bluuebunny/crossref_metadata_2025 huggingface dataset:
Using the Health Belief Model to understand intention to vaccinate for Lyme disease in the United States
Authors: Gould L. Hannah , Stark James H. , McFadden Brandon , Patel Niyati , Kelly Patrick H. , Riis Jason
Published: May 2024

Aims

A growing number of Lyme disease (LD) cases in the U.S. are reported in states neighbouring those with high‐incidence (>10 cases per 100,000 population) rates. Considering the evolving epidemiology, high‐incidence counties in many of these “neighbouring states,” and the forthcoming vaccines, understanding the drivers of vaccination intention is critical, particularly how drivers of intention in neighbouring states vary relative to regions currently classified as high incidence.

This study uses the Health Belief Model (HBM) to determine the key drivers associated with vaccine intention for U.S. adults and caregivers of children residing in neighbouring states.

Methods and Results

Using an established panel with quotas for age, sex, race/ethnicity and urbanity, we surveyed 887 adults and 822 caregivers of children residing in U.S. neighbouring states. Survey items included measures of intention and HBM constructs, all of which were assessed using 5‐point Likert scales. We subsequently used structural equation modelling to understand the influence of the HBM constructs on LD vaccine intention.

Estimates from structural equation modelling show that the HBM constructs explain much of the variation in intention to vaccinate against LD. Intentions to vaccinate for both adults and caregivers are positively influenced by cues to action, perceived susceptibility of LD, and perceived benefits to vaccination. Both are also negatively influenced by perceived barriers to vaccination. The caregiver's intention to vaccinate is also positively influenced by the perceived severity of LD.

Conclusion

The intention to vaccinate for respondents residing in LD neighbouring states is strongly influenced by recommendations from healthcare providers or the Centers for Disease Control and Prevention (CDC). As incidence rises in neighbouring states and the need to prevent disease becomes more overt, public health officials should strongly recognize the influence of healthcare providers and CDC recommendations on intention to vaccinate against LD.
The complementary dominance hypothesis: a model for remediating the ‘good’ hand in stroke survivors
Authors: Kitchen Nick M. , Dexheimer Brooke , Yuk Jisung , Maenza Candice , Ruelos Paul R. , Kim Taewon , Sainburg Robert L.
Published: May 2024

The complementary dominance hypothesis is a novel model of motor lateralization substantiated by decades of research examining interlimb differences in the control of upper extremity movements in neurotypical adults and hemisphere‐specific motor deficits in stroke survivors. In contrast to earlier ideas that attribute handedness to the specialization of one hemisphere, our model proposes complementary motor control specializations in each hemisphere. The dominant hemisphere mediates optimal control of limb dynamics as required for smooth and efficient movements, whereas the non‐dominant hemisphere mediates impedance control, important for countering unexpected mechanical conditions and achieving steady‐state limb positions. Importantly, this model proposes that each hemisphere contributes its specialization to both arms (though with greater influence from either arm's contralateral hemisphere) and thus predicts that lesions to one hemisphere should produce hemisphere‐specific motor deficits in not only the contralesional arm, but also the ipsilesional arm of stroke survivors – a powerful prediction now supported by a growing body of evidence. Such ipsilesional arm motor deficits vary with contralesional arm impairment, and thus individuals with little to no functional use of the contralesional arm experience both the greatest impairments in the ipsilesional arm, as well as the greatest reliance on it to serve as the main or sole manipulator for activities of daily living. Accordingly, we have proposed and tested a novel intervention that reduces hemisphere‐specific ipsilesional arm deficits and thereby improves functional independence in stroke survivors with severe contralesional impairment.

image
Role of the Flexion Relaxation Phenomenon in the Analysis of Low Back Pain Risk in the Powerlifter: A Proof-of-Principle Study
Authors: Marotta Nicola , de Sire Alessandro , Bartalotta Isabella , Sgro Maria , Zito Roberta , Invernizzi Marco , Ammendolia Antonio , Iona Teresa
Published: May 2024

Introduction: Unlike the most common training approaches for bodybuilding, powerlifting programs are generally based on maximum and submaximal loads, putting enormous stresses on the lumbar spine. The flexion relaxation phenomenon evaluation is a clinical tool used for low back pain (LBP) assessment. This study aimed to evaluate the role of the flexion relaxation phenomenon in the analysis of LBP in the powerlifters. Methods: Healthy professional powerlifters participated in the study. In fact, we divided the participants into a LBP-low-risk group and a LBP-high-risk group, based on a prior history of LBP. Outcome measures included flexion relaxation ratio (FRR) and trough surface electromyography collected during trunk maximum voluntary flexion; furthermore, during a bench press lifting, we measured the height of the arched back (ARCH), using a camera and the Kinovea video editing software, to consider a potential correlation with the risk of LBP. Results: We included a group of 18 male (aged 24–39 y) powerlifters of 93 kg category. We measured a nonsignificant mean difference of ARCH between low-risk LBP group and high-risk LBP subjects. Curiously, maximum voluntary flexions were both above the threshold of 3.2 μV; therefore, with an absence of appropriate myoelectric silence, on the contrary, the FRR ratios were higher than 9.5, considering the presence of the phenomenon, exclusively for the low-risk group. The lumbar arched back measurement data did not report any association with the LBP risk, regarding the maximum voluntary flexion value, and even more than the FRR there is a relationship with the presence or the absence of LBP risk. Conclusions: FRR could be considered as a useful parameter for studying the risk of LBP in powerlifting. The FRR index not only refers to the possible myoelectric silence of the lumbar muscles in trunk maximum forward flexion but also takes into account the energy value delivered by the lumbar muscles during the flexion. Furthermore, we can indicate that the size of the powerlifter ARCH may not be a determining factor in the occurrence of LBP.
Two Preservice Teachers’ Adaptive Competence in Lesson Planning Across Two High School Placements
Authors: Dehandschutter Toon , van der Mars Hans , Ward Phillip , Iserbyt Peter
Published: May 2024

Purpose: To investigate the adaptive competence of two physical education preservice teachers (PSTs) across two high school placements. Methods: Two PSTs each taught four 6-lesson frisbee units across two different high schools. Feedback on lesson plans prior to teaching, guided reflection-on-action, and repeated teaching was used to support adaptations to six core teaching practices. PSTs made three iterations of each lesson plan. Results: PSTs made 589 adaptations across the core teaching practices of management (28%), task presentation (16%), content development (16%), active supervision (16%), goals and assessment (7%), and rules and routines (3%). Between 16% and 30% of adaptations were made in the last iteration. The number of adaptations decreased from the first to the second school placement and from Lessons 1 to 6. Conclusion: Feedback on lesson plans, guided reflection-on-action, and repeated teaching aided PSTs’ development of adaptive competence during school placements.
Physical Activity and Motor Skill Development During Early Childhood: Investigating the Role of Parent Support
Authors: James Maeghan E. , Arbour-Nicitopoulos Kelly P. , Kwan Matthew , King-Dowling Sara , Cairney John
Published: May 2024

Purpose: This study examined the relationship between parent physical activity (PA) support and children’s motor skill development and PA during early childhood and explored the potential moderating effect of child PA and motor skills on these relationships. Methods: Participants (N = 589, 250 girls, meanage = 4.93 [0.59] y) were part of a larger, longitudinal cohort study. Motor skills were assessed using the Movement Assessment Battery for Children—Second Edition. Moderate to vigorous PA (MVPA) was measured using ActiGraph accelerometers. Five items were used to measure parent support frequency (1 = none, 3 = 3–4 times, 5 = daily). Moderation analyses were conducted to examine the moderating effect of MVPA and motor skills on the relationship between parent support and motor skills and MVPA, respectively. Results: Parent support was significantly related to motor skills (B = 14.45, P = .007), and child MVPA significantly moderated this relationship (B = −0.17, P = .021). The relationship between parent support and child MVPA did not reach significance (B = 2.89, P = .051); however, motor skills had a significant moderating effect (B = −0.08, P = .022). Conclusions: These novel findings suggest parent PA support is related to child motor skills and PA during early childhood, but this relationship is context dependent. Child-level characteristics should be considered in future parent PA support research.
Influence of acute dietary nitrate supplementation on oxygen delivery/consumption and critical impulse during maximal effort forearm exercise in males: a randomized crossover trial
Authors: Fenuta Alyssa M. , Drouin Patrick J. , Kohoko Zach I.N. , Lynn Mytchel J.T. , Tschakovsky Michael E.
Published: May 2024

Beetroot juice supplementation (BRJ) should increase nitric oxide bioavailability under conditions of muscle deoxygenation and acidosis that are a normal consequence of the maximal effort exercise test used to identify forearm critical impulse. We hypothesized BRJ would improve oxygen delivery:demand matching and forearm critical impulse performance. Healthy males (20.8 ± 2.4 years) participated in a randomized crossover trial between October 2017 and May 2018 (Queen’s University, Kingston, ON). Participants completed 10 min of rhythmic maximal effort forearm handgrip exercise 2.5 h post placebo (PL) vs. BRJ (9 completed PL/BRJ vs. 4 completed BRJ/PL) within a 2 week period. Data are presented as mean ± SD. There was a main effect of drink (PL > BRJ) for oxygen extraction ( P = 0.033, ηp2 = 0.351) and oxygen consumption/force ( P = 0.017, ηp2 = 0.417). There was a drink × time interaction (PL > BRJ) for oxygen consumption/force ( P = 0.035, ηp2 = 0.216) between 75 and 360 s (1.25–6 min) from exercise onset. BRJ did not influence oxygen delivery ( P = 0.953, ηp2 = 0.000), oxygen consumption ( P = 0.064, ηp2 = 0.278), metabolites ((lactate) ( P = 0.196, ηp2 = 0.135), pH ( P = 0.759, ηp2 = 0.008)) or power-duration performance parameters (critical impulse ( P = 0.379, d = 0.253), W′ ( P = 0.733, d = 0.097)). BRJ during all-out handgrip exercise does not influence oxygen delivery or exercise performance. Oxygen cost of contraction with BRJ is reduced as contraction impulse is declining during maximal effort exercise resulting in less oxygen extraction.
Folate and synthetic folic acid content in Canadian fortified foods two decades after mandatory fortification
Authors: Khanna Siya , Aufreiter Susanne , MacFarlane Amanda J. , Shakur Yaseer A. , O'Connor Deborah L.
Published: May 2024

In 1998, Health Canada mandated folic acid fortification of white flour and enriched grain products to prevent neural tube defects. At the time, neither the Canadian Nutrient File (CNF) nor product labels reflected the actual folate content of foods. We aimed to assess if 20 years post-fortification, the CNF values for total folate and synthetic folic acid accurately reflect amounts determined by direct analysis. Using the 2001 Food Expenditure Survey and ACNielsen Company data, we identified 10–15 of the most purchased fortified foods across seven food categories in Canada. Total folate concentrations were determined by tri-enzyme digestion and microbial assay. Folic acid concentrations were determined using liquid chromatography-tandem mass spectrometry. Except for “cooked pastas”, mean total folate content of foods ( n = 89) were significantly higher than CNF values across categories ( p < 0.05), reflecting 167% ± 54% of CNF values. Similarly, mean folic acid content of foods was higher than CNF values for all categories except “cooked pastas” ( p < 0.05), with a mean of 188% ± 94% of CNF values; the latter CNF values included uncooked pasta. In sum, 20 years post-fortification, and 10 years since the last direct measurement, CNF and product label values still underestimate actual total folate and the folic acid content of foods. These findings emphasize that dietary estimates established using the CNF may significantly underestimate actual intakes and thus caution should be exercised when interpreting estimates of nutritional adequacy based on these values.
An integrated population model and sensitivity assessment for a data-poor population of green sturgeon
Authors: Dudley Peter N. , Mora Ethan A. , Friedenberg Nick A. , Doukakis Phaedra
Published: May 2024

Conservation planning requires understanding population sizes and trajectories. As with many imperiled species, green sturgeon ( Acipenser medirostris) census data are limited, making it difficult to contextualize current threats and promote quantitative conservation goals. We combined spawning census, growth, and demographic data for the southern population of green sturgeon to build a population size and trajectory estimate. We generated a distribution of population size estimates and trajectories reflecting uncertainty from multiple sources. We then propagate these estimates through a demographic model to assess the potential impact of fishing bycatch. Our model suggests the population is below the recovery goal of 3000 adults. The most probable current total population estimate (including juveniles) is approximately 10 000 fish (5300–18 400 95% high density interval (HDI)), with 2400 adults (2197–2624 95% HDI). Simulated fishing bycatch pressure on the adults and subadults reduced abundance by a median value of 0.4% per year, which could be an impediment to recovery. Fisheries bycatch is one of many threats this population faces; this integrated framework may be used to assess how other threats may affect this population.
The impact of herbicide-resistant canola systems on the weed community dynamics in the Canadian Prairies
Authors: Chastko Theodore , Benaragama Dilshan I. , Leeson Julia L. , Willenborg Christian J.
Published: May 2024

Increased adoption of no-till and extensive cultivation of herbicide-resistant (HR) crops in low diversity rotations are some transformations that occurred during the 1970s–2000s in western Canada. Historical data analysis of weed survey data (Saskatchewan, Manitoba, and Alberta) in canola crops and a canola rotation field experiment were used to understand how tillage, herbicide-resistant cropping systems, and the temporal frequency of canola in the rotation can impact weed abundance, composition, and diversity in the Canadian prairies. Weed survey data revealed that the spatial frequency of many weed species declined in years after 1995 compared with before 1995, the year HR cultivars were introduced. False cleavers ( Galeum spurium L .) and volunteer wheat ( Triticum aestivum L.) were the only species showing a continuous increase in frequency throughout the surveyed years. Species richness showed no trajectories but varied depending on climatic conditions. Roundup-Ready (RR) systems were associated with green foxtail ( Setaria viridis L.) irrespective of their frequency in the rotation. Continuous canola systems had an obvious association with chickweed ( Stellaria media L.) regardless of herbicide systems. Wild oats ( Avena fatua L.) were predominant in the longer rotations with glufosinate and imidazolinone-resistant canola. In the field study, weeds associated before in crop herbicide application were relatively controlled after herbicides were applied, except for foxtail barley ( Hordeum Jubatum L.), which was not controlled in most HR systems. Overall, both weed survey data and field experiment data revealed greater control of most weed species due to the adoption of HR canola cultivars and the associated cultural practices.
Restoring forest ecosystem services through trait-based ecology
Authors: Aubin Isabelle , Deschênes Élise , Santala Kierann R. , Emilson Erik J.S. , Schoonmaker Amanda L. , McIntosh Anne C.S. , Bourgeois Bérenger , Cardou Françoise , Dupuch Angélique , Handa I. Tanya , Lapointe Mélanie , Lavigne Jonathan , Maheu Audrey , Nadeau Solange , Naeth M. Anne. , Neilson Eric W. , Wiebe Philip A.
Published: May 2024

Restoration is moving towards a more mechanistic approach that emphasizes restoration of ecosystem services. Trait-based approaches provide links between species identity and ecosystem functions and have been suggested as a promising way to formally integrate ecosystem services in the design of restoration programs. While practitioners have been routinely using informal knowledge on plant traits in their practices, these approaches are underutilized as operationalization remains challenging. The goal of this paper is to provide guidance for applied scientists and restoration practitioners looking to apply a trait-based approach to restore forest ecosystems. We present a five-step framework: (1) selection of services to be restored, (2) trait selection, (3) data acquisition, (4) analytical planning, and (5) empirical testing and monitoring. We use three Canadian case studies to illustrate the applicability of our framework and the variety of ways trait-based approaches can inform restoration practices: (1) restoration of urban woodlots after an insect outbreak, (2) restoration of a smelter-damaged landscape surrounding an urban area, and (3) reclamation of remote upland forests after oil- and gas-related disturbances. We describe the major mechanisms and traits that determine vegetation effects on ecosystem services of importance in each case study. We then discuss data availability, methodological constraints, comparability issues, analytical methods, and the importance of empirical testing and monitoring to ensure realistic prediction of service restoration. By outlining issues and offering practical information, we aim to contribute to a more robust use of traits in ecological restoration.
Data Validation Utilizing Expert Knowledge and Shape Constraints
Authors: Bachinger Florian , Ehrlinger Lisa , Kronberger Gabriel , Wöss Wolfram
Published: May 2024

Data validation is a primary concern in any data-driven application, as undetected data errors may negatively affect machine learning models and lead to suboptimal decisions. Data quality issues are usually detected manually by experts, which becomes infeasible and uneconomical for large volumes of data.

To enable automated data validation, we propose “shape constraint-based data validation,” a novel approach based on machine learning that incorporates expert knowledge in the form of shape constraints. Shape constraints can be used to describe expected (multivariate and nonlinear) patterns in valid data and enable the detection of invalid data that deviates from these expected patterns. Our approach can be divided into two steps: (1) shape-constrained prediction models are trained on data, and (2) their training error is analyzed to identify invalid data. The training error can be used as an indicator for invalid data because shape-constrained models can fit valid data better than invalid data.

We evaluate the approach on a benchmark suite consisting of synthetic datasets, which we have published for benchmarking similar data validation approaches. Additionally, we demonstrate the capabilities of the proposed approach with a real-world dataset consisting of measurements from a friction test bench in an industrial setting. Our approach detects subtle data errors that are difficult to identify even for domain experts.
Multi-Task Learning in Natural Language Processing: An Overview
Authors: Chen Shijie , Zhang Yu , Yang Qiang
Published: May 2024

Deep learning approaches have achieved great success in the field of Natural Language Processing (NLP). However, directly training deep neural models often suffer from overfitting and data scarcity problems that are pervasive in NLP tasks. In recent years, Multi-Task Learning (MTL), which can leverage useful information of related tasks to achieve simultaneous performance improvement on these tasks, has been used to handle these problems. In this article, we give an overview of the use of MTL in NLP tasks. We first review MTL architectures used in NLP tasks and categorize them into four classes, including parallel architecture, hierarchical architecture, modular architecture, and generative adversarial architecture. Then we present optimization techniques on loss construction, gradient regularization, data sampling, and task scheduling to properly train a multi-task model. After presenting applications of MTL in a variety of NLP tasks, we introduce some benchmark datasets. Finally, we make a conclusion and discuss several possible research directions in this field.
A Review of Symbolic, Subsymbolic and Hybrid Methods for Sequential Decision Making
Authors: Núñez-Molina Carlos , Mesejo Pablo , Fernández-Olivares Juan
Published: May 2024

In the field of Sequential Decision Making (SDM), two paradigms have historically vied for supremacy: Automated Planning (AP) and Reinforcement Learning (RL). In the spirit of reconciliation, this article reviews AP, RL and hybrid methods (e.g., novel learn to plan techniques) for solving Sequential Decision Processes (SDPs), focusing on their knowledge representation: symbolic, subsymbolic, or a combination. Additionally, it also covers methods for learning the SDP structure. Finally, we compare the advantages and drawbacks of the existing methods and conclude that neurosymbolic AI poses a promising approach for SDM, since it combines AP and RL with a hybrid knowledge representation.
Lightweight Hardware-Based Cache Side-Channel Attack Detection for Edge Devices (Edge-CaSCADe)
Authors: Bhade Pavitra , Paturel Joseph , Sentieys Olivier , Sinha Sharad
Published: May 2024

Cache Side-Channel Attacks (CSCAs) have been haunting most processor architectures for decades now. Existing approaches to mitigation of such attacks have certain drawbacks, namely software mishandling, performance overhead, and low throughput due to false alarms. Hence,“mitigation only when detected”should be the approach to minimize the effects of such drawbacks. We propose a novel methodology of fine-grained detection of timing-based CSCA using a hardware-based detection module.

We discuss the design, implementation, and use of our proposed detection module in processor architectures. Our approach successfully detects attacks that flush secret victim information from cache memory like Flush+Reload, Flush+Flush, Prime+Probe, Evict+Probe, and Prime+Abort, commonly known as cache timing attacks. Detection is on time with minimal performance overhead. The parameterizable number of counters used in our module allows detection of multiple attacks on multiple sensitive locations simultaneously. The fine-grained nature ensures negligible false alarms, severely reducing the need for any unnecessary mitigation. The proposed work is evaluated by synthesizing the entire detection algorithm as an attack detection block, Edge-CaSCADe, in a RISC-V processor as a target example. The detection results are checked under different workload conditions with respect to the number of attackers and the number of victims having RSA-, AES-, and ECC-based encryption schemes like ECIES, and on benchmark applications like MiBench and Embench. More than 98% detection accuracy within 2% of the beginning of an attack can be achieved with negligible false alarms. The detection module has an area and power overhead of 0.9% to 2% and 1% to 2.1% for the targeted RISC-V processor core without cache for one to five counters, respectively. The detection module does not affect the processor critical path and hence has no impact on its maximum operating frequency.
OTI-IoT: A Blockchain-based Operational Threat Intelligence Framework for Multi-vector DDoS Attacks
Authors: Aguru Aswani , Erukala Suresh
Published: May 2024

The Internet of Things (IoT) refers to a complex network comprising interconnected devices that transmit their data via the Internet. Due to their open environment, limited computation power, and absence of built-in security, IoT environments are susceptible to various cyberattacks. Denial of service (DDoS) attacks are among the most destructive types of threats. The Multi-vector DDoS attack is a contemporary and formidable form of DDoS wherein the attacker employs a collection of compromised IoT devices as zombies to initiate numerous DDoS attacks against a target server. A Blockchain-based Operational Threat Intelligence framework, OTI-IoT, is proposed in this article to counter multi-vector DDoS attacks in IoT networks. A “Prevent-then-Detect” methodology was utilized to deploy the OTI-IoT framework in two distinct stages. During Phase 1, the consortium Blockchain network validators employ the IPS module, composed of a smart contract for attack prevention and access control, and Proof of Voting consensus, to thwart attacks. Validators are outfitted with deep learning-based IDS instances to detect multi-vector DDoS attacks during Phase 2. Alert messages are generated by the IDS module’s alert generation and propagation smart contract in response to identifying malicious IoT sources. The feedback loop from the IDS module to the IPS module prevents incoming traffic from malicious sources. The proposed OTI framework capabilities are realized as an outcome of combining and storing the outcomes of the IDS and IPS modules on the consortium Blockchain. Each validator maintains a shared ledger containing information regarding threat sources to ensure robust security, transparency, and integrity. The operational execution of OTI-IoT occurs on an individual Ethereum Blockchain. The empirical findings indicate that our proposed framework is most suitable for real-time applications due to its ability to lower attack detection time, decreased block validation time, and higher attack prevention rate.
HKA: A Hierarchical Knowledge Alignment Framework for Multimodal Knowledge Graph Completion
Authors: Xu Yunhui , Li Youru , Xu Muhao , Zhu Zhenfeng , Zhao Yao
Published: May 2024

Recent years have witnessed the successful application of knowledge graph techniques in structured data processing, while how to incorporate knowledge from visual and textual modalities into knowledge graphs has been given less attention. To better organize them, Multimodal Knowledge Graphs (MKGs), comprising the structural triplets of traditional Knowledge Graphs (KGs) together with entity-related multimodal data (e.g., images and texts), have been introduced consecutively. However, it is still a great challenge to explore MKGs due to their inherent incompleteness. Although most existing Multimodal Knowledge Graph Completion (MKGC) approaches can infer missing triplets based on available factual triplets and multimodal information, they almost ignore the modal conflicts and supervisory effect, failing to achieve a more comprehensive understanding of entities. To address these issues, we propose a novel
H
ierarchical
K
nowledge
A
lignment (
HKA
) framework for MKGC. Specifically, a macro-knowledge alignment module is proposed to capture global semantic relevance between modalities for dealing with modal conflicts in MKG. Furthermore, a micro-knowledge alignment module is also developed to reveal the local consistency information through inter- and intra-modality supervisory effects more effectively. By integrating different modal predictions, a final decision can be made. Experimental results on three benchmark MKGC tasks have demonstrated the effectiveness of the proposed HKA framework.
An Open-Source ML-Based Full-Stack Optimization Framework for Machine Learning Accelerators
Authors: Esmaeilzadeh Hadi , Ghodrati Soroush , Kahng Andrew , Kim Joon Kyung , Kinzer Sean , Kundu Sayak , Mahapatra Rohan , Manasi Susmita Dey , Sapatnekar Sachin , Wang Zhiang , Zeng Ziqing
Published: May 2024

Parameterizable machine learning (ML) accelerators are the product of recent breakthroughs in ML. To fully enable their design space exploration (DSE), we propose a physical-design-driven, learning-based prediction framework for hardware-accelerated deep neural network (DNN) and non-DNN ML algorithms. It adopts a unified approach that combines power, performance, and area (PPA) analysis with frontend performance simulation, thereby achieving a realistic estimation of both backend PPA and system metrics such as runtime and energy. In addition, our framework includes a fully automated DSE technique, which optimizes backend and system metrics through an automated search of architectural and backend parameters. Experimental studies show that our approach consistently predicts backend PPA and system metrics with an average 7% or less prediction error for the ASIC implementation of two deep learning accelerator platforms, VTA and VeriGOOD-ML, in both a commercial 12 nm process and a research-oriented 45 nm process.
Selection of the Best in the Presence of Subjective Stochastic Constraints
Authors: Zhou Yuwei , Andradóttir Sigrún , Kim Seong-Hee
Published: May 2024

We consider the problem of finding a system with the best primary performance measure among a finite number of simulated systems in the presence of subjective stochastic constraints on secondary performance measures. When no feasible system exists, the decision maker may be willing to relax some constraint thresholds. We take multiple threshold values for each constraint as a user’s input and propose indifference-zone procedures that perform the phases of feasibility check and selection-of-the-best sequentially or simultaneously. Given that there is no change in the underlying simulated systems, our procedures recycle simulation observations to conduct feasibility checks across all potential thresholds. We prove that the proposed procedures yield the best system in the most desirable feasible region possible with at least a pre-specified probability. Our experimental results show that our procedures perform well with respect to the number of observations required to make a decision, as compared with straight-forward procedures that repeatedly solve the problem for each set of constraint thresholds, and that our simultaneously-running procedure provides the best overall performance.
SEMScene: Semantic-Consistency Enhanced Multi-Level Scene Graph Matching for Image-Text Retrieval
Authors: Liu Yuankun , Yuan Xiang , Li Haochen , Tan Zhijie , Huang Jinsong , Xiao Jingjie , Li Weiping , Mo Tong
Published: May 2024

Image-text retrieval, a fundamental cross-modal task, performs similarity reasoning for images and texts. The primary challenge for image-text retrieval is cross-modal semantic heterogeneity, where the semantic features of visual and textual modalities are rich but distinct. Scene graph is an effective representation for images and texts as it explicitly models objects and their relations. Existing scene graph based methods have not fully taken the features regarding various granularities implicit in scene graph into consideration (e.g., triplets), the inadequate feature matching incurs the absence of non-trivial semantic information (e.g., inner relations among triplets). Therefore, we propose a
S
emantic-Consistency
E
nhanced
M
ulti-Level
Scene
Graph Matching (SEMScene) network, which exploits the semantic relevance between visual and textual scene graphs from fine-grained to coarse-grained. Firstly, under the scene graph representation, we perform feature matching including low-level node matching, mid-level semantic triplet matching, and high-level holistic scene graph matching. Secondly, to enhance the semantic-consistency for object-fused triplets carrying key correlation information, we propose a dual-step constraint mechanism in mid-level matching. Thirdly, to guide the model to learn the semantic-consistency of matched image-text pairs, we devise effective loss functions for each stage of the dual-step constraint. Comprehensive experiments on Flickr30K and MS-COCO datasets demonstrate that SEMScene achieves state-of-the-art performances with significant improvements.
Perspective—Surface-display Techniques in Electrochemical Biosensor Designs for Health Monitoring
Authors: Weber Courtney J. , Whisonant Megan D. , Clay Olivia M. , Simoska Olja
Published: May 2024

Enzymatic and microbial electrochemical biosensors integrate enzymes and microorganisms as biological recognition elements into the sensor design and functionality. Enzyme-based sensors offer high sensitivity and selectivity for target analyte detection. However, these have limited stability necessary for continuous analyte monitoring. Contrarily, microbe-based electrochemical sensors provide a means for continuous analyte sensing but are associated with challenges related to analyte selectivity in complex samples. To address these limitations, surface-display methods, which bind enzymes to microbial surfaces, enhance biosensor selectivity and sensitivity. This perspective outlines the application of surface-display techniques, offering a promising avenue for health monitoring.
Experimental Investigation on the 3D Printing of Nylon Reinforced by Carbon Fiber through Fused Filament Fabrication Process, Effects of Extruder Temperature, and Printing Speed
Authors: Moradi Mahmoud , Malekshahi Beiranvand Zeinab , Salimi Nahid , Meiabadi Saleh , Lawrence Jonathan
Published: May 2024

This study investigated how the extruder temperature, printing speed, and specimen geometry interact during a tensile test of continuous carbon fiber‐reinforced nylon matrix composites produced by the fused deposition modelling (FDM) process. The investigation utilized statistical techniques. For this purpose, tensile examinations were done on manufactured samples using a testing apparatus. The study’s objective is to identify the most efficient specimen geometry for tensile testing result optimization and to maximize the 3D printing process’s capability for producing complex, freeform patterns in these composites. In this study, the input parameters required for the response surface methodology (RSM) were varying extruder temperature (240‐255°C) and printing speed (60‐80 mm/s), and experimental responses included modulus, elongation at break, and weight. The findings of the regression analysis showed output responses are influenced by both input variables. The results showed that the strength of the samples was significantly influenced by the input parameters. To draw the surface and residual plots, the software of design expert software was used. The interaction between the two input variables suggests raising the extruder temperature and decreasing printing speed, which leads to printing heavier samples. Inversely, the diversity between the forecasted and real responses for the optimal specimens is less than 10% which is assumed to be acceptable for the design of experiments (DOE). The analysis took into account the lower and upper ranges of the input variable with the goal of enhancing both the most modulus and fracture elongation while simultaneously degrading the weight of the specimens. To achieve this objective, the extruder temperature and printing speed are between 240 and 250°C and 65 and 75 mm/s, respectively.
Clinical Significance of the Prognostic Nutritional Index in Predicting Delirium among Critically Ill Patients: A Retrospective Cohort Study
Authors: Xu Feifei , Zhong Xi
Published: May 2024

Delirium is a serious and complex problem facing critically ill patients. This retrospective study aimed to explore the association between prognostic nutrition index (PNI) and delirium in critically ill patients in the intensive care unit (ICU). This study was based on the Medical Information Mart Intensive Care IV (MIMIC IV) database. Patients over 18 years of age were enrolled. Univariate and multivariate logistic regression analyses were performed to explore the association between PNI and delirium. Tendency analysis, subgroups analysis, and restricted cubic spline (RCS) were selected to further certify the association. The receiver operating characteristic curve (ROC) was adopted to assess the predictability of PNI to delirium. Propensity score matching (PSM) was used to reduce the interference of confounders. A total of 3,105 patients participated in this study. As the grade of malnutrition increases, the incidence of delirium increases in all models. The odds ratios (OR) of delirium in the fully adjusted model were 1.00 (reference), 1.04 (0.80, 1.36), 1.53 (1.17, 1.99), and 1.93 (1.44, 2.59). Strong U-shaped curves were found with RCS analysis between delirium and PNI in the subgroups of patients over 60 years of age and patients with chronic obstructive pulmonary disease (COPD). After PSM, the ORs of delirium were 1.44 (1.16, 1.79) and 1.53 (1.22, 1.93), respectively, in the univariate and multivariate logistic regression models. PNI is negatively associated with the prevalence of delirium in critically ill adults in the ICU. PNI is an independent risk factor for the incidence of delirium in adults in the ICU.
Quality Analysis and Shelf-Life Prediction of Antarctic Krill (Euphausia superba) Sauce Based on Kinetic Model and Back Propagation Neural Network Model
Authors: Chi Hai , Zhang Yuanxing , Zhao Lukai , Lin Na , Kang Wei
Published: May 2024

The study is aimed at determining how the quality of Antarctic krill (Euphausia superba) sauce (AkS) changed over time, including changes in color, moisture content, acid value (AV), peroxide value (POV), thiobarbituric acid reactive substance (TBARS), aerobic plate count, and sensory score. Quality variations and shelf life of AkS were estimated using kinetic model and back propagation (BP) neural network model. The results showed that sensory score, moisture content, and a∗ values of AkS declined as storage temperature increased at 4, 25, and 37°C. In addition, the L∗ values, b∗ values, AV, POV, and TBARS of AkS increased as storage duration increased, indicating that high storage temperature of the samples accelerated quality degradation. The primary reason for AkS degradation was the oxidation of proteins and lipids. The POV, TBARS, and total sensory evaluation rating exhibited a highly significant correlation, and therefore, POV and TBARS were selected as the indicators for the two models. The BP neural network outperformed the kinetic model in predicting quality changes over the whole storage period, with relative errors of less than 10%. In terms of shelf-life prediction, the BP neural network’s relative errors were 11.76% and 13.39% in POV and TBARS, respectively. POV and TBARS had experimental shelf lengths of 119 and 142 d, respectively. Compared with the kinetic model, the BP neural network model predicted the quality changes and shelf life of AkS with greater accuracy and stability. The findings offer fundamental insights and innovative concepts for the production of high-value Antarctic krill products, as well as the exploitation of Antarctic krill resources.
Determinants of Farmers’ Perceptions towards Socioecological Benefits of Agroforestry Practices in Northwestern Ethiopia
Authors: Marie Mequannt , Lemessa Debissa , Esa Ebrahim , Tadesse Behailu , Azadi Hossein
Published: May 2024

Agroforestry practices provide multiple ecosystem services in agricultural landscapes. However, within the local context, local communities hold divergent perceptions regarding the various roles of agroforestry, particularly in the Afrotropical Region. Hence, understanding these drivers is critical for adopting and promoting sustainable agroforestry practices. Here, we examined the factors that influenced farmers’ perceptions towards the socioecological benefits of agroforestry practices. A multistage stratified random sampling technique was applied to select 90 households along an urban-rural gradient. The data were gathered through semi-structured questionnaires and key informant interviews and analyzed using a descriptive statistics, chi-square tests, general linear model, and redundancy analysis with the help of R software version 4.3.2. The findings showed that approximately 69.7% of the respondents strongly agreed that agroforestry practices had benefits for society and the environment. However, 11.1% were neutral, and the others disagreed. The results of the general linear model analysis showed that household age, agricultural experience, access to land certification books, and training on tree conservation had a significantly positive effect on farmers’ perceptions of the advantages of agroforestry practices. Furthermore, farmers’ perceptions of the benefits of agroforestry in terms of income generation, educational and cultural values, and climate change mitigation were dependent on household age and farming experience. Overall, this study provides useful insights into the drivers of farmers’ perceptions of the advantages of agroforestry practices in Northwest Ethiopia. The findings of this study underscore the need for policymakers and practitioners to consider sociodemographic and institutional factors that influence farmers’ perceptions when developing policies and plans to advance the adoption and promote sustainable management of agroforestry practices. This supports the widespread adoption of agroforestry practices in tropical agroecosystems.
Herbage Yield and Nutritive Value of Selected Grasses in Subhumid Agroecological Environments in Ethiopia
Authors: Dereje Fantahun , Mengistu Ashenafi , Geleti Diriba , Diba Diriba , Feyissa Fekede
Published: May 2024

The introduction of improved forage varieties could play a crucial role in meeting the growing demand for livestock and livestock products in Ethiopia. However, different cultivars exhibit varied performance. Therefore, this study aimed to assess the performance of Brachiaria cultivars and other grass cultivars in two subhumid agroecological areas of Ethiopia. Four Brachiaria cultivars (B. brizantha (DZF-13379), B. humidicola (DZF-9222), B. decumbens (DZF-10871), and B. mutica (var. DZF-483), and two local grass cultivars, Rhodes grass (Chloris gayana var. Massaba) and Desho grass (Pennisetum glaucifolium var. Kindu Kosha), were evaluated in Bako and Bishoftu for three years during the rainy season. The experiment was carried out using a completely randomized block design. Results indicated that both cultivars and years had significant (P<0.001) effects on herbage dry matter (DM) yield, crude protein (CP) yield, and plant height, whereas location only affected DM yield. Significant cultivar × year interactions were observed for DM yield, CP yield, and plant height, while cultivar × location interactions had a significant effect on all parameters except for CP yield. Additionally, cultivars significantly (P<0.001) impacted the in vitro dry matter digestibility (IVDMD) as well as the nutritional content (CP, ash, acid detergent lignin (ADL), neutral detergent fiber (NDF), and acid detergent fiber (ADF). Overall, all cultivars demonstrated potential as alternative ruminant feeds, with B. mutica, followed by B. brizantha, showing superior performance in the subhumid agroecological areas of Ethiopia.
Influencing Factor on School-Age Children’s Holistic Health Care Who Using Online Media by Working-Age Parents in Northeast Thailand
Authors: Wonginchan Atchariya , Tangpukdee Juraporn , Wonginchan Suphasak , Kearttiwongkru Sureeporn , Konggungong Sujitra
Published: May 2024

Background. Thailand tends to have more access to online media. In 2020, the northeastern region had the highest usage rate. Especially school-age children have the highest usage rate. If misused for a long time, the consequences can adversely affect health in many aspects. Their parents should closely supervise and monitor them for this health problem. However, parents have limitations in caring because they have to work a lot. The aim was to study factors that influence working-age parents in holistic health care (HHC) of school-age children using online media in northeast Thailand. Materials and Methods. This research was a descriptive study, with a sample of parent amount 222. The questionnaire performed an item-objective congruence (IOC) = 0.60–1.00, the content validity index (CVI) = 0.90, and the Cronbach’s alpha coefficient was 0.93. The data collected then include the characteristics, use of online media, health problems, and health care. Finally, data were analyzed using descriptive statistics, frequency, percentage, standard deviation, and multiple regressions. Result. The results of the study are factors that influenced HHC with statistical significance (p≤.001) including the child’s gender, the child’s age, the child’s class, the type of internet, activities, the device, and the device owner. In addition, it was holistic health problems of school-age children such as physical, emotional, social, and intellectual. Importantly, these could explain the variation for HHC of 21.2% (adjusted R2 = 0.212), and the standard error in the model estimation was 25.06 (SEE = 25.06). Conclusion. Factors that influence school-age children’s health care are beneficial to preventing and solving health problems by using online media. Parents or health care professionals can use it as a guide to develop more effective health care strategies and policies. Ultimately, it will help to promote better health for children in an ever-changing digital world.
Infant and Young Child Feeding (IYCF) Practices in Rural and Urban Regions of Indonesia
Authors: Simbolon Demsa , Suryani Desri , Dayanti Heidy , Setia Agustina , Hasan Tobianus
Published: May 2024

Background. The global strategy on infant and child feeding recommends the best pattern of feeding for infants and young child feeding (IYCF) from birth to 24 months. Practices of proper feeding of children up to 2 years of age contribute to a child’s survival, growth, and development because they can prevent micronutrient deficiencies, morbidity, and obesity in later life. This study aims to determine the risk factors for the failure of IYCF practice in urban and rural Indonesia. Materials and Methods. The study used Indonesia Demographic and Health Survey (IDHS) 2017 data with a cross-sectional research design. The sampling technique used systematic probability proportional to size sampling. The number of samples was 4,869 children consisting of 2,424 children in urban and 2,445 children in rural. Bivariate analysis using chi-square test. Multivariate analysis using multivariate logistic regression. Results. The study found that most of the children in urban (79.3%) and rural (97.2%) did not practice IYCF under the recommendations. There is a difference in the proportion of IYCF practices between urban and rural areas (p <0.001). The risk factors for the failure of IYCF practice in urban areas are socioeconomic, maternal age, and age at first marriage. The poorest socioeconomic is at risk of 2.4 times, maternal age > 35 years are at risk of 1.5 times, age at first marriage is less than 21 years at risk of 2.1 times carrying out IYCF practices not according to recommendations compared to the comparison group. The risk factors for the failure of IYCF practice in rural are socioeconomic, maternal education, quantity, and quality of antenatal care (ANC). The poorest socioeconomic is at risk of 2.1 times, mothers with primary education are at 1.5 times, the quantity of ANC is not good at risk at 1.9 times, the quality of ANC is not good risk at 1.5 times practicing IYCF is not according to the recommendations compared to the comparison group. Conclusion. The practice of IYCF in Indonesia starting from giving early breastfeeding initiation, exclusive breastfeeding, and complementary feeding according to recommendations is still low. The priority of IYCF intervention through education and assistance to pregnant women needs to be a sustainable program.