By introducing environmental mediator, for avoiding randomness in selecting indicators and subjectivity in deciding interaction path coefficients to reveal interaction mechanism among forest industry, ecology and environmental capacity, structural Pressure–State–Impact–Response (PSIR) and quantitative Structural Equation Modeling (SEM) are utilized with data on thirty–one provinces of China. We can find that: (i) forest industry has a negative influence on ecology, whereas ecology has a positive influence on industry; (ii) the destructive influence that industry has on ecology is much greater than the positive effect ecology has on industry; and (iii) environmental capacity plays a partial mediating role between industry and ecology. According to these results we can conclude that: (i) forest industry and ecology are not in a symbiotic relationship and have not reached a level of ecological security; (ii) the interaction between industry and ecology is in a period of transition from antagonistic to beneficial; and (iii) industry dominates ecology. However, we prospect the following advice: (i) methods of forest industry should be changed; (ii) new modes of industrial integration and circular economy should be developed; and (iii) the ability of the environment to sustain itself should be enhanced. The level of forest ecological security to discuss the symbiotic mechanism of industry and ecology will propose to be measured in future.

Taiwan’s auditors have suffered from processing excessive audit data, including drawing audit evidence. This study advances sampling techniques by integrating machine learning with sampling. This machine learning integration helps avoid sampling bias, keep randomness and variability, and target the risker samples. We first apply a Naive Bayes classifier to classify data into some classes. Next, a user-based, item-based, or hybrid approach is employed to draw audit evidence. The representativeness index is the primary metric for measuring the representativeness of audit evidence. The user-based approach denotes the selection of samples between two percentiles in a class as audit evidence. It may be equivalent to a combination of monetary and variable sampling methods. The item-based approach represents the choice of risky samples as audit evidence. It may be identical to a combination of non-statistical and monetary sampling methods. Auditors can hybridize those user-based and item-based approaches to balance representativeness and riskiness in selecting audit evidence. Three experiments show that sampling using machine learning integration has the benefits of drawing unbiased samples, handling complex patterns, correlations, and unstructured data, and improving efficiency in sampling big data. However, the limitations are the classification accuracy output by machine learning algorithms and the range of prior probabilities.

In the dynamic landscape of the telecommunications industry, understanding and mitigating customer churn is paramount for maintaining competitive advantage and sustaining revenue streams. This comprehensive literature review delves into the intricacies of customer churn prediction, a crucial aspect in fortifying the bond between telecom businesses and their customers. The review methodically analyzes 11 articles published between 2000 and March 2024, spotlighting the strategic role of Customer Relationship Management and the prevalence of high churn rates that necessitate a shift from customer acquisition to retention strategies. Furthermore, the review navigates through the challenges inherent in churn prediction, including the complexities of managing large, imbalanced, and noisy datasets and the intricacies involved in model selection and optimization. Despite these challenges, the paper highlights the superior capability of machine learning techniques in handling complex datasets to provide accurate predictions, albeit acknowledging their practical complexities. The main aim of the study is to provide an overview, serving as a knowledge base for researchers interested in machine learning techniques within the realm of telecommunications.

Herein, we present the design technique of a resonant rectifier for piezoelectric (PE) energy harvesting. We propose two diode equivalents to reduce the voltage drop in the rectifier operation, a minuscule-drop-diode equivalent (MDDE) and a low-drop-diode equivalent (LDDE). The diode equivalents are embedded in resonant rectifier integrated circuits (ICs), which use symmetric bias-flip to reduce the power wasted for charging and discharging the internal capacitance of a PE transducer. The self-startup function is supported by synchronously generating control pulses for the bias-flip from the PE transducer. Two resonant rectifier ICs, using both MDDE and LDDE, are fabricated in a 0.18 μm CMOS process and their performances are characterized under external and self-power conditions. Under the external-power condition, the rectifier using LDDE delivers an output power POUT of 564 μW and a rectifier output voltage VRECT of 3.36 V with a power conversion efficiency (PCE) of 90.1%. Under self-power conditions, the rectifier using MDDE delivers a POUT of 288 μW and a VRECT of 2.4 V with a corresponding PCE of 74.6%. The result shows that the power extraction capability of the proposed rectifier is 5.9 and 3.0 times higher than that of a conventional full-bridge rectifier.

This paper proposes a novel single-stage buck-boost three-Level neutral-point-clamped (NPC) inverter with two independent dc sources coupled for the grid-tied photovoltaic (PV) application, which can effectively solve the unbalanced operational conditions generally appeared between two coupled independent PV sources induced by the unequal irradiation and temperature distribution. The proposed control scheme can simultaneously guarantee the maximum power point (MPP) operation of both PV sources and maintain the output waveform quality. Compared to the traditional two-stage PV inverter, the proposed NPC inverter could reduce the PV array voltage requirement and dc-link capacitors’ voltage rating, meanwhile show the advantage in operational efficiency. MATLAB simulations and the captured experimental results are presented to show the performance of the proposed three-level inverter.

The article presents several innovative systems developed through student laboratory projects, including two autonomous vehicles, a four-legged walking robot, an active ankle-foot orthosis, a ball on beam balancing mechanism, a ball on plate system, and a manipulator arm—all powered by pneumatic artificial muscles (PAMs). Fluidic muscles offer significant potential in various mechatronic systems, robots, and manipulators due to their flexible, lightweight nature and compliance. Their ability to produce smooth and adaptable motions is particularly beneficial in applications requiring natural and human-like movement, such as rehabilitation and assistive devices. Although PAMs powered control systems face challenges with nonlinear phenomena, their biologically inspired design holds promise for future applications across various industries. These may include innovative applicability in industrial, automotive, and aerospace sectors, as well as use in sports, medical aids, entertainment, and animatronics. Incorporating self-made laboratory systems actuated by PAMs into control systems education offers a comprehensive learning opportunity that combines theoretical concepts with hands-on experience. This approach develops the necessary skills of future engineers, expanding their understanding of technical principles and preparing them for future challenges in engineering practice.

Intelligent fault diagnosis techniques have replaced the time-consuming and unreliable human analysis, increasing the efficiency of fault diagnosis. Deep learning model can improve the accuracy of intelligent fault diagnosis with the help of its multilayer nonlinear mapping ability. This paper has proposed a novel method named Deep Convolutional Neural Networks with Wide First-layer Kernels (WDCNN). The proposed method uses raw vibration signals as input (data augmentation is used to generate more inputs), and uses the wide kernels in first convolutional layer for extracting feature and suppressing high frequency noise. Small convolutional kernels in the preceding layers are used for multilayer nonlinear mapping. AdaBN is implemented to improve the domain adaptation ability of the model. The proposed model addresses the problem that currently, the accuracy of CNN applied to fault diagnosis is not very high. WDCNN can not only achieve 100% classification accuracy on normal signals, but also outperform state of the art DNN model which is based on frequency features under different working load and noisy environment.

Origin, mechanics and properties of the Solar System are analysed in the framework of the Complete Relativity theory (by the same author). According to Complete Relativity, everything is relative. Any apparent absolutism (notably invariance to scale of dimensional constants, absolute elementariness, invariance to time) is an illusion stemming from limits imposed by [or on] polarized observers that will inevitably lead to misinterpretation of phenomena (another illusion) occurring on non-directly observable scales or even on observable but distant scales in space or time. If everything is relative, reference frames will exist where particles are planets and where planets are living beings. Earth is, therefore, analysed here in more detail, both as a particle and, as a living evolving being (of, hypothesized, extremely introverted intelligence). The analysis confirms the postulates and hypotheses of the theory (ie. existence of discrete vertical energy levels) with a significant degree of confidence. During the analysis, some new hypotheses have emerged. These are discussed and confirmed with various degrees of confidence. To increase confidence or refute some hypotheses, experimental verification is necessary. Main conclusions that stem from my research and are further confirmed in this paper are: universes are, indeed, completely relative; Solar System is a scaled (inflated, in some interpretations) Carbon isotope with a nucleus in a condensed (bosonic) state and components in various vertically excited states; life is common everywhere, albeit extroverted complex forms are present on planetary surfaces only during planetary neurogenesis; anthropogenic climate change is only a part (trigger from one perspective) of bigger global changes; major extinction events on a surface of a planet are relative extinctions, a regular part of transformation and transfer of life in the process of planetary neurogenesis.

In this paper, a method is developed for presenting a novel virtual torque sensor based on precise model and position measurements avoids the need of traditional strain gauges and amplifiers. More specifically, the harmonic drive compliance model and the Gaussian process regression (GPR) technique are used together to achieve virtual torque sensor measurement. While the harmonic drive compliance model provides the analytic part, the Gaussian process regression method is used to reconstruct the unmolded part based on motor-side and link-side joint angles as well as motor current. After an automatic offline calibration, the method allows for a lean online implementation. The virtual torque sensor measurement is compared with measurements of a commercial torque sensor, and the results have attested the effectiveness of the proposed method.

This paper aims to develop a hierarchical risk assessment model using the newly-developed evidential reasoning (ER) rule, which constitutes a generic conjunctive probabilistic reasoning process. In this paper, we first provide a brief introduction to the basics of the ER rule and emphasize the strengths for representing and aggregating uncertain information from multiple experts and sources. Further, we discuss the key steps of developing the hierarchical risk assessment framework systematically, including (1) formulation of risk assessment hierarchy, (2) representation of both qualitative and quantitative information, (3) elicitation of attribute weights and information reliabilities, (4) aggregation of assessment information using the ER rule and (5) quantification and ranking of risks using utility-based transformation. The proposed hierarchical risk assessment framework can potentially be implemented to various complex and uncertain systems. A case study on the fire/explosion risk assessment of marine vessels demonstrates the applicability of the proposed risk assessment model.

Abstract: High step-up voltage gain nonisolated DC-DC converter have attracted much attention in photovoltaic, fuel cells and other renewable energy system applications. In this paper, by combining input current ripple-free boost cell with coupled-inductor voltage-doubler cell, an input current ripple-free high voltage gain nonisolated converter is proposed. In addition, passive lossless clamp circuit is adopted to recycle the leakage inductor energy and to reduce the voltage spike across the power switch. By utilizing voltage-doubler cell consisting of diode and capacitor, the voltage stress of switch is further reduced and the resonance between the leakage inductor and the stray capacitor of the output diode is eliminated. A low switch-on-resistance low-voltage-rated MOSFET can therefore be employed to reduce the conduction loss and cost. The reverse recovery loss of output diode is reduced, and the efficiency of converter can be improved. Furthermore, the proposed converter can achieve nearly zero input current-ripple and make the design of electromagnetic interference (EMI) filter easy. Steady state analysis and operation mode of the converter is performed. Finally, experimental results are presented to verify the analysis results of the proposed converter.

Wood-borer extermination by a dry heat clean-up is the essential method from the point of view of sustainability of historical wooden structures like trusses and ceilings in heritage-listed buildings. The wooden components are heated by a hot air of temperature about 100°C until the temperature in the centre of the wooden structures reaches 55°C to kill the wood-borers. In this article we review the method and present solution of the heat equation for structural wooden components. We also suggest an approximate formula that enables to estimate the necessary heating time for different cross sections of wooden components and can be used in technical application of the method.

Estimating ground surface PM2.5 with fine spatiotemporal resolution is a critical technique for exposure assessments in epidemiological studies of its health risks. Previous studies have utilized monitoring, satellite remote sensing or air quality modeling data to evaluate the spatiotemporal variations of PM2.5 concentrations, but such studies rarely combined these data simultaneously. We develop a three-stage model to fuse PM2.5 monitoring data, satellite-derived aerosol optical depth (AOD) and community multi-scale air quality (CMAQ) simulations together and apply it to estimate daily PM2.5 at a spatial resolution of 0.1˚ over China. Performance of the three-stage model is evaluated using a cross-validation (CV) method step by step. CV results show that the finally fused estimator of PM2.5 is in good agreement with the observational data (RMSE = 23.00 μg/m^3 and R2 = 0.72) and outperforms either AOD-retrieved PM2.5 (R2 = 0.62) or CMAQ simulations (R2 = 0.51). According to step-specific CVs, in data fusion, AOD-retrieved PM2.5 plays a key role to reduce mean bias, whereas CMAQ provides all-spacetime-covered predictions, which avoids sampling bias caused by non-random incompleteness in satellite-derived AOD. Our fused products are more capable than either CMAQ simulations or AOD-based estimates in characterizing the polluting procedure during haze episodes and thus can support both chronic and acute exposure assessments of ambient PM2.5. Based on the products, averaged concentration of annual exposure to PM2.5 was 55.75 μg/m3, while averaged count of polluted days (PM2.5 > 75 μg/m3) was 81, across China during 2014. Fused estimates will be publicly available for future health-related studies.

Natural polluted insulator surfaces are always coated with various kinds of soluble constituents, and those constituents affect flashover performance differentially. Currently this fact is not considered either in laboratory experiments or field pollution degree measurements, causing the existing insulation selection method to be deficient. In this paper a systematical research on insulator flashover voltage gradient correction involving types of soluble pollution constituents was presented. Taking typical type glass insulator as the sample, its flashover tests polluted by typical soluble chemicals NaCl, NaNO₃, KNO₃, NH₄NO₃, MgSO₄, Ca(NO₃)₂ and CaSO₄ were carried out. Then the flashover gradient correction was made combining the flashover performance of each soluble constituent, the ESDD contribution of the seven constituents, as well as the saturation performance of CaSO₄. The correction was well verified with the flashover test results of insulator polluted by three types of soluble mixture. Research results indicate that the flashover gradient correction method proposed by this paper performs well in reducing the calculating error. It is recommended to carry out component measurements and flashover gradient correction to better select outdoor insulation configuration.

This study investigates the long-term stability of the Suomi National Polar-orbiting Partnership (S-NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) moderate-resolution Thermal Emissive Bands (M TEBs; M12 – M16) covering a period from February 2012 to August 2020. It also assesses inter-sensor consistency of the VIIRS M TEBs among three satellites-S-NPP, NOAA-20, and NOAA-21 over eight months spanning from March 18 to November 30, 2023. The field of interesting is limited to the ocean surface between 60°S and 60°N, specifically under clear-sky conditions. Taking radiative transfer modeling (RTM) as the transfer reference, we employed the Community Radiative Transfer Model (CRTM) to simulate VIIRS TEB brightness temperature (BTs), incorporating European Centre for Medium-range Weather Forecasts (ECMWF) reanalysis data as inputs. Our results reveal two key findings. Firstly, the reprocessed S-NPP VIIRS TEBs exhibit a robust long-term stability, as demonstrated through analyses of the observation minus background BT differences (O-B ∆BTs) between VIIRS measurements (O) and CRTM simulations (B). The drifts of the O-B BT differences are consistently less than 0.105 K/Decade across all S-NPP VIIRS M TEB bands. Notably, observations from VIIRS M14 and M16 stand out with drifts well within 0.04 K/Decade, reinforcing their exceptional reliability for climate change studies. Secondly, excellent inter-sensor consistency among these three VIIRS instruments is confirmed through the double-difference analysis method (O-O). This method relies on the O-B BT differences obtained from daily data. The mean inter-VIIRS O-O BT differences remain within 0.08 K for all M TEBs, except for M13. Even in the case of M13, the O-O BT differences between NOAA-21 and NOAA-20/S-NPP have values of 0.312 K and 0.234 K, respectively, which are comparable to the 0.2 K difference between some TEBs among VIIRS and MODIS. These disparities are primarily attributed to the significant differences in the Spectral Response Function (SRF) of NOAA-21 compared to NOAA-20 and S-NPP. Our study confirms the RTM-based TEB quality evaluation method’s versatility and effectiveness in assessing long-term sensor stability and inter-sensor consistency. The double-difference approach effectively mitigates uncertainties and biases inherent to CRTM simulations, establishing itself a robust mechanism for assessing inter-sensor consistency. In addition, we’ve observed that, except for M13, M12 always exhibits larger spatial variations of O-O BT differences with greater uncertainties compared to the other M TEBs. The influence of solar contribution through sea surface reflection on the Top of Atmosphere (TOA) radiance measurements during daytime in M12 should not be underestimated.

Utilization of wasted heat instead of fuel combustion is effective to reduce primary energy consumption for mitigating global warming problem. Because wasted heat sources are not necessarily located close to areas of heat demand, one of the difficulties is that wasted heat has to be transferred from heat source side to heat demand side, which may require heat transportation over long distance. From this point we proposed and have examined new idea of heat transportation using ammonia-water as the working fluid which system is named Solution Transportation Absorption chiller, in short STA. Our previous studies of STA were mainly the experimental investigation with STA facility which cooling power was 25RT (90kW). As a result, the COP of STA was found almost same value 0.65 with the conventional absorption chiller without depending on the transportation distances. The simulation using AspenHYSYS also examined with same experimental condition. The experimental data showed good agreement with the simulation calculation. In this study, we examined the large-scale cooling power STA on simulation. The examination cooling powers were from 90 kW(25RT) to 3517 kW(1000RT). All cooling power achieved around COP 0.64 including pump power consumptions. In addition, we performed the dynamic simulation. As the results, there was no effect of pipeline size on the cooling capacities and mass flow rates. Furthermore, the stability time of the cooling capacities and mass flow rates were almost same regardless of the pipeline size and cooling capacity. In other words, STA may be achieved the same COP even though having various complex conditions compared with the conventional absorption chiller.

Structural health monitoring has been studied by a number of researchers as well as various industries to keep up with the increasing demand for preventive maintenance routines. This work presents a novel method for reconstruct prompt, informed strain/stress responses at the hot spots of the structures based on strain measurements at remote locations. The structural responses measured from usage monitoring system at available locations are decomposed into modal responses using empirical mode decomposition. Transformation equations based on finite element modeling are derived to extrapolate the modal responses from the measured locations to critical locations where direct sensor measurements are not available. Then, two numerical examples (a two-span beam and a 19956-degree of freedom simplified airfoil) are used to demonstrate the overall reconstruction method. Finally, the present work investigates the effectiveness and accuracy of the method through a set of experiments conducted on an aluminium alloy cantilever beam commonly used in air vehicle and spacecraft. The experiments collect the vibration strain signals of the beam via optical fiber sensors. Reconstruction results are compared with theoretical solutions and a detailed error analysis is also provided.

This study explores optimizing Synthetic Aperture Radar (SAR) satellite constellation scheduling for multi-imaging missions in densely targeted areas using an in-house developed Modified Dynamic Programming (MDP) algorithm. By employing Mixed-Integer Linear Programming (MILP) to define the mission planning problem, the research aims to maximize observation of high-value targets within restricted planning horizons. Numerical simulations, covering a wide range of target numbers and satellite configurations, reveal the MDP algorithm’s superior mission allocation efficiency, enhanced success rates, and reduced revisit times, compared to the Greedy algorithm. The findings underscore the MDP algorithm’s improved operational efficiency and planning robustness for complex imaging tasks, demonstrating significant advancements over traditional approaches.

Aiming at the limitations of single-functionality, limited applicability, and complex designs prevalent in current metasurfaces, we propose a terahertz multifunctional and multiband tunable metasurface utilizing a VO2-metal hybrid structure. This metasurface structure comprises a top VO2-metal resonance layer, a middle polyimide dielectric layer, and a gold film reflective layer at the bottom. This metasurface exhibits multifunctionality, operating independently of polarization and incident angle. The varying conductivity states of the VO2 layers, enable the metasurface to achieve various terahertz functionalities, including single-band absorption, broadband THz absorption, and multiband perfect polarization conversion for linear (LP) and circularly polarized (CP) incident waves. Finally, we believe that the functional adaptability of the proposed metasurface expands the repertoire of options available for future terahertz device designs.

The variation of aerosols, especially dust aerosol, in time and space plays an important role in climate forcing studies. Aerosols can effectively reduce land surface longwave emission and re-emit energy at a colder temperature, making estimation of downwelling surface longwave radiation (DSLR) with satellite data difficult. Using the latest atmospheric radiative transfer code (MODTRAN 5.0), we simulate the outgoing longwave radiation (OLR) and DSLR under different land surface and atmospheric profile conditions. The results show that dust aerosol has an obvious “warming” effect to longwave radiation compared with other aerosols, that aerosol longwave radiative forcing (ALRF) increased with increasing aerosol optical depth (AOD), and that the atmospheric water vapor content (WVC) is critical to the understanding of ALRF. A method is proposed to improve the accuracy of DSLR estimation from satellite data for the skies under heavy dust aerosols. The AOD and atmospheric WVC under cloud-free conditions with a relatively simple satellite-based radiation model that yields the high accurate DSLR under heavy dust aerosol are used explicitly as model input to reduce the effects of dust aerosol on the estimation of DSLR. Validations of the proposed model with satellites data and field measurements show that it estimates the DSLR accurately under heavy dust aerosol skies. The root mean square errors (RMSEs) are 20.4 W/M² and 24.2 W/M² for Terra and Aqua satellites, respectively, at the Yingke site, and the biases are 2.7 W/M² and 9.6 W/M², respectively. For the Arvaikheer site, the RMSEs are 23.2 W/M² and 19.8 W/M² for Terra and Aqua, respectively, and the biases are 7.8 W/M² and 10.5 W/M², respectively. The proposed method is especially applicable to acquire relatively high accurate DSLR under heavy dust aerosol using MODIS data with available WVC and AOD data.

We have shown that three astrometric solar-system anomalies can be explained satisfactorily by using evolutionary gravitational constant G and speed of light c in the Einstein’s field equation. These are: a) the Pioneer acceleration anomaly; b) the anomalous secular increase of Moon-orbit eccentricity; and c) the anomalous secular change in the astronomical unit AU. The gravitational constant G and the speed of light c both increase as dG/dt = 5.4GH₀ and dc/dt = 1.8cH₀ with H₀ as the Hubble constant. We also show that the Planck’s constant ħ increases as dħ/dt = 1.8ħH₀. Additionally, the new approach fits the supernovae Ia redshift vs distance modulus data as well as the standard ΛCDM model with just one adjustable parameter H₀.

This paper explores the capability of various machine learning algorithms, including Random Forests and advanced gradient boosting techniques such as XGBoost, LightGBM, and CatBoost, to predict customer churn in the telecommunications sector. For this analysis, a dataset available to the public was employed. The performance of these algorithms was assessed using recognized metrics, including Accuracy, Precision, Recall, F1-score, and the Receiver Operating Characteristic Area Under Curve (ROC AUC). These metrics were evaluated at different phases: subsequent to data preprocessing and feature selection; following the application of SMOTE and ADASYN sampling methods; and after conducting hyperparameter tuning on the data that had been adjusted by SMOTE and ADASYN. The outcomes underscore the notable efficiency of upsampling techniques such as SMOTE and ADASYN in addressing the imbalance inherent in customer churn prediction. Notably, the application of random grid search for hyperparameter optimization did not significantly alter the results, which remained comparatively unchanged. The algorithms' performance post- ADASYN application marginally surpassed that observed after SMOTE application. Remarkably, LightGBM achieved an exceptional F1-score of 89% and an ROC AUC of 95% subsequent to the ADASYN sampling technique. This underlines the effectiveness of advanced boosting algorithms and upsampling methods like SMOTE and ADASYN in navigating the complexities of imbalanced datasets and intricate feature interdependencies.

One of the most crucial elements in deeply understanding humans on a psychological level is manifested through facial expressions. The analysis of a human behavior can be informed by their facial expressions, making it essential to employ indicators such as expression (Expr), valence-arousal (VA), and action units (AU). In this paper, we introduce the method proposed in the Challenge of the 6th Workshop and Competition on Affective Behavior Analysis in-the-wild (ABAW) at CVPR 2024. Our proposed method utilizes the multi-modal Aff-wild2 dataset, which is splitted into spatial and audio modalities. For the spatial data, we extract features using a SimMiM model that was pre-trained on a diverse set of facial expression data. For the audio data, we extract features using a WAV2VEC model. To fusion the extracted spatial and audio features, we employed the cascaded cross-attention mechanism of a transformer.

Almost all single-family detached house in Canada consume huge electricity for space heating and domestic hot water (DHW) purposes. There are many possibilities to design an energy-efficient house. A solar water heating system can be used for domestic water and space heating. Water temperature can be kept constant always by connecting a heat pump or oil burner because solar energy is intermittent. Proper and optimized solar photovoltaic and collector design, tank design, heat pump selection, house insulation, total demand calculation in each section are essential. Energy-Efficient house design has been proposed with water heating and space heating system and compared with the existing system, solar PV based systems, and solar collector based system. The tracking and non-tracking based solar thermal collector based and the solar photovoltaic based system has been compared in this paper and investigate the suitable one for practically applicable and acceptable by the people. Simulation has been done by using the PolySun software. It found that by implementing the proposed PV based system with tracking is highly suitable considering lower cost, high output power, flexibility, easy installation.

Abstract: This study investigates the transition to sustainable shipping industry within the framework of the IMO's 2050 carbon neutrality objective, focusing on socio-technical systems analysis. The research delves into the sustainable paradigm shift towards alternative marine fuels such as LNG, methanol, ammonia, and hydrogen from a socio-technical transition perspective, analyzing the multi-level per-spective challenges and opportunities. Specifically, it scrutinizes the implications of adopting these al-ternative fuels for global shipping, identifying technical uncertainties, financial constraints, and infra-structure needs. Through a comprehensive review of existing literature on ship emissions and alterna-tive fuels, coupled with socio-technical transition theory, the study addresses the multifaceted chal-lenges and opportunities in meeting the IMO's environmental objectives. It emphasizes the necessity of collaborative governance, innovative niche strategies, and a holistic approach to leverage the current window of opportunity in facilitating the maritime industry's transition to sustainable fuel options. Fi-nally, this research enhances our understanding of the intricate interplay between technological, social, industrial, and cultural dynamics in the greening of shipping and offers insights into the strategic adop-tion of environmentally friendly marine fuels for a sustainable future.

The accurate prediction of heat load profiles with a daily resolution is required for a broad range of applications such as potential studies, design, or predictive operation of heating systems. If the heat demand of a consumer is mainly caused by (production) processes independent of the ambient temperature, existing load profile prediction methods fail. To close this gap, this study develops two ex post machine learning models for the prediction of the heat demand with a daily resolution. The selected input features are commonly available to each consumer connected to public natural gas and electricity grids or operating an energy monitoring system: Ambient temperature, weekday, electricity consumption, and heat consumption of the last seven days directly before the predicted day. The study’s database covers electricity and natural gas consumption load profiles from 82 German consumers over a period of two years. Electricity and heat consumption correlate strongly with individual patterns for many consumers. Both, shallow and deep learning algorithms are evaluated. The highest accuracy is achieved by a Long Short Term Memory (LSTM) model with a median of R² of 0.94. The ex post model architecture makes the model suitable for automated anomaly detection in energy monitoring systems.

Sarcopenia is associated with clinical complications, which increase mortality. Effective sarcopenia risk screening in the elderly should be grounded in a comprehensive assessment, encompassing both nutritional status and medication use. Current screening tools, while emphasizing muscular per-formance, exhibit limited sensitivity in identifying elderly individuals at risk of sarcopenia. This cross-sectional study aimed to develop a sarcopenia risk screening tool, named Sarc-Global, with enhanced sensitivity, incorporating factors pertinent to the overall health of the elderly. Utilizing the criteria established by EWGSOP2 for diagnosing sarcopenia, we evaluated 395 communi-ty-dwelling elderly individuals. This evaluation served as a reference for assessing the efficacy of two validated questionnaires, Sarc-F and Sarc-CalF, and our newly developed Sarc-Global. Con-structed through multiple logistic regression, Sarc-Global integrates variables such as sex, age, medication use, body mass index, arm circumference, and handgrip strength – all significantly associated with sarcopenia (p

Prostate cancer is the second leading cause of death in males in America, with advanced prostate cancers exhibiting a 5-year survival rate of only 32%. Castration-resistance is often developed during the course of treatment, though its influences require further understanding. This study explores the human microbiome for its implications in castration-resistance and metastasis in prostate cancer. RNA sequencing data was downloaded for bone and soft tissue biopsies of patients with metastatic castration-resistant prostate cancer. These sequences were mapped to bacterial sequences to yield species-level abundance approximations. Numerous species were found to correlate to the expression of known markers of castration-resistance, including AR, PI3K, and AKT. Castration-resistance-associated signaling pathways were also enriched with these species, including PI3K-AKT signaling and endocrine resistance. For their implications in cancer aggression and metastasis, cancer stem cell markers were further explored for relation to these species. EGFR and SLC3A2 were widely downregulated with greater abundance of most species. Our results suggest that the microbiome is heavily associated with castration-resistance and stemness in prostate cancer. By considering the microbiome’s importance in these factors, we may better understand the highly aggressive and highly invasive nature of prostate cancer, allowing for needed improvement in the treatment of this disease.

We conducted this systematic review with the objective to understand the efficacy and safety of proton pump inhibitors [PPIs] in the pediatric population. We have used PubMed to identify randomized controlled trials [RCTs] published between 1st June 2010 and 30th June 2023, in patients from birth to 18 years old, with gastroesophageal reflux disease [GERD], who received treatment with any PPI. The literature yielded 76 articles and 13 met inclusion criteria. For infants, PPIs were equal to placebo in reducing GERD symptoms in 4 articles. In 1, the number of GER episodes and esophageal acid exposure were lower in infants who received PPIs and left lateral position, but there was no significant improvement in symptoms. In another publication, the combination of PPIs and feeding modifications [FM] was not more effective than PPIs alone. For children and adolescents, PPIs were effective in improving symptoms and achieving mucosal and histological healing, which was subsequently maintained. To conclude, PPIs are not effective to reduce symptoms related to GERD in infants, but are effective in older children, along with histological remission. Generally, PPIs are well tolerated, but it is important to not forget the possible adverse events [AE], especially if they are used for a long time.

Atmospheric turbulence characteristics are essential in determining the quality of astronomical images and implementing adaptive optics systems. In this study, the vertical distributions of optical turbulence at the Peak Terskol observatory (43.27472 ° N 42.50083 ° E, 3127 m a.s.l.) using the Era-5 re-analysis and scintillation measurements are investigated. For the closest reanalysis grid node to the observatory, vertical profiles of the structural constant of the air refractive index turbulent fluctuations Cn2 were obtained. The calculated Cn2(z) vertical profiles are compared with the vertical distribution of turbulence intensity obtained from tomographic measurements with Shack-Hartmann sensor. The atmospheric coherence length at the location of Terskol Peak was estimated. Using combination of atmospheric models and scheme paramaterization of turbulence, Cn2(z) profiles at Mt. Kurapdag were obtained. The values of atmospheric coherence length at the Peak Terskol are compared with estimated values of this length at the ten astronomical sites including Ali, Lenghu and Daocheng.

This research investigates the complex transformation of the Baltic Clean and Dirty Tankers markets from 1998 to 2023. Our study quantifies the market multifractality in two distinct periods, from 1998 to 2010 and 2010 to 2023. We compare multifractal measures of the original, surrogated, and shuffled data, which indicate high multifractality and a complex market structure influenced by temporal organization and inherent volatility. Shuffled data analysis emphasizes the critical role of temporal sequences within market behaviour. Additionally, the study delves into external factors such as regulatory changes, economic disturbances, technological innovation, and environmental concerns contributing to market complexity. A distinct shift in multifractal values between the two periods suggests an evolution from crisis-induced complexities to a market influenced by diversified and modern dynamics. Our findings offer a concise understanding of the multifractal nature of the tanker markets and how global crises can impact the markets, emphasizing the importance of considering systemic risks in freight market analysis.

Background and Objectives: In this current state, Type 2 Diabetes Mellitus (T2DM) is more prevalent in the population, and metformin is used as a first-line medication for treating it. There was up surge in the utilization of alternative therapies in the management of diabetes. Especially in diabetes, Herbal medicines are considered safe and reliable by majority of the population. Aim: The purpose of this research is aimed to estimate the safety and efficacy of Polyherbal Metabolite Compounds (PHMC) capsules in adults with T2DM. Materials and Methods: In this study, 60 T2DM patients aged 18-60 years were randomly assigned to Groups A (30) receiving Metformin 500 mg twice a day after food and Group B (30) receiving PHMC capsule 500 mg twice a day after food in a prospective, randomized and open-label clinical study. The in-silico simulation study was performed to selected plants major compounds on target protein Insulin-like Growth Factor-1 (1K3A). Result: The PHMC is found to be safe and effective in the management of T2DM. The FBS, PPBS and HbA1c were significantly lowered (p

This paper is regarding design and program an Micro-controller Arduino Uno board by using Arduino software to work as a photo-sensor(Active) single axial solar tracker system(SASTS). A solar panel, two photo-resistors (LDR) in two sides (north/south) of the photo-voltaic(PV) and a servo motor are connected to the Uno board, which is running a code that prepared by Arduino software IDE in advanced then it works as a tracking system. Here, the LDRs send the signal of presence or absence of the light to the board and based on that sent signal the Uno reflects a new signal to the servo motor to rotate and finds the light source. Lastly, the photo-sensor single axis tracker is made while Continuously, the system tries to face the panel to the sun and whilst changing the irradiance intensity it starts searching to find the angle of highest irradiance. Based on results that are extracted from the data, the tracker system significantly boosts the output efficiency of the solar panel. By using the Micro-controller Uno board, LDRs, servo motor and special designed mechanical base, the tracking system is constructed, based on acquired data the influence of the STS on the increasing the solar panel efficiency is more obvious. Significantly, the tracker system rises the efficiency of the PV .

Food systems will need to undergo considerable transformation. To be better prepared for and resilient to uncertainty and disturbances in the future, resource users and managers need to further develop knowledge about the food and farming system, with its dominating feedback structures and complexities, and to test robust and integrated system-based solutions. This paper investigates how participatory system dynamics modeling can be adapted to groups at the community level with low or no formal educational background. The paper also analyzes the refinement of workshop participants’ mental models as a consequence of a participatory system dynamics intervention. For this purpose, we ran two workshops with small-scale farmers in Zambia. Analysis of workshop data and post-workshop interviews shows that participatory system dynamics is well adaptable to support an audience-specific learning-by-doing approach. The use of pictures, objects and water glasses in combination with the basic aspects of causal loop diagramming makes for a well-balanced toolbox. Participants acquire understanding that is also relevant beyond systems thinking in that is offers a range of practical insights such as a critical evaluation of common food security strategies.

With the development of higher voltage power grid, the high and low voltage parallel loops are emerging, which lead to energy losses, even threaten the security and stability of power system. The multi-infeed HVDC configurations widely appearing in AC/DC interconnected power system make this situation even worse. Aimed at energy saving and system security, a decision optimization method for power grid operating condition with high and low voltage parallel loops is proposed in this paper. Firstly, considering hub substation distribution and power grid structure, parallel loop opening schemes are generated with GN algorithm. Then, candidate opening schemes are preliminarily selected from all these generated schemes based on a filtering index. Finally, with the influence on power system security, stability and operation economy in consideration, an evaluation model for candidate opening schemes is founded based on analytic hierarchy process (AHP). And a fuzzy evaluation algorithm is used to find the optimal scheme. Simulation results of New England 39-bus system and an actual power system validate the effectiveness and superiority of this proposed method.

Solar energy is a renewable and sustainable source of energy, and it has been used in addition of specified technologies, such as solar cooling. The demand for cooling continues to increase in line with environmental changes and a greater desire for human comfort. This study primarily aimed to analyze the performance of an evaporative cooling system powered by solar energy and assess the economic and environmental impact of this system. In this system, hot, dry incoming air is used to evaporate water, thus transforming part of its sensitive heat into latent heat without any variation in enthalpy or total heat. The outputted air then has a lower temperature and a greater humidity, thus providing a more comfortable, healthier environment for its users. To achieve this study’s objectives, numerical code was developed and implemented in MATLAB. The obtained results reveal that the efficiency of the evaporative cooler exceeds 90%, with maximum efficiency being reached at a high wet-bulb depression, while minimum efficiency was observed when the dry air has a high relative humidity and a low dry-bulb temperature.

Of all the renewable energy sources, solar photovoltaic (PV) power is estimated to be a popular source due to several advantages such as its free availability, absence of rotating parts, integration to building such as rooftops, and less maintenance cost. The nonlinear current-voltage (I–V) characteristics and power generated from a PV array primarily depend on solar insolation/irradiation and panel temperature. The extracted PV output power is influenced by the accuracy with which the nonlinear power–voltage (P–V) characteristic curve is traced by the maximum power point tracking (MPPT) controller. In this paper, a bio-inspired roach infestation optimization (RIO) algorithm is proposed to extract the maximum power from the PV system (PVS). To validate the usefulness of the RIO MPPT algorithm, MATLAB/Simulink simulations are performed under varying environmental conditions, for example, step changes in solar irradiance, and partial shading of the PV array. Furthermore, the search performance of the RIO algorithm is examined on different unconstrained benchmark functions, and it is that realized that the RIO algorithm has improved convergence characteristics in terms of finding the optimal solution than Particle swarm optimization (PSO). The results demonstrated that the RIO-based MPPT performs remarkably in tracking with high accuracy as the PSO-based MPPT.

The prohibitive costs of small-scale solar photovoltaic (PV) racks decreases PV adoption velocity. To overcome these costs challenges, an open hardware design method is used to develop two novel variable tilt racking designs. These are the first stilt mounted racking designs that allow manual change of tilt angle from zero to 90 degrees by varying the length of cables. The racks are designed using the calculated dead, wind and snow loads for Canada as conservative design for most of the rest of the world. Structural capacities of the wooden members are then ascertained and resisting bending moment, shear force, tensile force, and compressive force for them is calculated. A structural and truss analysis is performed to ensure that racking design with-stands the applicable forces. Moreover, implications of changing the tilt angle on the wooden members/cables used to build the system are also determined. The systems offer significant economic savings ranging from 1/3rd to 2/3rd the capital expenses of the commercially available alternatives. In addition, the racking designs are easy-to-build and require minimal manufacturing operations, which increases their accessibility. The stilt-mounted designs can be employed for agrivoltaic settings while allowing farm workers shaded ergonomic access to perform planting, weeding, and harvesting.

The fact that Thailand’s energy policy has set a new renewable energy target of 30% of total final energy consumption by 2036. It also has the potential of solar energy and community demands in remote areas. However, most of the renewable energy technology will still be able to achieve renewable energy goals, similar to the case of the national policy that promotes Solar Home System (SHS) in remote areas, lack of good handling. Therefore, achieving the goal of the renewable energy policy should be in position using the right strategy. This article presents the result of a case study in the Akha upland community, northern Thailand, where we used the mixing method and factor analysis to analyze strategies for SHS related criteria. The key scopes and challenge included bottom-up planning concepts and subsidies from expert persons, while contributions to factors have an impact on developing sustainable SHS, include the creating approval of SHS technologies, developing of SHS management, promoting of SHS technologies, and supporting of SHS policies, respectively. Mainly, social factors provide positive effects, which thus influence the sustainable development of process SHS in terms of the creation of approval. Furthermore, there should be managed appropriately for each community, for the positive imagery of solar power.

Significant efforts are being made across academia and industry to better characterize lithium-ion battery cells as reliance on the technology for applications ranging from green energy storage to electric mobility increases. The measurement of short-term and long-term volume expansion in lithium-ion battery cells is relevant for several reasons. For instance, it provides information about the quality and homogeneity of battery cells during charge and discharge cycles, as well as aging over it’s lifetime. The expansion measurements are useful for the evaluation of new materials and the improvement of end-of-line quality tests during cell production. These measurements may also indicate the safety of battery cells by aiding in predicting state of charge and state of health over the lifetime of the cell. Expansion measurements can also assess inhomogeneities on the electrodes and defects such as gas accumulation and lithium plating. In this review, we first establish the known mechanisms through which short term and long term volume expansion in lithium-ion battery cells occurs. We then explore the current state-of-the-art for both contact and non-contact measurements of volume expansion. This review compiles existing literature to outline the various options available to researchers aiming to make ex situ volume expansion measurements by doing post mortem analyses on individual components and in operando measurements on entire battery cells. Finally, we discuss the different considerations when selecting an appropriate measurement technique. The selection of the optimal method for measuring battery cell expansion depends on the objective of the characterization, duration, required resolution, and repeatability of results. Costs and required space for the measurement equipment are also considered.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) persists throughout the world with over 65 million registered cases of survivors with post-COVID-19 sequelae, also known as LongCOVID-19 (LongC). LongC survivors exhibit various symptoms that span multiple organ systems, including the nervous system. To search for neurological biomarkers of LongC, we investigated the soluble proteins present in the plasma and the proteins associated with plasma neuronal-enriched extracellular vesicles (nEVs) in 33 LongC patients with neurological impairment (nLongC), 12 COVID-19 survivors without any LongC symptoms (Cov), and 28 pre-COVID-19 healthy controls (HC). COVID-19 positive participants were infected from 2020-2022, not hospitalized, and were vaccinated or unvaccinated before infection. IL-1b was significantly increased in both nLongC and Cov and IL-8 elevated in only nLongC. Both brain-derived neurotropic factor and cortisol were significantly elevated in nLongC and Cov compared to HC. nEVs from people with nLongC had significantly elevated protein markers of neuronal dysfunction, including amyloid beta 42, pTau181 and TDP-43. This study shows chronic peripheral inflammation with increased stress after COVID-19 infection. Additionally, differentially expressed nEV neurodegenerative proteins were identified in people recovering from COVID-19 regardless of persistent symptoms.

One of the most important sources of energy is the sun. Taiwan is located at north 22-25° latitude. Due to its proximity to the equator, it experiences only a small angle of sunlight incidence. Its unique geographical location which can obtain sustainable and stable solar resources. This study takes research on the forecast of solar radiation to maximize the benefits of solar power generation, and develops methods that can predict the future solar radiation pattern to help reduce the costs of solar power generation. This study builds supervised machine learning models, known as deep neural network (DNN) and long short-term memory neural network (LSTM). The hybrid supervised and unsupervised model, namely cluster-based artificial neural network (k-means clustering and fuzzy C-means clustering-based models), was developed. After establishing these models, the study evaluated their prediction results. For different prediction periods, the study selected the best-performing model based on the results and proposed combining them to establish a real-time updated solar radiation forecast system capable of predicting the next 12 hours. The study area covered Kaohsiung, Hualien, and Penghu in Taiwan. Data from ground stations of the Central Weather Administration, collected between 1993 and 2021, as well as the solar angle parameters of each station, were used as input data for the model. The results of this study show that different models have their advantages and disadvantages in predicting different future times. Therefore, the hybrid prediction system can predict future solar radiation more accurately than a single model.

Volcanism powered by tidal forces inside celestial bodies can provide enough energy to keep important solvents for living systems in the liquid phase. Moreover, tidal forces and their environmental consequences may strongly influence habitability of planets and other celestial bodies and may result in special forms of live and living conditions. A prerequisite to calculate such tidal interactions and consequences is depending on simulations for tidal accelerations in a multi-body system. Unfortunately, from measurements in many extrasolar planetary systems only few physical and orbital parameters are well enough known for investigated celestial bodies. For calculating tidal acceleration vectors under missing most orbital parameter exactly, a simulation method is developed that is only based on a few basic parameters, easily measurable even in extrasolar planetary systems. Such a method as being presented here, allows finding a relation between the tidal acceleration vectors and potential heating inside celestial objects. Using values and results of our model approach to our solar system as a “gold standard” for feasibility allowed us to classify this heating in relation to different forms of volcanism. This “gold standard” approach gave us a classification measure for the relevance of tidal heating in other extrasolar systems with a reduced availability of exact physical parameters. We would help to estimate conditions for the identification of potential candidates for further sophisticated investigations by more complex established methods like viscoelastic multi-body theories. As a first example, we applied the procedures developed here to the extrasolar planetary system TRAPPIST-1 as an example to check our working hypothesis.

The aim of this research work is to study the influence of temperature, solar irradiation and wind dust on the production of solar energy. In addition, it aims to study the assessment of water quality, in particular groundwater used for cooling and cleaning photovoltaic panels (quality analysis). it's an important source, stable and sustainable can be used entirely for cooling and cleaning PV modules from the sales dust, in order to improve their performance. To date, all research studies carried out in this context of cooling and cleaning using water have not taken account of the nature of this water. We have taken a sample of water from a well in a rural area in the Darra Tafilalet region of Morocco. This region has an arid and semi-arid climate, with very high temperatures and dust-laden winds, particularly in summer. These meteorological characteristics influence the efficiency of cells, particularly photovoltaic (PV) panels, reducing their performance. Next, the water sample was analysed in the laboratory. The results obtained show that the groundwater in this area is very sensitive to cooling and cleaning photovoltaic panels. Moreover, these results indicate that the temperature, solar irradiation and wind-blown dust have a significant influence on energy production.

Urea cycle disorders (UCDs) are common inborn errors of metabolism, with an incidence of one in 30,000 births. They are caused by deficiencies in any of six enzymes and two carrier proteins, the most common being Ornithine Transcarbamylase Deficiency (OTCD). OTCD results in impairment to excrete nitrogen, causing toxic buildup of ammonia with resultant encephalopathy. Hyperammonemia (HA) induces the conversion of glutamate to glutamine in the brain. Excess glutamine in the brain causes osmotic changes cerebral edema, changes in astrocyte morphology, and cell death. Acute symptoms of HA include vomiting, hyperventilation, seizures, and irritability. Long-term neurological changes include deficits in working memory and executive function. To date, there are no predictors of prognosis of infants with neonatal onset OTCD outside of plasma ammonia level at presentation and duration of hyperammonemic coma. We provide a comprehensive analysis of a 16-year-old male with neonatal onset of OTCD as an example of how brain biomarkers may be useful to monitor disease course and outcome. This male presented at 8 days post natal with plasma ammonia and glutamine of 677 and 4024 micromol/L and had a missense mutation in Exon 4 (p.R129H). Treatment included protein restriction, sodium benzoate, and citrulline, arginine, and iron. He suffered recurrent acute hyperammonemic episodes despite compliance, triggered by infections or catabolic stressors. We discuss the long-term effects of the hyperammonemic episodes by following MRI based disease biomarkers.

This paper investigates an innovative photovoltaic cell configuration, denoted as “Au/CuSCN/RbGeI3/CdTe/ZnO/TiO2/FTO", using SCAPS-1D simulation. The research focuses on optimizing device performance by analyzing key components, including ZnO as the electron transport layer (ETL), RbGeI3 as the perovskite absorber layer, CuSCN as the hole transport layer (HTL), and CdTe as the active buffer layer. A comparison is made with the standard “Au/CuSCN/CH3NH3SnI3/CdTe/ZnO/TiO2/FTO" configuration under dark and illuminated conditions. Through an in-depth investigation involving temperature effects, generation rate analysis, RbGeI3 absorber layer thickness, Nd level, and interface defect levels, an optimized design for a unit area is achieved. The proposed solar cell demonstrates outstanding performance metrics, including an open circuit voltage (Voc) of 0.94V, a short circuit current density (Jsc) of 34.52mA/cm2, a fill factor (FF) of 77.77%, and an energy conversion efficiency (η) of 25.40%. These values significantly outperform the reference models with energy conversion efficiencies of 10.11% and 21.09%. Real-world applicability is validated by implementing these optimized characteristics in a photovoltaic system for Pakistan’s geographical location using PVSyst software. The suggested solar cell achieves a remarkable performance ratio (PR) of 85.6%, surpassing the PR of the reference basic solar cell model at 83%. The findings of this research offer a promising and superior solar cell design, incorporating innovative materials and optimization techniques to achieve exceptional energy conversion efficiency and overall performance. This study’s outcomes have the potential to advance photovoltaic technology and contribute to sustainable energy solutions worldwide.

Solids in the interstellar medium consist of an intimate mixture of silicate and carbonaceous grains. Because 99% of silicates in meteorites were reprocessed at high temperatures in the inner regions of the Solar Nebula, we propose that similar levels of heating of carbonaceous materials in the oxygen-rich Solar Nebula would have converted nearly all carbon in dust and grain coatings to CO. We discuss catalytic experiments on a variety of grain surfaces that not only produce gas-phase species such as CH4, C2H6, C6H6, C6H5OH or CH3CN, but also produce carbonaceous solids and fibers that would be much more readily incorporated into growing planetesimals. CO and other more volatile products of these surface mediated reactions were likely transported outwards along with chondrule fragments and small Calcium Aluminum Inclusions (CAIs) to enhance the organic content in the outer regions of the nebula where comets formed. Carbonaceous fibers formed on the surfaces of refractory oxides may have significantly improved the aggregation efficiency of chondrules and CAIs. Carbonaceous fibers incorporated into chondritic parent bodies might have served as the carbon source for the generation of more complex organic species during thermal or hydrous metamorphic processes on the evolving asteroid.

We present an Arduino-based automatic robotic system which is used for cutting grass or lawns, mostly healthy grass which needs to cut neatly like in a public park or a private garden. The purpose of this proposed project is to design a programmable automatic pattern design grass cutting robot with solar power which no longer requires time-consuming manual grass-cutting, and that can be operated wirelessly using an Android Smartphone via Bluetooth from a safe distance which is capable of cutting the grass in indeed required shapes and patterns; the cutting blade can also be adjusted to maintain the different length of the grass. The main focus was to design a prototype that can work with a little or no Physical user interaction. The proposed work is accomplished by using an Arduino microcontroller, DC geared Motors, IR obstacle detection sensor, motor shield, relay module, DC battery, solar panel, and Bluetooth module. The grass-cutting robot system can be moved to the location in the lawn remotely where the user wants to cut the grass directly or in desired patterns. The user can press the desired pattern button from the mobile application, and the system will start cutting grass in the similar design such as a circle, spiral, rectangle, and continue pattern. Also, with the assistance of sensors positioned at the front of the vehicle, an automatic barrier detection system is introduced to enhance safety measurements to prevent any risks. IR obstacle detector sensors are used to detect obstacles, if any obstacle is found in front of the robot while traveling; it avoids the barrier by taking a right/right turn or stop automatically appropriately, thereby preventing the collision. Also, the main aim of this project is the formation of a grass cutter that relieves the user from mowing their own grasses and reduces environmental and noise pollution. The proposed system is designed as a lab-scale prototype to experimentally validate the efficiency, accuracy, and affordability of the systems. The experimental results prove that the proposed work has all in one capability (Simple and Pattern based grass cutting with mobile-application, obstacle detection), is very easy to use, and can be easily assembled in a simple hardware circuit. We note that the systems proposed can be implemented on a large scale under real conditions in the future, which will be useful in robotics applications and cutting grass in playing grounds such as cricket, football, and hockey, etc.

Solar heating technology is a promising solution to promote China to achieve the “3060 double carbon” target as soon as possible. And seasonal thermal storage (STS) can effectively solve the mismatch problem of solar heating systems between the supply and demand of thermal energy. Due to the instability of solar radiation resources and heat demand, it is necessary to analyze the dynamic response characteristics of the system. Yet, related studies are still scarce. In this study, a solar heating system with a solar tower receiver and STS was introduced in north China. The dynamic performance of the system is analyzed with a dynamic simulated method in a typical day or typical operation modes, and the switch mechanism between multiple operation modes is also revealed. The impact of different heating strategies on system performance was analyzed. Results showed that the solar fraction of the system reached 89.4% in the third year, which was 3.6% higher than the first year. The quality-quantity heating operation strategies can be effective ways to improve the discharge efficiency of STS and the system performance without any heat pump. The electricity consumption of the pump on the heating side could be significantly reduced by 44.6% compared with the quality control.

Efficient implementation of clean energy technologies, including pioneering solutions like mobile solar PV systems mounted on trailers (MSPT), is pivotal for addressing energy challenges, especially in regions with constrained power grid access. This endeavour is vital for meeting escalating elec-tricity demands and aligning with the UN Sustainable Development Goal (SDG) aimed at ensuring dependable and sustainable energy provision in developing countries. This paper investigates the aerodynamic behaviour of a designed MSPT using numerical and experimental approaches. Spe-cifically, the study focuses on the effects of wind velocity and tilt angels on the drag and lift forces, as well as drag and lift coefficients on the panel used in the MSPT system. Additionally, the overall wind force acting on the entire MSPT (including 9 large solar PV panels) was examined, considering the combined effects of wind flow and the system's geometry. The simulations were conducted using ANSYS-Fluent software and experimental testing was performed utilizing scaled-down models to validate the accuracy of the simulation. The insights and the knowledge from this study can be utilized to optimize the design, installation, and operation of such systems, ultimately enhancing their performance, reliability, and paving ways for further improvements in mobile solar PV technology.

Load forecasting is an important component of power system and resource planning for electrical grids. The adoption of electric vehicles (EVs), behind-the-meter (BTM) solar, and heat pumps will significant change the amount and variability of loads. Electrification adoption and load forecasting in arctic regions and Alaska is limited. This paper provides the first load and electrification adoption forecast for the Alaska Railbelt transmission system, including yearly adoption rates of EVs, BTM solar, and heat pumps and hourly load data. Adoption rates are based on available historical data and compared to other regional and national trends. Two forecasts are created: 1) a moderate adoption forecast based on projections from current adoption rates and comparisons to other projections, and 2) an aggressive forecast, which provides a bookend comparison at the high adoption rate of 90% for all technologies. The results of these forecasts demonstrate a significant increase in both energy, 80% and 116% for moderate and aggressive, respectively and peak load demand, 113% and 219% for moderate and aggressive, respectively. Additionally, the results indicate a maximum hourly load change of 260% and 381% for the moderate and aggressive forecasts, respectively. These findings highlight a need for resource planning, which accounts for increases in demand and suggests that significant demand management is needed to smooth and control the load fluctuations as a result of the adoption of EVs, BTM solar, and heat pumps.

This paper aims to investigate the rate of expansion and extraction within the solar system. We carried out the Solar system expansion calculations to do such a review. The Universe is expected to look the same from every point in it. After the big bang, Universe is expanding at some speed. Astrophysicists have been in a race to measure precisely how fast the Universe is expanding since Hubble announced that galaxies were systematically moving away from Milky Way Galaxy with a current speed in 1929. Hubble’s observations came after Einstein’s general relativity, which inspired the big bang theory. According to the Big Bang theory, the Universe has created billions of years ago with an explosion and started to expand until today. The expansion of the Universe mostly happens in vast spaces, so clusters of galaxies move away from each other. For example, raising bread during baking will expand, but the raisings will stay the same size while moving each other to expand the bread. Observers have proven that an object (galaxies, a cluster of planets) held together by gravity has a patch of nonexpanding space produced by a gravitational field. However, some observers claimed the solar system is not expanding, while others claimed it is expanding. Does our solar system expand in an expanding Universe? The cosmological expansion of local systems is reviewed in the modern cosmological models. We showed answers related to this question with the help of literature. This review article revisited the proof of the Solar System’s expansion and its speed with about 0.32 nm/s in an expanding Universe.

Conventional models do not fully explain composition of the solar system – for example, the presence of p-nuclei and post-post-Fe-nuclei remains not yet understood (and is one of the great unresolved puzzles of nuclear astrophysics in general); other puzzles exist. We offer a hypothesis which can explain the appearance of non-native elements in the solar system, and a feasible scenario for its implementation. The hypothesis suggests that a nuclear-fission "event" occurred in the inner part of the solar system at the time currently defined as the birth of the system. Conventional models have never considered fission as a contributing nuclei-production mechanism. Upon examination of the existing models and factual data (presented in volumes of publications but never combined into an aggregate), we identified one plausible scenario by which a fission event (not demolishing the entire solar system) could occur: an encounter with a compact super-dense stellar "fragment" (with specific properties) and its "explosion" in fission-cascades. Such scenario also helps resolve other long-standing puzzles of the solar system. For example, it provides that the fission-produced nuclei subsequently transformed into the material that (eventually) accreted into the "rocky" objects in the system (terrestrial planets, asteroids, etc.) and enriched the pre-existed hydrogen-helium objects (the Sun and the gaseous giants) – this offers an explanation for the planets’ inner position and compositional differences within the predominantly hydrogen-helium rest of the solar system. Other implications also follow.

Presently, Jordan confronts a double substantial challenge of enhancing energy security and mitigating greenhouse gas emissions. One of the most encouraging avenues to attain the objectives of sustainable development, energy security, and environmental conservation is to enhance the integration of renewable energy into the generation of electricity. In pursuit of this goal, the Jordanian government intends to expand investments in the green energy sector, with solar and wind energy poised to play a key role in satisfying energy demands and cultivating an environmental sustainability. This paper aims to examine the distinctive dynamics, challenges, obstacles, and potential solutions corresponding to the establishment of community solar and wind farms in Jordan suburban areas. It aims to highlight the opportunities and barriers influencing the track of sustainable energy adoption in the country. Through a comprehensive analysis of the Jordanian context, the goal is to extract insights that can guide policies and practices. Additionally, the findings may serve as a valuable benchmark for other regions confronting similar challenges on their pursuit for a sustainable energy future.

This paper proposes the configuration of an Organic Rankine Cycle (ORC) coupled to a solar domestic hot water system (SDHWS), with the purpose of analyzing the cogeneration capacity of the system. A simulation of the SDHWS was conducted at different temperatures, observing its performance to determine the amounts of useable heat generated by the solar collector; thus, from an energy balance, the amount of heat that may be used by the ORC could be determined. The working fluid that would be suitable for the temperatures and pressures given in the system were selected. The best fluid for the given conditions of superheated vapor at 120 °C and 604 kPa and a condensation temperature of 60 °C and 115 kPa was acetone. The main parameters for the expander thermodynamic design that may be used in such ORC were obtained with the possibility of generating 443 kWh of annual electric energy, with 6.65 % global efficiency of solar to electric power, or an overall efficiency of the cogeneration system of 56.35 % with a solar collector of 2.84 m².

The spectra and global distributions of the X-ray emissions generated by the solar wind charge-exchange (SWCX) process in the terrestrial magnetosheath are investigated based on a global hybrid model and a global geocoronal hydrogen model. The solar wind O6+ ions which are the primary charge state for oxygen ions in solar wind are considered. The line emissivity of the charge-exchange born O5+ ions are calculated by Spectral Analysis System for Astrophysical and Lab oratory (SASAL). It is found that the emission lines from the O5+ ranges from 105.607 to 118.291 eV with the strong line at 107.047 eV. We then simulated the magnetosheath X-ray emission intensity distributions with a virtual camera at two positions of north pole and dusk at six stages during the passing of a perpendicular interplanetary shock combined with a tangential discontinuity structure through the Earth’s magnetosphere. During this process, the X-ray emission intensity increases with time, and the maximum value is 27.11 keV cm-2 s-1 sr-1 on the dayside, which is 4.5 times that before the solar wind structure reaches the Earth. A clear shock structure can be seen in the magnetosheath and moves earthward. The maximum emission intensity seen at dusk is always higher than that seen at north pole.

The per capita availability of freshwater resources on a global scale is gradually decreasing due to population growth, rapid urbanization, industrialization, and modern lifestyles. In this situation, wastewater management becomes a major concern. Conventional treatment processes used to treat wastewater discharges are not adequate for the advanced removal of pollutants, especially refractory organic pollutants, which are mainly organic compounds that are not easily biodegradable in the environment. The use of photocatalytic membrane reactors (PMRs) for tertiary wastewater treatment has attracted much interest in recent periods, and various configurations have been investigated. The performance of a PMR using an iron doped titanium photo catalyst and Polysulfone-based polymeric membrane for municipal wastewater treatment was evaluated under natural and simulated solar light. Obtained results proved that solar driven PMRs can remove organic loading (COD) with high efficiencies and may represent a suitable alternative for the tertiary treatment step of municipal wastewater.

Promoting the use of solar energy resources has always faced the challenges of instability and supply-demand mismatch. The key to solving these issues is to efficiently store and utilize solar energy resources using high-performance heat storage devices. This study designed a high-performance shell-and-tube phase change thermal storage device and established a numerical model using ANSYS software to summarize the device's dynamic melting law. To verify the accuracy of the numerical simulation, a performance testing platform for the phase change thermal storage device was built to investigate the impact of factors such as inlet water temperature, inlet water flow rate, type of heat storage, and initial temperature of the device, and to reveal the change law of the device's performance. The results show that the inlet water temperature has the most significant impact on the device's heat storage and release performance. When the device's heat storage or release is used for heating, changing the inlet water flow rate has a weak and limited effect on the device's performance. However, when the device's heat release is used to provide domestic hot water, increasing the makeup water temperature and reducing the inlet water flow rate can significantly improve the device's effective heat release. Furthermore, based on the experimental validation of the model's correctness, this study further simulated and studied the impact of different factors on the device's heat storage process to optimize its structural design and provide technical references for the device's actual operation and installation. The results show that the placement of fins has a negligible effect on the performance of the heat storage device while reducing the fin spacing and increasing the fin thickness can significantly improve the melting efficiency of the phase change material (PCM). Additionally, the heat storage characteristics of the device are significantly better in the vertical installation mode than in the horizontal installation mode. This study provides theoretical guidance and technical references for the design and use of phase change thermal storage devices.

This paper attempts to describe the large-scale (solar system-like, astronomical-scale) systems of the known world using the physical models and mathematical tools of relativistic quantum mechanics. The value of H_x can be introduced and approximated as an analogue of the constant ℏ. Based on the quantum mechanical approach, the proper time scale of the solar system can be determined.

Solar photovoltaic (PV) generation suffers from multiple serious issues, for example shading effects directly affect the performance of installed PV arrays. By studying these issues, it can play a key role in improving the limitations and performance of PV panels. The study of PV arrays under partial shading conditions is mentioned in many papers as a part of other studies and as an independent phenomenon. Therefore, it is essential to improve these studies and update them with new research and study this problem in detail to know how to limit the effects of partial shading irradiance conditions on PV arrays performances, including economical aspects. This paper proposes to study the effects of different partial shading irradiance conditions for a PV array in various configurations. The analysis is proved in detail through the simulation of the proposed PV array in the Matlab/Simulink environment. The obtained results show the performances of interconnected PV arrays under different partial shading irradiance and temperature conditions, including the solar array I-V and P-V characteristics. In addition, different PV array configurations with and without bypass and blocking protection diodes improve and minimize the effect of partial shading conditions and avoid the reductions in output power.

This paper focuses on designing and assessing Pumped Hydroelectric Energy Storage Systems (PHES), connected to the grid and PV system for self-consumption structured at Mutah university in an area of high solar potential. In focusing on PHES and PV literature was made to have data on the field, based on the grid code needed in Jordan. Next, a prospection to find the proper location for the installation was done. Afterward, a load profile was inserted to know the energy demand of the university. Then The productivity of the solar power plant of Mutah University was included. Finally, MATLAB software was used to realize the amount of energy to be stored, this data was used to implement the system which was chosen and sized. PHES layout was created to find the most accurate values for parameters to optimize the system performance, and to investigate the loss analysis. The system attains 9230.89 MWh/year. An annual load yields 4430 MWh/year, which covers the Mutah university demand with an estimated saving of 2039773 JD.

We have suggested earlier a new sustainable method for permafrost thermal stabilization that combines passive screening of solar radiation and precipitation with active solar-powered cooling of the near-surface soil layer thus preventing heat penetration in depth. Feasibility of this method has been shown by calculations, but needed experimental proof. In this article, we are presenting the results of soil temperature measurements obtained at the experimental implementation of this method outside of the permafrost area which actually meant higher thermal loads than in Polar Regions. We have shown that near-surface soil layer is kept frozen during the whole summer, even at air temperatures exceeding +30°C. Therefore, the method has been experimentally proven to be capable of sustaining soil frozen even in more extreme conditions than expected in permafrost areas. In addition to usual building and structure thermal stabilization, the method could be used to prevent the development of thermokarst, gas emission craters, and landslides; greenhouse gases, chemical, and biological pollution from the upper thawing layers at least in the area of human activities; protection against coastal erosion; and permafrost restoration after wildfires. Using commercially widely available components, the technology can be scaled up for virtually any size objects.

This study addresses the causal links between external factors and the main hydro-climatic variables. There is a gap in the literature on the description of a complete chain in addressing the structures of direct causal links of solar activity on the terrestrial variables. This is why, the present study uses the extensive facilities of the application of information theory in view of recent advances in different fields. Also, by other methods (e.g. neural networks) first are tested the existence non-linear links of solar-terrestrial influences on hydro-climate system. The results are promising related to the solar impact on terrestrial phenomena which is discriminant in space-time domain. The implications prove robust for determining the causal measure of climate variables under direct solar impact which makes it easier to consider solar activity in climate models, by appropriate parametrizations. This study found that hydro-climatic variables are sensitive to solar impact only for certain frequencies (periods) and these have a coherence with the Solar-Flux only for some lags of the Solar-Flux (in advance).

The technologies influencing alternative ways of transportation are augmenting in recent years as the need for transportation is increasing rapidly due to urbanization and motorization. In this paper, a solar powered electric auto-rickshaw (SPEA) is designed and developed for Indian conditions. The developed vehicle is comprehensively analyzed techno-economically for its viability in the Indian market. The performance analysis of SPEA results in an optimal charging rate of 2 kWh per day with an average solar irradiance of 325 W/m2. The discharging characteristics are studied based on different loading conditions. The vehicle achieved a maximum speed of 21.69 km/h with battery discharge rate of 296W at 90kg load and also reached a maximum discharge rate of 540W at 390kg loading with a maximum speed of 12.11 km/h. The environmental analysis of SPEA displayed yearly CO2 emissions of 1,777 kg, 1,987 kg and 1,938 kg using Compressed Natural Gas, Liquefied Petroleum Gas and gasoline engines respectively can be mitigated using SPEA. The results of financial analysis of SPEA were welcoming as the investor gets 24.44% lesser payback duration compared to gasoline run vehicle. Socio-Economic analysis of SPEA discussed its significant advantages and showed 18.73% and 3.9% increase in yearly income over gasoline driven and battery driven vehicles.

Since the energy sector is the main source of greenhouse gases, it has the highest potential for improvement. Improvements can be achieved by generating energy from renewable sources. Combined with thermal energy storage, concentrating solar power plants represent a promising technology for dispatchable renewable energy, ensuring a stable energy supply even in remote areas without contributing to greenhouse gas emissions during operation. However, it is important to note that greenhouse gases and other polluting emissions may occur during the manufacturing process of concentrating solar power plant components. This work analyses the design of the receiver to produce thermal energy for the existing solar dish CSP plant at the Energy Center of the Politecnico di Torino. The objective of this plant is to produce hot water for a small case study. Considering the average solar irradiance for Turin equal to 800 W/m2, the surface heat flux was obtained from the first part of the analysis, which was used to obtain the maximum internal temperature in the receiver equal to 873.7 °C. The maximum temperature obtained was a constraint for the selection of the material for the solar receiver. Of the various possibilities, copper was chosen. In the second part of the work, a Life Cycle Assessment was carried out to compare the emissions generated during the production of the main components of the CSP plant with the emissions generated by the methane-fuelled water heater that produces the same amount of water as the CSP plant. It can be concluded that the manufacture of the main components of the CSP plant results in lower greenhouse gas emissions than the operational phase of a conventional natural gas-fired water heater (1.6 kg CO2/day).

Recently it was found from Cassini data that the mean recession speed of Titan from Saturn is v = 11.3 ± 2.0 cm/yr which corresponds to a tidal quality factor of Saturn Q ≈ 100 while the standard estimate yields Q ≥ 6 · 10⁴ . It was assumed that such a large speed v is due to a resonance locking mechanism of five inner mid-sized moons of Saturn. In this paper, we show that an essential part of v may come from a local Hubble expansion, where the Hubble-Lemaˆıtre constant H0 recalculated to the Saturn-Titan distance D is 8.15 cm/(yrD). Our hypothesis is based on many other observations showing a slight expansion of the Solar system and also of our Galaxy at a rate comparable with H0. We demonstrate that the large disproportion in estimating the Q factor can be just caused by the local expansion effect. [Accepted for publication in "Gravitation and Cosmology". The paper is to appear in Vol. 28, Issue 2 (2022) of the journal Gravitation and Cosmology.]

The Sun is a huge gaseous body. However, we cannot observe events in the inner Sun due to the convection zone opacity according to previous models. Therefore, the flares originate from the front surface of the Sun. But the current study relied on the distance distribution of X-Ray solar flares, which concluded that the inner layers have much lower opacity than expected. It is even less than what was expected by the latest models based on helioseismology. This means that the flares may originate from the solar interior or solar core, and perhaps from the backside surface, and even appear to us from the frontside surface. Which the re-estimate and correct the currently listed solar flare’s location is needed. Additionally, the flare’s distance illustrations the solar interior layers and appears their boundaries from the core to the photosphere. This method allows us to monitor the variation of the core’s radius with time. The model of the flare’s distance has been developed in current study. But this needs to redevelopment after re-estimating the solar flares locations.

Road cracks significantly affect the serviceability and safety of roadways, especially in mountainous terrain. Traditional inspection methods, such as manual detection, are excessively time-consuming, labor-intensive, and inefficient. Additionally, multi-function detection vehicles equipped with various sensors are expensive and not suitable for mountainous roads due to poor conditions. To address these challenges, this study proposes a customized Unmanned Aerial Vehicles (UAV) inspection system designed for automatic crack detection. The system focuses on enhancing autonomous capabilities in mountainous terrains by incorporating embedded algorithms for route planning, autonomous navigation, and automatic crack detection. The slide window method is proposed to enhance the autonomous navigation of UAV flights by generating path planning in mountainous roads. This method compensates for GPS/IMU positioning errors, particularly in GPS-denied or GPS-drift scenarios. Moreover, the YOLOv8 algorithm is introduced to conduct autonomous crack detection from UAV imagery in the offboard module. To validate our UAV inspection system's performance, we conducted several experiments to assess its accuracy, robustness, and efficiency. The experimental results demonstrate that our UAS inspection system accurately estimates the flight trajectory along the mountainous road for autonomous navigation. Furthermore, the experimental detection results also demonstrate that the YOLOv8 algorithm achieves higher recognition accuracy compared to the YOLOv5, and YOLOv7-tiny algorithms, exceeding 3.1%,and 6.8% of mAP, respectively. In summary, the UAV inspection system proposed in this study can provide a useful tool and technological guidance for the regular inspection of mountainous roads.

Solar flares are broadly classified as impulsive or gradual. Ions accelerated in a gradual flare are thought to be accelerated through a shock acceleration mechanism, but the particle acceleration process in an impulsive flare is still largely unexplored. To understand the acceleration process, it is necessary to measure the high-energy gamma-rays and neutrons produced by the impulsive flare. Under such circumstances, on November 7, 2004, a huge X2.0 flare occurred on the solar surface, where ions were accelerated to energies greater than 10 GeV. The accelerated primary protons collided with the solar atmosphere and produced line gamma-rays and neutrons. These particles were received as neutrons and line gamma-rays, respectively. Neutrons of a few GeV, on the other hand, decay to produce secondary protons while traveling 0.06 au in the solar-terrestrial space. These secondary protons arrived at the magnetopause. Although the flux of secondary protons is very low, the effect of collecting secondary protons arriving in a wide region of the magnetosphere (the Funnel or Horn effect) has resulted in significant signals being received by the solar neutron telescope at Mt. Sierra Negra (4,600 m). This information suggests that ions on the solar surface are accelerated to over 10 GeV with an impulsive flare.

In this study, we present comprehensive climatologies of effective ultraviolet (UV) quantities and photosynthetically active radiation (PAR) over Cyprus for the period 2004 to 2023, leveraging the synergy of earth observation (EO) data and radiative transfer model simulations. The EO dataset, encompassing satellite and re-analysis data for aerosols, total ozone column, and water vapor, alongside cloud modification factors, to capture the nuanced dynamics of Cyprus's atmospheric conditions. With a temporal resolution of 15 minutes and a spatial of 0.05°x0.05°, these climatologies undergo rigorous validation against established satellite datasets and are further evaluated through comparisons with ground-based global horizontal irradiance measurements provided by the Meteorological Office of Cyprus. This dual-method validation approach not only underscores the models' accuracy but also highlights their proficiency in capturing intra-daily cloud coverage variations. Our analysis extends to investigating the long-term trends of these solar radiation quantities, examining their interplay with changes in cloud attenuation, aerosol optical depth (AOD), and total ozone column (TOC). Significant decreasing trends in the noon ultraviolet index (UVI), of -2 to -4% per decade have been found in Autumn, especially marked on the island’s Northeastern part, mainly originating from the (significant) positive trends in TOC. The significant decreasing trends in TOC, of -2 to -3% per decade that were found in Spring do not result in correspondingly significant positive trends in the noon UVI since variations in cloudiness and aerosols also have a strong impact on the UVI in this season. The seasonal trends in the Daylight Integral (DLI) were generally not significant. These insights provide a valuable foundation for further studies aimed at developing public health strategies and enhancing agricultural productivity, highlighting the critical importance of accurate and high-resolution climatological data.

Redox mediators comprising I–, Co3+, and Ti3C2Tx MXene were applied to dye-sensitized solar cells (DSCs). In the as-prepared DSCs (I-DSCs), wherein hole conduction occurred via the redox reaction of I−/I3− ions, the power conversion efficiency (PCE) was not altered by the addition of Ti3C2Tx MXene. The I-DSCs were exposed to light to produce Co2+/Co3+-based cells (Co-DSCs), wherein the holes were transferred via the redox reaction of Co2+/Co3+ ions. A PCE of 9.01% was achieved in a Co-DSC with Ti3C2Tx MXene (Ti3C2Tx-Co-DSC), which indicated an improvement from the PCE of a bare Co-DSC without Ti3C2Tx MXene (7.27%). It was also found that the presence of Ti3C2Tx MXene in the redox mediator increased the hole collection, dye regeneration, and electron injection efficiencies of the Ti3C2Tx-Co-DSC, leading to an improvement in both the short-circuit current and the PCE when compared with those of the bare Co-DSC without MXene.

The quiet-time solar wind electrons feature non-thermal characteristics when viewed from the perspective of their velocity distribution functions. They typically have an appearance of being composed of a denser thermal “core” population plus a tenuous energetic “halo” population. At first, such a feature was empirically fitted with the Kappa velocity space distribution function, but ever since the ground-breaking work by Tsallis, the space physics community has embraced the potential implication of the Kappa distribution as reflecting the non-extensive nature of the space plasma. From the viewpoint of microscopic plasma theory, the formation of non-thermal electron velocity distribution function can be interpreted in terms of the plasma being in a state of turbulent quasi equilibrium. Such a finding brings forth the possible existence of a profound inter-relationship between the non-extensive statistical state and the turbulent quasi-equilibrium state. The present paper further develops the idea of solar wind electrons being in the turbulent equilibrium, but unlike the previous model, which involves the electrostatic turbulence near the plasma oscillation frequency (i.e., Langmuir turbulence), the present paper considers the impact of transverse electromagnetic turbulence, particularly, the turbulence in the whistler-mode frequency range. It is found that the coupling of spontaneously-emitted thermal fluctuations and the background turbulence leads to the formation of non-thermal electron velocity distribution function of the type observed in the solar wind during quiet times. This demonstrates that the whistler-range turbulence represents an alternative mechanism for producing the Kappa-like non-thermal distribution, especially close to the Sun and in the near-Earth space environment.

Shared public buildings have become centers of innovation, integrating advanced technologies to meet evolving societal needs. With a heightened emphasis on occupants’ health and well-being, these buildings serve as hubs for technological convergence, facilitating seamless connectivity and intelligent data analysis and management. Within this context, environmental monitoring emerges as a foundational element, pivotal to all aspects of building management. This article provides findings from the nationally funded RE-START project, which focuses on shared public buildings, with special regard to educational and medical facilities. The project explores enhanced indoor air quality monitoring, focusing in particular on CO2 concentration that is directly correlated with occupancy, as a fundamental element for developing health and safety protocols, energy efficiency strategies, integration of smart building technologies, and data-driven energy management. The intersection of environmental monitoring, energy efficiency, security, and IoT technologies in in indoor spaces are relevant. The outcomes of the study reveal the delicate nature of all the involved components, that need to be carefully developed in an integrated manner.

Cyprus plans to drastically increase the share of renewable energy sources from 13.9% in 2020 to 22.9% in 2030. Solar energy can play a key role in the effort to fulfil this goal. The potential for production of solar energy over the island is much higher than most of European territory because of the low latitude of the island and the nearly cloudless summers. In this study, high quality and fine resolution satellite retrievals of aerosols and dust, from the newly developed MIDAS climatology, as well as information for clouds from CMSAF are used in order to quantify the effects of aerosols, dust, and clouds on the levels of surface solar radiation (SSR) and the corresponding financial loss for different types of installations for production of solar energy. An SSR climatology has been also developed based on the above information. Ground-based measurements were also incorporated to study the contribution of different species to the aerosol mixture and the effects of day-to-day variability of aerosols on SSR. Aerosols attenuate 5 – 10% of annual GHI and 15 – 35% of annual DNI, while clouds attenuate ~25 – 30% and 35 – 50% respectively. Dust is responsible for 30 – 50% of the overall attenuation by aerosols.

In this study the effects of three different main preparatory tillage operations [ploughing at 0.4 m (P40) and 0.20 m (P20) depth and minimum tillage at 0.20 m depth (MT) each of them carried out at two different soil water contents (WC) [low, 58% (LH) and high, 80% (HH) of field capacity] were investigated. The results obtained in this research show high values of soil strength in term of Penetration resistance (CI) and shear strength (SS) particularly in deeper soil layers at lower water content. Fossil-fuel energy requirements both for P40 LH and P20 LH were 25 and 35% higher with respect to the HH treatments and tractor slip were very high (P40 LH = 32.4%) with respect to the P40 HH treatment (16%). Therefore soil water content had significantly influenced tractor performance during soil ploughing, particularly at 0.40 m depth while MT was not influenced at all. A significant correlation between grain yield and soil penetration resistance was found highlighting how soil strength may be good indicator of its productivity. Obtained results during these field tests allowed considering MT and P20 treatments more suitable for this type of soil in climate change scenarios.

Railway has been used as one of the most crucial means of transportation in public mobility and economic development. For efficiently operating railways, the electrification system in railway infrastructure, which supplies electric power to trains, is essential facilities for stable train operation. Due to its important role, the electrification system needs to be rigorously and regularly inspected and managed. This paper presents a supervised learning method to classify Mobile Laser Scanning (MLS) data into ten target classes representing overhead wires, movable brackets and poles, which are recognized key objects in the electrification system. In general, the layout of railway electrification system shows a strong regularity of spatial relations among object classes. The proposed classifier is developed based on Conditional Random Field (CRF), which characterizes not only labeling homogeneity at short range, but also the layout compatibility between different object classes at long range in the probabilistic graphical model. This multi-range CRF model consists of a unary term and three pairwise contextual terms. In order to gain computational efficiency, MLS point clouds is converted into a set of line segments where the labeling process is applied. Support Vector Machine (SVM) is used as a local classifier considering only node features for producing the unary potentials of CRF model. As the short-range pairwise contextual term, Potts model is applied to enforce a local smoothness in short-range graph. While, long-range pairwise potentials are designed to enhance spatial regularities of both horizontal and vertical layouts among railway objects. We formulate two long-range pairwise potentials as the log posterior probability obtained by Naïve Bayes classifier. The directional layout compatibilities are characterized in probability look-up tables which represent co-occurrence rate of spatial relations in horizontal and vertical directions. The likelihood function is formulated by multivariate Gaussian distributions. In the proposed multi-range CRF model, the weight parameters to balance four sub-terms are estimated by applying the Stochastic Gradient Descent (SGD). The results show that the proposed multi-range CRF can effectively classify detailed railway elements, representing the average recall of 97.66% and the average precision of 97.07% for all classes.

A flow chart is presented depicting coupling in ocean-atmosphere system, initiated by solar decadal variability that involves El Niño Southern Oscillation (ENSO). Possible mechanisms for Canonic ENSO, Modoki ENSO and Canonic-Modoki ENSO are proposed considering their relevance to the decadal variation of Hadley, Walker circulation and mid-latitude jets. It also discusses subsequent teleconnections by ENSO on Indian Summer Monsoon (ISM) with a special emphasis on later two decades of the last century. Possible mechanisms relating to a disruption of the usual ENSO-ISM teleconnection for those decades are emphasised; the role of volcanos and the change in the sun- NAO (North Atlantic Oscillation) connection were attended. The regional Hadley circulation, via the NAO in the northern hemisphere and Indian Ocean Dipole in the southern hemisphere, during that period, may have a role in the change in ISM behaviour- which though captured in observation but missed by models. The representation of flow chart helps to improve understanding of various types of ENSO in both temporal as well as spatial scale. The overall study subsequently can benefit the modelling community by not only improved the representation of ENSO in models but also better representation of ISM teleconnection via regional Hadley cell.

Microscopic vision plays an important role in automated micro-assembly. However, some uncertain factors in the assembly process, such as occlusion and stains can lead to the mistakes of feature extraction. Herein, to solve the problem, the deep learning techniques are introduced into the feature recognition tasks, focusing on the attention mechanism and visualizing CNNs for DL-based microscopic vision. The main contributions are summarized as follows: The CBAM attention mechanism is combined with the YOLOv5 algorithm to improve the accuracy and robustness of feature extraction. The micropart feature occlusion experiment results show that at 70% occlusion degree, YOLOV5-CBAM can reach 97.9% [email protected], which is 4.6% higher than the original one. Visualization analysis of DL-based model is conducted using Grad-CAM to make the decision result more transparent and avoid potential visual detection risks during assembly. The heatmap matching degree between GT area and high-light area is increased by 27.81% on average, which further verify the effectiveness of attention mechanism in micropart feature localization. Additionally, micropart surface stain and droplet quality classification models based on ResNet50 are trained to replace the manual sorting. The visual results are consistent with human eye discernment and judgement, confirming the reliability of parts and droplets sorting.

The There are many factors influencing the performance of photovoltaic (PV) systems. Among these factors, temperature and solar radiation are two major parameters that have a large effect on the efficiency of PV systems. The cell temperature of PV panels is related to the ambient temperature while the solar radiation incident on the surface of the PV modules depends on the slope and azimuth of these modules. Furthermore, ground reflectance (albedo) affects the irradiance incident on the PV panel surface, which in turn affects the output of a PV system. Nevertheless, the effects of these factors on the economic performance of the solar PV systems are scarcely reported. This paper presents a complete design of a stand-alone PV/battery system to supply electric power for a mobile base station in Choman, Erbil, Iraq. The effects of different factors on the total electricity produced by PV arrays and its economic performance are simultaneously investigated. HOMER software has been used as a tool for the techno-economic and environmental analysis. As indicated from the simulation results, the PV array capacity and its economic performance are highly affected by the variation of the slope and azimuth. With a base case (albedo of 20% and average annual ambient temperature of 11°C), the best feasible system which is achieved by facing PV due to south with a tilt angle of 40° or 45°, is found to have net present cost (NPC) of 70595 $ and cost of energy (COE) of 0.54 $/kWh. Moreover, the results indicate that increasing the ground reflectance from 10% to 90% results in a 7.2% decrease in the PV array capacity and about 3% decrease in the NPC and COE. On the other hand, increasing the ambient temperature from 0°C to 40°C results in a 19.7% increase in the PV array capacity and an 8.2% increase in the NPC and COE. Furthermore, according to the ambient temperature of Choman, using PV modules with high sensitivity to temperature is found to be an attractive option. Provided simulation performance analysis proves that the studied parameters must be treated well to establish an enabling environment for solar energy development in Iraq.

This study presents a cost-effective IoT-based remote monitoring system for solar PV energy systems, along with a machine learning-based PV power estimator. Remote access is crucial for tracking PV systems installed in remote areas. In this system, an open-source and IoT-compatible data logger is employed. The data logger collects important performance data of the PV system and transfers it to a server. Real-time visualization of this data is displayed in the designed web and mobile monitoring interfaces. The measured data includes the current, voltage, and temperature information of the PV generator and battery, as well as environmental parameters such as temperature, radiation, humidity, and pressure. Subsequently, this data is used for PV power estimation using machine learning methods. This enables the identification of maintenance requirements and the prediction of potential issues in the PV system. When a problem occurs in the PV system, the user is alerted through the mobile application. Early detection and intervention prevent power loss and damage to system components. When comparing the results of linear regression, SVM, decision trees, random forests, and KNN machine learning methods for power estimation based on performance evaluation criteria, it was observed that the random forests algorithm provided the best results. In conclusion, the developed monitoring and estimation system, along with web and mobile interfaces, is suitable for large-scale PV energy systems.

Energy demand and consumption in recent times have witnessed a rapid proliferation influenced by technological developments, increased population and economic growth. This has fuelled research trends in the domain of energy management employing tri-generation systems such as the combined cooling, heating and power (CCHP) systems. Furthermore, the incorporation of renewable energy, especially solar energy, to complement the thermal input by fossil fuels has facilitated the effectiveness and sustainability of CCHP systems. This study proposes a new approach to improve the overall efficiency of CCHP systems and compute the optimal design parameters in order to assist decision makers to identify the best geometrical configuration. A multi-objective optimization formulation of a solar-assisted CCHP system was adopted to maximize the net power, the exergy efficiency and minimize the CO2 emission using the greywolf optimization technique. In addition, the effects of the decision variables on the objective functions were analysed. The proposed optimization approach yielded 100 set of Pareto optimal solutions which would serve as options to the decision maker to make a selection in order to improve the performance of a solar-assisted CCHP system. This study demonstrates that the proposed approach is potentially suitable for the optimization of a solar-assisted CCHP system.

This study focused on the predictive models incorporating machine learning techniques that induce new dynamics for forecasting energy generation, enabling effective planning, financing, and system monitoring. The research developed a machine learning-based power generation prediction model tailored explicitly for Kenya's Garissa solar power plant. The selected model demonstrated a root mean squared error of 5.23 during evaluation, resulting in a prediction accuracy of 90.42%. This high accuracy indicates that the model can be relied upon for precise generation prediction, facilitating effective planning, and system performance monitoring

This paper, we examined the technology to maximize the use of renewable energy. The passive ventilation system is expected to reduce the energy consumption of the fan power and the maintenance burden. In addition, the wall-mounted solar air heater can supply thermal energy without using any energy at all. Therefore, we propose a "passive ventilation system with a solar air heater" that combines a passive ventilation system with solar air heater to preheat the air supply and reduce the ventilation load. To evaluate the solar air heater performance in a real environment, we developed a simulation for calculating the heat collection capacity of the solar air heater, and then implemented the system in a real building for verification. Based on the measurement results, the effects of heating load reduction and prediction methods are presented.

This study presents application of demand response strategy in a standalone wind-solar-battery hybrid energy system (HES). Inputs for the designed HES are wind speed, solar radiation, temperature and load demand which is variable with time. In this study, hourly values of meteorological data and hourly load demand are considered in one year. An improved gravitational search algorithm (IGSA) is used to optimize the configuration of the standalone wind-solar-battery hybrid power system. The optimal objectives of the system are cost of the system in life cycle, the loss of power supply probability（LPSP）and the energy excess percentage（EXC）.The effect of demand response on economic benefit and energy storage allocation of the standalone wind-solar-battery system is studied. The obtained optimal configuration of the proposed HES can provide minimal energy cost with excellent performance and reduced waste and unmet load.

Sterol homeostasis in mammalian cells and tissues involves balancing three fundamental processes: de novo sterol biosynthesis; sterol import (e.g., from blood-borne lipoproteins); and sterol export. In complex tissues, composed of multiple different cell types (such as the retina), import and export also may involve intra-tissue, inter-cellular sterol exchange. Disruption of any of these processes can result in pathologies that impact the normal structure and function of the retina. Here, we provide a brief overview of what is known currently about sterol homeostasis in the vertebrate retina and offer a proposed path for future experimental work to further our understanding of these processes, with relevance to the development of novel therapeutic interventions for human diseases involving defective sterol homeostasis.

Major (exo)planetary and satellite bodies seem to concentrate at intermediate areas of the radial 1 distributions of all the objects orbiting in each (sub)system. We show that angular-momentum transport during secular evolution of (exo)planets and satellites necessarily results in the observed intermediate accumulation of the massive objects. We quantify the ‘middle’ as the mean of mean motions (orbital angular velocities) when three or more massive objects are involved. Radial evolution of the orbits is expected to be halted when the survivors settle near mean-motion resonances and angular-momentum transfer between them ceases (gravitational Landau damping). This dynamical behavior is opposite in direction to what has been theorized for viscous and magnetized accretion disks, in which gas spreads out and away from either side of any conceivable intermediate area. We present angular momentum transfer simulations in few-body systems, and we calculate the tidal dissipation timescales and the physical properties of the mean tidal field in planetary and satellite (sub)systems.

In view of the problem that the output thrust of solenoid actuator is affected by various factors and is difficult to calculate in actual working conditions, this paper proposes a semi-analytical model constructed by magnetic field subdomain method with internal and external boundary conditions in cylindrical coordinate system for calculation, and the general solution equations of magnetic vector potential for each subdomain are derived and solved by MATLAB. Taking a push-pull electromagnet as an example, the finite element simulation and experimental comparative analysis are carried out. The correctness and applicable conditions of the subdomain method are illustrated by comparing the gradient plot of magnetic vector potential, inductance curve and electromagnetic force. By calculating the output thrust after considering the core gravity, spring force and electromagnetic force, it is shown that this method has the advantage of computational flexibility compared with the finite element method, and it is easier to write special algorithms according to various working conditions to calculate the important parameters in engineering applications.

The present study analyzed the effect of temperature, pH, retention time, pretreatment and mixing ratio on the anerobic digestion process. The parameters during the anaerobic co digestion of cow manure and food waste were then optimized using the Taguchi experimental design method. ANOVA is carried out to find the significant parameters which influence the biogas production. Experimental tests were carried out at laboratory scale reactors kept at different temperatures (28 °C, 35 °C, and 50 °C). The specific methanogenic Performance (SMP) during anerobic digestion at higher temperatures was characterized with the analysis of acetate, propionate, butyrate, hydrogen, glucose, and formate. The improvement of biogas production with different pre-treatment i.e., ultrasonic, autoclave, and microwave techniques also analysed. The results showed that reactor that maintained at 35 °C showed the highest biogas production. While the reactor that maintained at lower temperature (28 °C) produced the lower volume of biogas. As the retention time increases, the amount of biogas produced increases. Methanogenic activities of microorganisms reduced at higher temperature conditions (65 °C). Biogas production increased by 28.1%, 20.23%, and 13.27 % when the of substrates treated with ultrasonic, autoclave, and microwave, respectively, compared to the untreated substrate. The optimized condition for the highest biogas production during anaerobic co digestion of food waste and cow manure is temperature of 35 °C, pH of 7 and mixing ratio (CM: FW=1.5:0.5). ANOVA showed that temperature is the most important input parameter affecting biogas production, followed by mixing ratio.

Background: As the only place in a store where all customers must pass through and wait, the checkout lane may be particularly influential over consumer purchases. Because most foods and beverages sold at checkout are unhealthy (e.g., candy/sweets, sugar-sweetened beverages, and salty snacks), policymakers and advocates have expressed growing interest in healthy checkout policies. To understand the extent to which such policies could improve nutrition equity, we as-sessed the prevalence and sociodemographic correlates of purchasing items from checkout. Methods: We assessed self-reported checkout purchasing and sociodemographic characteristics in a national convenience sample of adults (n=10,348) completing an online survey in 2021. Re-sults: Over one-third (36%) of participants reported purchasing foods or drinks from checkout during their last grocery shopping trip. Purchasing items from checkout was more common among men; adults <55 years of age; low-income consumers; Hispanic, non-Hispanic American Indian and Alaska Native, and non-Hispanic Black consumers; those with at least a bachelor’s degree; parents; and consumers diagnosed with type 2 diabetes or pre-diabetes (p-values<0.05). Conclusions: Purchasing foods or beverages from store checkouts is common and more prevalent among low-income and racial and ethnic minority groups. These results suggest that healthy checkout policies have the potential to improve nutrition equity.

The effective Supervisory Control and Data Acquisition (SCADA) system can improve the reliability, safety and economic benefits of microgrid operation. In this research, the lower central controller and upper WEB monitoring system are connected by the SCADA system which is as the hub of microgrid intelligent monitoring platform. This system contains a set of specific functions programmed by Java as a middleware which can provide control and communication between the central controller and the upper monitoring system. The system realizes the real-time data acquisition and storage, the control instructions parsing and transmitting, the microgrid security and stability, the load balancing and resource recovery of the microgrid. All that functions have been tested and verified in the actual operation.

The availability of current land cover and land use (LCLU) information for monitoring the status of land resources has considerable value in ensuring sustainable land use planning. Similarly, the need to provide updated information on the extent of LCLU change in West Africa has become apparent, given the increasing demand for land resources driven by the rapid population growth in the sub-region. SERVIR West Africa, a regional consortium jointly supported by USAID and NASA to foster the use of geospatial and earth observation data and science for decision-making on critical environmental and food security challenges, in collaboration with the Food and Agriculture Organization of the United Nations (FAO), is building technical capacity in the sub-region to develop and implement harmonized regional land cover classification system for LULC change monitoring in West Africa, based on the Land Cover Meta Language (LCML-ISO 19144-2). This article focuses on the process, potential opportunities, outputs of this collaboration, exemplary use cases, and recommendations for the next steps.

Aiming at the problems of high labor intensity and high cost of manual picking, high loss rate of mechanical picking seeds, low mechanization level and difficult to guarantee the quality of picking plates in the process of picking plates of edible sunflower. Based on the principle of manual plate taking, a plate taking control system for sunflower harvesting table was designed. Through the research and analysis of the principle of taking the plate of the edible sunflower harvesting table. According to the actual operation requirements, the overall scheme of the sunflower plate control system is determined, and the control strategy of imitating artificial low loss harvesting is designed. To reduce the grain loss in the process of taking the plate and improve the control accuracy of the system on the movement speed and displacement of the key components in the process of taking the plate, a trapezoidal acceleration and deceleration control algorithm is designed as the control algorithm of taking the plate. To verify the working performance of the plate control system, with the absolute error, relative error and total loss rate of the harvest as objectives. The bench and field verification experiments were both carried out. The bench experiment showed that the speed error of the plate parts was not more than 0.028 m/s. In the bench experiments of the device, the maximum positioning error was 1.25 mm, the average relative error is only 0.94 %, and the grain loss rate was not more than 2.26 %. It result showed that the system algorithm was reliable, the positioning accuracy was high, and the taking plate operation can be completed well. The field verification experiment showed that the forward speed of the unit was in the range of 0.4~0.8 m/s, and the total loss rate of harvest was less than 5 %. When the forward speed is 0.6 m/s, the minimum harvest loss rate is 2.32 %, and which indicated the control system meets the requirements of sunflower harvesting operation.

This paper aims to study the performance of solar collectors with various sizes under different weather conditions of different Japanese cities, i.e. Kofu city, Nagoya city and Yamagata city. The heat generated from the solar collector was used to conduct a biogas dry reforming reactor in order to produce H2 to feed a solid oxide fuel cell (SOFC). This study has revealed that the output temperature of solar collector, Tfb in April and July was higher than that in January and October irrespective of city. The optimum length of absorber (dx) of the collector was 4 m irrespective of city. It was clarified that Tfb in Yamagata city in January and October, i.e. winter and autumn is lower than that in Kofu city and Nagoya city especially, which is influenced by the tendency of a solar intensity (I) strongly, not velocity of surrounding air (ua). On the other hand, Tfb is almost the same in April and July, i.e. spring and summer, irrespective of city. The amount of produced H2 from the biogas dry reforming reactor and the power generated by SOFC using H2 in spring and summer were higher compared to the other seasons irrespective of city. This study has revealed that the highest available households number per month was 4.7 according to the investigation in this study.

Green spaces take part a vital role in reducing the harmful effects occurred through the process of rapid urbanization. This study focuses on evaluating the change of green space and per capita green space in Colombo District, Sri Lanka in between 2008 and 2015 using Landsat images. The NDVI differencing method and classification method were used to detect the change of land cover. According to the results, a gradual decline of green space from 629.1km2 to 591.16km2 with a rate of 6.03% can be depicted. The change is considerable in Kaduwela, Moratuwa and Maharagama DSDs and quite better in the areas such as Avissawella, Homagama and Padukka. The per capita green space has been decreased from 272.361 m2 to 248.811 m2. Though these rates are very well higher than the UN, EU and WHO standards for the district, Colombo DSD do not comply with UN and EU standards. The case would turn into worst in 2025, as the predictions with current rates. Therefore, the study recommends taking immediate actions to make a greener city. Further, a tax system is proposed to get the attraction of the general public.

Phase change materials (PCMs) have emerged as promising solutions for latent heat thermal energy storage (LHTES) systems, offering considerable potential for storing energy derived from renewable sources across various engineering applications. The present study focused on optimization of solar cooling system by integrating LHTES with different PCM tank configuration. TRNSYS simulation software was selected for the study and collected experimental data from laboratory system prototype was used for system validation. The results indicate that the use of PCM led to a noteworthy decrease of 6.2% in auxiliary energy consumption. Furthermore, the duration during which the heat carrier temperature flow exceeded 90°C from the storage tank to the auxiliary heater was extended by 27.8% when PCM was utilized, compared to its absence. The use of PCM in LHTES is more effective under variable weather conditions. On the day when changes in weather conditions were observed, around 98% of the cooling load was provided by produced sun energy. The results of the research can be used to optimize the solar cooling system, which will help reduce the environmental impact of cooling systems running on non-renewable fuels.

Power generation using renewable technologies has become a primordial option to satisfy the energy demand all over the world, being solar concentrating technologies widely applied for this purpose. A combination of Parabolic Trough Collector with Direct Steam Generation has been considered an excellent option for power generation, due to the economic cost and complexity in the plant are reduced. The thermal evaluation of the solar power plant as well as the PTC in the DSG process is very important in viability and economic analysis. In this sense, as the main objective of this work, a numerical tool for evaluating DSG with PTC technology has been developed. The software SOLEEC is a versatile, reliable, accurate and friendly to the user for thermally evaluating a DSG with PTC technology. The user has the possibility to compare the thermal behavior of different geometrical dimensions for a PTC; and even consider different materials; in order to satisfy the demand of superheated steam by a DSG process. The software has an error less than 5% when compared with literature results and in this paper is used to evaluate a power plant in Mexico obtaining that the change to DSG proposing different PTC could reduce the solar field about 35%.

The interest in virtual fencing, as opposed to traditional physical barriers, has increased since it makes it easier to move the boundaries of animal enclosures for agricultural and rewilding purposes. Despite this, the implementation of virtual fence systems, such as the Nofence© system, requires large financial investments. The necessity of individual collars also makes it more difficult to implement this system on a larger scale. If a herd of cattle tend to maintain cohesive social groups around dominant individuals, it might be possible to use fewer collars thereby making the system more cost efficient. This study investigates the pattern in spatial distribution in a herd of 17 Angus cows on Fanø in Denmark with GPS locations, using a Nofence© system. The aim of this paper is to determine how individuals position themselves in a herd, spatially, and identify a pattern in ranks. The method used in this study examines the distances between an individual to the rest of the herd mates using nearest neighbour and distribution plots. Contrary to the expectations, this study did not show a distinct pattern in herd distribution. While some tendencies in spatial distribution patterns were observed, only a low concordance could be found (W=0.15,p=3.02e−16). Therefore, interactions among individuals should be considered in future studies, rather than only investigating the herd as one unit. A cumulative curves of the ranks estimated over the entire periods allowed however a rough estimation of the hierarchy and allowed the identification of the highest ranked cows. Therefore it might be possible to use fewer collars if the high ranked cows are discovered, but the estimation of the hierarchy will require a period of observation overall several days as the hierarchical positions are fluctuating too much from days to days. Therefore the cumulative rank curves seems to be a valid solution.

The extensive collection of paper documents and books stored in the archives of universities worldwide is a hidden cultural heritage that is frequently inaccessible. To overcome this problem, the University of Genoa, Italy, seeks to collect, store, and digitize a wide variety of items, encompassing books, manuscripts, archival materials, and documents related to museum artifacts, which together form a cultural heritage of great importance and historical significance. To make such a cultural heritage accessible to both humans and machines, images and videos must be provided with alternate descriptions, metadata, and speech-to-text transcriptions while ancient texts, for which OCR techniques are often not effective, must be accompanied by word-for-word transcripts. This work presents the design of a transcription system for the “University Museum System” at the University of Genoa, Italy (SMA-UniGe), including user interface elements and users’ engagement techniques. The goal is to create an accessible digital heritage that can be enjoyed by all, facilitated by a community of digital volunteers who are eager to dedicate their time, have a great experience, socialize, and interact on the proposed transcription system. The system exploits gamification theory to transform the typically monotonous task of transcription into a captivating experience. This activity is in line with the so-called University third mission, i.e., the activity of public engagement that aims at generating knowledge outside the academic environment to the benefit of the social, cultural, and economic development.

Daily maximum surface air temperature (Tamax) is a crucial factor for understanding complex land surface processes under rapid climate change. Remote detection of Tamax has widely relied on the empirical relationship between air temperature and land surface temperature (LST), a product derived from remote sensing. However, little is known about how such a relationship is affected by the high heterogeneity in landscapes and dynamics in seasonality. This study aims to advance our understanding of the roles of land cover and seasonal variation in the estimation of Tamax using the MODIS (Moderate Resolution Imaging Spectroradiometer) LST product. We developed statistical models to link Tamax and LST in the middle and lower reaches of the Yangtze River in China for five major land-cover types (i.e., forest, shrub, water, impervious surface, cropland, and grassland) and two seasons (i.e., growing season and non-growing season). Results show that the performance of modeling the Tamax-LST relationship was highly dependent on land cover and seasonal variation. Estimating Tamax over grasslands and water bodies achieved superior performance; while uncertainties were high over forested lands that contained extensive heterogeneity in species types, plant structure, and topography. We further found that all the land-cover specific models developed for the plant non-growing season outperformed the corresponding models developed for the growing season. Discrepancies in model performance mainly occurred in the vegetated areas (forest, cropland, and shrub), suggesting an important role of plant phenology in defining the statistical relationship between Tamax and LST. For impervious surfaces, the challenge of capturing the high spatial heterogeneity in urban settings using the low-resolution MODIS data made Tamax estimation a difficult task, which was especially true in the growing season.

Scene classification plays an important role in the intelligent processing of high-resolution satellite (HRS) remotely sensed image. In HRS image classification, multiple features, e.g. shape, color, and texture features, are employed to represent scenes from different perspectives. Accordingly, effective integration of multiple features always results in better performance compared to methods based on a single feature in the interpretation of HRS image. In this paper, we introduce a multi-task joint sparse and low-rank representation model to combine the strength of multiple features for HRS image interpretation. Specifically, a multi-task learning formulation is applied to simultaneously consider sparse and low-rank structure across multiple tasks. The proposed model is optimized as a non-smooth convex optimization problem using an accelerated proximal gradient method. Experiments on two public scene classification datasets demonstrate that the proposed method achieves remarkable performance and improves upon the state-of-art methods in respective applications.