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Nucleic acid amplification testing has great potential for point-of-need diagnostic testing with high detection sensitivity and specificity. Current sample preparation is limited by a tedious workflow requiring multiple steps, reagents and instrumentation, hampering nucleic acid testing at point of need. In this study, we present the use of mixed cellulose ester (MCE) paper for DNA binding by ionic interaction under molecular crowding conditions and fluid transport by wicking. The poly(ethylene) glycol-based (PEG) reagent simultaneously provides the high pH for alkaline lysis and crowding effects for ionic binding of the DNA under high salt conditions. In this study, we introduce Paper-based Abridged Solid-Phase Extraction with Alkaline Poly(ethylene) Glycol Lysis (PASAP). The anionic mixed cellulose ester (MCE) paper is used as solid phase and allows for fluid transport by wicking, eliminating the need for pipetting skills and the use of a magnet to retain beads. Following the release of DNA from the cells due to the lytic activity of the PASAP solution, the DNA binds to the anionic surface of the MCE paper, concentrating at the bottom while the sample matrix is transported towards the top by wicking. The paper was washed by dipping it in 40% isopropanol for 10 s. After air-drying for 30 s, the bottom section of the paper (3 mm × 4 mm) was snapped off using the cap of a PCR tube and immersed in the colourimetric loop-mediated isothermal amplification (cLAMP) solution for direct amplification and colourimetric detection. The total sample processing was completed in 15 min and ready for amplification. cLAMP enabled the detection of 10 2 CFU/mL of Escherichia coli (E. coli) from culture media and the detection of E. coli in milk < 10 3  CFU/mL (10 CFU) after incubation at 68 °C for 60 min, demonstrating applicability of the method to complex biological samples., (© 2024. The Author(s).)

The pulp and paper industry generates vast quantities of paper sludge, posing significant environmental challenges due to its disposal in landfills or incineration. This study explores the potential of valorizing paper sludge by incorporating it into particleboard production. It aims to optimize sludge content and particle size to enhance board properties-a novel approach to waste management in the wood composites industry. Through systematic variation of sludge content (0-25%) and particle size (< 0.5 to > 2 mm), we assessed the mechanical and physical properties such as internal bond strength (IB), modulus of rupture (MOR), modulus of elasticity (MOE), water absorption (WA), and thickness swelling (TS). The findings indicate that incorporating paper sludge at moderate levels (5-15%) with optimized particle sizes (< 1 mm) significantly improves the mechanical properties of the particleboard, including increased IB, MOR, and MOE while reducing WA and TS. Principal Component Analysis (PCA) further supported these results, revealing that higher-density boards with enhanced mechanical properties absorb less water, highlighting the interrelationship between structural integrity and moisture resistance. The PCA also identified thickness swelling as an independent factor, suggesting that while mechanical properties can be optimized, additional strategies are needed to control swelling. In conclusion, this study demonstrates that up to 15% paper sludge can be effectively used in particleboard production without compromising quality, provided particle size is carefully controlled. This approach not only offers a sustainable solution for managing paper sludge but also contributes to the development of eco-friendly composite materials, aligning with circular economy principles., (© 2024. The Author(s).)

The paper industry is a major environmental polluter due to paper waste sludge (PWS), often disposed of in hazardous ways. The techniques are employed to disposing of PWS are posing significant environmental hazards and risks to well-being. This study aims to evaluate PWS as a potential replacement for commercial adsorbents like AC and ZEO in treating stabilized leachate. Contact angle analysis of PWS was 92.60°, reveals that PWS to be hydrophobic. Batch adsorption experiments were conducted with parameters set at 200 rpm stirring speed, 120 min contact time, and pH 7. Optimal conditions for COD and NH 3 -N removal were identified at 120 min contact time, 200 rpm stirring speed, pH 7, and 2.0 g PWS ratio. Removal percentages for COD and NH 3 -N were 62% and 52%, respectively. Based on the results of the isotherm and kinetic studies, it was observed that the Langmuir and Pseudo second order (PSO) model exhibited greater suitability compared to the Freundlich and Pseudo first order (PFO) model, as indicated by higher values of R-squared (R 2 ). The R-squared of Langmuir for COD and NH 3 -N were 0.9949 and 0.9919 and for Freundlich model were 0.9855 and 0.9828 respectively. Whereas the R-squared of PFO for COD and NH 3 -N were 0.9875 and 0.8883 and for PSO were 0.9987 and 0.9909 respectively., (© 2024. The Author(s).)

Microfluidic paper-based analytical devices often are combined with scanners as detectors. In this work, different scanning options offered by scanners: resolution, scanning mode, exposure to radiation, colour restoration, and saving format were tested. Moreover, different attempts to mathematical data treatment based on intensities of three channels-Red, Green and Blue, were studied. All measurements presented in this article were conducted for a model dye-bromothymol blue and a model analyte-zinc(II) ion (complexed with xylenol orange in a paper matrix). The article summarizes the scanning options and possibilities of mathematical calculations. Nevertheless, it is suggested that the best option is to use the prior prepared calculation file to paste obtained intensities and compare all presented in this article (and the most frequently used) equations to process intensities and decide which one should be used in the particular analysis., (© 2024. The Author(s).)

Anger suppression is important in our daily life, as its failure can sometimes lead to the breaking down of relationships in families. Thus, effective strategies to suppress or neutralise anger have been examined. This study shows that physical disposal of a piece of paper containing one's written thoughts on the cause of a provocative event neutralises anger, while holding the paper did not. In this study, participants wrote brief opinions about social problems and received a handwritten, insulting comment consisting of low evaluations about their composition from a confederate. Then, the participants wrote the cause and their thoughts about the provocative event. Half of the participants (disposal group) disposed of the paper in the trash can (Experiment 1) or in the shredder (Experiment 2), while the other half (retention group) kept it in a file on the desk. All the participants showed an increased subjective rating of anger after receiving the insulting feedback. However, the subjective anger for the disposal group decreased as low as the baseline period, while that of the retention group was still higher than that in the baseline period in both experiments. We propose this method as a powerful and simple way to eliminate anger., (© 2024. The Author(s).)

Oil-immersed transformers are expensive equipment in the electrical system, and their failure would lead to widespread blackouts and catastrophic economic losses. In this work, an elaborate diagnostic approach is proposed to evaluate twenty-six different transformers in-service to determine their operative status as per the IEC 60599:2022 standard and CIGRE brochure. The approach integrates dissolved gas analysis (DGA), transformer oil integrity analysis, visual inspections, and two Back Propagation Neural Network (BPNN) algorithms to predict the loss of life (LOL) of the transformers through condition monitoring of the cellulose paper. The first BPNN algorithm proposed is based on forecasting the degree of polymerization (DP) using 2-Furaldehyde (2FAL) concentration measured from oil samples using DGA, and the second BPNN algorithm proposed is based on forecasting transformer LOL using the 2FAL and DP data obtained from the first BPNN algorithm. The first algorithm produced a correlation coefficient of 0.970 when the DP was predicted using the 2FAL measured in oil and the second algorithm produced a correlation coefficient of 0.999 when the LOL was predicted using the 2FAL and DP output data obtained from the first algorithm. The results show that the BPNN can be utilized to forecast the DP and LOL of transformers in-service. Lastly, the results are used for hazard analysis and lifespan prediction based on the health index (HI) for each transformer to predict the expected years of service., (© 2024. The Author(s).)

This article presents the results of an experiment designed to study the impact of temperature on the characteristic parameters of creeping discharges. The insulating interfaces consist of a thermally enhanced cellulose surface immersed in mineral oil, palm kernel oil methyl ester (PKOME) and castor oil methyl ester (COME). The study was carried out under a standard negative lightning impulse voltage (1.2/50 μs). The article also presents the complete algorithms for calculating the maximum extension of the discharges, the ionisation rate and the charge produced by them. The results of the study show that temperature favors the propagation of discharges and the ionisation rate. It was observed that liquids with a higher dielectric constant and high electrical conductivity were more exposed to the impact of temperature. The results show ionisation increments of 0.973%/°C, 1.093%/°C and 1.076%/°C in mineral oil (MO), COME and PKOME respectively. The maximum extension of the discharges shows a linear evolution with the applied voltage and temperature but a non-linear increment with the temperature. As for the charge produced, it shows a constant increment with temperature and voltage in each liquid. These values are (5.839%/°C, 1.977%/kV), (6.047%/°C, 2.082%/kV) and (6.177%/°C, 2.113%/kV) respectively in MO, COME and PKOME., (© 2024. The Author(s).)

As the social organism, Polistes wasps build a communal nest using woody fibers with saliva for sustaining brood and adult population throughout the season. Limited information exists regarding the identification specific plant materials employed in wasp nest building. Thus, we firstly tested if the DNA metabarcoding approach utilizing rbcL and trnL molecular markers could identify the plant species quantitatively and qualitatively inform the mixed-origin woody samples. A threshold of 0.01 proportion of reads was applied for rbcL and trnL molecular markers, while this threshold for median proportion was 0.0025. In assessing taxa richness, the median proportion demonstrated superior performance, exhibiting higher taxa detection power, however, rbcL marker outperformed in quantitative analysis. Subsequently, we applied DNA metabarcoding to identify the plant materials from the nests of two Polistes species, P. mandarinus and P. rothneyi. The results showed that higher preference of Quercus and Robinia as the major nest building materials regardless of the surrounding plant communities, by two wasp species. Material diversity was higher for P. rothneyi than P. mandarinus, which may explain the abundance of this species possibly with heightened adaptive capacities in their nesting behavior. This study demonstrated that DNA metabarcoding could identify the complex nest-building plant materials of paper wasps and provide insights into their ecological interactions in the natural ecosystem., (© 2024. The Author(s).)

Energy storage devices (ESD) which are intended to power electronic devices, used in close contact of human skin, are desirable to be safe and non-toxic. In light of this requirement, Zn based energy storage devices seem to provide a viable pathway as they mostly employ aqueous based electrolytes which are safe and non-toxic in their functioning. Additionally, having a flexible ESD will play a crucial role as it will enable the ESD to conform to the varying shapes and sizes of wearable electronics which they energize. In this work, we have developed an inkjet-printed Zinc ion battery (IPZIB) with planar electrode configuration over bond paper substrate. Zn has been used as the negative electrode, MnO 2 is used as the positive electrode with Poly(3,4-ethylenedioxythiophene) polystyrene sulfonate (PEDOT:PSS) as the active binder. Conducting tracks of reduced graphene oxide (rGO) are used to construct the current collector on the paper substrate. The fabricated IPZIB delivered a high discharge capacity of 300.14 mAh g -1 at a current density of 200 mA g -1 . The energy density of the IPZIB is observed as 330.15 Wh kg -1 at a power density of 220 W kg -1 and retains an energy density of 94.36 Wh kg -1 at a high power density of 1650 W kg -1 . Finally, we have demonstrated the capability of the IPZIB to power a LED at various bending and folding conditions which indicates its potential to be used in the next generation flexible and wearable electronic devices., (© 2024. The Author(s).)

With the application of digital technology and its promotion of business model innovation, digital transformation has increasingly become an important strategic issue for enterprises. In this context, based on imprinting theory, we select all A-share listed enterprises in China from 2008 to 2022 as samples and study the relationship between the academic background of senior executives and the digital transformation of enterprises. The study results show that senior executives with academic backgrounds can significantly promote the digital transformation of enterprises. A test of the action mechanism shows that the academic background of senior executives plays a role in the promotion of the digital transformation of enterprises by improving enterprise innovation, and the degree of industry competition moderates the relationship between the academic background of senior executives and the digital transformation of enterprises. This paper applies imprint theory to explore the relationship between the academic background of executives and corporate digital transformation, expanding the research on how imprints affect corporate decision-making and the scope of imprint theory research, while also providing evidence to support government departments in formulating policies to encourage talented individuals with academic backgrounds to participate in corporate management., (© 2024. The Author(s).)

Given a large enough volume of data and precise, meaningful categories, training a statistical model to solve a classification problem is straightforward and has become a standard application of machine learning (ML). If the categories are not precise, but rather fuzzy, as in the case of scientific disciplines, the systematic failures of ML classification can be informative about properties of the underlying categories. Here we classify a large volume of academic publications using only the abstract as information. From the publications that are classified differently by journal categories and ML categories (i.e., misclassified publications, when using the journal assignment as ground truth) we construct a network among disciplines. Analysis of these misclassifications provides insight in two topics at the core of the science of science: (1) Mapping out the interplay of disciplines. We show that this misclassification network is informative about the interplay of academic disciplines and it is similar to, but distinct from, a citation-based map of science, where nodes are scientific disciplines and an edge indicates a strong co-citation count between publications in these disciplines. (2) Analyzing the success of interdisciplinarity. By evaluating the citation patterns of publications, we show that misclassification can be linked to interdisciplinarity and, furthermore, that misclassified articles have different citation frequencies than correctly classified articles: In the highest 10 percent of journals in each discipline, these misclassified articles are on average cited more frequently, while in the rest of the journals they are cited less frequently., (© 2024. The Author(s).)

Human-wildlife interactions are situated within dynamic systems, characterized by social and ecological complexity. Human-wildlife coexistence research, however, typically focuses on one component of these systems in isolation. We inadvertently followed this norm while carrying out semi-structured interviews of livestock-owners in Northern Tanzania. As existing literature highlighted that this area was a hotspot for livestock depredation, our research questions focused on human interactions with carnivores. Interestingly, almost three quarters (72%, n = 72 of 100) of study participants independently raised African elephants (Loxodonta africana) as presenting the greatest impediments to coexistence. By centering our interviews on carnivores, we omitted vital components of this complex system. To counteract the effects of this oversimplification, we changed our intended analytical process after data collection. Instead of conducting a quantitative analysis of rates of livestock depredation and perceptions of risk posed by a suite of sympatric carnivores, we applied a grounded theory approach to assess interactions across multiple dimensions of this complex system. Through this transparent effort to realign our approaches with the complexity of the study system, we highlight the importance of designing research approaches that effectively reflect the complexities inherent to human-wildlife coexistence., (© 2024. The Author(s).)

This study investigated the influence of social media on patients regarding aesthetic dental treatments according to sex, type of treatment, and time since their last treatment. A cross-sectional study was conducted using a web-based survey. Participants were students over 18 years of age who were social media users and had undergone aesthetic dental treatment. 504 responses were included. Data evaluation was made using Pearson chi-square test. The level of significance was set at p < .05. 29.37% of the respondents indicated that social media influenced their decision to have their aesthetic treatment. Regarding the influence of social media, no differences were observed between males and females, nor by type of aesthetic treatment. Participants who had undergone an aesthetic dental treatment within the last year were more influenced by social media than those who had undergone it more than two years ago. Posts from dental clinics or dentists were the type of content that most influenced participants. Social media can influence some patients' decision to undergo aesthetic dental treatment, thus dental practices and dentists should consider the increasing importance of social media in their daily practice., (© 2024. The Author(s).)

The ubiquitous presence of electronic devices demands robust hardware security mechanisms to safeguard sensitive information from threats. This paper presents a physical unclonable function (PUF) circuit based on magnetoresistive random access memory (MRAM). The circuit utilizes inherent characteristics arising from fabrication variations, specifically magnetic tunnel junction (MTJ) cell resistance, to produce corresponding outputs for applied challenges. In contrast to Arbiter PUF, the proposed effectively satisfies the strict avalanche criterion (SAC). Additionally, the grid-like structure of the proposed circuit preserves its resistance against machine learning-based modeling attacks. Various machine learning (ML) attacks employing multilayer perceptron (MLP), linear regression (LR), and support vector machine (SVM) networks are simulated for two-array and four-array architectures. The MLP-attack prediction accuracy was 53.61% for a two-array circuit and 49.87% for a four-array circuit, showcasing robust performance even under the worst-case process variations. In addition, deep learning-based modeling attacks in considerable high dimensions utilizing multiple networks such as convolutional neural network (CNN), recurrent neural network (RNN), MLP, and Larq are used with the accuracy of 50.31%, 50.25%, 50.31%, and 50.31%, respectively. The efficiency of the proposed circuit at the layout level is also investigated for simplified two-array architecture. The simulation results indicate that the proposed circuit offers intra and inter-hamming distance (HD) with a mean of 0.98% and 49.96%, respectively, and a mean diffuseness of 49.09%., (© 2024. The Author(s).)

With the increasing demand for heat dissipation of engineering vehicles, a dual-cycle cooling system is introduced in this paper to prevent the adverse effects of engineering vehicles' equipment when operating at the overheating temperature. The performance of the new system is analyzed through tests, and the results show that the dual-cycle cooling system can meet the thermal balance requirements of the engineering vehicle during the shovel operation. Compared with the traditional cooling system, the new cooling system improved performance in terms of volume, engine energy consumption and working oil efficiency. The oil consumption of a wheel loader using the dual-cycle cooling system is reduced by 1% per hour, and the temperature of its transmission oil and hydraulic oil is reduced by more than 10 °C. The new cooling system has bright future in energy saving and emission reduction of engineering vehicles., (© 2024. The Author(s).)

Chronic kidney disease (CKD) is a widespread condition with considerable health and economic impacts globally. However, existing methodologies for serum creatinine assessment often involve prolonged wait times and sophisticated equipment, such as spectrometers, hindering real-time diagnosis and care. Innovative solutions like point-of-care (POC) devices are emerging to address these challenges. In this context, there is a recognized need for remote, regular, automated, and low-cost analysis of serum creatinine levels, given its role as a critical parameter for CKD diagnosis and management. This study introduces a miniaturized system with integrated heater elements designed for precise serum creatinine measurement. The system operates based on the Jaffe method and accurate serum creatinine measurement within a microreservoir chip. Smartphone-based image processing using the hue-saturation-value (HSV) color space was applied to captured images of microreservoirs. The creatinine analyses were conducted in serum with a limit of detection of ~ 0.4 mg/dL and limit of quantification of ~ 1.3 mg/dL. Smartphone-based image processing employing the HSV color space outperformed spectrometric analysis for creatinine measurement conducted in serum. This pioneering technology and smartphone-based processing offer the potential for decentralized renal function testing, which could significantly contribute to improved patient care. The miniaturized system offers a low-cost alternative ($87 per device), potentially reducing healthcare expenditures (~ $0.5 per test) associated with CKD diagnosis and management. This innovation could greatly improve access to diagnosis and monitoring of CKD, especially in regions where access to sophisticated laboratory equipment is limited., (© 2024. The Author(s).)

Field observations frequently demonstrate stress fluctuations resulting from the reservoir depletion. The development of reservoirs, particularly the completion of infill wells and refracturing, can be significantly impacted by stress changes in and around drainage areas. Previous studies mainly focus on plane fractures and few studies consider the influence of complex transport and storage mechanism and irregular fracture geometry on stress evolution in shale gas reservoirs. Based on the embedded discrete fracture model (EDFM) and finite-volume method (FVM), a coupled geomechanics/fluid model has been successfully developed considering the adsorption, desorption, diffusion and slippage of shale gas. This model achieves coupling simulation of natural fractures, hydraulic fractures with complex geometry, storage and transport mechanism, reservoir stress, and pore-elastic effect. The open-source software OpenFOAM is used as the main solver for this model. The stress calculation and productivity simulation of the model are verified by the classical poroelasticity problem and the simulation results of published research and commercial simulator with EDFM respectively. The simulation results indicate that σ xx , σ yy , σ xy and Δσ changes with time and space due to the time effect and anisotropy of formation pressure depletion; Due to the influence of different mechanisms on shale gas storage and transport, the reservoir pressure and stress distribution under different mechanisms are different; Among them, the stress with full mechanisms differs the most compared to the stress without any mechanism. The reservoir with stronger stress sensitivity (smaller Biot coefficient) is less sensitive to formation pressure depletion, and the stress variation range is smaller. For reservoirs with weak stress sensitivity, formation pressure depletion is more likely to lead to stress reversal. Under the influence of fracture geometry, the pressure depletion regions caused by the three types of fracture geometry are approximately rectangular, parallelogram and square, respectively. The corresponding σ xx , σ yy and Δσ also have great differences in spatial distribution and values. Therefore, the time effect, shale gas storage and transport mechanism and the influence of complex fracture geometry should be considered when predicting pressure depletion induced stress under the condition of simultaneous production. This study is of great significance for understanding the evolution law of stress induced by pressure consumption, as well as the design of infill wells and repeated fracturing., (© 2024. The Author(s).)

The main chemical compositions of 201 surface sediments and 53 deep sediment samples from Chaohu Lake, China, were analysed. Since the surface sediments (0-2 cm depth) in Chaohu Lake are modern sediments, this paper mainly focuses on the deep sediments (50-100 cm depth) in Chaohu Lake. Particle size analysis and magnetization determination of the CH3 and CH4 column sediment samples were carried out. The age determination data of the CH-1 column sediment samples are reported. A systematic study of the rocks and their chemical compositional characteristics in the Chaohu Lake Basin was also carried out. The results of this study show that four positive chemical weathering indicators and one negative chemical weathering indicator are applicable to the study of Chaohu Lake. The mean CIA of the Chaohu Lake sediments was less than 65, indicating that the Chaohu Lake Basin experienced weak chemical weathering and that the palaeoclimate was cold and dry. Vertical variations in the mean grain size and magnetization in the CH3 and CH4 columnar sediments reflect changes in the depositional environment and climate during deposition of the Chaohu Lake sediments. The age data from the CH-1 column sediment samples directly indicate deposition of the deep sediments in Chaohu Lake during the Little Ice Age in eastern China (AD 1380-1880). The Th/U, Sc/Th, Rb/Sr, Na 2 O/K 2 O, CaO/MgO and OC/N ratios of the Chaohu sediments reflect palaeoclimate characteristics and the chemical compositions of the source rocks in the Chaohu Lake basin. The correlations of the CIA, CIW, PIA, and CIX with the chemical compositional ratios provide information on the palaeoclimate and the distribution of the chemical compositions. The CIA, CIW, PIA, and CIX were not correlated with Cd, Pb, As, Hg, or P. In contrast, the CIA, CIW, PIA, and CIX were significantly positively correlated with Cr and N. The WIP was inconsistently correlated with the selected chemical components. Therefore, the study of the correlations of chemical weathering indicators with four heavy metals and two eutrophication-related elements is of little significance. The study of the chemical weathering characteristics of deep sediments of inland lakes should be combined with assessment of the geological characteristics of the lake basins, particularly the analysis of the chemical composition of the rocks in the lake basins., (© 2024. The Author(s).)

Multiple-choice test generation is one of the most complex NLP problems, especially in languages other than English, where there is a lack of prior research. After a review of the literature, it has been verified that some methods like the usage of rule-based systems or primitive neural networks have led to the application of a recent architecture, the Transformer architecture, in the tasks of Answer Extraction (AE) and Question Generation (QG). Thereby, this study is centred in searching and developing better models for the AE and QG tasks in Spanish, using an answer-aware methodology. For this purpose, three multilingual models (mT5-base, mT0-base and BLOOMZ-560 M) have been fine-tuned using three different datasets: a translation to Spanish of the SQuAD dataset; SQAC, which is a dataset in Spanish; and their union (SQuAD + SQAC), which shows slightly better results. Regarding the models, the performance of mT5-base has been compared with that found in two newer models, mT0-base and BLOOMZ-560 M. These models were fine-tuned for multiple tasks in literature, including AE and QG, but, in general, the best results are obtained from the mT5 models trained in our study with the SQuAD + SQAC dataset. Nonetheless, some other good results are obtained from mT5 models trained only with the SQAC dataset. For their evaluation, the widely used BLEU1-4, METEOR and ROUGE-L metrics have been obtained, where mT5 outperforms some similar research works. Besides, CIDEr, SARI, GLEU, WER and the cosine similarity metrics have been calculated to present a benchmark within the AE and QG problems for future work., (© 2024. The Author(s).)

The concept of volume fracturing has revolutionized the conventional limits of low permeability, expanded the effective resource space, and significantly enhanced oil well production in tight oil reservoir development. This paper elucidates the mechanism of volume fracturing technology for tight sandstone reservoirs by considering multiple factors such as the initiation range of multi-fractures, influence of far-well horizontal principal stress on fracture initiation and propagation, degree of natural fractures development, and mechanical parameters of reservoir rock. Through simulation based on the mechanical parameters of reservoir rock, a comparative analysis was conducted between the model-calculated rock fracture pressure value and measured data from fracturing construction wells in the study area. The results revealed that there was a discrepancy within 10% between the model calculations and actual data. By simulating the effects of different injection volumes of fracturing fluid, pumping rates, and perforation methods on the fracture geometry, optimal design parameters for volume fracturing technology were obtained. Additionally, we propose optimization ideas and suggestions for construction parameters applicable to field operations. The simulation results indicate that a minimum recommended fluid volume scale exceeding 1800 m 3 is advised for the reservoir. Based on frictional calculations, it is recommended to have an on-site construction rate not less than 18.0 m 3 /min along with 36-48 holes/section for perforation purposes. The numerical simulation research presented in this paper provides a theoretical reference basis and practical guidance for the application of fracturing network technology in tight sandstone reservoirs., (© 2024. The Author(s).)

Aspect-Based Sentiment Analysis (ABSA) represents a fine-grained approach to sentiment analysis, aiming to pinpoint and evaluate sentiments associated with specific aspects within a text. ABSA encompasses a set of sub-tasks that together facilitate a detailed understanding of the multifaceted sentiment expressions. These tasks include aspect and opinion terms extraction (ATE and OTE), classification of sentiment at the aspect level (ALSC), the coupling of aspect and opinion terms extraction (AOE and AOPE), and the challenging integration of these elements into sentiment triplets (ASTE). Our research introduces a comprehensive framework capable of addressing the entire gamut of ABSA sub-tasks. This framework leverages the contextual strengths of BERT for nuanced language comprehension and employs a biaffine attention mechanism for the precise delineation of word relationships. To address the relational complexity inherent in ABSA, we incorporate a Multi-Layered Enhanced Graph Convolutional Network (MLEGCN) that utilizes advanced linguistic features to refine the model's interpretive capabilities. We also introduce a systematic refinement approach within MLEGCN to enhance word-pair representations, which leverages the implicit outcomes of aspect and opinion extractions to ascertain the compatibility of word pairs. We conduct extensive experiments on benchmark datasets, where our model significantly outperforms existing approaches. Our contributions establish a new paradigm for sentiment analysis, offering a robust tool for the nuanced extraction of sentiment information across diverse text corpora. This work is anticipated to have significant implications for the advancement of sentiment analysis technology, providing deeper insights into consumer preferences and opinions for a wide range of applications., (© 2024. The Author(s).)

To achieve high-efficiency combustion of heavy fuel oil (HFO), this study investigated the combustion characteristics of methanol/HFO droplets with methanol content from 10 to 30% using the suspension method under ambient temperature from 923 to 1023 K. The combustion of methanol/HFO droplets was summarized as a two-phase process consisting of six typical stages, emphasizing liquid phase. Especially, the fluctuation evaporation stage, induced by frequent and intense puffing, was identified as prominent character. Both the ignition delay and lifetime of HFO and methanol/HFO droplets decreased with increasing ambient temperatures. For the methanol/HFO droplet, the ignition delay and droplet lifetime increased with the increasing methanol content. Prominently, compared to HFO, HM10 had the most significant reduction in droplet lifetime and TINL under the same operating conditions, which indicated that the addition of 10% methanol accelerated the combustion process and reduced soot generation. Additionally, the thermos-dynamic characteristics of methanol/HFO droplets were investigated. Puffing was primarily attributed to superheating of methanol and pyrolysis of heavy components in HFO, which resulted in active and passive rupture of bubbles. Similarity and maximum deformation were employed to qualitatively distinguish between them. The obtained findings aimed to develop a promising alternative fuel to reduce emissions and preserve energy., (© 2024. The Author(s).)

Identification of an early biomarker and effective testing device to differentiate dry eye disease secondary to autoimmune disease (Sjögren's syndrome dry eye disease) from non-Sjögren's dry eye disease are prerequisites for appropriate treatment. We aimed to demonstrate the capacity of a new photo-detection device to evaluate tear lactoferrin levels as a tool for differentiating systemic conditions associated with dry eye disease. Patients with non-Sjögren's and Sjögren's syndrome dry eye disease (n = 54 and n = 52, respectively) and controls (n = 11) were enrolled. All participants completed the Ocular Surface Disease Index questionnaire. Tear collection was performed with Schirmer test, and tear break-up time was examined using a slit lamp. Tear lactoferrin was evaluated using our newly developed photo-detection device. The average lactoferrin concentration was significantly lower in samples from patients with non-Sjögren's dry eye disease (0.337 ± 0.227 mg/mL, n = 54) and Sjögren's syndrome dry eye disease (0.087 ± 0.010 mg/mL, n = 52) than in control samples (1.272 ± 0.54 mg/mL, n = 11) (p < 0.0001). Further, lactoferrin levels were lower in patients with Sjögren's syndrome dry eye disease than in those with non-Sjögren's dry eye disease (p < 0.001). Our cost-effective, antibody-free, highly sensitive photo-detection device for evaluating tear lactoferrin levels can assist ophthalmologists in differentiating different types of dry eye diseases., (© 2024. The Author(s).)

Crowd flow prediction has been studied for a variety of purposes, ranging from the private sector such as location selection of stores according to the characteristics of commercial districts and customer-tailored marketing to the public sector for social infrastructure design such as transportation networks. Its importance is even greater in light of the spread of contagious diseases such as COVID-19. In many cases, crowd flow can be divided into subgroups by common characteristics such as gender, age, location type, etc. If we use such hierarchical structure of the data effectively, we can improve prediction accuracy of crowd flow for subgroups. But the existing prediction models do not consider such hierarchical structure of the data. In this study, we propose a deep learning model based on global-local structure of the crowd flow data, which utilizes the overall(global) and subdivided by the types of sites(local) crowd flow data simultaneously to predict the crowd flow of each subgroup. The experiment result shows that the proposed model improves the prediction accuracy of each sub-divided subgroup by 5.2% (Table 5 Cat #9)-45.95% (Table 11 Cat #5), depending on the data set. This result comes from the comparison with the related works under the same condition that use target category data to predict each subgroup. In addition, when we refine the global data composition by considering the correlation between subgroups and excluding low correlated subgroups, the prediction accuracy is further improved by 5.6-48.65%., (© 2024. The Author(s).)

In the era of social media, the use of emojis and code-mixed language has become essential in online communication. However, selecting the appropriate emoji that matches a particular sentiment or emotion in the code-mixed text can be difficult. This paper presents a novel task of predicting multiple emojis in English-Hindi code-mixed sentences and proposes a new dataset called SENTIMOJI, which extends the SemEval 2020 Task 9 SentiMix dataset. Our approach is based on exploiting the relationship between emotion, sentiment, and emojis to build an end-to-end framework. We replace the self-attention sublayers in the transformer encoder with simple linear transformations and use the RMS-layer norm instead of the normal layer norm. Moreover, we employ Gated Linear Unit and Fully Connected layers to predict emojis and identify the emotion and sentiment of a tweet. Our experimental results on the SENTIMOJI dataset demonstrate that the proposed multi-task framework outperforms the single-task framework. We also show that emojis are strongly linked to sentiment and emotion and that identifying sentiment and emotion can aid in accurately predicting the most suitable emoji. Our work contributes to the field of natural language processing and can help in the development of more effective tools for sentiment analysis and emotion recognition in code-mixed languages. The codes and data will be available at https://www.iitp.ac.in/~ai-nlp-ml/resources.html#SENTIMOJI to facilitate research., (© 2024. The Author(s).)

We propose a novel model-selection method for dynamic networks. Our approach involves training a classifier on a large body of synthetic network data. The data is generated by simulating nine state-of-the-art random graph models for dynamic networks, with parameter range chosen to ensure exponential growth of the network size in time. We design a conceptually novel type of dynamic features that count new links received by a group of vertices in a particular time interval. The proposed features are easy to compute, analytically tractable, and interpretable. Our approach achieves a near-perfect classification of synthetic networks, exceeding the state-of-the-art by a large margin. Applying our classification method to real-world citation networks gives credibility to the claims in the literature that models with preferential attachment, fitness and aging fit real-world citation networks best, although sometimes, the predicted model does not involve vertex fitness., (© 2024. The Author(s).)

Healthcare fraud, waste and abuse are costly problems that have huge impact on society. Traditional approaches to identify non-compliant claims rely on auditing strategies requiring trained professionals, or on machine learning methods requiring labelled data and possibly lacking interpretability. We present Clais, a collaborative artificial intelligence system for claims analysis. Clais automatically extracts human-interpretable rules from healthcare policy documents (0.72 F1-score), and it enables professionals to edit and validate the extracted rules through an intuitive user interface. Clais executes the rules on claim records to identify non-compliance: on this task Clais significantly outperforms two baseline machine learning models, and its median F1-score is 1.0 (IQR = 0.83 to 1.0) when executing the extracted rules, and 1.0 (IQR = 1.0 to 1.0) when executing the same rules after human curation. Professionals confirm through a user study the usefulness of Clais in making their workflow simpler and more effective., (© 2024. The Author(s).)

The present study introduces "rock slope instability score (RSIS)" a novel classification system for assessing rock slope stability. It takes into account geological and geotechnical parameters, as well as the impact of human activities and triggering parameters, which have become more frequent due to climate change and few of them have been ignored in existing classifications. The study focuses on rock slopes of various lithologies from the Indian Himalayas. The development of this new classification system is based on the examination of 81 different rock slopes from various states of the Indian Himalayas. Extensive field surveys, rock sampling, geotechnical laboratory tests, and ground measurements have been conducted at the various slope sites to establish a comprehensive scoring system for the stability assessment. The distributions of weightage to each parameter have been considered, corresponding to its degree of impact in causing slope instability. Sensitivity analysis of all defined parameters of RSIS system has revealed that the majority of the parameters exhibit a strong positive correlation, with Pearson correlation coefficients ranging from 0.74 to 0.61. However, two parameters, namely discontinuity dip and the relationship between slope & discontinuity direction, gives moderate relationship with correlation coefficient values of 0.48 and 0.41, respectively. To avoid any designer biasness in the system, several individuals gathered data set at different times. The proposed classification system has demonstrated a strong correlation with the actual slope condition, and it is quite promising. The outcome of RSIS classification for studied 81 slopes classified 2 slopes under stable condition, 21 slopes as partially stable, 44 as unstable, and 14 as completely unstable., (© 2024. The Author(s).)

The humidity has often been measured through a single point sensor. Where, the humidity could be varied at different locations as well as depending on environmental conditions. The present paper developed the dual point humidity measuring sensor by using a polymer optical fiber (POF) based on a single illuminating fiber. The sensor's basic structure is to twist two fibers and bend them at a certain radius. However, the dual point sensor is developed through the cascading of twisted micro bend (TMB-1 and TMB-2). The twisting of fibers couples the light from one fiber to another fiber through the side coupling method. An increase in the humidity level leads to a change in the reflective index, which helps to get variation in coupled light intensity. To measure the humidity, the dual point sensors are placed into the control humidity chamber at two random positions. The power reading variation is significantly linear when the humidity level increases from 30 to 80%. The sensor has a fast response of about 1 s and a recovery time of about 4 s. Furthermore, the chemical coating is applied to improve the sensor's sensitivity. Between 30 and 80% range of humidity, the both sensors of dual point TMB-1 and TMB-2 have appropriate sensitivity and detection limits, which is about 680.8 nW/% and 763.9 nW/% and 1.37% and 1.98%, respectively. To measure the humidity at variable positions, the present dual points humidity sensor is well-stable, easy, and straightforward, which uses a less expensive method., (© 2024. The Author(s).)

Molecular diagnostics involving nucleic acids (DNA and RNA) are regarded as extremely functional tools. During the 2020 global health crisis, efforts intensified to optimize the production and delivery of molecular diagnostic kits for detecting SARS-CoV-2. During this period, RT-LAMP emerged as a significant focus. However, the thermolability of the reagents used in this technique necessitates special low-temperature infrastructure for transport, storage, and conservation. These requirements limit distribution capacity and necessitate cost-increasing adaptations. Consequently, this report details the development of a lyophilization protocol for reagents in a colorimetric RT-LAMP diagnostic kit to detect SARS-CoV-2, facilitating room-temperature transport and storage. We conducted tests to identify the ideal excipients that maintain the molecular integrity of the reagents and ensure their stability during room-temperature storage and transport. The optimal condition identified involved adding 5% PEG 8000 and 75 mM trehalose to the RT-LAMP reaction, which enabled stability at room temperature for up to 28 days and yielded an analytical and diagnostic sensitivity and specificity of 83.33% and 90%, respectively, for detecting SARS-CoV-2. This study presents the results of a lyophilized colorimetric RT-LAMP COVID-19 detection assay with diagnostic sensitivity and specificity comparable to RT-qPCR, particularly in samples with high viral load., (© 2024. The Author(s).)

We present an advanced electrochemical immunosensor designed to detect the vascular endothelial growth factor (VEGF) precisely. The sensor is constructed on a modified porous gold electrode through a fabrication process involving the deposition of silver and gold on an FTO substrate. Employing thermal annealing and a de-alloying process, the silver is eliminated from the electrode, producing a reproducible porous gold substrate. Utilizing a well-defined protocol, we immobilize the heavy-chain (VHH) antibody against VEGF on the gold substrate, facilitating VEGF detection through various electrochemical methods. Remarkably, this immunosensor performs well, featuring an impressive detection limit of 0.05 pg/mL and an extensive linear range from 0.1 pg/mL to 0.1 µg/mL. This emphasizes it's to measure biomarkers across a wide concentration spectrum precisely. The robust fabrication methodology in this research underscores its potential for widespread application, offering enhanced precision, reproducibility, and remarkable detection capabilities for the developed immunosensor., (© 2024. The Author(s).)

Plasma ice shape regulation is a technology which uses plasma actuator to regulate the continuous ice into safer intermittent ice by its significant thermal effect with limited energy. Whether plasma ice shape regulation could reduce flight risk is a new problem under the wing with continuous ice. The 3D printed ice shapes were arranged on the leading edge of the wing based on NACA0012 airfoil, aiming to simulate the configuration after ice shape regulation. And the aerodynamic parameters were obtained by wind tunnel experiments. The experimental results showed that the ratio of signal regulation ice width d to chord length of the wing b A determined the aerodynamic characteristics, and the aerodynamic characteristics changed better compared with configuration of the continuous ice. However, the flight risk of the wing under given regulation ratio is unknown. Based on the straight and swept wing after regulating, the flight safety boundaries were simulated by the reachable set method. Further, a method of quantitative assessment of flight risk is proposed. Quantitative values of risk were calculated. The results show that the flight risk all decreases from level 2 to level 4 compared with configuration of the continuous ice when d / b A equals 0.15 under conditions of swept and straight wing., (© 2024. The Author(s).)

The verified text data of wheat varieties is an important component of wheat germplasm information. To automatically obtain a structured description of the phenotypic and genetic characteristics of wheat varieties, the aim at solve the issues of fuzzy entity boundaries and overlapping relationships in unstructured wheat variety approval data, WGIE-DCWF (joint extraction model of wheat germplasm information entity relationship based on deep character and word fusion) was proposed. The encoding layer of the model deeply fused word semantic information and character information using the Transformer encoder of BERT. This allowed for the cascading fusion of contextual semantic feature information to achieve rich character vector representation and improve the recognition ability of entity features. The triple extraction layer of the model established a cascading pointer network, extracted the head entity, extracted the tail entity according to the relationship category, and decoded the output triplet. This approach improved the model's capability to extract overlapping relationships. The experimental results demonstrated that the WGIE-DCWF model performed exceptionally well on both the WGD (wheat germplasm dataset) and the public dataset DuIE. The WGIE-DCWF model not only achieved high performance on the evaluation datasets but also demonstrated good generalization. This provided valuable technical support for the construction of a wheat germplasm information knowledge base and is of great significance for wheat breeding, genetic research, cultivation management, and agricultural production., (© 2024. The Author(s).)

In recent years, the transformer-based language models have achieved remarkable success in the field of extractive text summarization. However, there are still some limitations in this kind of research. First, the transformer language model usually regards the text as a linear sequence, ignoring the inherent hierarchical structure information of the text. Second, for long text data, traditional extractive models often focus on global topic information, which poses challenges in how they capturing and integrating local contextual information within topic segments. To address these issues, we propose a long text extractive summarization model that employs a local topic information extraction module and a text hierarchical extraction module to capture the local topic information and document's hierarchical structure information of the original text. Our approach enhances the ability to determine whether a sentence belongs to the summary. In this experiment, ROUGE score is used as the experimental evaluation index, and evaluates the model on three large public datasets. Through experimental validation, the model demonstrates superior performance in terms of ROUGE-1, ROUGE-2, and ROUGE-L scores compared to current mainstream summarization models, affirming the effectiveness of incorporating local topic information and document hierarchical structure into the model., (© 2024. The Author(s).)

High-quality development plays a crucial role in China's economic progress in the new era. It represents a new concept of advancement and mirrors the increasing aspirations of the populace for an improved standard of living. In this context, the role of artificial intelligence (AI) in promoting sustainable development cannot be overemphasized. This paper explores how AI technologies can drive the transition to a green, low-carbon and circular economy. We have established an index system to measure the development level of the artificial intelligence industry and the high-quality development of the economy, which is relevant to the current state of the artificial intelligence industry and the advancement of the economy. Panel data from 2008 to 2017 has been utilized for this purpose. Global principal component analysis method and entropy value method are employed in the evaluation. Through in-depth analysis of the application of artificial intelligence and environmental protection in various provinces and cities, we clarify that artificial intelligence promotes innovation, saves resources, and is conducive to the development of green economy in the new era., (© 2024. The Author(s).)

Representative single anisotropic matrix block 2D Green's function models for depletion through fully-penetrating, vertical fractures through different numbers of fracture faces are constructed that analytically capture both fracture and block depletion with fracture-matrix mass transfer. The 1D Green's function for a fracture system is likewise solved in terms of the time evolution of average fracture pressure. While transient average pressure values are not inherently measurable, they are transformed into cumulative production or instantaneous flowrate values, thus producing new rate decline model functional forms. Primary variables in assembling the interacting systems model are the volume ratio, V f /V m , permeability ratio, k f /k x , and geometry, (a/b)(k y /k x ), with the last term accounting for both block shape and permeability anisotropy. We construct interacting systems models in terms of various ratios of V f /V m , and k f /k x for three fracture architecture prototypes: representative matrix blocks depleted by 4, 2, or 1 contacting fractures. The single matrix block models can be migrated to ones for heterogeneous systems using superposition and matrix block distributions, as demonstrated with a binary distribution of block sizes with variable fractions. Analytic solutions for rate decline problems can be used to understand the production signatures of naturally fractured reservoirs and interpretation of fracture volume fraction, permeability ratio, average matrix block size, and measures of heterogeneity., (© 2024. The Author(s).)

In mountain areas landslides many times endanger safety of transport infrastructures, and these must be stabilized with retaining wall structures. In this paper the validation of a new composite as a backfill material for landslide stabilization with a large scale demo retaining wall is presented. The new composite was made from residues of paper industry, which uses for its production deinking process. New composite was validated with the laboratory tests, construction of small demo sites and at the end with a large demo retaining wall structure with a length of 50 m. It was concluded that the paper sludge ash and the paper sludge are in proportion 70:30, compacted on the optimal water content and maximum dry density, reached sufficient uniaxial compressive and shear strength. However, the composite's hydration processes required the definition of an optimal time between the composite mixing and installation. In 2019, the retaining wall structure from the new composite was successfully built. The large demo structure is an example of the knowledge transfer from the laboratory to the construction site, in which composite and installing technology could be verified., (© 2024. The Author(s).)

The reference evapotranspiration (ETo) is an essential component in hydrological and ecological processes. The objective of this research is to develop an explicit model to estimate ETo only using commonly measurable meteorological parameters such as relative humidity, air temperature, and wind speed, where the measurements corresponding to solar radiation are omitted. The model was generated using Genetic Programming (GP), evaluated, and validated with reference data ETo using FAO56-PM. This reference data was obtained from different climates (warm-temperate and arid-warm) and latitudes, acquired from CIMIS stations in the state of California, United States, and the El Porvenir station in the state of Coahuila, located in north-central Mexico. After applying the proposed methodology, a total of 3754 results were generated, demonstrating a significant improvement in the estimation of ETo compared to the Hargreaves-Samani model. A particularly noteworthy result revealed that our approach outperformed the Hargreaves-Samani model in the training phase by 27%, and in the testing phase by 16%, on average. In order to achieve a generalized model, a dataset encompassing meteorological stations in two different climates (warm-temperate and arid-warm) and various latitudes was utilized. The obtained outcome unveiled a highly effective model for estimating ETo in diverse climatic contexts, eliminating the need for local adjustments. This model significantly surpassed the Hargreaves-Samani model, exhibiting superior performance by 17% during the training phase and 18% during the testing phase. These results conclusively underscore the capability of our approach to provide more accurate and reliable ETo estimates. These results conclusively underscore the capability of our approach to provide more accurate and reliable ETo estimates. Finally, to validate the model, four different datasets with climates similar to those used for model creation (warm-temperate, warm-arid) and different latitudes were employed. The validation stage results clearly indicate the superiority of our reference evapotranspiration ETo11 model over the Hargreaves-Samani model by 51% in warm-temperate climates. For the dataset with arid-warm climate, our model continued to show satisfactory results, surpassing the Hargreaves-Samani model by 8%. GP emerges as an innovative and effective alternative for simplified model development. This approach introduces a novel paradigm that facilitates the efficient development of models, standing out for its simplicity and effectiveness in generating solutions., (© 2024. The Author(s).)

Value alignment, the process of ensuring that artificial intelligence (AI) systems are aligned with human values and goals, is a critical issue in AI research. Existing scholarship has mainly studied how to encode moral values into agents to guide their behaviour. Less attention has been given to the normative questions of whose values and norms AI systems should be aligned with, and how these choices should be made. To tackle these questions, this paper presents the STELA process (SocioTEchnical Language agent Alignment), a methodology resting on sociotechnical traditions of participatory, inclusive, and community-centred processes. For STELA, we conduct a series of deliberative discussions with four historically underrepresented groups in the United States in order to understand their diverse priorities and concerns when interacting with AI systems. The results of our research suggest that community-centred deliberation on the outputs of large language models is a valuable tool for eliciting latent normative perspectives directly from differently situated groups. In addition to having the potential to engender an inclusive process that is robust to the needs of communities, this methodology can provide rich contextual insights for AI alignment., (© 2024. The Author(s).)

In the ongoing battle against adversarial attacks, adopting a suitable strategy to enhance model efficiency, bolster resistance to adversarial threats, and ensure practical deployment is crucial. To achieve this goal, a novel four-component methodology is introduced. First, introducing a pioneering batch-cumulative approach, the exponential particle swarm optimization (ExPSO) algorithm was developed for meticulous parameter fine-tuning within each batch. A cumulative updating loss function was employed for overall optimization, demonstrating remarkable superiority over traditional optimization techniques. Second, weight compression is applied to streamline the deep neural network (DNN) parameters, boosting the storage efficiency and accelerating inference. It also introduces complexity to deter potential attackers, enhancing model accuracy in adversarial settings. This study compresses the generative pre-trained transformer (GPT) by 65%, saving time and memory without causing performance loss. Compared to state-of-the-art methods, the proposed method achieves the lowest perplexity (14.28), the highest accuracy (93.72%), and an 8 × speedup in the central processing unit. The integration of the preceding two components involves the simultaneous training of multiple versions of the compressed GPT. This training occurs across various compression rates and different segments of a dataset and is ultimately associated with a novel multi-expert architecture. This enhancement significantly fortifies the model's resistance to adversarial attacks by introducing complexity into attackers' attempts to anticipate the model's prediction integration process. Consequently, this leads to a remarkable average performance improvement of 25% across 14 different attack scenarios and various datasets, surpassing the capabilities of current state-of-the-art methods., (© 2024. The Author(s).)

Digital transformation has emerged as a powerful force in reshaping the business landscape and enabling organizations to enhance their capabilities. One critical aspect of this change is how it impacts an enterprise's innovation ability. To explore this question, we select data regarding China's A-share listed enterprises from 2007 to 2021 as the research sample. We employ crawler technology to gather keywords related to "digital transformation" from annual reports, portraying detailed journeys of enterprises' digital transformation. Through descriptive statistics and multiple covariance tests, a linear relationship is established between digital transformation and innovation ability. Benchmark regression is conducted and a robustness test is utilized to determine the robustness of the benchmark regression. The mechanism, heterogeneity, and moderating effects of this study are also tested. The results reveal that digital transformation makes a significant positive contribution to the innovation capability of enterprises. Meanwhile, among different types of enterprises, the impact of digital transformation on enterprise innovation capability shows heterogeneity. In terms of the impact mechanism, digital transformation can enhance the innovation output of enterprises by reducing the agency cost and improving the risk-taking level of enterprises, so as to further improve the innovation capability of enterprises. The research results of this paper provide essential theoretical support for the digital transformation of enterprises and the government's formulation of enterprises' digitalization strategies. More profoundly, it provides significant reference for how to further promote the digital transformation of Chinese enterprises., (© 2024. The Author(s).)

In the process of developing tight oil and gas reservoirs, multistage fractured horizontal wells (NFHWs) can greatly increase the production rate, and the optimal design of its fracturing parameters is also an important means to further increase the production rate. Accurate production prediction is essential for the formulation of effective development strategies and development plans before and during project execution. In this study, a novel workflow incorporating machine learning (ML) and particle swarm optimization algorithms (PSO) is proposed to predict the production rate of multi-stage fractured horizontal wells in tight reservoirs and optimize the fracturing parameters. The researchers conducted 10,000 numerical simulation experiments to build a complete training and validation dataset, based on which five machine learning production prediction models were developed. As input variables for yield prediction, eight key factors affecting yield were selected. The results of the study show that among the five models, the random forest (RF) model best establishes the mapping relationship between feature variables and yield. After verifying the validity of the Random Forest-based yield prediction model, the researchers combined it with the particle swarm optimization algorithm to determine the optimal combination of fracturing parameters under the condition of maximizing the net present value. A hybrid model, called ML-PSO, is proposed to overcome the limitations of current production forecasting studies, which are difficult to maximize economic returns and optimize the fracturing scheme based on operator preferences (e.g., target NPV). The designed workflow can not only accurately and efficiently predict the production of multi-stage fractured horizontal wells in real-time, but also be used as a parameter selection tool to optimize the fracture design. This study promotes data-driven decision-making for oil and gas development, and its tight reservoir production forecasts provide the basis for accurate forecasting models for the oil and gas industry., (© 2024. The Author(s).)

Global climate change associated with increased carbon emissions has become a global concern. Resource-based cities, by estimations, have emerged as major contributors to carbon emissions, accounting for approximately one-third of the national total. This underscores their pivotal role in the pursuit of carbon neutrality goals. Despite this, resource-based cities have long been neglected in current climate change mitigation policy discussions. Accordingly, using exploratory spatial data analysis and Geographical Weighted Regression method, this study investigates the determinants of carbon emissions and their spatial pattern in 113 resource-based cities in China. It can be concluded that: (1) The proportion of carbon emissions from resource-based cities in the national total has shown a marginal increase between 2003 and 2017, and the emissions from these cities have not yet reached their peak. (2) A relatively stable spatial pattern of "northeast high, southwest low" characterizes carbon emissions in resource-based cities, displaying significant spatial autocorrelation. (3) Population size, economic development level, carbon abatement technology, and the proportion of resource-based industries all contribute to the increase in carbon emissions in these cities, with carbon abatement technology playing a predominant role. (4) There is a spatial variation in the strength of the effects of the various influences., (© 2024. The Author(s).)

During the start of the COVID-19 pandemic in 2020, lockdowns and movement restrictions were thought to negatively impact population mental health, since depression and anxiety symptoms were frequently reported. This study investigates the effect of COVID-19 mitigation measures on mental health across the United States, at county and state levels using difference-in-differences analysis. It examines the effect on mental health facility usage and the prevalence of mental illnesses, drawing on large-scale medical claims data for mental health patients joined with publicly available state- and county-specific COVID-19 cases and lockdown information. For consistency, the main focus is on two types of social distancing policies, stay-at-home and school closure orders. Results show that lockdown has significantly and causally increased the usage of mental health facilities in regions with lockdowns in comparison to regions without such lockdowns. Particularly, resource usage increased by 18% in regions with a lockdown compared to 1% decline in regions without a lockdown. Also, female populations have been exposed to a larger lockdown effect on their mental health. Diagnosis of panic disorders and reaction to severe stress significantly increased by the lockdown. Mental health was more sensitive to lockdowns than to the presence of the pandemic itself. The effects of the lockdown increased over an extended time to the end of December 2020., (© 2024. The Author(s).)

Animal-based metal threads were largely used between the 10th and the fifteenth century, in European, Middle Eastern and Far Eastern textile productions for the decoration of textiles and cloths. They belong to a larger group of metal threads, used either as flat threads or wrapped around a fiber core, that were backed by an organic support (animal or paper). This study focuses on the medieval production of metal threads backed by an animal membrane (e.g. gut membrane), or skin. A total of 91 samples were collected from a corpus of 66 textile fragments belonging to 54 catalogued objects. The relevance and novelty of the present study is represented by the combination of proteomics, cross-section analysis, and scanning electron microscopy (SEM-EDS and SEM-µXRF). The diversity of materials and manufacturing techniques found within each typology of thread, respectively, membrane-based metal threads and skin-based metal threads, hinted at different production technologies. Membrane-based threads were found to be invariably made from cattle gut membrane, coated with gilt-silver leaves. A possible sheep glue adhesive was found in a few samples. Skin-based threads were made from either goat or sheep leather, coated with metal leaves or powder. Within the three different types of coatings identified (silver, gold and gilt-silver), gold coatings were the most represented. Goat leather threads were associated with an egg-white binder, while sturgeon glue was identified as adhesive in all sheep leather threads. Collagen glue from other species (cattle, sheep, horse) was occasionally found in mixed adhesives. In two textiles, the finding of human proteins indicates past contamination due to handling or use. The analytical results show coherence between the fabrication patterns of animal-based metal threads and their probable geographical areas of manufacture, indicating that the study of materials and techniques provide further criteria to classify and group textiles, and trace correlations between manufacturing centers within Eurasian territories., (© 2024. The Author(s).)

Multi-image steganography, a technique for concealing information within multiple carrier mediums, finds remote sensing images to be particularly apt carriers due to their complex structures and abundant texture data. These characteristics bolster the resilience against steganalysis and enhance steganographic capacity. The efficacy of multi-image steganography hinges on the diplomatic strategy of cover selection and the meticulous allocation of the payload. Nevertheless, the majority of current methods, which are empirically formulated, predominantly focus on the texture complexity of individual images, thereby potentially undermining overall security. This paper introduces a security-oriented approach to steganographic payload allocation for multiple remote sensing images aimed at fortifying the security of multi-image steganography. Our primary contributions include employing a steganalysis pre-trained network to quantify texture complexity in remote sensing cover images, directly correlating it with security. Additionally, we have developed an adaptive payload allocation strategy for multiple images, which embeds a payload proximate to each image's maximal steganographic capacity while concurrently ensuring the security of the embedding process. Experimental results corroborate that our methodology excels in cover selection and payload allocation and achieves better undetectability against modern steganalysis tools., (© 2024. The Author(s).)

Teachers in Japanese schools employ alphabetical surname lists that call students sooner, with surnames appearing early on these lists. We conducted Internet surveys nearly every month from March 2020 to September 2022 with the same participants, wherein we asked participants where the alphabetical columns of their childhood and adult surnames were located. We aimed to identify how surname order is important for the formation of noncognitive skills. During the data collection period, the COVID-19 vaccines became available; Japanese people could receive their third dose starting in December 2021. The 19th wave of the survey was conducted in January 2022. Therefore, to examine how a surname's alphabetical order could influence intention to revaccinate, we used a subsample of data from December 2021 to September 2022. The major findings were as follows. Women with early surnames had an approximately 4% stronger likelihood of having such intentions than men with early surnames. Early name order was more strongly correlated with revaccination intention among women than among men. The surname effect for women was larger when a mixed-gender list was used compared with when it was not used. This effect was only observed for childhood surnames and not for adult surnames., (© 2024. The Author(s).)

In this study, gearbox radiated noise was successfully reduced through housing shape optimization. First, dynamic and structural-acoustic coupled analysis models of an agricultural UTV gearbox were developed. Then, the test equipment was configured to match that of the simulation model, and a test was conducted. The analysis and test results showed errors within 0.1 dB for vibration and 0.2 dB(A) for noise, indicating that the models were reliable. The housing design was then optimized using topology optimization based on the developed structural-acoustic coupling analysis model. The sound pressure level around the housing was used as an objective function for topology optimization. The optimal distribution of materials for the housing was also derived to reduce the radiated noise. Lastly, the housing rib was designed based on the optimization result, and an improvement in the radiated noise by approximately 2.43 dB(A) was confirmed in the operation area., (© 2024. The Author(s).)

In recent years, a variety of deep learning networks for cardiac MRI (CMR) segmentation have been developed and analyzed. However, nearly all of them are focused on cine CMR under breathold. In this work, accuracy of deep learning methods is assessed for volumetric analysis (via segmentation) of the left ventricle in real-time free-breathing CMR at rest and under exercise stress. Data from healthy volunteers (n = 15) for cine and real-time free-breathing CMR at rest and under exercise stress were analyzed retrospectively. Exercise stress was performed using an ergometer in the supine position. Segmentations of two deep learning methods, a commercially available technique (comDL) and an openly available network (nnU-Net), were compared to a reference model created via the manual correction of segmentations obtained with comDL. Segmentations of left ventricular endocardium (LV), left ventricular myocardium (MYO), and right ventricle (RV) are compared for both end-systolic and end-diastolic phases and analyzed with Dice's coefficient. The volumetric analysis includes the cardiac function parameters LV end-diastolic volume (EDV), LV end-systolic volume (ESV), and LV ejection fraction (EF), evaluated with respect to both absolute and relative differences. For cine CMR, nnU-Net and comDL achieve Dice's coefficients above 0.95 for LV and 0.9 for MYO, and RV. For real-time CMR, the accuracy of nnU-Net exceeds that of comDL overall. For real-time CMR at rest, nnU-Net achieves Dice's coefficients of 0.94 for LV, 0.89 for MYO, and 0.90 for RV and the mean absolute differences between nnU-Net and the reference are 2.9 mL for EDV, 3.5 mL for ESV, and 2.6% for EF. For real-time CMR under exercise stress, nnU-Net achieves Dice's coefficients of 0.92 for LV, 0.85 for MYO, and 0.83 for RV and the mean absolute differences between nnU-Net and reference are 11.4 mL for EDV, 2.9 mL for ESV, and 3.6% for EF. Deep learning methods designed or trained for cine CMR segmentation can perform well on real-time CMR. For real-time free-breathing CMR at rest, the performance of deep learning methods is comparable to inter-observer variability in cine CMR and is usable for fully automatic segmentation. For real-time CMR under exercise stress, the performance of nnU-Net could promise a higher degree of automation in the future., (© 2024. The Author(s).)

Remote work (REMOTE) causes an overlap between working and domestic demands. The study of the cardiac autonomic profile (CAP) by means of heart rate variability (HRV) provides information about the impact of REMOTE on workers' health. The primary aim was to determine whether CAP, self-perceived stress, environmental and workstation comfort are modified during REMOTE. The secondary aim was to explore how these indices are influenced by individual and environmental work-related factors. Fifty healthy office employees alternating REMOTE and in-office (OFFICE) working were enrolled, rated self-perceived stress, environmental and workstation comfort using a visual analogue scale and performed a 24-h electrocardiogram during REMOTE and OFFICE. Stress was lower (5.6 ± 2.2 vs. 6.4 ± 1.8), environmental comfort higher (7.7 ± 1.9 vs. 7.0 ± 1.5), and the workstation comfort poorer (6.2 ± 1.8 vs. 7.5 ± 1.2) during REMOTE. CAP was similar during REMOTE and OFFICE. CAP was influenced by some work-related factors, including the presence of offspring, absence of a dedicated workspace during REMOTE and number of working hours. All these variables determined a decreased vagal modulation. The working setting seems to impact the levels of perceived stress and comfort, but not the CAP. However, individual and environmental work-related factors reduce cardiac vagal modulation during REMOTE, potentially increasing the risk of developing cardiovascular diseases., (© 2024. The Author(s).)