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Abstract 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 ( 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 (

Abstract 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 102 CFU/mL of Escherichia coli (E. coli) from culture media and the detection of E. coli in milk

Abstract 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 NH3–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 NH3–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 (R2). The R-squared of Langmuir for COD and NH3–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 NH3–N were 0.9875 and 0.8883 and for PSO were 0.9987 and 0.9909 respectively.

Abstract 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.

Abstract 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.

Abstract 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, MnO2 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.

Abstract 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.

Abstract 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.

Abstract 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.

Abstract This work features a new corona discharge plasma technology for de-inking yellow, blue, and red colors on various papers. This work was developed to minimize the chemical and environmental impacts of de-inking processes. A nonchemical contribution, operating at room temperature and atmospheric pressure, reduces the environmental impact of the process. The deinkability factor (DEMLab) values for all papers are determined with the optimal assessment results provided by a 36-mm variation gap at 2-min (blue) and 10-min (yellow and red) plasma exposure times, followed by applied voltages of 20 kV (yellow), 16 kV (blue), and 20 kV (red). The corona discharge plasma led to 48.58% (yellow printed paper), 64.11% (blue printed paper), and 41.11% (red printed paper) deinkability without altering the physical properties of the paper itself. The change in the tensile strength for the plasma-exposed paper was relatively little, less than 10%, compared to that of common recycling. The tensile strength of the untreated white paper was 5065 ± 487.44 N/mm2, and that of the plasma-treated printed paper was 4593 ± 248.47 N/mm2. It appears that there is little impact on the physicochemical properties of paper induced by the corona plasma treatment during the de-inking process.

Abstract 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.

Abstract This study investigates the impact of air pollution on health outcomes in Middle Eastern countries, a region facing severe environmental challenges. As such, these are important in an effort to add up to policy-level as well as interventional changes that can be put in practice in the area of public health. Numeration analysis and association with health parameters was carried out by using Analytical tools such as, AIR Data, ARIMA,ANN, SVM and Exponential smoothing. Amongst the models, Support Vector Machine came again on top, with high accuracy yielding Mean Absolute Percentage Error of approximately 1%. Mortality of Air pollution in Qat from the case of Mortality of Air Pollution in Qatar is 959 while Auto regressive Integrated Moving average is 11.096, Exponential Smoothing 9.892 and Artificial Neural Networks are the source of inspiration for the development of this paper 4.61. The above perceptions indicate that there is need to adapt modeling strategies depending on the context and establish that it is possible to implement ML models in public health planning basket. This paper publishes the methodological frameworks for the purpose of modeling and analysis of the EHDs and serves as policy prescription for the policy makers to intending to reduce the effects of air borne pollution on health.

Abstract This paper introduces an enhanced time synchronization method different from IEEE 1588 (PTP). The proposed algorithm employs a unique synchronous message packet structure with a fixed length of 10 bytes and the highest system priority. This design enables preemptive transmission, effectively reducing transmission and queuing delays. Additionally, it applies a Kalman filtering model to mitigate noise interference, including clock drift, network delay jitter, and network asymmetry. The algorithm also features a clock drift compensation mechanism to continuously adjust the clock, ensuring high precision and stability in time synchronization. The algorithm proposed in this paper has the characteristics of being easy to implement, requiring minimal hardware resources, and being applicable to a variety of networks. Simulation results show that, with an 80 MHz crystal oscillator, the time offset between master and slave clocks does not exceed ± 1 clock cycle in symmetric communication links. In asymmetric links, the maximum time offset is within ± 3 clock cycles. Compared to the original PTP algorithm and the Kalman filter-based time synchronization algorithm, this method reduces the time offset from several microseconds to less than 40 ns.

Abstract Bridge construction collapse is one of the most common bridge safety accidents. At present, evaluation results are often affected by the ability and experience of the assessor. Therefore, it is difficult to quickly, accurately and effectively evaluate the risk in the process of bridge construction. Moreover, key factors that can prevent accidents can hardly find from the existing bridge construction safety management and evaluation method. This paper analyzes and classifies the artificial and environmental risk factors that affect the bridge construction stage, and establishes 26 risk factors in 5 categories according to the characteristics of bridge construction and the actual situation of the project. Random forest (RF) algorithm is a non-parametric machine learning method based on decision tree, which does not need to be scored by experts in advance and avoids the influence of subjective factors. Compared with other analysis methods, random forest algorithm has the advantages of accurate and robust risk assessment results. Based on the advantages of random forest algorithm and the characteristics of bridge construction risk, this paper uses random forest algorithm to evaluate the bridge construction risk, and ranks the importance of indicators, and identify the index that has a greater influence on the risk. In order to verify the applicability and feasibility of the proposed method, a typical urban complex pedestrian bridge was taken as an example for actual engineering evaluation and verification. The results obtained are basically consistent with the actual risk assessment results of the pedestrian bridge.

Abstract The vertical tube indirect evaporative cooler (VTIEC) could be preferred one to air-conditioning buildings if its water film quality could be improved. This paper proposed a new VTIEC with inner grooved tubes to improve the water film quality and obtained its performance by experimental method. The results indicated that inner grooved tubes have positive effects on the water film and performance of the new VTIEC. A low working air enthalpy at inlet can significantly improve the total cooling capacity from evaporation, and then VTIEC performance. In this paper conditions, the velocity ratio of 1.52 of the produced air to the working air is better one condition compared to 1.01 and 2.28. The produced air velocity is positive effect on the cooling capacity per unit area until the produced air velocity of about 4.05 m/s and is negative effect on COP. The maximum unit cooling capacity, wet-bulb efficiency and COP are 307.74W/m2, 81.7% and 44.97.The results will promote the application of the new VTIEC.

Abstract The calculation of the spectral blueing operator in the traditional spectral blueing method has singularity, which leads to poor performance in post-stack seimic frequency expansion. To this end, a frequency spreading technique based on matching pursuit (MP) and spectral blueing is proposed. Through time–frequency analysis processing, it is shown that the seismic signal extracted by matching tracking method has good stability and higher resolution. The specific process of the method in this paper firstly uses the matching tracking method to accurately divide the post-stack seismic data into multiple frequency-division seismic bodies; then, in the process of calculating the spectral blueing ization operators for each frequency band, the weighting idea is used to calculate the weights of the optimized spectral blueing ization operators for each frequency band based on the differences in energy in different frequency bands; finally, the optimized spectral blueing operator is convolved with seismic reflection coefficients to obtain high-resolution seismic data. The actual test results of poststack seismic data have proven that the frequency raising method proposed in this paper is superior to the traditional spectral blueing ization algorithm, greatly improving the high-frequency component information of poststack seismic data. After frequency extension, there are more seismic events and higher resolution. Finally, the practicability and rationality of the seismic data after frequency extraction are verified by a series of operations such as attribute extraction, well seismic calibration and inversion.

Abstract The problem of solving the equivalent resistance between two points for resistor networks has important significance in physics. This paper mainly changes and rewrites the formula for calculating the resistance between two points of an unconventional $$m\times n$$ m × n cylindrical resistor network with a zero resistor axis and any two left and right boundaries. To enhance the efficiency of calculating the equivalent resistance between two points, Chebyshev polynomials and hyperbolic cosine functions are employed to represent the new formula. And in the inference process, the famous discrete cosine transform of the third kind (DCT-III) is used to process the matrix. We give the equivalent resistance formula for several special cases, and display them by a three-dimensional graph. Subsequently, the calculation efficiency of the original formula and the rewritten formula are compared. At the end of the paper, a heuristic algorithm suitable for robot path planning on cylindrical environment is proposed.

Abstract In Europe mainly at winter season the PM levels exceed air quality limits, which correlated with the operation of solid-fired boilers. More and more people are returning to using these devices due to energy shortage caused by the pandemic and regional conflicts. In addition, the phenomena of co-burning fuels and municipal waste in residential boilers in primarily fuel poverty households increases further the amount of pollutants in the atmosphere. This study aims to correlate the quantity and quality of air pollutants with the type of fuel (wood and wastes) burned. Combustion experiments were conducted using oak fuel mixed with three waste groups: (1) plastics (PP, HDPE, PET); (2) textiles (polyester—PES, cotton—COT); and (3) papers (cardboard—CARD, glossy coated paper—GCP, 84C/PAP). The addition of waste to wood fuel altered the morphology of emitted particles. While waste burning doesn't always increase particle quantity, it significantly raises PAH concentrations. A strong relationship exists between waste type, particle morphology, and PAH quality, where with lower molecular weight PAHs linked to tar agglomerates and higher ones to soot agglomerates with inorganic crystals.

Abstract This paper introduces a novel design for a universal DC-DC and DC-AC converter tailored for DC/AC microgrid applications using Approximate Dynamic Programming and Artificial Neural Networks (ADP-ANN). The proposed converter is engineered to operate efficiently with both low-power battery and single-phase AC supply, utilizing identical side terminals and switches for both chopper and inverter configurations. This innovation reduces component redundancy and enhances operational versatility. The converter's design emphasizes minimal switch usage while ensuring efficient conversion to meet diverse load requirements from battery or AC sources. A conceptual example illustrates the design's principles, and comprehensive analyses compare the converter's performance across various operational modes. A test bench model, rated at 3000W, demonstrates the converter's efficacy in all five operational modes with AC/DC inputs. Experimental results confirm the system's robustness and adaptability, leveraging ADP-ANN for optimal performance. The paper concludes by outlining potential applications, including microgrids, electric vehicles, and renewable energy systems, highlighting the converter's key advantages such as reduced complexity, increased efficiency, and broad applicability.

Abstract A new DC–DC buck–boost converter with a wide conversion ratio is presented in this paper. The proposed buck–boost converter consists of a combination of modified boost converter and ZETA converter, which has the advantages of both converters such as continuous input/output current and positive polarity of the output voltage. The combination of these two converters achieves semi-quadratic voltage gain and makes the proposed converter suitable for industrial and renewable energy applications. With a voltage gain higher than that of the ZETA converter and modified boost converter, the proposed converter also reduces input current stress due to its continuity. Two operating states are available for this converter in continuous conduction mode. This converter has two switches that operate simultaneously and can be easily controlled. The converter's output voltage ripple and output capacitor current stress are reduced as a result of continuous output current. Computational analysis and the introduced structure efficiency considering the influence of parasitic elements are presented in this paper. The small signal modelling and closed-loop control, as well as simulation and experimental results are also presented. This converter has also been compared with other similar and recently presented topologies. Finally, a 40–60 W, 20–76 V for boost mode and 10 V for buck mode prototype was implemented to verify the accuracy of the computational analysis.

Abstract The Fenshui River Bridge is a steel–concrete composite girder bridge with a main span of 90 m. Due to the topographical constraints, the steel box girder was constructed without the use of auxiliary piers, employing incremental launching techniques. This article focuses on the construction technology used for the steel box girder of the Fenshui River Bridge. Firstly, using the influence matrix method, the cable force is determined based on the maximum cantilever state of the structure, with the vertical deformation of the front end of the guide beam and the horizontal deformation of the top of the tower as the control objectives. The unstressed cable length is then calculated based on the mechanical relationship between cable deformation and cable force. A calculation method for adaptive cable force is proposed, which is based on the variation of the stress-free cable length within the adaptive structural system. Next, the finite element analysis method was employed to determine the optimal layout position for the tower. The results indicate that during the incremental launching construction of the steel box girder, the calculated cable forces using the method proposed in this paper are in close agreement with the measured cable forces. At the maximum cantilever state of the structure, the calculated and measured values of the cable force resulted in a percentage difference of 3.96%. The calculated values of deformation and stress in key sections showed a percentage difference of 6.4% for deformation and 6.6% for stress. To maximize the effectiveness of the tower and cable, the tower should be positioned above the bridge pier when the guide beam crosses the maximum span. The findings of this paper can serve as a reference for the construction of similar types of bridges.

Abstract Comparing with cities, rural communities especially those declining rural communities have become vulnerable to natural disasters owing to their backward socioeconomic conditions. Taking Xun County of China’s Henan Province as the study area, the paper aims to evaluate rural community resilience to flood by unveiling the connection between individuals’ cognition, follow-up actions and the community resilience. Research results show that: (1) The logic chain exists as individual’s cognition to disaster leads to their constructive actions to cope with disaster, which contribute to community resilience. (2) At the cognition dimension, individual’s knowledge reserve of disaster prevention and their recognition to local authority are playing an important role in their decision making and follow-up behaviors when disaster occurs. (3) At the action dimension, individual’s familiarity with the disaster preparedness, efficient information transmission when disaster occurs and villagers’ following order and their unity of action all contribute to community resilience to disaster. The paper proposes ways to improve rural community resilience to disasters based on the research findings.

Abstract The aim of this paper is to explore whether and how urban road transport (URT) development affects haze pollution. One of the innovations of this paper is that URT development is measured by road accessibility with novel digital elevation model datasets, which have been used by few scholars. The endogenous problem caused by revere causality issue in the relationship between URT development and haze pollution is also considered. Based on the panel data of prefecture-level cities of Yangtze River Delta (YRD) region in China from 2011 to 2018, this paper uses long-lagged values of URT development as the instrumental variable, employing the two-stage least squares (2SLS) method. The study shows that URT development leads to an increase of haze pollution. Moreover, mechanism tests based on moderating and mediating models support the finding that decreasing haze pollution resulted from better connection effects, while rising agglomeration effects tend to bring about increasing haze pollution, and the latter effect is larger in magnitude than the former. Current URT development may have long-term negative consequences for livability of YRD cities, and urban decision makers should reconsider the effectiveness of the current road transport investment and construction.

Abstract Actual acquired air quality time series data are highly volatile and nonstationary, and accurately predicting nonlinear time series data containing complex noise is an ongoing challenge. This paper proposes an air quality prediction method based on empirical mode decomposition (EMD), a transformer and a bidirectional long short-term memory neural network (BiLSTM), which is good at addressing the ultrashort-term prediction of nonlinear time-series data and shows good performance for application to the air quality dataset of Patna, India (6:00 am on October 3, 2015–0:00 pm on July 1, 2020). The AQI sequence is first decomposed into intrinsic mode functions (IMFs) via EMD and subsequently predicted separately via the improved transformer algorithm based on BiLSTM, where linear prediction is performed for IMFs with simple trends. Finally, the predicted values of each IMF are integrated using BiLSTM to obtain the predicted AQI values. This paper predicts the AQI in Patna with a time window of 5 h, and the RMSE, MAE and MAPE are as low as 5.6853, 2.8230 and 2.23%, respectively. Moreover, the scalability of the proposed model is validated on air quality datasets from several other cities, and the results prove that the proposed hybrid model has high performance and broad application prospects in real-time air quality prediction.

Abstract Undercover videos have become a popular tool among NGOs to influence public opinion and generate engagement for the NGO’s cause. These videos are seen as a powerful and cost-effective way of bringing about social change, as they provide first-hand evidence and generate a strong emotional response among those who see them. In this paper, we empirically assess the impact of undercover videos on support for the cause. We in addition analyze whether the increased engagement among viewers is driven by the negative emotional reactions produced by the video. To do so, we design an online experiment that enables us to estimate both the total and emotion-mediated treatment effects on engagement by randomly exposing participants to an undercover video (of animal abuse) and randomly introducing a cooling-off period. Using a representative sample of the French population (N=3,310), we find that the video successfully increases actions in favor of animals (i.e., donations to NGOs and petitions), but we fail to prove that this effect is due to the presence of primary emotions induced by the video. Last, we investigate whether activists correctly anticipate their undercover videos’ (emotional) impact via a prediction study involving activists (exploratory analysis). Protocol registration This manuscript is a Stage-2 working paper of a Registered Report that received In-Principle-Acceptance from Scientific Reports on November 20th, 2023 [ Link to Stage-1 ]. The Stage-1 that received In-Principal-Acceptance can be found here: https://osf.io/8cg2d .

Abstract Injection molding is a common plastic processing technique that allows melted plastic to be injected into a mold through pressure to form differently shaped plastic parts. In injection molding, in-mold electronics (IME) can include various circuit components, such as sensors, amplifiers, and filters. These components can be injected into the mold to form a whole within the melted plastic and can therefore be very easily integrated into the molded part. The brain–computer interface (BCI) is a direct connection pathway between a human or animal brain and an external device. Through BCIs, individuals can use their own brain signals to control these components, enabling more natural and intuitive interactions. In addition, brain–computer interfaces can also be used to assist in medical treatments, such as controlling prosthetic limbs or helping paralyzed patients regain mobility. Brain–computer interfaces can be realized in two ways: invasively and noninvasively, and in this paper, we adopt a noninvasive approach. First, a helmet model is designed according to head shape, and second, a printed circuit film is made to receive EEG signals and an IME injection mold for the helmet plastic parts. In the electronic film, conductive ink is printed to connect each component. However, improper parameterization during the injection molding process can lead to node displacements and residual stress changes in the molded part, which can damage the circuits in the electronic film and affect its performance. Therefore, in this paper, the use of the BCI molding process to ensure that the node displacement reaches the optimal value is studied. Second, the multistrategy differential evolutionary algorithm is used to optimize the injection molding parameters in the process of brain–computer interface formation. The relationship between the injection molding parameters and the actual target value is investigated through Latin hypercubic sampling, and the optimized parameters are compared with the target parameters to obtain the optimal parameter combination. Under the optimal parameters, the node displacement can be optimized from 0.585 to 0.027 mm, and the optimization rate can reach 95.38%. Ultimately, by detecting whether the voltage difference between the output inputs is within the permissible range, the reliability of the brain–computer interface after node displacement optimization can be evaluated.

Abstract The study of hydrogen concentration distribution law of hydrogen-doped methane pipeline is directly related to the safety and stability of hydrogen-doped methane pipeline network. Based on the theory of fluid dynamics, this paper established a model of hydrogen-doped methane pipeline and simulated the operation and shutdown status of hydrogen-doped methane pipeline by adopting the computational fluid dynamics method and selecting the mixture multiphase model and standard k - ε turbulence model. This paper investigates the hydrogen concentration distribution law in hydrogen-doped methane pipelines as well as the influence law of different hydrogen-doping ratios, operating flow velocities, operating pressures, shutdown time and gas usage on the hydrogen concentration distribution in gas pipeline. The results show that: under the operation condition, there is a weak uneven distribution of hydrogen in the pipeline, the hydrogen-doping ratio, flow velocity, pressure on the hydrogen volume fraction of the change in the 0.9% or less, the effect can be ignored; in the shutdown status, there is a clear stratification phenomenon, the hydrogen-doping ratio increased from 10 to 25%, the change in the volume fraction of hydrogen in the 11.2% or less, a positive correlation; with the extension of the shutdown time to 900s, the pipeline firstly appeared obvious stratification phenomenon in the branch pipe, the thickness of the gas with hydrogen volume fraction above 40% on the upper wall surface of the branch pipe increased to 0.7 mm, and after the shutdown time was extended to 10 h, obvious stratification phenomenon appeared in the main pipeline, and the volume fraction of hydrogen near the top of the main pipe of about 16.5 mm was above 30%, which was positively correlated; In the shutdown status, the shutdown time has the greatest effect on the stratification phenomenon in the pipe, followed by the hydrogen-doping ratio, and the gas usage has the least effect.

Abstract Modern power systems high voltage transmission systems either HVDC or HVAC has mandated the presence of two types of circuit breakers (CB), HVAC-CB and HVDC-CB. That required two different production lines, higher costs and more complicated manufacturing process. A solution is proposed in this paper which is a concept design for a universal HVCB (UHVCB) that is applicable to both HVDC and HVAC system. Such a concept would allow a faster, easier production and more economical unit cost for CBs that would benefit the entire industry from manufacturers to utilities. The design of HVDC-CB was used as the foundation the proposed UHVCB such that L–C branches are used. The UHVCB was tested in both HVAC and HVDC transmission systems. The results showed the reliable performance of UHVCB in both systems. The recorded transient recovery voltage (TRV) was reduced from 750 to 430 kV when UHVCB was used instead of conventional HVCB. The testing included both technical and economic aspects. The performance of the UHVCB was tested by varying the parameters for L–C shunt branches in both systems. That included varying the value of L in range 0.3–1 mH and 10–30 µF for C. The most important conclusion from this paper that a UHVCB that is applicable in HVDC and HVAC systems is achievable and this paper is only an initial step in achieving this goal.

Abstract In a multi-level quantum system Fano coherences stand for the formation of quantum coherences due to the interaction with the continuum of modes characterizing an incoherent process. In this paper we propose a V-type three-level quantum system on which we certify the presence of genuinely quantum traits underlying the generation of Fano coherences. We do this by determining work conditions that allows for the loss of positivity of the Kirkwood-Dirac quasiprobability distribution of the stochastic energy changes within the discrete system. We also show the existence of nonequilibrium regimes where the generation of Fano coherences leads to a non-negligible excess energy given by the amount of energy that is left over with respect to the energy of the system at the beginning of the transformation. Excess energy is attained provided the initial state of the discrete system is in a superposition of the energy eigenbasis. We conclude the paper by studying the thermodynamic efficiency of the whole process.

Abstract Hybrid energy systems (HESs) are the most important sources of energy demand-supply, have developed significantly around the world. Microgrids, renewable energy sources, remote telecommunications stations, greenhouses, etc., are being considered as HESs applications. Optimal sizing of these systems is considered as one of the important issues related to energy management. In this paper, the Response Surface Methodology (RSM) is proposed for the optimal sizing of a Photovoltaic (PV) system in a HESs. The suggested procedure solves the optimization problem by considering the factors affecting PV output power about the environmental conditions of the HESs. Providing a mathematical model for each of the input parameters and the ability to assessment the sensitivity of each of the input variables are the most important advantages of the proposed technique. In this paper, the RSM provides the most optimal sizing related to the PV system by considering climatic and geographical factors in the study site, and technical and economic issues related to the HESs. The optimal model obtained is evaluated by the Analysis of Variance (ANOVA) evaluation method, which is one of the important techniques of statistical evaluation. It should be noted that the RSM technique can be utilized to optimize all components of any HES.

Abstract With the development of society, online reviews are increasingly becoming a crucial factor in decision-making. Especially for entertainment products such as movies, they are preferred for their affordability and high entertainment factor. Therefore, this paper proposes a movie recommendation model that considers user personalization using a probabilistic linguistic approach based on online reviews. Firstly, the method constructs a quantitative sentiment framework that transforms comments into a multi-granular probabilistic sentiment language. Secondly, we build the decision-making trial and evaluation laboratory (DEMATEL) method for probabilistic linguistic environments to explore interrelationships between product attributes, and improve the distance measure and score function to better integrate probabilistic linguistic information into DEMATEL weight calculations. Furthermore, to account for risk preferences, the model employs the extended TODIM (an acronym in Portuguese for interactive and multicriteria decision making) methodology to determine the ranking of alternatives. Finally, we design Douban movie ranking experiments to demonstrate the validity of the model. Compared with other methods, this paper incorporates the emotional tendency of movie attributes and user preference into the decision-making process leading to more reasonable results.

Abstract Remote Sensing Image Object Detection (RSIOD) faces the challenges of multi-scale objects, dense overlap of objects and uneven data distribution in practical applications. In order to solve these problems, this paper proposes a YOLO-ACPHD RSIOD algorithm. The algorithm adopts Adaptive Condition Awareness Technology (ACAT), which can dynamically adjust the parameters of the convolution kernel, so as to adapt to the objects of different scales and positions. Compared with the traditional fixed convolution kernel, this dynamic adjustment can better adapt to the diversity of scale, direction and shape of the object, thus improving the accuracy and robustness of Object Detection (OD). In addition, a High-Dimensional Decoupling Technology (HDDT) is used to reduce the amount of calculation to 1/N by performing deep convolution on the input data and then performing spatial convolution on each channel. When dealing with large-scale Remote Sensing Image (RSI) data, this reduction in computation can significantly improve the efficiency of the algorithm and accelerate the speed of OD, so as to better adapt to the needs of practical application scenarios. Through the experimental verification of the RSOD RSI data set, the YOLO-ACPHD model in this paper shows very satisfactory performance. The F1 value reaches 0.99, the Precision value reaches 1, the Precision-Recall value reaches 0.994, the Recall value reaches 1, and the mAP value reaches 99.36 $$\%$$ % , which indicates that the model shows the highest level in the accuracy and comprehensiveness of OD.

Abstract Peripheral Capillary Oxygen Saturation (SpO2) has received increasing attention during the COVID-19 pandemic. Clinical investigations have demonstrated that individuals afflicted with COVID-19 exhibit notably reduced levels of SpO2 before the deterioration of their health status. To cost-effectively enable individuals to monitor their SpO2, this paper proposes a novel neural network model named “ITSCAN” based on Temporal Shift Module. Benefiting from the widespread use of smartphones, this model can assess an individual’s SpO2 in real time, utilizing standard facial video footage, with a temporal granularity of seconds. The model is interweaved by two distinct branches: the motion branch, responsible for extracting spatiotemporal data features and the appearance branch, focusing on the correlation between feature channels and the location information of feature map using coordinate attention mechanisms. Accordingly, the SpO2 estimator generates the corresponding SpO2 value. This paper summarizes for the first time 5 loss functions commonly used in the SpO2 estimation model. Subsequently, a novel loss function has been contributed through the examination of various combinations and careful selection of hyperparameters. Comprehensive ablation experiments analyze the independent impact of each module on the overall model performance. Finally, the experimental results based on the public dataset (VIPL-HR) show that our model has obvious advantages in MAE (1.10%) and RMSE (1.19%) compared with related work, which implies more accuracy of the proposed method to contribute to public health.

Abstract The ceiling structure in a long-narrow space has a significant impact on the thickness of the fire smoke layer. This paper mainly studies the influence of the height of the ceiling structure on the thickness of the smoke layer before the ceiling structure. Firstly, a theoretical analysis is conducted on the relationship between the critical thickness that affects the smoke flow state and the smoke layer thickness directly below the ceiling structure, and a formula for calculating the smoke layer thickness under gradient flow conditions is derived. Then a small-scale experimental platform was built to study the deviation of actual fire smoke relative to theoretical calculations. Experimental results show that due to the smoke cannot maintain gradient flow when crossing the ceiling structure, there is difference with theoretical calculations in the smoke layer thickness directly below the ceiling structure and some distance before the ceiling structure (which may be of greater concern for personnel evacuation). The gradient flow coefficient and thickness coefficient are defined to represent the above differences. After analysis, it was found that the gradient flow coefficient and thickness coefficient showed different linear variation patterns when the height of the ceiling structure was longer than 35 cm or shorter than 30 cm. Through numerical analysis, this study suggests that the main reason for the change in this linear pattern is whether the potential energy conversed from kinetic energy of the thin layer close to the ceiling structure can directly make the smoke to cross the ceiling structure. The research conclusions of this paper can assist in the engineering design of building smoke control and personnel evacuation.

Abstract Detection of printed circuit board assembly (PCBA) defects is crucial for improving the efficiency of PCBA manufacturing. This paper proposes PCBA-YOLO, a YOLOv5-based method that can effectively increase the accuracy of PCBA defect detection. First, the spatial pyramid pooling module with cross-stage partial structure is replaced in the neck network of YOLOv5 to capture the resolution features at multiple scales. Second, large kernel convolution is introduced in the backbone network to obtain larger effective receptive fields while reducing computational overhead. Finally, an SIoU loss function that considers the angular cost is utilized to enhance the convergence speed of the model. In addition, an assembled PCBA defect detection dataset named PCBA-DET is created in this paper, containing the corresponding defect categories and annotations of defect locations. The experimental results on the PCB defect dataset demonstrate that the improved method has lower loss values and higher accuracy. Evaluated on the PCBA-DET dataset, the mean average precision reaches 97.3 $$\%$$ % , achieving a real-time detection performance of 322.6 frames per second, which considers both the detection accuracy and the model size compared to the YOLO series of detection networks. The source code and PCBA-DET dataset can be accessed at https://github.com/ismh16/PCBA-Dataset .

Abstract Time-modulated array antenna (TMAA) is a new type of array antenna based on time modulation technology. By introducing "time" as the fourth dimensional design freedom into the design of conventional array antennas in three-dimensional space, the array antenna has time modulation characteristics, which better controls the radiation characteristics of the array antenna and achieves the best far-field radiation pattern synthesis. This paper designs a Time-modulated linear array (TMLA) with low sidelobe level (SLL) and low sideband level (SBL) based on the chaotic exchange nonlinear dandelion optimization (CENDO) algorithm. Three optimization methods are studied: firstly, determining the optimal on-time (τ nn ) for each array element; The second is to determine the optimal on-time (τ nn ) and optimal uniform array element spacing (d) for each array element; The third is to determine the optimal opening time (t on ), closing time (t off ), and optimal uniform array element spacing (d) for each array element. To achieve simultaneous reduction of sidelobe level and suppression of harmonic interference. The same array model contains different harmonic frequency radiation. In this article, we only considered two harmonic frequencies, namely the first sideband frequency and the second sideband frequency. Because the harmonic of other sideband frequencies has a very small impact on the radiation of the fundamental wave, it can be ignored. To demonstrate the stronger ability of the CENDO algorithm in optimizing Time-modulated array antennas, and in line with the principle of fairness and impartiality, this paper also simulates different Time-modulated array models and compares the results of the CENDO algorithm with other published literature. It is concluded that this study shows lower SLL and lower SBL in different models. This provides a more scientific and accurate explanation of the superiority of the CENDO algorithm compared to other algorithms in the field of antenna optimization in electromagnetics. At the same time, this also provides great research value and fundamental support for designing high-performance Time-modulated array antennas in subsequent engineering applications.

Abstract This paper investigates a novel category of probability distributions and a specific member within this category. We have formulated a new family of trigonometric distributions by utilizing the odds ratio derived from the distribution function of a base distribution. This newly devised distribution family termed the “Sine pie-power odd-G family” of distributions, is constructed through a transformation involving the sine function. The paper presents an overview of the fundamental characteristics inherent to this proposed distribution family. Using the Weibull distribution as a base reference, we have introduced a member belonging to the proposed distribution family. This member demonstrates various hazard functions such as j, reverse-j, increasing, decreasing, or bathtub shapes. The paper examines essential statistical attributes of this newly introduced distribution. The estimation of the distribution’s parameters is carried out via the maximum likelihood estimation method. The accuracy of the parameter estimation procedure is validated through Monte Carlo simulations. The outcomes of these simulations reveal a reduction in biases and mean square errors as sample sizes increase, even for small samples. Two sets of real-engineering data are considered to demonstrate the proposed distribution’s applicability. The performance of the suggested distribution is evaluated using some model selection criteria and goodness-of-fit test statistics. Empirical evidence from these evaluations substantiates that the proposed model outperforms six existing models.

Abstract This paper discusses the simultaneous management of active and reactive power of a flexible renewable energy-based virtual power plant placed in a smart distribution system, based on the economic, operational, and voltage security objectives of the distribution system operator. The formulated problem aims to specify the minimum weighted sum of energy cost, energy loss, and voltage security index, considering the optimal power flow model, voltage security formulation, and the operating model of the virtual power plant. The virtual unit includes renewable sources, like wind systems, photovoltaic, and bio-waste units. Flexibility resources include electric vehicle parking lot and price-based demand response. In the mentioned scheme, parameters of load, renewable sources, electric vehicles, and energy prices are uncertain. This paper utilizes the Unscented Transformation method for modeling uncertainties. Fuzzy decision-making is utilized to extract a compromised solution. The suggested approach innovatively considers the simultaneous management of active and reactive power of a virtual unit with electric vehicles and price-based demand response. This is performed to promote economic, operational, and network security objectives. According to numerical results, the approach with optimal power management of renewable virtual units is capable of boosting the economic, operation, and voltage security status of the network by approximately 43%, 47–62%, and 26.9%, respectively, to power flow studies. Only price-based demand response can improve the voltage security, operation, and economic states of the network by about 19.5%, 35–47%, and 44%, respectively, compared to the power flow model.

Abstract Radar observation variables reflect the precipitation amount of strong convective precipitation processes, which accurate forecast is an important difficulty in weather forecasting. Current forecasting methods are mostly based on radar echo extrapolation, which has the insufficiency of input information and the ineffectiveness of model architecture. This paper presents a Bidirectional Long Short-Term Memory forecasting method for strong convective precipitation based on the attention mechanism and residual neural network (ResNet-Attention-BiLSTM). First, this paper uses ResNet to effectively extract the key information of extreme weather and solves the problem of regression to the mean of the prediction model by learning the residuals of the radar observation data. Second, this paper uses the attention mechanism to adaptively weight the fusion of the features to enhance the extraction of the important features of the precipitation image data. On this basis, this paper presents a novel spatio-temporal reasoning method for radar observations and establishes a precipitation forecasting model, which captures the past and future time-order relationship of the sequence data. Finally, this paper conducts experiments based on the real collected data of a strong convective precipitation process and compares its performance with the existing models, the mean absolute percentage error of this model was reduced by 15.94% (1 km), 18.72% (3 km), and 14.91% (7 km), and the coefficient of determination ( $$R^{2}$$ R 2 ) was increased by 10.89% (1 km), 9.61% (3 km), and 9.29% (7 km), which proves the state of the art and effectiveness of this forecasting model.

Abstract In the actual production process of oil fields, the real-time and accurate acquisition of dynamic recorder data from the pumping unit is of great significance for the diagnosis of well failures. The traditional method of obtaining the card of the dynamometer usually includes installing a load sensor on the auxiliary head of the pumping unit. However, due to the harsh environment of the oil field production site, these load sensors often suffer from damage, distortion, and aging, resulting in large measurement errors and low reliability. This paper proposes a mixed model of pumping based on motor electrical parameter data and CNN convolutional neural network, which has good consistency with actual data in terms of predictive performance. Thus, the highlights of this paper can be summed up in two points: (1) Based on the mathematical model of the AC motor, the speed of the motor and the torque output of the motor are accurately estimated. (2) The convolutional neural network is introduced to compensate for the errors caused by the defects of the pumping unit mechanism model.

Abstract Since 2016, China has progressively relaxed family planning policies to stimulate birth rates. This paper examines the behavioral health repercussions of China’s 2016 universal two-child policy (UTCP) by analyzing sleep pattern data from China Family Panel Studies. Napping is a composite indicator that denotes health outcomes, job quality, and personal well-being. It reveals work conditions and environments to some extent. UTCP may lead to heightened social expectations regarding pregnancy likelihood, and changes in social expectations within the workplace may make work environments less equitable and more stressful for females. Leveraging a difference-in-difference model, this paper explores how napping behaviors among the working-age cohort have responded to the policy shifts. Our analysis reveals a gender discrepancy in response to the policy, specifically, females exhibit a discernible reduction in the likelihood of napping, as well as in the duration of both daytime naps and daily sleep. Conversely, such effects are not significant among males. These results suggest policy consequences extend beyond individuals directly impacted by childbirth or contemplating parenthood. Hence, while promoting fertility is still the government’s goal, policymakers are encouraged to consider the broader challenges the female population faces from social and workplace environment factors.

Abstract Building extraction aims to extract building pixels from remote sensing imagery, which plays a significant role in urban planning, dynamic urban monitoring, and many other applications. UNet3+ is widely applied in building extraction from remote sensing images. However, it still faces issues such as low segmentation accuracy, imprecise boundary delineation, and the complexity of network models. Therefore, based on the UNet3+ model, this paper proposes a 3D Joint Attention (3DJA) module that effectively enhances the correlation between local and global features, obtaining more accurate object semantic information and enhancing feature representation. The 3DJA module models semantic interdependence in the vertical and horizontal dimensions to obtain feature map spatial encoding information, as well as in the channel dimensions to increase the correlation between dependent channel graphs. In addition, a bottleneck module is constructed to reduce the number of network parameters and improve model training efficiency. Many experiments are conducted on publicly accessible WHU,INRIA and Massachusetts building dataset, and the benchmarks, BOMSC-Net, CVNet, SCA-Net, SPCL-Net, ACMFNet, MFCF-Net models are selected for comparison with the 3DJA-UNet3+ model proposed in this paper. The experimental results show that 3DJA-UNet3+ achieves competitive results in three evaluation indicators: overall accuracy, mean intersection over union, and F1-score. The code will be available at https://github.com/EnjiLi/3DJA-UNet3Plus .

Abstract The power industry’s low carbon transition is pivotal for achieving carbon reduction and sustainable development. This study uses the super epsilon-based measurement (Super-EBM) model and the Malmquist index to evaluate the power industry’s low carbon transition efficiency using data from 30 provinces in China from 2010 to 2020, and utilizes the Tobit model to comprehensively analyze the factors affecting the low carbon transition of power industry. In addition, this paper examines the spatial differences in the power industry's low carbon transition efficiency as well as its distributional characteristics and dynamic evolutionary patterns. Conclusion is drawn as follows this paper analyzes the regional differences, spatial distribution characteristics and dynamic evolutionary trends of the power industry’s low carbon transition. The main conclusions are as follows: (1) The power industry’s low carbon transition efficiency in China shows an uptrend, with the western China region having the highest overall level of efficiency, greater fluctuations in the central China region, and more stability in the eastern China region, technological progress is a central factor in increasing total factor productivity, the efficiency of the power industry’s low carbon transition is positively influenced by the electricity prices, and negatively influenced by the energy structure, environmental regulations and economic structure; (2) the Intraregional differences and hypervariable density are the main reasons sources of the overall differences in the efficiency of the power industry’s low carbon transition; Intraregional differences in the eastern, central, and western China regions are decreasing year by year, but the efficiency of the power industry’s low carbon transition in the western China region is still distributed in a multipolar way; (3) The dynamic evolutionary trends of the efficiency distribution of the low carbon transition in power industry is influenced by the type of spatial lag in the neighboring area. Where areas with low efficiency makes it difficult to achieve short-term leapfrog development, and areas with a cluster of high-efficiency provinces are prone to “Siphon Effect”. The findings provide a theoretical basis for promoting the efficiency of the power industry's low carbon transition and coordinating the strategic adjustment of economic and environmental green development.

Abstract Identification of high consequence areas is an important task in pipeline integrity management. However, traditional identification methods are generally characterized by low efficiency, high cost and low accuracy. For this reason, this paper proposes a recognition method based on the improved algorithm Mask Region-based Convolutional Neural Network. Coordinate attention mechanism module is introduced into the traditional Mask R-CNN algorithm to improve the recognition accuracy and reduce the training time. For the identification results, GIS tools are utilized to establish high consequence zones along both sides of the pipeline, and the grade and scope of the high consequence zones are determined according to relevant specifications.In this paper, this method is used to identify the high-consequence area of a pipeline section in Guangdong Province, the results show that: 1, the improved algorithm in the identification of densely populated, geologic hazards, flammable and explosive high consequence zones of the average accuracy of the identification of 1.7%, 3.4%, 3.9%. 2, The method in this paper identifies 8 more building elements and 0.311 more kilometers of pipeline mileage compared to traditional identification methods. The method of this paper can provide a reference for the early identification of high consequence areas of pipelines.

Abstract Some important problems in science do not have analytical solutions in four dimensions including QCD, but they are integrable in two dimensions. For many years, scientists have been trying to find a relation between two-dimensional and four-dimensional space-time to explain the real problem in four dimensions by accurately solving the appropriate model in two dimensions. In this paper, an interesting relation between $$gY M_2$$ g Y M 2 (generalized two-dimensional Yang–Mills) and Maxwell construction has been found, which can be a starting point for finding more relations between two-dimensional and four-dimensional space-time, so this paper can play an important role in the advancement of science. For this purpose, first, the large-N behavior of the quartic-cubic generalized two-dimensional Yang–Mills U(N) on a sphere is investigated for finite cubic couplings. It is shown that there are two phase transitions one of which is of third order, which is similar to previous papers, and the other one is of second order, which is a novel result. Second, $$gY M_2$$ g Y M 2 (for $$ G(z) = z^ m + \lambda \, z^n; m = 4, 6; n < m$$ G ( z ) = z m + λ z n ; m = 4 , 6 ; n < m ) and Maxwell construction are compared with each other and a relationship between two-dimensional space-time, which is integrable, and four-dimensional space-time is obtained.

Abstract This research addresses the issue of the heritage preventive conservation in the perspective of energy sustainability, for contributing to the achievement of the Sustainable Development Goals (SDGs) and towards the EU Green Deal. The study analyses and compares four cases associated with different microclimate thresholds as suggested by the standard EN 16893:2018 (Cases 1–3) and as derived from the outputs of three degradation models for preserving paper, wood, and canvas paintings (Case 4). Weather-based indices (degree and gram days) were calculated to estimate trends in the potential energy demand of collection facilities in three European cities belonging to different Köppen-Geiger climate zones (Cfb, Csa, and Dfb), under recent past (1981–2010) and near/far future climate scenarios (2021–2050 and 2071–2100) from two Shared Socioeconomic Pathways (SSP2-4.5 and SSP5-8.5). The findings suggest that adapting facilities’ management strategies to focus on collections preservation can facilitate the achievement of 5 out of 17 SDGs, offering a viable alternative to costly energy retrofits and encouraging the development of shared solutions for similar facilities in the same climate zone. The results can contribute to inform the revision of EN 16893 and to face major challenges such as the preservation of paper collections in southern latitudes.

Abstract Printing as a process itself generates many environmental concerns. The paper addresses ink management in terms of environmental issues in the label printing industry, focusing on its environmental implications. The goal is to demonstrate how a proper ink management system impacts overall printing process efficiency and environmental sustainability for printing companies. The paper introduces an empirical approach to managing components for label and packaging production, utilizing automatic ink dispensing systems. The results demonstrate that the proper management of ink dispensing to minimize waste in packaging printing is crucial for optimizing operating print costs, potentially reducing the amount of ink needed to prepare colors by 52% and achieving energy savings of 37%. This approach fulfills the goal of sustainability by addressing environmental, economic, and social concerns. By optimizing ink usage and energy consumption, companies can significantly reduce operating costs and enhance economic performance. Simultaneously, these practices improve product quality, meet consumer demands for sustainable packaging, and create better working conditions for employees. Future directions and practical implications for supporting operational excellence in production are also discussed.

Abstract This paper presents a new methodology for addressing imbalanced class data for failure prediction in Water Distribution Networks (WDNs). The proposed methodology relies on existing approaches including under-sampling, over-sampling, and class weighting as primary strategies. These techniques aim to treat the imbalanced datasets by adjusting the representation of minority and majority classes. Under-sampling reduces data in the majority class, over-sampling adds data to the minority class, and class weighting assigns unequal weights based on class counts to balance the influence of each class during machine learning (ML) model training. In this paper, the mentioned approaches were used at levels other than “balance point” to construct pipe failure prediction models for a WDN with highly imbalanced data. F1-score, and AUC–ROC, were selected to evaluate model performance. Results revealed that under-sampling above the balance point yields the highest F1-score, while over-sampling below the balance point achieves optimal results. Employing class weights during training and prediction emphasises the efficacy of lower weights than the balance. Combining under-sampling and over-sampling to the same ratio for both majority and minority classes showed limited improvement. However, a more effective predictive model emerged when over-sampling the minority class and under-sampling the majority class to different ratios, followed by applying class weights to balance data.

Abstract This paper primarily explores the modeling method of n-level complex helical structures with coal mining machine cables as the research object. The paper first elaborately introduces the modeling method of n-level helix curves based on parametric equations and coordinate transformations, and compensates for the n-level helix curves with corrected pitch, which can obtain more accurate n-level helix curves and improve the accuracy of n-level helix curves modeling. Subsequently, based on this high-precision n-level helix curves modeling method, the paper elaborates on the method of solving pitch and twisting radius of multi-layer helical structure. Calculation scripts were written based on the above methods, which can be used to batch calculate the twisting radius and pitch of each layer structure in multi-layer structures when satisfying the conditions of in-layer tangency, inter-layer tangency, and extrusion deformation, and retain the actual results through logical judgment. Then, based on the above two methods, the paper developed a modeling method for braided structures based on piecewise functions containing fifth-order polynomials, which can effectively avoid the problem of insufficiently dense arrangement of braided lines and easy interference in traditional methods. Finally, a set of modeling tools was developed using C# and Python in Grasshopper to implement the modeling algorithm. Taking the MCPT-1.9/3.3 3120 + 170 + 4 * 10 coal mining machine cable as an example. The cable was modeled using both the method proposed in this paper and the traditional method. Comparative data shows that the method proposed in this paper can reduce errors by 3.31E6 times in the second-level and above helical structures. In addition this paper compares the standard line length, measured line length, and the line length established by the proposed model, showing that the relative errors are both less than 0.1941%. This paper provides a new, systematic, high-precision, and full-process cable modeling method, in which all parameters except the process parameters are accurately solved by equations. It lays a theoretical foundation for the high-precision simulation and intelligent sensing cables, which is of great significance for improving the safety, stability, and efficient development of the coal mining industry. The research results of the paper can not only be applied to the modeling of coal mining machine cables but also can be extended to the modeling of other complex multi-layer helical structures.

Abstract This study explores integrating blockchain technology into the Internet of Medical Things (IoMT) to address security and privacy challenges. Blockchain’s transparency, confidentiality, and decentralization offer significant potential benefits in the healthcare domain. The research examines various blockchain components, layers, and protocols, highlighting their role in IoMT. It also explores IoMT applications, security challenges, and methods for integrating blockchain to enhance security. Blockchain integration can be vital in securing and managing this data while preserving patient privacy. It also opens up new possibilities in healthcare, medical research, and data management. The results provide a practical approach to handling a large amount of data from IoMT devices. This strategy makes effective use of data resource fragmentation and encryption techniques. It is essential to have well-defined standards and norms, especially in the healthcare sector, where upholding safety and protecting the confidentiality of information are critical. These results illustrate that it is essential to follow standards like HIPAA, and blockchain technology can help ensure these criteria are met. Furthermore, the study explores the potential benefits of blockchain technology for enhancing inter-system communication in the healthcare industry while maintaining patient privacy protection. The results highlight the effectiveness of blockchain’s consistency and cryptographic techniques in combining identity management and healthcare data protection, protecting patient privacy and data integrity. Blockchain is an unchangeable distributed ledger system. In short, the paper provides important insights into how blockchain technology may transform the healthcare industry by effectively addressing significant challenges and generating legal, safe, and interoperable solutions. Researchers, doctors, and graduate students are the audience for our paper.