bluuebunny/crossref_metadata_2025 huggingface dataset:
Using Intelligent Edge Devices for Predictive Maintenance on Injection Molds
Authors: Nunes Pedro , Rocha Eugénio , Santos José Paulo
Published: June 2023

A considerable part of enterprises’ total expenses is dedicated to maintenance interventions. Predictive maintenance (PdM) has appeared as a solution to decrease these costs; however, the necessity of end-to-end solutions in deploying predictive models and the fact that these models are often difficult to interpret by maintenance practitioners hinder the adoption of PdM approaches. In this work, we propose a flexible architecture for PdM to recommend maintenance actions. The proposed architecture is based on containerized microservices on intelligent edge devices together with a hybrid model which fuses generalized fault trees (GFTs) and anomaly detection. Results on injection molds carried out at OLI, a Portuguese company, show that the proposed solution is suitable for deploying predictive models and services such as data preprocessing, sensor management, and data flow control, among others. These services run near the shop floor, allowing for greater flexibility, as they may be remotely managed and customized according to the company’s requirements. The results of the GFT model show an estimated reduction of more than 63% in current maintenance costs, while the distribution of analytics tasks by the edge devices reduces the burden on the network, requiring only 0.2% of the current cloud storage.
Application of Feedforward Cascade Compound Control Based on Improved Predictive Functional Control in Heat Exchanger Outlet Temperature System
Authors: Miao Wanjun , Xu Bing
Published: June 2023

Aiming at the problems of large delay and poor anti-disturbance ability in the outlet temperature control system of the heat exchanger to optimize the control accuracy of the system and improve the control performance, this paper proposes a control scheme combining predictive functional control with proportional-integral-derivative control. Using the incremental proportional-integral-derivative control algorithm to improve the optimization objective function of the predictive functional control algorithm, a predictive functional control optimization model with a proportional-integral-derivative structure is established. The feedforward compensation control is adopted to eliminate the influence of external disturbances on the heat exchanger temperature control system. Through simulation, the proposed control scheme is compared with the feedforward cascade compound control scheme based on a proportional-integral-derivative main controller. The results show that the scheme has a small over harmonic and strong anti-interference ability. The adaptability and stability of the system are significantly improved, and the exit temperature of the heat exchanger can be effectively controlled.
True Non-Contrast Phase versus Virtual-Non Contrast: “Lights and Shadows” of Dual Energy CT Angiography in Peripheral Arterial Disease
Authors: Floridi Chiara , Cacioppa Laura Maria , Agliata Giacomo , Cellina Michaela , Rossini Nicolo’ , Valeri Tommaso , Curzi Martina , Felicioli Alessandro , Bruno Alessandra , Rosati Marzia , Candelari Roberto , Giovagnoni Andrea
Published: June 2023

(1) Background: The value of dual-energy CT angiography (DE-CTA) in the detection of peripheral arterial disease (PAD) has been widely recognized. We aim to evaluate the diagnostic accuracy of virtual non-contrast (VNC) imaging of DE-CTA compared to true non-contrast phase (TNC). (2) Methods: Our Internal Review Board (IRB) approved prospective study enrolled 40 patients (28 men, 12 women; median age 69 y, range 41–93 y) who underwent lower extremity DE-CTA for symptomatic PAD. Mean attenuation values of TNC and VNC were obtained by placing circular regions of interest (ROI) at five levels from the aortic to the popliteal arterial lumen, reported in Hounsfield units (HU), and compared using a two-sample t-test. The subjective quality of VNC images was assessed by two independent radiologists with 10 and 4 years of CTA-imaging experience according to a 4-point scale and verified by the intra-class correlation coefficient (ICC). Dose Length Product (DLP) values of each DE-CTA examination were also considered. (3) Results: Except for the external iliac artery, VNC attenuation values were significantly lower than the corresponding TNC values at all levels, with a mean difference ranging from 14.1 and 8.7 HU. At qualitative analysis, VNC images were considered excellent to diagnose in 40%, good in 50%, and sufficient in 10% of cases. No cases of non-diagnostic VNC imaging were reported. Avoiding the TNC phase, a mean reduction in DLP of 54% for each DE-CTA was estimated. (4) Conclusions: TNC and VNC images showed comparable reliability and diagnostic accuracy in the detection of PAD. VNC may be considered a promising substitute for TNC from the perspectives of dose reduction and workflow optimization.
Prediction of Structural Damage Trends Based on the Integration of LSTM and SVR
Authors: Liu Yiyan
Published: June 2023

Currently, accidents in civil engineering buildings occur frequently, resulting in significant economic damage and a large number of casualties. Therefore, it is particularly important to predict the trend of early damage to building structures. Early structural damages are difficult to correctly identify, and obtaining the required accuracy using a single traditional time-series prediction method is difficult. In this study, we propose a novel method based on the integration of support vector regression (SVR) and long short-term memory (LSTM) networks to predict structural damage trends. First, the acceleration vibration signal of the structure is decomposed using the variational mode decomposition (VMD) method, and the decomposed components are transformed with Hilbert transform to obtain the instantaneous frequency. Then, the instantaneous frequency is input into the LSTM–SVR integrated model for damage trend prediction. The results indicate that the VMD method effectively eliminates modal aliasing and decomposes various intrinsic components of the signal. Compared with individual LSTM and SVR models, the integration model has a higher prediction accuracy for small samples in a chaotic time series that is 6.56%, 2.56%, and 3.7%, respectively. The standard deviation of the absolute percentage error (SDAPE) values of the three operating conditions under the integrated method decreased 0.0994, 0.0869, and 0.0921, which improved the stability of prediction. The mean absolute percentage error (MAPE) of the integration method is an order of magnitude higher than that of the LSTM model.
Comparison of Different Configurations for Shoreline Pond Electrode Station for HVDC Transmission Systems—Part I: Electric Field Study for Frames of Linear Electrode Arrangement Based on a Simplified Analytical Model
Authors: Kontargyri Vassiliki T. , Tsekouras George J. , Prousalidis John M. , Tsirekis Costantinos D. , Leontaritis Konstantinos , Alexandris John C. , Kanellos Fotis D. , Deligianni Panagiota M. , Kontaxis Panagiotis A. , Moronis Antonios X.
Published: June 2023

During the design of a shoreline electrode station for High-Voltage Direct Current (HVDC) interconnections, the location of the electrodes plays a critical part, especially in the development of the near-electric field. The basic structure is their linear placement, in the form of successive frames, parallel to the longitudinal axis of the breakwater, as proposed by CIGRE and implemented in existing projects. However, this arrangement requires a considerable breakwater length, which may not be permissible, as in the case of Stachtoroi, one of the two electrode stations being built for the 1 GW, ±500 kV HVDC interconnection between Crete and mainland Greece. This troubled the preliminary study team of the electrode stations, which investigated other possible configurations. In this paper, configurations of linear placements of electrode frames are studied and compared at the preliminary study level in terms of electric field effects (especially the near-field), using an analytical simplified model and the superposition method, to determine the most appropriate arrangement of electrodes that will cover the respective requirements of CIGRE directives B4.61/2017. These arrangements are practically evaluated for two different electrode station locations at Korakia in Crete and at Stachtoroi in Aegina for the Crete–mainland-Greece interconnection, resulting in interesting alternative solutions.
A Long-Term Traffic Flow Prediction Model Based on Variational Mode Decomposition and Auto-Correlation Mechanism
Authors: Guo Kaixin , Yu Xin , Liu Gaoxiang , Tang Shaohu
Published: June 2023

Traffic flow forecasting, as an integral part of intelligent transportation systems, plays a critical part in traffic planning. Previous studies have primarily focused on short-term traffic flow prediction, paying insufficient attention to long-term prediction. In this study, we propose a hybrid model that utilizes variational mode decomposition (VMD) and the auto-correlation mechanism for long-term prediction. In view of the periodic and stochastic characteristics of traffic flow, VMD is able to decompose the data into intrinsic mode functions with different frequencies, which in turn helps the model extract the internal features of the data and better capture the changes of traffic flow data in the cycle. Additionally, we improve the residual structure by adding a convolutional layer to propose a correction module and use it together with the auto-correlation mechanism to jointly build an encoder and decoder to extract features from different data components (intrinsic mode functions) and fuse the extracted features for output. To meet the requirements of long-term forecasting, we set the traffic flow forecast length to 4 levels: 96, 192, 336, and 720. We validated our model using the departure statistics dataset of a taxi parking lot at Beijing Capital International Airport and achieved the best prediction performance in terms of mean squared error and mean absolute error, compared to the baseline model.
Research on Digital Meter Reading Method of Inspection Robot Based on Deep Learning
Authors: Lin Wenwei , Zhao Ziyang , Tao Jin , Lian Chaoming , Zhang Chentao
Published: June 2023

Aiming at solving the issue of blurred images and difficult recognition of digital meters encountered by inspection robots in the inspection process, this paper proposes a deep-learning-based method for blurred image restoration and LED digital identification. Firstly, fast Fourier transform (FFT) is used to perform blur detection on the acquired images. Then, the blurred images are recovered using spatial-attention-improved adversarial neural networks. Finally, the digital meter region is extracted using the polygon-YOLOv5 model and corrected via perspective transformation. The digits in the image are extracted using the YOLOv5s model, and then recognized by the CRNN for digit recognition. It is experimentally verified that the improved adversarial neural network in this paper achieves 26.562 in the PSNR metric and 0.861 in the SSIM metric. The missing rate of the digital meter reading method proposed in the paper is only 1% and the accuracy rate is 98%. The method proposed in this paper effectively overcomes the image blurring problem caused by the detection robot during the detection process. This method solves the problems of inaccurate positioning and low digital recognition accuracy of LED digital meters in complex and changeable environments, and provides a new method for reading digital meters.
Full Anchor Cable Support Mechanism and Application of Roadway with Thick Soft Rock Mass Immediate Roof
Authors: Yang Yongjie , Meng Lingren , Zhang Tianli
Published: June 2023

The focus of this paper is the thick soft rock mass roof of the track roadway in the No. 3606 panel of the Chaili Coal Mine. Due to its substantial thickness, the soft rock mass roof of the roadway is susceptible to damage and deformation during the mining process. In order to preserve the integrity of the roadway roof, a full anchor cable support scheme is proposed after studying the mechanism of bolt-anchor cable support. The supporting parameters and feasibility of the scheme were determined through support experience and numerical simulation analyses in the field. Moreover, on-site monitoring and data analysis were conducted, revealing that the anchor cables and anchor bolts played a stable role in supporting the roadway. The displacement of the roadway’s roof and floor was minimal, as was the displacement of the two ribs. The overall deformation of the roadway was minor. Practice demonstrated that the full anchor cable support method was effective in supporting the immediate roof of thick soft rock.
Design and Development of an Optimal Control Model in System Dynamics through State-Space Representation
Authors: Barrios Sánchez Jorge Manuel , Baeza Serrato Roberto
Published: June 2023

Control engineering and state-space representation are valuable tools in the analysis and design of dynamic systems. In this research, a methodology is proposed that uses these approaches to transform a system-dynamics simulation model into a mathematical model. This is achieved by expressing input, output and state variables as input, output and state vectors, respectively, allowing the representation of the model in matrix form. The resulting model is linear and time-invariant, facilitating its analysis and design. Through the use of this methodology, the system transfer matrix is obtained, which allows the analysis and design of the optimal control of the simulation model. The Ackermann gain-control technique is used to determine the optimal control of the system, which results in a shorter settlement time. This research proposal seeks to mathematically strengthen simulation models and provide an analytical alternative through modern control engineering in SD simulation models. This would allow more informed and effective decisions in the implementation of dynamic systems.
Modelling and Control Methods in Path Tracking Control for Autonomous Agricultural Vehicles: A Review of State of the Art and Challenges
Authors: Wang Quanyu , He Jin , Lu Caiyun , Wang Chao , Lin Han , Yang Hanyu , Li Hang , Wu Zhengyang
Published: June 2023

This paper provides a review of path-tracking strategies used in autonomous agricultural vehicles, mainly from two aspects: vehicle model construction and the development and improvement of path-tracking algorithms. Vehicle models are grouped into numerous types based on the structural characteristics and working conditions, including wheeled tractors, tracked tractors, rice transplanters, high clearance sprays, agricultural robots, agricultural tractor–trailers, etc. The application and improvement of path-tracking control methods are summarized based on the different working scenes and types of agricultural machinery. This study explores each of these methods in terms of accuracy, stability, robustness, and disadvantages/advantages. The main challenges in the field of agricultural vehicle path tracking control are defined, and future research directions are offered based on critical reviews. This review aims to provide a reference for determining which controllers to use in path-tracking control development for an autonomous agricultural vehicle.
Hand Joint Hypermobility among Dental Students—A Cross-Sectional Study
Authors: Sangalli Linda , Alabsy Melisa , Lingle Dana , Khazaal Nada , West-Pelak Ella , Mitchell John C.
Published: June 2023

(1) Background: Joint hypermobility refers to joint movement beyond normal range. Dentistry requires fine motor skills and manual dexterity; thus, the presence of hand joint hypermobility requires early assessment and intervention. The aim of this cross-sectional study was to assess the prevalence of hand joint hypermobility among first-year dental students and investigate correlations between the severity of their hypermobility and muscular strength. (2) Methods: First-year dental students were evaluated by an occupational therapy team composed of certified hand therapists. The extension of distal interphalangeal, proximal interphalangeal, and metacarpophalangeal finger joints was assessed with bilateral testing of digit hypermobility (with a cut-off of >20° for hypermobility) and classified as high, moderate, or low risk according to number/types of joints involved. Muscular strength was assessed by comparing grip strength, lateral pinch, and three-point pinch to norm values. Right- vs. left-handed individuals, and females vs. males were compared with an independent t-test; the relationships between the variables were investigated using the Pearson correlation. (3) Results: Out of 141 dental students (24.6 ± 3.0, 70% females, 87% right-handed), 15% and 16% were classified as having a high and moderate risk of hypermobility, respectively, with no sex difference. Right-handed students scored significantly higher in the right lateral pinch than left-handed students (18.2 ± 4.8 vs. 13.2 ± 4.1, p = 0.045). A higher severity of hypermobility was associated with bilateral lower grip and pinch strength (p < 0.05). (4) Conclusions: One third of the first-year dental students exhibited moderate to severe hand joint hypermobility. Moreover, those identified as having moderate to severe hand joint hypermobility showed a decreased grip strength.
Mittag-Leffler-Type Stability of BAM Neural Networks Modeled by the Generalized Proportional Riemann–Liouville Fractional Derivative
Authors: Agarwal Ravi P. , Hristova Snezhana , O’Regan Donal
Published: June 2023

The main goal of the paper is to use a generalized proportional Riemann–Liouville fractional derivative (GPRLFD) to model BAM neural networks and to study some stability properties of the equilibrium. Initially, several properties of the GPRLFD are proved, such as the fractional derivative of a squared function. Additionally, some comparison results for GPRLFD are provided. Two types of equilibrium of the BAM model with GPRLFD are defined. In connection with the applied fractional derivative and its singularity at the initial time, the Mittag-Leffler exponential stability in time of the equilibrium is introduced and studied. An example is given, illustrating the meaning of the equilibrium as well as its stability properties.
Knowable Moments in Stochastics: Knowing Their Advantages
Authors: Koutsoyiannis Demetris
Published: June 2023

Knowable moments, abbreviated as K-moments, are redefined as expectations of maxima or minima of a number of stochastic variables that are a sample of the variable of interest. The new definition enables applicability of the concept to any type of variable, continuous or discrete, and generalization for transformations thereof. While K-moments share some characteristics with classical and other moments, as well as with order statistics, they also have some unique features, which make them useful in relevant applications. These include the fact that they are knowable, i.e., reliably estimated from a sample for high orders. Moreover, unlike other moment types, K-moment values can be assigned values of distribution function by making optimal use of the entire dataset. In addition, K-moments offer the unique advantage of considering the estimation bias when the data are not an independent sample but a time series from a process with dependence. Both for samples and time series, the K-moment concept offers a strategy of model fitting, including its visualization, that is not shared with other methods. This enables utilization of the highest possible moment orders, which are particularly useful in modelling extremes that are closely associated with high-order moments.
Retinal Vascular Image Segmentation Using Improved UNet Based on Residual Module
Authors: Huang Ko-Wei , Yang Yao-Ren , Huang Zih-Hao , Liu Yi-Yang , Lee Shih-Hsiung
Published: June 2023

In recent years, deep learning technology for clinical diagnosis has progressed considerably, and the value of medical imaging continues to increase. In the past, clinicians evaluated medical images according to their individual expertise. In contrast, the application of artificial intelligence technology for automatic analysis and diagnostic assistance to support clinicians in evaluating medical information more efficiently has become an important trend. In this study, we propose a machine learning architecture designed to segment images of retinal blood vessels based on an improved U-Net neural network model. The proposed model incorporates a residual module to extract features more effectively, and includes a full-scale skip connection to combine low level details with high-level features at different scales. The results of an experimental evaluation show that the model was able to segment images of retinal vessels accurately. The proposed method also outperformed several existing models on the benchmark datasets DRIVE and ROSE, including U-Net, ResUNet, U-Net3+, ResUNet++, and CaraNet.
DNA-Binding Protein Dps Protects Escherichia coli Cells against Multiple Stresses during Desiccation
Authors: Loiko Nataliya , Tereshkina Ksenia , Kovalenko Vladislav , Moiseenko Andrey , Tereshkin Eduard , Sokolova Olga S. , Krupyanskii Yurii
Published: June 2023

Gradual dehydration is one of the frequent lethal yet poorly understood stresses that bacterial cells constantly face in the environment when their micro ecotopes dry out, as well as in industrial processes. Bacteria successfully survive extreme desiccation through complex rearrangements at the structural, physiological, and molecular levels, in which proteins are involved. The DNA-binding protein Dps has previously been shown to protect bacterial cells from many adverse effects. In our work, using engineered genetic models of E. coli to produce bacterial cells with overproduction of Dps protein, the protective function of Dps protein under multiple desiccation stresses was demonstrated for the first time. It was shown that the titer of viable cells after rehydration in the experimental variants with Dps protein overexpression was 1.5–8.5 times higher. Scanning electron microscopy was used to show a change in cell morphology upon rehydration. It was also proved that immobilization in the extracellular matrix, which is greater when the Dps protein is overexpressed, helps the cells survive. Transmission electron microscopy revealed disruption of the crystal structure of DNA–Dps crystals in E. coli cells that underwent desiccation stress and subsequent watering. Coarse-grained molecular dynamics simulations showed the protective function of Dps in DNA–Dps co-crystals during desiccation. The data obtained are important for improving biotechnological processes in which bacterial cells undergo desiccation.
Exploring the Potential of GANs in Biological Sequence Analysis
Authors: Murad Taslim , Ali Sarwan , Patterson Murray
Published: June 2023

Biological sequence analysis is an essential step toward building a deeper understanding of the underlying functions, structures, and behaviors of the sequences. It can help in identifying the characteristics of the associated organisms, such as viruses, etc., and building prevention mechanisms to eradicate their spread and impact, as viruses are known to cause epidemics that can become global pandemics. New tools for biological sequence analysis are provided by machine learning (ML) technologies to effectively analyze the functions and structures of the sequences. However, these ML-based methods undergo challenges with data imbalance, generally associated with biological sequence datasets, which hinders their performance. Although various strategies are present to address this issue, such as the SMOTE algorithm, which creates synthetic data, however, they focus on local information rather than the overall class distribution. In this work, we explore a novel approach to handle the data imbalance issue based on generative adversarial networks (GANs), which use the overall data distribution. GANs are utilized to generate synthetic data that closely resembles real data, thus, these generated data can be employed to enhance the ML models’ performance by eradicating the class imbalance problem for biological sequence analysis. We perform four distinct classification tasks by using four different sequence datasets (Influenza A Virus, PALMdb, VDjDB, Host) and our results illustrate that GANs can improve the overall classification performance.
Method of Monitoring 26S Proteasome in Cells Revealed the Crucial Role of PSMA3 C-Terminus in 26S Integrity
Authors: Steinberger Shirel , Adler Julia , Shaul Yosef
Published: June 2023

Proteasomes critically regulate proteostasis via protein degradation. Proteasomes are multi-subunit complexes composed of the 20S proteolytic core particle (20S CP) that, in association with one or two 19S regulatory particles (19S RPs), generates the 26S proteasome, which is the major proteasomal complex in cells. Native gel protocols are used to investigate the 26S/20S ratio. However, a simple method for detecting these proteasome complexes in cells is missing. To this end, using CRISPR technology, we YFP-tagged the endogenous PSMB6 (β1) gene, a 20S CP subunit, and co-tagged endogenous PSMD6 (Rpn7), a 19S RP subunit, with the mScarlet fluorescent protein. We observed the colocalization of the YFP and mScarlet fluorescent proteins in the cells, with higher nuclear accumulation. Nuclear proteasomal granules are formed under osmotic stress, and all were positive for YFP and mScarlet. Previously, we have reported that PSMD1 knockdown, one of the 19 RP subunits, gives rise to a high level of “free” 20S CPs. Intriguingly, under this condition, the 20S-YFP remained nuclear, whereas the PSMD6-mScarlet was mostly in cytoplasm, demonstrating the distinct subcellular distribution of uncapped 20S CPs. Lately, we have shown that the PSMA3 (α7) C-terminus, a 20S CP subunit, binds multiple intrinsically disordered proteins (IDPs). Remarkably, the truncation of the PSMA3 C-terminus is phenotypically reminiscent of PSMD1 knockdown. These data suggest that the PSMA3 C-terminal region is critical for 26S proteasome integrity.
Inhibition of the Mitochondrial Carnitine/Acylcarnitine Carrier by Itaconate through Irreversible Binding to Cysteine 136: Possible Pathophysiological Implications
Authors: Giangregorio Nicola , Tonazzi Annamaria , Console Lara , Scalise Mariafrancesca , Indiveri Cesare
Published: June 2023

Background: The carnitine/acylcarnitine carrier (CAC) represents the route of delivering acyl moieties to the mitochondrial matrix for accomplishing the fatty acid β-oxidation. The CAC has a couple of Cys residues (C136 and C155) most reactive toward ROS and redox signaling compounds such as GSH, NO, and H2S. Among physiological compounds reacting with Cys, itaconate is produced during inflammation and represents the connection between oxidative metabolism and immune responses. The possible interaction between the CAC and itaconate has been investigated. Methods: the modulatory effects of itaconate on the transport activity of the native and recombinant CAC were tested using the proteoliposome experimental model together with site-directed mutagenesis and computational analysis. Results: Itaconate reacts with the CAC causing irreversible inhibition. Dose–response experiment performed with the native and recombinant protein showed IC50 for itaconate of 11 ± 4.6 mM and 8.4 ± 2.9 mM, respectively. The IC50 decreased to 3.8 ± 1.0 mM by lowering the pH from pH 7.0 to pH 6.5. Inhibition kinetics revealed a non-competitive type of inhibition. C136 is the main target of itaconate, as demonstrated by the increased IC50 of mutants in which this Cys was substituted by Val. The central role of C136 was confirmed by covalent docking. Administration of dimethyl itaconate to HeLa cells inhibited the CAC transport activity, suggesting that itaconate could react with the CAC also in intact cells.
Insights on the Role of Polyphenols in Combating Cancer Drug Resistance
Authors: Farhan Mohd
Published: June 2023

Chemotherapy resistance is still a serious problem in the treatment of most cancers. Many cellular and molecular mechanisms contribute to both inherent and acquired drug resistance. They include the use of unaffected growth-signaling pathways, changes in the tumor microenvironment, and the active transport of medicines out of the cell. The antioxidant capacity of polyphenols and their potential to inhibit the activation of procarcinogens, cancer cell proliferation, metastasis, and angiogenesis, as well as to promote the inhibition or downregulation of active drug efflux transporters, have been linked to a reduced risk of cancer in epidemiological studies. Polyphenols also have the ability to alter immunological responses and inflammatory cascades, as well as trigger apoptosis in cancer cells. The discovery of the relationship between abnormal growth signaling and metabolic dysfunction in cancer cells highlights the importance of further investigating the effects of dietary polyphenols, including their ability to boost the efficacy of chemotherapy and avoid multidrug resistance (MDR). Here, it is summarized what is known regarding the effectiveness of natural polyphenolic compounds in counteracting the resistance that might develop to cancer drugs as a result of a variety of different mechanisms.
Clinical Considerations for Patients Experiencing Acute Kidney Injury Following Percutaneous Nephrolithotomy
Authors: Reich Daniel A. , Adiyeke Esra , Ozrazgat-Baslanti Tezcan , Rabley Andrew K. , Bozorgmehri Shahab , Bihorac Azra , Bird Vincent G.
Published: June 2023

Acute kidney injury (AKI) is a common postoperative outcome in urology patients undergoing surgery for nephrolithiasis. The objective of this study was to determine the prevalence of postoperative AKI and its degrees of severity, identify risk factors, and understand the resultant outcomes of AKI in patients with nephrolithiasis undergoing percutaneous nephrolithotomy (PCNL). A cohort of patients admitted between 2012 and 2019 to a single tertiary-care institution who had undergone PCNL was retrospectively analyzed. Among 417 (n = 326 patients) encounters, 24.9% (n = 104) had AKI. Approximately one-quarter of AKI patients (n = 18) progressed to Stage 2 or higher AKI. Hypertension, peripheral vascular disease, chronic kidney disease, and chronic anemia were significant risk factors of post-PCNL AKI. Corticosteroids and antifungals were associated with increased odds of AKI. Cardiovascular, neurologic complications, sepsis, and prolonged intensive care unit (ICU) stay percentages were higher in AKI patients. Hospital and ICU length of stay was greater in the AKI group. Provided the limited literature regarding postoperative AKI following PCNL, and the detriment that AKI can have on clinical outcomes, it is important to continue studying this topic to better understand how to optimize patient care to address patient- and procedure-specific risk factors.
Multifaceted Effects of Kinase Inhibitors on Pancreatic Cancer Cells Reveals Pivotal Entities with Therapeutic Implications
Authors: Kim Yoo Na , Patil Ketki , Ma Jeonghwa , Dufek Griffin A. , Pai S. Balakrishna
Published: June 2023

Pancreatic cancer is one of the most aggressive forms of cancer and is the seventh leading cause of cancer deaths worldwide. Pancreatic ductal adenocarcinoma (PDAC) accounts for over 90% of pancreatic cancers. Most pancreatic cancers are recalcitrant to radiation, chemotherapy, and immunotherapy, highlighting the urgent need for novel treatment options for this deadly disease. To this end, we screened a library of kinase inhibitors in the PDAC cell lines PANC-1 and BxPC-3 and identified two highly potent molecules: Aurora kinase inhibitor AT 9283 (AT) and EGFR kinase inhibitor WZ 3146 (WZ). Both AT and WZ exhibited a dose-dependent inhibition of viability in both cell lines. Thus, we conducted an in-depth multilevel (cellular, molecular, and proteomic) analysis with AT and WZ in PANC-1 cells, which harbor KRAS mutation and exhibit quasimesenchymal properties representing pancreatic cancer cells as having intrinsic chemoresistance and the potential for differential response to therapy. Elucidation of the molecular mechanism of action of AT and WZ revealed an impact on the programmed cell death pathway with an increase in apoptotic, multicaspase, and caspase 3/7 positive cells. Additionally, the key survival molecule Bcl-2 was impacted. Moreover, cell cycle arrest was observed with both kinase inhibitors. Additionally, an increase in superoxide radicals was observed in the AT-treated group. Importantly, proteomic profiling revealed differentially regulated key entities with multifaceted effects, which could have a deleterious impact on PDAC. These findings suggest potential targets for efficacious treatment, including a possible increase in the efficacy of immunotherapy using PD-L1 antibody due to the upregulation of lactoferrin and radixin. Furthermore, combination therapy outcomes with gemcitabine/platinum drugs may also be more effective due to an increase in the NADH dehydrogenase complex. Notably, protein–protein interaction analysis (STRING) revealed possible enrichment of reactome pathway entities. Additionally, novel therapy options, such as vimentin-antibody--drug conjugates, could be explored. Therefore, future studies with the two kinases as monotherapy/combination therapy are warranted.
Designing Dynamic Sustainable Business Models to Assess the Feasibility of an Industrial Symbiosis: The Case of the Retro-Port Area of Trieste
Authors: Bordoli Laura , Scire’ Giovanni , Rossi Francesca
Published: June 2023

This study evaluates the managerial sustainability of implementing a decision support system for industrial symbiosis through a recent business modeling methodology. The decision support system is intended to facilitate the identification of implementable industrial symbiosis good practices in a pilot project located in the industrial retro-port area of Trieste. To evaluate the feasibility of this project, a qualitative “Dynamic business model canvas for sustainability” was designed and supported by a system dynamics approach using causal loop diagrams. The study analyzed various governance scenarios for managing the decision support system (public, private, or public–private partnership) and the financing system. The analysis concludes by discussing the research limitations of this study and future opportunities, emphasizing the importance of a resilient system that can handle operational and regulatory shocks, given the project’s public relevance. Further study opportunities could be provided through quantitative simulations and potential numerical validations with stakeholders in the local area, considering potential regulatory constraints.
Unsupervised Hierarchical Clustering of Head and Neck Cancer Patients by Pre-Treatment Plasma Metabolomics Creates Prognostic Metabolic Subtypes
Authors: Eldridge Ronald , Qin Zhaohui , Saba Nabil , Houser Madelyn , Hayes D. , Miller Andrew , Bruner Deborah , Jones Dean , Xiao Canhua
Published: June 2023

There is growing evidence that the metabolism is deeply intertwined with head and neck squamous cell carcinoma (HNSCC) progression and survival but little is known about circulating metabolite patterns and their clinical potential. We performed unsupervised hierarchical clustering of 209 HNSCC patients via pre-treatment plasma metabolomics to identify metabolic subtypes. We annotated the subtypes via pathway enrichment analysis and investigated their association with overall and progression-free survival. We stratified the survival analyses by smoking history. High-resolution metabolomics extracted 186 laboratory-confirmed metabolites. The optimal model created two patient clusters, of subtypes A and B, corresponding to 41% and 59% of the study population, respectively. Fatty acid biosynthesis, acetyl-CoA transport, arginine and proline, as well as the galactose metabolism pathways differentiated the subtypes. Relative to subtype B, subtype A patients experienced significantly worse overall and progression-free survival but only among ever-smokers. The estimated three-year overall survival was 61% for subtype A and 86% for subtype B; log-rank p = 0.001. The association with survival was independent of HPV status and other HNSCC risk factors (adjusted hazard ratio = 3.58, 95% CI: 1.46, 8.78). Our findings suggest that a non-invasive metabolomic biomarker would add crucial information to clinical risk stratification and raise translational research questions about testing such a biomarker in clinical trials.
Changes in the Histology of Lung Cancer in Northern Italy: Impact on Incidence and Mortality
Authors: Mangone Lucia , Marinelli Francesco , Bisceglia Isabella , Zambelli Alessandro , Zanelli Francesca , Pagano Maria , Alberti Giulia , Morabito Fortunato , Pinto Carmine
Published: June 2023

This study assessed the incidence, mortality, and survival of lung cancer subtypes of NSCSLC (non-small-cell lung cancer), SCLC (small-cell lung cancer), and other morphologies. It is an observational epidemiological study using 7197 cases from the Reggio Emilia Cancer Registry recorded between 2001 and 2020 in males and females. The incidence of NSCLC in 5104 males indicates a significant 3% annual increase until 2013 and then a decline of −3.2% that is not statistically significant; until 2014, mortality increased significantly (3.2%), but it then decreased non-significantly (−12.1%), especially squamous cell cancer. In 2093 females, the incidence and mortality trends continued to rise significantly through 2012, and then they began to slightly decline (not statistically significant). The two-year relative survival of NSCLC increased from 32% to 38% in males and from 42% to 56% in females. SCLC in males decreased significantly both in incidence and mortality, while in women, it showed a slight increase (significantly for incidence but not for mortality). This study is important because it analyzes the decrease in lung cancer in males and the increase in females in relation to the different histotypes. Our study’s findings confirmed a decline in male incidence and death beginning in 2013. We were unable to determine if the drop in cigarette smoking and the introduction of new drugs such as EGFR in first-line therapy were responsible for the lower incidence.
Updated Review and Clinical Recommendations for the Diagnosis and Treatment of Patients with Retroperitoneal Sarcoma by the Spanish Sarcoma Research Group (GEIS)
Authors: Álvarez Álvarez Rosa , Manzano Aránzazu , Agra Pujol Carolina , Artigas Raventós Vicente , Correa Raquel , Cruz Jurado Josefina , Fernandez Juan Angel , Garcia del Muro Xavier , Gonzalez Jose Antonio , Hindi Nadia , Lozano Lominchar Pablo , Martínez-Trufero Javier , Méndez Ramiro , Muñoz Mercedes , Muñoz Casares Cristobal , Orbis Castellanos Francisco , Orellana Fernandez Ruth , Paniagua González Miguel , Redondo Andres , Valverde Morales Claudia , Asencio Jose Manuel
Published: June 2023

Soft tissue sarcomas (STS) are an uncommon and biologically heterogeneous group of tumors arising from mesenchymal cells. The incidence is estimated at five cases per 100,000 people per year. Retroperitoneal sarcomas (RPS) account for 10–15% of all STS, and their management depends on their anatomical characteristics and histotype. Due to their very low incidence, it is recommended that RPS be treated in reference centers and evaluated by an experienced multidisciplinary team (MDT). In Spain, the Spanish Group for Research in Sarcomas (GEIS) brings together experts from various specialties to promote research on sarcomas and improve treatment results. This paper summarizes the GEIS recommendations for the diagnosis, treatment, and follow-up of patients with RPS.
A Parallel Computing Approach to Gene Expression and Phenotype Correlation for Identifying Retinitis Pigmentosa Modifiers in Drosophila
Authors: Metah Chawin , Khalifa Amal , Palu Rebecca
Published: June 2023

As a genetic eye disorder, retinitis pigmentosa (RP) has been a focus of researchers to find a diagnosis through either genome-wide association (GWA) or RNAseq analysis. In fact, GWA and RNAseq are considered two complementary approaches to gaining a more comprehensive understanding of the genetics of different diseases. However, RNAseq analysis can provide information about the specific mechanisms underlying the disease and the potential targets for therapy. This research proposes a new approach to differential gene expression (DGE) analysis, which is the heart of the core-analysis phase in any RNAseq study. Based on the Drosophila Genetic Reference Panel (DGRP), the gene expression dataset is computationally analyzed in light of eye-size phenotypes. We utilized the foreach and the doParallel R packages to run the code on a multicore machine to reduce the running time of the original algorithm, which exhibited an exponential time complexity. Experimental results showed an outstanding performance, reducing the running time by 95% while using 32 processes. In addition, more candidate modifier genes for RP were identified by increasing the scope of the analysis and considering more datasets that represent different phenotype models.
Proactive Ransomware Detection Using Extremely Fast Decision Tree (EFDT) Algorithm: A Case Study
Authors: Ba’abbad Ibrahim , Batarfi Omar
Published: June 2023

Several malware variants have attacked systems and data over time. Ransomware is among the most harmful malware since it causes huge losses. In order to get a ransom, ransomware is software that locks the victim’s machine or encrypts his personal information. Numerous research has been conducted to stop and quickly recognize ransomware attacks. For proactive forecasting, artificial intelligence (AI) techniques are used. Traditional machine learning/deep learning (ML/DL) techniques, however, take a lot of time and decrease the accuracy and latency performance of network monitoring. In this study, we utilized the Hoeffding trees classifier as one of the stream data mining classification techniques to detect and prevent ransomware attacks. Three Hoeffding trees classifier algorithms are selected to be applied to the Resilient Information Systems Security (RISS) research group dataset. After configuration, Massive Online Analysis (MOA) software is utilized as a testing framework. The results of Hoeffding tree classifier algorithms are then assessed to choose the enhanced model with the highest accuracy and latency performance. In conclusion, the 99.41% classification accuracy was the highest result achieved by the EFDT algorithm in 66 ms.
Phase Structures and Dielectric Properties of (n + 1)SrO − nCeO2 (n = 2) Microwave Ceramic Systems with TiO2 Addition
Authors: Su Qi , Qu Jingjing , Liu Fei , Yuan Changlai , Liu Xiao , Su Mingwei , Meng Liufang , Chen Guohua
Published: June 2023

Ti4+-ion-doped (n + 1)SrO − nCeO2 (n = 2) ceramic systems were prepared with the conventional solid-state reaction method, and the effects of the phase structures and compositions, sintering behaviors, microstructures and microwave dielectric properties of these ceramic systems were investigated in detail as a function of TiO2 content. The analytical results of the XRD patterns show that the pure (n + 1)SrO − nCeO2 (n = 2) system is a composite-phase ceramic system with coexisting SrCeO3 and Sr2CeO4 phases (represented as a SrCeO3 + Sr2CeO4 system), which belong to the orthogonal structures of the Pmcn (62) and Pbam (55) space groups, respectively. For the xTiO2-(1 − x) (SrCeO3 + Sr2CeO4) (x = 0.1–0.4) ceramic samples, the secondary phase Sr2TiO4 can also be detected within the range of the investigated components. Meanwhile, the Raman spectroscopy, SEM-EDS, and HRTEM (SAED) analysis results also verified the correctness and consistency of the phase structures and compositions for all the given specimens. In addition, complex impedance spectroscopy was used to detect the conductive behavior of these compound ceramic systems, and the calculation results show that the appropriate addition of Ti4+-ions can make the SrCeO3 + Sr2CeO4 system have better thermal stability. The composition of x = 0.2 multiphase structural ceramic sample sintered at 1330 °C for 4 h has a near zero τf value of ~−4.6 ppm/°C, a moderate εr of ~40.3 and a higher Q × f~44,020 GHz (at 6.56 GHz). The relatively superior-performing ceramics developed in this work are expected to provide a promising microwave dielectric material for communication components.
Ultrasound Elastography to Assess Age of Deep Vein Thrombosis: A Systematic Review
Authors: Santini Paolo , Esposto Giorgio , Ainora Maria Elena , Lupascu Andrea , Gasbarrini Antonio , Zocco Maria Assunta , Pola Roberto
Published: June 2023

Background and aims: Deep-vein thrombosis (DVT) is a widely diffused condition, and its accurate staging has major clinical and therapeutic implications. Ultrasound elastography (UE) is a rapidly evolving imaging technique that allows quantification of elastic tissue properties and could play a crucial role in determining thrombus age. The aim of this review is to find clinical evidence regarding the application of UE in the evaluation of DVT and its usefulness in differentiating thrombosis age. Methods: A literature search of clinical studies was performed to identify the ability of UE of discriminate acute, subacute, and chronic DVT. Heterogeneity and publication bias were calculated. In accordance with the study protocol, a qualitative analysis of the evidence was planned. The results were summarized with a comprehensive summary table of study characteristics and baseline characteristics of participant patients. Results: Nine studies matched the predetermined eligibility requirements for this systematic review regarding the risk of bias; the greatest criticalities were found within the domains of patient selection and index test. Based on the quality assessment, two publications were excluded from the qualitative synthesis because of the presence of significant applicability concerns. Among the seven studies that were considered eligible for qualitative synthesis, four evaluated strain elastography and three evaluated shear-wave elastography. Despite significant differences concerning study design, thrombus age definitions, and patient characteristics, nearly all studies demonstrated an increase in thrombus stiffness according to DVT age. Conclusions: UE could play a key role in routine ultrasound examination of DVT. The measurement of thrombus stiffness has a high biological plausibility and its use is supported by the finding of a correlation between the stiffness and the progression of the DVT age.
Evaluation of Different Registration Algorithms to Reduce Motion Artifacts in CT-Thermography (CTT)
Authors: Kostyrko Bogdan , Rubarth Kerstin , Althoff Christian , Zibell Miriam , Neizert Christina Ann , Poch Franz , Torsello Giovanni Federico , Gebauer Bernhard , Lehmann Kai , Niehues Stefan Markus , Mews Jürgen , Diekhoff Torsten , Pohlan Julian
Published: June 2023

Computed tomography (CT)-based Thermography (CTT) is currently being investigated as a non-invasive temperature monitoring method during ablation procedures. Since multiple CT scans with defined time intervals were acquired during this procedure, interscan motion artifacts can occur between the images, so registration is required. The aim of this study was to investigate different registration algorithms and their combinations for minimizing inter-scan motion artifacts during thermal ablation. Four CTT datasets were acquired using microwave ablation (MWA) of normal liver tissue performed in an in vivo porcine model. During each ablation, spectral CT volume scans were sequentially acquired. Based on initial reconstructions, rigid or elastic registration, or a combination of these, were carried out and rated by 15 radiologists. Friedman’s test was used to compare rating results in reader assessments and revealed significant differences for the ablation probe movement rating only (p = 0.006; range, 5.3–6.6 points). Regarding this parameter, readers assessed rigid registration as inferior to other registrations. Quantitative analysis of ablation probe movement yielded a significantly decreased distance for combined registration as compared with unregistered data. In this study, registration was found to have the greatest influence on ablation probe movement, with connected registration being superior to only one registration process.