bluuebunny/crossref_metadata_2025 huggingface dataset:
Data Quality Improvement and Internal Data Audit of the Chinese Neonatal Network Data Collection System
Authors: Sun Jianhua , Cao Yun , Hei Mingyan , Sun Huiqing , Wang Laishuan , Zhou Wei , Chen Xiafang , Jiang Siyuan , Zhang Huayan , Ma Xiaolu , Wu Hui , Li Xiaoying , Shi Yuan , Gu Xinyue , Wang Yanchen , Yang Tongling , Lu Yulan , Zhou Wenhao , Chen Chao , Lee Shoo K. , Du Lizhong ,   The Chinese Neonatal Network
Published: May 2023

Background: The Chinese Neonatal Network (CHNN) is a nationwide neonatal network that aims to improve clinical neonatal care quality and short- and long-term health outcomes of infants. This study aims to assess the quality of the Chinese Neonatal Network database by conducting an internal audit of data extraction.

Methods: A data audit was performed by independently replicating the data collection and entry process in all 58 tertiary neonatal intensive care units (NICU) participating in the CHNN. Eighty-eight data elements selected for re-abstraction were classified into three categories (critical, important, less important), and agreement rates for original and re-abstracted data were predefined. Three to five records were randomly selected at each site for re-abstraction, including one short- (0–7 days), two medium- (8–28 days), and two long-stay (more than 28 days) cases. Agreement rates for each data item were calculated for individual NICUs and across the network, respectively.

Results: A total of 283 cases and 24,904 data fields were re-abstracted. The agreement rates for original and re-abstracted data elements were 96.1% overall, and 97.2, 94.3, and 96.6% for critical, important, and less important data elements, respectively. Individual site variation for discrepancies ranged between 0.0 and 18.4% for all collected data elements.

Conclusion: The completeness, precision, and quality of data in the CHNN database are high, providing assurance for multipurpose use, including health service evaluation, quality improvement, clinical trials, and other research.
Optimal planting pattern of cotton is regulated by irrigation amount under mulch drip irrigation
Authors: Zuo Wenqing , Wu Baojian , Wang Yuxuan , Xu Shouzhen , Tian Jingshan , Jiu Xingli , Dong Hengyi , Zhang Wangfeng
Published: May 2023

Objective

It is of great importance to explore agronomic management measures for water conservation and cotton yield in arid areas.

Methods

A four–year field experiment was conducted to evaluate cotton yield and soil water consumption under four row spacing configurations (high/low density with 66+10 cm wide, narrow row spacing, RS66+10H and RS66+10L; high/low density with 76 cm equal row spacing, RS76H and RS76L) and two irrigation amounts (CI:conventional drip irrigation; LI:limited drip irrigation) during the growing seasons in Shihezi, Xinjiang.

Results

A quadratic relationship was observed between the maximum LAI (LAImax) and seed yield. Canopy apparent transpiration rate(CAT), daily water consumption intensity (DWCI) and crop evapotranspiration (ETC) were positively and linearly correlated with LAI. The seed yields, lint yields, and ETC under CI were 6.6–18.3%,7.1–20.8% and 22.9–32.6%higher than those observed under LI, respectively. The RS66+10H under CI had the highest seed and lint yields. RS76L had an optimum LAImax range, which ensured a higher canopy apparent photosynthesis and daily dry matter accumulation and reached the same yield level as RS66+10H; however, soil water consumption in RS76L was reduced ETC by 51–60 mm at a depth of 20–60 cm at a radius of 19–38 cm from the cotton row,and water use efficiency increased by 5.6–8.3%compared to RS66+10H under CI.

Conclusion

A 5.0<LAImax<5.5 is optimum for cotton production in northern Xinjiang, and RS76L under CI is recommended for high yield and can further reduce water consumption. Under LI, the seed and lint yield of RS66+10H were 3.7–6.0% and 4.6–6.9% higher than those of RS76L, respectively. In addition, high-density planting can exploit the potential of soil water to increase cotton yields under water shortage conditions.
Research on the influence and mechanism of human–vehicle moral matching on trust in autonomous vehicles
Authors: Chen Na , Zu Yao , Song Jing
Published: May 2023

Introduction

Autonomous vehicles can have social attributes and make ethical decisions during driving. In this study, we investigated the impact of human-vehicle moral matching on trust in autonomous vehicles and its mechanism.

Methods

A 2*2 experiment involving 200 participants was conducted.

Results

The results of the data analysis show that utilitarian moral individuals have greater trust than deontological moral individuals. Perceived value and perceived risk play a double-edged role in people’s trust in autonomous vehicles. People’s moral type has a positive impact on trust through perceived value and a negative impact through perceived risk. Vehicle moral type moderates the impact of human moral type on trust through perceived value and perceived risk.

Discussion

The conclusion shows that heterogeneous moral matching (people are utilitarian, vehicles are deontology) has a more positive effect on trust than homogenous moral matching (both people and vehicles are deontology or utilitarian), which is consistent with the assumption of selfish preferences of individuals. The results of this study provide theoretical expansion for the fields related to human-vehicle interaction and AI social attributes and provide exploratory suggestions for the functional design of autonomous vehicles.
Involvement, leadership and social practice to the development of postgraduate attributes: evidence from extracurricular education with Chinese characteristics
Authors: Li Sen , Hu Yusheng , Wang Lingling , Xia Enjun
Published: May 2023

Limited by students’ time and energy, participation in extracurricular activities is not necessarily beneficial to the development of postgraduate attributes. Thus, it is necessary to explore the impact path of extracurricular activities and education outcomes on the development of postgraduate attributes. From a configuration perspective, this study identifies the asymmetric causal effects of engagement and extracurricular education on postgraduate attributes. First, this study proposes a theoretical framework for postgraduate attribute development in extracurricular education with Chinese characteristics based on the input-environment-output (IEO) theory. Second, 166 academic scholarship applications submitted by the whole third-grade postgraduates who are from a science and engineering school at a double first-class university in China are taken as the sample. Finally, utilizing data envelopment analysis (DEA) and fuzzy set qualitative comparative analysis (fsQCA), this study conducts the effect of the combination of causal conditions on the development of postgraduate attributes. Results are as follows: (1) the development efficiency of postgraduate attribute in extracurricular education with Chinese characteristics is practical but still insufficient; (2) four configurations consistently linked to high development efficiency of postgraduate attributes. Specifically, in context with outstanding academic research achievement and excellent moral character, participating in extracurricular education or not consistently linked to high development efficiency. In contrast, in a context characterized by academic achievement or moral award not outstanding enough, involvement in extracurricular activities or social practice is consistently linked to high development efficiency. In addition, no configuration links student leadership to high development efficiency, and non-scientific research ability is consistently linked to low development efficiency; (3) there is an asymmetric causal relationship between the high and low development efficiency paths, indicating that the conditions affecting the development of postgraduate attributes have multiple concurrencies. These findings provide a new practical path and perspective for promoting the development of postgraduate attributes through extracurricular education with Chinese characteristics.
Planetary protection: an international concern and responsibility
Authors: Coustenis Athena , Hedman Niklas , Doran Peter T. , Al Shehhi Omar , Ammannito Eleonora , Fujimoto Masaki , Grasset Olivier , Groen Frank , Hayes Alexander G. , Ilyin Vyacheslav , Kumar K. Praveen , Morisset Caroline-Emmanuelle , Mustin Christian , Olsson-Francis Karen , Peng Jing , Prieto-Ballesteros Olga , Raulin Francois , Rettberg Petra , Sinibaldi Silvio , Suzuki Yohey , Xu Kanyan , Zaitsev Maxim
Published: May 2023

Planetary protection is a set of measures agreed upon at an international level to ensure the protection of scientific investigation during space exploration. As space becomes more accessible with traditional and new actors launching complex and innovative projects that involve robotics (including sample return) and human exploration, we have the responsibility to protect the pristine environments that we explore and our own biosphere. In this sense, the Committee on Space Research (COSPAR) provides the international standard for planetary protection as well as a forum for international consultation. COSPAR has formulated a Planetary Protection Policy with associated requirements for responsible space exploration. Although not legally binding under international law, the standard offered by the Policy with its associated requirements is internationally endorsed along with implementation guidelines supplied for reference in support States’ compliance with Article IX of the United Nations Outer Space Treaty of 1967. Indeed, States parties to the Outer Space Treaty (under Article VI) are responsible for any space activities in their countries, governmental and non-governmental. The main goal of this Policy is to avoid compromising the search for any lifeforms on other celestial bodies and to protect the Earth from a potential threat posed by extraterrestrial samples returned by an interplanetary mission. The COSPAR Planetary Protection Policy has defined five categories, depending on the target and objective of the specific space mission. Associated to these categories are requirements are various degrees of rigor in the contamination control applied. The Policy is assessed regularly and updated with input from new scientific findings and in conjunction with the fast-evolving space exploration milieu. The COSPAR Panel on Planetary Protection (PPP) is a designated international committee composed of scientists, agency representatives and space experts. Its role is to support and revise the COSPAR Policy and its related requirements (https://cosparhq.cnes.fr/scientific-structure/panels/panel-on-planetary-protection-ppp/). The Panel’s activities deal with the individual needs of a space mission while exercising swift care and expertise to ensure sustainable exploration of the Solar System.
Prognostic value of albumin-bilirubin score in pancreatic cancer patients after pancreatoduodenectomy with liver metastasis following radiofrequency ablation
Authors: Zhang Lei , Zhang Xuefei , Wu Bolin , Han Xue , Guo Cunli , Li Bo , Jing Hui , Cheng Wen
Published: May 2023

Objective: The current study aimed to investigate the prognostic value of albumin-bilirubin (ALBI) score in predicting clinical outcomes of pancreatic cancer patients after pancreatoduodenectomy with liver metastasis following radiofrequency ablation.

Methods: This retrospective study included 90 pancreatic cancer patients after pancreatoduodenectomy with liver metastasis from January 2012 to December 2018. In this study, the Chi-square or Fisher’s exact tests, the receiver operating characteristic (ROC) curve, Kaplan-Meier method and Log-rank test, univariate and multivariate Cox proportional hazard regression analyses, nomogram, calibration curves and decision curve analysis were used for all statistical analysis.

Results: We analyzed the optimal cut-off value of ALBI by ROC curve, and the optimal cut-off value was −2.60. According to ALBI score, these patients were divided into two groups: low ALBI group (n = 33) and high ALBI group (n = 57). Patients with low ALBI score was significantly related to longer progression free survival (PFS) (p = 0.0002, HR: 3.039, 95% CI: 1.772–5.210) and overall survival (OS) (p = 0.0005, HR: 2.697, 95% CI: 1.539–4.720). The 1-, 3-, and 5-year PFS and OS rates in low ALBI group were higher than those in high ALBI group. ALBI was a potential independent prognostic factor for pancreatic cancer patients after pancreatoduodenectomy with liver metastasis following radiofrequency ablation. Moreover, the nomogram was used to predict the 1-, 3-, and 5-year survival probabilities of PFS and OS. The calibration curve shown that the prediction line matched the reference line well for postoperative 3-year PFS and OS. The DCA shown that nomogram model was better than the only ALBI, and indicated the ability for clinical decision-making, especially in 1-year PFS, and 3-, 5-year OS.

Conclusion: ALBI is a potential independent factor for PFS and OS, and can predict the prognosis of pancreatic cancer patients after pancreatoduodenectomy with liver metastasis following radiofrequency ablation.
Obesity is Associated With Delayed Graft Function in Kidney Transplant Recipients: A Paired Kidney Analysis
Authors: Shi Bree , Ying Tracey , Xu Josephine , Wyburn Kate , Laurence Jerome , Chadban Steven J
Published: May 2023

Obesity is increasingly prevalent among candidates for kidney transplantation. Existing studies have shown conflicting post-transplant outcomes for obese patients which may relate to confounding bias from donor-related characteristics that were unaccounted for. We used ANZDATA Registry data to compare graft and patient survival between obese (BMI >27.5 kg/m2 Asians; >30 kg/m2 non-Asians) and non-obese kidney transplant recipients, while controlling for donor characteristics by comparing recipients of paired kidneys. We selected transplant pairs (2000–2020) where a deceased donor supplied one kidney to an obese candidate and the other to a non-obese candidate. We compared the incidence of delayed graft function (DGF), graft failure and death by multivariable models. We identified 1,522 pairs. Obesity was associated with an increased risk of DGF (aRR = 1.26, 95% CI 1.11–1.44, p < 0.001). Obese recipients were more likely to experience death-censored graft failure (aHR = 1.25, 95% CI 1.05–1.49, p = 0.012), and more likely to die with function (aHR = 1.32, 95% CI 1.15–1.56, p = 0.001), versus non-obese recipients. Long-term patient survival was significantly worse in obese patients with 10- and 15-year survival of 71% and 56% compared to 77% and 63% in non-obese patients. Addressing obesity is an unmet clinical need in kidney transplantation.
Adaptive PID Control via Sliding Mode for Position Tracking of Quadrotor MAV: Simulation and Real-Time Experiment Evaluation
Authors: Noordin Aminurrashid , Mohd Basri Mohd Ariffanan , Mohamed Zaharuddin
Published: May 2023

The lightweight nature of micro air vehicles (MAVs) makes them highly sensitive to perturbations, thus emphasizing the need for effective control strategies that can sustain attitude stability throughout translational movement. This study evaluates the performance of two controllers (Proportional-Integral-Derivative (PID) and Adaptive PID based on Sliding Mode Control (SMC)) on a MAV that is subjected to external disturbances. These controllers are initially simulated using MATLAB®/Simulink™ and then implemented in real-time on the Parrot Mambo Minidrone. The observation on the waypoint follower and the orbit follower in both simulation and experiment showed that the Adaptive PID (APID) controller is more effective and robust than the PID controller against external disturbances such as wind gusts. The study provides evidence of the potential of the APID control scheme in enhancing the resilience and stability of MAVs, making them suitable for various applications including surveillance, search and rescue, and environmental monitoring.
Agricultural Water Quality Assessment and Application in the Yellow River Delta
Authors: Sun Yunpeng , Chen Xiaobing , Luo Yongming , Cao Dan , Feng Hongyu , Zhang Xin , Yao Rongjiang
Published: May 2023

Irrigation is the primary agricultural utilization of water resources worldwide, and it produces 36% of the food. The quality of irrigation water influences crop growth and food safety. The coastal river delta region supplies huge area lands for developing agriculture, and the irrigation in this area is composed of many resources for its special location. This study investigated the agricultural water with different resources and evaluated its quality in the Yellow River Delta. The water samples included irrigation water, underground water, and drainage water. The sampling points were designed in the typical areas of Kenli county and Guangrao county in Dongying city, which is the core area of the Yellow River Delta. Through testing the ions composition and the parameters of electrical conductivity (EC), pH, and so on, six evaluation methods were conducted to assess the water quality. The results suggested that the Yellow River water has good quality for irrigation or unconventional water utilization. The high concentration of Na+ and Cl− was the primary problem of the water resource. The rainfall was also another water resource supplement in this region. Therefore, developing saline water irrigation incorporated with salt-tolerant crop cultivation is a rational measure for improving coastal agriculture in the Yellow River Delta.
Co-Infection by Leptospira montravelensis and Leptospira interrogans Serovar Pomona in Urine Samples of Donkeys and Pigs in Sardinia, Italy
Authors: Piredda Ivana , Bertoldi Loris , Pedditzi Aureliana , Pintore Pierangela , Palmas Bruna , Chisu Valentina
Published: May 2023

Saprophytic leptospires are spirochetes enclosed within the non-pathogenic clade of the genus Leptospira, which in turn is subdivided into two subclades S1 and S2. To date, the microorganisms included in these subclades have been isolated from the environment in various parts of the world, and are believed to have no known animal reservoirs. After a case of Leptospira interrogans serovar Pomona was notified to the owner of a farm in Sardinia, all of the farm animals (11 pigs and 3 donkeys) were examined for the presence of Leptospira. Sera of all tested animals resulted positive for antibodies to Leptospira using a microscopic agglutination test (MAT). Moreover, nine (82%) kidney samples from pigs and three urine samples collected from donkeys (100%) tested positive for Leptospira DNA after qPCR. Results obtained after MLST analysis and sequencing of rrs, rpoB, and secY genes, performed on six Leptospira strains isolated in culture, revealed the presence of the genomospecies L. interrogans serovar Pomona in the kidney samples. Conversely, whole-genome sequencing combined with mean nucleotide identity revealed the presence of the saprophytic L. montravelensis in the urine samples. Our results report, for the first time, the isolation of a saprophytic species from mammalian urine, suggesting a new ecological specialization for these bacteria, with a possible transition from free-living to a symbiotic lifestyle. Further studies will have to be conducted to understand the evolution of virulence of these bacteria, potential infectivity, and possible public health implications.
In Vitro Activity of Novel Topoisomerase Inhibitors against Francisella tularensis and Burkholderia pseudomallei
Authors: Whelan Adam O. , Cooper Ian , Ooi Nicola , Orr David , Blades Kevin , Kirkham James , Lyons Amanda , Barnes Kay B. , Richards Mark I. , Salisbury Anne-Marie , Craighead Mark , Harding Sarah V.
Published: May 2023

Antimicrobial resistance is a global issue, and the investigation of alternative therapies that are not traditional antibiotics are warranted. Novel bacterial type II topoisomerase inhibitors (NBTIs) have recently emerged as a novel class of antibiotics with reduced potential for cross-resistance to fluoroquinolones due to their novel mechanism of action. This study investigated the in vitro activity of a series of cyclohexyl–oxazolidinone bacterial topoisomerase inhibitors against type strains of Francisella tularensis and Burkholderia pseudomallei. Broth microdilution, time-kill, and cell infection assays were performed to determine activity against these biothreat pathogens. Two candidates were identified that demonstrated in vitro activity in multiple assays that in some instances was equivalent to ciprofloxacin and doxycycline. These data warrant the further evaluation of these novel NBTIs and future iterations in vitro and in vivo.
State Residualisation and Kron Reduction for Model Order Reduction of Energy Systems
Authors: Zhao Xianxian , Kestelyn Xavier , Cossart Quentin , Colas Frédéric , Flynn Damian
Published: May 2023

Greater numbers of power electronics (PEs) converters are being connected to energy systems due to the development of renewable energy sources, high-voltage transmission, and PE-interfaced loads. Given that power electronics-based devices and synchronous machines have very different dynamic behaviours, some modelling approximations, which may commonly be applied to run transient simulations of transmission systems, may not be optimal for future grids. Indeed, the systematic utilisation of the phasor approximation for power lines, implemented in most transient simulation programs, is increasingly not appropriate anymore. In order to avoid the requirement for full electromagnetic transient simulations, which can be resource-demanding and time-consuming, this paper proposes a combination of an event-based state residualisation approximation and the Kron reduction technique. The proposed technique has the advantage of allowing accurate transient simulations based on the optimal reduction of the number of state variables, depending on the observed variables, the considered events, and the tolerated approximation error, along with simplifying power systems equations for accelerated simulations.
Artificial Bee Colony Algorithm with Pareto-Based Approach for Multi-Objective Three-Dimensional Single Container Loading Problems
Authors: Phongmoo Suriya , Leksakul Komgrit , Charoenchai Nivit , Boonmee Chawis
Published: May 2023

The ongoing container shortage crisis has presented significant challenges for the freight forwarding industry, requiring companies to implement adaptive measures in order to maintain peak operational efficiency. This article presents a novel mathematical model and artificial bee colony algorithm (ABC) with a Pareto-based approach to solve single-container-loading problems. The goal is to fit a set of boxes with strongly heterogeneous boxes into a container with a specific dimension to minimize the broken space and maximize profits. Furthermore, the proposed algorithm incorporates the bottom-left fill method, which is a heuristic strategy for packing containers. We conducted numerical testing to identify optimal parameters using the C~ metric method. Subsequently, we evaluated the performance of our proposed algorithm by comparing it to other heuristics and meta-heuristic approaches using the relative improvement (RI) value. Our analysis showed that our algorithm outperformed the other approaches and achieved the best results. These results demonstrate the effectiveness of the proposed algorithm in solving real-world single-container-loading problems for freight forwarding companies.
A Study on the Sensitivities of an Ice Protection System Combining Thermoelectric and Superhydrophobic Coating to Icing Environment Parameters
Authors: Yu Lei , Wu Yuan , Zhao Huanyu , Zhu Dongyu
Published: May 2023

The hybrid Ice Protection System combining thermoelectric and superhydrophobic coating is efficient and benefits from low-energy consumption. In order to explore the application details of superhydrophobic coating, this paper investigated the sensitivities of the Ice Protection System parameters including the range of the superhydrophobic coating, heating range and power to icing environmental parameters. In this paper, an icing wind tunnel test was adopted to study the performance of this Ice Protection System under different icing conditions, as well as the influence of the superhydrophobic coating range, heating range and power variation on ice protection. The results showed that the superhydrophobic coating is effective only when it covers the droplet impingement area, with the heating power requirement emerging as a critical design consideration that is extremely sensitive to environmental temperature changes. Additionally, median volumetric diameter determines the protection area to be protected, while liquid water content variation has little effect on the designed Ice Protection System in contrast.
Special Issue on Synchrotron Radiation for Medical Applications
Authors: Itzhak Orion
Published: May 2023

X-ray radiation is a widely used tool in the fields of medical physics and diagnostic imaging [...]
Stretching and Forming Limit Curve of Steel–Glass Fibre Reinforced and Non-Reinforced Polyamide–Steel Sandwich Materials
Authors: Hua Wei , Harhash Mohamed , Ziegmann Gerhard , Carradò Adele , Palkowski Heinz
Published: May 2023

This paper focuses on investigating the forming behaviour of sandwich materials composed of steel sheets and glass fibre-reinforced polyamide 6 (GF-PA6), i.e., thermoplastic-based fibre metal laminates (FML). Stretching and forming limit curve (FLC) determination of FML with different cover/core layer thickness ratios at various forming temperatures, i.e., at room temperature (RT), 200 and 235 °C, are the main approaches for characterizing their formability. In addition, the formability of mono-materials and non-reinforced sandwich materials is investigated as a reference. For a successful test and reliable results, several technical issues are considered, such as the suitable lubrication configuration and digital image correlation at elevated forming temperatures. The results revealed that the formability of non-reinforced sandwich materials with different core layer thicknesses exhibited compared formability to their monolithic steel sheet and no remarkable improvement in their formability with increasing the temperature up to 200 °C. Conversely, the formability of FML shows significant improvement (approx. 300%) with increasing temperature with a forming depth of about 33 mm at 235 °C compared to only 12 mm at RT.
Amidoalkyl Naphthols as Important Bioactive Substances and Building Blocks: A Review on the Current Catalytic Mannich-Type Synthetic Approaches
Authors: Petkov Hristo , Simeonov Svilen P.
Published: May 2023

Currently, 1-amidoalkyl-2-naphthol derivatives are of increasing interest due to their biological activities and further use in the preparation of other important bioactive molecules, such as aminoalkyl naphthols and oxazines. The synthesis of 1-amidoalkyl-2-naphthol moiety is usually achieved by employing one-pot multicomponent Mannich reactions. This review covers the recent reports on 1-amidoalkyl-2-naphthols’ preparation with the use of different catalysts and summarizes the available published data for the period of the last 3 years. It also puts emphasis on the structure, synthetic transformation and biological importance of this class of products.
Special Issue on Nano- and Biomagnetism
Authors: Caizer Costica
Published: May 2023

Magnetism in nanometer range [...]
G Protein-Coupled Receptors and the Rise of Type 2 Diabetes in Children
Authors: Dallatana Alessia , Cremonesi Linda , Trombetta Maddalena , Fracasso Giulio , Nocini Riccardo , Giacomello Luca , Innamorati Giulio
Published: May 2023

The human genome counts hundreds of GPCRs specialized to sense thousands of different extracellular cues, including light, odorants and nutrients in addition to hormones. Primordial GPCRs were likely glucose transporters that became sensors to monitor the abundance of nutrients and direct the cell to switch from aerobic metabolism to fermentation. Human β cells express multiple GPCRs that contribute to regulate glucose homeostasis, cooperating with many others expressed by a variety of cell types and tissues. These GPCRs are intensely studied as pharmacological targets to treat type 2 diabetes in adults. The dramatic rise of type 2 diabetes incidence in pediatric age is likely correlated to the rapidly evolving lifestyle of children and adolescents of the new century. Current pharmacological treatments are based on therapies designed for adults, while youth and puberty are characterized by a different hormonal balance related to glucose metabolism. This review focuses on GPCRs functional traits that are relevant for β cells function, with an emphasis on aspects that could help to differentiate new treatments specifically addressed to young type 2 diabetes patients.
Discriminating Glioblastoma from Peritumoral Tissue by a Nanohole Array-Based Optical and Label-Free Biosensor
Authors: García-Milán Víctor , Franco Alfredo , Zvezdanova Margarita Estreya , Marcos Sara , Martin-Laez Rubén , Moreno Fernando , Velasquez Carlos , Fernandez-Luna José L.
Published: May 2023

In glioblastoma (GBM) patients, maximal safe resection remains a challenge today due to its invasiveness and diffuse parenchymal infiltration. In this context, plasmonic biosensors could potentially help to discriminate tumor tissue from peritumoral parenchyma based on differences in their optical properties. A nanostructured gold biosensor was used ex vivo to identify tumor tissue in a prospective series of 35 GBM patients who underwent surgical treatment. For each patient, two paired samples, tumor and peritumoral tissue, were extracted. Then, the imprint left by each sample on the surface of the biosensor was individually analyzed, obtaining the difference between their refractive indices. The tumor and non-tumor origins of each tissue were assessed by histopathological analysis. The refractive index (RI) values obtained by analyzing the imprint of the tissue were significantly lower (p = 0.0047) in the peritumoral samples (1.341, Interquartile Range (IQR) 1.339–1.349) compared with the tumor samples (1.350, IQR 1.344–1.363). The ROC (receiver operating characteristic) curve showed the capacity of the biosensor to discriminate between both tissues (area under the curve, 0.8779, p < 0.0001). The Youden index provided an optimal RI cut-off point of 0.003. The sensitivity and specificity of the biosensor were 81% and 80%, respectively. Overall, the plasmonic-based nanostructured biosensor is a label-free system with the potential to be used for real-time intraoperative discrimination between tumor and peritumoral tissue in patients with GBM.
Terson Syndrome: Not to Be Missed in Patients with Disorders of Consciousness
Authors: Maslias Errikos , Vijiala Sergiu , Epiney Jean-Benoit , Konstantinidis Lazaros , Kawasaki Aki , Diserens Karin
Published: May 2023

The diagnosis of clinical cognitive motor dissociation (cCMD) can be hindered by pitfalls during standardized clinical evaluation based on gold-standard neurobehavioral rating scales. We introduce here a new pitfall, by reporting two cases of Terson syndrome (TS) after subarachnoid haemorrhage (SAH) caused by the rupture of an anterior communicant artery aneurysm, hospitalized in the Acute Neurorehabilitation Unit (ANR) of CHUV. TS is reported to occur in 8–19.3% of patients suffering from SAH. It can lead to significant visual impairment and if unrecognized, may impair the patient’s capacity to interact appropriately with the environment; it thus presents an important pitfall in recognizing clinical cognitive–motor dissociation (cCMD) in patients with altered states of consciousness. An early ophthalmological exam should be considered in all patients with SAH and disorders of consciousness or visual complaints.
The Environmental and Energy Renovation of a District as a Step towards the Smart Community: A Case Study of Tehran
Authors: Pompei Laura , Rosa Flavio , Nardecchia Fabio , Piras Giuseppe
Published: May 2023

As the world’s third-largest oil and natural gas producer, Iran consumed enormous amounts of non-renewable energy during the last twenty years. There are many obsolete buildings in the Iranian building stock, which required energy renovation. Many studies in the literature proposed energy retrofitting strategies to increase the efficiency of buildings, but few of them involve an energy network for the entire neighbourhood (such as district heating). Moreover, energy renovation is not sufficient to improve the smartness level of a community; in fact, it is essential to evaluate sustainable and social aspects. In this direction, this study aims to develop a comprehensive analysis of the current criticalities of a district in Tehran (District 5), proposing strategies to face the pollution of the city, provide a healthy environment for the citizens, and renovate the old buildings. The application of a decision support method is presented to set a priority ranking, pointing out the positive and negative impacts of each evaluated scenario. The energy renovation solution involved the installation of two storage tanks and solar collectors in each building and the connection with the district heating powered by waste to the energy plant. A multi-level car parking system and a noise mapping application were evaluated to solve mobility and pollution problems. Moving to the results, the priority ranking assesses that the most affordable action is the installation of a Solar Water Heater since energy and environmental indicators demonstrate its efficacy compared to the other solutions.
A Machine Learning Pipeline to Forecast the Electricity and Heat Consumption in a City District
Authors: Antonesi Gabriel , Cioara Tudor , Toderean Liana , Anghel Ionut , De Mulder Chaim
Published: May 2023

The shift towards renewable energy integration into smart grids has led to complex management processes, which require finer-grained energy and heat generation/ demand forecasting while considering data from monitoring devices and the integration of smaller multi-energy sub-systems at the community, district, or buildings level. However, energy prediction is challenging due to the high variability in the electrical and thermal energy demands of building occupants, the heterogenous characteristics of the energy assets or buildings in a district, and the length of the forecasting horizon. In this paper, we define a data-driven machine-learning pipeline to predict the electricity and thermal consumption of buildings and energy assets from a city district in 24 h intervals. Each pipeline’s step is divided into sensors’ data processing and model integration, data enrichment and features engineering, and multilayer perceptron model training. To address some of the drawbacks of using the multi-layer perceptron model, such as slow convergence rate and risk of overfitting, and to ensure a lower error in the energy prediction process features, an engineering technique was employed. We incorporated weather data features and interaction features derived from fusing the energy data with statistical models to capture the nonlinear patterns of the electrical and heat demands. The proposed approach was successfully validated in a real-world environment, a city district in Gent, Belgium. It featured good prediction results for electricity and heat production and consumption of various assets without considering the physical characteristics, making it viable and easily applicable in broader urban areas. The evaluation of energy prediction accuracy yielded good results, with a Mean Absolute Error (MAE) falling within the range of 0.003 to 3.27, and a Mean Absolute Scaled Error (MASE) ranging from 7 × 10−5 to 2.57 × 10−3.
Chemical Diversity and Redox Values Change as a Function of Temporal Variations of the Essential Oil of a Tropical Forest Shrub
Authors: Costa-Oliveira Claudete da , Gouvêa-Silva João Gabriel , Brito Machado Daniel de , Felisberto Jéssica Regina Sales , Queiroz George Azevedo de , Guimarães Elsie Franklin , Ramos Ygor Jessé , Moreira Davyson de Lima
Published: May 2023

This study investigated the chemical phenotypical variability of Piper lhotzkyanum Kunth (Piperaceae), a shrub found in Brazilian tropical forests, over time (different periods of the day and seasons) and under natural conditions. For this, essential oils (EOs) from the leaves were collected in different seasons and times of the day and analyzed by gas chromatography coupled with mass spectrometry, and gas chromatography coupled with a flame ionization detector. The indices were applied to evaluate the chemical diversity as well as the dynamics of redox of the mixtures. The results showed that the EOs were dominated by non-oxygenated sesquiterpenes, with β-elemene, E-caryophyllene, and α-zingiberene being the main compounds identified in all collections. Temporal and seasonal analyses revealed important fluctuations in the chemical composition, redox, and chemical diversity indices of the species. A correlation between climatic factors and the variation in redox and chemical diversity was observed, highlighting the chemical phenotypic plasticity P. lhotzkyanum. This study resolves a previously unanswered question by confirming that natural light does not produce interconversion of major compounds. The adaptation capacity of this species to the environmental changes suggests new cultivation strategies to maximize the quality of EO and promote a more sustainable future in partnership with nature.
Counteracting Data Bias and Class Imbalance—Towards a Useful and Reliable Retinal Disease Recognition System
Authors: Chłopowiec Adam R. , Karanowski Konrad , Skrzypczak Tomasz , Grzesiuk Mateusz , Chłopowiec Adrian B. , Tabakov Martin
Published: May 2023

Multiple studies presented satisfactory performances for the treatment of various ocular diseases. To date, there has been no study that describes a multiclass model, medically accurate, and trained on large diverse dataset. No study has addressed a class imbalance problem in one giant dataset originating from multiple large diverse eye fundus image collections. To ensure a real-life clinical environment and mitigate the problem of biased medical image data, 22 publicly available datasets were merged. To secure medical validity only Diabetic Retinopathy (DR), Age-Related Macular Degeneration (AMD) and Glaucoma (GL) were included. The state-of-the-art models ConvNext, RegNet and ResNet were utilized. In the resulting dataset, there were 86,415 normal, 3787 GL, 632 AMD and 34,379 DR fundus images. ConvNextTiny achieved the best results in terms of recognizing most of the examined eye diseases with the most metrics. The overall accuracy was 80.46 ± 1.48. Specific accuracy values were: 80.01 ± 1.10 for normal eye fundus, 97.20 ± 0.66 for GL, 98.14 ± 0.31 for AMD, 80.66 ± 1.27 for DR. A suitable screening model for the most prevalent retinal diseases in ageing societies was designed. The model was developed on a diverse, combined large dataset which made the obtained results less biased and more generalizable.
EF-TTOA: Development of a UAV Path Planner and Obstacle Avoidance Control Framework for Static and Moving Obstacles
Authors: Du Hongbao , Wang Zhengjie , Zhang Xiaoning
Published: May 2023

With the increasing applications of unmanned aerial vehicles (UAVs) in surveying, mapping, rescue, etc., the security of autonomous flight in complex environments becomes a crucial issue. Deploying autonomous UAVs in complex environments typically requires them to have accurate dynamic obstacle perception, such as the detection of birds and other flying vehicles at high altitudes, as well as humans and ground vehicles at low altitudes or indoors. This work’s primary goal is to cope with both static and moving obstacles in the environment by developing a new framework for UAV planning and control. Firstly, the point clouds acquired from the depth camera are divided into dynamic and static points, and then the velocity of the point cloud clusters is estimated. The static point cloud is used as the input for the local mapping. Path finding is simplified by identifying key points among static points. Secondly, the design of a trajectory tracking and obstacle avoidance controller based on the control barrier function guarantees security for moving and static obstacles. The path-finding module can stably search for the shortest path, and the controller can deal with moving obstacles with high-frequency. Therefore, the UAV can deal with both long-term planning and immediate emergencies. The framework proposed in this work enables a UAV to operate in a wider field, with better security and real-time performance.
Characterizing Highly Effective Technology and Engineering Educators
Authors: Hughes Andrew John , Love Tyler S. , Dill Kristine
Published: May 2023

There have been numerous definitions and models proposed in attempts to better conceptualize effective educators; however, there is no consensus on a definition or model that characterizes effective educators in all contexts. Specific to technology and engineering (T&E) education, the Standards for Technological and Engineering Literacy (STEL) proposed three elements (core standards and benchmarks, T&E practices, and T&E contexts) for standardization of instruction to ensure more effective T&E educators. However, this requires educators to possess a broad spectrum of integrative knowledge and practices to guide authentic T&E teaching and learning experiences, something which the literature has shown is not always correlated with teaching experience. This article examines various definitions and characteristics of effective educators as presented throughout the literature considered within the context of T&E education. The information presented in this article has implications for helping educators, educator preparation programs, and professional development providers identify and develop competencies that the literature suggests can result in more effective T&E educators.
Imperfect-Information Game AI Agent Based on Reinforcement Learning Using Tree Search and a Deep Neural Network
Authors: Ouyang Xin , Zhou Ting
Published: May 2023

In the field of computer intelligence, it has always been a challenge to construct an agent model that can be adapted to various complex tasks. In recent years, based on the planning algorithm of Monte Carlo tree search (MCTS), a new idea has been proposed to solve the AI problems of two-player zero-sum games such as chess and Go. However, most of the games in the real environment rely on imperfect information, so it is impossible to directly use the normal tree search planning algorithm to construct a decision-making model. Mahjong, which is a popular multiplayer game with a long history in China, attracts great attention from AI researchers because it contains a large game state space and a lot of hidden information. In this paper, we utilize an agent learning approach that leverages deep learning, reinforcement learning, and dropout learning techniques to implement a Mahjong AI game agent. First, we improve the state transition of the tree search based on the learned MDP model, the player position variable and transition information are introduced into the tree search algorithm to construct a multiplayer search tree. Then, the model training based on a deep reinforcement learning method ensures the stable and sustainable training process of the learned MDP model. Finally, we utilize the strategy data generated by the tree search and use the dropout learning method to train the normal decision-making agent. The experimental results demonstrate the efficiency and stability performance of the agent trained by our proposed method compared with existing agents in terms of test data accuracy, tournament ranking performance, and online match performance. The agent plays against human players and acts like real humans.
A Blockchain-Based Incentive Mechanism for Sharing Cyber Threat Intelligence
Authors: Ma Xingbang , Yu Dongsheng , Du Yanhui , Li Lanting , Ni Wenkai , Lv Haibin
Published: May 2023

With the development of the Internet, cyberattacks are becoming increasingly complex, sustained, and organized. Cyber threat intelligence sharing is one of the effective ways to alleviate the pressure on organizational or individual cyber security defense. However, the current cyber threat intelligence sharing lacks effective incentive mechanisms, resulting in mutual distrust and a lack of motivation to share among sharing members, making the security of sharing questionable. In this paper, we propose a blockchain-based cyber threat intelligence sharing mechanism (B-CTISM) to address the problems of free riding and lack of trust among sharing members faced in cyber threat intelligence sharing. We use evolutionary game theory to analyze the incentive strategy; the resulting evolutionarily stable strategy achieves the effect of promoting sharing and effectively curbing free-riding behavior. Then, the incentive strategy is deployed to smart contracts running in the trusted environment of blockchain, whose decentralization and tamper-evident properties can provide a trusted environment for participating members and establish trust without a third-party central institution to achieve secure and efficient cyber threat intelligence sharing. Finally, the effectiveness of the B-CTISM in facilitating and regulating threat intelligence sharing is verified through experimental simulation and comparative analysis.
Application of Machine Learning in Fuel Cell Research
Authors: Su Danqi , Zheng Jiayang , Ma Junjie , Dong Zizhe , Chen Zhangjie , Qin Yanzhou
Published: May 2023

A fuel cell is an energy conversion device that utilizes hydrogen energy through an electrochemical reaction. Despite their many advantages, such as high efficiency, zero emissions, and fast startup, fuel cells have not yet been fully commercialized due to deficiencies in service life, cost, and performance. Efficient evaluation methods for performance and service life are critical for the design and optimization of fuel cells. The purpose of this paper was to review the application of common machine learning algorithms in fuel cells. The significance and status of machine learning applications in fuel cells are briefly described. Common machine learning algorithms, such as artificial neural networks, support vector machines, and random forests are introduced, and their applications in fuel cell performance prediction and optimization are comprehensively elaborated. The review revealed that machine learning algorithms can be successfully used for performance prediction, service life prediction, and fault diagnosis in fuel cells, with good accuracy in solving nonlinear problems. Combined with optimization algorithms, machine learning models can further carry out the optimization of design and operating parameters to achieve multiple optimization goals with good accuracy and efficiency. It is expected that this review paper could help the reader comprehend the state of the art of machine learning applications in fuel fuels and shed light on further development directions in fuel cell research.
Electro-Mechanical Modeling and Evaluation of Electric Load Haul Dump Based on Field Measurements
Authors: Freire Gabriel , Ramirez Guillermo , Gómez René , Skrzypkowski Krzysztof , Zagórski Krzysztof
Published: May 2023

In underground mining, conventional loader equipment uses diesel as a power source, implying different drawbacks, such as combustion gases, low visibility, worker’s health problems, and high ventilation requirements. Thus, hybrid and electric loaders are being developed by the main industry suppliers who prefer clean technology. In this study, we analyzed the performance of an electro-mechanical powertrain through a dynamic model of underground-loader equipment using field data. This electric LHD model was compared to a diesel loader under the same operational conditions. For the case study, the results showed that the proposed electro-mechanical model, considering 14 tons of capacity, consumed 86.8 kWh, representing 60.5% less energy than the diesel loader with similar speed and torque characteristics. Thus, the proposed methodology is a valuable tool for operators, process engineers, and decision-makers, allowing an energy-efficiency evaluation for electric LHD adoption, based on the current operational data available for conventional equipment.