Parallel Approach Research Articles

KMPR-AEP: Knowledge-Enhanced Multi-task Parallelized Recommendation Algorithm Incorporating Attention-Embedded Propagation

Existing knowledge graph-based recommendation algorithms either use Knowledge Graphs (KGs) as auxiliary information or use KG prediction tasks as regular terms to constrain recommendation with multi-task learning. However, the first method, which introduces multi-hop neighbors to enhance item representations, also weakens the relationship information within individual triples to some extent. The second method ignores neighboring information and thus fails to capture the long-range connectivity between items, which leads to a lack of utilization of the structural information in the KG. In response to the limitations of existing recommendation algorithms that do not fully leverage the relational and structural information within KGs, we propose a novel method named Knowledge-Enhanced Multi-Task Parallelized Recommendation Algorithm Incorporating Attention-Embedded Propagation (KMPR-AEP). It employs a parallel approach to simultaneously incorporate KG as auxiliary information and a regular term. We take into account the multi-hop neighbor information of users and items as well as enhance the relations among individual triples, which considers both the structural and relational information of KG. Extensive experiments conducted on three real-world datasets for CTR prediction and Top-K recommendation scenarios demonstrate the superiority of our proposed method over the state-of-the-art. On average, our KMPR-AEP shows improvements of 2.44% in AUC, 3.49% in ACC, and 2.64% in F1.

Fabrication of 60μm-Thick Free-Standing Bilayer Solid Electrolytes for Solid-State Batteries Using Solution Processing and Lamination

Development of solid-state batteries with a high voltage cathode and lithium metal anode could enable high energy density while avoiding safety issues of flammable liquid electrolytes. However, the solid electrolyte (SE) should meet multiple requirements, including 1) electrochemical stability at both the cathode and anode side interfaces, 2) small thickness (tens of μm) to maximize energy density of the cell, and 3) fabrication with a scalable manufacturing method. Using bilayer SEs with different electrochemical stability windows could enable stability at both cathode|SE and anode|SE interfaces. In this work, we developed a manufacturing process to fabricate thin (60 μm) free-standing bilayer SE films from slurry casting and lamination, which can be used for large scale manufacturing of SSBs.Halide SE (Li3InCl6) and sulfide SE (Li6PS5Cl) were slurry casted on different substrates, and then were dried in vacuum before being laminated onto each other to make a free-standing bilayer film. The parallel slurry casting approach eliminates cross-compatibility requirements of the solvents used for slurry preparation. The resulting bilayer films after densification were pin-hole free without intermixing between the two phases. The ionic conductivity of the bilayer SE film was ~70% of the bilayer SE pellet (1 mm thickness in total) made with a conventional powder pressing method. However, because the thickness of bilayer SE film (60 μm) was much smaller than the bilayer SE pellet (1 mm), the bilayer SE film had ~10x lower area-specific resistance (ASR) compared to the bilayer SE pellet. Because of the lower total cell resistance, the cell made with the bilayer SE film had a better rate capability compared to the reference cell in pellet geometry.The strategy developed in this work could be integrated into current manufacturing infrastructure of battery fabrication as it is based on slurry casting, which is already a well-established fabrication method for lithium-ion batteries. Moreover, it could be used to fabricate multi-layered solid-state architectures in general, which are challenging to manufacture due to solvent compatibility issues during slurry casting. The study will assist the progress of solid-state battery manufacturing by providing a scalable manufacture method to prepare thin free-standing bilayer SEs.

Transfer and recovery of DNA and metal particles: A proof-of-concept application of a parallel strategy by DNA and environmental scanning electron microscopy analysis

According to the principle of Locard “Every contact leaves a trace", when touching a surface, a bi-directional transfer of self and non-self-DNA residing on the hands and touched objects can occur. Metals are commonly encountered in forensic evidence and, during hand contact with these surfaces, a transfer of metal particles could occur together with the transfer of human DNA. This study proposes a proof-concept approach for the original detection of metal particles and touch DNA to track the activity performed by a donor and particularly to assess the metallic substrate touched before the contact with a subsequent surface. To this scope, a scenario of contact events was simulated by three volunteers, who participated in fingerprint deposition firstly on copper and then on plastic and glass surfaces. Twenty-four stubs were collected on the hands of volunteers and the secondary surfaces and then analyzed by environmental scanning electron microscopy (ESEM). DNA was quantified only from copper and plastic surfaces. Ten additional volunteers followed the same protocol of deposition on copper and then on plastic surfaces to evaluate DNA transfer only. On 20 touch DNA samples, the copper surface yielded significantly lower DNA amounts, ranging from 0.001 to 0.129 ng/μl, compared to the secondary touched plastic surface, ranging from 0.007 to 0.362 ng/μl. ESEM-EDS analysis showed that copper particles could be abundantly detected on the hands of the volunteers after contact with the copper surface. Particles containing silicates with copper were shown on plastic, while they were only found in 1/3 of samples on glass. Our proof-of-concept study has shown that ESEM-EDS analysis has the potential to detect copper particles transferred to the hands of volunteers during contact with a copper metallic surface and deposited on secondarily touched items. The results suggest that this original ESEM-DNA parallel approach could potentially allow the tracking of DNA transfer and metal particles at a crime scene, although this represents only a first step and further research on a wider casuistry could help to address the interpretation of results given activity level propositions.

A Parallel Approach to Enhance the Performance of Supervised Machine Learning Realized in a Multicore Environment

Machine learning models play a critical role in applications such as image recognition, natural language processing, and medical diagnosis, where accuracy and efficiency are paramount. As datasets grow in complexity, so too do the computational demands of classification techniques. Previous research has achieved high accuracy but required significant computational time. This paper proposes a parallel architecture for Ensemble Machine Learning Models, harnessing multicore CPUs to expedite performance. The primary objective is to enhance machine learning efficiency without compromising accuracy through parallel computing. This study focuses on benchmark ensemble models including Random Forest, XGBoost, ADABoost, and K Nearest Neighbors. These models are applied to tasks such as wine quality classification and fraud detection in credit card transactions. The results demonstrate that, compared to single-core processing, machine learning tasks run 1.7 times and 3.8 times faster for small and large datasets on quad-core CPUs, respectively.

Optimization of such strategy as frequency modulation for grid-side energy storage control-terminal in response to frequency fluctuations in the new power system grid

Abstract This paper focuses on the refinement of the control strategy for grid-side energy storage systems as they engage in primary and secondary frequency modulation within an emerging smart grid. To attain an optimal outcome, traditional frequency modulation methods, which often require high-precision operations and extended adjustment periods, fall short of the stringent demands posed by modern power systems for swift and responsive frequency modulation. Under such circumstances, the grid-side energy storage control-terminal system presents a viable solution for managing energy storage, thereby providing rapid and precise assistance to the power system’s frequency modulation. By implementing a control mechanism that can adaptively switch between ARR signal-based and ACE signal-based frequency adjustments, a segmented parallel control approach ensures that the energy storage system can promptly and effectively modify the power within the grid system, facilitating swift frequency modulation.

Numerical solutions of KDV and mKDV equations: Using sequence and multi-core parallelization implementation

In this study, we use the explicit difference method and also the combination of the explicit difference method with the implicit method to numerically solve the third order of Korteweg–de Vries (KDV) and modified Korteweg–de Vries (mKDV) equations. This method is applied to obtain soliton solutions on several problems and the results obtained are compared with each other and the exact solution of the problem. Also, due to that these problems are very ill-posed, when the time increases, the waveform of solitons and the input parameters of these methods are carefully examined. The L∞, L2 and mean-square errors of the solutions show that these methods can be applied to different problems and have very good accuracy. These methods are widely used in combination with other algorithms, but their very high execution time, especially in this category of problems, is always a big limitation. In this paper, a parallel approach for implementing these methods is presented and very good results have been obtained compared to sequential implementation. This is a reference to further study solutions of other models of KDV and mKDV problems as well as solving their inverse problems.

Parallel indirect time-of-flight ranging using on-chip dual-frequency combs

The significant advancements in autonomous vehicle applications demand detection solutions capable of swiftly recognizing and classifying objects amidst rapidly changing and low-visibility conditions. Light detection and ranging (LiDAR) has emerged as a robust solution, overcoming challenges associated with camera imaging, particularly in adverse weather conditions or low illumination. Rapid object recognition is crucial in dynamic environments, but the speed of conventional LiDARs is often constrained by the 2D scanning of the laser beam across the entire scene. In this study, we introduce a parallelization approach for the indirect time-of-flight (iToF) ranging technique. This method enables efficient and high-speed formation of 1D clouds, offering the potential to have extended range capabilities without being constrained by the laser coherence length. The application potential spans mid-range autonomous vehicles ranging to high-resolution imaging. It utilizes dual-frequency combs with slightly different repetition rates. The method leverages the topology of the target object to influence the phase of the beating signal between the comb lines in the RF domain. This approach enables parallel ranging in one direction, confining the scanning process to a single dimension, and offers the potential for high-speed LiDAR systems. A tri-comb approach will be discussed that can provide an extended unambiguous range without compromising the resolution due to the range–resolution trade-off in iToF techniques. The study starts by explaining the technique for parallel detection of distance and velocity. It then presents a theoretical estimation of phase noise for dual combs, followed by an analysis of distance and velocity detection limits, illustrating their maximum and minimum extents. Finally, a study on the mutual interference conditions between two similar LiDAR systems is presented, demonstrating the feasibility of designing simultaneously operating LiDARs to avoid mutual interference.

A parallel machine learning-based approach for tsunami waves forecasting using regression trees

Following a seismic event, tsunami early warning systems (TEWSs) try to provide precise forecasts of the maximum height of incoming waves at designated target points along the coast. This information is crucial to trigger early warnings in areas where the impact of tsunami waves is predicted to be dangerous (or potentially cause destruction), to help the management of the potential impact of a tsunami as well as reduce environmental destruction and losses of human lives. For such a reason, it is crucial that TEWSs produce predictions with short computation time while maintaining a high prediction accuracy. This paper presents a parallel machine learning approach, based on regression trees, to discover tsunami predictive models from simulation data. In order to achieve the results in a short time, the proposed approach relies on the parallelization of the most time consuming tasks and on incremental learning executions, in order to achieve higher performances in terms of execution time, efficiency and scalability. The experimental evaluation, performed on two real tsunami cases occurred in the Western and Eastern Mediterranean basin in 2003 and 2017, shows reasonable advantages in terms of scalability and execution time, which is an important benefit in a urgent-computing scenarios.

Query-Based Object Visual Tracking with Parallel Sequence Generation.

Query decoders have been shown to achieve good performance in object detection. However, they suffer from insufficient object tracking performance. Sequence-to-sequence learning in this context has recently been explored, with the idea of describing a target as a sequence of discrete tokens. In this study, we experimentally determine that, with appropriate representation, a parallel approach for predicting a target coordinate sequence with a query decoder can achieve good performance and speed. We propose a concise query-based tracking framework for predicting a target coordinate sequence in a parallel manner, named QPSTrack. A set of queries are designed to be responsible for different coordinates of the tracked target. All the queries jointly represent a target rather than a traditional one-to-one matching pattern between the query and target. Moreover, we adopt an adaptive decoding scheme including a one-layer adaptive decoder and learnable adaptive inputs for the decoder. This decoding scheme assists the queries in decoding the template-guided search features better. Furthermore, we explore the use of the plain ViT-Base, ViT-Large, and lightweight hierarchical LeViT architectures as the encoder backbone, providing a family of three variants in total. All the trackers are found to obtain a good trade-off between speed and performance; for instance, our tracker QPSTrack-B256 with the ViT-Base encoder achieves a 69.1% AUC on the LaSOT benchmark at 104.8 FPS.

The thermal conductivity of graphite composite insulation boards: A theoretical and experimental study

Graphite composite insulation boards (GCIB) have emerged as a promising solution in the construction industry, offering a combination of fire resistance, high temperature stability, and excellent insulation properties. As their usage continues to increase, it is crucial to develop and validate a more accurate theoretical model for the effective design and optimization of building insulation systems. Traditional models including series and parallel models are employed for estimating the thermal conductivity of GCIB but revealed higher relative errors and lower theoretical calculation accuracy. Therefore, the primary objective is to design and validate a more efficient theoretical model for thermal conductivity prediction in GCIB. This paper investigates the thermal conductivity of GCIB under varying composition ratios by designing a novel Parallel-Series Parallel (PSP) approach. The PSP model is designed based on a single basic cell where the graphite polystyrene particles are employed as the matrix material while cement, vitrified microspheres, and silica fume are used as inclusion materials. Then, the thermal conductivity unit is divided into three parallel heat conduction subunits where the matrix materials and inclusion materials in the three subunits are integrated in a series-parallel manner to provide a more realistic representation of the heat transfer mechanisms within the composite. For a comprehensive assessment, the study encompasses theoretical analysis, empirical assessment, and finite element (FE) simulations to illustrate its thermal properties. The predicted thermal conductivity reveals perfect consensus in comparison with the FE and experimental test outcomes by achieving lower relative errors of 2.7 %∼3.8 % and 1.9 %∼ 3.0 % respectively. The model validated through numerical simulations and sample experiments illustrates a significant improvement in accuracy of about 76.4 %∼94.3 % when compared to traditional series and parallel methods. Prominently, the findings indicate that the thermal conductivity of GCIBs declines considerably as the volumetric ratio of graphite polystyrene particles (Ф1/Ф3) increases and stabilizes when the proportion reaches 10, emphasizing the importance of optimizing material composition to enhance the thermal performance of GCIB. Overall, the validated PSP model by accurately determining the thermal conductivity of GCIB serves as a reliable tool for designing and optimizing high-performance insulation materials, contributing to energy efficiency and sustainability in building and construction industries.

Substantial parallel mediation contribution by cognitive domains in the relationship between adolescents' physical fitness and academic achievements: the Cogni-Action Project.

To determine how cognitive domains mediate the link between fitness components, their global score (GFS), and adolescents' academic achievement (ACA) across various school subjects. In this study, 1,296 adolescents aged 10-14 participated. GFS was computed by three fitness components (strength, muscular, and cardiorespiratory fitness) through the ALPHA-fitness test battery. ACA was determined by five school subjects (Language, English, Mathematics, Science, and History) and two academic scores (a) "Academic Average" (five subjects) and (b) "Academic-PISA" (Language, Mathematics, and Science). A principal component analysis was performed to establish four factors (working memory [WM], cognitive flexibility [CF], inhibitory control [IC], and fluid reasoning [FR]). A parallel mediation approach was implemented with 5,000 bootstrapped samples controlled for sex, maturity, central obesity, having breakfast before cognitive tasks, schools, and school vulnerability. Total, direct, indirect effects, and mediation percentages were estimated. Overall, the finding showed a full parallel mediation effect for Language (92.5%) and English (53.9%), while a partial mediation for Mathematics (43.0%), Science (43.8%), History (45.9%), "Academic Average" (50.6%), and "Academic-PISA" (51.5%). In particular, WM, IC, and FR mediated all school subjects except mathematics, where IC was not significant. CF has not mediated any relationship between GF and academic performance. This study underscores the pivotal role of cognitive domains, specifically WM, IC, and FR, in mediating the link between physical fitness and academic performance in adolescents. These insights have relevant implications for educational and public health policies.

Implementation of social needs screening for minoritized patients newly diagnosed with breast cancer: a mixed methods evaluation in a pragmatic patient navigation trial

BackgroundSocial needs inhibit receipt of timely medical care. Social needs screening is a vital part of comprehensive cancer care, and patient navigators are well-positioned to screen for and address social needs. This mixed methods project describes social needs screening implementation in a prospective pragmatic patient navigation intervention trial for minoritized women newly diagnosed with breast cancer.MethodsTranslating Research Into Practice (TRIP) was conducted at five cancer care sites in Boston, MA from 2018 to 2022. The patient navigation intervention protocol included completion of a social needs screening survey covering 9 domains (e.g., food, transportation) within 90 days of intake. We estimated the proportion of patients who received a social needs screening within 90 days of navigation intake. A multivariable log binomial regression model estimated the adjusted rate ratios (aRR) and 95% confidence intervals (CI) of patient socio-demographic characteristics and screening delivery. Key informant interviews with navigators (n = 8) and patients (n = 21) assessed screening acceptability and factors that facilitate and impede implementation. Using a convergent, parallel mixed methods approach, findings from each data source were integrated to interpret study results.ResultsPatients’ (n = 588) mean age was 59 (SD = 13); 45% were non-Hispanic Black and 27% were Hispanic. Sixty-nine percent of patients in the navigators’ caseloads received social needs screening. Patients of non-Hispanic Black race/ethnicity (aRR = 1.25; 95% CI = 1.06–1.48) and those with Medicare insurance (aRR = 1.13; 95% CI = 1.04–1.23) were more likely to be screened. Screening was universally acceptable to navigators and generally acceptable to patients. Systems-based supports for improving implementation were identified.ConclusionsSocial needs screening was acceptable, yet with modest implementation. Continued systems-based efforts to integrate social needs screening in medical care are needed.

Bio-based connections and hybrid planar truss: A parallel genetic algorithm approach for model updating

Bolted steel to laminated bio-based material connections experience significant performance challenges due to the nonlinear response and high stress concentrations at their joints. This paper introduces an innovative 3D plasticity-fracture continuum Finite Element (FE) model that significantly advances the simulation of such truss joints by integrating Hill's yielding criteria with an element removal methodology for fracture simulation. This novel approach captures both plastic and fracture behaviors simultaneously, a capability not sufficiently addressed in existing models. We detail the theoretical framework for these models, including the derivation of constitutive equations and the algorithms necessary for their implementation in ABAQUS. Additionally, it is provided a low-fidelity modeling of truss joints, offering a comprehensive analysis of connector elements, joint models, and parametric modeling via Python scripting. The model's efficacy is demonstrated through identification of connection and of hybrid planar trusses under cyclic loading, which validates the practical applicability of the method. To optimize computational efficiency, we developed a Parallel Genetic Algorithm (PGA) that integrates seamlessly with ABAQUS and Python to facilitate parameter calibration. This integration not only enhances the model's accuracy but also reduces computational load, making it feasible for complex engineering applications. Our findings illustrate a significant improvement in modeling accuracy and efficiency, establishing a robust methodology for analyzing truss joints in bio-based construction materials.

Toward Graph Self-Supervised Learning With Contrastive Adjusted Zooming.

Graph representation learning (GRL) is critical for graph-structured data analysis. However, most of the existing graph neural networks (GNNs) heavily rely on labeling information, which is normally expensive to obtain in the real world. Although some existing works aim to effectively learn graph representations in an unsupervised manner, they suffer from certain limitations, such as the heavy reliance on monotone contrastiveness and limited scalability. To overcome the aforementioned problems, in light of the recent advancements in graph contrastive learning, we introduce a novel self-supervised GRL algorithm via graph contrastive adjusted zooming, namely, G-Zoom, to learn node representations by leveraging the proposed adjusted zooming scheme. Specifically, this mechanism enables G-Zoom to explore and extract self-supervision signals from a graph from multiple scales: micro (i.e., node level), meso (i.e., neighborhood level), and macro (i.e., subgraph level). First, we generate two augmented views of the input graph via two different graph augmentations. Then, we establish three different contrastiveness on the above three scales progressively, from node, neighboring, to subgraph level, where we maximize the agreement between graph representations across scales. While we can extract valuable clues from a given graph on the micro and macro perspectives, the neighboring-level contrastiveness offers G-Zoom the capability of a customizable option based on our adjusted zooming scheme to manually choose an optimal viewpoint that lies between the micro and macro perspectives to better understand the graph data. In addition, to make our model scalable to large graphs, we use a parallel graph diffusion approach to decouple model training from the graph size. We have conducted extensive experiments on real-world datasets, and the results demonstrate that our proposed model outperforms the state-of-the-art methods consistently.

Energy price prediction based on decomposed price dynamics: A parallel neural network approach

The growing uncertainties of the world due to geographic tensions, and weather conditions challenge the traditional method to achieve a reliable price prediction of energy future for both asset pricing and risk management. Following the initial success of deep learning models in energy price prediction, we attempt to establish a better architecture of neural networks to improve the prediction accuracy. We propose a novel Parallel Hybrid Neural Network (PHNN) model that utilizes independent sub-networks to effectively capture the distinct features of various sequences. Empirical results demonstrate that the PHNN model exhibits a significant performance enhancement of 16.68%, 14.09%, and 2.34% over the EMD–LSTM, the informer model, and the single LSTM model, respectively. In particular, the PHNN outperforms the single LSTM, which is trained on the same inputs, by 2.34% overall while by a remarkable 4.11% during event periods. This suggests that the PHNN derives notable benefits from its distinct architecture, particularly during the initial phase of extreme events characterized by significant price trend changes. Additionally, the study explores the potential benefits of incorporating additional event-tracking indicators for energy futures price forecasting. However, the findings suggest that these indicators may not consistently provide effective complementary information.

A Secure Parallel Pattern Mining System for Medical Internet of Things.

In this paper, a new generic parallel pattern mining framework called multi-objective Decomposition for Parallel Pattern-Mining (MD-PPM) is developed to solve challenges in the Internet of Medical Things through big data exploration. MD-PPM discovers important patterns by using decomposition and parallel mining methods to explore connectivity between medical data. First, a new technique, the multi-objective k-means algorithm, is used to aggregate medical data. A parallel pattern mining approach based on GPU and MapReduce architectures is also used to create useful patterns. To ensure complete privacy and security of the medical data, blockchain technology has been integrated throughout the system. Several tests were conducted to demonstrate the high performance of two sequential and graph pattern mining problems on large medical data and to evaluate the developed MD-PPM framework. From our results, our proposed MD-PPM has achieved strong results in terms of memory usage and computation time in terms of efficiency. Moreover, MD-PPM performs well in terms of accuracy and feasibility compared to existing models.

Agriculture data analysis using parallel k-nearest neighbour classification algorithm

A cost-effective and effective agriculture management system is created by utilizing data analytics (DA), internet of things (IoT), and cloud computing (CC). Geographic information system (GIS) technology and remote sensing predictions give users and stakeholders access to a variety of sensory data, including rainfall patterns and weather-related information (such as pressure, humidity, and temperatures). They have unstructured format for sensory data. The current systems do a poor job of analysing such data since they cannot effectively balance speed and memory usage. An effective categorization model (ECM) on agriculture management system is proposed to address this research difficulty. First, a classification technique called priority-based k-nearest neighbour (KNN) is provided to categorize unstructured multi-dimensional data into a structured form. Additionally, the Hadoop MapReduce (HMR) framework is used to do classification utilizing a parallel approach. Data from real-time IoT sensors used in agriculture is the subject of experiments. The suggested approach significantly outperforms previous approaches that are computing time, memory efficiency, model accuracy, and speedup.

Central role of the ramAR locus in the multidrug resistance in ESBL-Enterobacterales.

The aim of this study was to evaluate the proportion of resistance to a temocillin, tigecycline, ciprofloxacin, and chloramphenicol phenotype called t2c2 that resulted from mutations within the ramAR locus among extended-spectrum β-lactamases-Enterobacterales (ESBL-E) isolated in three intensive care units for 3 years in a French university hospital. Two parallel approaches were performed on all 443 ESBL-E included: (i) the minimal inhibitory concentrations of temocillin, tigecycline, ciprofloxacin, and chloramphenicol were determined and (ii) the genomes obtained from the Illumina sequencing platform were analyzed to determine multilocus sequence types, resistomes, and diversity of several tetR-associated genes including ramAR operon. Among the 443 ESBL-E strains included, isolates of Escherichia coli (n = 194), Klebsiella pneumoniae (n = 122), and Enterobacter cloacae complex (Ecc) (n = 127) were found. Thirty-one ESBL-E strains (7%), 16 K. pneumoniae (13.1%), and 15 Ecc (11.8%) presented the t2c2 phenotype in addition to their ESBL profile, whereas no E. coli presented these resistances. The t2c2 phenotype was invariably reversible by the addition of Phe-Arg-β-naphthylamide, indicating a role of resistance-nodulation-division pumps in these observations. Mutations associated with the t2c2 phenotype were restricted to RamR, the ramAR intergenic region (IR), and AcrR. Mutations in RamR consisted of C- or N-terminal deletions and amino acid substitutions inside its DNA-binding domain or within key sites of protein-substrate interactions. The ramAR IR showed nucleotide substitutions involved in the RamR DNA-binding domain. This diversity of sequences suggested that RamR and the ramAR IR represent major genetic events for bacterial antimicrobial resistance.IMPORTANCEMorbimortality caused by infectious diseases is very high among patients hospitalized in intensive care units (ICUs). A part of these outcomes can be explained by antibiotic resistance, which delays the appropriate therapy. The transferable antibiotic resistance gene is a well-known mechanism to explain the high rate of multidrug resistance (MDR) bacteria in ICUs. This study describes the prevalence of chromosomal mutations, which led to additional antibiotic resistance among MDR bacteria. More than 12% of Klebsiella pneumoniae and Enterobacter cloacae complex strains presented mutations within the ramAR locus associated with a dysregulation of an efflux pump called AcrAB-TolC and a porin: OmpF. These dysregulations led to an increase in antibiotic output notably tigecycline, ciprofloxacin, and chloramphenicol associated with a decrease of input for beta-lactam, especially temocillin. Mutations within transcriptional regulators such as ramAR locus played a major role in antibiotic resistance dissemination and need to be further explored.

Psychological distress among healthcare professionals in Mbarara, following the 2022 Ebola Virus Disease outbreak in Uganda: a mixed methods study

BackgroundThe 2022 Ebola Virus Disease (EVD) outbreak occurred at a time when Uganda was still battling the social and psychological challenges of the COVID-19 pandemic; placing health care professionals (HCPs) at a much higher risk of developing psychological distress. Psychological distress among HCPs can cause decreased workplace productivity and ineffective management of their patients. The current study aimed to investigate and understand psychological distress among HCPS in Mbarara city in Southwestern Uganda following the 2022 EVD outbreak.MethodWe enrolled 200 HCPs through convenient sampling from one private and one public health facility in Mbarara city in Southwestern Uganda, in a cross-sectional convergent parallel mixed method approach where qualitative and quantitative data were collected concurrently. Quantitative data, utilizing the Kessler Psychological Distress (K10) Scale, provided us with a quantitative measure of the prevalence of psychological distress among HCPs, and were analyzed using STATA version 16. Qualitative data, on the other hand, offered deeper insights into the nature, perceptions, and contextual factors influencing this distress, and were analyzed using emergent theme analysis.ResultsThe prevalence of psychological distress was 59.5% and it was higher among females (63.9%) compared to males (36.1%). HCPs vividly expressed distress and anxiety, with heightened suspicion that every patient might be an EVD carrier, creating a pervasive sense of unsafety in the workplace. However, the outbreak had an educational affect where concerns about the announcement of another EVD outbreak were diverse, with HCPs expressing anxiety, despair, and dissatisfaction with the country’s management of potential outbreaks.ConclusionHigh levels of psychological distress were experienced by HCPs in Southwestern Uganda as a result of the 2022 EVD pandemic. HCPs express a wide range of feelings, such as dread, anxiety, despair, pessimism, and discontent with the way the outbreaks are handled throughout the nation. We recommend implementation of comprehensive psychosocial support programs tailored to the unique needs of HCPs, including counseling services, stress management workshops, and peer support networks.

Parallel Interactions Between Linguistic and Contextual Factors in Bilinguals

AbstractThe necessity for introducing interactionist and parallelism approaches in different branches of cognitive science emerged as a reaction to classical sequential stage‐based models. Functional psychological models that emphasized and explained how different components interact, dynamically producing cognitive and perceptual states, influenced multiple disciplines. Chiefly among them were experimental psycholinguistics and the many applied areas that dealt with humans’ ability to process different types of information in different contexts. Understanding how bilinguals represent and process verbal and visual input, how their neural and psychological states facilitate such interactions, and how linguistic and nonlinguistic processing overlap, has now emerged as an important area of multidisciplinary research. In this article, we will review available evidence from different language‐speaking groups of bilinguals in India with a focus on situational context. In the discussion, we will address models of language processing in bilinguals within a cognitive psychological approach with a focus on existent models of inhibitory control. The paper's stated goal will be to show that the parallel architecture framework can serve as a theoretical foundation for examining bilingual language processing and its interface with external factors such as social context.