bluuebunny/crossref_metadata_2025 huggingface dataset:
EEG measurements-based study for evaluating acoustic human perception: A pilot study
Authors: Mansi Silvia Angela , Jamali Reza , Battista Gianmarco , Arnesano Marco , Martarelli Milena , Chiariotti Paolo , Castellini Paolo
Published: July 2024

Sound quality analysis and sound design are well-known human-centered strategies to evaluate the subjective perception of noise and to design machines and environments with pleasant and comfortable acoustic signatures. The subjective acoustic perception is conventionally measured by means of sound quality metrics determined through a correlation process with jury test results. The exploitation of electroencephalogram (EEG) measurements during the jury test for the registration of the brain activity in response to the acoustic stimuli presented to the jurors can allow us to estimate the jurors’ perception directly from their physiological response. This study presents results from the application of an EEG wearable device to investigate changes in the EEG frequency domain at different acoustic stimuli. Forty-three participants were recruited, and the EEG signals were recorded using the wearable sensor. The analysis of power spectral densities (PSDs) was performed to investigate features correlated to acoustic sensation induced by audio stimuli. Statistically significant differences were found between the three audio stimuli. The results bring to the conclusion that wearable sensors could be used for EEG acquisition applied to acoustic perception evaluation.
Editorial to selected papers from the 2023 IMEKO International Conference on ‘Metrology for Archaeology and Cultural Heritage’ – part 1
Authors: Leccese Fabio
Published: July 2024

This Special Issue collects the extended version of some of the contributions presented at the 2024 IMEKO International Conference on Metrology for Archaeology and Cultural Heritage, held in Rome (Italy) from the 19th to the 21st of October 2023. This international conference brought together experts with different expertise but united by the interest in the characterization and conservation of Cultural Heritage (CH).
Smectite Illitization Geothermometry of the Upper Indus Basin Hydrocarbon Source Rocks (Pakistan)
Authors: Ali A. , Ullah S.
Published: July 2024

—Smectite illitization geothermometry has been used to assess the thermal maturity and hydrocarbon generation potential of the late Paleocene to early Eocene Patala Formation in the Upper Indus Basin. X-ray diffraction (XRD) detected illite, muscovite, quartz, kaolinite, chlorite, and calcite. Comparison between air-dried (AD) and ethylene glycol (EG)-solvated XRD patterns reveals the absence of discrete smectite and interstratified illite-smectite (I-Sm). Additionally, authigenic illite-2M1 indicates that the Patala Formation has entered the late-stage diagenetic zone or the low anchizone, which lies in the R3 illitization zone. Abundant SiO2 and Al2O3 in the Patala clay fraction indicate the substitution of tetrahedral Si4+ by Al3+ within the smectite interlayers. Likewise, the relative abundance of K2O to CaO and MgO indicates the exchange of K+ with Ca2+ and Mg2+ during smectite illitization. Scanning electron microscopy (SEM) reveals in situ growth of platy illite crystals that form within the R3 zone. Furthermore, the total organic carbon (TOC) of the Patala shale exposed in Tirah suggests a poor to good source rock. The absence of discrete smectite and I-Sm, combined with the detection of discrete illite in the Patala shale, suggests that hydrocarbon might have potentially migrated from the source to the reservoir rock during smectite illitization.
Technology Focus: Reservoir Simulation (July 2024)
Authors: Abraham Anson
Published: July 2024

The early days of artificial intelligence (AI) and machine learning (ML) were filled with promise, but now we’re witnessing a wave of practical applications transforming reservoir engineering. The integration of AI/ML with traditional workflows marks a turning point, unleashing the immense potential of these proven techniques to address our everyday challenges in reservoir simulation.

These advancements lead to more-accurate models and faster simulation turnaround times, allowing us to model various scenarios more efficiently. These techniques enable us to understand and simulate reservoir behavior with greater fidelity and focus on the underlying physics. This deeper understanding translates to more-confident decisions regarding reservoir management and development, with a clearer picture of potential risks.

Physics-informed machine learning emerges as a significant step forward, improving our understanding and providing better models while boosting runtimes, convergence, and overall performance. Paper IPTC 23730 highlights its application in tackling computationally intensive tasks such as critical temperature prediction with high accuracy, leading to significant speed-ups in simulations, particularly for complex compositional models undergoing miscible gas injection.

Paper IPTC 23935 presents a fundamentally different approach to reservoir simulation studies. Adaptive models use multiple smaller, specialized models instead of one giant model. These models can be developed and run independently, allowing for parallel workflows and significantly reduced turnaround times. Adaptive models foster a culture of rapid experimentation and iteration, aligning perfectly with the fail-fast approach—prioritizing the quick evaluation of ideas and discarding those that don’t show promise early on.

Paper SPE 214855 provides a comprehensive overview of a complex problem—wellbore modeling. This paper offers practical insights directly relevant to my work.

These advancements represent a significant leap forward, paving the way for more-powerful and -versatile reservoir models. As research and development continue, these techniques are poised to revolutionize how we manage and optimize oil and gas reservoirs. I hope you enjoy reading this selection of papers and find them enlightening.

Recommended additional reading at OnePetro: www.onepetro.org.

SPE 216722 Developing Consistent Relative Permeability and Capillary Pressure Models for Reservoir Simulation of Carbon Capture and Storage Projects by L.S. Lun, ExxonMobil, et al.
Hybrid Swin Transformer is Gaze Estimation need
Authors: Wang Yanxia , CHENG ZHANG , Liang Wei , Huang Fenglin
Published: July 2024

In recent years, models based on the Swin Transformer architecture have significantly improved the performance of many computer vision tasks. However, the performance of the Swin Transformer architecture in gaze estimation tasks remains to be verified. In this paper, in order to introduce Swin Transformer into gaze estimation, Pure Swin Transformer(PST-Net) is proposed by improving Swin-T. Based on this, in order to further improve the performance of PSTNet and reduce the computational complexity, a new lightweight model, Hybrid Swin Transformer(HST-Net), is proposed. To the best of our knowledge, we are the first to apply Swin Transformer in gaze estimation. HST-Net significantly improves performance by adding CNNs before and after the Swin Transformer Block. Experiments show that HST-Net significantly outperforms PST-Net and that the model’s generalization performance is significantly better than previous methods. We further perform experiments to explore the effect of HST-Net based on a range of publicly available datasets and analyze the impact of the depthwise separable convolution after the Swin Transformer Block on the final results. The experiments demonstrate that after pre-training, HST-Net outperforms GazeTRHybrid in all benchmark tests, achieving optimal performance while reducing parameters by 56.4% and FLOPs by 75.4%.
Synergistic impacts of clean energy demonstration policies on pollution and carbon reduction
Authors: Cui LiZhi , Sun Wang
Published: July 2024

The promotion of synergies between pollution and carbon reduction is a key strategic direction for building a beautiful China, with the development of clean energy being a positive step towards achieving this goal. In this study, we used China's provincial panel data from 2005 to 2020 to examine the clean energy demonstration province (CEDP) pilot as a quasi-natural experiment and utilized the synthetic control and difference-in-differences model to assess the synergistic effect of CEDP formulation on pollution and carbon reduction. We found that CEDP formulation synergistically reduced pollution and carbon and that policy implementation effectively reduced the PM2.5 emission intensity and carbon emission intensity of the pilot provinces. These were robust findings even after multiple tests. There was regional heterogeneity in the synergistic effect of CEDPs, and the policy effect of conducting the CEDP pilots was more significant in the central and western regions than in the eastern regions. Further mechanism tests showed that industrial upgrading and energy efficiency improvement were important channels for CEDP formulation to achieve the synergistic effect of pollution and carbon reduction. We proposed to enhance the evaluation system for CEDP formulation and implementation, systematically expand the coverage of CEDP pilots, and reinforce the mechanism for industrial upgrading and energy efficiency improvement.
The challenge of uncertainty: can sustainability be achieved under unpredictable ecological dynamics?
Authors: Chiaravalloti Rafael , Dyble Mark , Homewood Katherine , Burgman Mark , Canty Steven , Moreau Marie-Annick , Galuccio Malena , Balet Domenec Ruiz I , Wadt Lucia Helena , Nunes André , Mills Morena
Published: July 2024

Unpredictable environmental dynamics may be the greatest challenge of our time1,2. However, sustainability initiatives are designed with the assumption that people’s access to natural resources is relatively predictable3. This assumption is based on the principle that to use resources sustainably users must have the ability to exclude free riders through clear boundaries4. A lack of boundaries is linked to open access and the tragedy of the commons5. However, clear and enforceable boundaries are not always possible, especially in areas with unpredictable natural resource dynamics6,7. How sustainability can be achieved in unpredictable systems is still poorly explored8. Here we show that many socio-ecological systems have developed customary systems to deal with unpredictability and achieve sustainability. We found that predictability levels below 50% lead socio-ecological systems to display broad reciprocal access across socially and spatially fuzzy boundaries and high levels of mobility. Our results demonstrate that functioning governance structures do not occur only when people have predictable access to resources and clear boundaries to resource extraction are imposed. We show that to deal with the increasing level of unpredictability around the world9, sustainability science and practice must recognise the possibility of governance structures based on mobility and information/resource sharing6.
Sustainable Construction in Nigeria: A Socioeconomic Impact Analysis
Authors: Unegbu H. C. O. , Yawas D. S. , Dan-asabe B. , Alabi A. A.
Published: July 2024

Sustainable construction practices represent a critical pathway towards fostering economic development and social well-being within the Nigerian construction industry. This research aims to elucidate the socioeconomic impacts of sustainable construction practices in Nigeria, employing a quantitative approach and utilizing simulated data for analyses. Through a comprehensive survey of 370 construction industry professionals, this study investigates the perceptions of sustainable practices such as energy efficiency, cost reduction, and local economic development. Findings revealed a strong consensus among respondents regarding the positive socioeconomic impacts of sustainable strategies, showcasing correlations between these practices and economic growth, job creation, and the appreciation of property values. The regression analysis highlighted the significant influence of these sustainable practices on socioeconomic aspects, emphasizing the interconnectedness between sustainable construction and economic advancement. The implications of this study underscore the importance of integrating sustainable strategies within the Nigerian construction sector, advocating for policy adjustments to leverage the benefits of these practices. This research contributes to the growing discourse on sustainable construction practices, emphasizing their potential to drive economic growth and enhance community well-being within Nigeria.
Food contamination with fipronil alters gene expression associated with foraging in Africanized honey bees
Authors: Lima Yan Souza , Lippi Isabella Cristina de Castro , Scheffer Jaine da Luz , Lunardi Juliana Sartori , Alvarez Marcus Vinícius Niz , Kadri Samir Moura , Orsi Ricardo de Oliveira
Published: July 2024

Taking into consideration that bees can be contaminated by pesticides through the ingestion of contaminated floral resources, we can utilize genetic techniques to assess effects that are scarcely observed in behavioral studies. This study aimed to investigate the genetic effects of ingesting lethal and sublethal doses of the insecticide fipronil in foraging honey bees during two periods of acute exposure. Bees were exposed to fipronil through contaminated honey syrup at two dosages (LD50 = 0.19 μg/bee; LD50/100 = 0.0019 μg/bee) and for two durations (one and four hours). Following exposure, we measured syrup consumption per bee, analyzed the transcriptome of bee brain tissue, and identified differentially expressed genes (DEGs), categorizing them functionally based on Gene Ontology (GO). The results revealed a significant genetic response in honey bees after exposure to fipronil, regardless of the dosage used. Fipronil affected various metabolic, transport, and cellular regulation pathways, as well as detoxification processes and xenobiotic substance detection. Additionally, downregulation of several DEGs belonging to the Olfactory Binding Protein (OBP) family was observed, suggesting potential physiological alterations in bees that may lead to disoriented behaviors and reduced foraging efficiency.
Clustering temporal step-counting patterns for 24 hours with machine learning revealed potential heterogeneity in the categorization by a traditional tertile procedure
Authors: Nawrin Saida Salima , Inada Hitoshi , Momma Haruki , nagatomi Ryoichi
Published: July 2024

Background
Physical activity is a crucial aspect of health benefits in the public society. Although studies on the temporal physical activity patterns might lead to the protocol for efficient intervention/program, a standardized procedure to determine and analyze the temporal physical activity patterns remains to be developed. Here, we attempted to develop a procedure to cluster 24-hour patterns of physical activity as step counts measured with an accelerometer-based wearable sensor.
Methods
Data was collected from 42 healthy participants, comprising 35 males and 7 females, at the Sendai Oroshisho center in 2008. This is a cross sectional study using unsupervised machine learning, specifically the kernel k-means algorithm with the global alignment kernel was applied on a total of 815 days from 42 participants, and 6 activity patterns were identified. Further, the probability of each 24-hour step-counting pattern was calculated for every participant., and was used in MATLAB to apply spectral clustering, and 5 activity behaviors were identified.
Results
We could identify six 24-hour step-counting patterns and five daily step-behavioral clusters. When the amount of physical activity was categorized into tertile groups reflecting highly active, moderately active, and low active, each tertile group consisted of different proportions of six 24-hour step-counting patterns.
Conclusions
Our study introduces a novel approach using an unsupervised machine learning method to categorize daily hourly activity, revealing six distinct step counting patterns and five clusters representing daily step behaviors. Our procedure would be reliable for finding and clustering physical activity patterns/behaviors and reveal heterogeneity in the categorization by a traditional tertile procedure using total step amount.
Oxytocin Supplementation Demonstrates Potential in Reducing Breast Cancer Growth via Regulation of the PIK3CA/ULK1/Autophagy Axis
Authors: Xu Shuo-Han , Zhou Feng-Jia , Liu Shuai , Fan Xin-Yu , Yan Ying
Published: July 2024

Breast cancer stands as a leading cause of morbidity and mortality among patients worldwide. The emerging trend in cancer prevention and treatment involves harnessing endogenous substances to regulate disease occurrence and progression. This study aimed to decipher the inhibitory effect of oxytocin on breast cancer and its underlying mechanism. Leveraging network pharmacology and molecular docking, we screened oxytocin's key targets in breast cancer. Our in vitro analysis revealed oxytocin's anti-proliferative, anti-migratory, and apoptosis-inducing properties in human breast cancer cell lines, MCF-7 and ZR-75-1. Additionally, oxytocin significantly suppressed the growth of MCF-7 transplanted tumors in nude mice. Western blot analysis detected oxytocin's modulation of PIK3CA expression and autophagy pathway factors, including ULK1, p62, LC3-Ⅰ, and LC3-Ⅱ, leading us to propose a mechanism involving the PIK3CA/ULK1/autophagy axis. The findings indicate that oxytocin effectively inhibits breast cancer cell proliferation and migration, promotes apoptosis, and reduces tumor growth, potentially through regulation of the PIK3CA/ULK1/autophagy pathway. Low-dose oxytocin supplementation emerges as a promising strategy for breast cancer prevention and treatment.
GCN2 inhibition reduces mutant SOD1 clustering and toxicity and delays disease progression in an Amyotrophic Lateral Sclerosis mouse model
Authors: Ortiz Didio Alberto , Peregrín Nuria , Valencia Miguel , Vinueza-Gavilanes Rodrigo , Marín-Ordovas Elisa , Ferrero Roberto , Nicolás María Jesús , González-Aseguinolaza Gloria , Arrasate Montserrat , Aragón Tomás
Published: July 2024

The disruption of protein folding homeostasis in motoneurons (MNs), and the ensuing accumulation of protein aggregates, is one of the main molecular hallmarks of amyotrophic lateral sclerosis (ALS) pathology, and has been recapitulated in cellular and animal disease models. The loss of proteostasis and other stresses in the MN trigger the activation of a general stress mechanism, the integrated stress response (ISR). The ISR is initiated by either of four stress-sensing kinases (GCN2, HRI, PERK and PKR) which, upon activation by distinct insults, promote a dramatic remodeling of gene expression to combat stress and promote survival. Paradoxically, in pathologies where stress is chronic or overwhelming, the ISR can also promote neuronal death. In ALS experimental models, extensive evidence demonstrates a key role of this mechanism in the progression of disease, and has inspired many attempts to develop ALS therapies based on ISR modulation.
In our group, we recently discovered that the downstream ISR inhibitor ISRIB increases survival of a neuronal ALS model based on the expression of the neurotoxic ALS allele, SOD1 G93A. In the current study, we found that ISR inhibition is sufficient to prevent the concentration of mutant SOD1 into cytosolic foci, suggesting that ISR is required for SOD1 protein aggregation. Through a systematic CRISPR Cas9 approach and pharmacological inhibition, we demonstrate that, unexpectedly, the ISR kinase GCN2 is required for SOD1 clustering in cell lines and primary neuronal cultures. Moreover, genetic or pharmacological GCN2 inhibition strongly enhances survival of neurons overexpressing mutant SOD1. Finally, GCN2 pharmacological inhibition in fALS SOD1G93A transgenic mice delayed muscle denervation, strength loss, weight loss, and the appearance of ALS symptoms. Based on these findings, we propose GCN2 as a new potential therapeutic target for ALS.
Tyrer-Cuzick Lifetime Risk is Not Associated with Non-BRCA1/2 Pathogenic Variants for Breast Carcinoma
Authors: Rao Divya , Wernecke Chloe , Baron Lisa , Cai Steven , Beitsch Peter , Patel Rakesh , Whitworth Pat , Rosen Barry , Tran Nhan Nguyen , Hughes Kevin , Baron Paul L.
Published: July 2024

Purpose: The Tyrer-Cuzick lifetime risk score (TC LR) is used to estimate the probability of an unaffected female patient developing breast cancer as well as their likelihood of carrying a BRCA1/BRCA2 pathogenic variant (PV), considering factors such as family history, personal history, and breast density. The purpose of this study is to determine if TC LR is associated with non-BRCA1/2 PVs.
Methods: A population of 964 patients, enrolled from 2019-2022, were evaluated for 12 PVs and variants of unknown significance (VUS) using lab agnostic genetic testing. TC LR scores were compared between the three patient cohorts - BRCA PVs, non-BRCA PVs, and PV negative, using Kruskal-Wallis test and DSCF adjustment for multiple comparisons. Chi-square test and logistic regression were used to compare the groups.
Results: This study found an average TC LR of 7.71%. The presence of PVs and VUS was evaluated, with 12.03% of patients were found to have a PV (average TC LR 8.98%). Out of those with PVs, 52% had non-BRCA1/2 PVs (average TC LR 5.47%). A total of 102 patients (10.58%) had a VUS (average TC LR 8.29%).
Conclusion: TC LR scores were found to be low in patients with non-BRCA1/2 PVs. The study suggests that the decision for genetic testing should not be influenced by TC LR but should be based on individual patient’s family history, NCCN guidelines, or the TC genetic risk score.
Vulnerability Assessment and Decision-Making in Older Patients with Head and Neck Cancers Planned for Chemoradiation: Experience from a Tertiary Care Cancer Centre in India
Authors: Noronha Vanita , Sarkar Laboni , Pillai Anupa , Rao Abhijith Rajaram , Kumar Anita , Dhekale Ratan , Mahajan Sarika , Daptardar Anuradha , Sonkusare Lekhika , Vagal Manjusha , Mahajan Purabi , Timmanpyati Shivshankar , Gota Vikram , Laskar Sarbani , Budrukar Ashwini , Swain Monali , Shetake Ankush , Chowdhury Oindrila Roy , Pawar Akash , Prabhash Kumar
Published: July 2024

Objectives
We aimed to evaluate clinical characteristics and outcomes of patients with head and neck squamous cell cancers (HNSCC) planned for chemoradiation enrolled in our geriatric clinic and the role of geriatric assessment (GA) in treatment decision-making.
Methods
Patients aged over 60 years with locally advanced HNSCC planned for chemoradiation referred to the geriatric clinic from 2018 to 2023 were identified from the clinic database.
Results
Of 164 patients identified, 36/148 (24.3%) patients were vulnerable in the domain of function and falls, 41/84 (48.8%) in comorbidities, 88/148 (55.4%) in nutrition, 35.148 (23.6%) in psychological, 6/87 (6.9%) in cognition, 25 (11.8%) in polypharmacy and 8 (5.4%) in social domains. 76 (51.4%) were frail, with 2 or more vulnerabilities.
35 (21.3%) patients were planned for radiotherapy alone, and 117 (71.3%) patients ultimately received chemoradiation. Decision on chemotherapy regimen was made awaiting GA in 99 (84.6%) patients. Among them, tentative chemotherapy regimen was changed after GA in 13 (13.1%) patients. 24-month OS was 64.7% (95% CI: 52.2%-80.3%). Factors significantly associated with poorer overall status (OS) were Eastern Cooperative Oncology Group performance status (PS) >=2 (hazard ratio (HR) 5.38, CI 2.39-12.1), vulnerability in nutrition (HR 2.52, CI 1.17-5.39), higher CARG score (HR 9.92, CI 3.91-25.16) and frailty (HR 10.51, CI 2.89-38.22). Factors impacting grade 3/4 toxicity included vulnerability in nutrition, higher CARG score and PS >=2.
Conclusion
71.3% patients planned for chemoradiation went on to receive concurrent chemotherapy after GA, with vulnerabilities in nutrition, frailty, performance status and CARG score being predictive for OS.
Optimization of Subcritical Water Pretreatment on Glutinous Rice Husk for Efficient Sugar and Hydrogen Production
Authors: Aziz Izzah Farhana Ab , Man Hasfalina Che , Dau Anelka , Hashim Norhashila , Hamzah Muhammad Hazwan , Hamzah Adila Fazliyana Aili
Published: July 2024

There are thousands of different rice cultivars available worldwide, and glutinous rice (Oryza sativa var. glutinosa), sometimes known as sticky rice, is one of the most well-known varieties. Glutinous Rice Husk (GRH) is categorized as lignocellulosic biomass obtained as a by-product of rice manufacturing. The GRH is residue with a substantial amount of cellulose, hemicelluloses, and lignin, but it is difficult to degrade GRH during anaerobic digestion or fermentation. This research aims to investigate the factors affecting the sugar production of GRH by subcritical water (SCW) pretreatment. It is expected that the sugar produced could increase biohydrogen production. The biohydrogen yield was compared between raw and SCW-treated GRH as a fermentation substrate. Response Surface Methodology (RSM) was utilized to determine the ideal parameters for the subcritical water pretreatment of GRH using a Central Composite Design in the Design Expert Software. SCW pretreatment with temperature (195°C), solid-to-liquid ratio (1:10), and reaction time (17.5 minutes) were selected for the optimum sugar production (647.2 mg/L total sugar) of GRH. This pretreatment improves hydrogen production during dark fermentation by increasing the availability of easily accessible simple sugars for microbes. Additionally, it improves the lignocellulosic structure of GRH to enhance the fermentation process. Dark fermentation with SCW-treated GRH (0.73 mL H2/g-TS) obtained higher hydrogen yield than raw GRH (0.09 mL H2/g-TS). These findings highlight the potential of SCW pretreatment as an effective strategy for maximizing hydrogen production from GRH and contributing to using lignocellulosic biomass for renewable energy applications.
Multi-view unsupervised feature selection based on consensus indicator graph learning with adaptive similarity
Authors: Ma Yingcang , Zou Jinlin , Yang Xiaofei , Xing Zhiwei , Deng Yongmei
Published: July 2024

Abstract: How to obtain discriminate features with consensus and complementary information between different views is crucial for multi-view feature selection. However, most of the existing methods often emphasize the consistency of multi-view structures and ignore their heterogeneous information. To tackle this issue, this study proposes multi-view unsupervised feature selection of consensus indicator graph learning with adaptive similarity(CIAS). Adaptive similarity learning of multiple views plays an important role in maintaining heterogeneous information in the selected features. Concretely, structural graphs with only 0-1 values, can be learned from the dissimilarity between the samples. Then, a shared indicator graph with consistent information is fused with the above indicator graphs of different views. Finally, the feature selection matrices of different views are guided by the shared indicator graph. It can solve the proposed model efficiently using an alternating iterative approach. Furthermore, the experimental results demonstrate our algorithm's superiority when compared to six new related algorithms.
Effect of Cementation Ratio and Molarity on Mechanical Properties of MICP-Treated Sand Subjected to Freeze-Thaw Cycles
Authors: Abbasi Mehdi , Hosseinpour Iman , Barari Amin , Mirmoradi Seyed Hamed
Published: July 2024

This study investigates the efficacy of microbial-induced carbonate precipitation (MICP) on the mechanical properties of poorly graded sand through a set of laboratory experiments. Unconfined Compressive Strength (UCS), Ultrasonic Pulse Velocity (UPV), Scanning Electron Microscopy (SEM), and calcium carbonate assessments were conducted to evaluate the influence of MICP under varying cementation concentrations, cementation ratios, and injection cycles. To this end, treated samples underwent 3, 14, and 21 injection cycles with cementation ratios ranging from 10–90% and molarities of 0.25, 0.5, 0.75, and 1 mol/L. Optimally stabilized samples were then subjected to 2, 4, 6, 8, 10, and 12 freeze-thaw cycles to evaluate their thermal durability. Correlation relationships were also developed to predict the compressive strength and stiffness of MICP-treated sand. Results demonstrated that MICP treatment effectively enhanced the UCS and stiffness by forming interlocking zones between the sand particles. Accordingly, the maximum UCS, secant stiffness, and constrained modulus were achieved at 14.98% calcite content using Sporosarcina pasteurii bacteria accompanied by a 50% cementation ratio and molarity of 0.75 mol/L over 21 injection cycles. Also, the optimally stabilized specimens exhibited 70% and 90% retention in USC and stiffness after 12 freeze-thaw cycles, confirming their sustainability under harsh thermal conditions.
Effect of Medial Stabilizer Chest Position on Pectus Bar Dislocation
Authors: Melhado Caroline , Highet Alexandra , Mukherjee Neal , Ozgediz Doruk , Idowu Olajire , Kim Sunghoon
Published: July 2024

Purpose:The current standard method for pectus excavatum (PE) repair is the Nuss procedure. One major postoperative complication is the displacement of the implanted metal bar, which is used to remodel the chest wall. Blocking the possible ways that the bar can be displaced with the use of stabilizers and peri/intracostal sutures has reduced the incidence of bar displacement. Despite the modifications, bar dislocation is often reported. We adopted the medial position stabilizer placement method and imposed no postoperative restrictions. In this study, we analyzed the bar dislocation rate with this modification and concurrent postoperative full activity.
Methods:Nuss procedure modification where stabilizers are placed bilaterally in the medial location was done on patients irrespective of age and Haller index greater than 3.25. A single bar was used for all patients. Cryoanalgesia was performed on every patient. No postoperative restrictions were imposed on the patients. Full immediate activities including sports were allowed.
Results: 114 patients (103 male, 11 female) were analyzed from 2016 to 2023. The median age was 15 years old. There was zero incidence of bar displacement. The combined incidence of other postoperative complications was 4%: 2 wound infection and 2 hematoma formation both needing incision and drainage.
Conclusion: Bilateral medial stabilizer placement resulted in no incidence of bar dislocation. Return to immediate full activities after Nuss procedure did not appear to increase the incidence of bar displacement if stabilizers are placed medially.
Enhancing Si-Nanowire Solar Cell Performance through Fabrication and Annealing Optimization
Authors: Muduli Sakti Prasanna , Kale Paresh
Published: July 2024

The notable optical and electrical features of Si nanowires (SiNWs) outperform conventional bulk silicon, including a large surface area, antireflective properties, and shorter carrier transportation paths for photovoltaics. However, the key challenge lies in the fabrication and doping of SiNWs for p-n junction. The cost-effective metal-assisted chemical etching (MACE) lets the electrolyte etch the rear surface of the substrate. The dot electrode pattern on the front side and the close-periphery electrode on the rear side reduce the photocurrent collection. The spin-on-doping (SOD) leaves phosphorus clusters on the surface during diffusion, which needs dissolution and activation for doping uniformity. The work employs a modified MACE setup to prevent the electrolyte influence on the rear side and increase the photocurrent collection by modifying the front and rear electrode patterns. The increment in the annealing temperature up to 900 ºC dissolves the surface phosphorus clusters and activates the interstitial phosphorus atoms. The optical measurements and Hall mobility confirm the increased active phosphorus atoms. However, the surface oxidation, tip dissolution, and surface defects reduce the power conversion efficiency above the annealing temperature of 900 ºC. Due to increased shunt resistance, the fabrication modification and the annealing temperature optimization improve the power conversion efficiency and FF by 33.7% and 37.6%, respectively.
MicroRNA-718 inhibits mitochondrial fusion and ameliorates IMQ-induced psoriasis inflammation mediated by PHB and STAT1
Authors: Rani Himani , Saini Neeru
Published: July 2024

Multiple inflammatory pathways contribute to the development of psoriasis, leading to the hyperproliferation and dedifferentiation of keratinocytes at the affected site. The precise etiology of psoriasis pathogenesis remains unclear. Given that a single miRNA can regulate a cellular process involving multiple genes, there has been a rise in miRNA-based therapy studies over the past few decades. The current study aimed to investigate the therapeutic potential of miR-718 overexpression in treating psoriasis and to elucidate its underlying mechanisms using an imiquimod (IMQ)-induced mouse model and human keratinocytes (HaCaT). Additionally, the drug tofacitinib was incorporated in the in-vivo study to provide further insights. We observed that miR-718 overexpression leads to the inhibition of JAK/STAT signaling, as evidenced by the reduced expression of STAT1, JAK1, JAK2, and JAK3, directly inhibiting STAT1, both in-vitro and in-vivo. Moreover, the expression of STAT2 and STAT3 was also found to be downregulated in in-vitrostudies. In-vivo studies further show that miR-718 decreases the NF-kB, critical mediators of inflammation, upon ectopic expression in psoriatic mice. Immunohistochemistry (IHC) results indicate reduced acanthosis and parakeratosis in IMQ-induced psoriatic mice, potentially resulting from halted JAK/STAT signaling. In the miR-718 transfected mice skin, there was decreased expression of VEGF and matrix metalloproteases (MMP7 and MMP9), as shown by IHC and western blotting, respectively. The study also demonstrated that miR-718 represses mitochondrial fusion by inhibiting MFN1, MFN2, PHB, and OPA1 in HaCaT cells, while increasing DRP1 expression. Understanding the mechanism by which miR-718 ameliorates psoriasis not only provides new insight but also raises hopes for translating miR-718 as potential therapeutic agent for psoriasis.
Nurse Adherence to the Use of Personal Protective Equipment While Handling Chemotherapy: A Multicenter Cross-Sectional Study in Palestine
Authors: LAMA NADER , SACA-HAZBOUN HANAN
Published: July 2024

Purpose
The purpose of this study was to assess the level of compliance among oncology nurses with personal protective equipment while handling chemotherapy and determine factors hindering its use.
Method
In this cross-sectional study, 100 nurses from six Palestinian hospitals completed a paper-based questionnaire measuring compliance with personal protective equipment use, knowledge of exposure, self-efficacy, barriers, perceived risk, conflict of interest, interpersonal influences, and workplace safety climate. A bivariate correlation analysis was used.
Results
Nurses were experienced in oncology, well-educated, received training on the use of personal protective equipment, and, on average, treated 1–5 patients per day. Knowledge on chemotherapy exposure was low (Mean = 6.77, Standard Deviation ± 1.87); as was self-efficacy (13.22 ± 3.32). Perceived barriers to using personal protective equipment were moderate (29.93 ± 9.00); as was workplace safety climate (54.11 ± 11.43). Total precaution use during chemotherapy administration was moderate (2.3 ± 0.67). Nurse characteristics did not predict personal protective equipment use. Bivariate correlation showed fewer barriers and better interpersonal influences were independent predictors of higher personal protective equipment use.
Conclusions
This study demonstrated that Palestinian oncology nurses exhibited a moderate level of adherence to the use of personal protective equipment. Compliance with personal protective equipment was highest during chemotherapy administration and lowest during disposal activities. Further research is necessary to comprehend the impact of nurses' beliefs and attitudes on compliance levels. Additionally, future studies should consider other healthcare providers inadvertently exposed to chemotherapy.
3D Human Pose Estimation Based on Conditional Dual-Branch Diffusion Model
Authors: Li Jinghua , Bai Zhuowei , Kong Dehui , Chen Dongpan , Li Qianxing , Yin Baocai
Published: July 2024

Thanks to the development of 2D keypoint detectors, monocular 3D human pose estimation (HPE) via 2D-to-3D lifting approaches have achieved remarkable improvements. However, monocular 3D HPE is still a challenging problem due to the inherent depth ambiguities and occlusions. Recently, diffusion models have achieved great success in the field of image generation. Inspired by this, we transform 3D human pose estimation problem into a reverse diffusion process, and propose a dual-branch diffusion model that could fully explore the global and local correlations between joints. Furthermore, we propose conditional dual-branch diffusion model to enhance the performance of 3D human pose estimation, in which the joint-level semantic information are regarded as the condition of the diffusion model, and integrated into the joint-level representations of 2D pose to enhance the expression of joints. The proposed method is verified on two widely used datasets and the experimental results have demonstrated the superiority.
Bayesian optimization-based XGBoost for performance Prediction of Carbon Nanotube Membranes
Authors: Wu Bin , chen Pengjie , Wei Mingjie
Published: July 2024

Given the complex relationship between the structural features of carbon nanotube (CNT) membranes and their water permeability, predicting the performance of CNT membranes poses a significant challenge. The Bayesian optimization-based Extreme Gradient Boosting (Bayes-XGBoost) algorithm demonstrates considerable potential in capturing the intricate influences of various feature parameters on water permeability. An experimental dataset comprising 572 sets of data derived from molecular dynamics simulations serves as the characteristic dataset for machine learning, utilizing the Bayes-XGBoost algorithm to elucidate the connection between the structural features of CNT membranes and their filtration performance. The results indicate that, in predicting the permeability of CNT membranes, the Bayes-XGBoost algorithm achieves an impressive prediction accuracy of 97.82%, exhibiting faster convergence speed and higher predictive precision compared to traditional machine learning algorithms. Additionally, the optimal combination of CNT membrane feature parameters was identified through a genetic algorithm, providing robust support for the design and fabrication of high-performance CNT membranes. This highlights the significant potential of the Bayes-XGBoost in the field of material design.
Do children laugh like their parents? Conversational laughter mimicry occurrence and acoustic alignment in middle-childhood
Authors: Mazzocconi Chiara , O’Brien Benjamin , Bodur Kübra , Fourtassi Abdellah
Published: July 2024

Laughter is ubiquitous and crucial in social interactions. In addition to conveying meaning, it is a valuable means for monitoring interlocutor mental states and mutual cognitive alignment. Laughter has been shown to be informative about the pragmatic development of pre-school children. The current paper investigates laughter mimicry and acoustic alignment in middle childhood in a corpus of parent-child and parent-adult computer-mediated conversations. We performed both distributional and acoustic analyses. While there was no significant differences in terms of laughter frequency between children and adults, laughter mimicry was significantly less frequent in parent-child than in parent-adult interactions, although balanced at an intra-dyad level. A spectro-temporal modulation analysis revealed that mimicking laughter exhibited an increase in low-frequency spectral modulations compared to non-mimicking laughter for all participants, partly explained by heightened arousal. To test whether this pattern was related to a local alignment to the partner’s preceding laughter, we compared distances from genuine and pseudo-random initiating-mimicry laughter pairs. In parent-adult interactions, confirming previous studies, laughs constituting genuine pairs were acoustically more similar than those constituting pseudo-random pairs. No such pattern was found in parent-child interactions. Our results suggest that although laughter production during middle-childhood resembles adult-like use in terms of frequency, arousal, and balance between interactants, it is still significantly different when considering laughter mimicry, both in terms of transitional probabilities and acoustic alignment between interlocutors. Our study forms a basis for discussion on current theoretical models of alignment, its interplay with interactional dynamics, and the entanglement of laughter use with pragmatic development.
A Noval Approach based on Dual-Branch Encoder and Attention Skip Connections Decoder for Hard Exudate Segmentation
Authors: Li Bo , Zou Beiji , Xiao Xiaoxia , Peng Qinghua , Yan Junfeng , Zhang Wensheng , Li Yang
Published: July 2024

Diabetic retinopathy (DR) is a chronic condition that can lead to significant vision loss and even blindness. Existing deep networks for hard exudate segmentation in fundus images face two primary challenges: (1) The receptive field of traditional convolution operations is limited, resulting in poor hard exudate extraction performance; (2) Due to the irregular distribution and different sizes of fine exudates, it is easy to lose information about tiny exudates during the feature extraction process. To address these challenges, we propose DBASNet, a novel lesion segmentation model. In order to solve the problem of insufficient segmentation caused by the limitations of the receptive field, we propose a new multi-scale attention feature extraction (MAT) module. Combined with the dual encoder structure, the features extracted by MAT and EfficientNet in the dual branches are fused to effectively expand the perceptual field and avoid information loss. We also propose an attentional skip connection (AS) module in the decoder to filter and retain channel and spatial information, enrich skip connections and carry feature information of tiny lesions. Experiments on publicly available datasets IDRiD and E-Ophtha-EX demonstrate effectiveness of our method. DBASNet achieves 79.48, 80.35, 79.81, and 66.64% of recall, precision, Dice, and IOU metrics on IDRiD and 52.73, 60.33, 56.16, and 39.82% on E-Ophtha-EX, respectively. DBASNet outperforms some state-of-the-art approaches. The quantitative and qualitative findings unequivocally establish the pre-eminence of DBASNet in the field of lesion segmentation relevant to diabetic retinopathy.
Sex Dependent Intergenerational Effects of Lead in Mouse Model
Authors: Banda Nelly , Soe Nyein , Yabe John , Doya Rio , Yohannes Yared , Ikenaka Yoshinori , Ishizuka Mayumi , Nakayama Shouta
Published: July 2024

Lead contaminated soils remains one of the major sources of lead (Pb) exposure with high impact on fertility in both males and females, pregnancy outcomes and child brain development. We investigated inhalation and ingestion as medium of Pb exposure via soil in male and female mice. Exposure was then withdrawn prior to mating thus we investigated the lingering effects of Pb on fertility in F0 generation and neurological development in F1 generation. Lead exposure reduced conception rates with a cumulative decrement being recorded when both males and females are exposed. Male testicular gene expression revealed reduction in fertility was caused by oxidative stress. Cessation of exposure did not alleviate neurological deficits in F1 generation, as offspring of exposed individuals performed poorly in the water Morris test when compared to control. The severity of the effects was also parent dependent and sex dependent. Paternal Pb exposure also caused some neurological and motor deficits, with effects being more pronounced in female offspring. The female offspring of paternal Pb exposure showed gene expression of changes occurring more in prefrontal cortex than in hippocampus, a trend recorded in their sires. This trend suggests that Pb neurological effects may be X- chromosome linked.
Ceftazidime-Avibactam for the Treatment of Febrile Neutropenia in HSCT Recipients and Acute Leukemia Patients Post Induction Chemotherapy
Authors: Frem Jim Abi , Khazzeka Alicia , Allaw Fatima , Doueiry Caren , Ghoussaini Racha , Mohamad Rayan , Kanafani Zeina A.
Published: July 2024

Background
Febrile neutropenia is a common and major complication in patients with acute leukemia or those undergoing hematopoietic stem cell transplantation (HSCT). Understanding patient characteristics and patterns of susceptibility in febrile neutropenia is essential to provide the appropriate antimicrobial therapy. First-line agents should have Pseudomonas coverage, but with the increase in multi-drug resistant organisms, ceftazidime-avibactam has emerged as a new therapy in febrile neutropenia.
Methods
This is a retrospective case-control study of a total of 300 admissions (143 patients) between January 2009 and December 2017. Patients with hematologic neoplasms and patients that underwent HSCT who satisfied the definition of febrile neutropenia and treated with ceftazidime-avibactam (CAZAVI) were included in the study. A bivariate regression model to explore independent predictors of septic shock and mortality was constructed.
Results
Patients who received ceftazidime-avibactam (CAZAVI) were more likely to have a microbiologically documented infection (59.0% vs. 28.3%). Almost all complications were significantly more frequent in the CAZAVI group, with sepsis being the most common, occurring in 59.0% of patients in the CAZAVI arm. Our multivariable logistic regression analysis showed that receiving CAZAVI was an independent risk factor for both sepsis and mortality (aOR 6.33 [95% CI 2.81–14.30] and 7.82 [2.63–23.26], respectively).
Conclusion
Knowing the most common organisms isolated during a neutropenic fever episode as well as the patterns of resistance, compounded with an understanding of the risk factors for morbidity and mortality in such a vulnerable population, is key to providing them with appropriate prophylactic and therapeutic management. In addition, more studies should be done on the effectiveness of ceftazidime-avibactam in treating febrile neutropenia in the population at hand.
Deep Learning-Empowered Channel Estimation for 6G Vehicle-to-Vehicle Communications
Authors: Chen Xin , Hou Zhiwei , Zhu Yaolin
Published: July 2024

Vehicle-to-Vehicle (V2V) communications play a vital role in intelligent transportation. Especially in the 6G environments, the accuracy and efficiency of channel estimation techniques for V2V communication are crucial for realizing reliable autonomous driving and traffic systems. Although the convolutional neural network (CNN) has exhibited notable effectiveness in channel estimation for wireless communication systems, there are still severe open challenges in achieving desirable performance and computation complexity. To fill the gap, a novel deep learning-based channel estimation network (CEN) for multi-scene V2V channel estimation is proposed in this paper. Firstly, a novel bidirectional long short-term memory (Bi-LSTM) framework is introduced for V2V channel estimation. Then, the fully connected neural network (FCNN) network is used for the output dimensionality reduction. Finally, the temporal averaging (TA) processing is designed for eliminating the noise. Simulation results show that the proposed channel estimation scheme is superior to traditional channel estimation algorithms with desirable performance and lower computational load in urban environments.