bluuebunny/crossref_metadata_2025 huggingface dataset:
Structural Design and Control Research of Multi-Segmented Biomimetic Millipede Robot
Authors: Yin Hao , Shi Ruiqi , Liu Jiang
Published: May 2024

Due to their advantages of good stability, adaptability, and flexibility, multi-legged robots are increasingly important in fields such as rescue, military, and healthcare. This study focuses on the millipede, a multi-segmented organism, and designs a novel multi-segment biomimetic robot based on an in-depth investigation of the millipede’s biological characteristics and locomotion mechanisms. Key leg joints of millipede locomotion are targeted, and a mathematical model of the biomimetic robot’s leg joint structure is established for kinematic analysis. Furthermore, a central pattern generator (CPG) control strategy is studied for multi-jointed biomimetic millipede robots. Inspired by the millipede’s neural system, a simplified single-loop CPG network model is constructed, reducing the number of oscillators from 48 to 16. Experimental trials are conducted using a prototype to test walking in a wave-like gait, walking with a leg removed, and walking on complex terrain. The results demonstrate that under CPG waveform input conditions, the robot can walk stably, and the impact of a leg failure on overall locomotion is acceptable, with minimal speed loss observed when walking on complex terrain. The research on the structure and motion control algorithms of multi-jointed biomimetic robots lays a technical foundation, expanding their potential applications in exploring unknown environments, rescue missions, agriculture, and other fields.
Fabrication of Multiple-Channel Electrochemical Microneedle Electrode Array via Separated Functionalization and Assembly Method
Authors: Huang Xin-Shuo , Huang Shuang , Zheng Shan-Tao , Liang Bao-Ming , Zhang Tao , Yue Wan , Liu Fan-Mao , Shi Peng , Xie Xi , Chen Hui-Jiuan
Published: May 2024

Real-time monitoring of physiological indicators inside the body is pivotal for contemporary diagnostics and treatments. Implantable electrodes can not only track specific biomarkers but also facilitate therapeutic interventions. By modifying biometric components, implantable electrodes enable in situ metabolite detection in living tissues, notably beneficial in invasive glucose monitoring, which effectively alleviates the self-blood-glucose-managing burden for patients. However, the development of implantable electrochemical electrodes, especially multi-channel sensing devices, still faces challenges: (1) The complexity of direct preparation hinders functionalized or multi-parameter sensing on a small scale. (2) The fine structure of individual electrodes results in low spatial resolution for sensor functionalization. (3) There is limited conductivity due to simple device structures and weakly conductive electrode materials (such as silicon or polymers). To address these challenges, we developed multiple-channel electrochemical microneedle electrode arrays (MCEMEAs) via a separated functionalization and assembly process. Two-dimensional microneedle (2dMN)-based and one-dimensional microneedle (1dMN)-based electrodes were prepared by laser patterning, which were then modified as sensing electrodes by electrochemical deposition and glucose oxidase decoration to achieve separated functionalization and reduce mutual interference. The electrodes were then assembled into 2dMN- and 1dMN-based multi-channel electrochemical arrays (MCEAs), respectively, to avoid damaging functionalized coatings. In vitro and in vivo results demonstrated that the as-prepared MCEAs exhibit excellent transdermal capability, detection sensitivity, selectivity, and reproducibility, which was capable of real-time, in situ glucose concentration monitoring.
Stress Concentration Factors of Concrete-Filled Double-Skin Tubular K-Joints
Authors: Xia Qian , Ma Ling , Li Gang , Hu Chao , Zhang Lei , Xu Fei , Liu Zhenhai
Published: May 2024

Tubular joints are important connecting parts of a welded steel tube structure. The S-N curves based on the hot spot stress (HSS) method are often used to evaluate the fatigue life of tubular joints in practical engineering. The stress concentration factor (SCF) is a key parameter to calculate HSS. In this paper, stress concentration tests of hollow-section and concrete-filled double-skin tubular (CFDST) K-joints were carried out, respectively, and then finite element models of K-joints considering the weld were established. The developed models were validated with the experimental results. The influence of key geometrical parameters, such as the diameter ratio of brace to chord β, the diameter to thickness ratio of chord γ, the wall thickness ratio of brace to chord τ, brace angle θ, and hollow section ratio ζ on the distribution and key position of SCFs along the weld toe, was discussed. Parametric studies were conducted to obtain the calculating equations for the SCF values of CFDST K-joints. The results demonstrate that infill concrete can effectively reduce SCFs along the weld on the chord. When the hollow section ratio was reduced to 0.317, the SCF was reduced by 77.2%. Notably, the SCF reduction rate was sensitive to γ and θ, with a decrease observed as γ increased. The hollow section ratio ζ had a less pronounced effect on SCF distribution patterns, but as ζ decreased, the chord’s stiffness improved, suggesting a potential approach to enhance joint performance. The distribution of SCFs is similar for joints of the same type but different geometric configurations. The innovatively integrated hollow section ratio in the CFDST design equation significantly simplifies and enhances the precision of SCF calculations for CFDST K-joints.
Efficacy of Pembrolizumab vs. Nivolumab Plus Ipilimumab in Metastatic NSCLC in Relation to PD-L1 and TMB Status
Authors: Shalata Walid , Maimon Rabinovich Natalie , Agbarya Abed , Yakobson Alexander , Dudnik Yulia , Abu Jama Ashraf , Cohen Ahron Yehonatan , Shalata Sondos , Abu Hamed Ahmad , Ilan Ber Tahel , Machluf Oshri , Shoham Levin Gal , Meirovitz Amichay
Published: May 2024

The efficacy of immune checkpoint inhibitor (ICI) therapy concerning programmed death ligand 1 (PD-L1) status is well established in patients diagnosed with non-small cell lung cancer (NSCLC). However, there remains a paucity of evidence regarding the efficacy concerning tumor mutational burden (TMB) in both clinical trials and real-world data (RWD). In the current article, clinicopathological and molecular epidemiological data were meticulously collected, and treatment modalities were meticulously recorded. The final analysis included a study population of 194 patients. Median age was 67 years (range 37–86), with the majority being male (71.13%), and 85.71% of patients were either current or former smokers at diagnosis. Adenocarcinoma accounted for most diagnoses (71.65%), followed by squamous cell carcinoma (24.23%). In terms of PD-L1 status, 42.78% had an expression level below 1%, 28.35% had an expression between 1–49%, and 28.87% had an expression above 50%. The TMB ranged from 0 to 75, with a median of 10.31 (range 0–75) for PD-L1 expression below 1%, with a median of 9.73 (range 0.95–39.63) for PD-L1 expression between 1–49%, and a median of 9.72 (range 0.95–48) for PD-L1 expression above 50%. Corresponding to patients with low PDL-1 less than 1% and low TMB (0–5), the median overall survival (mOS) was 16 (p = 0.18), and 15 months (p = 0.22), patients with medium PDL-1 (1–49%) and medium TMB (5–10), the mOS was 15 (p = 0.18) and 16 months (p = 0.22), patients with high PDL-1 (>50) and high TMB (>10), the mOS was 24 (p = 0.18) and 21 (p = 0.22) months. This study represents the largest academic RWD dataset concerning PD-L1 and TMB status in patients with locally advanced and metastatic NSCLC.
Insights in Molecular Therapies for Hepatocellular Carcinoma
Authors: Heumann Philipp , Albert Andreas , Gülow Karsten , Tümen Deniz , Müller Martina , Kandulski Arne
Published: May 2024

We conducted a comprehensive review of the current literature of published data and clinical trials (MEDLINE), as well as published congress contributions and active recruiting clinical trials on targeted therapies in hepatocellular carcinoma. Combinations of different agents and medical therapy along with radiological interventions were analyzed for the setting of advanced HCC. Those settings were also analyzed in combination with adjuvant situations after resection or radiological treatments. We summarized the current knowledge for each therapeutic setting and combination that currently is or has been under clinical evaluation. We further discuss the results in the background of current treatment guidelines. In addition, we review the pathophysiological mechanisms and pathways for each of these investigated targets and drugs to further elucidate the molecular background and underlying mechanisms of action. Established and recommended targeted treatment options that already exist for patients are considered for systemic treatment: atezolizumab/bevacizumab, durvalumab/tremelimumab, sorafenib, lenvatinib, cabozantinib, regorafenib, and ramucirumab. Combination treatment for systemic treatment and local ablative treatment or transarterial chemoembolization and adjuvant and neoadjuvant treatment strategies are under clinical investigation.
Diagnostic Biomarkers in Renal Cell Tumors According to the Latest WHO Classification: A Focus on Selected New Entities
Authors: Sanguedolce Francesca , Mazzucchelli Roberta , Falagario Ugo Giovanni , Cormio Angelo , Zanelli Magda , Palicelli Andrea , Zizzo Maurizio , Eccher Albino , Brunelli Matteo , Galosi Andrea Benedetto , Carrieri Giuseppe , Cormio Luigi
Published: May 2024

The fifth edition of the World Health Organization (WHO) classification for urogenital tumors, released in 2022, introduces some novelties in the chapter on renal epithelial tumors compared to the previous 2016 classification. Significant changes include the recognition of new disease entities and adjustments in the nomenclature for certain pathologies. Notably, each tumor entity now includes minimum essential and desirable criteria for reliable diagnosis. This classification highlights the importance of biological and molecular characterization alongside traditional cytological and architectural features. In this view, immunophenotyping through immunohistochemistry (IHC) plays a crucial role in bridging morphology and genetics. This article aims to present and discuss the role of key immunohistochemical markers that support the diagnosis of new entities recognized in the WHO classification, focusing on critical topics associated with single markers, in the context of specific tumors, such as the clear cell capillary renal cell tumor (CCPRCT), eosinophilic solid and cystic renal cell carcinoma (ESC-RCC), and so-called “other oncocytic tumors”, namely the eosinophilic vacuolated tumor (EVT) and low-grade oncocytic tumor (LOT). Their distinctive characteristics and immunophenotypic profiles, along with insights regarding diagnostic challenges and the differential diagnosis of these tumors, are provided. This state-of-the-art review offers valuable insights in biomarkers associated with novel renal tumors, as well as a tool to implement diagnostic strategies in routine practice.
Novel Endocrine Therapeutic Opportunities for Estrogen Receptor-Positive Ovarian Cancer—What Can We Learn from Breast Cancer?
Authors: Ottenbourgs Tine , Van Nieuwenhuysen Els
Published: May 2024

Low-grade serous ovarian cancer (LGSOC) is a rare ovarian malignancy primarily affecting younger women and is characterized by an indolent growth pattern. It exhibits indolent growth and high estrogen/progesterone receptor expression, suggesting potential responsiveness to endocrine therapy. However, treatment efficacy remains limited due to the development of endocrine resistance. The mechanisms of resistance, whether primary or acquired, are still largely unknown and present a significant hurdle in achieving favorable treatment outcomes with endocrine therapy in these patients. In estrogen receptor-positive breast cancer, mechanisms of endocrine resistance have been largely explored and novel treatment strategies to overcome resistance have emerged. Considering the shared estrogen receptor positivity in LGSOC and breast cancer, we wanted to explore whether there are any parallel mechanisms of resistance and whether we can extend endocrine breast cancer treatments to LGSOC. This review aims to highlight the underlying molecular mechanisms possibly driving endocrine resistance in ovarian cancer, while also exploring the available therapeutic opportunities to overcome this resistance. By unraveling the potential pathways involved and examining emerging strategies, this review explores valuable insights for advancing treatment options and improving patient outcomes in LGSOC, which has limited therapeutic options available.
Direct Synthesis of Calcium Lactate through the Reaction of Glycerol with Calcium Hydroxide Catalyzed by Bimetallic AuCu/SiO2 Nanocatalysts
Authors: Li Changqing , Cui Xinyue , Wang Aili , Yin Hengbo , Li Yuting , Lin Qiao , Guo Junjie
Published: May 2024

Bimetallic AuCu/SiO2 nanosized catalysts were prepared using the wet chemical reduction technique. From among Au0.1–1.5Cu10/SiO2 catalysts, the Au0.5Cu10/SiO2 catalyst gave the highest yield of calcium lactate of 87% at a glycerol conversion of 96% when the reaction of glycerol with calcium hydroxide at a mole ratio of calcium hydroxide to glycerol of 0.8:1 was conducted under an anaerobic atmosphere at 200 °C for 2 h. The interactions between metallic Au0 and Cu0 nanoparticles facilitate calcium lactate formation. The simulation of glycerol consumption rate with an empirical power-function reaction kinetics equation yielded a reaction activation energy of 44.3 kJ∙mol−1, revealing that the catalytic reaction of glycerol with calcium hydroxide to calcium lactate can be conducted by overcoming a mild energy barrier. The synthesis of calcium lactate through the catalytic reaction of glycerol with calcium hydroxide on a bimetallic AuCu/SiO2 nanosized catalyst under a safe anaerobic atmosphere is an alternative to the conventional calcium lactate production technique through the reaction of expensive lactic acid with calcium hydroxide.
Diversity of Freshwater Calanoid Copepods (Crustacea: Copepoda: Calanoida) in North-Eastern China
Authors: Ding Ruirui , Liu Le , Shu Shusen , Li Yun , Chen Feizhou
Published: May 2024

The distribution and diversity of calanoid copepods were investigated using samples collected from 37 lakes in North-eastern (NE) China in 2019. A total of 10 calanoid copepods belonging to eight genera and three families were identified. Among them, Heterocope soldatovi Rylov, 1922 was recorded for the first time in China. Species from the family Centropagidae were more widespread than those from the families Temoridae and Diaptomidae. Sinocalanus doerrii (Brehm, 1909), previously not recorded in NE China, is now widespread alongside Boeckella triarticulata (Thomson G.M., 1883), and the latter species is also prevalent in that region. Canonical correspondence analysis (CCA) and redundancy analysis (RDA) revealed that calanoid copepods were significantly correlated with total phosphorus (TP), total nitrogen, conductivity, nitrate nitrogen, altitude, and dissolved organic carbon. TP was the most important environmental variable that impacted the distribution of calanoid copepods, including both fresh and saline–alkaline lakes. Integrating historical records, a total of 21 calanoid copepods were distributed in NE China, and we also gave comments on the ecology and distribution of these species.
Interannual Variation in the Zooplankton Community of the North Adriatic Sea under Short-Term Climatic Anomalies
Authors: Menicucci Samuele , De Felice Andrea , Biagiotti Ilaria , Canduci Giovanni , Costantini Ilaria , Palermino Antonio , Centurelli Michele , Leonori Iole
Published: May 2024

Zooplankton are a pivotal component of the pelagic community, and their abundance and distribution are often strongly dependent on environmental conditions at sea. However, climate change can pose significant challenges to planktonic organisms. Therefore, in this study, we tried to address the possible effect of short-term climatic anomalies on the zooplankton community in the North Adriatic Sea, comparing mesozooplankton composition in June between two years with very different temperature and rainfall levels, i.e., 2019 and 2022. Environmental conditions at sea were significantly different, since 2022 faced rising temperatures in the northern part of the area and higher salinity and lower chlorophyll values in coastal samples. Our data unveiled a community shift, from a Noctiluca-dominated community to a crustacean-dominated one, and revealed that even offshore areas can be subject to changes, despite having quite stable environmental parameters. Our findings confirmed the influence of river inputs and temperature on the Adriatic community’s distribution and composition, highlighting how climate-driven changes could have unpredictable effects on the whole Adriatic ecosystem. Indeed, each species has its own role in ecosystem functioning and climatic anomalies could uncouple the fine-scale connections that make up the pelagic trophic web.
Coastal Eukaryotic Plankton Diversity of the Southern Adriatic as Revealed by Metabarcoding
Authors: Baricevic Ana , Kogovsek Tjasa , Smodlaka Tankovic Mirta , Grizancic Lana , Knjaz Mia , Vlasicek Ivan , Podolsak Ivan , Kuzat Natasa , Pfannkuchen Martin , Maric Pfannkuchen Daniela
Published: May 2024

Plankton studies serve as a basis for marine ecosystem research, but knowledge of marine plankton is still incomplete due to its extreme taxonomic and functional complexity. The application of metabarcoding is very valuable for the characterisation of the plankton community. The plankton community of the Southern Adriatic is subject to strong environmental fluctuations and changes, which underlines the need for frequent, reliable and comprehensive characterisation of the plankton. The aim of this study was to determine the taxonomic composition and seasonal distribution of eukaryotic plankton in the Southern Adriatic. Plankton samples were collected monthly for one year at the coastal station of the Southern Adriatic and metabarcoding was used for taxonomic identification. The results showed a high taxonomic diversity and dynamic seasonal distribution patterns for both the protist and metazoan plankton communities. Metabarcoding revealed both the core, year-round plankton community and previously unrecorded plankton organisms in the Southern Adriatic. The results provide for the first time a comprehensive overview of the plankton community in this area by metabarcoding. The identified seasonal patterns of plankton genera and species in the Southern Adriatic will contribute to the understanding of plankton interactions and future changes in community diversity characterisation.
Effect of Bleaching Agents on Healthy Enamel, White Spots, and Carious Lesions: A Systematic Review and Meta-Analysis
Authors: Gkavela Grigoria , Kakouris Vlassios , Pappa Eftychia , Rahiotis Christos
Published: May 2024

This systematic review examines studies focusing on tooth bleaching and its effects on healthy enamel or incipient caries and bacterial adhesion. The aim is to explore the impact of different bleaching agents on incipient caries lesions and healthy enamel. Clinical studies, in vitro studies, and observational studies that compared at least two groups were included. A search strategy was used to select studies from the MEDLINE via Pubmed and Scopus databases. Two evaluators performed data extraction, screening, and quality assessment independently. Only studies written in English were included. From 968 initial records, 28 studies were selected for a full-text evaluation. Of these, 7 studies were classified as cluster 1 (bacterial adherence on teeth), 12 studies as cluster 2 (no bacteria involved), 4 studies as cluster 3 (no teeth deployment), and 5 clinical studies were cluster 4. Of the selected studies, 6 (21.4%) supported increased bacterial attachment capacity and cariogenic dynamics, 4 (14.3%) decreased adhesion and cariogenic activity, 7 (25%) showed no difference, and 11 (39.3%) followed a different methodological approach and could not be categorized. The risk of bias appeared to be high, mainly because of the different methodologies in the studies, so we cannot reach a confident conclusion. Nevertheless, as far as carbamide peroxide bleaching is concerned, there does not seem to be a clinically significant alteration, neither in microorganism counts nor in enamel microstructure.
PPSwarm: Multi-UAV Path Planning Based on Hybrid PSO in Complex Scenarios
Authors: Meng Qicheng , Chen Kai , Qu Qingjun
Published: May 2024

Evolutionary algorithms exhibit flexibility and global search advantages in multi-UAV path planning, effectively addressing complex constraints. However, when there are numerous obstacles in the environment, especially narrow passageways, the algorithm often struggles to quickly find a viable path. Additionally, collaborative constraints among multiple UAVs complicate the search space, making algorithm convergence challenging. To address these issues, we propose a novel hybrid particle swarm optimization algorithm called PPSwarm. This approach initially employs the RRT* algorithm to generate an initial path, rapidly identifying a feasible solution in complex environments. Subsequently, we adopt a priority planning method to assign priorities to UAVs, simplifying collaboration among them. Furthermore, by introducing a path randomization strategy, we enhance the diversity of the particle swarm, thereby avoiding local optimum solutions. The experimental results show that, in comparison to algorithms such as DE, PSO, ABC, GWO, and SPSO, the PPSwarm algorithm demonstrates significant advantages in terms of path quality, convergence speed, and runtime when addressing path planning issues for 40 UAVs across four different scenarios. In larger-scale experiments involving 500 UAVs, the proposed algorithm also exhibits excellent processing capability and scalability.
Deep Learning-Based Multi-Feature Fusion for Communication and Radar Signal Sensing
Authors: Li Ting , Liu Tian , Song Zhangli , Zhang Lin , Ma Yiming
Published: May 2024

Recent years witness the rapid development of communication and radar technologies, and many transmitters are equipped with both communication and radar functionalities. To keep track of the working state of a target dual-functional transmitter, it is crucial to sense the modulation mode of the emitted signals. In this paper, we propose a deep learning-based intelligent modulation sensing technique for dual-functional transmitters. Different from existing modulation sensing methods, which usually focus on communication signals, we take both communication and radar signals into consideration. Typically, the dominant features of communication signals lie in the time domain, while those of radar signals lie in both time and frequency domains. To enhance the sensing accuracy, we first exploit real and complex value convolution operations to extract both time-domain and frequency-domain features of emitted signals from the target transmitter. Then, we fuse the extracted features by assigning proper weights with the attention mechanism. Simulation results reveal that the proposed technique can improve the sensing accuracy by up to 4% on average compared with benchmarks.
Trend Research on Maritime Autonomous Surface Ships (MASSs) Based on Shipboard Electronics: Focusing on Text Mining and Network Analysis
Authors: Kim Jinsick , Han Sungwon , Lee Hyeyoung , Koo Byeongsoo , Nam Moonju , Jang Kukjin , Lee Jooyeoun , Chung Myoungsug
Published: May 2024

The growing adoption of electric propulsion systems in Maritime Autonomous Surface Ships (MASSs) necessitates advancements in shipboard electronics for safe, efficient, and reliable operation. These advancements are crucial for tasks such as real-time sensor data processing, control algorithms for autonomous navigation, and robust decision-making capabilities. This study investigates research trends in MASSs, using bibliographic analysis to identify policy and future research directions in this evolving field. We analyze 3363 MASS-related articles from the Web of Science database, employing co-occurrence word analysis and latent Dirichlet allocation (LDA) topic modeling. The findings reveal a rapidly growing field dominated by image recognition research. Keywords such as “datum”, “image”, and “detection” suggest a focus on collecting and analyzing marine data, particularly with deep learning for synthetic aperture radar imagery. LDA confirms this, with “image analysis and classification research” as the leading topic. The study also identifies national and organizational leaders in MASS research. However, research on Arctic routes lags behind that on other areas. This work provides valuable insights for policymakers and researchers, promoting a deeper understanding of MASSs and informing future policy and research agendas regarding the integration of electric propulsion systems within the maritime industry.
Techno-Economic and Environmental Analysis of the Integration of PV Systems into Hybrid Vessels
Authors: McAllister Lewis , Wang Haibin
Published: May 2024

Solar energy is one type of clean energy resource, and currently the IMO, EU and UK are targeting net zero carbon emissions by 2050. This paper delves into the integration of photovoltaic (PV) systems into hybrid vessels in order to meet their strategies and targets. The technical challenges that come with designing such systems as well as their economic and environmental impacts are examined. By optimizing the usage of harnessed solar energy, we discover the operational strategy that provides maximal benefits through day-to-day savings as well as over the 25 year lifespan of solar panels. It demonstrates impressive economic viability, with cost savings of up to GBP 4.55 per day and a payoff period as short as 9 years. It also displays a modest emission reduction of up to 8.002 kg of CO2, which serves as proof for a pathway to greener practices in the maritime industry. This report highlights the operational flexibility that a hybrid vessel possesses once paired with a PV system through the ability to withstand regulatory and market changes. Also, when looking ahead, further adoption of PV technology creates opportunities for innovation in adopting renewable energy solutions in maritime transportation.
Brief Analysis of the Location and Determination of Maximum Capacity of Distributed Generation in Electrical Systems Considering Demand Scenarios in Ecuador
Authors: De la Cruz Roger David , Tipán Luis Fernando , Cuji Cristian Cristobal
Published: May 2024

This research focuses on evaluating the importance of the use of renewable sources through distributed generation and its implication in the operation of electrical systems given that its incorporation has a direct impact on the expansion of the capacity of the networks, the minimization losses, and the impact on end users, all supported by the growth of demand. Under this context, the study focuses on incorporating distributed generation (DG), taking scenarios of base, medium, and peak demand and the modeling of the network, and subsequently evaluating the service quality indices and operating costs in addition to the electrical variables of the system. For this purpose, the present work proposes an optimization model to be solved using the Matlab (2021b) computational program together with GAMS (37.1.0 Major release (11 November 2021)) and mixed-integer nonlinear programming, determining the optimal insertion and determination of the maximum capacity of distributed generators while complying with the technical restrictions of the system and applying optimal AC power flows. Localizing and determining maximum capacity for distributed generation (DG) in electrical systems are critical aspects of modern grid planning and operation. With the increasing penetration of renewable energy sources and the growing complexity of energy demand patterns, efficient integration of DG has become paramount for ensuring grid reliability and sustainability. In this context, the analysis of DG localization and capacity determination considering demand scenarios emerges as a critical area of research in electrical engineering. By employing advanced optimization techniques such as mixed-integer nonlinear programming (MINP), this research addresses the multidimensional challenges associated with DG deployment, including technical constraints, economic considerations, and environmental impacts. Understanding the contribution of this optimization approach to electrical engineering is fundamental for optimizing grid performance, enhancing renewable energy integration, and supporting the transition towards more resilient and sustainable energy systems. Consequently, investigating this optimization model represents a crucial step towards advancing the state-of-the-art in grid planning and facilitating the transition to a cleaner and more efficient energy future.
Reservoir Simulations of Hydrogen Generation from Natural Gas with CO2 EOR: A Case Study
Authors: Miłek Krzysztof , Szott Wiesław , Tyburcy Jarosław , Lew Alicja
Published: May 2024

This paper addresses the problem of hydrogen generation from hydrocarbon gases using Steam Methane Reforming (SMR) with byproduct CO2 injected into and stored in a partially depleted oil reservoir. It focuses on the reservoir aspects of the problem using numerical simulation of the processes. To this aim, a numerical model of a real oil reservoir was constructed and calibrated based on its 30-year production history. An algorithm was developed to quantify the CO2 amount from the SMR process as well as from the produced fluids, and optionally, from external sources. Multiple simulation forecasts were performed for oil and gas production from the reservoir, hydrogen generation, and concomitant injection of the byproduct CO2 back to the same reservoir. EOR from miscible oil displacement was found to occur in the reservoir. Various scenarios of the forecasts confirmed the effectiveness of the adopted strategy for the same source of hydrocarbons and CO2 sink. Detailed simulation results are discussed, and both the advantages and drawbacks of the proposed approach for blue hydrogen generation are concluded. In particular, the question of reservoir fluid balance was emphasized, and its consequences were presented. The presented technology, using CO2 from hydrogen production and other sources to increase oil production, also has a significant impact on the protection of the natural environment via the elimination of CO2 emission to the atmosphere with concomitant production of H2.
Comparison of the Sample Preparation Strategies and Impacts on the Tensile Strength of Gas Shale with Variable Moisture Conditions
Authors: Shu Liuqing , Xie Lingzhi , He Bo , Zhang Yao
Published: May 2024

Moisture significantly affects the mechanical behavior of gas shale and further determines the hydraulic fracturing performance, as it is more attractive. Nevertheless, batch experiments have usually involved variable methodologies regarding the preparation of moisture-contained shale specimens in the sequence (and/or frequency) of drying and soaking treatments. Accordingly, this work investigates how the preparation methodology influences the test results of moisture-contained shale samples. This study compares three commonly used shale sample preparation strategies for acquiring different moisture contents, that is, “dry-wet”, “dry-wet-dry”, and “wet-dry-wet” strategies, followed by a Brazilian splitting test for the mechanical parameters. The results show that under the same saturation conditions, the longer the soaking time during sample preparation, the higher the degradation degree of shale tensile strength. Meanwhile, prolonged soaking can lead to a more discrete distribution of strength values, and the failure mode may deviate from the Brazilian splitting theory model. Under the combined influence of moisture content and soaking time, the tensile strength of shale decreases approximately linearly with increasing saturation, while the degradation degree increases nonlinearly with increasing saturation, and the degradation rate changes from slow to fast. According to the observation of the microstructure of hydrated shale, prolonged soaking can lead to an increase in the expansion of clay minerals in shale by hydration, resulting in looser and more fragmented internal structure, and further degradation in shale strength. In order to weaken the interference of hydration when studying the effect of moisture content on the tensile strength of shale, the soaking time should be minimized as much as possible during the preparation process.
Integration of Chemical Looping Combustion in the Graz Power Cycle
Authors: Arnaiz del Pozo Carlos , Sánchez-Orgaz Susana , Navarro-Calvo Alberto , Jiménez Álvaro Ángel , Cloete Schalk
Published: May 2024

Effective decarbonization of the power generation sector requires a multi-pronged approach, including the implementation of CO2 capture and storage (CCS) technologies. The Graz cycle features oxy-combustion CO2 capture in a power production scheme which can result in higher thermal efficiencies than that of a combined cycle. However, the auxiliary consumption required by the air separation unit to provide pure O2 results in a significant energy penalty relative to an unabated plant. In order to mitigate this penalty, the present study explores the possibility of chemical looping combustion (CLC) as an alternative means to supply oxygen for conversion of the fuel. For a midscale power plant, despite reducing the levelized cost of electricity (LCOE) by approximately 12.6% at a CO2 tax of EUR 100/ton and a natural gas price of EUR 6.5/GJ and eliminating the energy penalty of CCS relative to an unabated combined cycle, the cost reductions of CLC in the Graz cycle were not compelling relative to commercially available post-combustion CO2 capture with amines. Although the central assumptions yielded a 3% lower cost for the Graz-CLC cycle, an uncertainty quantification study revealed an 85.3% overlap in the interquartile LCOE range with that of the amine benchmark, indicating that the potential economic benefit is small compared to the uncertainty of the assessment. Thus, this study indicates that the potential of CLC in gas-fired power production is limited, even when considering highly efficient advanced configurations like the Graz cycle.
Assessing the Additional Benefits of Thailand’s Approaches to Reduce Motor Vehicle Emissions
Authors: Thanatrakolsri Pantitcha , Sirithian Duanpen
Published: May 2024

Air pollutants and greenhouse gases (GHGs) represent major challenges in our era, contributing to climate change and global health issues. These problems arise from a variety of well-known sources, including motor vehicles. Almost all nations, Thailand included, have formulated and implemented policies to curb greenhouse gas (GHG) emissions in line with the requirements and commitments of the Paris Agreement. The evaluation of specific air pollutants and GHG emissions originating from road vehicles utilises the Thailand database, referencing the year 2019. Data intersections from 2019 to 2022 are grounded in actual data collected from relevant departments in Thailand, while projections for 2023–2030 are forecasted based on the baseline year. The secondary database used in the International Vehicle Emission model is adjusted according to real-world driving data to accurately reflect country-specific emission factors. Dynamic emission factors for specific air pollutants and GHGs are evaluated and integrated with the average Vehicle Kilometres Travelled (VKT) for each vehicle category. The Business-As-Usual (BAU) scenario is then examined, based on existing policies aimed at reducing air pollutants and GHG emissions in Thailand’s transport sector. These policies include strategies for the adoption of electric vehicles and the promotion of public transport to reduce VKT. Under the BAU scenario, the overall number of road vehicles in Thailand, including passenger cars, motorcycles, pickups, vans, trucks, and buses, is expected to increase by approximately 6.58% by 2030, leading to a rise in specific air pollutants and GHG emissions compared to the 2019 baseline. However, by adhering to Thailand’s strategies and transitioning to new electric passenger cars and buses, greenhouse gas emissions and specific air pollutants from the road transport sector will be significantly reduced.
Machine Learning Models for Regional Photovoltaic Power Generation Forecasting with Limited Plant-Specific Data
Authors: Tucci Mauro , Piazzi Antonio , Thomopulos Dimitri
Published: May 2024

Predicting electricity production from renewable energy sources, such as solar photovoltaic installations, is crucial for effective grid management and energy planning in the transition towards a sustainable future. This study proposes machine learning approaches for predicting electricity production from solar photovoltaic installations at a regional level in Italy, not using data on individual installations. Addressing the challenge of diverse data availability between pinpoint meteorological inputs and aggregated power data for entire regions, we propose leveraging meteorological data from the centroid of each Italian province within each region. Particular attention is given to the selection of the best input features, which leads to augmenting the input with 1-hour-lagged meteorological data and previous-hour power data. Several ML approaches were compared and examined, optimizing the hyperparameters through five-fold cross-validation. The hourly predictions encompass a time horizon ranging from 1 to 24 h. Among tested methods, Kernel Ridge Regression and Random Forest Regression emerge as the most effective models for our specific application. We also performed experiments to assess how frequently the models should be retrained and how frequently the hyperparameters should be optimized in order to comprise between accuracy and computational costs. Our results indicate that once trained, the model can provide accurate predictions for extended periods without frequent retraining, highlighting its long-term reliability.
Multi-Time-Scale Low-Carbon Economic Dispatch Method for Virtual Power Plants Considering Pumped Storage Coordination
Authors: Zhang Junwei , Liu Dongyuan , Lyu Ling , Zhang Liang , Du Huachen , Luan Hanzhang , Zheng Lidong
Published: May 2024

Low carbon operation of power systems is a key way to achieve the goal of energy power carbon peaking and carbon neutrality. In order to promote the low carbon transition of energy and power and the coordinated and optimized operation of distributed energy sources in virtual power plants (VPP), this paper proposes a framework for collaborative utilization of pumped storage–carbon capture–power-to-gas (P2G) technologies. It also constructs a multi-time scale low carbon economic dispatch model for VPP to minimize the internal resource operation cost of VPP in each time period. During the intraday scheduling stage, the day-ahead scheduling results as the planned output and the energy flow is then dynamically corrected at a short-term resolution in the framework. This allows for the exploration of the low-carbon potential of each aggregation unit within the virtual power plant. The results of the simulation indicate that the strategy and model proposed in this paper can effectively encourage the consumption of renewable energy sources, promote the low-carbon operation of power system power, and serve as a valuable reference for the low-carbon economic operation of the power system.
Emerging Approaches to Profile Accessible Chromatin from Formalin-Fixed Paraffin-Embedded Sections
Authors: Sunitha Kumary Vishnu Udayakumaran Nair , Venters Bryan J. , Raman Karthikeyan , Sen Sagnik , Estève Pierre-Olivier , Cowles Martis W. , Keogh Michael-Christopher , Pradhan Sriharsa
Published: May 2024

Nucleosomes are non-uniformly distributed across eukaryotic genomes, with stretches of ‘open’ chromatin strongly associated with transcriptionally active promoters and enhancers. Understanding chromatin accessibility patterns in normal tissue and how they are altered in pathologies can provide critical insights to development and disease. With the advent of high-throughput sequencing, a variety of strategies have been devised to identify open regions across the genome, including DNase-seq, MNase-seq, FAIRE-seq, ATAC-seq, and NicE-seq. However, the broad application of such methods to FFPE (formalin-fixed paraffin-embedded) tissues has been curtailed by the major technical challenges imposed by highly fixed and often damaged genomic material. Here, we review the most common approaches for mapping open chromatin regions, recent optimizations to overcome the challenges of working with FFPE tissue, and a brief overview of a typical data pipeline with analysis considerations.
pFedBASC: Personalized Federated Learning with Blockchain-Assisted Semi-Centralized Framework
Authors: Zhang Yu , Peng Xiaowei , Xian Hequn
Published: May 2024

As network technology advances, there is an increasing need for a trusted new-generation information management system. Blockchain technology provides a decentralized, transparent, and tamper-proof foundation. Meanwhile, data islands have become a significant obstacle for machine learning applications. Although federated learning (FL) ensures data privacy protection, server-side security concerns persist. Traditional methods have employed a blockchain system in FL frameworks to maintain a tamper-proof global model database. In this context, we propose a novel personalized federated learning (pFL) with blockchain-assisted semi-centralized framework, pFedBASC. This approach, tailored for the Internet of Things (IoT) scenarios, constructs a semi-centralized IoT structure and utilizes trusted network connections to support FL. We concentrate on designing the aggregation process and FL algorithm, as well as the block structure. To address data heterogeneity and communication costs, we propose a pFL method called FedHype. In this method, each client is assigned a compact hypernetwork (HN) alongside a normal target network (TN) whose parameters are generated by the HN. Clients pull together other clients’ HNs for local aggregation to personalize their TNs, reducing communication costs. Furthermore, FedHype can be integrated with other existing algorithms, enhancing its functionality. Experimental results reveal that pFedBASC effectively tackles data heterogeneity issues while maintaining positive accuracy, communication efficiency, and robustness.
Impact of the Local Dynamics on Exit Choice Behaviour in Evacuation Model
Authors: Xing Sensen , Wang Cheng , Gao Dongli , Wang Wei , Yuen Anthony Chun Yin , Lee Eric Wai Ming , Yeoh Guan Heng , Chan Qing Nian
Published: May 2024

This study investigated the interplay between exit selection models and local pedestrian movement patterns within floor field frameworks. Specifically, this investigation analysed the performance of a multinomial logit exit choice model, incorporating both expected utility theory and cumulative prospect theory frameworks when coupled with three distinct local-level pedestrian movement models (FF-Von Neumann, FF-Moore, and NSFF). The expected utility theory framework considers the deterministic component as a linear relationship, while the cumulative prospect theory framework further considers the decision-maker’s risky attitudes by transforming objective terms into subjective terms using a power value function. The core objective was to comprehend how local movement dynamics, as represented by the floor field models, influence decision-making during exit selection. Comparative analyses revealed intriguing variations between the three local models, despite their shared expected utility theory-based exit choice framework. These discrepancies stemmed from the diverse pedestrian trajectory behaviours generated by each model. Consequently, these local dynamics impacted the decision-maker’s assessment of critical factors, such as the number of evacuees close to the decision-maker (NCDM) and the number of evacuees close to an exit (NCE), which the exit choice model incorporates. These assessments, in turn, significantly affected higher-level decision-making. The integration of the three models with the multinomial logit exit choice model, using either cumulative prospect theory and expected utility theory frameworks, further strengthened the observed bilateral relationship. While the specific nature of this relationship varied depending on the chosen framework and its implementation details, these consistent findings demonstrate the robustness of the results. This reinforced the influence of local-level pedestrian dynamics on higher-level exit selection, highlighting the importance of accurate crowd dynamics modelling, especially when advanced exit choice models consider local movement factors.
Improving the Properties of Gelatin-Based Films by Incorporation of “Pitanga” Leaf Extract and Crystalline Nanocellulose
Authors: Tessaro Larissa , Pereira Ana Gabrielle R. , Martelli-Tosi Milena , Sobral Paulo José do Amaral
Published: May 2024

Biopolymer-based films can be activated by the incorporation of active compounds into their matrix. Plant extracts are rich in phenolic compounds, which have antimicrobial and/or antioxidant properties. The aim of this study was to produce gelatin-based active films and nanocomposite films incorporated with “pitanga” (Eugenia uniflora L.) leaf extract (PLE) and/or crystalline nanocellulose extracted from soybean straw (CN), and to study the physicochemical, functional, microstructural, thermal, UV/Vis light barrier, and antioxidant properties of these materials. PLE enhanced some film properties, such as tensile strength (from 30.2 MPa to 40.6 MPa), elastic modulus (from 9.3 MPa to 11.3 MPa), the UV/Vis light barrier, and antioxidant activity, in addition to affecting the microstructural, surface, and color properties. These improvements were even more significant in nanocomposites simultaneously containing PLE and CN (59.5 MPa for tensile strength and 15.1 MPa for elastic modulus), and these composites also had lower moisture content (12.2% compared to 13.5–14.4% for other treatments) and solubility in water (from 48.9% to 44.1%). These improvements may be the result of interactions that occur between PLE’s polyphenols and gelatin, mainly in the presence of CN, probably due to the formation of a stable PLE–CN–gelatin complex. These results are relevant for the food packaging sector, as the activated nanocomposite films exhibited enhanced active, barrier, and mechanical properties due to the presence of PLE and CN, in addition to being entirely produced with sustainable components from natural and renewable sources.