Miracle-dz/UniFAR-FTUs huggingface dataset:
Artificial Intelligence–Enabled Software Prototype to Inform Opioid Pharmacovigilance From Electronic Health Records: Development and Usability Study

Background: The use of patient health and treatment information captured in structured and unstructured formats in computerized electronic health record (EHR) repositories could potentially augment the detection of safety signals for drug products regulated by the US Food and Drug Administration (FDA). Natural language processing and other artificial intelligence (AI) techniques provide novel methodologies that could be leveraged to extract clinically useful information from EHR resources. Objective: Our aim is to develop a novel AI-enabled software prototype to identify adverse drug event (ADE) safety signals from free-text discharge summaries in EHRs to enhance opioid drug safety and research activities at the FDA. Methods: We developed a prototype for web-based software that leverages keyword and trigger-phrase searching with rule-based algorithms and deep learning to extract candidate ADEs for specific opioid drugs from discharge summaries in the Medical Information Mart for Intensive Care III (MIMIC III) database. The prototype uses MedSpacy components to identify relevant sections of discharge summaries and a pretrained natural language processing (NLP) model, Spark NLP for Healthcare, for named entity recognition. Fifteen FDA staff members provided feedback on the prototype’s features and functionalities. Results: Using the prototype, we were able to identify known, labeled, opioid-related adverse drug reactions from text in EHRs. The AI-enabled model achieved accuracy, recall, precision, and F1-scores of 0.66, 0.69, 0.64, and 0.67, respectively. FDA participants assessed the prototype as highly desirable in user satisfaction, visualizations, and in the potential to support drug safety signal detection for opioid drugs from EHR data while saving time and manual effort. Actionable design recommendations included (1) enlarging the tabs and visualizations; (2) enabling more flexibility and customizations to fit end users’ individual needs; (3) providing additional instructional resources; (4) adding multiple graph export functionality; and (5) adding project summaries. Conclusions: The novel prototype uses innovative AI-based techniques to automate searching for, extracting, and analyzing clinically useful information captured in unstructured text in EHRs. It increases efficiency in harnessing real-world data for opioid drug safety and increases the usability of the data to support regulatory review while decreasing the manual research burden.
The Lagrangian and Hamiltonian Formalisms of Mechanics

This paper serves to demonstrate and summarize my research into Analytical Mechanics. Advanced mechanics is an area of physics that Physics students are certainly never exposed to due to the mathematical complexity of the subject. However, concepts in analytical mechanics are extended to cutting edge areas of modern physics, particularly quantum physics, statistical physics and relativistic physics. The Lagrangian and Hamiltonian reformulations of classical mechanics are rich in beautiful, elegant mathematics and present a far more general and abstract way of understanding observable phenomena than purely Newtonian approaches. Moreover, these concepts are extended and abstracted to other areas of theoretical physics, so an understanding of these formulations is invaluable to a physics student. Computing physical problems using Lagrangians and Hamiltonians are remarkably easy and reduce the computational complexity of solving for the dynamics of large complex physical systems. The difficult part is following and understanding the logic behind the development of these esoteric concepts. The mathematical proofs are not arduous by any means, yet the challenge lies in understanding how and why some of the greatest physicists and mathematicians of antiquity even thought of these things. This was by far the biggest enigma I faced in my research as there are little to no resources that explain the motivation and more importantly, the thought process behind analytical mechanics. A lot of proofs in textbooks, though logically sound, will work backwards from the premise instead of working towards the premise which leaves one wondering how some concepts were conceived of at all. Thus, this paper will largely focus on logic and intuition with minimal computation and problem solving as there are already a plethora of detailed resources available that provide myriad rigorous examples and practice problems. Furthermore, I will also investigate the Lagrangian and Hamiltonian formalisms through a geometric interpretation.
The impact of portfolio-based writing instruction on writing performance and anxiety of EFL students

: Although the usefulness of alternative assessment in second language (L2) classrooms has been extensively recognized by scholars, the use of the various types of alternative assessment in English as a Foreign Language (EFL) contexts has not received adequate attention by L2 practitioners. To contribute to this line of research, the present research sought to examine the impact of a portfolio-based writing instruction on writing performance and writing anxiety of EFL students. To this end, a number of 41 EFL learners were recruited as the participants of this study. They were then randomly divided to an experimental group (N=21) and a control group (N=20). The participants in the experimental group received portfolio-based writing instruction, whereas the control group received the regular writing instruction with no archiving of students’ drafts in portfolios. Timed-writing tasks and the Second Language Writing Anxiety Inventory (SLWAI) were employed to collect the data. The results obtained from ANCOVA analysis revealed that the portfolio-based writing instruction aided the participants in improving their writing performance more than the control group. Moreover, it was found that the use of portfolios significantly reduced the L2 writing anxiety of the participants while the traditional writing instruction did not have any significant impact on L2 writing anxiety of the control group. The pedagogical implications for portfolio-based writing instruction are discussed finally.
Comment on ‘Relativity, potential energy, and mass’

The case has been made by Eugene Hecht (2016 Eur. J. Phys. 37 065804) that potential energy is a conceptual fiction, i.e. is not an objective physical quantity. This was based on a claimed absence of measurability of potential energy, changes in the mass of a body being measurable, and by a particular interpretation of the mass-energy equation from the special theory of relativity. However, if potential energy lacks measurability then kinetic energy is not measurable in the same sense. The absence of such measurability being used to conclude that potential energy does not have physical objectivity would then also require that kinetic energy has no physical objectivity either. Further, it is shown that appealing to special relativity does not help secure the conclusion of physical non-objectivity of potential energy.
H-Matrix Arithmetic for Fast Direct and Iterative Method of Moment Solution of Surface-Volume-Surface EFIE for 3-D Radiation Problems

Hierarchical (H-) matrix based fast direct and iterative algorithms are presented for acceleration of the Method of Moment (MoM) solution of the Surface-Volume-Surface Electric Field Integral Equation (SVS-EFIE) formulated for scattering and radiation problems on homogeneous dielectric objects. As the SVS-EFIE features the product of the integral operator mapping the tangential equivalent electric current on the surface of the scatterer to the volume polarization current and the integral operator mapping the volume polarization current to the tangential component of the scattered electric field, its MoM discretization produces the product of non-square matrices. Formation of the non-square H-matrices for the MoM discretized integral operators is described. The algorithms for arithmetics pertinent to the product of the non-square H-matrices are explained. The memory and CPU time complexity scaling of the required H-matrix operations are analyzed in details and verified numerically. The numerical validation of the proposed algorithm is provided for both the lowloss dielectric objects as well as for the high-loss biological tissues found in the bioelectromagnetics applications. The numerical experiments demonstrate a significant reduction of memory usage and a considerable speedup for CPU time compared to näıve MoM, thus, enabling solution of the large-scale scattering and radiation problems with the SVS-EFIE.
Learning three-dimensional models of object structure from images

Recognizing objects in images is an effortless task for most people. Automating this task with computers, however, presents a difficult challenge attributable to large variations in object appearance, shape, and pose. The problem is further compounded by ambiguity from projecting 3-D objects into a 2-D image. In this thesis we present an approach to resolve these issues by modeling object structure with a collection of connected 3-D geometric primitives and a separate model for the camera. From sets of images we simultaneously learn a generative, statistical model for the object representation and parameters of the imaging system. By learning 3-D structure models we are going beyond recognition towards quantifying object shape and understanding its variation. We explore our approach in the context of microscopic images of biological structure and single view images of man-made objects composed of block-like parts, such as furniture. We express detected features from both domains as statistically generated by an image likelihood conditioned on models for the object structure and imaging system. Our representation of biological structure focuses on Alternaria, a genus of fungus comprising ellipsoid and cylinder shaped substructures. In the case of man-made furniture objects, we represent structure with spatially contiguous assemblages of blocks arbitrarily constructed according to a small set of design constraints. We learn the models with Bayesian statistical inference over structure and camera parameters per image, and for man-made objects, across categories, such as chairs. We develop a reversible-jump MCMC sampling algorithm to explore topology hypotheses, and a hybrid of Metropolis-Hastings and stochastic dynamics to search within topologies. Our results demonstrate that we can infer both 3-D object and camera parameters simultaneously from images, and that doing so improves understanding of structure in images. We further show how 3-D structure models can be inferred from single view images, and that learned category parameters capture structure variation that is useful for recognition.
Pneumonia knowledge and care seeking behavior for children under-five years in Jigawa, Northwest Nigeria: a cross-sectional study

Background Between 2013 and 2022, Nigeria did not meet globally defined targets for pneumonia control, despite some scale-up of vaccinations, oxygen and antibiotics. A deliberate focus on community-based programs is needed to improve coverage of protective, preventive and treatment interventions. We therefore aimed to describe caregiver knowledge and care seeking behaviour for childhood pneumonia, in a high child mortality setting in Nigeria, to inform the development of effective community-based interventions for pneumonia control. Methods We conducted a cross-sectional household survey in Kiyawa Local Government Area, Jigawa State, Nigeria between December 2019 and March 2020. We asked caregivers about their knowledge of pneumonia symptoms, prevention, risks, and treatment. A score of 1 was assigned for each correct response. We showed them videos of pneumonia specific symptoms and asked (1) if their child had any respiratory symptoms in the 2-weeks prior; (2) their subsequent care-seeking behaviour. Multivariate regressions explored socio-demographic and clinical factors associated with care seeking. Results We surveyed 1,661 eligible women, with 2,828 children under-five. Only 4.9% of women could name both cough and difficulty/fast breathing as pneumonia symptoms, and the composite knowledge scores for pneumonia prevention, risks and treatment were low. Overall, 19.0% (536/2828) of children had a report of pneumonia specific symptoms in the prior two-weeks, and of these 32.3% (176/536) were taken for care. The odds of care seeking was higher among children: with fever (AOR:2:45 [95% CI: 1.38–4.34]); from wealthiest homes (AOR: 2:13 [95% CI: 1.03–4.38]) and whose mother first married at 20–26 years compared to 15–19 years (AOR: 5.15 [95% CI: 1.38–19.26]). Notably, the caregiver’s knowledge of pneumonia was not associated with care seeking. Conclusion While some socio-demographic factors were associated with care seeking for children with symptoms of Acute Respiratory Infection (ARI), caregiver’s knowledge of the disease was not. Therefore, when designing public health interventions to address child mortality, information-giving alone is likely to be insufficient.
Deep learning system assisted detection and localization of lumbar spondylolisthesis

Objective: Explore a new deep learning (DL) object detection algorithm for clinical auxiliary diagnosis of lumbar spondylolisthesis and compare it with doctors’ evaluation to verify the effectiveness and feasibility of the DL algorithm in the diagnosis of lumbar spondylolisthesis. Methods: Lumbar lateral radiographs of 1,596 patients with lumbar spondylolisthesis from three medical institutions were collected, and senior orthopedic surgeons and radiologists jointly diagnosed and marked them to establish a database. These radiographs were randomly divided into a training set (n = 1,117), a validation set (n = 240), and a test set (n = 239) in a ratio of 0.7 : 0.15: 0.15. We trained two DL models for automatic detection of spondylolisthesis and evaluated their diagnostic performance by PR curves, areas under the curve, precision, recall, F1-score. Then we chose the model with better performance and compared its results with professionals’ evaluation. Results: A total of 1,780 annotations were marked for training (1,242), validation (263), and test (275). The Faster Region-based Convolutional Neural Network (R-CNN) showed better precision (0.935), recall (0.935), and F1-score (0.935) in the detection of spondylolisthesis, which outperformed the doctor group with precision (0.927), recall (0.892), f1-score (0.910). In addition, with the assistance of the DL model, the precision of the doctor group increased by 4.8%, the recall by 8.2%, the F1-score by 6.4%, and the average diagnosis time per plain X-ray was shortened by 7.139 s. Conclusion: The DL detection algorithm is an effective method for clinical diagnosis of lumbar spondylolisthesis. It can be used as an assistant expert to improve the accuracy of lumbar spondylolisthesis diagnosis and reduce the clinical workloads.
Pain and participation in social activities in people with relapsing remitting and progressive multiple sclerosis

Background Differences in pain between subtypes of multiple sclerosis are understudied. Objective To compare the prevalence of pain, and the association between pain and: (a) pain interference and (b) social participation in people with relapsing–remitting multiple sclerosis and progressive multiple sclerosis. Methods Participants completed the McGill Pain Questionnaire Short-Form-2, Pain Effects Scale and Ability to Participate in Social Roles and Activities-V2.0 questionnaires. We tested the association between multiple sclerosis subtype, pain severity, and pain interference/social participation using quantile regression. Results Of 231 participants (relapsing–remitting multiple sclerosis: 161, progressive multiple sclerosis: 70), 82.3% were women. The prevalence of pain was 95.2%, of more than mild pain was 38.1%, and of pain-related limitations was 87%; there were no differences between multiple sclerosis subtypes. Compared to participants with relapsing–remitting multiple sclerosis, those with progressive multiple sclerosis reported higher pain interference (mean (standard deviation) Pain Effects Scale; progressive multiple sclerosis: 15[6.0] vs relapsing–remitting multiple sclerosis: 13[5], p = 0.039) and lower social participation (Ability to Participate in Social Roles and Activities T-scores 45[9.0] vs 48.3[8.9], p = 0.011). However, on multivariable analysis accounting for age, physical disability, mood/anxiety and fatigue, multiple sclerosis subtype was not associated with differences in pain interference or social participation. Conclusions Pain was nearly ubiquitous. Over one-third of individuals with relapsing–remitting multiple sclerosis and progressive multiple sclerosis reported pronounced pain, although this did not differ by multiple sclerosis subtype.
A Comprehensive Survey on Nature Inspired Heuristic Algorithms for Energy Efficiency in Wireless Sensor Network

- Today’s Engineering Study is highly motivated towards the Nature Inspired heuristic computational algorithm as they have the competence to give better results as compared to the conventional methods. Wireless Sensor Networks (WSNs) have become gradually prevalent due to their wide array of applications. Developing an energy-efficient routing algorithm is an important issue in the conspiratorial of WSNs applications as the lifetime of the sensor node is based on limited battery-powered devices. Therefore, to maximize the lifetime of the sensor node an extensive survey of energy-efficient routing algorithms based on natural science has been addressed in this paper. The paper will take the readers into a detailed survey of existing energy-optimized routing algorithms considering the natural science (heuristic) as the base and considering parameters related to energy efficiency as the core objective.
Benson's algorithm for nonconvex multiobjective problems via nonsmooth Wolfe duality

‎In this paper‎, ‎we propose an algorithm to obtain an approximation set of the (weakly) nondominated points of nonsmooth multiobjective optimization problems with equality and inequality constraints‎. ‎We use an extension of the Wolfe duality to construct the separating hyperplane in Benson's outer algorithm for multiobjective programming problems with subdifferentiable functions‎. ‎We also formulate an infinitive approximation set of the (weakly) nondominated points of biobjective optimization problems‎. ‎Moreover‎, ‎we provide some numerical examples to illustrate the advantage of our algorithm.
More environment-friendly and safer working gas mixtures for Bakelite RPCs operated in streamer mode

This paper presents experimental results of RPCs performances with different working gas mixtures. Owing to Freon's high global warming potential, its threat to RPCs aging and its large consumption in large particle physics experiments, studies to minimize the concentration of HFC-134A, and even its complete replacement, have been undertaken. In addition, the reduction of iso-butane is also a favorable strategy, due to the flammability level of the gas mixture. Freon-less working gas mixture of Ar/HFC-134A/i-C4H10/CO2=20/0/8/72 was chosen with plateau efficiency of 86.3% and noise rate of 0.61 Hz/cm2. For working gas with lower ratio of Freon, Ar/HFC-134A/i-C4H10/CO2=20/20/8/52 was suggested with plateau efficiency of 91.0% and noise rate of 0.19 Hz/cm2, in which Freon was decreased by 22% compared to the BESIII RPC gas mixture. Furthermore, iso-butane was decreased to 6% with RPC's efficiency of 90% and noise rate of 0.20 Hz/cm2 achieved. Finally, the explanation of RPC's different performances at various working gas mixtures has been validated by the investigation of secondary streamers. This study will be helpful for RPC's application in future large particle physics experiments, in which RPCs can run in streamer mode.
The Natuna Regent's Leadership Style in 2021-2024 Period: A Leadership Characterized by Archipelagic Region

This research analyzed and described the leadership style of the Natuna Regent during the 2021-2024 period, which is characterized by the archipelagic regions. To achieve this, the Thoha theory comprising four types of leadership styles, namely directive, consultative, participative, and delegation, was employed. Furthermore, a qualitative approach with a descriptive design was employed, with data collected through interviews, observations, and documentation. The collected data were subsequently analyzed through data reduction and by drawing conclusions.The results showed that the Natuna Regent exhibits a collaborative leadership style that encompasses all four styles tailored to the specific needs of the organization. Supporting factors include the Regent's in-depth understanding of governance issues, non-authoritarian approach, welcoming attitude, strong communication skills, and encouragement of innovative ideas from all individuals. However, inhibiting factors include certain Regional Apparatus Organizations (RAO) that have not fully fulfilled their responsibilities, subordinates or RAO displaying unresponsiveness towards directives, and program implementation lacking sufficient support from the regional budget. Efforts to overcome these inhibiting factors in the leadership style of the Natuna Regent include encompassing consolidating relationships with all regional apparatus, establishing a shared mission and vision, fostering innovation among the regional apparatus and RAO heads, promoting collaboration through the Regional Leadership Coordination Forum (Forkopimda), and ensuring seamless task execution without obstacles.
An Adaptive Multiscale Gaussian Co-Occurrence Filtering Decomposition Method for Multispectral and SAR Image Fusion

Spectral information and backscatter information are both exclusively important bases for land cover classification, and these two kinds of information are found in multispectral images and SAR images, respectively. Therefore, the fusion of complementary information of multispectral and SAR images can effectively improve land cover classification accuracy. However, the existing fusion methods of multispectral and SAR images generally have some problems, such as insensitivity to edge information, serious interference by speckle noise, and unreasonable settings of fusion rules, which lead to unsatisfactory results of land cover classification. To solve this issue, a fusion method based on adaptive multiscale Gaussian co-occurrence filtering decomposition is proposed. Gaussian filtering and adaptive co-occurrence filtering are applied to the original image to smooth out speckle noise and interference edges within the textures while preserving edge information between textures. Through multi-scale spatial decomposition, the separation of detail information, edge information and basic information is realized, while multi-layer fusion of image features is performed. Finally, the fused image with low noise interference, clear boundary and uniform pixel convergence is generated. Experimental results show that the proposed method generally performs the best in eight evaluation indexes compared with ten other methods. The overall accuracy, average accuracy and Kappa coefficient of land cover classification are increased by 7.674%, 6.776%, and 0.098, respectively, compared with those of the original multispectral image in Area 1, and by 6.904%, 7.649%, and 0.089, respectively, compared with those of the original multispectral image in Area 2.