bluuebunny/crossref_metadata_2025 huggingface dataset:
Background subtraction challenges in motion detection using Gaussian mixture model: a survey
Authors: Mohd Aris Nor Afiqah , Suhana Jamaian Siti
Published: March 2023

Motion detection is becoming prominent for computer vision applications. The background subtraction method that uses the Gaussian mixture model (GMM) is utilized frequently in camera or video settings. However, there is still more work that needs to be done to develop a reliable, accurate and high-performing technique due to various challenges. The degree of difficulty for this challenge is primarily determined by how the object to be detected is defined. It could be influenced by the changes in the object posture or deformations. In this context, we describe and bring together the most significant challenges faced by the background subtraction techniques based on GMM for dealing with a crucial background situation. Therefore, the findings of this study can be used to identify the most appropriate GMM version based on the crucial background situation.
Image captioning to aid blind and visually impaired outdoor navigation
Authors: Faurina Ruvita , Jelita Anisa , Vatresia Arie , Agustian Indra
Published: March 2023

Artificial intelligence technology has dramatically improved the quality of services for human needs, one of which is technology to improve the quality of services for the blind and visually impaired, particularly technology that can help them understand visual sights to facilitate navigation in their daily lives. This study developed an image captioning model to aid the blind and visually impaired in outdoor navigation. The image captioning model employs the encoder-decoder method, with the convolutional neural network (CNN) feature extraction and attention layer as encoders and the long short-term memory (LSTM) as decoders. ResNet101 and ResNet152 are used in the encoder to extract image features. The results of the extraction and caption are forwarded to the attention layer and the LSTM network. The attention layer uses the Bahdanau attention mechanism. The accuracy of the model is calculated using the bilingual evaluation understudy score (BLEU), metric for evaluation of translation with explicit ordering (METEOR) and recall-oriented understudy for gisting evaluation-longest common subsequence (ROUGE-L). ResNet101 performed the best on BLEU-4, scoring 91.811% and 94.0337% in the METEOR evaluation. The captioning results show that the model is quite successful in displaying a simple caption that is suitable for each image.
Fault tolerant control of two tanks system using gain-scheduled type-2 fuzzy sliding mode controller
Authors: Loukal Keltoum , Bouguerra Abderrahmen , Zeghlache Samir
Published: March 2023

To save the robustness of type 2 fuzzy logic control technique and to avoid the high energy consumption that represents the sliding mode control (SMC) technique control technique, without failing the performance of the system, we propose a new fault tolerant control method based on gain-scheduled sliding mode control with interval type 2 fuzzy logic (FTCGST2FSMC) applied to the hydraulic system (two tanks system) with an actuator fault. The proposed control scheme avoids a difficult modeling, due to the chatter effect of the SMC, guarantees the stability studied by Lyapunov with the robustness of the system. The gains of the control with the SMC controller are modified and changed by an adaptation with a technique based on type 2 fuzzy logic, used to improve the gains of the controller when the fault is added, the proposed FTCGST2FSMC controller has been compared with the sliding mode controller. The results obtained confirm the robustness and the performance of this method, in the presence of the actuator fault effect.
Particle swarm optimization for the optimal layout of wind turbines inside a wind farm
Authors: El jaadi Mariam , Haidi Touria , Bouabdallaoui Doha
Published: March 2023

The wind turbine’s output power is heavily affected by the arrangement of the wind turbine location. Wind farm planning endeavors to firstly maximize the farm’s output energy. Secondly, it seeks to minimize the effects of the wake phenomenon. This paper attempts to find the best possible location of a wind turbine inside a square farm using the particle swarm optimization (PSO) method whilst focusing on the three salient cases: the steadiness of wind direction and speed, the variability of the flow direction with a steady speed, and the variability of direction for three discrete wind speeds. The proposed algorithm generated results that will be contrasted to previous studies on the same topic with different metaheuristic methods such as a genetic algorithm. When compared to the optimum findings from prior research, the suggested approach has a reduced cost. It is developed by language C through MATLAB environment considering a square with the dimensions 2×2 kilometers.
Machine learning models applied in analyzing breast cancer classification accuracy
Authors: Bokhare Anuja , Jha Puja
Published: March 2023

There have been many attempts made to classify breast cancer data, since this classification is critical in a wide variety of applications related to the detection of anomalies, failures, and risks. In this study machine learning (ML) models are reviewed and compared. This paper presents the classification of breast cancer data using various ML models. The effectiveness of models comparatively evaluated through result using benchmark of accuracy which was not done earlier. The models considered for the study are k-nearest neighbor (kNN), decision tree classifier, support vector machine (SVM), random forest (RF), SVM kernels, logistic regression, Naïve Bayes. These classifiers were tested, analyzed and compared with each other. The classifier, decision tree, gets the highest accuracy i.e. 97.08% among all these models is termed as the best ML algorithm for the breast cancer data set.
Text grouping: a comprehensive guide
Authors: Palai Padarabinda , Agrawal Kaushiki , Prasad Mishra Debani , Reddy Salkuti Surender
Published: March 2023

Text keywords have huge variance and to bridge the gap between the country business segment which provides negligible information and the keywords that have a huge longtail it is imperative for us to categorize the queries that provide a middle ground and also serve a few other purposes. The paper will present those in-depth. Query categorization falls into the segment of 'Multi-Class Classification' in the domain of natural language processing (NLP). However, business requirements require the implementation of any technique that could provide as accurate results as possible. So, to solve this problem the paper discusses an amalgamation of approaches like TF-IDF (term frequency-inverse document frequency), neural networks, cosine similarity, transformers-all of which fix specific issues.
A novel approach for sizing and optimization of hybrid solar-PV, biogas-generator, and batteries system for rural electrification: case study
Authors: Ebeya Cheibany Cheikh , Ali Mohamed Mahmoud , Ndongo Mamoudou , Sidibba Ahmed , Yetilmezsoy Kaan , Kiyan Emel , Bilal Boudy , Jedou Eslemhoum , Diagne Halima Maiguizo , Youm Issakha
Published: March 2023

A new approach for sizing a hybrid solar-PV-battery and biogas generator for power generation was suggested in this study, based on the variation of energy resources and the load profile. Biogas has enormous potential and numerous economic and social advantages. In this respect, the monthly solar radiation, temperature, and biogas produced from biomass resources were used as model inputs. This study considers the total annualized of the components for the feasibility analysis. HOMER Pro® software-based outcomes in the proposed methodology revealed that the proposed solar biogas hybrid system was sufficient to meet the load requirements of the village (Ain Farba village in the Hodh El Gharbi area located in the northeast of the Mauritania country) with a net present cost over the 25-year lifespan of 61,144 $ and the energy cost was determined to be 0.0473 $/kWh.
Fault detection using acceptance ratio analysis on polycrystalline grid-connected photovoltaics system
Authors: Muhammad Nurmalessa , Roland Fiona , Zainuddin Hedzlin
Published: March 2023

Around the world, electricity generation from PV photovoltaic systems is increasing, achieving 10-20% PV system efficiency. However, PV systems degrade due to the technology and the operating conditions and become worse in tropical climate countries. Hence, degradation is one of the key performance indicators for the reliability assessment of a PV system. This paper presents the acceptance ratio (AR) analysis grid-connected photovoltaic (GCPV) located on the campus of the Universiti Teknologi MARA, Shah Alam, Malaysia as the key performance indicators. A comparative analysis of the actual and predicted AC Power and AR of the polycrystalline GCPV system is carried out over monitoring of a one-year period. MATLAB software is chosen to simulate the output power using actual data. Malaysian Standard MS2692:2020 has noted that the AR value must ≥ 0.9 to classify as accepted in testing and commissioning tests and AR < 0.9 has been indicated as a non-accepted GCPV system. The results of acceptance ratio (AR), yield (Y), specific yield (SY), and performance ratio (PR) show that almost half of the AR’s data results show below 0.9 with the performance ratio of PV systems was less than 75%, indicating that the systems needed to be completely replaced.
Fuzzy logic control based maximum power point tracking technique in standalone photovoltaic system
Authors: Hasbi Azmi Muhammad , Noor Siti Zaliha Mohammad , Musa Suleiman
Published: March 2023

This study describes the development of a smart technique for tracking the highest power point on a standalone photovoltaic (SAPV) system when temperature and irradiance conditions are changing using fuzzy logic control (FLC). The PV systems comprises of a PV array, a boost converter, a controller for tracking the maximum power point, and an inverter to power the AC loads. The FLC-based maximum power point tracking (MPPT) is proposed because the technique is not complex and does not need a deep comprehension of the particular model of the system. Furthermore, the technique is efficient and fast response in tracking maximum power (MP) from the PV array. The technique overcame the limitation of the conventional MPPT technique that resulted in slow tracking of the MP and was not accurate on the optimal position of the PV array output power. The SAPV system is integrated with the sealed lead acid (SLA) battery bank as an alternative power source and makes up for power shortages by supplying energy to the load without interruption during power shortages. A system for managing the battery has been included in the system to preserve the battery's longevity by controlling the battery's charging process. The PV system is able to supply single-phase output AC voltage of 230 Vrms and has low total harmonic distortion (THD) that is suitable for home appliances. The achieved simulation results demonstrate the effectiveness of the suggested fuzzy logic controller in tracking the MPP.
A study of power management approach in a grid connected hybrid renewable generation system
Authors: Rathod Pranita , Bhuyan Sujit Kumar , Mishra Sanjay Kumar
Published: March 2023

Where sun irradiation is not continuous throughout the day, photovoltaic (PV) cells are used to supply solar power to the grid. A suitable power management plan is required to provide a steady power supply to the grid. The hybrid renewable generation system (HRGS) interconnected to the grid is the subject of this study, which provides a revolutionary power management method and control strategy. PV devices, a battery storage unit (BSU) charged by electricity generated from solar energy, and an auxiliary unit make up the HRGS concept (AU). Maximum power point tracking (MPPT) uses an improved perturb and observe (P&O) method to track maximal energy from solar irradiation. The peculiarity of this approach is that it uses a modified MPPT algorithm to handle power management throughout the process, allowing it to manage continuous power delivery to the grid based on load demand. It is capable of working under any load situation. The action of an L-C filter is utilized to decrease the total harmonic distortion (THD). The HRGS scheme's performance appraisals work correctly, achieve maximum productivity, and continually managing the power provided to the grid in a satisfactory way.
Comparison of fuzzy time series, ANN and wavelet techniques for short term load forecasting
Authors: Khatoon Shahida , Ibraheem Ibraheem , Gupta Priti , Shahid Mohammad
Published: March 2023

The present article presents the load forecasting for a power system (substation) load demands using techniques based on fuzzy time series (FTS), artificial neural network (ANN), and wavelet transform (WT). The mean absolute percentage error (MAPE), integral absolute error (IAE), integral of time multiplied error (ITAE), integral square error (ISE) along with integral time multiplied square error (ITSE) criteria are used for determining the performance indices and minimizing the error. From the investigations of the results obtained in the study, it is inferred that forecasting of electric load based on WT and ANN offers less error as compared to FTS. The suggested integrated model captures the useful properties of artificial neural networks and wavelet transforms in time series and is found to be accurate for real-time data.
An intelligent overcurrent relay to protect transmission lines based on artificial neural network
Authors: Al-Jawady Noha Abed-Al-Bary , Ibrahim Mohammed Ahmed , Khalaf Laith A. , Abed Mahmood Natiq
Published: March 2023

Power systems are susceptible to faults due to system failures or natural calamities. This could be caused by damage to power system components, resulting in an interruption of power delivery to clients. Overcurrent relays are important relays that protect distribution feeders, transmission lines, transformers, and other components. The intelligent relay can perform both primary and secondary functions. Line-to-ground (L-G) faults are the most common occurrence in long transmission lines, posing a serious threat to electrical equipment. This article presents improved fault classification for transmission line overcurrent protection and highlights the use of artificial neural network (ANN) techniques to protect transmission lines of 100 km (terco type). An ANN is used to classify the faults. A back propagation neural network (BPNN) is used in this case. The neural network has been trained to classify faults in transmission lines for overcurrent protection. Various fault conditions are considered. In the event of a fault condition, the output of a neural network will be a tripping signal. The MATLAB neural network tool and the Simulink package are used to model the suggested method.
Operation and control of a 1-Φ induction machine fed from a typical transformer-less photovoltaic (PV) cells
Authors: Shyaa Suha Sabah , Jasim Nihad Abdulkudhur , Takheal Yihia Hussein , Mahdy Donya Nuhad
Published: March 2023

The work given in this paper illustrates how an adjustable speed 1-Φ induction machine's auxiliary winding is driven. Trina Solar's TSM-250PA05.08 array type model, which is made up of 1 series and 8 parallel-connected photovoltaic (PV) modules, feeds a single induction machine. PV system with perturb and observe (P&O) technique for maximum power point tracking (MPPT) tracking. The induction motor has been controlled using the field-oriented control (FOC) controlling approach. The outcomes of the 1-induction motor simulation were closely related to the parameters of insolation change. The outcomes are taken into account for the system's reaction FOC, which had been demonstrated in electromagnetic torque Te, main and auxiliary winding currents, and motor rotor speed.
Feedforward neural network emulation of a PID continuous-time controller for quadcopter attitude digital control
Authors: Mohammed Baba , Said Bennaceur , Fateh Bounaama
Published: March 2023

Quadcopters are popular UAVs owing to their compact size and maneuverability. Quadcopters are unmanned aircraft guided by remote control, and the demand for them is increasing due to their widespread surveillance, goods delivery, aerial photography, and defense applications. Nonlinear quadcopter operation makes control system implementation very challenging. In this paper, based on artificial intelligence (AI), we train a feedforward neural network (FFNN) controller of a traditional proportional integral derivative (PID). The conventional (PID) is generally tuned to improve the quadcopter control and performance. FFNN can perform offline learning between the inputs and outputs of the controller to learn its behavior. Once the learning is complete, we replace the PID controller with the neural network controller, to get a controller that can maintain system stability,and overcome the limitations of hardware implementation problems caused by the classical PID controller.
The design of inductive power transfer system for fish aquarium using class-E inverter and LCCL impedance matching
Authors: Saat Shakir , Yusop Yusmarnita , Husin Siti Huzaimah , Awal Md Rabiul , Shapri Ahmad Husni Mohd
Published: March 2023

Nowadays, the accessories in fish aquarium systems such as water pump, internal filter, and LED light are powered using cables. This will cause to safety issue such as tangled wire, and risk of electric shock. Therefore, this work aims to design a wireless power transfer system to power up the fish aquarium’ accessories wirelessly without require any physical connection between source and loads. To be more specific, inductive power transfer (IPT) is used instead of capacitive power transfer (CPT) due to high efficiency ability owned by IPT. Class-E inverter circuit is proposed in this paper to convert DC to AC at nearly 100% efficiency. The inverter provides a low switching loss which is important as the circuit operates at high frequency, 1 MHz operating frequency. Furthemore, LCCL impedance matching circuit is added into receiver part of the system to enhance the efficiency of overall circuit. The analyses were done based on the distance between the coils and the misalignment of the coils against the output power and efficiency. The developed prototype is supplied by 24 V DC and capable to deliver 5 W power. The overall efficiency of the prototype is 87.81%.
An elevated gain DC-DC converter with active switched inductor for PV application
Authors: Mahalingam Kalarathi , Jothimani Gnanavadivel
Published: March 2023

Innovative power electronic converters have emerged because of renewable energy expansion in the past two decades. Direct current DC grids can operate independently or connected to power grids. A DC grid has distributed generation (DG) units, such as solar panels and, fuel cells. It is necessary to have a DC-DC converter in DC grids to raise output voltage. Boost converters have limited voltage gain, and their switching stress is often equal to their output voltage. This work proposes a converter which gives high gain and less stress when compared to other recent converters. At 0.8 duty ratio, this converter produces an elevated gain of 59. An experimental prototype is built for the proposed converter and the results are presented.
High frequency isolated bidirectional dual active bridge DC-DC converters and its application to distributed energy systems: an overview
Authors: Bathala Kiran , Kishan Dharavath , Harischandrappa Nagendrappa
Published: March 2023

Among the DC-DC converters, an isolated bidirectional dual active bridge converter is a core circuit for high-frequency power converters in distributed energy system applications. These high-frequency power conversion systems attract academia and industry due to various advantages, such as high-power density, less weight, reduced noise, high efficiency, low cost and high reliability. First, the importance of power electronic converters in modern-day life is introduced. Second, a topological overview of voltage-fed and current-fed isolated bidirectional dual active bridge converters is presented with their importance in integrating hybrid power sources. Third, switching modes of isolated bidirectional DC-DC converters are also presented with a degree of freedom of control.  Forth, performance evaluation of voltage-fed and current-fed isolated bidirectional converters has been presented with an example. Their suitability in integrating fuel cells and photovoltaics with energy storage systems in low to medium-power applications is presented.
Transcranial Direct-Current Stimulation in Subacute Aphasia: A Randomized Controlled Trial
Authors: Stockbridge Melissa D. , Elm Jordan , Breining Bonnie L. , Tippett Donna C. , Sebastian Rajani , Cassarly Christy , Teklehaimanot Abeba , Spell Leigh Ann , Sheppard Shannon M. , Vitti Emilia , Ruch Kristina , Goldberg Emily B. , Kelly Catherine , Keator Lynsey M. , Fridriksson Julius , Hillis Argye E.
Published: March 2023

Background:

Transcranial direct-current stimulation (tDCS) is a promising adjunct to therapy for chronic aphasia.

Methods:

This single-center, randomized, double-blind, sham-controlled efficacy trial tested the hypothesis that anodal tDCS augments language therapy in subacute aphasia. Secondarily, we compared the effect of tDCS on discourse measures and quality of life and compared the effects on naming to previous findings in chronic stroke. Right-handed English speakers with aphasia <3 months after left hemisphere ischemic stroke were included, unless they had prior neurological or psychiatric disease or injury or were taking certain medications (34 excluded; final sample, 58). Participants were randomized 1:1, controlling for age, aphasia type, and severity, to receive 20 minutes of tDCS (1 mA) or sham-tDCS in addition to fifteen 45-minute sessions of naming treatment (plus standard care). The primary outcome variable was change in naming accuracy of untrained pictures pretreatment to 1-week posttreatment.

Results:

Baseline characteristics were similar between the tDCS (N=30) and sham (N=28) groups: patients were 65 years old, 53% male, and 2 months from stroke onset on average. In intent-to-treat analysis, the adjusted mean change from baseline to 1-week posttreatment in picture naming was 22.3 (95% CI, 13.5–31.2) for tDCS and 18.5 (9.6–27.4) for sham and was not significantly different. Content and efficiency of picture description improved more with tDCS than sham. Groups did not differ in quality of life improvement. No patients were withdrawn due to adverse events.

Conclusions:

tDCS did not improve recovery of picture naming but did improve recovery of discourse. Discourse skills are critical to participation. Future research should examine tDCS in a larger sample with richer functional outcomes.

Registration:

URL:
https://www.clinicaltrials.gov
; Unique identifier: NCT02674490.
Cerebrovascular Reactivity Has Negligible Contribution to Hemodynamic Lag After Stroke: Implications for Functional Magnetic Resonance Imaging Studies
Authors: Braban Andra , Leech Robert , Murphy Kevin , Geranmayeh Fatemeh
Published: March 2023

Background:

Functional magnetic resonance imaging (fMRI) is ubiquitously used to study poststroke recovery. However, the fMRI–derived hemodynamic responses are vulnerable to vascular insult which can result in reduced magnitude and temporal delays (lag) in the hemodynamic response function (HRF). The cause of HRF lag remains controversial, and a better understanding of it is required to ensure accurate interpretation of poststroke fMRI studies. In this longitudinal study, we investigate the relationship between hemodynamic lag and cerebrovascular reactivity (CVR) following stroke.

Methods:

Voxel-wise lag maps were calculated relative to a mean gray matter reference signal for 27 healthy controls and 59 patients with stroke across 2 time points (≈2 weeks and ≈4 months poststroke) and 2 conditions: resting-state and breath-holding. The breath-holding condition was additionally used to calculate CVR in response to hypercapnia. HRF lag was computed for both conditions across tissue compartments: lesion, perilesional tissue, unaffected tissue of the lesioned hemisphere, and their homolog regions in the unaffected hemisphere. CVR and lag maps were correlated. Group, condition, and time effects were assessed using ANOVA analyses.

Results:

Compared with the average gray matter signal, a relative hemodynamic lead was observed in the primary sensorimotor cortices in resting-state and bilateral inferior parietal cortices in the breath-holding condition. Whole-brain hemodynamic lag was significantly correlated across conditions irrespective of group, with regional differences across conditions suggestive of a neural network pattern. Patients showed relative lag in the lesioned hemisphere which significantly reduced over time. Breath-hold derived lag and CVR had no significant voxel-wise correlation in controls, or patients within the lesioned hemisphere or the homologous regions of the lesion and perilesional tissue in the right hemisphere (mean
r
<0.1).

Conclusions:

The contribution of altered CVR to HRF lag was negligible. We suggest that HRF lag is largely independent of CVR, and could partly reflect intrinsic neural network dynamics among other factors.
Distinct Roles of Cyclones and Anticyclones in Setting the Midwinter Minimum of the North Pacific Eddy Activity: A Lagrangian Perspective
Authors: Okajima Satoru , Nakamura Hisashi , Kaspi Yohai
Published: March 2023

The North Pacific storm-track activity is suppressed substantially under the excessively strong westerlies to form a distinct minimum in midwinter, which seems inconsistent with linear baroclinic instability theory. This “midwinter minimum” of the storm-track activity has been intensively investigated for decades as a test case for storm-track dynamics. However, the mechanisms controlling it are yet to be fully unveiled and are still under debate. Here we investigate the detailed seasonal evolution of the climatological density of surface migratory anticyclones over the North Pacific, in comparison with its counterpart for cyclones, based on a Lagrangian tracking algorithm. We demonstrate that the frequency of surface cyclones over the North Pacific maximizes in midwinter, whereas that of anticyclones exhibits a distinct midwinter minimum under the upstream influence, especially from the Japan Sea region. In midwinter, it is only on such a rare occasion that prominent weakening of the East Asian winter monsoon allows a migratory surface anticyclone to form over the Japan Sea, despite the unfavorable climatological-mean conditions due to persistent monsoonal cold-air outbreaks and the excessively strong upper-tropospheric westerlies. The midwinter minimum of the North Pacific anticyclone density suggests that anticyclones are likely the key to understanding the midwinter minimum of the North Pacific storm-track activity as measured by Eulerian eddy statistics.
Laparoscopic Graham Patch for Anterior Duodenal Perforation in a 6-Year-Old
Authors: McCullers Michelle R. , Shin Caroline C. , Anderson Christopher M.
Published: March 2023

Peptic ulcer disease causing perforation is extremely rare in children and primarily affects teenagers. We present a case of a perforated peptic ulcer in a 6-year-old with abdominal pain and emesis with CT findings of moderate pneumoperitoneum and pelvic free fluid without a distinct cause. He was emergently transferred, found to be peritonitic, and taken to the operating room for diagnostic laparoscopy revealing an anterior duodenal ulcer, and underwent laparoscopic Graham patch repair. Postoperatively, the child had positive fecal antigen for H. pylori. He was treated with triple therapy and underwent subsequent testing to confirm eradication. Perforated peptic ulcer is an uncommon pediatric surgical problem, and imaging may not be diagnostic as in the case presented here. Thus, clinicians need to maintain a high index of suspicion when evaluating children with free air and a surgical abdomen in the setting of long-standing abdominal pain.
“Let’s Talk About Stealing Sh*t”: Online Socialization and Its Potential to Influence Offline Offending
Authors: Brown Samantha J. , Reid Jonathan C. , Myers Wesley
Published: March 2023

Social media use has significantly increased in recent years, yet relatively few studies have applied criminological theory to understand how online spaces may influence offline criminal behavior. This study uses content analysis techniques to explore how social media users are exposed to socialization processes on an online forum that promotes shoplifting. Findings indicate that concepts derived from the social learning perspective—such as motivations for shoplifting, techniques for participating in this behavior, and reinforcement for offending—were manifested and spread in this online community. These results suggest that social media users can be exposed to key elements of social learning processes that could impact their offline behavior. The implications of these findings for research and policy are considered.
Trade union strength, business power, and labor policy reform: The cases of Argentina and Chile in comparative perspective
Authors: Pérez Ahumada Pablo
Published: March 2023

In this article, I explain why pro-labor reforms succeed or fail. Focusing on the cases of Argentina and Chile, I show that labor reforms are more successful in extending trade union rights when unions successfully build associational power and employers are less able to do so. Consistent with this argument, a quantitative analysis of time-series cross-sectional data from 78 countries suggests that the level of class power disparity is negatively correlated with the extension of workers’ collective rights. At the end of the article, I discuss how these results have implications for the study of labor reforms and power resources.
The Association Between Resiliency Factors and Suicide Indicators Among American Indian/Alaska Native Middle School Students in New Mexico: A Strength-Based Analysis
Authors: Parshall Carolyn , Qeadan Fares , Espinoza Judith , English Kevin
Published: March 2023

Objective:

Suicide is a leading cause of death among American Indian/Alaska Native (AI/AN) young people aged 10-19 years in the United States, but data collection and reporting in this population are lacking. We examined results of an oversample project in New Mexico to determine the association between resiliency factors and suicide-related behaviors among AI/AN middle school students.

Methods:

We conducted analyses using data from the 2019 New Mexico Youth Risk and Resiliency Survey for students in grades 6 through 8. An oversampling method was used to increase the sample size of AI/AN students. We used logistic regression to determine the association between resiliency factors and suicide indicators among AI/AN students, stratified by sex.

Results:

Among female AI/AN students, community support had the strongest protective effect against having seriously thought about suicide (adjusted odds ratio [aOR] = 0.23; 95% CI, 0.14-0.38), while family support was significantly associated with the lowest odds of having made a suicide plan (aOR = 0.15; 95% CI, 0.08-0.28) and having attempted suicide (aOR = 0.21; 95% CI, 0.13-0.34) ( P < .001 for all). Among male AI/AN students, school support had the strongest protective effect against all 3 outcomes: seriously thought about suicide (aOR = 0.34; 95% CI, 0.19-0.62; P < .001), having made a suicide plan (aOR = 0.19; 95% CI, 0.09-0.39; P < .001), and having attempted suicide (aOR = 0.27; 95% CI, 0.12-0.65; P = .003).

Conclusions:

Oversampling AI/AN young people can help accurately quantify and understand health risk behaviors and strengths of this population, leading to improved health and wellness. Family, community, and school-based support should be considered in interventions geared toward suicide prevention among AI/AN young people.
Factors Affecting SARS-CoV-2 Vaccination Intent and Decision Making Among African American, Native American, and Hispanic Participants in a Qualitative Study
Authors: Schwartz Benjamin , Brewer Janesse , Budigan Haley , Bernier Roger , Dudley Matthew Z. , Kan Lilly , Proveaux Tina M. , Roberts Robin , Tafoya Nadine , Hamlin Mary Davis , Moore Lucy , Hughes Michael , Turner Brian , Al-Dahir Sara , Velasco Elizabeth , Privor-Dumm Lois , Veloz Wendie , White Jennifer A. , Dubois Shantel , Ooton Jenn , Kipp Billie Jo , Show Ty J. , Salu Kemi , Chavez Brisa , Montes Maria Del Pilar , Najera Rene , King Terris , Salmon Daniel A.
Published: March 2023

Limited studies are available on how decisions and perceptions on SARS-CoV-2 vaccination have changed since the start of vaccination availability. We performed a qualitative study to identify factors critical to SARS-CoV-2 vaccination decision making and how perspectives evolved among African American/Black, Native American, and Hispanic communities disproportionately affected by COVID-19 and social and economic disadvantage. We conducted 16 virtual meetings, with 232 participants in wave 1 meetings (December 2020) and with 206 returning participants in wave 2 meetings (January and February 2021). Wave 1 vaccine concerns in all communities included information needs, vaccine safety, and speed of vaccine development. Lack of trust in government and the pharmaceutical industry was influential, particularly among African American/Black and Native American participants. Participants showed more willingness to get vaccinated at wave 2 than at wave 1, indicating that many of their information needs had been addressed. Hesitancy remained greater among African American/Black and Native American participants than among Hispanic participants. Participants in all groups indicated that conversations tailored to their community and with those most trustworthy to them would be helpful. To overcome vaccine hesitancy, we propose a model of fully considered SARS-CoV-2 vaccine decision making, whereby public health departments supply information, align with community values and recognize lived experiences, offer support for decision making, and make vaccination easy and convenient.
Green supply chain transformation and emission reduction based on machine learning
Authors: Wu Tao , Zuo Minxin
Published: March 2023

Artificial intelligence techniques provide more possibilities for supply chain transformations in the face of global warming and environmental degradation. This study examines the Cournot game model of two competing supply chains with various carbon emission technologies as well as the possibility of upgrading machine learning technology. The investment risk of a supply chain's technology upgrade is either symmetric or asymmetric information. In the case of symmetric information, results show that the machine learning technology upgrade risk does not affect the market equilibrium outcomes of the duopoly model. However, in the case of asymmetric information, technology upgrade risk is vital in determining the quantities and prices of competition equilibrium. To achieve the goal of green supply chain transformation, the government should provide more technology and financial support to traditional supply chains to upgrade their machine learning technology on carbon emissions.
Shakedown of a plate with a circular hole: An educational problem
Authors: Chen Yifeng , Hills David Anthony
Published: March 2023

The shakedown limit for an infinite plate containing a circular hole and subject to oscillatory arbitrary remote loading is found, first by using Melan’s lower bound theorem, and secondly by using a finite element model. It is shown that in some cases the limit found from the Melan theorem using the solution for the residual state of stress for an over-pressurized hole provides the exact solution, specifically when the limiting factor is the reversal of the state of stress at a particular point, while in other cases the shakedown limit is rather higher.
Who Does Light Rail Serve? Examining Gendered Mobilities and Light-Rail Transit in Montreal, Canada
Authors: Villafuerte-Diaz Julian , Victoriano-Habit Rodrigo , Soliz Aryana , El-Geneidy Ahmed
Published: March 2023

Investment in light-rail transit (LRT) has been one of the main strategies of large metropolitan areas in the last decade to tackle environmental, economic, and social issues. In Montreal, Canada, a C$7 billion LRT system is currently under construction and is expected to significantly affect mobility patterns across the metropolitan region. It is crucial to identify how the impacts of such large public investments vary across societal groups to assess whether the distribution of benefits is fair and equitable. Using data from an online survey and a binary logistic modeling approach, we investigated the ways in which intentions to use this new LRT system differ across gender identities. First, we found that women are less likely than men to have an intention to use LRT. Our modeling results show that there are statistically significant differences across gender identities in the effect of certain sociodemographic and travel-behavior characteristics that explain the intention to use the LRT system. In respect of trip purpose, while women and men intend to use LRT for work trips to the same extent, men intend to use LRT for leisure and discretionary travel more than women. Our findings can help in guiding further research into gender gaps in transport studies and inform practitioners on how gender can be considered in LRT policy decisions so that the benefits of major public-transit investments are more equitably distributed.
Predicting Choice of Filtering of Motorized Two Wheelers in Urban Mixed Traffic
Authors: Damani Jaikishan , Vedagiri Perumal
Published: March 2023

Moving through lateral gaps defined by slower vehicles, or “filtering,” is a common characteristic of motorized two wheelers (MTWs) in mixed traffic conditions. Despite its potential benefits such as reduced journey time and emissions, filtering is a critical maneuver, owing to the lesser conspicuity of MTWs and their almost non-existent physical protection from crashes. An in-depth knowledge of filtering behavior is necessary for various theoretical and practical applications. In an attempt to model this distinct driving characteristic of MTWs, this study investigates the filtering interactions of MTWs in urban mixed traffic conditions. Specifically, the behavioral differences between following and filtering interactions were investigated while considering the conditions found in heterogeneous traffic. The choice of filtering was modeled based on multiple factors including spatial parameters and classification parameters. This paper employs a utility-based binary logit model (BLM) and two machine learning (ML) based models: random forest (RF); and adaptive neuro fuzzy inference system (ANFIS) to predict the filtering choice of MTWs in urban mixed traffic. A comparative analysis revealed that, generally if not always, the ML based models (prediction accuracy of RF and ANFIS was 90.07% and 96.02%, respectively) performed better than utility-based models (prediction accuracy of BLM was 80.05%). Owing to the better performance measures of the ANFIS technique, it can be considered a powerful tool for predicting following and filtering choices of MTWs, useful for policy makers designing intelligent transportation systems and microsimulation models.
Unpromising Futures: Early-Career GPs’ Narrative Accounts of Meaningful Work during a Professional Workforce Crisis
Authors: Laverty Louise , Checkland Katherine , Spooner Sharon
Published: March 2023

Over the past few decades, the intensification and reorganisation of work have led to growing precarity, insecurity and uncertainty for employees, affecting even professionals tied to traditionally model employers. Doctors, in particular, have seen substantial changes to their work: having to work harder, longer and more intensely with reductions in expected autonomy, deference and respect. This article focuses on how early-career GPs make sense of and navigate meaningful work in the context of a current workforce crisis. Drawing on 15 narrative interviews and 10 focus groups with early-career GPs, the findings show that meaningful work during a crisis is understood temporally, with imagined futures perceived as increasingly impossible due to changes to the structure and orientation of medical work, leading to different career plans. Utilising Adam and Groves’ approach to futures as a conceptual lens, the article focuses on how multiple, often clashing, future orientations impact meaningful work.
Comparison of anterior cervical discectomy and fusion versus anterior cervical corpectomy and fusion in the treatment of localized ossification of the posterior longitudinal ligament
Authors: Chen Tangyiheng , Wang Yujie , Zhou Hong , Lin Cheng , Li Xuefeng , Yang Huilin , Liu Yijie , Jiang Weimin
Published: March 2023

Background

The retrospective study was conducted to compare the efficacy of anterior cervical discectomy and fusion (ACDF) and anterior cervical corpectomy and fusion (ACCF) for localized ossification of the posterior longitudinal ligament (OPLL) by evaluating clinical and radiologic outcomes.

Methods

We reviewed 151 patients to assess the effects of treatment for one or two levels localized OPLL. Perioperative parameters, such as blood loss, operation time and complications, were recorded. Radiologic outcomes, such as the occupying ratio (OR), fusion status, cervical lordosis angle, segmental angle, disc space height, T1 slope, and C2–C7 sagittal vertical axis (SVA), were assessed. Clinical indices, such as the JOA scores and VAS scores, were investigated to compare the two surgical options.

Results

There were no significant differences in the JOA scores or VAS scores between the two groups ( p > 0.05). The operation time, volume of blood loss and incidence of dysphagia were significantly less in the ACDF group than in the ACCF group ( p < 0.05). In addition, cervical lordosis, segmental angle and disc space height were significantly different from their preoperative evaluations. No adjacent segment degenerated in the ACDF group. The subsidence rates of implants were 5.2% in the ACDF group and 28.4% in the ACCF group. The degeneration of the ACCF group was 4.1%. The incidence of CSF leaks was 7.8% in the ACDF group and 13.5% in the ACCF group. All the patients ultimately achieved successful fusion.

Conclusion

Although both options achieved satisfactory primary clinical and radiographic efficacies, ACDF was associated with a shorter surgical procedure, less intraoperative blood loss, better radiologic outcomes, and lower incidence of dysphagia than ACCF.
Qualitative Findings From a Survey on Patient Experiences and Satisfaction with Lung Cancer Screening
Authors: Pérez-Morales Jaileene , Pathak Rashmi , Reyes Monica , Tolbert Haley , Tirbene Rajwantee , Gray Jhanelle E. , Simmons Vani N. , Schabath Matthew B. , Quinn Gwendolyn P.
Published: March 2023

Background

To reveal successes and potential limitations of the lung cancer screening program, we conducted a survey that included both quantitative and open-ended questions to measure patient experiences and satisfaction with screening.

Methods

We report on the five open-ended items related to barriers to returning for screening, experience with other cancer prevention screenings, positive and negative experiences, and suggestions for improving future appointments. The open-ended responses were analyzed using constant comparison method and inductive content analysis.

Results

Respondents (182 patients, 86% response rate for open-ended questions) provided generally positive comments about their lung cancer screening experience. Negative comments were related to desire for more information about results, long wait times for results, and billing issues. Suggestions for improvements included: scheduling on-line appointments and text or email reminders, lower costs, and responding to uncertainty about eligibility criteria.

Conclusion

Findings provide insights about patient experiences and satisfaction with lung cancer screening which is important given low uptake. Ongoing patient-centered feedback may improve the lung cancer screening experience and increase follow-up screening rates.
A Longitudinal Study of Thrombosis and Bleeding Outcomes With Thromboprophylaxis in Pregnant Women at Intermediate and High Risk of VTE
Authors: Schapkaitz E. , Libhaber E. , Gerber A. , Rhemtula H. , Zamparini J. , Jacobson B.F , Büller H.R
Published: March 2023

Background

The efficacy and safety of thromboprophylaxis in pregnancy at intermediate to high risk of venous thrombo-embolism (VTE) is an area of ongoing research.

Aim

This study aimed to assess thrombosis and bleeding outcomes associated with thromboprophylaxis in women at risk of VTE.

Methods

A cohort of 129 pregnancies, who received thromboprophylaxis for the prevention of VTE, were identified from a specialist obstetric clinic in Johannesburg, South Africa. Intermediate-risk pregnancies, with medical comorbidities or multiple low risks, were managed with fixed low-dose enoxaparin antepartum and for a median (interquartile range) of 4 (4) weeks postpartum. High-risk pregnancies, with a history of previous VTE, were managed with anti-Xa adjusted enoxaparin antepartum and for a median of 6 (0) weeks postpartum. Pregnancy-related VTE was objectively confirmed. Major bleeding, clinically relevant nonmajor bleeding (CRNMB) and minor bleeding were defined according to the International Society on Thrombosis and Hemostasis Scientific Subcommittee.

Results

Venous thrombo-embolism occurred antepartum in 1.4% (95% CI: 0.04-7.7) of intermediate and 3.4% (95% CI: 0.4-11.7) of high-risk pregnancies. Bleeding events occurred in 7.1% (95% CI: 2.4-15.9) of intermediate and 8.5% (95% CI: 2.8-18.7) of high-risk pregnancies. Of these bleeding events, 3.1% (95% CI: 1.0-8.0) were classified as major bleeding. On univariate analysis, no independent predictors of bleeding were identified.

Conclusion

The rates of thrombosis and bleeding in this predominantly African population were consistent with similar studies and can be used to inform pregnant women of the benefits of anticoagulation and the risks of potential bleeding.
Perioperative Outcomes of Patients with Bleeding Disorders Undergoing Major Surgery at an Academic Hemophilia Treatment Center
Authors: Rhoades Ruben , French Zachary , Yang Amy , Walsh Karen , Drelich Douglass A. , McKenzie Steven E.
Published: March 2023

Persons with bleeding disorders (PwBD) are at high risk for bleeding with invasive procedures. However, the risk of bleeding in PwBD undergoing major surgery and outcomes of patients managed perioperatively at a hemophilia treatment center (HTC) are not well described. We performed a retrospective review of surgical outcomes among PwBD undergoing major surgery between January 1st, 2017 and December 31st, 2019 at the Cardeza Foundation Hemophilia and Thrombosis Center in Philadelphia, PA. The primary outcome was postoperative bleeding, assessed according to the ISTH-SSC's 2010 definition. Secondary outcomes included use of unplanned postoperative hemostatic therapy, LOS, and 30-day readmission rate. Results were compared to non-PwBD population from a surgical database, matched for surgery, age, and sex. During the study period, 50 PwBD underwent 63 major surgeries. The most common diagnoses were VWD (64%) and hemophilia A (20.0%). The most common surgical procedure category was orthopedic (33.3%), predominantly arthroplasties. Postoperatively,4.8% of procedures were complicated by major bleeding and 1.6% by non-major bleeding. The mean LOS was 1.65 days, and 30-day readmission rate was 1.6%. In comparison to matched, non-PwBD patients in a national surgical database undergoing the same procedures, study patients had a similar rate of bleeding complications per procedure (5.0% vs 1.04% P = .071, Fisher's exact test). PwBD undergoing major surgeries have low rates of major bleeding when receiving comprehensive care at an HTC. Bleeding and hospital readmission rates were similar to non-PwBD baseline in a large database.