bluuebunny/crossref_metadata_2025 huggingface dataset:
Pentacoordinate Mixed-Ligand Zn(II) Complexes with Flufenamato and Niflumato Ligands: Crystal Structure, Spectral Characterization and Biological Properties
Authors: Smolková Romana , Smolko Lukáš , Poupon Morgane , Samoľová Erika
Published: October 2023

Mixed-ligand complexes featuring a carboxylato ligand and bidentate chelate N,N′-donor ligands along with a single halogenido ligand are rarely subjected to studies despite their interesting physical, chemical and biological properties. A direct synthesis involving non-steroidal anti-inflammatory drugs flufenamic acid and niflumic acid and sterically hindering N,N′-donor ligands (neocuproine and 2,2′-biquinoline) has been used to prepare mononuclear mixed-ligand zinc complexes with a composition of [ZnCl(flu)(neo)] (1A and 1B) and [ZnCl(nif)(biq)] (2). Single-crystal X-ray diffraction data have shown that the carboxylate binding mode differs in the two polymorphs 1A and 1B, which form molecules of same composition, and the deviations in molecular geometry are further reflected in their supramolecular structures. The inter- and intramolecular interactions and their contributions to the stability of the crystal structures has been investigated by Hirshfeld surface analysis. The spectral properties of the prepared complexes in solid-state and solution have been analyzed. Additionally, the interaction with the biomacromolecules (fish sperm DNA, human and bovine serum albumins), studied by fluorescence assays, showed a stronger binding of 1A and 1B in comparison with 2.
Pretreatment Cancer-Related Cognitive Impairment in Hodgkin Lymphoma Patients
Authors: Juríčková Veronika , Fayette Dan , Jonáš Juraj , Fajnerová Iveta , Kozák Tomáš , Horáček Jiří
Published: October 2023

Background: Cancer-related cognitive impairment (CRCI) is one of the most serious side effects of cancer that negatively impacts the quality of life of cancer patients and survivors. There is evidence of CRCI in Hodgkin lymphoma patients (HL); however, there is a lack of studies examining the presence of cognitive deficits before starting any treatment in HL patients. Methods: Forty adult patients (N = 40) newly diagnosed with HL (with no previous cancer diagnoses) and 40 healthy controls (N = 40) matched for age, sex, education, and premorbid intellect completed the neuropsychological battery and subjective and objective measures of affective distress and quality of life. Results: The results showed impairment in three out of six cognitive domains: verbal memory and learning, speed of processing/psychomotor speed, and abstraction/executive functions in the HL patients before the initiation of any treatment. The speed of processing/psychomotor speed domain is negatively correlated with depression. Conclusion: Cognitive deterioration in verbal memory and learning and abstraction/executive functions domains in HL patients seems to occur before the initiation of treatment independently of anxiety, depression, or physical symptoms. This suggests that HL itself may cause cognitive deficits in these cognitive domains. However, the underlying causes of CRCI still remain unclear.
Human–Shark Interactions: Citizen Science Potential in Boosting Shark Research on Madeira Island
Authors: Berimbau Luís , Larrea Ander , Costa Ana Cristina , Torres Paulo
Published: October 2023

Sharks are regularly considered to be an indicator of the ocean ecosystem’s health and are generally difficult to study in their natural environment. Citizen science has been increasing, being a cost-effective method for particularly important species that have low encounter rates or are logistically challenging to sample. Madeira Island has a considerable gap in terms of its coastal shark species data and studies, which this work aims to complement. To achieve this goal, online questionnaires to citizens and specific interviews of spearfishers were conducted, with questions related to species size, distribution, and behaviour. Exactly 129 reports of shark sightings were obtained, including seven different species exhibiting four different types of behaviour around the coastal areas of the different municipalities of Madeira Island. Individuals seem to aggregate around the east and west edges of the island due to localised upwelling phenomena. Also, a relation was found between size and distance to coast and depth, as smaller sharks tend to stay close to the shore, probably reflecting the role of Madeira as a nursery area, but further studies are necessary to confirm the reliability of this hypothesis.
Precision and Accuracy of Radiological Bone Age Assessment in Children among Different Ethnic Groups: A Systematic Review
Authors: Martín Pérez Sebastián Eustaquio , Martín Pérez Isidro Miguel , Vega González Jesús María , Molina Suárez Ruth , León Hernández Coromoto , Rodríguez Hernández Fidel , Herrera Perez Mario
Published: October 2023

Aim: The aim was to identify, evaluate, and summarize the findings of relevant individual studies on the precision and accuracy of radiological BA assessment procedures among children from different ethnic groups. Materials and Methods: A qualitative systematic review was carried out following the MOOSE statement and previously registered in PROSPERO (CRD42023449512). A search was performed in MEDLINE (PubMed) (n = 561), the Cochrane Library (n = 261), CINAHL (n = 103), Web of Science (WOS) (n = 181), and institutional repositories (n = 37) using MeSH and free terms combined with the Booleans “AND” and “OR”. NOS and ROBINS-E were used to assess the methodological quality and the risk of bias of the included studies, respectively. Results: A total of 51 articles (n = 20,100) on radiological BA assessment procedures were precise in terms of intra-observer and inter-observer reliability for all ethnic groups. In Caucasian and Hispanic children, the Greulich–Pyle Atlas (GPA) was accurate at all ages, but in youths, Tanner–Whitehouse radius–ulna–short bones 3 (TW3-RUS) could be an alternative. In Asian and Arab subjects, GPA and Tanner–Whitehouse 3 (TW3) overestimated the BA in adolescents near adulthood. In African youths, GPA overestimated the BA while TW3 was more accurate. Conclusion: GPA and TW3 radiological BA assessment procedures are both precise but their accuracy in estimating CA among children of different ethnic groups can be altered by racial bias.
Clinical Value of Mean Platelet Volume to Platelet Ratio (MPR) in Distinguishing Mass-Forming Chronic Pancreatitis and Pancreatic Cancer
Authors: Wang Han-Xuan , Li Yu-Lin , Huang Jin-Can , Ma You-Wei , Lang Ren , Lyu Shao-Cheng
Published: October 2023

Background: Correctly distinguishing mass-forming chronic pancreatitis (MFCP) from pancreatic cancer (PC) is of clinical significance to determine optimal therapy and improve the prognosis of patients. According to research, inflammation status in PC is different from that in MFCP. Mean platelet volume/platelet ratio (MPR) is a platelet-related inflammation index which has been proven to be valuable in the diagnosis and prognosis of various malignant cancers due to the change in mean platelet volume and platelet count under abnormal inflammatory conditions caused by tumors. Thus, we conducted this study to investigate the clinical value of MPR in distinguishing MFCP from PC. Methods: We retrospectively analyzed the data of 422 patients who were suspected to have PC during imaging examination at our department from January 2012 to December 2021. Included patients were divided into the PC (n = 383) and MFCP groups (n = 39), according to their pathological diagnosis. Clinical data including MPR were compared within these two groups and the diagnostic value was explored using logistic regression. The ROC curve between MPR and PC occurrence was drawn and an optimal cut-off value was obtained. Propensity score matching was applied to match MFCP patients with PC patients according to their age and carbohydrate antigen 19-9 (CA19-9). Differences in MPR between groups were compared to verify our findings. Results: The area under the ROC curve between MPR and PC occurrence was 0.728 (95%CI: 0.652–0.805) and the optimal cut-off value was 0.045 with a 69.2% sensitivity and 68.0% accuracy. For all the included patients, MPRs in the MFCP and PC groups were 0.04 (0.04, 0.06) and 0.06 (0.04, 0.07), respectively (p = 0.005). In patients with matching propensity scores, MPRs in the MFCP and PC groups were 0.04 (0.03, 0.06) and 0.06 (0.05, 0.08), respectively (p = 0.005). Multiple logistic regression in all included patients and matched patients confirmed MPR and CA19-9 as independent risk markers in distinguishing PC. Combining CA19-9 with MPR can increase the sensitivity and accuracy in diagnosing PC to 93.2% and 89.5%, respectively. Conclusion: MPR in PC patients is significantly higher than that in MFCP patients and may be adopted as a potential indicator to distinguish MFCP and PC. Its differential diagnosis capacity can be improved if combined with CA19-9.
The Diagnosis of Malignant Pleural Effusion Using Tumor-Marker Combinations: A Cost-Effectiveness Analysis Based on a Stacking Model
Authors: Wang Jingyuan , Zhou Jiangjie , Wu Hanyu , Chen Yangyu , Liang Baosheng
Published: October 2023

Purpose: By incorporating the cost of multiple tumor-marker tests, this work aims to comprehensively evaluate the financial burden of patients and the accuracy of machine learning models in diagnosing malignant pleural effusion (MPE) using tumor-marker combinations. Methods: Carcinoembryonic antigen (CEA), carbohydrate antigen (CA)19-9, CA125, and CA15-3 were collected from pleural effusion (PE) and peripheral blood (PB) of 319 patients with pleural effusion. A stacked ensemble (stacking) model based on five machine learning models was utilized to evaluate the diagnostic accuracy of tumor markers. We evaluated the discriminatory accuracy of various tumor-marker combinations using the area under the curve (AUC), sensitivity, and specificity. To evaluate the cost-effectiveness of different tumor-marker combinations, a comprehensive score (C-score) with a tuning parameter w was proposed. Results: In most scenarios, the stacking model outperformed the five individual machine learning models in terms of AUC. Among the eight tumor markers, the CEA in PE (PE.CEA) showed the best AUC of 0.902. Among all tumor-marker combinations, the PE.CA19-9 + PE.CA15-3 + PE.CEA + PB.CEA combination (C9 combination) achieved the highest AUC of 0.946. When w puts more weight on the cost, the highest C-score was achieved with the single PE.CEA marker. As w puts over 0.8 weight on AUC, the C-score favored diagnostic models with more expensive tumor-marker combinations. Specifically, when w was set to 0.99, the C9 combination achieved the best C-score. Conclusion: The stacking diagnostic model using PE.CEA is a relatively accurate and affordable choice in diagnosing MPE for patients without medical insurance or in a low economic level. The stacking model using the combination PE.CA19-9 + PE.CA15-3 + PE.CEA + PB.CEA is the most accurate diagnostic model and the best choice for patients without an economic burden. From a cost-effectiveness perspective, the stacking diagnostic model with PE.CA19-9 + PE.CA15-3 + PE.CEA combination is particularly recommended, as it gains the best trade-off between the low cost and high effectiveness.
Embracing AI: The Imperative Tool for Echo Labs to Stay Ahead of the Curve
Authors: Vasile Corina , Iriart Xavier
Published: October 2023

Advancements in artificial intelligence (AI) have rapidly transformed various sectors, and the field of echocardiography is no exception. AI-driven technologies hold immense potential to revolutionize echo labs’ diagnostic capabilities and improve patient care. This paper explores the importance for echo labs to embrace AI and stay ahead of the curve in harnessing its power. Our manuscript provides an overview of the growing impact of AI on medical imaging, specifically echocardiography. It highlights how AI-driven algorithms can enhance image quality, automate measurements, and accurately diagnose cardiovascular diseases. Additionally, we emphasize the importance of training echo lab professionals in AI implementation to optimize its integration into routine clinical practice. By embracing AI, echo labs can overcome challenges such as workload burden and diagnostic accuracy variability, improving efficiency and patient outcomes. This paper highlights the need for collaboration between echocardiography laboratory experts, AI researchers, and industry stakeholders to drive innovation and establish standardized protocols for implementing AI in echocardiography. In conclusion, this article emphasizes the importance of AI adoption in echocardiography labs, urging practitioners to proactively integrate AI technologies into their workflow and take advantage of their present opportunities. Embracing AI is not just a choice but an imperative for echo labs to maintain their leadership and excel in delivering state-of-the-art cardiac care in the era of advanced medical technologies.
Medical Imaging Applications of Federated Learning
Authors: Sandhu Sukhveer Singh , Gorji Hamed Taheri , Tavakolian Pantea , Tavakolian Kouhyar , Akhbardeh Alireza
Published: October 2023

Since its introduction in 2016, researchers have applied the idea of Federated Learning (FL) to several domains ranging from edge computing to banking. The technique’s inherent security benefits, privacy-preserving capabilities, ease of scalability, and ability to transcend data biases have motivated researchers to use this tool on healthcare datasets. While several reviews exist detailing FL and its applications, this review focuses solely on the different applications of FL to medical imaging datasets, grouping applications by diseases, modality, and/or part of the body. This Systematic Literature review was conducted by querying and consolidating results from ArXiv, IEEE Xplorer, and PubMed. Furthermore, we provide a detailed description of FL architecture, models, descriptions of the performance achieved by FL models, and how results compare with traditional Machine Learning (ML) models. Additionally, we discuss the security benefits, highlighting two primary forms of privacy-preserving techniques, including homomorphic encryption and differential privacy. Finally, we provide some background information and context regarding where the contributions lie. The background information is organized into the following categories: architecture/setup type, data-related topics, security, and learning types. While progress has been made within the field of FL and medical imaging, much room for improvement and understanding remains, with an emphasis on security and data issues remaining the primary concerns for researchers. Therefore, improvements are constantly pushing the field forward. Finally, we highlighted the challenges in deploying FL in medical imaging applications and provided recommendations for future directions.
Increased Salivary IL-1 Beta Level Is Associated with Poor Sleep Quality in University Students
Authors: Ballestar-Tarín María Luisa , Ibáñez-del Valle Vanessa , Mafla-España Mayra Alejandra , Cauli Omar , Navarro-Martínez Rut
Published: October 2023

Poor sleep quality is a major public health concern for all ages. In particular, university students often face stress levels and changes in social life habits that negatively influence their quality of sleep. This could be associated with psychological well-being in terms of anxiety and depressive symptoms, stress levels, and a poor self-perceived health status. The increases in the pro-inflammatory cytokine interleukin 6 (IL-6), IL-1 beta (IL-1β), and tumor necrosis factor alpha (TNFα), in blood have been linked to poor sleep quality in many diseases, but data on salivary cytokine levels in students are missing or are seldom analyzed. In this study we determined the quality of sleep in a sample of university students and the role of psychological assessment and factors affecting sleep (alcohol intake, tobacco, consumption of stimulant drinks, exercise, and body mass index). We also aimed to shed new light on the associations between sleep quality and salivary inflammatory cytokines (IL-1β, IL-6, and TNFα). Sleep quality was measured with the Athens Insomnia Scale (AIS) and Pittsburgh Sleep Quality Index (PSQI). Perceived stress was assessed using Cohen’s Perceived Stress Scale (PSS), and the Goldberg Anxiety and Depression Scale (GADS) was used to assess the level of anxiety or depression. Perceived health status was measured with a visual analogue. Saliva samples was taken in the morning and the inflammatory cytokines was measured via enzyme-linked immunoassay. There was a direct and significant association between the salivary IL-1β concentration and AIS score (r = 0.248; p = 0.038, Pearson correlation) and Pittsburgh scale score (r = 0.274; p = 0.022, Pearson correlation). The relationship between IL-1β and AIS controlling for sex, age, and chronic disease, is still significant (r = 0.260; p = 0.033). The relationship between IL-1β and PSQI controlling for the influence of these variables is also significant (r = 0.279; p = 0.022). Salivary IL-1β concentrations were not significantly associated with any of the scores of the other psychological assessments (PSS, anxiety, depression symptoms, or self-perceived health). Salivary TNFα was significantly and inversely associated with self-perceived health (r = −0.259; p = 0.033, Pearson correlation), but the salivary IL-6 concentration was not associated with any of the sleep quality scale or psychological assessment scores. Our results provide a novel relationship between pro-inflammatory cytokine IL-1β in saliva and poor sleep quality. However, the role of inflammation in poor sleep quality requires further study to identify strategies that could lower inflammation and thus, likely improve sleep quality.
Intelligent Resource Allocation Using an Artificial Ecosystem Optimizer with Deep Learning on UAV Networks
Authors: Rafiq Ahsan , Alkanhel Reem , Muthanna Mohammed Saleh Ali , Mokrov Evgeny , Aziz Ahmed , Muthanna Ammar
Published: October 2023

An Unmanned Aerial Vehicle (UAV)-based cellular network over a millimeter wave (mmWave) frequency band addresses the necessities of flexible coverage and high data rate in the next-generation network. But, the use of a wide range of antennas and higher propagation loss in mmWave networks results in high power utilization and UAVs are limited by low-capacity onboard batteries. To cut down the energy cost of UAV-aided mmWave networks, Energy Harvesting (EH) is a promising solution. But, it is a challenge to sustain strong connectivity in UAV-based terrestrial cellular networks due to the random nature of renewable energy. With this motivation, this article introduces an intelligent resource allocation using an artificial ecosystem optimizer with a deep learning (IRA-AEODL) technique on UAV networks. The presented IRA-AEODL technique aims to effectually allot the resources in wireless UAV networks. In this case, the IRA-AEODL technique focuses on the maximization of system utility over all users, combined user association, energy scheduling, and trajectory design. To optimally allocate the UAV policies, the stacked sparse autoencoder (SSAE) model is used in the UAV networks. For the hyperparameter tuning process, the AEO algorithm is used for enhancing the performance of the SSAE model. The experimental results of the IRA-AEODL technique are examined under different aspects and the outcomes stated the improved performance of the IRA-AEODL approach over recent state of art approaches.
Dynamical Analyses Show That Professional Archers Exhibit Tighter, Finer and More Fluid Dynamical Control Than Neophytes
Authors: Azadjou Hesam , Błażkiewicz Michalina , Erwin Andrew , Valero-Cuevas Francisco J.
Published: October 2023

Quantifying the dynamical features of discrete tasks is essential to understanding athletic performance for many sports that are not repetitive or cyclical. We compared three dynamical features of the (i) bow hand, (ii) drawing hand, and (iii) center of mass during a single bow-draw movement between professional and neophyte archers: dispersion (convex hull volume of their phase portraits), persistence (tendency to continue a trend as per Hurst exponents), and regularity (sample entropy). Although differences in the two groups are expected due to their differences in skill, our results demonstrate we can quantify these differences. The center of mass of professional athletes exhibits tighter movements compared to neophyte archers (6.3 < 11.2 convex hull volume), which are nevertheless less persistent (0.82 < 0.86 Hurst exponent) and less regular (0.035 > 0.025 sample entropy). In particular, the movements of the bow hand and center of mass differed more between groups in Hurst exponent analysis, and the drawing hand and center of mass were more different in sample entropy analysis. This suggests tighter neuromuscular control over the more fluid dynamics of the movement that exhibits more active corrections that are more individualized. Our work, therefore, provides proof of principle of how well-established dynamical analysis techniques can be used to quantify the nature and features of neuromuscular expertise for discrete movements in elite athletes.
Viscoelastic Slider Blocks as a Model for a Seismogenic Fault
Authors: Motuzas Charlotte A. , Shcherbakov Robert
Published: October 2023

In this work, a model is proposed to examine the role of viscoelasticity in the generation of simulated earthquake-like events. This model serves to investigate how nonlinear processes in the Earth’s crust affect the triggering and decay patterns of earthquake sequences. These synthetic earthquake events are numerically simulated using a slider-block model containing viscoelastic standard linear solid (SLS) elements to reproduce the dynamics of an earthquake fault. The simulated system exhibits elements of self-organized criticality, and results in the generation of avalanches that behave similarly to naturally occurring seismic events. The model behavior is analyzed using the Epidemic-Type Aftershock Sequence (ETAS) model, which suitably represents the observed triggering and decay patterns; however, parameter estimates deviate from those resulting from natural aftershock sequences. Simulated aftershock sequences from this model are characterized by slightly larger p-values, indicating a faster-than-normal decay of aftershock rates within the system. The ETAS fit, along with realistic simulated frequency-size distributions, supports the inclusion of viscoelastic rheology to model the seismogenic fault dynamics.
The Two Facing Square Flaps Method for Release of Anterior and Posterior Axillary Line Burn Contractures
Authors: Quong Whitney Laurel , Ogawa Rei
Published: October 2023

With improved burn outcomes and survival rates, the focus of management in large burns has shifted from merely survival towards optimizing form and function for the burn survivor. Due to its unique structural features and functional demands however, the axilla is prone to contracture formation, and remains complex to reconstruct. Where contractures involve both the posterior and anterior axillary lines, the two facing square flaps method is a suitable choice for a wide range of patients. The flap design is flexible, and is relatively safe with a sufficient blood supply. Superior lengthening of approximately 3–4 times can be achieved, and is maintainable. In this surgical technique paper, we describe the strategy of the two facing square flaps, and present two patterns of its application, with representative cases of the local flap method.
A Scalable Spatial–Temporal Correlated Non-Stationary Channel Fading Generation Method
Authors: Fang Sheng , Mao Tongbao , Hua Boyu , Ding Yuan , Song Maozhong , Zhou Qiangjun , Zhu Qiuming
Published: October 2023

To address the challenges of complex implementation structures and high hardware resource consumption in multiple-input multiple-output (MIMO) channel emulators, this paper proposes a hardware generation method for spatial–temporal correlated non-stationary channel fading. Firstly, a hardware generation architecture is developed for field-programmable gate array (FPGA) platforms, which can also reduce the complexity of the channel emulator. Secondly, an improved CORDIC method is introduced to reduce algorithm latency and hardware consumption while expanding the function convergence domain, with a relative error maintained at the level of 10-4. Furthermore, based on the idea of time-division multiplexing, an efficient hardware operation of a lower triangular matrix is adopted to minimize the consumption of hardware resources. Finally, the measured results demonstrate that the statistical characteristics of the channel fading generated by the proposed method are in good agreement with the theoretical ones, with an average error of less than 2%. Additionally, under identical simulation conditions, hardware resource consumption is reduced by 6.87%. These findings provide compelling evidence of the enhanced efficiency and accuracy in simulating MIMO channels achieved through the proposed method.
Neuro-Robotic Synergy: Crafting the Secure Future of Industries in the Post Pandemic Era
Authors: Gueye Thierno , Iqbal Asif , Wang Yanen , Mushtaq Ray Tahir , Bakar Muhammad S. Abu
Published: October 2023

In recent years, ICSs have become increasingly commonplace in virtually every industry. The abbreviation “ICSs” refers to industrial control systems. These are specially designed computers used for monitoring, managing, and controlling procedures and tasks across a wide range of industries and vital infrastructure sectors. Production, power, disinfection of water, transport, and other sectors all greatly benefit from ICS use. The authors of this paper aim to detect ICS cyber hazards in industry. This article is the result of the writers’ extensive research on ICS programs and the impact of cyberattacks on them as well. The study narrowed its attention to just three ICS applications because there are simply too many to count: power plants, water reservoirs, and gas pipelines. The present paper focuses on the development and evaluation of neural networks for use in cyberattacks. An early form of neural network, the residual system, came first in the field. When a breach is detected in the ICS, the neural network sorts it into one of several categories. The produced datasets must not compromise users’ privacy or cause harm to the relevant industry if they fall into the wrong hands. An encoding device, decoder, pseudo-encoder, and critical model neural networks work together to generate random data. Finally, a set of trials is conducted in which a residual neural network is utilized to classify cyberattacks based on both the created and original datasets. Results from a series of studies indicate that using the created dataset is an effective technique to train high-quality neural networks for use in cybersecurity on a large amount of data without sacrificing the accuracy of the models. The Kullback-Leibler and Jensen-Shannon divergences also serve as the theoretical foundation and technique, respectively. In particular, the paper recommends operational and maintenance cybersecurity standards for ICS. This entails such things as secure password practices, patch management, and anti-malware defense. Physical safeguards for ICS is another topic that is covered.
Multi-Objective Optimization for Solar-Hydrogen-Battery-Integrated Electric Vehicle Charging Stations with Energy Exchange
Authors: Duan Lijia , Guo Zekun , Taylor Gareth , Lai Chun Sing
Published: October 2023

The importance of electric vehicle charging stations (EVCS) is increasing as electric vehicles (EV) become more widely used. EVCS with multiple low-carbon energy sources can promote sustainable energy development. This paper presents an optimization methodology for direct energy exchange between multi-geographic dispersed EVCSs in London, UK. The charging stations (CSs) incorporate solar panels, hydrogen, battery energy storage systems, and grids to support their operations. EVs are used to allow the energy exchange of charging stations. The objective function of the solar-hydrogen-battery storage electric vehicle charging station (SHS-EVCS) includes the minimization of both capital and operation and maintenance (O&M) costs, as well as the reduction in greenhouse gas emissions. The system constraints encompass the power output limits of individual components and the need to maintain a power balance between the SHS-EVCSs and the EV charging demand. To evaluate and compare the proposed SHS-EVCSs, two multi-objective optimization algorithms, namely the Non-dominated Sorting Genetic Algorithm (NSGA-II) and the Multi-objective Evolutionary Algorithm Based on Decomposition (MOEA/D), are employed. The findings indicate that NSGA-II outperforms MOEA/D in terms of achieving higher-quality solutions. During the optimization process, various factors are considered, including the sizing of solar panels and hydrogen storage tanks, the capacity of electric vehicle chargers, and the volume of energy exchanged between the two stations. The application of the optimized SHS-EVCSs results in substantial cost savings, thereby emphasizing the practical benefits of the proposed approach.
Information Fusion for 5G IoT: An Improved 3D Localisation Approach Using K-DNN and Multi-Layered Hybrid Radiomap
Authors: El Boudani Brahim , Dagiuklas Tasos , Kanaris Loizos , Iqbal Muddesar , Chrysoulas Christos
Published: October 2023

Indoor positioning is a core enabler for various 5G identity and context-aware applications requiring precise and real-time simultaneous localisation and mapping (SLAM). In this work, we propose a K-nearest neighbours and deep neural network (K-DNN) algorithm to improve 3D indoor positioning. Our implementation uses a novel data-augmentation concept for the received signal strength (RSS)-based fingerprint technique to produce a 3D fused hybrid. In the offline phase, a machine learning (ML) approach is used to train a model on a radiomap dataset that is collected during the offline phase. The proposed algorithm is implemented on the constructed hybrid multi-layered radiomap to improve the 3D localisation accuracy. In our implementation, the proposed approach is based on the fusion of the prominent 5G IoT signals of Bluetooth Low Energy (BLE) and the ubiquitous WLAN. As a result, we achieved a 91% classification accuracy in 1D and a submeter accuracy in 2D.
Influence of Fluidised Bed Inventory on the Performance of Limestone Sorbent in Calcium Looping for Thermochemical Energy Storage
Authors: Di Lauro Francesca , Tregambi Claudio , Montagnaro Fabio , Molignano Laura , Salatino Piero , Solimene Roberto
Published: October 2023

This research work deals with the application of the calcium looping concept for thermochemical energy storage. Experiments were carried out in a lab-scale fluidised bed reactor, which was electrically heated. An Italian limestone (98.5% CaCO3, 420–590 μm) was present in the bed alone, or in combination with silica sand/silicon carbide (this last material was chosen as per its high absorption capacity in the solar spectrum). Calcium looping tests (20 calcination/carbonation cycles) were carried out under operating conditions resembling the “closed-loop” scheme (calcination at 950 °C, carbonation at 850 °C, fluidising atmosphere composed of pure CO2 in both cases). Carbonation degree, particle size distribution, and particle bulk density were measured as cycles progressed, together with the application of a model equation to relate carbonation degree to the number of cycles. Mutual relationships between the nature of the bed material and possible interactions, the degree of CaO carbonation, the generation of fragments, and changes in particle density and porosity are critically discussed. An investigation of the segregation behaviour of the bed material has been carried out through tests in a devoted fluidisation column, equipped with a needle-type capacitive probe (to measure solid concentration).
Development of Predictive Model for Hydrogen-Natural Gas/Diesel Dual Fuel Engine
Authors: Sehili Youcef , Loubar Khaled , Tarabet Lyes , Cerdoun Mahfoudh , Lacroix Clément
Published: October 2023

Faced with environmental issues and depleting oil reserves, engine research is venturing into novel paths, such as the dual-fuel engine. This has motivated the development of numerical models that provide highly accurate predictive tools. In this context, 0D/quasi-D modeling is necessary, with a compromise between control of computation time and acceptable prediction level, which will certainly enable the various studies on the dual fuel mode to be explored at reduced cost. The aim of the present study is to develop a combustion model adapted to the hydrogen-natural gas (HNG)/diesel dual fuel engine to ensure 0D/1D simulations over a wide load range and under different gas mixture compositions. This model is based on the separation of the different types of combustion in this mode, by first treating the combustion of the pilot fuel by jet modeling, then the combustion of the gas mixture (HNG) by a mathematical model based on the Gaussian function. This phase separation is carefully combined with a mathematical treatment of the heat release rate, in order to determine ignition delays for both phases and model each of them separately. The modeling approach unveiled in this work is based on a phenomenological aspect, where the distinction between pilot and primary fuel combustion is ensured with phase separation allowing precise monitoring of the combustion sequence with the detection of the start and end of each phase and the contribution of each to the overall heat release rate. The results confirm the predictive power of the model developed with a maximum error of around 2%. This accurate prediction is particularly evident at high loads with high hydrogen enrichment, where the combustion sequence becomes complicated.
Research on Transformation of Connate Water to Movable Water in Water-Bearing Tight Gas Reservoirs
Authors: Chen Fuhu , Wang Zengding , Fu Shuaishi , Li Aifen , Zhong Junjie
Published: October 2023

The Dongsheng gas field is a water-bearing tight gas reservoir characterized by high connate water saturation. During gas production, the transformation of connate water into movable water introduces a unique water production mode, significantly impacting gas reservoir recovery. Current experimental and theoretical methods for assessing formation water mobility are static and do not address the transformation mechanism from connate into movable water. In this study, we considered dynamic changes in formation stress and proposed the mechanism for the transformation of connate water into movable water during depressurization, involving the expansion of connate water films and the reduction of pore volume. We developed a novel methodology to calculate the dynamic changes in movable and connate water saturation in tight reservoirs due to reservoir pressure reduction. Furthermore, we quantitatively evaluated the transformation of connate water into movable water in the Dongsheng gas field through laboratory experiments (including formation water expansion tests, connate water tests, and porosity stress sensitivity tests) and theoretical calculations. Results show that under original stress, the initial connate water saturation in the Dongsheng gas field ranges from 50.09% to 58.5%. As reservoir pressure decreases, the maximum increase in movable water saturation ranges from 6.1% to 8.4% due to the transformation of connate water into movable water. This explains why formation water is produced in large quantities during gas production. Therefore, considering the transition of connate water to movable water is crucial when evaluating water production risk. These findings offer valuable guidance for selecting optimal well locations and development layers to reduce reservoir water production risks.
Influence of Al2O3/SiNx Rear-Side Stacked Passivation on the Performance of Polycrystalline PERC Solar Cells
Authors: Fan Weitao , Shen Honglie , Liu Biao , Zhao Lei , Zhang Xin , Pan Hong
Published: October 2023

In recent years, polycrystalline passivated emitter and rear cell (PERC) solar cells have developed rapidly, but less research has been conducted on the preparation process of their rear side passivation layers on standard solar cell production lines. In this work, a Al2O3/SiNx rear side stacked passivation layer for polycrystalline PERC solar cells was prepared using the plasma- enhanced chemical vapor deposition (PECVD) method. The effects of different Al2O3 layer thicknesses (6.8~25.6 nm), SiNx layer thicknesses (65~150 nm) and SiNx refractive indices (2.0~2.2) on the passivation effect and electrical performance were systematically investigated, which were adjusted by TMA flow rate, conveyor belt speed and the flow ratio of SiH4 and NH3, respectively. In addition, external quantum efficiency (EQE) and elevated temperature-induced degradation experiments were also carried out to check the cell performance. The results showed that the best passivation effect was achieved at 10.8 nm Al2O3 layer, 120 nm SiNx layer and 2.2 SiNx layer refractive index. Under the optimal conditions mentioned above, the highest efficiency was 19.20%, corresponding Voc was 647 mV, Isc was 9.21 A and FF was 79.18%. Meanwhile, when the refraction index was 2.2, the EQE of the cell in the long-wavelength band (800–1000 nm) was improved. Moreover, the decrease in conversion efficiency after 45 h LeTID was around 0.55% under the different refraction indices. The above results can provide a reference for the industrial production of polycrystalline PERC solar cells.
Analyzing the Performance of Double Spiral Tube Ground Heat Exchangers in a Zero-Energy Building Using Measurement Data
Authors: Yang Kunning , Katsura Takao , Nagasaka Shigeyuki , Nagano Katsunori
Published: October 2023

A ground-source heat pump (GSHP) system is a renewable energy technology that effectively reduces greenhouse gas emissions and consequently mitigates the progression of global warming. The thermal efficiency of ground heat exchangers (GHEs) is a critical component in the GSHP system that must be accurately estimated for its long-term operationality. Therefore, in this study, the thermal performance of double-spiral GHEs incorporated within the thermal piles of a zero-energy building in Sapporo, Japan, was investigated using the actual measured data obtained from the site and a novel metric, namely, the coefficient of heat extraction/injection, for a more precise evaluation. Moreover, this study assessed the coefficient of performance (COP) of the GSHP units during various periods of cooling or heating. The temperature of the circulating fluid remained within an ideal operational range over an operational period of 2 years, and the COP calculations indicated a high operational efficiency. The results derived in this study substantially exceeded those of traditional U-tube GHEs, indicating the enhanced efficiency and superior performance of large-diameter thermal piles with augmented thermal capacity. Our findings suggest that GSHP systems with double-spiral-tube GHEs have superior efficiency than conventional GHEs.
Possibility of Energy Recovery from Airflow around an SUV-Class Car Based on Wind Tunnel Testing
Authors: Ruchała Paweł , Orynycz Olga , Stryczniewicz Wit , Tucki Karol
Published: October 2023

For many years, technological progress has been observed in the field of minimizing energy consumption by devices and increasing the efficiency of energy generation from freely available sources. Energy harvesting (EH) is one of the ways to increase the energy available in vehicles. The manuscript presents the results of a series of laboratory tests carried out in a wind tunnel using a 1:10 scale model of an SUV. The aim of the tests was to measure the air velocity in the footsteps of the car. The speed field has been identified at more than 188,000 points in the space behind or next to the car, considering the symmetry of the vehicle. The total energy was aggregated for 2760 points in a vertical plane perpendicular to the plane of symmetry. From the tests carried out, it was found that the highest speed was achieved just behind the trunk of the car, at a distance of about 20% of the length of the car. Interestingly, the speed in this area was higher than the speed of the car.
Predicting the Performance of PEM Fuel Cells by Determining Dehydration or Flooding in the Cell Using Machine Learning Models
Authors: Zaveri Jaydev Chetan , Dhanushkodi Shankar Raman , Kumar C. Ramesh , Taler Jan , Majdak Marek , Węglowski Bohdan
Published: October 2023

Modern industries encourages the use of hydrogen as an energy carrier to decarbonize the electricity grid, Polymeric Electrolyte membrane fuel cell which uses hydrogen as a fuel to produce electricity, is an efficient and reliable ‘power to gas’ technology. However, a key issue obstructing the advancement of PEMFCs is the unpredictability of their performance and failure events caused by flooding and dehydration. The accurate prediction of these two events is required to avoid any catastrophic failure in the cell. A typical approach used to predict failure modes relies on modeling failure-induced performance losses and monitoring the voltage of a cell. Data-driven machine learning models must be developed to address these challenges. Herein, we present a machine learning model for the prediction of the failure modes of operating cells. The model predicted the relative humidity of a cell by considering the cell voltage and current density as the input parameters. Advanced regression techniques, such as support vector machine, decision tree regression, random forest regression and artificial neural network, were used to improve the predictions. Features related to the model were derived from cell polarization data. The model’s results were validated with real-time test data obtained from the cell. The statistical machine learning models accurately provided information on the flooding- and dehydration-induced failure events.
An Update on Experimental Therapeutic Strategies for Thin Endometrium
Authors: Tang Yiqun , Frisendahl Caroline , Lalitkumar Parameswaran Grace , Gemzell-Danielsson Kristina
Published: October 2023

Infertility caused by a thin endometrium remains a significant challenge in assisted reproduction and is often associated with a low success rate after treatment with assisted reproductive technology. There is a lack of consensus in the field concerning both its diagnostic criteria and clinical management. The currently available treatment options are few with limited efficacy. Recent advances in cell therapy and bioengineering have, however, shown promising results for the treatment of a thin endometrium. Notably, these novel interventions have demonstrated the ability to increase endometrial thickness, restore endometrial function, and improve reproductive outcomes. In this comprehensive review, we focus on a critical evaluation of these emerging therapeutic strategies for a thin endometrium including platelet-rich plasma, exosomes derived from stem cells, and bioengineering-based techniques. By synthesizing the findings from available clinical trials, we highlight the promising outcomes achieved so far and underscore the importance of robust clinical trials in assessing the safety and efficacy of these interventions in the future. Continued research efforts to unravel the intricate mechanisms involved in endometrial repair and regeneration will also be essential to enhance our understanding of this multifactorial condition and to identify novel treatment targets for future therapeutic interventions.
Characteristics and Roles of Large Trees in Giant Panda Habitat of Wanglang Nature Reserve
Authors: Yang Zihan , Wang Xiaorong , Kang Dongwei
Published: October 2023

Specific studies of large trees in giant panda (Ailuropoda melanoleuca) habitats are currently lacking. To fill this research gap, a field survey on large trees in Wanglang Nature Reserve of China was conducted. According to a previous definition of a large tree (DBH ≥ 60 cm), a total of 19 large trees, composed of Abies fargesii var. faxoniana (73.7%), Betula albosinensis (15.8%), and Juniperus saltuaria (10.5%), were found in 50% of 30 10 m × 10 m giant panda habitat plots (contained feces of giant panda). Compared to the habitat where large trees were absent, the giant panda habitat where large trees were present was characterized by larger-sized tree diameter and more bamboo clumps. The mean proportion of large trees in the basal area of the tree layer in habitats where large trees were present was 65.5%. Considering large trees are a scarce resource and play a crucial role in habitat composition and habitat use, large trees and their habitats need to be given priority protection. To achieve more accurate protection, continuous and dynamic monitoring of large trees is needed, and the investigation of large trees should form a part of future national surveys of giant pandas.
Unsupervised Deep Learning Approach for Characterizing Fractality in Dried Drop Patterns of Differently Mixed Viscum album Preparations
Authors: Acuña Carlos , Kokornaczyk Maria Olga , Baumgartner Stephan , Castelán Mario
Published: October 2023

This paper presents a novel unsupervised deep learning methodology for the analysis of self-assembled structures formed in evaporating droplets. The proposed approach focuses on clustering these structures based on their texture similarity to characterize three different mixing procedures (turbulent, laminar, and diffusion-based) applied to produce Viscum album Quercus 10−3 according to the European Pharmacopoeia guidelines for the production of homeopathic remedies. Texture clustering departs from obtaining a comprehensive texture representation of the full texture patch database using a convolutional neural network. This representation is then dimensionally reduced to facilitate clustering through advanced machine learning techniques. Following this methodology, 13 clusters were found and their degree of fractality determined by means of Local Connected Fractal Dimension histograms, which allowed for characterization of the different production modalities. As a consequence, each image was represented as a vector in R13, enabling classification of mixing procedures via support vectors. As a main result, our study highlights the clear differences between turbulent and laminar mixing procedures based on their fractal characteristics, while also revealing the nuanced nature of the diffusion process, which incorporates aspects from both mixing types. Furthermore, our unsupervised clustering approach offers a scalable and automated solution for analyzing the databases of evaporated droplets.