contributions of this research can be evaluating the human body impulse radio transfer function. And the design of the wireless radio system with the body shadowing effects and ambient environments.
(p,q)-biclique counting and enumeration for large sparse bipartite graphs
Authors: Yang Jianye , Peng Yun , Zhang Wenjie
Published: February 2022

In this paper, we study the problem of (
p
, q)-biclique counting and enumeration for large sparse bipartite graphs. Given a bipartite
G
= (
U, V
, E), and two integer parameters
p
and q, we aim to efficiently count and enumerate all (p, q)-bicliques in
G
, where a (p, q)-biclique
B
(
L, R
) is a complete subgraph of
G
with
L
⊆
U, R
⊆
V
, |L| = p, and |R| = q. The problem of (p, q)-biclique counting and enumeration has many applications, such as graph neural network information aggregation, densest subgraph detection, and cohesive subgroup analysis, etc. Despite the wide range of applications, to the best of our knowledge, we note that there is no efficient and scalable solution to this problem in the literature.

This problem is computationally challenging, due to the worst-case exponential number of (p, q)-bicliques. In this paper, we propose a competitive branch-and-bound baseline method, namely BCList, which explores the search space in a depth-first manner, together with a variety of pruning techniques. Although BCList offers a useful computation framework to our problem, its worst-case time complexity is exponential to
p + q.
To alleviate this, we propose an advanced approach, called BCList++. Particularly, BCList++ applies a layer based exploring strategy to enumerate (
p, q
)-bicliques by anchoring the search on either
U
or
V
only, which has a worst-case time complexity exponential to either
p
or
q
only. Consequently, a vital task is to choose a layer with the least computation cost. To this end, we develop a cost model, which is built upon an unbiased estimator for the density of 2-hop graph induced by
U
or
V.
To improve computation efficiency, BCList++ exploits pre-allocated arrays and vertex labeling techniques such that the frequent subgraph creating operations can be substituted by array element switching operations. We conduct extensive experiments on 16 real-life datasets, and the experimental results demonstrate that BCList++ significantly outperforms the baseline methods by up to 3 orders of magnitude. We show via a case study that (p, q)-bicliques optimize the efficiency of graph neural networks.
Butterfly counting on uncertain bipartite graphs
Authors: Zhou Alexander , Wang Yue , Chen Lei
Published: February 2022

When considering uncertain bipartite networks, the number of instances of the popular graphlet structure the butterfly may be used as an important metric to quickly gauge information about the network. This Uncertain Butterfly Count has practical usages in a variety of areas such as biomedical/biological fields, E-Commerce and road networks. In this paper we formally define the uncertain butterfly structure (in which the existential probability of the butterfly is greater than or equal to some user-defined threshold
t
) as well as the Uncertain Butterfly Counting Problem (to determine the number of unique instances of this structure on any uncertain bipartite network). We then examine exact solutions by proposing a non-trivial baseline
(UBFC)
as well as an improved solution
(IUBFC)
which reduces the time complexity and employs heuristics to further reduce the runtime in practice. In addition to exact solutions, we propose two approximate solutions via sampling,
UBS
and
PES
, which can be used to quickly estimate the Uncertain Butterfly Count, a powerful tool when the exact count is unnecessary. Using a range of networks with different edge existential probability distributions, we validate the efficiency and effectiveness of our solutions.
Origami
Authors: Arman Arif , Loguinov Dmitri
Published: February 2022

Mergesort is a popular algorithm for sorting real-world workloads as it is immune to data skewness, suitable for parallelization using vectorized intrinsics, and relatively simple to multi-thread. In this paper, we introduce
Origami
, an in-memory merge-sort framework that is optimized for scalar, as well as all current SIMD (single-instruction multiple-data) CPU architectures. For each vector-extension set (e.g., SSE, AVX2, AVX-512), we present an in-register sorter for small sequences that is up to 8× faster than prior methods and a branchless streaming merger that achieves up to a 1.5× speed-up over the naive merge. In addition, we introduce a cache-residing quad-merge tree to avoid bottlenecking on memory bandwidth and a parallel partitioning scheme to maximize thread-level concurrency. We develop an end-to-end sort with these components and produce a highly utilized mergesort pipeline by reducing the synchronization overhead between threads. Single-threaded Origami performs up to 2× faster than the closest competitor and achieves a nearly perfect speed-up in multi-core environments.
ParChain
Authors: Yu Shangdi , Wang Yiqiu , Gu Yan , Dhulipala Laxman , Shun Julian
Published: February 2022

This paper studies the hierarchical clustering problem, where the goal is to produce a dendrogram that represents clusters at varying scales of a data set. We propose the ParChain framework for designing parallel hierarchical agglomerative clustering (HAC) algorithms, and using the framework we obtain novel parallel algorithms for the complete linkage, average linkage, and Ward's linkage criteria. Compared to most previous parallel HAC algorithms, which require quadratic memory, our new algorithms require only linear memory, and are scalable to large data sets. ParChain is based on our parallelization of the nearest-neighbor chain algorithm, and enables multiple clusters to be merged on every round. We introduce two key optimizations that are critical for efficiency: a range query optimization that reduces the number of distance computations required when finding nearest neighbors of clusters, and a caching optimization that stores a subset of previously computed distances, which are likely to be reused.

Experimentally, we show that our highly-optimized implementations using 48 cores with two-way hyper-threading achieve 5.8--110.1x speedup over state-of-the-art parallel HAC algorithms and achieve 13.75--54.23x self-relative speedup. Compared to state-of-the-art algorithms, our algorithms require up to 237.3x less space. Our algorithms are able to scale to data set sizes with tens of millions of points, which existing algorithms are not able to handle.
Scabbard
Authors: Theodorakis Georgios , Kounelis Fotios , Pietzuch Peter , Pirk Holger
Published: February 2022

Single-node multi-core stream processing engines (SPEs) can process hundreds of millions of tuples per second. Yet making them fault-tolerant with exactly-once semantics while retaining this performance is an open challenge: due to the limited I/O bandwidth of a single-node, it becomes infeasible to persist all stream data and operator state during execution. Instead, single-node SPEs rely on upstream distributed systems, such as Apache Kafka, to recover stream data after failure, necessitating complex cluster-based deployments. This lack of built-in fault-tolerance features has hindered the adoption of single-node SPEs.

We describe Scabbard, the first single-node SPE that supports exactly-once fault-tolerance semantics despite limited local I/O bandwidth. Scabbard achieves this by integrating persistence operations with the query workload. Within the operator graph, Scabbard determines when to persist streams based on the selectivity of operators: by persisting streams after operators that discard data, it can substantially reduce the required I/O bandwidth. As part of the operator graph, Scabbard supports parallel persistence operations and uses markers to decide when to discard persisted data. The persisted data volume is further reduced using workload-specific compression: Scabbard monitors stream statistics and dynamically generates computationally efficient compression operators. Our experiments show that Scabbard can execute stream queries that process over 200 million tuples per second while recovering from failures with sub-second latencies.
Enabling SQL-based training data debugging for federated learning
Authors: Liu Yejia , Wu Weiyuan , Flokas Lampros , Wang Jiannan , Wu Eugene
Published: February 2022

How can we debug a logistic regression model in a federated learning setting when seeing the model behave unexpectedly (e.g., the model rejects all high-income customers' loan applications)? The SQL-based training data debugging framework has proved effective to fix this kind of issue in a non-federated learning setting. Given an unexpected query result over model predictions, this framework automatically removes the label errors from training data such that the unexpected behavior disappears in the retrained model. In this paper, we enable this powerful framework for federated learning. The key challenge is how to develop a security protocol for federated debugging which is proved to be secure, efficient, and accurate. Achieving this goal requires us to investigate how to seamlessly integrate the techniques from multiple fields (Databases, Machine Learning, and Cybersecurity). We first propose FedRain, which extends Rain, the state-of-the-art SQL-based training data debugging framework, to our federated learning setting. We address several technical challenges to make FedRain work and analyze its security guarantee and time complexity. The analysis results show that FedRain falls short in terms of both efficiency and security. To overcome these limitations, we redesign our security protocol and propose Frog, a novel SQL-based training data debugging framework tailored for federated learning. Our theoretical analysis shows that Frog is more secure, more accurate, and more efficient than FedRain. We conduct extensive experiments using several real-world datasets and a case study. The experimental results are consistent with our theoretical analysis and validate the effectiveness of Frog in practice.
Leveraging query logs and machine learning for parametric query optimization
Authors: Vaidya Kapil , Dutt Anshuman , Narasayya Vivek , Chaudhuri Surajit
Published: February 2022

Parametric query optimization (PQO) must address two problems: identify a relatively small number of plans to cache for a parameterized query (populateCache), and efficiently select the best cached plan to use for executing any instance of the parameterized query (getPlan). Our approach decouples these two decisions. We formulate populateCache as an optimization problem with the goal of identifying a set of plans that minimizes the optimizer estimated cost of queries in the log, and present an efficient algorithm. For getPlan, we leverage query logs to train machine learning (ML) models to choose the lowest optimizer-estimated cost plan from the cached plans. We conduct extensive experiments using complex parameterized queries from benchmarks and real workloads. Our algorithm for populateCache achieves low geometric mean sub-optimality (1.2) even for complex queries using relatively few plans, and scales well to large query logs. The mean latency of our ML model based getPlan technique (~ 210μ
sec
) is between one to four orders of magnitude faster compared to prior PQO techniques. The mean sub-optimality is low (1.05), and the 95
th
percentile sub-optimality (1.3) is between 1.1× and 25× lower compared to prior techniques. Finally, we present an efficient algorithm for getPlan that leverages execution time information in query logs to circumvent inaccuracies of the query optimizer's cost estimates.
Pre-training summarization models of structured datasets for cardinality estimation
Authors: Lu Yao , Kandula Srikanth , König Arnd Christian , Chaudhuri Surajit
Published: February 2022

We consider the problem of pre-training models which convert structured datasets into succinct summaries that can be used to answer cardinality estimation queries. Doing so avoids per-dataset training and, in our experiments, reduces the time to construct summaries by up to 100×. When datasets change, our summaries are incrementally updateable. Our key insights are to use multiple summaries per dataset, use learned summaries for columnsets for which other simpler techniques do not achieve high accuracy, and that analogous to similar pre-trained models for images and text, structured datasets have some common frequency and correlation patterns which our models learn to capture by pre-training on a large and diverse corpus of datasets.
The next 50 years in database indexing or
Authors: Dittrich Jens , Nix Joris , Schön Christian
Published: February 2022

Index structures are a building block of query processing and computer science in general. Since the dawn of computer technology there have been index structures. And since then, a myriad of index structures are being invented and published each and every year.

In this paper we argue that the very idea of "inventing an index" is a misleading concept in the first place. It is the analogue of "inventing a physical query plan". This paper is a paradigm shift in which we propose to drop the idea to handcraft index structures (as done for binary search trees over B-trees to any form of learned index) altogether. We present a new automatic index breeding framework coined
Genetic Generic Generation of Index Structures (GENE)
. It is based on the observation that almost all index structures are assembled along three principal dimensions: (1) structural building blocks, e.g., a B-tree is assembled from two different structural node types (inner and leaf nodes), (2) a couple of invariants, e.g., for a B-tree all paths have the same length, and (3) decisions on the internal layout of nodes (row or column layout, etc.). We propose a generic indexing framework that can mimic many existing index structures along those dimensions. Based on that framework we propose a generic genetic index generation algorithm that, given a workload and an optimization goal, can automatically assemble and mutate, in other words 'breed' new index structure 'species'. In our experiments we follow multiple goals. We reexamine some good old wisdom from database technology. Given a specific workload, will GENE even breed an index that is equivalent to what our textbooks and papers currently recommend for such a workload? Or can we do even more? Our initial results strongly indicate that generated indexes are the next step in designing index structures.
DARLING
Authors: Chapnik Koral , Kolchinsky Ilya , Schuster Assaf
Published: February 2022

Complex event processing (CEP) is widely employed to detect user-defined combinations, or patterns, of events in massive streams of incoming data. Numerous applications such as healthcare, fraud detection, and more, use CEP technologies to capture critical alerts, threats, or vital notifications. This requires that the technology meet real-time detection constraints. Multiple optimization techniques have been developed to minimize the processing time for CEP, including parallelization techniques, pattern rewriting, and more. However, these techniques may not suffice or may not be applicable when an unpredictable peak in the input event stream exceeds the system capacity. In such cases, one immediate possible solution is to drop some of the load in a technique known as load shedding.

We present a novel load shedding mechanism for real-time complex event processing. Our approach uses statistics that are gathered to detect overload. The solution makes data-driven load shedding decisions to drop the less important events such that we preserve a given latency bound while minimizing the degradation in the quality of results. An extensive experimental evaluation on a broad set of real-life patterns and datasets demonstrates the superiority of our approach over the state-of-the-art techniques.
Rearchitecting in-memory object stores for low latency
Authors: Zhuo Danyang , Zhang Kaiyuan , Li Zhuohan , Zhuang Siyuan , Wang Stephanie , Chen Ang , Stoica Ion
Published: February 2022

Low latency is increasingly critical for modern workloads, to the extent that compute functions are explicitly scheduled to be co-located with their in-memory object stores for faster access. However, the traditional object store architecture mandates that clients interact with the server via inter-process communication (IPC). This poses a significant performance bottleneck for low-latency workloads. Meanwhile, in many important emerging AI workloads, such as parallel tree search and reinforcement learning, all the worker processes accessing the object store belong to a single user.

We design Lightning, an in-memory object store rearchitected for modern, low-latency workloads in a single-user, multi-process setting. Lightning departs from the traditional design by adopting a shared memory model, enabling clients to directly access the object store without IPC boundary. Instead, client isolation is achieved by a novel integration of Intel Memory Protect Keys (MPK) hardware, transaction logging, and formal verification. Our evaluations show that Lightning outperforms state-of-the-art in-memory object stores by up to 9.0x on five standard NoSQL workloads and up to 4.5x in scaling up a Python tree search program. Lightning improves the throughput of a popular reinforcement learning framework that uses an in-memory object store for data sharing by up to 40%.
Theoretically and practically efficient parallel nucleus decomposition
Authors: Shi Jessica , Dhulipala Laxman , Shun Julian
Published: February 2022

This paper studies the nucleus decomposition problem, which has been shown to be useful in finding dense substructures in graphs. We present a novel parallel algorithm that is efficient both in theory and in practice. Our algorithm achieves a work complexity matching the best sequential algorithm while also having low depth (parallel running time), which significantly improves upon the only existing parallel nucleus decomposition algorithm (Sariyüce
et al.
, PVLDB 2018). The key to the theoretical efficiency of our algorithm is a new lemma that bounds the amount of work done when peeling cliques from the graph, combined with the use of a theoretically-efficient parallel algorithms for clique listing and bucketing. We introduce several new practical optimizations, including a new multi-level hash table structure to store information on cliques space-efficiently and a technique for traversing this structure cache-efficiently. On a 30-core machine with two-way hyper-threading on real-world graphs, we achieve up to a 55x speedup over the state-of-the-art parallel nucleus decomposition algorithm by Sariyüce
et al.
, and up to a 40x self-relative parallel speedup. We are able to efficiently compute larger nucleus decompositions than prior work on several million-scale graphs for the first time.
APEX
Authors: Lu Baotong , Ding Jialin , Lo Eric , Minhas Umar Farooq , Wang Tianzheng
Published: February 2022

The recently released persistent memory (PM) offers high performance, persistence, and is cheaper than DRAM. This opens up new possibilities for indexes that operate and persist data directly on the memory bus. Recent learned indexes exploit data distribution and have shown great potential for some workloads. However, none support persistence or instant recovery, and existing PM-based indexes typically evolve B+-trees without considering learned indexes.

This paper proposes APEX, a new PM-optimized learned index that offers high performance, persistence, concurrency, and instant recovery. APEX is based on ALEX, a state-of-the-art updatable learned index, to combine and adapt the best of past PM optimizations and learned indexes, allowing it to reduce PM accesses while still exploiting machine learning. Our evaluation on Intel DCPMM shows that APEX can perform up to ~15× better than existing PM indexes and can recover from failures in ~42ms.
Efficient label-constrained shortest path queries on road networks
Authors: Zhang Junhua , Yuan Long , Li Wentao , Qin Lu , Zhang Ying
Published: February 2022

Computing the shortest path between two vertices is a fundamental problem in road networks. Most of the existing works assume that the edges in the road networks have no labels, but in many real applications, the edges have labels and label constraints may be placed on the edges appearing on a valid shortest path. Hence, we study the label-constrained shortest path queries in this paper. In order to process such queries efficiently, we adopt an index-based approach and propose a novel index structure, LSD-Index, based on
tree decomposition.
With LSD-Index, we design an efficient query processing algorithm with good performance guarantees. Moreover, we also propose an algorithm to construct LSD-Index and further improve the efficiency of index construction by exploiting the parallel computing techniques. We conduct extensive performance studies using large real road networks including the whole USA road network. Compared with the state-of-the-art approach, the experimental results demonstrate that our algorithm not only achieves up to 2 orders of magnitude speedup in query processing time but also consumes much less index space. Meanwhile, the indexing time is also competitive, especially that for the parallel index construction algorithm.
Physician-Assisted Suicide, the Right to Die, and Misconceptions About Life
Authors: De Andrade Pedro Fior Mota , Moreno Mario Tito Ferreira
Published: February 2022

In this paper, we analyze the legal situation regarding physician-assisted suicide in the world. Our hypothesis is that the prohibitive stance on physician-assisted suicide in most societies in the world today seems to be related to our moral attitudes toward suicide. This brings us to a discussion about life itself. We claim that the total lack of legal protection for physician-assisted suicide from international organizations and most countries in the world lies in a philosophical assumption that supports much of our common beliefs about what life really is. This assumption states that life must be thought of as not belonging exclusively to the individual. We argue that this assumption is a misconception that can be heard in the debate on suicide. Consequently we endorse a different conception of life which tends to favor a pro-choice stance towards suicide and assisted death in general. This position takes the patient’s decision about when to die more seriously. We conclude that there is no reason why physician-assisted suicide does not deserve legal protection as an individual right to die under international law.
Percieved Stress in Emerging Adulthood: The Role of Sense of Control and the Mediation Effects of Religiosity and Materialistic Values
Authors: Masoom Muhammad Rehan
Published: February 2022

The research addresses the effect of sense of control on perceived stress by controlling for the intervening effects of Religiosity and Materialism. A total of 609 emerging adults living in Dhaka city participated in the survey; surveyors used a 48-item structural closed-ended questionnaire to collect the responses. The elicited responses were quantified, and structural equation models were formulated to identify any associations among the variables of interest. The findings suggest that sense of control is a strong determinant of perceived stress; neither religiosity nor materialism mediate the association. The study advances the transactional model of stress and coping.
Modeling of texture evolution in copper under equal channel angular pressing
Authors: Baik Seung Chul , Estrin Yuri , Hellmig Ralph Jörg , Jeong Hyo-Tae , Brokmeier H.-G. , Kime Hyoung Seop
Published: February 2022

Texture evolution was analyzed with the full-constraint Taylor model for an idealized perfectly plastic face-centered cubic material as well as for real, strain-hardening copper subjected to equal channel angular pressing (ECAP). For the idealized material, the stress in the plastically deformed part of the billet was shown to be uniform leading to complete filling of the die. Finite element simulations showed that plastic deformation is localized in a narrow shear zone and that the plastic strain and texture in the billet become uniform after ECAP. A simplified recipe for texture calculation akin to that proposed by Gholinia et al. was suggested: it reduces the deformation under ECAP to a combination of two rotations separated by tension-compression. For the case of copper, a strain hardening model based on dislocation density evolution was used. It was shown that due to significant strain hardening during the first ECAP pass, the flowing material does not fill the outside die corner and a strain and texture non-uniformity develops. A gradual decrease of the strain hardening in subsequent ECAP passes leads to a more uniform strain and texture across the billet. The simulated pole figures were shown to be in good agreement with the neutron diffraction data for copper deformed by ECAP (Routes A, Cr, Bγ and Bcr) suggesting that the model used provides a reliable modeling tool for simulating texture evolution under ECAP.
Finite element analysis of the thermo-mechanical fatigue of DD8 single crystal nickel-based superalloy
Authors: Zhou L. , Li S. X. , Wang Y. C. , Zang Q. S. , Lu K.
Published: February 2022

A nonlinear finite element method is presented for analysing the thermo-mechanical fatigue behavior of the DD8 single crystal nickel-based superalloy. The specimens had axial orientations along the [001] direction. In-phase and out-of-phase thermo-mechanical fatigue were simulated under mechanical strain control with a temperature variation from 450 to 900 °C. The cyclic mechanical behavior, the equivalent stress and plastic strain were investigated in both γ' phase and γ matrix.
Factors influencing the extent of hydrogen-enhanced brittle cracking in a Cu-strengthened HSLA steel during monotonic loading
Authors: Chattoraj Indranil , Kumar Anil , Das Swapan K. , Tarafder Mita , Tarafder Soumitra
Published: February 2022

The extent of brittle crack as a function of hydrogen charging conditions was studied for a HSLA steel using circumferentially notched cylindrical tensile samples. Two different notch depths were used. The effect of hydrogen could be well represented by an effective hydrogen potential which was defined using a representative hydrogen concentration and a diffusive time parameter, for relatively faster strain rates. The high triaxiality in deep-notched samples led to the initiation of ductile failure mechanisms overwhelming the brittle cracking process.
Coarsening kinetics of Cu particles in an Fe-1.5% Cu alloy
Authors: Monzen Ryoichi , Takada Kenichi , Matsuda Kenji
Published: February 2022

The Ostwald ripening of spherical and rod-shaped Cu precipitate particles in an Fe-1.5 mass% Cu alloy aged between 873 and 1023 K has been examined by measuring both the particle size by electron microscopy and the Cu concentration in the α-Fe matrix by electrical resistivity. The average radius of spherical and rod-shaped particles increases with ageing time t as t
1/3, in agreement with the growth by diffusion-controlled coarsening. The kinetics of the depletion of supersaturation with t obey the predicted t
– 1/3 time law. The Cu/α-Fe interfacial energy and the diffusivity of Cu in α-Fe have been independently derived from the data on coarsening. The interfacial energies are measured as 0.57 and 1.1 Jm–2 for the spherical and rod-shaped particles respectively. The pre-exponential factor and the activation energy for bulk diffusion are found to be 4.54α10 –5 m2s– 1 and 241 kJmol – 1.
Characterisation of melamine formaldehyde microspheres synthesised with prolonged microencapsulated reaction time
Authors: Leskovšek Mirjam , Kortnik Jože , Stankovič Elesini Urška , Šumiga Boštjan
Published: February 2022

The aim of the research was to identify the influence of different microencapsulated reaction time on the morphology, size, infrared spectral, thermal and micromechanical properties of melamine formaldehyde microspheres, synthesised with modified in situ polymerisation. Microspheres are microencapsulated particles with a blurred boundary of the core and shell due to their same composition. The synthesis of microspheres was paused after 1, 3, 9 and 15 h, and stopped after 23 h. The scanning electron microscopy and granulometric analysis were used to study the morphology and size of microspheres. Regardless of the reaction time, the produced microspheres were spherical in shape and with a rough surface. The average size of microspheres was almost identical (0.709–0.790 µm), while the volume size distribution curve of the particles became narrower with prolonged reaction time. The curing mechanism of melamine formaldehyde resin was studied using the Fourier-transform infrared spectroscopy and thermal analysis, and nano-indentation identification. The results revealed a slightly more crosslinked structure: with minimal (neglected) increased thermal weight loss (only up to 0.5%) and minor increased Young’s modulus (up to 2.3%). Using a nano-indenter, the hardness of synthesised particles improved by up to 14.8% after 23 h reaction time.
Design of an Automatic Monitoring Model for Real-Time Data Flow in Network Based on Fuzzy Clustering Analysis for Health Analysis
Authors: Lv Yang
Published: February 2022

The current automatic monitoring model of real-time data flow in network has poor data clustering ability, which leads to a poor data flow clustering effect and a high memory occupation rate when the model is applied. To solve this problem, based on the data processing point of view, this paper designs an automatic monitoring model for real-time data flow in network based on fuzzy clustering analysis. The threshold sampling method is used to complete the dynamic sampling of real-time data in network. The Principal Component Analysis (PCA) method sorts out the eigenvectors in the data flow and solves them. According to the collected data flow samples, fuzzy clustering analysis algorithm combined with feature vector is used to realize the automatic monitoring of real-time data flow in network. So far, the design of automatic monitoring model for real-time data flow in network based on fuzzy clustering analysis is completed. The experimental results show that the fuzzy clustering model is better than the current model.
A New Concept of Jig Rotary Holder System for 3-Axis CNC Milling Machine Operated by the Main Machine Control
Authors: Silva F. J. G. , Campilho R. D. S. G. , Sousa V. F. C. , Coelho L. F. P. , Ferreira L. P. , Pereira M. T. , Matos J.
Published: February 2022

This study aims to develop a new jig holding system that is able to be controlled by a Computer Numeric Control (CNC) installed on three-axis machining centers, which can drastically improve the productivity in machining operations, enabling the machining of unparallel plans in the same setup. An action research methodology was adopted for this work, which, through a practical approach, intends to generate transferrable knowledge to other organizations whose situations are like those in this study. Together, the practical actions and the knowledge acquired create the changes needed for improving these processes. By conducting a case study, it was observed that savings of about 40 % can be easily achieved for parts with low geometric complexity. If the complexity of the parts increases, it is expected that these savings can be even higher. The return of investment is less than 2 years, which is usually affordable for enterprises. Through this study, it was possible to develop a new jig holding system that can be attached to a three-axis CNC machining center and clearly expands its functions and productivity. With this system, it is possible to work in different planes of the part in sequence, as well as use a double-sided table for the jigs, doubling the production batch each time the machine is loaded. Moreover, a list of key settings has been created with the main requirements and recommendations to adopt this kind of production system, which can be highlighted as the main research output.
Internal Forced Migration in Ukraine: Legal Aspects
Authors: Butyrskyi Andrii , Reznikova Viktoriia
Published: February 2022

The article is devoted to the study of the problems of forced internal migration in Ukraine. Forced migration significantly affects the geography, structure and dynamics of the population of Ukraine and its regions, the level of development of productive forces in the regions, the state of the labor market and the socio-economic characteristics of living standards of different categories of the population.

At present, migration processes in Ukraine are difficult to track, given that the last demographic census was conducted in 2001, and therefore it is difficult to establish the actual number of people in our country, and, accordingly, almost impossible to determine the number of internally displaced persons. According to the results of the study, the authors conclude that internally displaced persons (under current legislation) have the same rights and freedoms as other citizens of Ukraine permanently residing in Ukraine. However, in practice, internally displaced persons face restrictions on their rights and freedoms due to subjective factors. The basic rights that the state guarantees to internally displaced persons include: the right to employment, pensions, compulsory state social insurance, education, suffrage and economic rights. It is expedient to introduce tax benefits for enterprises founded by internally displaced persons, as well as to provide these benefits for enterprises that employ internally displaced persons.
The Effectiveness of Portofolio Assessment in English Writing Skill of Islamic Junior High School
Authors: Hafiz Hairori Sahrul , Hakim M Ikhwanul , Harun Muh Alawi
Published: February 2022

Seeing the observation result, the evidence from teachers' reports and final tests show that writing is the weakest aspect of students' work in English and supported by semi-structured interview given with a group of students having problem in English writing. The method was pre-experimental, and the Eighth grade of A is chosen as the sample. The findings show that there is significant difference in the mean score between students’ writing before and after treatment. It is also indicated from the result of hypothesis testing, it results in Ho is rejected because 0.000 < 0.025 (2-tailed) and based on t-test criteria, if t-test is bigger than t-table, it means that Ha is accepted and Ho is rejected. Besides students’ writing skill achievement with portfolio assessment is better than students without the assessment at the level of believe 95%. Portfolio assessment has positive correlation with students’ writing skill, it shows from the result of r-test in this study is 0.671 and the level of correlation is in the level of strong. Therefore, the assessment of teachers should have well-prepared materials, assignments, and other activities being appropriate time and it is better implemented in small class because it can be easily managed.
Influence Of Accreditation Readiness In Improving The Quality Of Teacher Teaching In PAUD Banguntapan Bantul Subdistrict
Authors: Hibana Hibana , Surahman Susilo
Published: February 2022

Recognition of educational institutions is demonstrated through accreditation and quality of teaching. This research aims to explain the influence of PAUD accreditation readiness on the quality of teaching in 40 PAUDs in Banguntapan. Research methodology uses quantitative descriptive. The results showed the existence of six criteria for the willingness of accreditation mechanisms that must be met, among others: first, general requirements; Second, special requirements; third, classification of accreditation applications; fourth, the completeness of the document; fifth, validation and verification; and sixth, accreditation results. The results showed X2h>X2t, where there was a significant influence on accreditation readiness to the quality of teaching in PAUD Banguntapan. Meanwhile, six schools focus on general requirements, and six schools also on specific needs. Twelve schools prepare for the early stages of the school's survival. Furthermore, eight schools in the preparation stage of accreditation application classification; seven schools in the readiness stage of the process and awaiting visit; ten schools in terms of validation and verification; And three schools are fully prepared for accreditation.
Improvement Learning Outcomes of Sepaktakraw Basic Techniques Using Audiovisual Media During Covid-19 Pandemic in PJOK Students of Sriwijaya University
Authors: Al-Amien M. Razif , Iyakrus Iyakrus , Sumarni Sri
Published: February 2022

This study aims to produce audiovisual based learning media during Covid-19 Pandemic to improve the Sepaktakraw basic technique in Physical Education, Sport, Health and Recreation Students in Sriwijaya University with a total of 41 students by using a limited trial (small group) to 11 students and a vast trial (large group) by using 30 students. Analysing technique in this research is a linear regression model. The results in this study indicate that the implementation of audiovisual media based learning process with Sepaktakraw basic technique is used in Physical Education, Sport, Health and Recreation Students of Sriwijaya University. According to material experts of obtained a percentage of 88% (very good) and media experts of obtained of 84,61% (very good) category. The pre-test results was found to have average value of 64,67 and the post-test result was found to have average value of 90,80 with F count < 0,05, that means is a significant difference between student knowledge before and after watching the video of Sepaktakraw basic technique. It can be concluded that audiovisual based learning of media to improve Sepaktakraw game technique in Physical Education, Sport, Health and Recreation Students in Sriwijaya University are valid, practical and feasible to use.