bluuebunny/crossref_metadata_2025 huggingface dataset:
Building a Puerto Rico 'Better than the One We Lost': Hurricane San Felipe II and the Puerto Rican New Deal
Authors: Standen Alex
Published: August 2021

This essay examines the connection between Hurricane San Felipe II (1928) and the Puerto Rican New Deal, an agrarian reconstruction plan initiated in 1933 to redistribute land, diversify agricultural production and create a more self-sufficient economy. I argue that San Felipe II played a critical role in intensifying and exposing the environmental conditions that gave rise to the Puerto Rican New Deal. In response, Puerto Rican workers, farmers and reformers used the hurricane to call attention to their plight, put pressure on colonial officials and initiate a series of reforms that laid the foundation for the more comprehensive measures of the New Deal era. I show how, in the aftermath of the storm, Puerto Ricans leveraged the specific impacts of the storm, what I call 'disaster populism', to defend their interests and demand agrarian reconstruction.
The enhanced deep Plug-and-play super-resolution algorithm with residual channel attention networks
Authors: Pan Hongguang , Wen Fan , Huang Xiangdong , Lei Xinyu , Yang Xiaoling
Published: August 2021

In the field of super-resolution image reconstruction, as a learning-based method, deep plug-and-play super-resolution (DPSR) algorithm can be used to find the blur kernel by using the existing blind deblurring methods. However, DPSR is not flexible enough in processing images with high- and low-frequency information. Considering a channel attention mechanism can distinguish low-frequency information and features in low-resolution images, in this paper, we firstly introduce this mechanism and design a new residual channel attention networks (RCAN); then the RCAN is adopted to replace deep feature extraction part in DPSR to achieve the adaptive adjustment of channel characteristics. Through four test experiments based on Set5, Set14, Urban100 and BSD100 datasets, we find that, under different blur kernels and different scale factors, the average peak signal to noise ratio (PSNR) and structural similarity (SSIM) values of our proposed method increase by 0.31dB and 0.55%, respectively; under different noise levels, the average PSNR and SSIM values increase by 0.26dB and 0.51%, respectively.
Document image binarization using difference of concatenated convolutions
Authors: Jyothi R.L. , Rahiman M. Abdul
Published: August 2021

Binarization is the most important stage in historical document image processing. Efficient working of character and word recognition algorithms depend on effective segmentation methods. Segmentation algorithms in turn depend on images free of noises and degradations. Most of these historical documents are illegible with degradations like bleeding through degradation, faded ink or faint characters, uneven illumination, contrast variation, etc. For effective processing of these document images, efficient binarization algorithms should be devised. Here a simple modified version of the Convolutional Neural Network (CNN) is proposed for historical document binarization. AOD-Net architecture for generating dehazed images from hazed images is modified to create the proposed network.The new CNN model is created by incorporating Difference of Concatenation layer (DOC), Enhancement layer (EN) and Thresholding layer into AOD-Net to make it suitable for binarization of highly degraded document images. The DOC layer and EN layer work effectively in solving degradation that exists in the form of low pass noises. The complexity of working of the proposed model is reduced by decreasing the number of layers and by introducing filters in convolution layers that work with low inter-pixel dependency. This modified version of CNN works effectively with a variety of highly degraded documents when tested with the benchmark historical datasets. The main highlight of the proposed network is that it works efficiently in a generalized manner for any type of document images without further parameter tuning. Another important highlight of this method is that it can handle most of the degradation categories present in document images. In this work, the performance of the proposed model is compared with Otsu, Sauvola, and three recent Deep Learning-based models.
A topological reduction for predicting of a lung cancer disease based on generalized rough sets
Authors: El-Bably M. K. , Abo-Tabl E. A.
Published: August 2021

The present work proposes new styles of rough sets by using different neighborhoods which are made from a general binary relation. The proposed approximations represent a generalization to Pawlak’s rough sets and some of its generalizations, where the accuracy of these approximations is enhanced significantly. Comparisons are obtained between the methods proposed and the previous ones. Moreover, we extend the notion of “nano-topology”, which have introduced by Thivagar and Richard [49], to any binary relation. Besides, to demonstrate the importance of the suggested approaches for deciding on an effective tool for diagnosing lung cancer diseases, we include a medical application of lung cancer disease to identify the most risk factors for this disease and help the doctor in decision-making. Finally, two algorithms are given for decision-making problems. These algorithms are tested on hypothetical data for comparison with already existing methods.
Generating fuzzy sets from the families of sets
Authors: Wu Hsien-Chung
Published: August 2021

The main purpose of this paper is to establish a mechanical procedure to determine the membership functions using the data collected from the economic and engineering problems. Determining the membership functions from the collected data may depend on the subjective viewpoint of decision makers. The mechanical procedure proposed in this paper can get rid of the subjective bias of decision makers. The concept of solid families is also proposed by regarding the sets in a family to be continuously varied. The desired fuzzy sets will be generated in the sense that its α-level sets will be identical to the sets of the original family. In order to achieve this purpose, any arbitrary families will be rearranged as the nested families by applying some suitable functions to the original families that are formulated from the collected data.
An iterative scheme for optimal and energy effective power allocation using multi-scale resource GOA-EM in distributed antenna system
Authors: Sakthidasan Sankaran K. , Gao Xiao-Zhi
Published: August 2021

Nowadays, numerous algorithms on power allocation have been proposed for maximizing the EE (Energy efficiency) and SE (Spectral efficiency) in the Distributed Antenna System (DAS). Moreover, the conservative techniques employed for power allocation seem to be problematic, due to their high computational complexity. The main objective of this paper focuses on optimizing the power allocation in order to enhance the EE and SE along with the improved antenna capacity using an effective optimization approach with the clustering model. To obtain the optimized power allocation and antenna capacity, Multi-scale resource Grasshopper Optimization Algorithm (Multi-scale resource GOA) scheme is proposed and employed. Furthermore, clustering is developed based on the Discriminative cluster-based Expectation maximization (DC-EM) clustering algorithms, which also helps to reduce the interference rate and computational complexity. The performance analysis is made under various scenarios and circumstances. The proposed system (DAS with GOA-EM) is assessed and compared with the existing approaches in terms of both the EE and SE, which demonstrates that its superiority.
Efficient common objects localization based on deep hybrid Siamese network
Authors: Moussa Mona M. , Shoitan Rasha , Abdallah Mohamed S.
Published: August 2021

Finding the common objects in a set of images is considered one of the recent challenges in different computer vision tasks. Most of the conventional methods have proposed unsupervised and weakly supervised co-localization methods to find the common objects; however, these methods require producing a huge amount of region proposals. This paper tackles this problem by exploiting supervised learning benefits to localize the common object in a set of unlabeled images containing multiple objects or with no common objects. Two stages are proposed to localize the common objects: the candidate box generation stage and the matching and clustering stage. In the candidate box generation stage, the objects are localized and surrounded by the bounding boxes. The matching and clustering stage is applied on the generated bounding boxes and creates a distance matrix based on a trained Siamese network to reflect the matching percentage. Hierarchical clustering uses the generated distance matrix to find the common objects and create clusters for each one. The proposed method is trained on PASCAL VOC 2007 dataset; on the other hand, it is assessed by applying different experiments on PASCAL VOC 2007 6×2 and Object Discovery datasets, respectively. The results reveal that the proposed method outperforms the conventional methods by 8% to 40% in terms of corloc metric.
A variable-order fractional discrete grey model and its application
Authors: Meixin Huang , Caixia Liu
Published: August 2021

Fractional order grey model is effective in describing the uncertainty of the system. In this paper, we propose a novel variable-order fractional discrete grey model (short for VOFDGM(1,1)) by combining the discrete grey model and variable-order fractional accumulation, which is a more general form of the DGM(1,1). The detailed modeling procedure of the presented model is first systematically studied, in particular, matrix perturbation theory is used to prove the validity in terms of the stability of the model, and then, the model parameters are optimized by the whale optimization algorithm. The accuracy of the proposed model is verified by comparing it with classical models on six data sequences with different forms. Finally, the model is applied to predict the electricity consumption of Beijing and Liaoning Province of China, and the results show that the model has a better prediction performance compared with the other four commonly-used grey models. To the best of our knowledge, this is the first time that the variable-order fractional accumulation is introduced into the discrete grey model, which greatly increases the prediction accuracy of the DGM(1,1) and extends the application range of grey models.
Multidimensional analysis model of agricultural product supply chain competition based on mean fuzzy
Authors: Chen Jing
Published: August 2021

Efficient and reliable fresh agricultural products supply chain is the key to meet the demand of consumers for fresh agricultural products, and also the guarantee for suppliers to realize their economic benefits. Therefore, a multi-dimensional analysis model of agricultural products supply chain competition based on fuzzy mean value is proposed. Firstly, the information distribution model of multi-dimensional analysis of agricultural product supply chain competition is proposed. On this basis, the multi-dimensional analysis information scheduling fusion of agricultural product supply chain competition is processed. Then, the application of mean value fuzzy in agricultural product supply chain is analyzed. According to the identification module of agricultural product information code, the fuzzy comprehensive evaluation model of supply chain and the mean fuzzy analytic hierarchy process, the competition of agricultural product supply chain is established Dimension analysis model. The experimental results show that the performance score of agricultural product supply chain is higher, the accuracy of supply chain information diagnosis is higher, and the clustering of agricultural product supply chain information diagnosis is better.
Predicting mental disorder from noisy questionnaires: an anomaly detection approach based on keyword extraction and machine learning techniques
Authors: Zhou Qing , Shi Xi , Ge Liang
Published: August 2021

The early warning of mental disorders is of great importance for the psychological well-being of college students. The accuracy of conventional scaling methods on questionnaires is generally low in predicting mental disorders, as the questionnaires contain much noise, and the processing on the questionnaires is rudimentary. To address this problem, we propose a novel anomaly detection framework on questionnaires, which represents each questionnaire as a document, and applies keyword extraction and machine learning techniques to detect abnormal questionnaires. We also propose a new keyword statistic for the calculation of option significance and three interpretable machine learning models for the calculation of question significance. Experiments demonstrate the effectiveness of our proposed methods.
Finite-time synchronization for fuzzy inertial cellular neural networks with time-varying delays via integral inequality
Authors: Wang Zhenjie , Cui Wenxia , Jin Wenbin
Published: August 2021

This paper mainly considers the finite-time synchronization problem of fuzzy inertial cellular neural networks (FICNNs) with time-varying delays. By constructing the suitable Lyapunov functional, and using integral inequality techniques, several sufficient criteria have been proposed to ensure the finite-time synchronization for the addressed (FICNNs). Without applying the known finite-time stability theorem, which is widely used to solve the finite-time synchronization problems for (FICNNs). In this paper, the proposed method is relatively convenient to solve finite-time synchronization problem of the addressed system, this paper extends the research works on the finite-time synchronization of (FICNNs). Finally, numerical simulations illustrated verify the effectiveness of the proposed results.
“Choose your opponent”: A new knockout design for hybrid tournaments
Authors: Guyon Julien
Published: August 2021

We present a new, simple knockout format for sports tournaments, that we call “Choose Your Opponent”, where the teams that have performed best during a preliminary group stage can choose their opponents during the subsequent knockout stage. The main benefit of this format is that it essentially solves a recently identified incentive compatibility problem when more than one teams from a group advance to the knockout stage, by effectively canceling the risk of tanking. This new design also makes the group stage more exciting, by giving teams a strong incentive to perform at their best level, and more fair, by limiting the risk of collusion and making sure that the best group winners are fairly rewarded in the knockout round. The choosing procedure would add a new, exciting strategic component to the competition. Advancing teams would choose their opponent during new, much anticipated TV shows which would attract a lot of media attention. We illustrate how this new format would work for the round of 16 of the UEFA Champions League, the most popular soccer club competition in the world.
Stimulus responsive 3D structures by self-morphing polymeric materials
Authors:   , Deng Heng
Published: August 2021

[ACCESS RESTRICTED TO THE UNIVERSITY OF MISSOURI-COLUMBIA AT REQUEST OF AUTHOR.] Inspired by ubiquitous phenomena of shape transformation in nature, extensive research effort has been devoted to self-morphing materials in recent decades. The self-morphing materials transfer their shapes from 2D planar films into 3D structures under environmental triggers such as humidity, pH, temperature, and light. Due to the extreme values of shape transformation, this kind of materials are invaluable for fabrication of various devices and systems, including flexible electronics, displays, artificial muscles, microfluidic valves and gates, actuation components in soft robotics and so on. The quintessence of fabricating such materials lies in programming structural anisotropies in them. Although various strategies and techniques have been developed to realize such a goal, this research area is still in its infant stages. New strategies and new techniques are still strongly desired. This dissertation focuses on exploring new possibilities of generating and programming anisotropies to develop novel self-morphing materials. New types of anisotropies are realized by controlling the distribution of polymeric crystal phase (Chapter 2), swellable guest medium (Chapter 3), laser induced graphene (Chapter 4), phase change microstructures (Chapter 5), and soft-stiff hybridized structures (Chapter 6) in self-morphing materials. Programmable shape changing behaviors, such as bending, folding, helical curling and buckling, were demonstrated on these materials by pattering the anisotropic structures. Moreover, for the first time, we demonstrate that the CO2 laser direct writing, which is normally used as a cutting tool in industry, has shown great potential in programming anisotropies in these newly developed self-morphing materials. These demonstrated strategies and techniques offer unique capabilities in fabricating functional self-morphing devices such as soft gripper, locomotive robot, rewritable paper, reconfigurable pneumatic actuator, and acoustic metamaterials.
Development of highly selective fluorescent sensors for detection of glycolipids in vesicles and visualization of glutamate for neuronal imaging
Authors:   , Xu Ming
Published: August 2021

[ACCESS RESTRICTED TO THE UNIVERSITY OF MISSOURI-COLUMBIA AT REQUEST OF AUTHOR.] Fluorescent sensors are very useful tools for exploring chemical biology and advanced medical research. Herein, we propose four different fluorescent sensor systems for the recognition of some important biological molecules. The first sensor system is a multi-component fluorescent sensor complex for the sensing of glycolipids. The glycolipid sensor system is a novel design that takes advantage of supramolecular self-assembly. Results show that it can bind with both the sugar headgroup and hydrocarbon tail of glycolipids, and turn on the fluorescence of the sensor system. The second sensor is a cell-impermeable fluorescent sensor system for the recognition and extraction of glycolipids from vesicles. To avoid the fluorescence enhancement caused by the hydrophobic effect from cell membrane, we designed a series of cell-impermeable sensor complexes. In addition, these complexes were fully explored by vesicle studies. Another fluorescent sensor is NS600 which was developed for detecting and imaging glutamate in neurons. This sensor system that utilizes a nucleophilic aromatic substitution for glutamate binding, and produces a 270-fold fluorescence enchantment upon glutamate binding. Also, it overcomes drawbacks of previous glutamate sensors including low signal response and poor sensitivity. It enables a clear and accurate visualization of glutamate in cultural neurons. The last sensor is NS570, a cell-impermeable glutamate sensor which could be loaded into synaptic vesicles by vesicle cycling. This sensor is a reversible chemical sensor that gives a 2600-fold fluorescence enhancement upon the titration with glutamate and can be used to monitor the release of neuronal glutamate in real time.
Breast cancer imaging and detection using microwave technique
Authors:   , Abed Ahmed Maher
Published: August 2021

[ACCESS RESTRICTED TO THE UNIVERSITY OF MISSOURI-COLUMBIA AT REQUEST OF AUTHOR.] Worldwide, breast cancer continues to be the top cause of death among women and the second-leading cause of cancer death after lung cancer. Thus, it has become a great global concern. Years of research on both diagnostic and therapeutic breast cancer detection and imaging using microwave techniques has resulted in a variety of novel approaches and studies. These approaches and studies utilize numerical breast phantoms that model structural complexities, tissue heterogeneity, and dispersive dielectric properties. In this dissertation, a microwave breast cancer detection technique was investigated and Ultra-Wide Band (UWB) radar imaging was used. A UWB antenna was designed and modeled using CST Microwave Studio and was used for Ultra-Wide Band microwave breast cancer detection. A new calibration approach for microwave breast cancer detection was proposed to calibrate the signals before applying the beamforming algorithms. A simulation was also used to validate the proposed techniques. Two signal calibration approaches were proposed to remove the high magnitude clutter from the signals. The two approaches are based on the state-space method Autoregressive Moving Average (ARMA). The first approach is derived from Estimation of Signal Parameters via Rotational Invariance Techniques (ESPRIT). This algorithm is referred to as the Pole Isolation via ESPRIT algorithm (PI-ESPRIT). The second approach is also derived from a previously proposed algorithm for microwave breast cancer detection. Th second calibration algorithm is referred to as the Modified Pole Removal algorithm. The Modified Pole Removal algorithm works to detect tumors, in contrast to the previously proposed algorithm (Pole Removal) that shows a lack of tumor detection. Three beamforming techniques were used to focus the signals onto the voxels through the breast phantom. Another beamforming algorithm was proposed, along with the Transmitting-Receiving Antenna Separation Distance (TRASD), which allows for the reduction of the late time clutter effect and improvement of the Signal to Clutter Ratio (SCR) when using the PI-ESPRIT algorithm. Using CST simulation tool, antennas arrayed around the breast are designed to simulate the transmitting/receiving signals. Numerical phantoms, with complicated structures and dispersive dielectric properties of breast tissue, have been developed with the CST simulation tool for simulating electromagnetic propagation. The received signals are imported into the MATLAB program to investigate the proposed approaches and compare them to conventional approaches. Overall, the results have shown improvement.
Elephant in the room : a study of the impact of emotional experiences on burnout among Chinese reporters
Authors:   , Guo Lei
Published: August 2021

This dissertation is conceived to examine the emotional labor of Chinese frontline reporters and its effects on their job burnout. For both detailed descriptive and generalizable findings, the mixed-method approach combining qualitative in-depth interviews with 31 reporters and a quantitative survey with 276 reporters was employed in the study. Findings from the in-depth interview demonstrate that reporters' emotional labor engagement mainly occurs while interacting with their subjects. In response to different scenarios and the subject personalities, they leverage various emotional labor strategies, including surface acting, deep acting, and natural expression to express desirable emotions. Additionally, according to their description, reporters' daily journalistic practices, occupational attitudes, mental and physical well-being, as well as personal life might be impacted by their involvement in the complex emotional mechanism. The follow-up survey reveals the effect of the demand on emotions at work and reporters' experience of engaging in surface acting magnify their levels of job burnout. Meanwhile, the use of problem-focused coping strategies can reduce reporters' job burnout caused by emotional labor engagement. Findings in this study fill the gap in understanding the mechanism of reporters' emotional labor engagement and its impacts on their job burnout. The theoretical and empirical implications of these findings are discussed.
Alternative learning strategies for spatio-temporal processes of complex animal behavior
Authors:   , Schafer Toryn L. J.
Published: August 2021

The estimation of spatio-temporal dynamics of animal behavior processes is complicated by nonlinear interactions. Alternative learning methods such as machine learning, deep learning, and reinforcement learning have proven successful for approximating nonlinear system mechanisms for prediction and classification. These alternative learning frameworks can be linked to statistical models in a hierarchical framework to improve ecological inference and prediction in the presence of uncertainty. This dissertation provides three methodological extensions of alternative learning with statistical uncertainty quantification for modeling animal behavior dynamics at different scales. First, an efficient Bayesian Markov model is developed to provide inference on white-fronted geese behavior from individual accelerometer and location data while accounting for classification uncertainty. Second, nonlinear basis function expansions produced by a spatio-temporal echo state network are used as features in a hierarchical generalized linear model for predicting spatial patterns of mallard duck settling pattern counts. Lastly, Bayesian inverse reinforcement learning is developed to estimate the behavioral state costs for collective animal groups.
Aptamers that bind filovirus GP : a new frontier for detection and neutralization of Ebola and Marburg virus
Authors:   , Tawiah Kwaku Dwumah
Published: August 2021

Aptamers are single chained, nucleic acid-based affinity probes that bind to their targets with strong affinity and specificity. They are made through an in vitro combinatorial selection method, wherein large libraries of nucleic acids with randomized sequences are subjected to an iterative process of affinity enrichment, partitioning, and amplification. Evolved libraries are sequenced, and the individual sequences are screened and characterized for their structure and function. Aptamers have been developed to target many molecules, including small molecules, purified proteins, whole cells, bacteria, and viruses. They have been developed for therapeutics and as research and diagnostic probes. Aptamers that have an affinity for virus surfaces are excellent probes for developing low-cost biosensors and potentially antiviral therapeutics. In this work, I present the development of aptamers that have an affinity for filovirus surfaces. I first describe the development of an improved method for purifying highly lytic vesicular stomatitis virus-based filovirus GP displayed surrogate viruses. Filoviruses are highly pathogenic and thus require highly secured containment facilities for their studies. The use of attenuated surrogates facilitates filovirus research at biosafety level 2 facilities. This work outlines the steps required to propagate and generate pure virus particles to be used as selection targets. I then describe the development of aptamer probes that differentially recognize GPs from MARV and EBOV. This work represents the first step in the development of aptamer based-low-cost point of care devices for filovirus disease diagnostics. Finally, this work describes the use of a hybrid selection approach that combines two different selection platforms to generate aptamers that bind to EBOV surfaces.
ANALISIS DAN PERENCANAAN GEDUNG “STUDI KASUS PERENCANAAN GEDUNG BELAJAR PONDOK PESANTREN TAHFIDZ AL–QU’RAN (PPTQ) BERTINGKAT TIGA DESA SUNGAI RAYA KECAMATAN BATANG TUAKA KABUPATEN INDRAGIRI HILIR
Authors: Ali Akbar M. Yoga
Published: August 2021

Abstract

Building planning in this study is a three-story Islamic boarding school building planning. The building function in this planning is used for the male students' learning building, with planning data based on Permendiknas No. 24/2007. Structural analysis to calculate internal forces on the structure uses two methods, namely the Cross method and the Cantilever Method. The Cross method or also known as the moment distribution method is used to calculate the vertical forces acting on the structure, in the analysis process this method carries out a moment distribution and induction (cary over) of the primary moment (Fixed End Moment) for a number of rounds (iterations) in order to get balance at each knot point. While the cantilever method is used to calculate the horizontal force acting on the structure. The cantilever method is an approximate method for calculating the shear and moment forces developed in beams and columns of a frame or structure due to lateral loads. The applied lateral loads usually include wind loads and earthquake loads, which must be taken into account when planning a building structure. The assumptions used in this method are that the contraflexure points (or the moment diagram inflection points) in the vertical and horizontal sections are located at the midpoint.

Abstrak

Perencanaan gedung dalam penelitian ini adalah perencanaan gedung Pondok Pesantren bertingkat tiga. Fungsi gedung di perencanaan ini digunakan untuk gedung belajar santri putra, dengan data – data perencanaan berdasarkan Permendiknas No 24 Tahun 2007. Analisa struktur untuk menghitung gaya – gaya dalam pada struktur mengunakan dua metode yaitu metode Cross dan Metode Kantilever. Metode Cross atau biasa juga disebut metode distribusi momen digunakan untuk menghitung gaya – gaya vertikal yang bekerja pada struktur, dalam proses analisis metode ini melalukan distribusi momen dan induksi (cary over) terhadap momen primer  (Fixed End Momen) sebanyak beberapa putaran (iterasi) guna mendapatkan keseimbangan di setiap titik simpul. Sedangkan metode Kantilever digunakan untuk menghitung gaya Horizontal yang bekerja pada struktur. Metode Kantilever adalah metode perkiraan untuk menghitung gaya geser dan momen yang dikembangkan dalam balok dan kolom bingkai atau struktur karena beban lateral. Beban lateral yang diterapkan biasanya mencakup beban angin dan beban gempa, yang harus dipertimbangkan saat merencanaan struktur bangunan. Asumsi-asumsi yang digunakan dalam metode ini adalah bahwa titik-titik contraflexure (atau titik-titik infleksi diagram momen) di bagian vertikal dan horizontal terletak di titik tengah.
PERENCANAAN GEDUNG ASRAMA PUTRI PONDOK PESANTREN TAHFIDZ QUR’AN (PPTQ) PARIT 24 TEMBILAHAN HILIR KECAMATAN TEMBILAHAN KABUPATEN INDRAGIRI HILIR”
Authors: Zahara Hana
Published: August 2021

Abstract

The building is an important element in the development of an area. In the construction of a building must pay attention to matters relating to the conditions of the surrounding environment. For this reason, planning is necessary in accordance with the function of the building and the area to be built and must calculate how much material is needed. The planning of the female dormitory building is planned to fulfill the Indragiri Hilir district government program, namely 1 Village 1 Tahfidz House by following the Operational Technical Guidelines (PTO) of Maju Inhil Jaya Plus Integrated Village (DMIJPT) 2019 concerning Tahfidz Houses. The calculation of the structure of the dormitory uses the Cross method for calculating vertical loads and the Portal method for calculating horizontal loads

 

Abstrak

Gedung merupakan salah satu unsur penting dalam hal pengembangan suatu daerah. Dalam pembangunan sebuah gedung harus memperhatikan hal-hal yang berkaitan dengan kondisi lingkungan sekitar. Untuk itu perlu dilakukan perencanaan yang sesuai dengan fungsi gedung dan wilayah yang akan dibangun serta harus menghitung berapa besar kebutuhan material yang diperlukan. Perencanaan Gedung asrama putri ini direncanakan untuk memenuhi program  pemerintah kabupaten Indragiri Hilir yaitu 1 Desa 1 Rumah Tahfidz dengan mengikuti Petunjuk Teknis Operasional (PTO) Desa Maju Inhil Jaya Plus Terintegritas (DMIJPT) Tahun 2019 tentang Rumah Tahfidz. Perhitungan struktur gedung asrama ini menggunakan metode Cross untuk perhitungan beban vertikal dan Metode Portal untuk perhitungan beban horizontal.
ANALISIS DAN PERENCANAAN GEDUNG ASRAMA PUTRI TAHFIDZ DESA SEI RAYA KECAMATAN BATANG TUAKA KABUPATEN INDRAGIRI HILIR
Authors: Mailiyani Nadila
Published: August 2021

Abstract

The female dormitory building in Sei Raya village, indragiri hilir district is planned to fulfill government activities, namely the basic Guidelines for the 1 Village 1 Tahfidz House Program by following the DMIJPT 2019 Operational Technical Guidelines (PTO) on Tahfidz Houses. Planning for dormitory building analysis calculations using the Engilab Beam application. The length of the building is 48 m wide 12 m from the data so it is dilated. The dilatation used is two-column dilatation.

 

Abstrak

Gedung asrama putri di desa Sei Raya keabupaten indragiri hilir  dierencanakan untuk memenuhi kegiatan pemerintah yaitu tentang Petunjuk dasar Program 1 Desa 1 Rumah Tahfidz dengan mengikuti Petunjuk Teknis Operasional (PTO) DMIJPT Tahun 2019 Tentang Rumah Tahfidz. Perencanaan gedung asrama analisa perhitungan menggunakan Aplikasi  Engilab Beam. Panjang gedung adalah 48 m lebar 12 m dari data tersebut maka  dilakukan Dilatasi. Dilatasi yang dipakai adalah dilatasi dua kolom
Cardiac Septal Defects in Children: Hemodynamics, Clinical Manifestations and Detection
Authors: Ria Nova  , Sukman Tulus Putra  , Siti Nurmaini  , Radiyati Umi Partan
Published: August 2021

Cardiac septal defect in children is one of the congenital heart defects characterized by atrial septal defects (ASD), ventricular septal defects (VSD) and defects in both atrial and ventricular septum (AVSD). The hemodynamic changes that occur are caused by a left to the right shunt. Differences in location, size of the defect and pulmonary vascular resistance make hemodynamic differences and clinical manifestations between the three types of cardiac septal defects. Detection of cardiac septal defects can be done by clinical examination by listening to the characteristic heart sounds and murmurs for each defect. However, clinical examination alone is often still difficult to determine the type of cardiac septal defect so that several supporting examinations such as photothorax, ECG, echocardiogram and cardiac catheterization need to be done to help establish the diagnosis.
A CLINICAL STUDY OF RISK FACTORS AND MATERNAL OUTCOME IN INTRA-UTERINE FETAL DEATH
Authors: Prasad Anubha Manu , Agrawal . Manju , Laddha Ankit
Published: August 2021

Background: The objective of the present study was to examine the etiological factors, both maternal and fetal, that caused IUFD.

Methods: It was a hospital based prospective observational study conducted in the Department of Obstetrics and Gynaecology at Jhalawar Medical College from January 2020 to December 2020. 120 cases of IUFD of ?24 weeks of gestation or foetuses weighing ?500 gramswere included in the study.

Results: The incidence of IUFD was 37.8/1000 total birth. IUFD occurredmainly in 25-35yrs age group (55%), unbooked cases (70%),multigravida (59.2%), preterm (<37 weeks of gestation). The risk factors were PIH (49.2%), infections (9.2%), hypothyroidism (5%), Rh negative with hydrops fetalis (5%),GDM (4.2%), chronic hypertension (2.5%), previous H/O LBW (2.5%), H/O prior stillbirth (5%), hyperthyroidism (1.7%), oligohydramnios (46.7%), APH (20.8%) and congenital fetal anomalies (19.2%).

Conclusion: IUFD rate still remains high. Therefore, regular ANC check up and early identification of risk factors like PIH, anaemia, infections, genetic counselling and timely referral to well equipped centre may help prevent IUFD and maternal mortality and morbidity.

Keywords: IUFD, ANC, Preterm
THE LEVEL OF DANGER IN CONTROLLED AIRPORT SECURITY
Authors: Kurdel P.
Published: August 2021

The functionality of the aircraft complex is determined by quality and safety. This is defined by the relationship between the entities airport – aircraft – airspace. Operational safe management and control at the airport is a directed action of independent factors. Their expression is embedded in the feeling of security and safety of each air traffic participant. Fulfilment of the ethical characteristics of security requires the implementation of management so that the control at the airport is carried out in the relevant security zone. Construction of a safe state can be achieved if the chosen method of safety experiments will faithfully reflect the real error in the control of air transport. The correctness of safety control procedures can be verified by simulation and models built on a physical basis. The motive of the article is to show one way of effective identification of errors in the management of security controls actors of air transport.
METHOD OF OPERATIONAL INVESTIGATION OF ROLLING STOCK DERAILMENT
Authors: Domin R.
Published: August 2021

The article presents the principles of the method of operational investigation of rolling stock derailment. This method is intended to identify the most significant mechanical reasons for transport accidents in order to find directions to further improving the safety requirements for the state of maintenance of freight cars and tracks on the basis of the data. The developed method establishes a general methodological order of conducting computer simulation of the dynamics of rolling stock of multivariate studies on calculating the safety indicators of train’s motion, and shows the significance of technical factors that provoked a certain event. By this method, the operational investigation of rolling stock derailment is based on the development of adequate computer models of dynamics of cars in trains. In this case, in detail, all the features of the car, which descended from the rails, are first. The presented method is based on the principles of factor analysis, where the intervals of varying events are presented in natural units of measurement. In this case, the assessment of security indicators for each research of the experiment plan is also carried out taking into account their dimension. As a result, the function of multiple regressions on the chosen indicator of motion safety allows not only to determine the significance of the factor of the ascent, but also to establish the level of factors, with exceeding the criteria for motion safety, that is, to determine the boundaries of the region with an acceptable level of motion safety.
DEVELOPMENT OF THE METHOD OF CLUSTERIZATION OF TRAIN SITUATIONS
Authors: Gorobchenko O.
Published: August 2021

The introduction of intelligent locomotive control systems requires better approaches to assessing and monitoring the current train situation than those used in modern traction rolling stock. Automatic detection of complex abnormal situations is currently not provided. For example, determining the inefficiency of the brakes, speeding, the presence of obstacles or people on the track, the deterioration of the traction properties of rolling stock, etc. relies solely on the driver of the locomotive. Given the important impact of these factors on traffic safety, it is proposed to include in the functions of automated and intelligent traffic control systems recognition of abnormal situations and notification of its occurrence. When driving a train, all objects of classification (train situations) are divided into a finite number of classes. A finite number of precedent objects are known and studied for each class. The task of pattern recognition is to assign a new recognizable situation to a class. The classifier or decisive rule is the rule of assigning the image of a train situation to one of the classes on the basis of its vector of features. An order of classification of train situations has been developed, which allows to allocate clusters of any complex shape, provided that different parts of such clusters are connected by chains of close to each other elements. The measure of difference is the square of the Euclidean distance.
DEVELOPMENT OF A SYSTEM MODEL OF TECHNICAL OPERATION MANAGEMENT IN TRANSPORT INFRASTRUCTURE PROJECTS
Authors: Kulbovskyi I.
Published: August 2021

The article considers the problems of quality management of repair and operation of technical means in projects of development and maintenance of transport infrastructure, which is the most important factor in improving living standards, economic, social and environmental safety. The project quality management system and planning methodology are considered. It is investigated that in order to improve this system it is necessary to solve the problem of integration of quality management principles and their adaptation to transport infrastructure projects on quality management of repair and operation of technical means and the ways of solution are shown. A set of interrelated organizational and technological tasks and measures aimed at restoring the normative transport and operational indicators of repair and operation of technical means of transport is considered. It is investigated that the process of project quality assurance at the level of quality management in transport infrastructure projects consists of the adoption of planned systematic measures to ensure the implementation of all envisaged processes. On the basis of researches the system model of criteria by means of which it is possible to define system efficiency of development and realization of the corresponding projects of transport infrastructure is offered. As a result of solving this problem, a model of management and decision support for diagnostics of technical means in transport infrastructure projects has been developed, which opens prospects for planning and timely maintenance and repair of technical means of transport to ensure and improve traffic safety.
ASSESSMENT CARTS STRENGTH INDICATORS WAGONS WITH EXPIRED SERVICE LIFE
Authors: Fomin O.
Published: August 2021

Currently, a significant number of units of specialized cars of 1520 mm gauge in Ukraine has a service life that exceeds that assigned by the manufacturer. At the same time, many years of experience in diagnosing and analyzing the operation of carts of these cars by specialized organizations, shows that their technical condition after long operation shows that the specified service life specified in the technical conditions in most cases far from the limit. This is largely due to the significant margin of safety, which is laid down in the design, and the peculiarities of the operation of a particular type of car. The paper considers the issues of assessing the operation of such cars on the example of the carriage of the weighing car, bogie model 18-102, and the carriage of the passenger car model KVZ-TsNII used in the track economy for transportation of workers. The average daily mileage of such cars is 60-70 percent less than the average mileage that is laid down when calculating the service life assigned by the manufacturer. The evaluation and methods of determining the strength of the frames of carriages of special cars are performed. Conclusions are made based on the results of the analysis of the strength of the carriages of special cars.
Registro de Diversinervus elegans Silvestri (Hymenoptera: Encyrtidae) para la ciudad de Bogotá, Colombia
Authors:   Jardín Botánico de Bogotá, Durán Prieto Juliana , Ocampo Valentina
Published: August 2021

Diversinervus elegans Silvestri (1915) es una especie deavispa parasitoide de la familia Encyrtidae (Hymenoptera:Chalcidoidea) perteneciente a la subfamilia Encyrtinae, ala tribu Cheiloneurini y al género Diversinervus Silvestri(1915), el cual incluye otras once especies a nivel mundial(Noyes, 2018).
The Witch
Authors: O'Connor Paul
Published: August 2021

This is a book review of Brandon Grafius, The Witch (Devil’s Advocates) (Liverpool, UK: Auteur Publishing in partnership with Liverpool University Press, 2020).
Rheological behavior of magnetic pulp fiber suspensions
Authors: SANCHEZ JORGE , PINEDA XIOMARA , QUINTANA GERMAN , HERRERA ADRIANA P.
Published: August 2021

This paper is focused on the rheology of magnetic pulp suspensions in absence and presence of an external magnetic field. Magnetic fibers were prepared by the lumen loading method using bleached eucalyptus fibers and cobalt ferrite (CoFe2O4) nanoparticles. The effect of mass consistency, temperature, concentration of magnetic fibers, and magnetic field strength on yield stress and apparent viscosity of the suspensions were investigated. In the absence of an applied field, a dependence of yield stress with consistency, as well as with the percentage of magnetic fibers present in the suspension, was found. In flow tests, all the suspensions exhibited shear-thinning behavior, showing that the viscosity is only affected by the consistency of the suspension. On the other hand, magnetorheological measurements show a negative effect of the applied magnetic field on the viscosity of the suspension.
Preparation of regenerated cellulose from rice straw lignocellulosic waste and its use for reinforced paper products
Authors: ALAM IZHAR , KUMAR JITENDRA , SHARMA CHHAYA
Published: August 2021

Rice straw waste is a lignocellulosic waste produced by farmers in large quantities. In this study, regenerated cellulose (RC) from rice straw was prepared by dissolving rice straw holocellulose (HC) in NaOH/Urea/Thiourea/Water solution by the freeze-thawing process. The crystallinity index of RC was calculated at 31%, which is out of the crystallinity range of 39%–69% that has been previously suggested. The study indicated that the RC is amorphous with a low degree of polymerization (638) and higher hydroxyl group content as compared to HC. The fiber length of RC was found to be 26.7% shorter; however, the width of RC was 21.2% higher as compared to HC. Reduced kinked fiber content was observed in the fraction of RC (18.3%) as compared with HC (39.1%), and a higher curl index of fiber was observed more so in HC (10.5%) than RC (5.6%). Because of the regeneration process, the fiber length was reduced and a fines element content of about 96% was observed in RC compared to the initial fines content of HC (56.9%). Irrespective of the high fines element content of RC, the composite paper of rice straw bleached pulp and RC fibers was developed with an increase in the tensile index from 41.4 N.m/g to 71.2 N.m/g and an increase in the burst index from 4.7 kPa.m2/g to 5.3 kPa.m2/g with the addition of 5% and 15% RC, respectively. However, enhanced tear index of paper was observed up to 5% and then it declined upon further addition of RC. The study revealed that regenerated cellulose can be used as a strength additive to overcome the shortcomings of low mechanical properties in paper products.
Production of antimicrobial paper using nanosilver, nanocellulose, and chitosan from a coronavirus perspective
Authors: PATEL MAHENDRA
Published: August 2021

The pulp and paper industry has an opportunity to play a vital role in breaking the spread of the COVID-19 pandemic through production that supports widespread use of antimicrobial paper. This paper provides a brief review of paper and paper-related industries, such as those producing relevant additives, and R&D organizations that are actively engaged in developing antimicrobial papers.
The focus here is on the potential of three nano-additives for use in production of antimicrobial papers that com-bat coronavirus: nanosilver, nanocellulose, and chitosan. Various recent developments in relevant areas and concepts underlining the fight against coronavirus are also covered, as are related terms and concepts.
Do Cash Flow and Accounting Profit Information Affect Stock Prices?
Authors: Yuliati Anik , Muniroh Indah
Published: August 2021

This study aims to determine the effect of cash flow statement information and accounting profit on stock prices. The object of this research is a food & beverage sub-sector manufacturing company listed on the Indonesia Stock Exchange in 2015-2019. The sampling technique used a purposive sampling technique with ten samples of financial statements that meet the criteria. Theanalysis technique uses the help of Partial Lease Square (PLS) with SmartPLS 3.0 Software. The study results show that the Cash Flow Statement of operating and funding information does not affect stock prices. On the other hand, information on the investing Cash Flow Statement and accounting profit affect stock prices. The more investors intend to buy or keep shares; the stock price will increase. Vice versa, if the number of investors who intend to sell or release shares increases, the share price will decrease. Based on these results, it is suggested that investors in deciding on selling or buying stock shares may pay more attention to the information from investing cash flow and accounting profit.