bluuebunny/crossref_metadata_2025 huggingface dataset:
Exercise training alters skeletal muscle microvascular endothelial cell properties in recent postmenopausal females
Authors: Nørregaard L. B. , Hansen C. C. , Wickham K. A. , Møller S. , Olsen K. , Ehlers T. , Bangsbo J. , Hellsten Y.
Published: June 2024

The present study examined and compared the impact of exercise training on redox and molecular properties of human microvascular endothelial cells derived from skeletal muscle biopsies from sedentary recent (RPF, ≤ 5 years as postmenopausal) and late (LPF, ≥ 10 years as postmenopausal) postmenopausal females. Resting skeletal muscle biopsies were obtained before and after 8 weeks of intense aerobic exercise training for isolation of microvascular endothelial cells and determination of skeletal muscle angiogenic proteins and capillarisation. The microvascular endothelial cells were analysed for mitochondrial respiration and production of reactive oxygen species (ROS), glycolysis and proteins related to vascular function, redox balance and oestrogen receptors. Exercise training led to a reduced endothelial cell ROS formation (∼50%; P = 0.009 and P = 0.020 for intact and permeabilized cells (state 3), respectively) in RPF only, with no effect on endothelial mitochondrial capacity in either group. Basal endothelial cell lactate formation was higher (7%; P = 0.028), indicating increased glycolysis, after compared to before the exercise training period in RPF only. Baseline endothelial G protein‐coupled oestrogen receptor (P = 0.028) and muscle capillarisation (P = 0.028) was lower in LPF than in RPF. Muscle vascular endothelial growth factor protein was higher (32%; P = 0.002) following exercise training in LPF only. Exercise training did not influence endothelial cell proliferation or skeletal muscle capillarisation in either group, but the CD31 level in the muscle tissue, indicating endothelial cell content, was higher (>50%; P < 0.05) in both groups. In conclusion, 8 weeks of intense aerobic exercise training reduces ROS formation and enhances glycolysis in microvascular endothelial cells from RPF but does not induce skeletal muscle angiogenesis.

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Key points

Late postmenopausal females have been reported to achieve limited vascular adaptations to exercise training.

There is a paucity of data on the effect of exercise training on isolated skeletal muscle microvascular endothelial cells (MMECs).

In this study the formation of reactive oxygen species in MMECs was reduced and glycolysis increased after 8 weeks of aerobic exercise training in recent but not late postmenopausal females.

Late postmenopausal females had lower levels of G protein‐coupled oestrogen receptor in MMECs and lower skeletal muscle capillary density at baseline.

Eight weeks of intense exercise training altered MMEC properties but did not induce skeletal muscle angiogenesis in postmenopausal females.
Test–Retest Reliability of Running Economy and Metabolic and Cardiorespiratory Parameters During a Multistage Incremental Treadmill Test in Male Middle- and Long-Distance Runners
Authors: Brady Aidan J. , Roantree Mark , Egan Brendan
Published: June 2024

This study investigated the test–retest reliability of running economy (RE) and metabolic and cardiorespiratory parameters related to endurance running performance using a multistage incremental treadmill test. On two occasions separated by 21–28 days, 12 male middle- and long-distance runners ran at 10, 11, 12, 13, and 14 km/hr for 8 min each stage, immediately followed by a ramp test to volitional exhaustion. Carbohydrate (10% maltodextrin solution) was consumed before and during the test to provide ∼1 g/min of exercise. RE, minute ventilation (), oxygen consumption (), carbon dioxide production (), respiratory exchange ratio (RER), heart rate (HR), ratings of perceived exertion (RPE), and blood glucose and lactate concentrations were recorded for each stage and at volitional exhaustion. Time-to-exhaustion (TTE) and peak oxygen consumption () during the ramp test were also recorded. Absolute reliability, calculated as the coefficient of variation (CV) between repeated measures, ranged from 2.3% to 3.1% for RE, whereas relative reliability, calculated as the intraclass correlation coefficient (ICC), ranged from .42 to .79. , , , , RER, and HR had a CV of 1.1%–4.3% across all stages. TTE and RPE had a CV of 7.2% and 2.3%–10.8%, respectively, while glucose and lactate had a CV of 4.0%–17.8%. All other parameters, except for blood glucose, were demonstrated to have good-to-excellent relative reliability assessed by ICC. Measures of RE, , and TTE were reliable during this two-phase multistage incremental treadmill test in a cohort of trained and highly trained male middle- and long-distance runners.
Velocity–Load Jump Testing Predicts Acceleration Performance in Elite Speed Skaters: But Does Movement Specificity Matter?
Authors: Zukowski Matthew , Herzog Walter , Jordan Matthew J.
Published: June 2024

Purpose: In this study, we compared the influence of movement specificity during velocity–load jump testing to predict on-ice acceleration performance in elite speed skaters. Methods: Elite long-track speed skaters (N = 27) performed velocity–load testing with 3 external loads during unilateral horizontal jumping, lateral jumping, and bilateral vertical countermovement jumping. For the unilateral tests, external load conditions were set to 10 N, 7.5% and 15% of external load relative to body weight. For the countermovement jumping, load conditions were body weight and 30% and 60% of external load relative to body weight. On-ice performance measures were obtained during maximal 50-m accelerations from a standing start, including maximal skating speed, maximal acceleration capacity, and maximum horizontal power. The 100-m split time from a 500-m race was also obtained. Regularized regression models were used to identify the most important predictors of on-ice acceleration performance. In addition to regularized regression coefficients, Pearson correlation coefficients (r) were calculated for all variables retained by the model to assess interrelationships between single predictors and on-ice performance measures. Results: The countermovement jump with 30% of body mass demonstrated the strongest association with maximal skating speed, maximum horizontal power, and 100-m time (regularized regression coefficient = .16−.49, r = .84−.97, P < .001). Horizontal jump with 15% of body mass was the strongest predictor of maximal acceleration capacity performance (regularized regression coefficient = .08, r = .83, P < .001). Conclusions: The findings of this study suggest that mechanical specificity rather than movement specificity was more relevant for predicting on-ice acceleration performance.
Synchronous Group-Based Online Exercise Programs for Older Adults Living in the Community: A Scoping Review
Authors: Fuentes Diaz Maria Fernanda , Leadbetter Brianna , Pitre Vanessa , Nowell Sarah , Sénéchal Martin , Bouchard Danielle R.
Published: June 2024

Older adults are the least physically active group with specific barriers to regular exercise, and online exercise programs could overcome some of those barriers. This scoping review aimed to describe the characteristics of supervised group-based synchronous online exercise programs for older adults living in the community, their feasibility, acceptability, and potential benefits. MEDLINE (Ovid), Embase, SPORTDiscus, and the Cumulative Index to Nursing and Allied Health Literature were searched until November 2022. The included studies met the following criteria: participants aged 50 years and above, a minimum of a 6-week group-based supervised and synchronous intervention, and original articles available in English. Eighteen articles were included, with 1,178 participants (67% female, average age of 71 [57–93] years), most (83%) published in the past 3 years. From the limited reported studies, delivering supervised, synchronous online exercise programs (one to three times/week, between 8 and 32 weeks) for older adults living in the community seems feasible, accepted, and can improve physical function.
Preparing Teachers for Physical Activity Leadership: Status of Comprehensive School Physical Activity Program in Physical Education Teacher Education
Authors: McKown Hayley B. , Merica Christopher B. , Egan Cate A.
Published: June 2024

Purpose: The purpose of this study was to better understand Comprehensive School Physical Activity Program (CSPAP) curricular offerings related to physical activity leadership training competencies that occur within physical education teacher education programs across the United States. Methods: Physical education teacher education program stakeholders were emailed an online survey about physical activity leadership training competencies and CSPAP training in physical education teacher education. A total of 142 participants (28% response rate) completed the survey. Descriptive statistics were used, and open-ended questions were analyzed. Results: Participants reported training preservice physical education teachers in CSPAP components: quality physical education (84%), physical activity before/after school (61%), physical activity during school (78%), staff involvement (50%), and family and community engagement (64%). Reported physical activity leadership competency training for preservice physical education teachers included: physical activity content knowledge (99%), leadership competencies (93%), communication and promotion (38%), and collaboration competencies (78%). Discussion/Conclusion: Training classroom teachers and physical education teachers to promote, sustain, and implement CSPAPs is vital to CSPAP uptake in schools.
Are Gender Stereotypes Still Prevalent in Physical Education? Spanish Teachers’ and Students’ Beliefs and Attitudes Toward Gender Equity
Authors: Castro-García Marina , Barquero-Ruiz Carmen , López-Villar Cristina
Published: June 2024

Purpose: Grounded in doing gender theory, the purpose is to explore physical education teachers’ and students’ beliefs and attitudes toward gender equity in physical education and sports and to identify possible aspects to be addressed. Method: This is a cross-sectional study in which a random sampling by multistage clusters was followed. Participants included 90 physical education teachers and 644 secondary school students, who completed two different questionnaires. Data were analyzed using descriptive statistics and t tests, one-factor analysis of variance, and two-way analysis of variance comparisons. Findings: Most students and teachers showed equitable beliefs and attitudes at a sociocultural level. The intersection of teachers’ age and self-identified sex/gender, and the self-identified sex/gender of students, played a significant role in the stereotypes. There was a gap between teachers’ results and students’ perceptions. Conclusion: Findings emphasize the need to implement critical feminist curricular approaches, especially with preservice teachers. Moreover, it is important that these approaches work on masculinities.
High-Resolution Diatomic Spectroscopy near the Dissociation Threshold
Authors: Stolyarov A. V.
Published: June 2024

Current progress in and prospects for high-resolution molecular laser spectroscopy used for quantum-mechanical modeling of the energy and radiation properties of the rovibronic states of diatomic molecules near the dissociation threshold are discussed at the experimental (spectroscopic) level of accuracy, which is impossible without taking into account all types of intramolecular interactions. The weakly bound, quasibound, and continuum rovibronic states localized near the dissociation threshold actively participate in the formation of stable molecules during spontaneous or laser-stimulated association of colliding atoms, which leads to cooling of the initial reaction medium. Laser-induced fluorescence (LIF) combined with high-resolution Fourier transform spectroscopy is a unique experimental technique, which allows the study of all the three (bound, quasibound, and continuum) parts of the molecular spectrum simultaneously. LIF experiments combined with precision ab initio electronic structure calculations and global nonadiabatic analysis of quasidegenerate rovibronic states converging to the same dissociation limit make it possible to study the structural and dynamic properties of isolated molecules over a very wide range of their electronic-vibrational-rotational excitation.
Study protocol OKRA: orientation compass for the preparing, delivering and following up on breaking bad news conversations in paediatric oncology
Authors: Krieger Theresia , Dittmer Kerstin , Reinert David , Hömberg Marc , Tepe Andrea , Hamm Isabel , Vitinius Frank
Published: June 2024

Introduction

In paediatric oncology, ‘breaking bad news’—BBN—like cancer diagnosis is perceived as particularly challenging. Enabling a trialogue between children with their relatives and health professionals requires profound communication skills. Lacking the skills, experience or adequate support tools might result in negative consequences for both paediatric oncologists as BBN transmitters and BBN receivers as children with cancer and their relatives.

In contrast to oncology for adults, multiperspective studies that explore BBN experience and specific support needs are rare, especially in Germany. Systematically developed and practically piloted support instruments, which address the specific needs of paediatric oncology, are missing.

Objective

To systematically design and mature in practice an orientation compass for preparing, delivering and following up on BBN conversations in paediatric oncology—so-called Orientierungskompass zur Übermittlung schwerwiegender Nachrichten in der Kinderonkologie (OKRA).

Methods and analysis

OKRA is based on a QUAL-quant mixed study design, comprising two phases. Four groups will contribute (1) experts through personal experience (representatives for children receiving BBN and their parents), (2) medical care providers and representatives of national medical societies, (3) ambulant psychosocial/psychological support providers and (4) researchers. In phase 1, multiperspective knowledge is generated through a participatory group Delphi that involves in-depth interviews, focus group discussions and questionnaires. This process culminates in formulating theses for a high-quality BBN process (output phase 1). In phase 2, based on the theses, a pilot orientation compass is designed. Through iterative cycles with the participatory action research method, this instrument will be piloted in three paediatric oncological settings and consequently optimised.

Ethics and dissemination

OKRA was approved on 19 September 2023 by the ethics committee of the Medical Faculty of the University of Cologne (No. 23-1187). After project completion, the OKRA compass will be distributed to multidisciplinary paediatric oncology teams throughout Germany.

Trial registration number

DRKS00031691.
Normal range and risk factors for deviating body temperatures during the first 24 hours in term-born infants under standardised care: an observational study
Authors: Tveiten Lars , Diep Lien My , Halvorsen Thomas , Markestad Trond
Published: June 2024

Objective

Body temperature for a known ambient temperature is not known for infants born at term. We aimed to determine the normal range and the incidences of hypothermia and hyperthermia during the first 24 hours of life in healthy term-born infants nursed according to WHO recommendations.

Design

Prospective observational study.

Setting

Norwegian single centre district hospital. Infants were observed during skin-to-skin care or when dressed in cots.

Participants

Convenience sample of 951 healthy infants born at term.

Methods

Delivery room temperature was aimed at 26–30°C and rooming-in temperature at 24°C. We measured rectal and room temperatures at 2, 4, 8, 16 and 24 hours of age.

Main outcome measures

Percentile curves for rectal temperature. Proportions and risk factors for hypothermia and hyperthermia.

Results

The mean (SD) room temperature was 24.0°C (1.1), 23.8°C (1.0), 23.8°C (1.0)., 23.7°C (0.9) and 23.8°C (0.9). The median (2.5, 97.5 percentile) rectal temperature was 36.9°C (35.7–37.9), 36.8°C (35.9–37.5), 36.9°C (36.1–37.5), 37.0°C (36.4–37.7) and 37.1°C (36.5–37.7). Hypothermia (<36.5°C) occurred in 28% of the infants, 82% of incidents during the first 8 hours. Risk factors for hypothermia were low birth weight (OR 3.1 (95% CI, 2.0 to 4.6), per kg), male sex, being born at night and nursed in a cot versus skin to skin. Hyperthermia (>37.5°C) occurred in 12% and most commonly in large infants after 8 hours of life. Risk factors for hyperthermia were high birth weight (OR 2.2 (95% CI, 1.4 to 3.5), per kg), being awake, nursed skin to skin and being born through heavily stained amniotic fluid.

Conclusions

Term-born infants were at risk of hypothermia during the first hours after birth even when nursed in an assumed adequate thermal environment and at risk of hyperthermia after 8 hours of age.
Model for the prediction of residual shear strength of unsaturated soils
Authors: Yang Xiuhan , Vanapalli Sai K.
Published: June 2024

The residual shear strength (RSS) of unsaturated soils is a crucial parameter for the reliable analysis and design of geo-structures constructed with or within unsaturated soils undergoing large shear deformation. For investigating the RSS of unsaturated soils, two sets of data are specifically generated on the poorly graded sand with silt and Indian Head till using suction-controlled ring shear tests and three more sets (i.e., silty sand (SM), silty clayey sand, and fat clay) are gathered from the literature. A model is proposed extending two approaches for predicting the RSS for both coarse- and fine-grained unsaturated soils. In this model, the suction contribution was calculated considering the loss of degree of saturation due to shearing, which was described as a nonlinear function of degree of saturation. The capability of the proposed model is validated with the five sets of data using two different approaches. The best-fitting approach that is based on three fitting parameters provides good predictions. The approximate approach performs well for four studied soils, except for SM soil; this approach is simple for use in engineering practice because no fitting parameters are required. The proposed model is valid for the suction range where degree of saturation is higher than the residual degree of saturation.
Technical Debt Monitoring Decision Making with Skin in the Game
Authors: Fungprasertkul Suwichak , Bahsoon Rami , Kazman Rick
Published: June 2024

Technical Debt Management (TDM) can suffer from unpredictability, communication gaps and the inaccessibility of relevant information, which hamper the effectiveness of its decision making. These issues can stem from division among decision-makers which takes root in unfair consequences of decisions among different decision-makers. One mitigation route is Skin in the Game thinking, which enforces transparency, fairness and shared responsibility during collective decision-making under uncertainty. This article illustrates characteristics which require Skin in the Game thinking in Technical Debt (TD) identification, measurement, prioritisation and monitoring. We point out crucial problems in TD monitoring rooted in asymmetric information and asymmetric payoff between different factions of decision-makers. A systematic TD monitoring method is presented to mitigate the said problems. The method leverages Replicator Dynamics and Behavioural Learning. The method supports decision-makers with automated TD monitoring decisions; it informs decision-makers when human interventions are required. Two publicly available industrial projects with a non-trivial number of TD and timestamps are utilised to evaluate the application of our method. Mann–Whitney U hypothesis tests are conducted on samples of decisions from our method and the baseline. The statistical evidence indicates that our method can produce cost-effective and contextual TD monitoring decisions.
Multi Fine-Grained Fusion Network for Depression Detection
Authors: Zhou Li , Liu Zhenyu , Li Yutong , Duan Yuchi , Yu Huimin , Hu Bin
Published: June 2024

Depression is an illness that involves emotional and mental health. Currently, depression detection through interviews is the most popular way. With the advancement of natural language processing and sentiment analysis, automated interview-based depression detection is strongly supported. However, current multimodal depression detection models fail to adequately capture the fine-grained features of depressive behaviors, making it difficult for the models to accurately characterize the subtle changes in depressive symptoms. To address this problem, we propose a Multi Fine-Grained Fusion Network (MFFNet). The core idea of this model is to extract and fuse the information of different scale feature pairs through a Multi-Scale Fastformer (MSfastformer), and then use the Recurrent Pyramid Model to integrate the features of different resolutions, promoting the interaction of multi-level information. Through the interaction of multi-scale and multi-resolution features, it aims to explore richer feature representations. To validate the effectiveness of our proposed MFFNet model, we conduct experiments on two depression interview datasets. The experimental results show that the MFFNet model performs better in depression detection compared to other benchmark multimodal models.
CASPER: Context-Aware IoT Anomaly Detection System for Industrial Robotic Arms
Authors: Kayan Hakan , Heartfield Ryan , Rana Omer , Burnap Pete , Perera Charith
Published: June 2024

Industrial cyber-physical systems (ICPS) are widely employed in supervising and controlling critical infrastructures, with manufacturing systems that incorporate industrial robotic arms being a prominent example. The increasing adoption of ubiquitous computing technologies in these systems has led to benefits such as real-time monitoring, reduced maintenance costs, and high interconnectivity. This adoption has also brought cybersecurity vulnerabilities exploited by adversaries disrupting manufacturing processes via manipulating actuator behaviors. Previous incidents in the industrial cyber domain prove that adversaries launch sophisticated attacks rendering network-based anomaly detection mechanisms insufficient as the “physics” involved in the process is overlooked. To address this issue, we propose an IoT-based cyber-physical anomaly detection system that can detect motion-based behavioral changes in an industrial robotic arm. We apply both statistical and state-of-the-art machine learning methods to real-time Inertial Measurement Unit data collected from an edge development board attached to an arm doing a pick-and-place operation. To generate anomalies, we modify the joint velocity of the arm. Our goal is to create an air-gapped secondary protection layer to detect “physical” anomalies without depending on the integrity of network data, thus augmenting overall anomaly detection capability. Our empirical results show that the proposed system, which utilizes 1D convolutional neural networks, can successfully detect motion-based anomalies on a real-world industrial robotic arm. The significance of our work lies in its contribution to developing a comprehensive solution for ICPS security, which goes beyond conventional network-based methods.
On Formal Methods Thinking in Computer Science Education
Authors: Dongol Brijesh , Dubois Catherine , Hallerstede Stefan , Hehner Eric , Morgan Carroll , Müller Peter , Ribeiro Leila , Silva Alexandra , Smith Graeme , de Vink Erik
Published: June 2024

Formal Methods (FMs) radically improve the quality of the code artefacts they help to produce. They are simple, probably accessible to first-year undergraduate students and certainly to second-year students and beyond. Nevertheless, in many cases, they are not part of a general recommendation for course curricula, i.e., they are not taught — and yet they are valuable.

One reason for this is that teaching “Formal Methods” is often confused with teaching logic and theory. This article advocates what we call
FM thinking
: the application of ideas from
Formal
Methods applied in informal, lightweight, practical and accessible ways. We will argue here that
FM thinking
should be part of the recommended curriculum for every Computer Science student, for even students who train only in that “thinking” will become much better programmers. However, there will be others who, exposed to those ideas, will be ideally positioned to go further into the more theoretical background: why the techniques work, how they can be automated, and how new ones can be developed. Those students would follow subsequently a specialised, more theoretical stream, including topics such as semantics, logics, verification and proof-automation techniques.
Behavior Expectation‐Based Anomaly Detection in Bridge Deflection Using AOA‐BiLSTM‐TPA: Considering Temperature and Traffic‐Induced Temporal Patterns
Authors: Qu Guang , Xia Ye , Sun Limin , Xin Gongfeng
Published: June 2024

In the realm of structural health monitoring (SHM), understanding the expected behavior of a structure is vital for the timely identification of anomalous activities. Existing methods often model only the physical quantities of monitoring data, neglecting the corresponding temporal information. To address this, this paper presents an innovative deep learning framework that synergistically combines a BiLSTM model, fortified by a temporal pattern attention (TPA) mechanism, with time‐encoded temperature and traffic‐induced deflection‐temporal patterns. The arithmetic optimization algorithm (AOA) is employed for optimal hyperparameter tuning, and incremental learning was implemented to enable real‐time updates of the model. Based on the proposed framework, an anomaly detection method was subsequently developed. This method is bidirectional: it uses quantile loss to provide expected ranges for structural behavior, identifying isolated anomalies, while the windowed normalized mutual information (WNMI) based on multivariate kernel density estimation (MKDE) helps detect trend variability caused by decreases in structural stiffness. This framework and the anomaly detection method were validated using data from an operational cable‐stayed bridge. The results demonstrate that the method effectively predicts structural behavior and detects anomalies, highlighting the critical role of temporal information in SHM.
Evaluation of Proximate Composition, Physicochemical Properties, and Sensory Attributes of Instant Flour from Brewery Spent Grain, by Blending with Maize (Zea mays L.) and Germinated Chickpea (Cicer arietinum L.)
Authors: Mekonnen Wosen Ewketu , Augchew Engeda Dessalegn , Terefe Zemenu Kerie
Published: June 2024

Brewer’s spent grain (BSG) is a nutritional-rich by-product of the brewing industry used in different food product development processes. “Corn Soya Blend” (CSB) is prepared from heat-treated maize and soybeans according to the specifications set by the World Food Program (WFP). Three instant formulations—IF20 (70% maize, 10% chickpea, and 20% BSG), IF15 (70% maize, 15% chickpea, and 15% BSG), and IF10 (70% maize, 20% chickpea, and 10% BSG)—were developed in this research. Proximate composition, functional properties, and antinutritional factors were analysed. The sensory quality of porridge samples developed from the instant flour was evaluated using a consumer-oriented panel (food science and technology students) at a five-point hedonic scale. Accordingly, moisture, crude fibre, crude protein, total ash, and crude fat contents increased significantly (p<0.05) as a result of BSG ratio inclusion. Bulk density decreased significantly (p<0.05) while the BSG proportion increased but water absorption capacity increased when the proportion of BSG increased. Phytate and tannin contents were also increased while the BSG proportion increased. However, an increase in germinated chickpea proportion significantly (p<0.05) decreased phytate and tannin contents. While BSG increased, the overall acceptability of porridge samples decreased, with the exception of mouthfeel. According to this study, up to 15% of BSG, 70% maize, and 15% chickpea could be used for instant flour preparation which has a comparable sensory characteristic with the commercial CSB. Hence, it can be used as a substitute for corn-soya mix.
Genomic Characterization of HLJDZD55: The First L1B PRRSV in China
Authors: Li Jinhao , Xu Hu , Li Chao , Zhao Jing , Gong Bangjun , Sun Qi , Guo Zhenyang , Zhang Siyu , Zhang Menglin , Xiang Lirun , Tang Yan-dong , Wu Jianan , Wang Qian , Peng Jinmei , Zhou Guohui , Liu Huairan , An Tongqing , Cai Xuehui , Tian Zhi-Jun , Zhang Hongliang
Published: June 2024

Porcine reproductive and respiratory syndrome virus (PRRSV) critically threatens the pig industry in China. Lineage 1 PRRSV, which is divided into L1A–L1F and L1H–L1J, is widely recognized as the most extensively genetically diverse lineage globally. L1A (NADC34‐like) and L1C (NADC30‐like) PRRSVs have become the current major circulating strains in China. Notably, strains from other branches of L1 have not been reported in China. During our epidemiological investigation of PRRSV, we identified a new strain named HLJDZD55. Phylogenetic analysis of the ORF5 gene revealed that HLJDZD55 belongs to the L1B branch. Alignment of deduced amino acid sequences based on the Nsp2 gene indicated that HLJDZD55 has a discontinuous deletion of 131 amino acids (111 + 1 + 19). We further sequenced the whole genome of HLJDZD55, and phylogenetic analysis based on the whole‐genome sequence revealed that HLJDZD55 belongs to the L1C branch. Recombination analysis of the whole genome demonstrated that HLJDZD55 is a recombinant strain of TJZH‐1607 (L1C, identified in China) and Minnesota 14 (L1B, identified in the USA). These findings suggested that HLJDZD55 is a newly emerged lineage 1 PRRSV in China and is closely related to L1B PRRSV in the US, which may have been introduced from the U.S. strain and subsequently recombined with the local Chinese strain and underwent evolution. Taken together, these results demonstrated the emergence of L1B PRRSV in China for the first time.
Harnessing Hydrogen: A Comprehensive Literature Review on Strategic Launching Initiatives in the Global Energy Market
Authors: Sakib Ahmed Nazmus , Mehjabin Farian , Schmidt Jeffrey B. , Haque Mushaida , Saha Koushik , Bhuiyan Md Monjur Hossain
Published: June 2024

In the transition towards sustainable energy sources, hydrogen has emerged as the predominant alternative after fossil fuels. This comprehensive literature review investigates into the evolving landscape of hydrogen launching strategies within the energy market. Numerous countries have strategically initiated efforts to introduce products based on hydrogen, highlighting the global acknowledgment of the potential inherent in hydrogen. Despite witnessing significant growth in the adoption of hydrogen energy worldwide, barriers to its widespread commercialization persist. This review identifies key barriers, including cost structures, technical complexities, and marketing challenges, hindering the establishment of a robust hydrogen energy market. The article critically examines the actions already undertaken to harness hydrogen energy for commercial purposes, looking at scientific insights and statistical data. By carefully examining the current state of hydrogen energy initiatives, the review highlights gaps in existing strategies that require thoughtful consideration. Insights derived from scientific and statistical analyses inform the identification of barriers and contribute to a nuanced understanding of the challenges faced in the journey towards hydrogen energy commercialization. Ultimately, the article presents a strategic proposal outlining marketing actions geared towards expediting the commercialization and widespread adoption of hydrogen energy. Grounded in a thorough review of existing literature and supported by scientific and statistical evidence, this comprehensive analysis offers valuable insights for stakeholders seeking to navigate the intricate landscape of hydrogen energy in the global energy market.
The Influence of Processing Parameters on the Process and Product Properties of Polylactic Acid/Natural Rubber Blown Films
Authors: Pongpakdee Chananchida , Akaradechakul Kornchanok , Pattamaprom Cattaleeya
Published: June 2024

This research investigates key parameters in the pilot-scale production of polylactic acid (PLA)/natural rubber (NR) blends for film-blowing applications. The study is aimed at analyzing the influence of process parameters including screw speed, output rate, compounding steps, and screw configuration on die stock pressure, temperature, %Torque, and retention time. The compounds were characterized for morphology, melt flow index, and film processability, whereby the resulting films were evaluated for surface appearance, transparency, and mechanical properties. It was found that the MFI value could be a good indicator of effective natural rubber dispersion, where higher screw speed and output rate contributed to slightly improved rubber dispersion due to higher shearing effects. Furthermore, an increase in the number of compounding steps and screw configurations with moderate mixing intensity led to further improved rubber dispersion. This was reflected by the reduced average rubber size from 4.1 to 1.0 μm, lower viscosity (higher MFI), smoother films, and improved mechanical properties.
How Do Patients Use Self-Care to Manage Nonspecific Symptoms Prior to a Cancer Diagnosis? A Rapid Review to Inform Future Interventions to Reduce Delays in Presentation to Primary Care
Authors: Wilson Georgia , Brewer Hannah R. , Flanagan James M. , von Wagner Christian , Hirst Yasemin
Published: June 2024

Background. A timely diagnosis of cancer is important for patient outcomes. The delay in the patient interval (time from symptom interpretation to seeking help) is often the longest throughout the cancer patient pathway. Factors extending this interval include vague symptom profiles increasing the difficulty of symptom appraisal and individual demographics influencing help-seeking behaviours. An underexplored and potential source of delay in cancer diagnosis is associated with managing symptoms using self-care activities prior to presentation to healthcare. Methods. This study aimed to characterise the use of self-care activities in the context of managing nonspecific symptoms, prior to cancer diagnosis and their effect on the length of the patient interval. Eligible publications were identified using a rapid systematic review, and their qualitative self-care data were extracted and analysed using thematic synthesis. Results. Forty-five qualitative research papers between 2009 and 2024 were included in the final review. Self-care was used as part of an iterative process, often resulting in delayed presentation to healthcare, if methods were effective in managing nonspecific symptoms. Across the literature, varying types of self-care activities were reported across all cancers with nonspecific symptoms, including the use of over-the-counter or alternative medications, lifestyle changes, and watchful waiting. The individual’s decision to self-care was either prompted externally by a healthcare professional (HCP) (e.g., community pharmacists) or prompted by the individual depending on the availability of home remedies and medication. Patients used self-care when there was a low perceived need to seek healthcare, to determine whether healthcare was required, or to avoid the use of healthcare. However, across the literature, there is limited evidence to understand the variation by cancer type, symptoms, and individual characteristics. Conclusions. The findings of this rapid review demonstrate that self-care activities could hinder prompt help-seeking and delay cancer diagnosis among people who are experiencing nonspecific cancer symptoms. However, more evidence is needed to understand which individual factors facilitate the adoption of self-care behaviours over prompt help-seeking for nonspecific cancer symptoms.
Challenges of Employing the Underlying Tenets of English as an International Language in Iran
Authors: Hashemian Mahmood , Heidari Vincheh Maryam , Mousavian Rad Seyed Ehsan
Published: June 2024

Concerning the complexities that are closely related to the rapid spread of English around the world over the past few decades, English as an international language (EIL) was proposed to represent a revisiting of the established notions within the field of teaching English as a foreign language (TEFL) by holding a positive attitude toward non-native English-speaking teachers (NNESTs) and their cultures. Given that English is not their first language, non-native English language learners, such as Iranian English as a foreign language (EFL) learners, may feel frustrated by the current status of EIL discussion. Consequently, searching for the challenges that they may struggle against seems to be an unexplored issue, which is the purpose of this qualitative study. Six Iranian Ph.D. students of TEFL who attended their EIL course were selected. The collection of student journals was combined with semistructured interviews with each participant. After analyzing the data using MAXQDA software and grounded theory, the challenges of EIL in Iran were categorized into five categories: (a) disownership of English, (b) Iranian cultural dominance in high school English textbooks, (c) prevalence of discrimination against NNESTs, (d) acceptance of only American and British varieties, and (e) lack of intercultural competence. The findings bear some implications for TEFL professionals to to explore the effects of challenges associated with employing the EIL tenets in EFL settings and to suggest potential solutions.
Network Intrusion Detection Using Knapsack Optimization, Mutual Information Gain, and Machine Learning
Authors: Afolabi Akindele S. , Akinola Olubunmi A.
Published: June 2024

The security of communication networks can be compromised through both known and novel attack methods. Protection against such attacks may be achieved through the use of an intrusion detection system (IDS), which can be designed by training machine learning models to detect cyberattacks. In this paper, the KOMIG (knapsack optimization and mutual information gain) IDS was developed to detect network intrusions. The KOMIG IDS combined the strengths of optimization and machine learning together to achieve a high intrusion detection performance. Specifically, KOMIG IDS comprises a 2-stage feature selection procedure; the first was accomplished with a knapsack optimization algorithm and the second with a mutual information gain filter. In particular, we developed an optimization model for the selection of the most important features from a network intrusion dataset. Then, a new set of features was synthesized from the selected features and combined with the selected features to form a candidate features set. Next, we applied an information gain filter to the candidate features set to prune out redundant features, leaving only the features that possess the maximum information gain, which were used to train machine learning models. The proposed KOMIG IDS was applied to the UNSW-NB15 dataset, which is a well-known network intrusion evaluation dataset, and the resulting data, after optimization operation, were used to train four machine learning models, namely, logistic regression (LR), random forest (RF), decision tree (DT), and K-nearest neighbors (KNN). Simulation experiments were conducted, and the results revealed that our proposed KNN-based KOMIG IDS outperformed comparative schemes by achieving an accuracy score of 97.14%, a recall score of 99.46%, a precision score of 95.53%, and an F1 score of 97.46%.
Application of Fully Connected Neural Network‐Based PyTorch in Concrete Compressive Strength Prediction
Authors: Dong Xuwei , Liu Yang , Dai Jinpeng
Published: June 2024

Compressive strength of concrete is an important parameter in the design of concrete structures and the prediction of their durability. Therefore, it is of great significance to predict the compressive strength of concrete. In this study, a fully connected neural network model is developed using the PyTorch framework to predict the compressive strength of concrete and compared with six other machine learning models. These models are multiple linear regression, K‐nearest neighbor regression, support vector machine, decision tree, random forest, light gradient boosting machine, and artificial neural network. The model is trained using 4,253 data with seven input parameters, including cement (C), fly ash (F), mineral powder (K), fine aggregate (FA), coarse aggregate (CA), water reducer admixture (WRA), and water (W). Three thousand six hundred twenty‐one data in the datasets are used to train the prediction model after data cleaning, and 632 data are used to validate the model. The results show that the fully connected neural network model based on PyTorch frame can predict the compressive strength of concrete with higher accuracy. Therefore, it is a reliable and useful method to optimize the artificial network model. So, it has important application value in practice. The strength of concrete can be predicted in advance, making the project more efficient and reducing costs. Besides, by adjusting the mix ratio, combining the strength prediction results in different environments and industries to ensure the quality of construction.
Knowledge, Attitude, and Practice (KAP) of ICU Nurses towards Tracheal Intubation Patients’ Postextubation Dysphagia: A Cross-Sectional Study
Authors: Sun Shanshan , Tao Lei , Yang Lin , Zhang Zhigang
Published: June 2024

Aim. The aim of this study was to understand the current status of knowledge-attitude-practice of ICU nurses in tertiary care hospitals regarding swallowing disorders after extubation of tracheally intubated patients and to analyse the influencing factors. Design. A cross-sectional study. Background. Most patients admitted to the ICU have an endotracheal tube, which may be the cause of acute and/or chronic problems after extubation. Therefore, training of ICU nurses and early extubation are essential to prevent these problems. Methods. A convenience sample of clinical nurses (n = 627) was selected from Grade A Hospitals in Northwest, North, and Central China as the study population. Survey instruments included the Questionnaire on ICU Nurses’ Knowledge, Attitude, and Practice of Postextubation Swallowing Disorders in Patients with Tracheal Intubation. Data Sources. Data were sourced from structured questionnaire responses. Results. A total of 647 ICU nurses participated in this survey, with 627 valid questionnaires. The three dimensions of knowledge, attitude, practice, and the total score of the questionnaire on ICU nurses' knowledge, attitude, and practice of postextubation swallowing disorders in patients with tracheal intubation were (6.46±3.09), (7.53±1.69), (4.89±2.00), and (18.88±5.18), respectively. Multiple linear regression analysis showed that the factors affecting the total score of PED among ICU nurses were age, nationality, professional title, job satisfaction, and mode of employment. Gender, age, nationality, and job satisfaction were the factors that influenced the score of knowledge. The influencing factors of attitude score include gender, age, nationality, section, professional title, job satisfaction, and mode of employment. The influencing factors of the score of knowledge include professional title and job satisfaction. Conclusion. The current status of ICU nurses’ knowledge-attitude-practice regarding postextubation dysphagia in tracheally intubated patients is generally at a moderate to low level, and the level of knowledge-attitude-practice needs to be further improved. Implications for Nursing Management. The results of the study showed that the knowledge, attitude, and practice of ICU nurses towards tracheal intubation patients’ postextubation dysphagia were in the lower middle level. Therefore, it is necessary to improve the knowledge, attitude, and practice of ICU nurses towards tracheal intubation patients’ postextubation dysphagia. This may include, but is not limited to, the development of tools for assessing PED, systematic and professional training, and the development of multidisciplinary collaborative models.
Safeguarding data privacy: strategies to counteract internal and external hacking threats
Authors: Jamal Hassan , Ahmed Algeelani Nasir , Al-Sammarraie Najeeb
Published: June 2024

In the digital age, the protection of data privacy has become increasingly important. Hackers, whether internal or external to an organization, could cause significant damage by stealing sensitive data, causing financial loss, compromising the privacy of individuals, or damaging the organization's reputation. This scientific research aimed to make substantial contributions by emphasizing the importance of addressing both internal and external hacking threats to protect sensitive information. The main theme of their work revolved around building a multi-layered defense system that included technological solutions like firewalls, encryption, and intrusion detection systems. The specific goals of their design and development approach were to establish clear policies and procedures for data handling, access control, and incident response, as well as to enhance data privacy strategies to stay ahead of evolving hacking techniques. The authors also highlighted the significance of employee awareness and training programs, collaboration with cybersecurity experts, and staying up-to-date with regulatory requirements to create a robust data privacy framework.
Deep learning technique for plant disease detection
Authors: Samson Adekunle Temitope , Oladayo Lawrence Morolake , Omotayo Alabi Oluwaseyi , A. Afolorunso Adenrele , Nse Ebong Godwin , Abiola Oladipupo Matthew
Published: June 2024

A nation's economy is primarily reliant on agricultural growth. However, several plant diseases seriously impair crop growth, both in terms of quantity and quality. Due to a lack of subject matter specialists and low contrast data, accurate diagnosis of many diseases by hand is highly difficult and time-consuming. The farm management system is therefore looking for a method for automatically detecting early illnesses. To overcome these challenges and correctly classify the different diseases, an efficient and small deep learning-based framework (E-GreenNet) is proposed. A MobileNetV3Small model is used as the foundation of our end-to-end architecture to produce finely tuned, discriminative, and noticeable features. Furthermore, the new plant composite (PC), plantvillage (PV), and data repository of leaf images (DRLI) datasets are used to independently train our proposed model, and test samples are used to evaluate its actual performance. The suggested model achieved accuracy rates of 1.00 percent, 0.96 percent, and 0.99 percent on the given datasets after a rigorous experimental study. Additionally, a comparative investigation of our proposed technique against the state-of-the-art (SOTA) reveals extremely high discriminative scores.