bluuebunny/crossref_metadata_2025 huggingface dataset:
Multiphysics Studies of 3D Plate Fin Heat Exchanger Filled with Ortho-Para-Hydrogen Conversion Catalyst for Hydrogen Liquefaction
Authors: Tang Liangguang , Yamaguchi Doki , Orellana Jose , Tian Wendy
Published: October 2024

A comprehensive 3D Multiphysics model was developed to simulate a plate fin heat exchanger designed for hydrogen liquefaction, incorporating an ortho-para hydrogen conversion catalyst in the hot fin channel. The model encompassed the 3D serrate fin structure, turbulent flow within the cold fin channel, and porous flow through the catalytic hot fin channel. Species transportation within the hot fin channel is coupled with ortho-para hydrogen conversion kinetics, while heat transfer mechanisms between the hot and cold fin channels are rigorously accounted for. Additionally, the state-of-the-art equation of state is employed to accurately describe the thermodynamic properties of ortho- and para-hydrogen within the model. Numerous operational parameters, including the gas hourly space velocity, cold gas velocity, ortho-para hydrogen conversion kinetics, and operating pressure, were systematically varied to identify the kinetic and heat transfer constraints during the heat exchanger operation. The findings revealed that the ortho-para hydrogen conversion kinetic parameter predominantly governs operations requiring high gas hourly space velocity, particularly in large-scale hydrogen liquefaction processes. Furthermore, a significant pressure drop within the catalytic filled channel was observed; however, operating at higher pressure mitigates this issue while mildly enhancing ortho-para hydrogen conversion kinetics.
Detecting Urban Traffic Anomalies Using Traffic-Monitoring Data
Authors: Mao Yunkun , Shi Yilin , Lu Binbin
Published: October 2024

Traffic anomaly detection is crucial for urban management, yet current research is often confined to small-scale endeavors. This study collected 9 months of real-time Wuhan traffic-monitoring data from Amap. We propose Traffic-ConvLSTM, a multi-scale spatial-temporal technique based on long short-term memory (LSTM) networks and convolutional neural networks (CNNs) to effectively achieve long-term anomaly detection at the city level. First, we converted traffic track points into an image representation, which enables spatial correlation between traffic flow and roads and correlations between traffic flow and roads, as well as the surrounding environment, to be captured. Second, the model utilizes convolution kernels of different sizes to extract spatial features at road-, regional-, and city-level scales while incorporating the temporal features of different time steps to capture hourly, daily, and weekly dynamics. Additionally, varying weights are assigned to the convolution kernels and temporal features of varying spatio-temporal scales to capture the heterogeneous strengths of spatio-temporal correlations within patterns of traffic anomalies. The proposed Traffic-ConvLSTM model exhibits improved performance over existing techniques in the task of identifying long-term and large-scale traffic anomaly occurrences. Furthermore, the analysis reveals significant traffic anomalies during holidays and urban sporting events. The diverse travel patterns observed in response to various activities offer insights for large-scale urban traffic anomaly management, providing recommendations for city-level traffic-control strategies.
A Study of the Evolution of Haze Microblog Concerns Based on a Co-Word Network Analysis
Authors: Lu Haiyue , Rui Xiaoping , Li Runkui , Zhang Guangyuan , Zhang Ziqian , Wu Mingguang
Published: October 2024

Haze is a phenomenon caused by excessive PM2.5 (air-borne particulate matter having a diameter of fewer than 2.5 μm) and other pollutants and results from the interaction between specific climatic conditions and human activities. It significantly impacts human health, transportation, and the natural environment and has aroused widespread concern. However, the influence of haze on human mental health, being hidden and indirect, is often overlooked. When haze pollution occurs, people express their feelings and concerns about haze events on media such as Weibo. At present, few studies focus on haze public opinion, as well as the changing trends in people’s discussion of haze since its emergence, which is of great significance for haze response and resource management. Based on the perspective of topic analysis, this study explores the psychological impact of haze on people by exploring the feelings of netizens in haze public opinion and investigates the evolution of people’s concerns based on long-term public opinion data. In this study, seven typical provinces and cities in China with severe haze pollution were selected as the research area. Based on data on the “haze” theme from Weibo from 2013 to 2019, first, the microblog posts were preprocessed, and the keyword co-word network was constructed. Second, the Louvain algorithm was used to detect the topic community. Based on this, the cosine similarity was calculated to realize the temporal evolution analysis of topics. The results show that with the development and change in haze pollution, the content and intensity of the topics netizens pay attention to have changed, including five types: merger, split, survival, transformation, and rebirth/extinction. People’s attention to haze shows obvious spatial differences, and it is related to the degree of haze pollution, which is bipolar. Areas with severe haze tend to pay more attention to haze itself and its influence, while areas with light haze pay more attention to haze control. The research results can provide valuable insights for governments and relevant departments in guiding public opinion and resource allocation.
Multimodal Identification of Molecular Factors Linked to Severe Diabetic Foot Ulcers Using Artificial Intelligence
Authors: Omo-Okhuasuyi Anita , Jin Yu-Fang , ElHefnawi Mahmoud , Chen Yidong , Flores Mario
Published: October 2024

Diabetic foot ulcers (DFUs) are a severe complication of diabetes mellitus (DM), which often lead to hospitalization and non-traumatic amputations in the United States. Diabetes prevalence estimates in South Texas exceed the national estimate and the number of diagnosed cases is higher among Hispanic adults compared to their non-Hispanic white counterparts. San Antonio, a predominantly Hispanic city, reports significantly higher annual rates of diabetic amputations compared to Texas. The late identification of severe foot ulcers minimizes the likelihood of reducing amputation risk. The aim of this study was to identify molecular factors related to the severity of DFUs by leveraging a multimodal approach. We first utilized electronic health records (EHRs) from two large demographic groups, encompassing thousands of patients, to identify blood tests such as cholesterol, blood sugar, and specific protein tests that are significantly associated with severe DFUs. Next, we translated the protein components from these blood tests into their ribonucleic acid (RNA) counterparts and analyzed them using public bulk and single-cell RNA sequencing datasets. Using these data, we applied a machine learning pipeline to uncover cell-type-specific and molecular factors associated with varying degrees of DFU severity. Our results showed that several blood test results, such as the Albumin/Creatinine Ratio (ACR) and cholesterol and coagulation tissue factor levels, correlated with DFU severity across key demographic groups. These tests exhibited varying degrees of significance based on demographic differences. Using bulk RNA-Sequenced (RNA-Seq) data, we found that apolipoprotein E (APOE) protein, a component of lipoproteins that are responsible for cholesterol transport and metabolism, is linked to DFU severity. Furthermore, the single-cell RNA-Seq (scRNA-seq) analysis revealed a cluster of cells identified as keratinocytes that showed overexpression of APOE in severe DFU cases. Overall, this study demonstrates how integrating extensive EHRs data with single-cell transcriptomics can refine the search for molecular markers and identify cell-type-specific and molecular factors associated with DFU severity while considering key demographic differences.
Epidermal RORα Maintains Barrier Integrity and Prevents Allergic Inflammation by Regulating Late Differentiation and Lipid Metabolism
Authors: Hua Xiangmei , Ficaro Maria K. , Wallace Nicole L. , Dai Jun
Published: October 2024

The skin epidermis provides a barrier that is imperative for preventing transepidermal water loss (TEWL) and protecting against environmental stimuli. The underlying molecular mechanisms for regulating barrier functions and sustaining its integrity remain unclear. RORα is a nuclear receptor highly expressed in the epidermis of normal skin. Clinical studies showed that the epidermal RORα expression is significantly reduced in the lesions of multiple inflammatory skin diseases. In this study, we investigate the central roles of RORα in stabilizing skin barrier function using mice with an epidermis-specific Rora gene deletion (RoraEKO). While lacking spontaneous skin lesions or dermatitis, RoraEKO mice exhibited an elevated TEWL rate and skin characteristics of barrier dysfunction. Immunostaining and Western blot analysis revealed low levels of cornified envelope proteins in the RoraEKO epidermis, suggesting disturbed late epidermal differentiation. In addition, an RNA-seq analysis showed the altered expression of genes related to “keratinization” and “lipid metabolism” in RORα deficient epidermis. A lipidomic analysis further uncovered an aberrant ceramide composition in the RoraEKO epidermis. Importantly, epidermal Rora ablation greatly exaggerated percutaneous allergic inflammatory responses to oxazolone in an allergic contact dermatitis (ACD) mouse model. Our results substantiate the essence of epidermal RORα in maintaining late keratinocyte differentiation and normal barrier function while suppressing cutaneous inflammation.
Mitogenomic Architecture of Atlantic Emperor Lethrinus atlanticus (Actinopterygii: Spariformes): Insights into the Lineage Diversification in Atlantic Ocean
Authors: Kundu Shantanu , Kang Hye-Eun , Go Yunji , Bang Gyurim , Jang Yengju , Htoo Hsu , Aini Sarifah , Kim Hyun-Woo
Published: October 2024

The evolutionary history of emperors, particularly in the Atlantic and Indo-West Pacific Oceans, remains largely unmapped. This study explores the maternal lineage evolution of Lethrinids by examining the complete mitogenome of Lethrinus atlanticus, which is endemic to the Eastern Atlantic Ocean. Utilizing advanced next-generation sequencing, we found that the mitogenome spans 16,789 base pairs and encompasses 37 genes, including 13 protein-coding genes (PCGs), two ribosomal RNAs, 22 transfer RNAs, and an AT-rich control region (CR). Our analysis indicates a preference for AT base pairs in the L. atlanticus mitogenome (53.10%). Most PCGs begin with the ATG codon, except for COI, which starts with GTG. Relative synonymous codon usage reveals high frequencies for alanine, leucine, proline, serine, and threonine. The ratio of nonsynonymous to synonymous substitutions suggests strong negative selection across all PCGs in Lethrinus species. Most transfer RNAs exhibit typical cloverleaf structures, with the exception of tRNA-serine (GCT), which lacks a dihydrouracil stem. Comparative analysis of conserved sequence blocks across the CRs of three Lethrinus species shows notable differences in length and nucleotide composition. Phylogenetic analysis using concatenated PCGs clearly distinguishes all Lethrinus species, including L. atlanticus, and sheds light on the evolutionary relationships among Spariformes species. The estimated divergence time of approximately 20.67 million years between L. atlanticus and its Indo-West Pacific relatives provides insights into their historical separation and colonization during the late Oligocene. The distribution of Lethrinids may be influenced by ocean currents and ecological factors, potentially leading to their speciation across the Eastern Atlantic and Indo-West Pacific. This study enhances our understanding of the genetic diversity and phylogenetic relationships within Lethrinus species. Further exploration of other emperor fish mitogenomes and comprehensive genomic data could provide vital insights into their genetic makeup, evolutionary history, and environmental adaptability in marine ecosystems globally.
Newborn Screening for Sickle Cell Disease in Catalonia between 2015 and 2022—Epidemiology and Impact on Clinical Events
Authors: González de Aledo-Castillo José Manuel , Argudo-Ramírez Ana , Beneitez-Pastor David , Collado-Gimbert Anna , Almazán Castro Francisco , Roig-Bosch Sílvia , Andrés-Masó Anna , Ruiz-Llobet Anna , Pedrals-Portabella Georgina , Medina-Santamaria David , Nadal-Rey Gemma , Espigares-Salvia Marina , Coll-Sibina Maria Teresa , Algar-Serrano Marcelina , Torrent-Español Montserrat , Leoz-Allegretti Pilar , Rodríguez-Pebé Anabel , García-Bernal Marta , Solà-Segura Elisabet , García-Gallego Amparo , Prats-Viedma Blanca , López-Galera Rosa María , Paredes-Fuentes Abraham J. , Pajares García Sonia , Delgado-López Giovanna , Blanco-Álvarez Adoración , Tazón-Vega Bárbara , Díaz de Heredia Cristina , Mañú-Pereira María del Mar , Marín-Soria José Luis , García-Villoria Judit , Velasco-Puyó Pablo ,   on behalf of the Sickle Cell Disease Newborn Screening Group of Catalonia
Published: October 2024

In 2015, Catalonia introduced sickle cell disease (SCD) screening in its newborn screening (NBS) program along with standard-of-care treatments like penicillin, hydroxyurea, and anti-pneumococcal vaccination. Few studies have assessed the clinical impact of introducing NBS programs on SCD patients. We analyzed the incidence of SCD and related hemoglobinopathies in Catalonia and the change in clinical events occurring after introducing NBS. Screening 506,996 newborns from 2015 to 2022, we conducted a retrospective multicenter study including 100 screened (SG) and 95 unscreened (UG) SCD patients and analyzed SCD-related clinical events over the first six years of life. We diagnosed 160 cases of SCD, with an incidence of 1 in 3169 newborns. The SG had a significantly lower median age at diagnosis (0.1 y vs. 1.68 y, p < 0.0001), and initiated penicillin prophylaxis (0.12 y vs. 1.86 y, p < 0.0001) and hydroxyurea treatment earlier (1.42 y vs. 4.5 y, p < 0.0001). The SG experienced fewer median SCD-related clinical events (vaso-occlusive crisis, acute chest syndrome, infections of probable bacterial origin, acute anemia requiring transfusion, acute splenic sequestration, and pathological transcranial Doppler echography) per year of follow-up (0.19 vs. 0.77, p < 0.0001), a reduced number of annual emergency department visits (0.37 vs. 0.76, p < 0.0001), and fewer hospitalizations (0.33 vs. 0.72, p < 0.0001). SCD screening in Catalonia’s NBS program has effectively reduced morbidity and improved affected children’s quality of life.
Topic: Arthropod Biodiversity: Ecological and Functional Aspects
Authors: Sollai Giorgia , Giglio Anita , Giulianini Piero G. , Crnjar Roberto , Solari Paolo
Published: October 2024

Invertebrate animals with a segmented body, exoskeleton, and articulated appendages represent the largest phylum in the animal kingdom, Arthropoda, and account for over 80% of all known living species [...]
Spring Runoff Simulation of Snow-Dominant Catchment in Steppe Regions: A Comparison Study of Lumped Conceptual Models
Authors: Eroshenko Stanislav , Shmakov Evgeniy , Klimenko Dmitry , Iumanova Irina
Published: October 2024

This paper explores the application of conceptual hydrological models in optimizing the operation of hydroelectric power plants (HPPs) in steppe regions, a crucial aspect of promoting low-carbon energy solutions. The study aims to identify the most suitable conceptual hydrological model for predicting reservoir inflows from multiple catchments in a steppe region, where spring runoff dominates the annual water volume and requires careful consideration of snowfall. Two well-known conceptual models, HBV and GR6J-CemaNeige, which incorporate snow-melting processes, were evaluated. The research also investigated the best approach to preprocessing historical data to enhance model accuracy. Furthermore, the study emphasizes the importance of accurately defining low-water periods to ensure reliable HPP operation through more accurate inflow forecasting. A hypothesis was proposed to explore the relationship between atmospheric circulation and the definition of low-water periods; however, the findings did not support this hypothesis. Overall, the results suggest that combining the conceptual models under consideration can lead to more accurate forecasts, underscoring the need for integrated approaches in managing HPP reservoirs and promoting sustainable energy production.
Outpatient Management Protocol for Uncomplicated Diverticulitis: A 3-Year Monocentric Experience in a Tertiary Hospital
Authors: Burgard Marie , Litchinko Alexis , Meyer Jeremy , Toso Christian , Ris Frédéric , Delaune Vaihere
Published: October 2024

Background/Objectives: The management of acute uncomplicated diverticulitis (AUD) has shifted towards outpatient care in the last decade, challenging the traditional inpatient approach. We aimed to analyze the safety and feasibility of a structured outpatient treatment pathway for AUD in a tertiary hospital. Methods: We conducted a retrospective observational cohort analysis of patients who underwent outpatient management for AUD at the Geneva University Hospitals from 2019 to 2021. Patient demographics, selection criteria, treatment protocols, and outcomes were analyzed. Results: Two-hundred and twenty patients were included in the outpatient cohort. Four patients (1.8%) required hospitalization due to the failure of outpatient management, whereas the majority of patients (116 patients, 98.2%) experienced a successful resolution of their symptoms without hospitalization. In a univariate analysis, factors associated with treatment failure included elevated white blood cell counts at admission (14 G/l vs. 10.6 G/l, p = 0.049) and the first follow-up appointment, (10.7 G/l vs. 7.4 G/l, p = 0.011) and the presence of free air on their CT scan (25% vs. 2,3%, p = 0.033). In a multivariate analysis, the presence of free air was the only identified risk factor for unsuccessful outpatient management (p = 0.05). We observed high rates of follow-up compliance (99.1%). Conclusion: Under the condition of a warranted outpatient follow-up appointment and with adequate selection criteria, outpatient management appears to be an effective approach for most patients with AUD, emphasizing the importance of tailored therapeutic interventions and vigilant clinical assessments for optimal outcomes.
Prognostic Significance of Psoas Muscle Index in Unresectable Hepatocellular Carcinoma: Comparative Analysis of Lenvatinib and Atezolizumab Plus Bevacizumab
Authors: Shigefuku Ryuta , Iwasa Motoh , Tanaka Hideaki , Tsukimoto Mone , Tamai Yasuyuki , Fujiwara Naoto , Yoshikawa Kyoko , Tameda Masahiko , Ogura Suguru , Nakagawa Hayato
Published: October 2024

Background and Aims: Skeletal muscle loss has been identified as a prognostic factor in patients with unresectable hepatocellular carcinoma (uHCC) undergoing treatment with lenvatinib (LEN). While atezolizumab plus bevacizumab (ATZ-BEV) is recommended as first-line therapy for uHCC, the impact of skeletal muscle loss in these patients remains unclear. Methods: We enrolled 97 patients treated with either LEN or ATZ-BEV as their first-line therapy and divided them into two groups based on the presence or absence of a low psoas muscle index (low PMI) before treatment. We compared patient characteristics and overall survival (OS) between the groups. Additionally, we investigated the transition of the PMI during drug therapy, specifically before treatment, at the initial evaluation, and after the end of treatment. Results: Seventy percent of patients in the LEN group and seventy-one percent in the ATZ-BEV group had a low PMI. Multivariate analysis across all patients revealed a low PMI (hazard ratio [HR] 3.25, p = 0.0004) as a prognostic factor for OS. The PMI decreased more in the LEN group compared to the ATZ-BEV group. In the Barcelona Clinic Liver Cancer—C group, the OS of ATZ-BEV therapy was significantly better than that of LEN therapy when a low PMI was present (p = 0.046). Conclusions: A low PMI emerges as a significant prognostic factor in uHCC patients undergoing drug therapy, not only in LEN therapy but also in ATZ-BEV therapy. Additionally, ATZ-BEV therapy may be more favorable for sarcopenic patients with advanced HCC stages.
Arterial Hypertension: Novel Pharmacological Targets and Future Perspectives
Authors: Popa Irene Paula , Clim Andreea , Pînzariu Alin Constantin , Lazăr Cristina Iuliana , Popa Ștefan , Tudorancea Ivona Maria , Moscalu Mihaela , Șerban Dragomir N. , Șerban Ionela Lăcrămioara , Costache-Enache Irina-Iuliana , Tudorancea Ionuț
Published: October 2024

Arterial hypertension (HTN) is one of the major global contributors to cardiovascular diseases and premature mortality, particularly due to its impact on vital organs and the coexistence of various comorbidities such as chronic renal disease, diabetes, cerebrovascular diseases, and obesity. Regardless of the accessibility of several well-established pharmacological treatments, the percentage of patients achieving adequate blood pressure (BP) control is still significantly lower than recommended levels. Therefore, the pharmacological and non-pharmacological management of HTN is currently the major focus of healthcare systems. Various strategies are being applied, such as the development of new pharmacological agents that target different underlying physiopathological mechanisms or associated comorbidities. Additionally, a novel group of interventional techniques has emerged in recent years, specifically for situations when blood pressure is not properly controlled despite the use of multiple antihypertensives in maximum doses or when patients are unable to tolerate or desire not to receive antihypertensive medications. Nonetheless, reducing the focus on antihypertensive medication development by the pharmaceutical industry and increasing recognition of ineffective HTN control due to poor drug adherence demands ongoing research into alternative approaches to treatment. The aim of this review is to summarize the potential novel pharmacological targets for the treatment of arterial hypertension as well as the future perspectives of the treatment strategy.
Phrenoplasty Techniques for the Reconstruction of Basal Chest Wall Defects
Authors: Puma Francesco , Ceccarelli Silvia , Melis Alberto , Pourmolkara Domenico , Coviello Eleonora , Amatucci Riccardo , Daddi Niccolò , Vannucci Jacopo
Published: October 2024

Background: Primary and secondary tumors of the abdominal lower third of the bony thorax are relatively rare. Therefore, indications and techniques for chest wall reconstructions in this area are not well defined. Methods: The techniques for reconstructing basal chest wall defects using the diaphragm are described. Indications for phrenoplasty are limited to reconstruction after full-thickness resection of at least two of the last four ribs in the midaxillary line. The diaphragm can be used for reconstructive purposes both if it is intact and if it is partially involved in the resection of the chest wall. Results: At our institution, the abovementioned reconstructive technique was successfully performed in five patients with an uneventful post-operative course. Conclusions: The main advantages of these methods are the use of promptly available, high-quality autologous tissue and the exclusion of the pleural space from the defect area, thus transforming a thoracic defect into an abdominal one. The disadvantage is a variable reduction in the volume of the hemithorax. These techniques could be compared with other reconstruction techniques using pre-/post-operative respiratory functional tests.
Assessment of Motor Imagery and Its Relationship to Functional Impairment in Post-Stroke Patients: A Descriptive Cross-Sectional Study
Authors: Martínez-Rolando Lidia , Martín-Casas Patricia , Pérez-Pérez Teresa
Published: October 2024

Background: Motor and cognitive sequelae are common in patients who have experienced a stroke. Recent advances in neuroscience have enabled the development of novel therapeutic approaches, such as motor imagery, which facilitate motor learning. The objective of this study is to examine the relationship between implicit and explicit motor imagery abilities and their correlation with functional impairment in post-stroke patients. Methods: A descriptive cross-sectional study was conducted with 36 patients who had experienced a stroke between March 2008 and March 2023. The capacity to generate both implicit and explicit motor imagery and to perform physical functions was evaluated. The relationship between implicit and explicit motor imagery measures was investigated using Pearson’s correlation coefficient. The factorial structure, which encompasses the capacity to generate motor imagery, whether implicit or explicit, and physical function, was subjected to analysis. Results: A correlation was identified between the time taken to identify images and the accuracy of this process, with the right hand (R = 0.474), the left hand (R = 0.568), and the left foot (R = 0.344) all demonstrating significant associations. Additionally, a notable correlation was observed between the two subscales of the KVIQ-10 scale (R = 0.749). No association was identified between the capacity to generate implicit and explicit motor imagery. Two- and three-factor solutions were obtained for the right and left hemibodies, respectively. On both sides, accuracy in identifying images and physical function constituted a single factor, while time to generate images for both hands and feet constituted a second factor. Conclusions: In conclusion, no significant data were reported regarding the association between the capacity to generate implicit and explicit motor imagery in the studied sample. However, the ability to generate implicit motor imagery was related to physical function, suggesting that it may serve as a screening criterion for implementing specific therapeutic approaches in post-stroke patients.
Bond Strength and Corrosion Protection Properties of Hot-Dip Galvanized Prestressing Reinforcement in Normal-Strength Concrete
Authors: Pokorný Petr , Chobotský Tomáš , Prodanovic Nikola , Steinerová Veronika , Hurtig Karel
Published: October 2024

Several prestressing reinforced structures have recently collapsed due to chloride-induced steel corrosion. This study investigates the effect of the corrosion of hot-dip galvanized conventional prestressing steel reinforcement under hydrogen evolution on bond strength in normal-strength concrete. The impact of hydrogen evolution on the porosity of cement paste at the interfacial transition zone (ITZ) is verified through image analysis. The whole surface of prestressing strands is hot-dip galvanized, and their corrosion behavior when embedded in the cement paste is investigated by measuring the time dependence of the open-circuit potential. Concerning the uniformity of the hot-dip galvanized coating and its composition, it is advisable to coat the individual wires of the prestressing reinforcement and subsequently form a strand. It is demonstrated that the corrosion of the coating under the evolution of hydrogen in the cement paste reduces the bond strength of hot-dip galvanized reinforcement in normal-strength concrete. Image analysis after 28 days of cement paste aging indicates insignificant filling of hydrogen-generated pores by zinc corrosion products. Applying an additional surface treatment (topcoat) stable in an alkaline environment is necessary to avoid corrosion of the coating under hydrogen evolution and limit the risk of bond strength reduction.
Energy Restoration in Data Drivers for Low-Power Digitally Driven OLEDoS Microdisplays
Authors: Gohardehi Sheida , Sachdev Manoj
Published: October 2024

Microdisplays are widely used in near-to-eye (NTE) applications that operate with batteries, and reducing the power consumption of microdisplays is key to increasing their battery life. This paper proposes a digital data driver with a data energy recycling feature to reduce its dynamic power consumption. According to the measurement results obtained from a proof-of-concept array fabricated using TSMC 65 nm technology, the power consumption of the display data driver demonstrates an average reduction of 16% when tested with 10 random black-and-white images or a 14.4% decrease when evaluated using four real-life test images.
Numerical Simulation of Saltwater Intrusion in the Yangtze River Estuary Based on a Finite Volume Coastal Ocean Model
Authors: Wang Xinjun , Shi Haiyun , Cao Yuhan , Dong Changming , Li Chunhui
Published: October 2024

Saltwater intrusion is a common issue in the Yangtze River estuary (YRE), significantly affecting the nearshore ecological environment and human activities. Using 20 years of runoff data, a high-resolution Finite-Volume Coastal Ocean Model (FVCOM) is constructed to simulate the lower reaches and estuary of the Yangtze River. This model is employed to analyze full-depth current and salinity characteristics and to explore the influence of factors such as runoff, wind, tides, and riverbank morphology on saltwater intrusion in the YRE. The model’s accuracy is validated by comparing its output with current speed and salinity observations and comparing long-term salinity variations with reanalysis data. The comparison shows that the model well reproduces the saltwater intrusion in the YRE. Over the long term, the salinity pattern exhibits a “high–low–high” variation. Analyzing the vertical structure of the intrusion, it is observed that during summer, surface waters are heated, resulting in stronger stratification, weaker mixing, and a more pronounced saltwater intrusion in the YRE. Using power spectrum and correlation analyses, runoff is identified as the factor having the greatest impact on saltwater intrusion, followed by meridional wind and changes in riverbank morphology. This study of the variations in long-term saltwater intrusion has important reference value for the protection of freshwater resources in the YRE.
New Insights into Tyrrhenian Sea Warming and Heat Penetration through Long-Term Expendable Bathythermograph Data
Authors: Ciuffardi Tiziana , Lo Bue Nadia , Raiteri Giancarlo , Marullo Salvatore , Artale Vincenzo
Published: October 2024

The warming trend of the Mediterranean region is already well known, but there is still a lack of information on its seasonal/annual to multidecadal time scales and its distribution in all water masses, including deep water. New temporal and spatial evidence of this thermal variability has been presented in the Tyrrhenian Sea, thanks to twenty-year continuous monitoring by eXpendable BathyThermographs (XBTs) along a fixed route from Genoa to Palermo. The Tyrrhenian Sea is one of the deepest Mediterranean sub-basins (with a maximum depth of about 4000 m), but its interaction with neighbouring basins is controlled by topographical factors, such as the Sardinian Channel to the south and the Corsican Channel to the north. The way in which the warm signal, originating from the Levantine sub-basin, and entering from the south, affects the entire Tyrrhenian Basin spreading rapidly northwards is studied, considering its peculiarities, such as topography, surface circulation, and strong stratification, as well as its climate variability. The warming trend observed for the Tyrrhenian Sea is consistent with the trend for the Mediterranean Sea as a whole. However, the Tyrrhenian Sea shows some peculiar features: around 2014, a shift to a new equilibrium (warmer) state was detected, with mean values along the monitored route that were significantly higher than the previous period (from 1999 to 2013), especially for the subsurface level, from 100 to 450 m depth.
Cyber–Physical Security Assessment for Maritime Vessels: Study on Drillship DP System Using American Petroleum Institute Security Risk Analysis and Bow-Tie Analysis
Authors: Progoulakis Iosif , Dagkinis Ioannis K. , Dimakopoulou Anastasia , Lilas Theodoros , Nikitakos Nikitas , Psomas Panagiotis M.
Published: October 2024

The maritime industry’s increasing integration of IT/OT systems into vessel operations has significantly elevated its exposure to cyber–physical threats, making the development of effective cyber risk management strategies a necessity. This paper provides an outlook of the current landscape of cyber security threats and vulnerabilities for the maritime sector and vessels. An outline of the relevant governmental and industry directives, standards, and guidelines for cyber security in maritime vessels is given. Considering maritime vessels as critical elements of the maritime critical infrastructure sector, a number of relevant cyber–physical security assessment methods are presented. Bridging cyber–physical security, process safety, and security, API SRA (American Petroleum Institute Security Risk Analysis) and BTA (Bow-Tie Analysis) are presented as the most applicable cyber–physical security assessment methods for complex maritime vessels, such as an offshore oil and gas drillship. The scenario of a cyber-attack on the Dynamic Positioning (DP) system of a drillship is presented with the use of API SRA and BTA. The difficulties in the implementation of NIST CSF v2.0 and IACS UR E26 and UR E27 in the maritime sector are also discussed. The need for intensified research on and the formulation of bespoke cyber security measures to mitigate the evolving cyber threats within the maritime domain is highlighted. The need for the allocation of training and resources for the reinforcement of the capacity of a maritime vessel’s crew in the mitigation of cyber threats and safe maritime operations is emphasized.
Morphological Response of Coastal Dunes to Typhoons and Winter Monsoons on Pingtan Island, China
Authors: Yang Lin , Dong Yuxiang , Huang Dequan
Published: October 2024

Wind plays a crucial role in the formation of coastal dunes, and in China, these dunes are shaped by the combined effects of typhoons and winter monsoons. However, the unique characteristics of Chinese coastal dunes impacted by these forces remain poorly understood, as prior research has predominantly focused on their separate impacts. This study employed RTK-GPS technology to conduct 14 high-precision morphological assessments of coastal dunes in Tannan Bay, Pingtan Island, Fujian, China, between 2014 and 2017, aiming to investigate the response patterns of coastal dunes to typhoons and winter monsoons. Our findings indicate that coastal dunes respond variably to typhoons of differing intensities, with considerable height changes across different sections; however, winter monsoons contribute to an overall increase in dune height. Both dune volume and height increased due to continuous sediment accumulation at the base of the windward slopes. Additionally, the average high-water level advanced seaward by 3.0–4.0 m. We concluded that in Tannan Bay, typhoons exert only a temporary “braking” effect on dune morphology, whereas the winter monsoon is the primary driver of its long-term evolution. These findings contribute to a comprehensive understanding of coastal dune dynamics and provide insights for effective coastal sand management and disaster prevention strategies.
Research on Rapid Detection of Underwater Targets Based on Global Differential Model Compression
Authors: Li Weishan , Li Yilin , Li Ruixue , Shen Haozhe , Li Wenjun , Yue Keqiang
Published: October 2024

Large-scale deep learning algorithms have emerged as the primary technology for underwater target detection, demonstrating exceptional inference effectiveness and accuracy. However, the real-time capabilities of these high-accuracy algorithms rely heavily on high-performance computing resources like CPUs and GPUs. It presents a challenge for deploying them on underwater embedded devices, where communication is limited and computational and energy resources are constrained. To overcome this, this paper focuses on constructing a lightweight yet highly accurate deep learning model suitable for real-time underwater target detection on edge devices. We develop a new lightweight model, named YOLO-TN, for real-time underwater object recognition on edge devices using a self-constructed image dataset captured by an underwater unmanned vehicle. This model is obtained by compressing the classical YOLO-V5, utilizing a globally differentiable deep neural architecture search method and a network pruning technique. Experimental results show that the YOLO-TN achieves a mean average precision (mAP) of 0.5425 and an inference speed of 28.6 FPS on embedded devices, while its parameter size is between 0.4 M and 0.6 M. This performance is a fifth of the parameter size and twelve times the FPS of the YOLO-V5 model, with almost no loss in inference accuracy. In conclusion, this framework significantly enhances the feasibility of deploying large-scale deep learning models on edge devices with high precision and compactness, ensuring real-time inference and offline deployment capabilities. This research is pivotal in addressing the computational challenges faced in underwater operations.
Energy Efficiency Maximization for Multi-UAV-IRS-Assisted Marine Vehicle Systems
Authors: Zhang Chaoyue , Lin Bin , Li Chao , Qi Shuang
Published: October 2024

Mobile edge computing is envisioned as a prospective technology for supporting time-sensitive and computation-intensive applications in marine vehicle systems. However, the offloading performance is highly impacted by the poor wireless channel. Recently, an Unmanned Aerial Vehicle (UAV) equipped with an Intelligent Reflecting Surface (IRS), i.e., UIRS, has drawn attention due to its capability to control wireless signals so as to improve the data rate. In this paper, we consider a multi-UIRS-assisted marine vehicle system where UIRSs are deployed to assist in the computation offloading of Unmanned Surface Vehicles (USVs). To improve energy efficiency, the optimization problem of the association relationships, computation resources of USVs, multi-UIRS phase shifts, and multi-UIRS trajectories is formulated. To solve the mixed-integer nonlinear programming problem, we decompose it into two layers and propose an integrated convex optimization and deep reinforcement learning algorithm to attain the near-optimal solution. Specifically, the inner layer solves the discrete variables by using the convex optimization based on Dinkelbach and relaxation methods, and the outer layer optimizes the continuous variables based on the Multi-Agent Twin Delayed Deep Deterministic Policy Gradient (MATD3). The numerical results demonstrate that the proposed algorithm can effectively improve the energy efficiency of the multi-UIRS-assisted marine vehicle system in comparison with the benchmarks.
Discovery of Gibellula floridensis from Infected Spiders and Analysis of the Surrounding Fungal Entomopathogen Community
Authors: Joseph Ross A. , Masoudi Abolfazl , Valdiviezo Mateo J. , Keyhani Nemat O.
Published: October 2024

Characterization of fungal spider pathogens lags far behind their insect counterparts. In addition, little to nothing is known concerning the ecological reservoir and/or fungal entomopathogen community surrounding infection sites. Five infected spider cadavers were identified in the neo-tropical climate of north-central Florida, USA, from three of which viable cultures were obtained. Multi-locus molecular phylogenetic and morphological characterization identified one isolate as a new Gibellula species, here named, Gibellula floridensis, and the other isolates highly similar to Parengyodontium album. The fungal entomopathogen community surrounding infected spiders was sampled at different habitats/trophic levels, including soil, leaf litter, leaf, and twig, and analyzed using ITS amplicon sequencing. These data revealed broad but differential distribution of insect-pathogenic fungi between habitats and variation between sites, with members of genera belonging to Metarhizium and Metacordyceps from Clavicipitaceae, Purpureocillium and Polycephalomyces from Ophiocordyceps, and Akanthomyces and Simplicillium from Cordycipitaceae predominating. However, no sequences corresponding to Gibellula or Parengyodontium, even at the genera levels, could be detected. Potential explanations for these findings are discussed. These data highlight novel discovery of fungal spider pathogens and open the broader question regarding the environmental distribution and ecological niches of such host-specific pathogens.
Soil Degradation and Contamination Due to Armed Conflict in Ukraine
Authors: Solokha Maksym , Demyanyuk Olena , Symochko Lyudmyla , Mazur Svitlana , Vynokurova Nadiya , Sementsova Kateryna , Mariychuk Ruslan
Published: October 2024

The impact of the active hostilities associated with Russia’s large-scale armed invasion of the territory of Ukraine on soil degradation as a result of military actions has resulted in soil damage due to heavy military armored vehicles. Debris from destroyed military equipment, ammunition, and fuel remnants lead to multi-factor damage to the soil system, causing local and global pollution and losses of soil resources. In all the studied cases, mechanical, chemical, and physical soil degradation were observed. This was manifested in changes in granulometric fractions at explosion sites, burning areas, and locations with heavy-metal contamination. Equipment incineration has resulted in an increase in the sand fraction (2.0–0.05 mm) by 1.2–1.8 times and a decrease in the clay fraction (<0.002 mm) by 1.1–1.2 times. The soil contamination levels with regard to heavy metals significantly surpass health standards, with the highest pollution levels observed for Pb, Zn, and Cd. Across all affected areas, changes occurred in the microbiome structure (a 20.5-fold increase in the proportion of mycelial organisms), microbiological process activity was suppressed (a 1.2-fold decrease), microbial biomass (a 2.1-fold decrease) was reduced, and high soil toxicity (99.8%) was observed. Explosions and the pyrolysis of armored vehicles have a significant impact on soil mesobiota and plants. The results indicate the existence of complex interactions between various factors in the soil environment post-explosion, significantly affecting soil health.
Supporting Human–Robot Interaction in Manufacturing with Augmented Reality and Effective Human–Computer Interaction: A Review and Framework
Authors: Subramanian Karthik , Thomas Liya , Sahin Melis , Sahin Ferat
Published: October 2024

The integration of Augmented Reality (AR) into Human–Robot Interaction (HRI) represents a significant advancement in collaborative technologies. This paper provides a comprehensive review of AR applications within HRI with a focus on manufacturing, emphasizing their role in enhancing collaboration, trust, and safety. By aggregating findings from numerous studies, this research highlights key challenges, including the need for improved Situational Awareness, enhanced safety, and more effective communication between humans and robots. A framework developed from the literature is presented, detailing the critical elements of AR necessary for advancing HRI. The framework outlines effective methods for continuously evaluating AR systems for HRI. The framework is supported with the help of two case studies and another ongoing research endeavor presented in this paper. This structured approach focuses on enhancing collaboration and safety, with a strong emphasis on integrating best practices from Human–Computer Interaction (HCI) centered around user experience and design.
Co-CrackSegment: A New Collaborative Deep Learning Framework for Pixel-Level Semantic Segmentation of Concrete Cracks
Authors: Alkayem Nizar Faisal , Mayya Ali , Shen Lei , Zhang Xin , Asteris Panagiotis G. , Wang Qiang , Cao Maosen
Published: October 2024

In an era of massive construction, damaged and aging infrastructure are becoming more common. Defects, such as cracking, spalling, etc., are main types of structural damage that widely occur. Hence, ensuring the safe operation of existing infrastructure through health monitoring has emerged as an important challenge facing engineers. In recent years, intelligent approaches, such as data-driven machines and deep learning crack detection have gradually dominated over traditional methods. Among them, the semantic segmentation using deep learning models is a process of the characterization of accurate locations and portraits of cracks using pixel-level classification. Most available studies rely on single-model knowledge to perform this task. However, it is well-known that the single model might suffer from low variance and low ability to generalize in case of data alteration. By leveraging the ensemble deep learning philosophy, a novel collaborative semantic segmentation of concrete cracks method called Co-CrackSegment is proposed. Firstly, five models, namely the U-net, SegNet, DeepCrack19, DeepLabV3-ResNet50, and DeepLabV3-ResNet101 are trained to serve as core models for the ensemble model Co-CrackSegment. To build the ensemble model Co-CrackSegment, a new iterative approach based on the best evaluation metrics, namely the Dice score, IoU, pixel accuracy, precision, and recall metrics is developed. Results show that the Co-CrackSegment exhibits a prominent performance compared with core models and weighted average ensemble by means of the considered best statistical metrics.
Two-Stage Satellite Combined-Task Scheduling Based on Task Merging Mechanism
Authors: Yu Jing , Guo Jiawei , Xing Lining , Song Yanjie , Liu Zhaohui
Published: October 2024

Satellites adopt a single-task observation mode in traditional patterns. Although this mode boasts high imaging accuracy, it is accompanied by a limited number of observed tasks and a low utilization rate of satellite resources. This limitation becomes particularly pronounced when dealing with extensive and densely populated observation task sets because the inherent mobility of satellites often leads to conflicts among numerous tasks. To address this issue, this paper introduces a novel multi-task merging mechanism aimed at enhancing the observation rate of satellites by resolving task conflicts. Initially, this paper presents a task merging method based on the proposed multitask merging mechanism, referred to as the constrained graph (CG) task merging approach. This method can merge tasks while adhering to the minimal requirements specified by users. Subsequently, a multi-satellite merging scheduling model is established based on the combined task set. Considering the satellite combined-task scheduling problem (SCTSP), an enhanced fireworks algorithm (EFWA) is proposed that incorporates the CG task synthesis mechanism. This algorithm incorporates local search strategies and a population disturbance mechanism to enhance both the solution quality and convergence speed. Finally, the efficacy of the CG algorithm was validated through a multitude of simulation experiments. Moreover, the effectiveness of the EFWA is confirmed via extensive comparisons with other algorithms, including the basic ant colony optimization (ACO) algorithm, enhanced ant colony optimization (EACO) algorithm, fireworks algorithm (FWA), and EFWA. When the number of tasks in the observation area are dense, such as in the case where the number of tasks is 700, the CG + EFWA (CG is adopted in the task merging stage and EFWA is adopted in the satellite combined-task scheduling stage) method improves observation benefits by 70.35% (compared to CG + EACO, CG is adopted in the task merging stage and EACO is adopted in the satellite combined-task scheduling stage), 78.93% (compared to MS + EFWA, MS is adopted in the task merging stage and EFWA is adopted in the satellite combined-task scheduling stage), and 39.03% (compared to MS + EACO, MS is adopted in the task merging stage and EACO is adopted in the satellite combined-task scheduling stage).