bluuebunny/crossref_metadata_2025_split huggingface dataset:
Stenting of a partially obstructed nasolacrimal duct using a steerable angle‐tipped hydrophilic guidewire in a cat
Authors: Park Shin Ae , Oetelaar Garrett , Kim Sun Young 
Published: July 2022

AbstractObjectiveThis case report describes the successful correction of partial obstruction of the NL duct in a cat by means of a modified retrograde NL duct cannulation using a steerable angle‐tipped hydrophilic guidewire (AH guidewire) following a paranasal incision.Animal StudiedA 2‐year‐old neutered male American domestic shorthair cat was referred to the Purdue University Veterinary Hospital (PUVH) for chronic epiphora suspected secondary to nasolacrimal (NL) system obstruction.ProceduresAt the first visit, the cat had epiphora OD and facial dermatitis but no other abnormalities on physical and ophthalmic examinations. Computed tomography (CT)‐dacryocystorhinography revealed partial obstruction of the NL duct secondary to stenosis near the distal root of the right maxillary third premolar (107). A digital three‐dimensional (3D) model of the right maxilla and NL duct was created for inspection and virtual cannulation of the NL. The model was 3D printed and cannulation of the NL duct was rehearsed with various stent materials. Retrograde NL stenting with the guidance of a steerable angle‐tipped hydrophilic guidewire was conducted following a paranasal incision. A urethral catheter was cannulated over the guidewire and maintained for 44 days.ResultsThe epiphora resolved immediately after stenting. At 21 days post‐stenting, the cat developed acute bullous keratopathy secondary to self‐trauma which was treated with a third eyelid flap. On the final follow‐up communication with the owner at 210 days post‐stenting, no epiphora or any other concerns were reported.ConclusionTo the authors' knowledge, this is the first report of successful NL stenting and resolution of epiphora in a cat with a partial NL system obstruction.
Effectiveness of an Intelligent Foot Orthosis in Lateral Fall Prevention
Authors: Yamamoto Rieko , Itami Sho , Kawabata Masashi , Shiraishi Toshihiko 
Published: July 2022

Abstract
The aim of this study was to validate the effectiveness of the newly developed intelligent foot orthosis (IFO) at preventing lateral falls. The IFO is a wearable fall prevention system based on using a small magnetorheological brake to control the height of the lateral sole. Experiments were performed to compare the walking motions on a lateral slope under four conditions: without IFO, with IFO current-OFF, with IFO current-ON, and with IFO control-ON. The mediolateral center of gravity and center of pressure horizontal distance (ML COG–COP HD) were measured in three-dimensional motion analysis to represent the change in posture on the frontal plane. To observe the corresponding muscular activity, surface electromyography (EMG) was performed to obtain the mean and peak root-mean-square (RMS) for the tibia anterior (TA) and peroneus longus (PL) in the first half of the stance phase when the IFO applied control. In the results, ML COG-COP HD increased significantly under the “with IFO control-ON” compared to the “without IFO” and “with IFO current-ON” conditions. The mean RMS of the TA was significantly decreased under the “with IFO current-ON” and “with IFO control-ON” conditions compared to the “without IFO” condition. These results demonstrate that the posture moved away from the lateral fall direction primarily due to IFO assistance rather than muscular activity, which would be a consequence of human postural control. Thus, the IFO does appear to help prevent lateral falls.
Dynamic Resource Allocation in Systems-of-Systems Using a Heuristic-Based Interpretable Deep Reinforcement Learning
Authors: Chen Qiliang , Heydari Babak 
Published: July 2022

Abstract
Systems-of-systems (SoS) often include multiple agents that interact in both cooperative and competitive modes. Moreover, they involve multiple resources, including energy, information, and bandwidth. If these resources are limited, agents need to decide how to share resources cooperatively to reach the system-level goal, while performing the tasks assigned to them autonomously. This paper takes a step toward addressing these challenges by proposing a dynamic two-tier learning framework, based on deep reinforcement learning that enables dynamic resource allocation while acknowledging the autonomy of systems constituents. The two-tier learning framework that decouples the learning process of the SoS constituents from that of the resource manager ensures that the autonomy and learning of the SoS constituents are not compromised as a result of interventions executed by the resource manager. We apply the proposed two-tier learning framework on a customized OpenAI Gym environment and compare the results of the proposed framework to baseline methods of resource allocation to show the superior performance of the two-tier learning scheme across a different set of SoS key parameters. We then use the results of this experiment and apply our heuristic inference method to interpret the decisions of the resource manager for a range of environment and agent parameters.
Numerical Investigation on Heat Flux Variation in Fuel Bundle Over Fully Voided Concentric Channel of IPHWR Under Severe Accidental Scenario
Authors: Kashyap Ishan , Kumar Sidharth , Singh Saurav Kumar , Sharma Mukesh 
Published: July 2022

Abstract
A steady-state thermal analysis is performed on a fully voided channel of an Indian pressurized heavy water reactor (IPHWR) by providing variable heat flux inputs of 800 W/m2, 1500 W/m2, 2500 W/m2, 3500 W/m2, and 4500 W/m2 for evaluating the numerical results of temperature distributions in pressure tube (PT) and calandria tube (CT) at concentric scenarios using a numerical approach. The top to bottom temperature difference of PT and CT is found to be 0.57% and 3.31%, respectively, for 800 W/m2 input heat flux, and this variation decreases to 0.015% and 0.05%, respectively, for 4500 W/m2 input heat flux, thus pointing out that increasing the heat flux leads to the decreased top to bottom temperature difference. Also, the temperature distribution pattern is found to be similar for all the input heat fluxes, and for a particular heat flux, the change in circumferential temperature is found to be negligible. The boiling phenomenon starts at 2500 W/m2, and the volume fraction of moderator vapor increases as the heat flux is increased, the average density of moderators inside the enclosure decreases, and continuous variation in film thickness is also observed with respect to time for a particular heat flux value. The results also showed that the moderator acted as an effective heat sink for higher heat input rates.
Comparison of Sensorimotor Integration and Skill-Related Physical Fitness Components Between College Athletes With and Without Forward Head Posture
Authors: Moustafa Ibrahim , Kim Meeyoung , Harrison Deed E. 
Published: July 2022

Objective: To evaluate sensorimotor integration and skill-related physical fitness components for participants with forward head posture (FHP) compared with strictly matched controls with normal head alignment. Material and Methods: We measured FHP, sensorimotor processing, and skill-related physical fitness variables in 50 participants with FHP and in 50 participants matched for age, gender, and body mass index with normal FHP, defined as having a craniovertebral angle >55°. Sensorimotor processing and integration variables were: (1) amplitudes of the spinal N13, (2) brainstem P14, (3) parietal N20 and P27, and (4) frontal N30 potentials. The skill-related physical fitness variables selected for the study were (1) T-test agility, (2) leg power, (3) stork static balance test, and (4) Y-balance test. Results: There was a statistically significant difference between the FHP group and control group for the sensorimotor integration variable: frontal N30 potentials (P < .05). Additionally, between-group differences were found for the sensorimotor processing variables: amplitudes of spinal N13, brainstem P14, and parietal N20, and P27 (P < .05). Statistically significant differences between groups for the skill-related physical fitness variables were also identified: T-test agility, leg power, stork static balance test, and Y-balance test (P < .05). The magnitude of the craniovertebral angle showed a correlation with all measured variables (P < .05). Conclusion: College athletes with FHP exhibited altered sensorimotor processing and integration measurements and less efficient skill-related physical fitness compared with athletes with normal sagittal head posture alignment.
LAMA2-related muscular dystrophy mimicking multiple sclerosis
Authors: Koshorek Jacqueline , de Macena Sobreira Nara , Saidha Shiv 
Published: July 2022

Laminin-alpha2-related muscular dystrophy (LAMA2-MD) is a genetic condition due to reduced LAMA2, a protein found throughout the nervous system. Late-onset LAMA2-MD may present with proximal muscle weakness, joint contractures, neuropathy, epilepsy and/or cardiorespiratory issues, and is less common than the neonatal form. We describe a novel phenotype of LAMA2-MD with progressive myelopathy and spinal cord abnormalities.A woman was referred for evaluation of multiple sclerosis (MS) with progressive gait difficulty and abnormal neuroimaging showing white matter abnormalities in the brain and spinal cord. Ancillary testing was not consistent with primary neuroinflammatory disorders, systemic autoimmunity or infection. Metabolic workup revealed low cyanocobalamin. Genetic testing identified two LAMA2-MD variants.Genetic disorders can mimic treatable neurological conditions. Chronic progressive course, involvement of the peripheral and central nervous systems, and confluent white matter abnormalities should be investigated with molecular testing that includes LAMA2 sequencing to ensure proper diagnosis and management.
Effect of the mass ratio on the escape in the 4-body ring problem
Authors: Belgharbi Ibrahim , Navarro Juan F. 
Published: July 2022

AbstractThe aim of this paper is to analyze the effect of the mass ratio on the distribution of short times of escape and the probability of escape of a particle from the 4-body ring configuration. To this purpose, we carry out a numerical exploration of the problem, considering three different values of the mass ratio between the central and the primary bodies and, for each of these values, a pair of values of the Jacobi constant.
Validity and feasibility of using a seated push-up test among community-dwelling older adults
Authors: Poncumhak Puttipong , Phadungkit Supaporn , Chokphukiao Pakwipa , Intaruk Roongnapa , Amatachaya Pipatana , Amatachaya Sugalya 
Published: July 2022

Background: Older individuals face a high risk of mobility and body composition decline, which can affect their independence. In light of a current uncertain healthcare situation created by the coronavirus (COVID-19) pandemic, healthcare paradigm has been shifted with increased demand for a practical measure to promote standard home healthcare services for all individuals, including older adults. Objective: This study explored the feasibility and validity of seated push-up tests (SPUTs) as clinical measures to reflect the body composition, muscle strength, and mobility among community-dwelling older individuals, aged [Formula: see text] years ([Formula: see text]). Methods: Participants were cross-sectionally assessed using SPUTs with various demanding forms, including the 1-time SPUT (1SPUT) along with its upper limb loading SPUT (ULL-SPUT), 5-time SPUT (5SPUT), 10-time SPUT (10SPUT), and 1-min SPUT (1minSPUT) and standard measures. Results: Participants who passed and failed a 1SPUT showed significant differences in the outcomes of all standard measures ([Formula: see text]). The ULL-SPUT significantly correlated to all body composition, muscle strength, and mobility ([Formula: see text]–0.785; [Formula: see text]). Outcomes of 1minSPUT significantly correlated with muscle strength and mobility outcomes ([Formula: see text]–0.526; [Formula: see text]). Participants reported no adverse effects following the SPUTs. Conclusion: The findings suggest the use of the 1SPUT, ULL-SPUT, and 1minSPUT as practical measures to reflect the body composition, muscle strength, and mobility of older individuals, according to their functional levels. The tests may especially clinically benefit those with lower limb limitations and those in settings with limited space and equipment.
Black-box Safety Analysis and Retraining of DNNs based on Feature Extraction and Clustering
Authors: Attaoui Mohammed , Fahmy Hazem , Pastore Fabrizio , Briand Lionel 
Published: July 2022

Deep neural networks (DNNs) have demonstrated superior performance over classical machine learning to support many features in safety-critical systems. Although DNNs are now widely used in such systems (e.g., self driving cars), there is limited progress regarding automated support for functional safety analysis in DNN-based systems. For example, the identification of root causes of errors, to enable both risk analysis and DNN retraining, remains an open problem. In this article, we propose SAFE, a black-box approach to automatically characterize the root causes of DNN errors. SAFE relies on a transfer learning model pre-trained on ImageNet to extract the features from error-inducing images. It then applies a density-based clustering algorithm to detect arbitrary shaped clusters of images modeling plausible causes of error. Last, clusters are used to effectively retrain and improve the DNN. The black-box nature of SAFE is motivated by our objective not to require changes or even access to the DNN internals to facilitate adoption.
Experimental results show the superior ability of SAFE in identifying different root causes of DNN errors based on case studies in the automotive domain. It also yields significant improvements in DNN accuracy after retraining, while saving significant execution time and memory when compared to alternatives.
The Level of HbA1c Evaluates the Extent of Coronary Atherosclerosis Lesions and the Prognosis in Diabetes with Acute Coronary Syndrome
Authors: Xu Shaodong , Zhou Birong , Zhou Bingfeng , Zheng Jianfa , Xu Qinghe , Wang Bing , Fu Minmin , Meng Ying 
Published: July 2022

Background. The level of HbA1c can reflect the average level of blood glucose over 3 months, which is the gold standard indicator for monitoring blood glucose. The relationship between the level of HbA1c and the extent of coronary atherosclerosis lesions or the prognosis in diabetes with acute coronary syndrome (ACS) remains poorly understood. Aims. To explore whether the level of HbA1c can evaluate the extent of coronary atherosclerosis lesions or the prognosis in diabetes with acute coronary syndrome (ACS) using the SYNTAX score, the Global Registry of Acute Coronary Events (GRACE) score, left ventricular function (LVEF), left ventricular end-diastolic volume (LVEDV), and major adverse cardiac events (MACEs) in the hospital and 12 months after discharge. Methods. This study was a prospective, randomized, open-label, and parallel group study. Patients with diabetes with ACS were recruited into this study indiscriminately, and all the participants were divided into two groups according to the level of HbA1c:

HbA
1
c

level
≤
7
%
group

and

HbA
1
c

level
>
7
%
group

. The followings were used as the evaluation indicators: SYNTAX score, GRACE score, LVEF, LVEDV, and MACEs in hospital and 12 months after discharge. Results. A total of 233 patients with diabetes and ACS were enrolled and assigned to two groups according to their level of HbA1c: the

HbA
1
c
≤
7
%
group

(

n
=
92

) and the

HbA
1
c
>
7
%
group

(

n
=
141

). The results showed that the proportion of STEMI was higher in the HbA1c ≤7% group (

p
<
0.05

), while the proportion of NSTEMI has not significantly higher in the HbA1c >7% group (

p
>
0.05

). Regression analysis indicated that HbA1c level was significantly positively correlated with GRACE score (

r
=
0.156

,

F
=
5.784

,

p
=
0.017

,

n
=
233

) and SYNTAX score (

r
=
0.237

,

F
=
13.788

,

p
<
0.001

,

n
=
233

), and there were no statistically significant differences in LVEDV and LVEF between the two groups (

p
>
0.05

). The total MACEs rate showed no significant difference between the two groups during hospitalization (

p
>
0.05

) but showed significant differences at 12 months after discharge (

p
<
0.05

). Conclusions. This study shows that HbA1c level was positively correlated with the extent of coronary atherosclerosis lesions and the prognosis in diabetes with ACS. The higher the HbA1c level is, the more severe the coronary atherosclerotic lesion and the worse the prognosis in diabetes with ACS are.
Application of the simplex method on profit maximization in Baker's Cottage
Authors: Mohd Azman Nur Zafira , Akmal Mohamed Nurul , Farihan Mohamed Nurul , Musa Muzirah 
Published: July 2022

Linear programming is an <span lang="EN-US">operational research technique widely used to identify and optimize management decision. Its application encourages businesses to increase their output. Instead, however, many organizations most commonly adopt the trial-and-error method. As such, companies find it challenging to distribute scarce resources in a manner that maximizes profit. This study focuses on implementing linear programming to optimize the profit of a manufacturing sector based on the optimized (best possible, efficient) use of raw materials. Our study uses the data gathered on five market bread types from Baker's Cottage reports, i.e., chicken floss, spicy floss, Frank Cheese, Mexico bun, and doughnut. This attribute has been recognized as a linear programming problem mathematically built that was solved using Excel software. The result showed that the Baker’s Cottage unit had to produce 332 loaves of Chicken Floss and 196 loaves of Frank Cheese, as these products objectively contributed to the profit. In contrast, other types of bread did not have to be produced, as their value turned to zero to achieve the maximum monthly profit.</span>
Early wildfire detection using machine learning model deployed in the fog/edge layers of IoT
Authors: Grari Mounir , Idrissi Idriss , Boukabous Mohammed , Moussaoui Omar , Azizi Mostafa , Moussaoui Mimoun 
Published: July 2022

<span lang="EN-US">The impact of wildfires, even following the fire's extinguishment, continues to affect harmfully public health and prosperity. Wildfires are becoming increasingly frequent and severe, and make the world's biodiversity in a growing serious danger. The fires are responsible for negative economic consequences for individuals, corporations, and authorities. Researchers are developing new approaches for detecting and monitoring wildfires, that make use of advances in computer vision, machine learning, and remote sensing technologies. IoT sensors help to improve the efficiency of detecting active forest fires. In this paper, we propose a novel approach for predicting wildfires, based on machine learning. It uses a regression model that we train over NASA's fire information for resource management system (FIRMS) dataset to predict fire radiant power in megawatts. The analysis of the obtained simulation results (more than 99% in the R2 metric) shows that the ensemble learning model is an effective method for predicting wildfires using an IoT device equipped with several sensors that could potentially collect the same data as the FIRMS dataset, such as smart cameras or drones.</span>
An interpolation based steganographic technique with least-significant-bit and pixel value differencing in a pixel block
Authors: Majumder Jayeeta , Pradhan Chittaranjan 
Published: July 2022

Over the <span lang="EN-US">past few years, in order to improve the hiding capacity and the peak signal-to-noise ratio (PSNR) value, several steganographic techniques have been developed. Steganography has become a popular technique to transmit secret data through any medium. In image steganography, the human eye cannot easily identify the hidden data which is embedded into the image. Small changes are also not detected by the human eye. High hidden capacity along with high visual quality is provided by the pixel value differencing (PVD) method. This paper first proposes the method of interpolation between the pixel blocks and then applies the least-significant-bit (LSB) substitution technique with the PVD method. At the starting phase, the original image is fixedto a 2x2 block, then the nearest neighbor interpolation (NNI) technique is implemented. In the next phase, the upper left pixel isembedded by the k-bit LSB substitution method along with hidden data. The newly generated neighbouring pixel value is measured. Thus, data is hidden from three directions. Through this paper using two different range tables, the new algorithm is proposed. We observed that in both cases, PSNR and the hiding capacity are improved.</span>
Dual embedding with input embedding and output embedding for better word representation
Authors: Ahn Yoonjoo , Rhee Eugene , Lee Jihoon 
Published: July 2022

Recent <span lang="EN-US">studies in distributed vector representations for words have variety of ways to represent words. We propose a various ways using input embedding and output embedding to better represent words than single model. We compared the performance in terms of word analogy and word similarity with each input and output embeddings and various dual embeddings which are the combination of those two embeddings. Performance evaluation results show that the proposed dual embeddings outperform each single embedding, especially with the way of simply adding input and output embeddings. We figured out two things in this paper, i) not only input embedding but also output embedding has such meaning to represent the words and ii) combining input embedding and output embedding as dual embedding outperforms the single embedding when we use input embedding and output embedding individually.</span>
Early disease prediction algorithm for hypertension-based diseases using data aware algorithms
Authors: Shaikh Yasmeen , Parvati Vasudev , Biradar Sangappa Ramachandra 
Published: July 2022

This paper <span lang="EN-US">implements a data aware early prediction of hypertension-based diseases. Automated data preprocessing method that adopts for both balanced and unbalanced data is the data aware method included in the disease classification algorithm. Proposed data aware data preprocessing method is evaluated on the ensemble learning based classification algorithm for early disease prediction. Data aware preprocessing method adopts isolation forest algorithm for outlier detection as part of the automation. Automated sampling method of applying the sampling corresponding to either balanced or unbalanced data is adopted. Performance evaluation of the proposed data aware algorithm using isolation forest algorithm for anomaly detection is experimented. Python based implementation of the proposed data aware classification algorithm inferred a better area under the curve (AUC) receiver operating characteristics (ROC) curve for isolation forest implementation in data preprocessing automation thus developed. While the individual classifiers multilayer perceptron classifier approached till 0.918 (AUC) in the ROC-AUC curve. The ensemble learning algorithm that included multilayer perceptron classifier, logistic regression classifier, support vector classifier and decision tree algorithm with the isolation forest-based anomaly detection algorithm performed better than the individual machine learning algorithm with 0.922 (AUC) in the ROC-AUC curve.</span>