bluuebunny/crossref_metadata_2025_split huggingface dataset:
Some Issues of Ensuring Completeness and Reliability of Operational Data Obtained in the Course of Instrument Inspection of Steel Underground Gas Pipelines
Authors: Strutsky N. V. , Romaniuk V. N. 
Published: February 2024

In the long term, along with the growth of nuclear power and related changes in the fuel and energy balance of the country, natural gas will retain an important place in the national economy, including in the production of heat and electricity Accordingly, the importance of gas distribution networks, which directly supply fuel to the Republic's consumers, will remain for a long time. According with the Concept of the National Strategy for Sustainable Development of the Republic of Belarus for the period until 2035, the main task in the gas sector is to maintain production assets at a level ensuring safe energy supply. Practice shows that corrosion factor has the greatest potential to influence the technical condition of steel underground distribution gas pipelines. To compensate for corrosion processes, steel underground pipelines are equipped with special protective means, in particular, insulation coatings. One of the key operational characteristics of insulation is its integrity, which is controlled through periodic (comprehensive) instrumental technical inspection. Based on the inspection results, statistics of identified defects of protective coatings is formed. The work examines the issues of ensuring the reliability of operational data, and highlights the experience of implementing specialized software packages in gas supply organizations of the Beltopgaz State Production Association for recording and processing the results of instrument surveys of gas distribution pipelines. The issue of the influence of organizational and production aspect (features of technology, local practice of planning and performing specific types of work on technical operation) on the structure of operational data, which requires separate study and accounting for their further statistical processing and use, has been considered in the paper.
Broadband nonlinear modulation of incoherent light using a transparent optoelectronic neuron array
Authors: Duan Xiangfeng , Zhang Dehui , Xu Dong , Li Yuhang , Luo Yi , Hu Jingtian , Zhou Jingxuan , Zhang Yucheng , Zhou Boxuan , Wang Peiqi , Li Xurong , Bai Bijie , Ren Huaying , Wang Laiyuan , Jarrahi Mona , Huang Yu , Ozcan Aydogan , Zhang Ao 
Published: February 2024

Abstract
Nonlinear optical processing of ambient natural light is highly desired for computational imaging and sensing. Strong optical nonlinear response under weak broadband incoherent light is essential for this purpose. By merging 2D transparent phototransistors (TPTs) with liquid crystal (LC) modulators, we create an optoelectronic neuron array that allows self-amplitude modulation of spatially incoherent light, achieving a large nonlinear contrast over a broad spectrum at orders-of-magnitude lower intensity than achievable in most optical nonlinear materials. We fabricated a 10,000-pixel array of optoelectronic neurons, and experimentally demonstrated an intelligent imaging system that instantlyattenuates input glares while retaining the weaker-intensity objects captured by a cellphone camera. This intelligent glare-reduction is important for various imaging applications, including autonomous driving, machine vision, and security cameras. The rapid nonlinear processing of incoherent broadband light might also find applications in optical computing, where nonlinear activation functions for ambient light conditions are highly sought.
Beautiful Mind: a meta-heuristic algorithm for generating minimal covering array
Authors: Esfandyari Sajad , Rafe Vahid , Pira Einollah , Yousofvand liela 
Published: February 2024

Abstract
Today, the application of meta-heuristic algorithms in solving problems is very important. This importance has led to the development of hundreds of types of meta-heuristic algorithms by researchers. The reason for the high number of such algorithms is that an algorithm may be superior to its competitors in a particular problem. Generating a test set in Combinatorial Testing (CT) is one of the thousands of problems that can be solved by meta-heuristic algorithms and hundreds of algorithms have been proposed in this regard. The main challenge in producing a test set in CT is becoming trapped in local optima that several solutions have been offered to overcome this problem. Since the proposed solutions are very slow in terms of time, it is still possible to produce better results by applying other solutions. Continuing our research in the field of CT, we have tried to present a new meta-heuristic solution called Beautiful Mind (BM), which simulates the human way to reach the answer. In fact, the proposed algorithm considers the human intelligence and emotional coefficient to find the answer. The evaluation results show that the proposed approach is much stronger than the existing solutions.
Adsorption Investigation of Polyacrylic Acid on Sub-micron Titanium Dioxide Surface and Associated Effects on Flocculation
Authors: Sun Xianfeng , Zheng Shenke , Cai Zhonghao , Wang Dong , Zhou Boxin , Chen Chubin , Wang Daowei 
Published: February 2024

Abstract

This work investigated the flocculation of sub-micron titanium dioxide (TiO2) by polyacrylic acids (PAAs), with a focus on studying the correlation between PAAs adsorption conformation and flocculation efficiency/flocs property. The adsorption analysis of PAAs on the TiO2 surface was studied by attenuated total reflection Fourier transform infrared (ATR-FTIR), X-ray photoelectron spectroscopy (XPS), CHNS elemental analyzer coupled with quartz crystal microbalance with dissipation monitoring (QCM-D). Adsorption densities were fitted using Langmuir and Freundlich isotherm models. The flocculation process of TiO2 by PAAs of different molecular weights (MWs) was monitored in real-time by photometric dispersion analyzer (PDA), and the floc structure was studied by scanning electron microscope (SEM). Results indicated that PAAs adsorption followed the Langmuir isotherm, forming monolayer adsorption on the TiO2 surface regardless of MWs through hydrogen and bidentate chelating chemical bonds. The adsorption layer thickness increased with increasing MW, which could promote flocs size by providing molecular “bridges”; but impeded the flocculation rate. For sub-micron particles, polyacrylic acid with a medium MW (512 kDa) was the optimum polymer owing to obtain the fastest flocculation rate and relatively dense flocs in the present research. PAA with high MW resulted in larger flocs with more porositise, while the low MW PAA can only generate smaller flocs with slower flocculation rate.
Sustainable solar energy deployment: a multicriteria decision-making approach for site suitability and greenhouse gas emission reduction
Authors: Richards Delmaria , Yabar Helmut , Mizunoya Takeshi , Koon Randy Koon , Tran Gia Hong , Esopere Yannick 
Published: February 2024

Abstract

Conventional power generation methods have led to adverse environmental impacts. Thus, the need for a strategic transition to alternative energy sources arises. This paper addresses land suitability and allocation for utility-scale solar farms based on environmental and socioeconomic planning. It employs multicriteria decision-making (MCDM) techniques, namely, the analytical hierarchy process (AHP) and fuzzy logic via a geographic information system (GIS). Environmental, economic, technical, social, and safety energy-optimization criteria are applied. A case study of Jamaica was utilized to explain the logic and efficiency of the model, where sites were determined by assigning weights to the criteria based on responses from a questionnaire with stakeholders and energy expert respondents. Information from published literature also determined criteria constraints for buffered distances and prohibited zones of ecological importance, land use areas, and hydrologic geomorphology. The fuzzy logic models of zero (unacceptable) and one (acceptable) are compared against the AHP model, where weights are applied according to relative importance. Finally, 16 suitable sites are identified based on their coordinates, specific available land, global horizontal irradiance (GHI), photovoltaic power potential (PVOUT), optimal tilt of the PV module (OPTM), temperature, elevation, and potential energy based on the silicon PV model. The suitability maps reveal that the total potential energy generation ranges from 19.96 to 109.15 GWh per year, with total land availability ranging from 666.90 to 2824.10 km2. Moreover, the amount of fossil fuel-based energy displaced is equivalent to 64,210 barrels of oil (BOE) or 1.94% per year, with 51,365.84 million tons of CO2e or a 1.71% reduction in total greenhouse emissions, saving the country US$3.28 million in social benefits from CO2 abated. The study's results can aid energy planners, potential investors, policymakers, and researchers identify suitable locations for utility-scale solar farms while minimizing environmental damage and optimizing land use to achieve sustainable renewable energy expansion for increased energy security.
A machine learning model that outperforms conventional global subseasonal forecast models
Authors: Li Hao , Chen Lei , Zhong Xiaohui , Wu Jie , Chen Deliang , Xie Shang-Ping , Chao Qingchen , Lin Chensen , Hu Zixin , Lu Bo , Qi Yuan 
Published: February 2024

Abstract

Skillful subseasonal forecasts beyond 2 weeks is critical to various sectors of society but pose a grand scientific challenge. Recently, machine learning based weather forecasting models have made remarkable advancements, outperforming the most successful numerical weather predictions (NWP) generated by the European Centre for Medium-Range Weather Forecasts (ECMWF). However, currently, no machine learning based subseasonal forecasting model surpasses conventional models. Here, we introduce FuXi Subseasonal-to-Seasonal (FuXi-S2S), a machine learning based subseasonal forecasting model that provides global daily mean forecasts for up to 42 days, covering 5 upper-air atmospheric variables at 13 pressure levels and 11 surface variables. FuXi-S2S integrates an enhanced FuXi base model with a perturbation module for flow-dependent perturbations in hidden features, which is a crucial for estimating uncertainty and enhancing subseasonal skill. The model is developed using 72 years of daily statistics from ECMWF ERA5 reanalysis data. When compared to the state-of-the-art ECMWF Subseasonal-to-Seasonal (S2S) reforecasts using a conventional model, the FuXi-S2S forecasts demonstrate superior deterministic and ensemble forecasts for total precipitation (TP), outgoing longwave radiation (OLR), and geopotential at 500 hPa (Z500). Moreover, it demonstrates comparable performance in predicting global 2-meter temperature (T2M), with clear advantages in land areas. Regarding forecasts for extreme TP, FuXi-S2S outperforms ECMWF S2S globally. The improved performance of FuXi-S2S is primarily due to its superior capability of predicting the Madden–Julian Oscillation (MJO), a key source of subseasonal predictability and a significant driver of weather patterns around the world. FuXi-S2S successfully extends the skillful MJO prediction from 30 days to 36 days.
Scalable synthesis of Cu(-Ag) oxide clusters via spark ablation for the highly selective electrochemical conversion of CO2 to acetaldehyde
Authors: Koolen Cedric , Pedersen Jack , Zijlstra Bernardus , Winzely Maximilian , Zhang Jie , Pfeiffer Tobias , Vrijburg Wilbert , Li Mo , Agarwal Ayush , Akbari Zohreh , Kuddusi Yasemen , Herranz Juan , Safonova Olga , Schmidt-Ott Andreas , Luo Wen , Züttel Andreas 
Published: February 2024

Abstract

The electrochemical conversion of CO2 into the base chemical acetaldehyde offers a sustainable and green alternative to the Wacker process. However, current electrocatalysts cannot effectively compete with heterogeneous processes due to their limited selectivity towards acetaldehyde, resulting in low energy efficiencies. Herein, we report a theory-guided synthesis of a series of Cu(-Ag) oxide cluster catalysts (~ 1.5 nm) immobilized on various hetero-atom doped carbonaceous supports, produced via spark ablation of Cu (and Ag) electrodes. These catalysts achieved an impressive acetaldehyde selectivity of up to 92% at only 600 mV from the equilibrium potential. Further, the catalyst exhibited exceptional catalytic stability during a rigorous 30-hour stress test involving 3 repeated start-stop cycles. In situ X-ray absorption spectroscopy showed that the initial oxide clusters were completely reduced under cathodic potential and maintained their metallic nature even after exposure to air, explaining the stable performance of the catalyst. First-principles simulations further elucidated the possible mechanism of CO2 conversion to acetaldehyde.
Sulfur/reduced graphite-oxide and dual-anion solid polymer-electrolyte integrated structure for high-loading practical all-solid-state lithium–sulfur battery
Authors: Kim Jae-Kwang , Jin En Mei , Han Jinseok , Kim Guk-Tae , Park Ganghwan , Baek Dong-Ho , Li Huan , Cui Meng Yang , Jin Bo , Jeong Sang Mun 
Published: February 2024

Abstract

Demand for high-capacity batteries with long cycle life and safety has been increasing owing to the expanding mid-to-large battery market. Li–S batteries are suitable energy-storage devices because of their reversibility, high theoretical capacity, and inexpensive construction materials. However, their performance is limited by various factors, including the shuttle effect and dendrite growth at the anode. Here, an integrated electrode for use in all-solid-state (ASS) Li–S batteries was formed via hot-pressing. In detail, S particles dispersed in a functionalized reduced graphite oxide (rGO) cathode with a binder-less polymer electrolyte (PE) and a dual-anion ionic liquid-containing cross-linked poly(ethylene oxide)–Li bis(fluoromethanesulfonyl)imide–N-butyl-N-methylpyrrolidinium bis(trifluoromethanesulfonyl) imide-based solid polymer electrolyte (SPE, PEO–LiFSI0.1(Pyr14TFSI)0.4) were hot-pressed into an integrated electrode, serving as both the cathode and electrolyte. The resultant S/rGO-based solid-state Li–S batteries exhibited more stable performance than Li–S batteries using liquid electrolytes, indicating that the dual-anion SPE layer effectively suppressed the dendritic L formation and the shuttle effect with high ionic conductivity. At 0.1 C, the battery discharge capacities were 957 and 576 mAh g− 1 in the first cycle and after 100 cycles, respectively. At 1 C, the reversible capacity was 590 and 417 mAh g− 1 in the first cycle and after 100 cycles, respectively (capacity retention = 71%). Therefore, the proposed S/rGO/PE//LiFSI0.1(Pyr14TFSI)0.4-integrated electrodes are beneficial for ASS Li–S batteries.
Patient care and access to clinical trials in gynaecological oncology: Global Implications of the Early Phase of the COVID-19 pandemic
Authors: Nasser Sara , Fotopoulou Christina , Gültekin Murat , Dimitrova Desislava , Bilir Esra , Inci Gülhan , Morice Philippe , Mirza Mansoor Raza , Martin Antonio Gonzalez , Berek Jonathan , Sehouli Jalid 
Published: February 2024

Abstract

Purpose:
Our prospective international survey evaluated the impact of the early phase of the COVID-19 pandemic on the management gynaecological malignancies from the multidisciplinary physicians' perspective with particular focus on clinical infrastructures, and trial participation.
Methods:
Our survey consisted of 53 COVID-related questions. It was sent to healthcare professionals in gynaecological oncology centres across Europe and Pan-Arabian region via the study groups and gynaecological societies from April 2020 to October 2020. All healthcare professionals treating gynaecological cancers were able to participate our survey.
Results:
A total of 255 answers were collected from 30 countries. The majority (73%) of participants were gynaecological oncologists from university hospitals (71%) with at least an intensive care unit with cardiopulmonary-support available at their institutions. Most institutions continued to perform elective surgeries only for oncological cases (98%). Patients had to wait on average two weeks longer for their surgery appointments compared to previous years (range 0-12 weeks). Most cases that were prioritised for surgical intervention across all gynaecological tumours were early-stage disease (74%), primary situation (61%), and good ECOG status (63%). The radicality of surgery did not change in the majority of cases (78%) across all tumour types. During the pandemic, only 38% of clinicians stated they would start a new clinical trial. Almost half of the participants stated the pandemic negatively impacted the financial structure and support for clinical trials. Approximately 20% of clinicians didn't feel well informed regarding clinical algorithm for COVID-19 patients throughout the pandemic. Thirty percent stated that they are currently having trouble in providing adequate medical care due to staff shortage.
Conclusions:
Despite well-established guidelines, pandemic clearly affected clinical research and patientcare. Our survey underlines the necessity for building robust emergency algorithms tailored to gynaecological oncology to minimize negative impact in crises and to preserve access to clinical trials.
Large scale multi-objective optimization algorithm with multiple strategies
Authors: Pei Qianru , Zou Feng , Chen Debao 
Published: February 2024

Abstract
When dealing with large-scale multi-objective optimization problems (LSMOP), enhancing the dimensionality of decision variables tends to render the MOEA/D algorithm poor scalability in decision space and more susceptible to converging toward local optima. In response to this issue, this article proposed an improved large-scale MOEA/D algorithm with multiple strategies (MSMOEA/D). In the MSMOEA/D algorithm, a hybrid initialization strategy based on automatic encoder was introduced into multi-objective optimization to provide a better initial population. Moreover, a neighborhood adjustment strategy based on the aggregation function value was proposed, which dynamically adjusted the neighborhood in accordance with the evolutionary progression of the current population and the change degree of the aggregation function value, and thus can obtained better search capabilities. Furthermore, a mutation selection strategy based on non-dominated sorting is adopted within the optimization process. Different subproblems select mutation strategies according to the number of individuals located at the first level of non-dominated sorting to avoid the population falling into local optima and enhance the overall performance of the algorithm. Finally, both the MSMOEA/D algorithm and other existing algorithms are evaluated using LSMOP and DTLZ test problems. The experimental outcomes substantiate the effectiveness of the improved algorithm in solving LSMOPs.
Use of digital food records in clinical settings - lessons in translation from a randomised controlled trial
Authors: Barnett Amandine , Mayr Hannah L , Keating Shelley E , Conley Marguerite M , Webb Lindsey , Jegatheesan Dev K , Staudacher Heidi , Macdonald Graeme A , Kelly Jaimon , Campbell Katrina L , Hickman Ingrid J 
Published: February 2024

Abstract

Background
Digital food records provide an efficient way to collect and assess dietary information remotely; however, there is limited research into factors which may impact their translation into clinical settings.
Methods
The study aimed to examine factors that may impact integration of a digital food record (mobile food diary app, electronically linked to a nutrition analysis software program) into clinical dietetic practice. This was achieved by assessing 1) the source and rate of data errors received (participant data entry or software) 2) the impact of dietitian-adjusted data on dietary variables; and 3) acceptance of use in a complex chronic disease cohort. Study participants were adults from specialist liver and kidney disease clinics who were enrolled in a randomised controlled feasibility trial. Participants recorded their dietary intake for three days using a mobile food diary app (Research Food diary, Xyris Software Pty Ltd) and this was analysed via desktop electronic nutrition analysis software (FoodWorks, Xyris Software Pty Ltd). Each participant’s digital food record was verified by a dietitian and errors were corrected. Dietary variables from digital food records before (participant-unadjusted data) and after (dietitian-adjusted data) were compared by non-parametric Wilcoxon signed ranks test, Bland Altman and Passing Balok analysis. Participant acceptance of using the mobile food diary app was assessed via online surveys and interviews.
Results
Errors appeared in 93% of the records. Dietitian-adjusted median data were higher for all macronutrients, five core food groups and other variables including energy and fibre intake than participant-unadjusted data (p < 0.05, median changes between 10–65%) of 59 participant records(median age 51 years, interquartile range 38–58). There was poor agreement between participant-unadjusted and dietitian-adjusted data for dietary variables including saturated fat, vegetables and added sugar intake. Sixty-four percent of participants who completed the survey (n = 32/50) found the app easy to use, while interviews with 29 participants provided insights into several technical challenges experienced.
Conclusion
Significant barriers to integrating digital food records into clinical practice exist related to errors that require dietitian adjustment to improve data quality for clinical decision making. Overall, there was variable acceptance towards the mobile food diary app.
Trial registration:
Australia and New Zealand Trials Registry ACTRN12620001282976. https://www.anzctr.org.au/Trial/Registration/TrialReview.aspx?id=378337