Nitrogen (N) is an essential macro nutrient for crop growth; therefore, N deficit can greatly limit crop growth and production. In the North China Plain (NCP), winter wheat is one of the main food crops, and its yield has increased from approximately 4000 kg ha-1 to 6000 kg ha-1 in the last two decades. Determining the proper N application rates at different growth stages and in all seasons is very important for the sustainable and high production of wheat in the NCP. A field experiment with five N application rates (250, 200, 150, 100, and 40 kgN·ha-1, designated as N250, N200, N150, N100, and N40, respectively) was conducted during the 2017-2018 and 2018-2019 winter wheat seasons to investigate the effects of the N application rate on water- and fertiliz-er-utilization efficiency, and on the crop growth and yield of winter wheat under sprinkler fer-tigation conditions. The results showed that, in the N application range of 40-200 kg ha-1, crop yield and water- and fertilizer-use efficiencies increased as the N application rate increased; however, further increases in the N application rate did not have additional benefits. The N up-take after regreening of winter wheat linearly increased with crop growth. Considering the wheat yield and N-use efficiency, the optimal N application range was 200 kg ha-1, and the best top-dressing strategy was equal amounts of N applied at the regreening, jointing, and grain-filling stages. The results of this study will be useful for optimizing field N management to achieve high wheat yield production in the NCP and in regions with similar climatic and soil environment conditions.

Background: Telemonitoring systems in cardiology have shown potential in improving chronic cardiovascular disease (CVD) management. This study aims to evaluate the impact of telemonitoring, mainly through mobile applications, on patient outcomes such as self-care, blood pressure control, quality of life, and hospitalization. Methods: We systematically reviewed studies assessing telemonitoring methods for patients with chronic CVD. The analysis included studies from various geographic regions and healthcare settings, focusing on qualitative outcomes without performing a meta-analysis. Results: Telemonitoring was found to aid in maintaining blood pressure and significantly enhance self-care abilities. Improvements in quality of life were observed in some studies, though results varied. Most studies indicated telemonitoring could effectively manage blood pressure and reduce hypertension-related complications. However, the heterogeneity of interventions and outcomes measured across trials posed challenges for a comprehensive meta-analysis. Conclusions: Integrating telemonitoring systems into routine care can significantly improve disease management and patient outcomes for chronic CVD patients. Future research should standardize telemonitoring interventions and outcome measures, conduct long-term studies, and evaluate the cost-effectiveness of these systems. Greater blindness in future randomized controlled trials and more studies on atrial fibrillation are also necessary. Significant potential exists for telemonitoring to improve patient outcomes and assist in managing chronic illnesses.

Lean body mass (LBM) is correlated with powerlifting performance in athletes competing in different bodyweight classes. However, it remains unknown whether changes in LBM are correlated with performance changes in powerlifters preparing for a competition. Aim of the study was to investigate the changes in LBM and performance in powerlifters preparing for a competition. Eight male powerlifters (age 31.7±9.8years, height 1.77±0.06m, weight 99.2±14.6kg) and three female powerlifters (age 32.7±16.3years, height 1.54±0.06m, weight 66.6±20.9kg), participated in the study. Athletes followed individualized periodized training programs for 12 weeks aiming to maximize their performance for the national championship. Maximum strength (1-RM) in squat, bench press, and deadlift, body composition, grip strength, anaerobic power, quadriceps’ cross sectional area and vastus lateralis muscle architecture were measured before and after the training period. Significant increases were found after the training period in squat (5.8±7.0%, P

Semi-Interlocking Masonry (SIM) represents an innovative building system developed at the Centre for Infrastructure Performance and Reliability at The University of Newcastle, Australia. This system employs a unique approach to interlocking mortar-less engineered masonry panels constructed from SIM units. These units are designed to offer substantial energy dissipation capacity, primarily attributed to the friction occurring on the sliding bed joints between the units within the panel during seismic events. The primary aim of this study is to evaluate the viability of incorporating semi-interlocking masonry as infill panels in the construction of multi-storey buildings across diverse geographical locations with varying seismic conditions in Australia. To assess the feasibility of SIM panels in different conditions (according to Australian Standard AS1170) in Australia, a comprehensive analysis using Strand7 is conducted on a three-storey structure incorporating traditional masonry infill panels and SIM panels. The comparison of displacement and base shear capacities is carried out in response to seismic events across various scenarios in different locations within Australia. The insights gained from this comparative analysis contribute valuable information regarding the viability of implementing SIM as a contemporary construction material.

Deep Eutectic Solvents (DESs) show promising abilities for the delamination of multilayer packaging films that are used in food packaging and in pharmaceutical blister packs. Due to the complexity of their structure, the recycling of these materials is a challenging task, leading to the easiest or cheapest option, landfilling. Towards the development of ‘green’ solvents for the efficient waste management and recycling, the conducted research focuses on the preparation of a range of hydrophobic and hydrophilic DES based on carboxylic acids. Also, their chemical and physical characterization, such as DSC, rheometer and density measurements, was followed for the determination of their properties. The laminate packaging waste products that were tested were Al/PE, PE/Al/PET, Al/PE/paper and PVC/PE/Al. Different batches of solvent systems were tested based on various naturally derived aliphatic and aromatic organic compounds as well as amino acids. The separated films were collected and studied further for the examination of the delamination extend. Results showed full or partial delamination of some materials as well as challenges of solvent systems. Variables such as temperature, agitation rate, mixing time and solvents ratios were monitored and collected during the design of experimental process. The results showed that the used DESs can offer an efficient, low-energy, affordable and green option for the delamination tested packaging materials.

GR4J (Genie Rural a 4 parame`tres Journalier) is a rainfall-runoff model widely used to modelling rainfall into discharge. In this model, the input data used are daily rainfall and evapotranspiration data, calibrated using daily observed discharge data. This model seeks the optimum parameter value that produces the minimum deviation or error. The Nash-Sutcliffe Coefficient and Relative Volume Error (RVE) are used to estimate the errors that occur. One of the modelling parameters, namely X4 (peak time), has a value that does not match the observed hydrograph peak time (Tp). For this reason, it is necessary to adjust the modelling equation so that the unit hydrograph of the model appropriately in terms of value and shape with observed unit hydrograph. This study examines ten watersheds in Java Island so that it is expected to represent catchment conditions in tropical areas and developing countries. A relationship was obtained between the difference peak time from modelling (X4) and observed Tp. Adjustments Formula can be made to the general GR4J modelling equation for unit hydrograph in Java Island watersheds.

This study profiled morphological and laboratory-derived performance biomechanics by position of American style football players training for the National Football League draft. Fifty-five players were categorized into Big (e.g., lineman; n=17), Big-skill (e.g., tight end; n=11), and Skill (e.g., receiver; n=27) groups. Body fat (BF%), lean body mass (LBM), total body mass, running force and ground contact time (GCT), countermovement jump height (CMJ-JH), CMJ normalized peak power (CMJ-NPP), and reactive strength were measured in a laboratory. Asymmetry was calculated for running force, GCT, and CMJ eccentric and concentric impulse (IMP). MANOVA determined between-group differences, and radar plots for morphological and performance characteristics were created using Z-scores. There was a significant between-group difference, F(26,80)=5.70, p<0.001;Wilk’s Λ=0.123, partial η2=0.649. Fisher’s least squares difference post-hocs showed Skill had greater JH, CMJ-NPP, reactive strength, and running force versus Big but not Big-skill, p<0.05. Big had greater BF%, LBM, total body mass, and GCT than Skill and Big-Skill, p<0.05. Big-skill had greater GCT asymmetry than Skill and Big, p<0.05. Asymmetries in running forces, and CMJ eccentric and concentric IMP were not different, p>0.05. Morphological and performance biomechanics differences are pronounced between Skill and Big. Big-skill possess characteristics from both groups. Laboratory-derived results provide specialized profiles of draft trainees.

The literature offers limited information on the effect of obesity on the rate of force development, a critical parameter for mobility in older adults. The objectives of this study were; to explore the influence of obesity on RFD in older adults and to examine the association between this neuromuscular parameter and walking speed. 42 older adults were classified into two groups: the control group (CG, n=22; age=81.13±4.02 years; Body mass index (BMI)=25.13±3.35 kg/m²), and the obese group (OG, n=20; age=77.71±2.95 years; BMI=34.46±3.25 kg/m²). Walking speed (m/s) was measured using the 10-meter walking test. Neuromuscular parameters of the plantar flexors were evaluated during a maximal voluntary contraction test using a dynamometer. The RFD was calculated from the linear slop of the force – time curve in two phases: from the onset of the contraction to 50 ms (RFD50-100) and from 100 to 200 ms (RFD100-200). The gait speed was lower in the OG compared to the CG (p

This paper presents a new semi-explicit algorithm for parameters estimation in a time-fractional generalization of dual-phase-lag heat conduction model with the Caputo fractional derivatives. It is shown that this model can be derived from a general linear constitutive relation for the heat transfer by conduction when the heat conduction relaxation kernel contains the Mittag-Leffler function. The model can be used to describe heat conduction phenomena in a material with power-law memory. The proposed algorithm of parameters estimation is based on the time integral characteristics method. The explicit representations of the thermal diffusivity and the fractional analogues of the thermal relaxation time and the thermal retardation are obtained via a Laplace transform of the temperature field and utilized in the algorithm. An implicit relation is derived for the order of fractional differentiation. In the algorithm, this relation is resolved numerically. An example illustrates the proposed technique.

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.

Among earth’s microbial populations are the genes, functional capacities, generational memory and sentient cognition that enable microorganisms to adapt and thrive in the full range of ecological conditions found on planet earth. In prior sequential publications, we considered fundamental properties (e.g., use of and responses to sound, light, vibrations) and features of microorganisms that cause them to be critical co-partners of human and other holobionts as well as the potential benefits for humanity that can be gained by fully applying quantum-related and novel capacities of microbial life. In this narrative review we: 1) discuss key concepts concerning a microbially-enhanced and sustainable future, and 2) focus on electric- and magnetic- based features of microorganisms that make them pivotal for myriad benefits ranging from beyond pharma-based health and wellness to free- and/or renewable energy and restorative agriculture to improved human networking. While the benefits are many, the risks posed by hazardous electric or magnetic field exposures to both holobionts and microbial communities are significant. This review concludes that microbes and their remarkable capacities offer both humanity and the planet a much brighter future if we reverse the demonization of microbes and wanton microbiome degradation that has predominated much of the past century.

Cross-linked polyethylene (XLPE) is a highly all-important material renowned for its exceptional physical and chemical properties, and it is extensively utilized in various industries such as con-struction, piping, cable insulation and medical equipment. The cross-linking process of polyethylene involves chemical or physical bonding of polymer chains to enhance its resistance to heat, chemicals and mechanical stress. This study offers a comprehensive overview of recent research and potential advancements in this field, with a particular focus on the development of efficient techniques and environmentally sustainable cross-linking systems. It evaluates the opportunities and challenges faced by cross-linked polyethylene materials across diverse applications, providing valuable insights for future research in this area.

Isothermal interrupted hot compression tests of 5754 aluminum were conducted on Gleeble-3500 thermo-mechanical simulator at temperature 350 °C and 450 °C, strain rate 0.1 s-1 and 1s−1. These tests simulated flat rolling to investigate how softening behaviors respond to controlled parameters, such as deformation temperature, strain rate and delay times. This data allowed the parameters for the hot rolling process to be optimized. The delay times during interrupted compression vary between 5s and 60s. The dynamic softening at each pass and metadynamic recrystallization at the intervals of deformation passes were analyzed in detail. 0.2% offset yield strength is applied to calculate the softening fraction undergoing metadynamic recrystallization. A kinetic model was developed to describe metadynamic recrystallization behaviors of the hot deformed 5754 aluminum alloy. Furthermore, the time constant for 50% recrystallization was expressed as functions related to the temperature and the strain rate. The experimental and calculated results were found to be in close agreement, which verified the developed model.

A comprehensive modeling framework for the thermoforming of polymer matrix woven laminate composite was developed. Two numerical indicators, the slip path length and traction magnitude, have been identified to be positively correlated to matrix smearing and wrinkling defects. The material model has been calibrated with picture-frame experimental results, and the prediction accuracy for intra-ply shear and thickness distribution were examined with measurements on the physically formed parts. Finally, a parametric study was conducted to determine the relationship between various process parameters and the quality of the formed part. For the trapezoidal part, orienting the laminate at 45-degrees to the mold axis reduces the likelihood of matrix smear and wrinkling defects. Although this laminate orientation yielded a greater spatial variation of part thickness, the thickness deviation is lower than that for the 0-degree orientation case. Higher forming rates were found to result in more defects due to increased slip path lengths and traction magnitudes. Optimal forming speeds should balance rapid shaping within the operating temperature window while avoiding tear. It was discovered that shallower molds benefit from faster ramp rates, while deeper molds require slower rates to manage extensive shearing, stretching, and bending. Faster forming rates lead to smaller thickness increases at high intra-ply shear regions, indicating a shift from intra-ply shear to out-of-plane bending due to the visco-plastic effect of the molten laminate, and can negatively impact part quality. Lastly, it was shown that a well-conceived strategy using darts could improve the part quality by reducing the magnitude of the defect indicators.

The vast array of interconnected microbes across earth’s ecosystems and within holobionts has been called the “Internet of Microbes.” Research into the fundamental microbial properties has demonstrated that bacteria and archaea are masters of energy and information collection, storage, transformation, and dissemination using both “wired” and wireless (at a distance) capacities. These features suggest that microbes are quantum entangled within and between earth’s holobionts, and that specific tools affecting energy and information functions could provide an effective strategy for managing microbial populations. Microbes use four critically important microbial networking functions (sound, light, electricity, and magnetism). This narrative review focuses on microbial management based on two of the four: sound and light (as well as related vibrations). These are examined as: 1) tools for managing microbial populations, 2) tools to support new technologies, and 3) tools for healing humans and other holobionts. Given microbial sensitivity to sound, light, and vibrations, it is critical that microbial populations and microbe-laden holobionts are protected from potentially damaging exposures. We conclude that sound, light, and vibrations are useful therapeutic tools that can help support useful microbial populations and help to address ongoing challenges of holobiont disease.

The digital model of airborne frequency selective surface radomes (AFSSRs) is the basis of design, simulation analysis, manufacturing, and other related research of AFSSRs. This paper proposes a rapid modeling method of AFSSRs based on dynamic customizable primitives. Firstly, a layered digital model construction scheme of AFSSR is presented based on the typical radome wall structure. Then, according to the characteristics of various surface configurations and complex wireframe information of AFSSRs, the dynamic primitives are raised to express the boundary and contour information of all kinds of radomes. Focusing on the undevelopable characteristics of the aero-dynamic shape surface of the AFSSR, the arrangement solution and mapping method for frequency selective elements on undevelopable surfaces are proposed. Furthermore, the imple-mentation logic of this method on the creation of each layer model and the assembly of the whole machine model is introduced. Finally, according to this method, a rapid modeling system (RMS) is established, and the modeling effect and efficiency of the RMS are demonstrated and compared, which verifies the feasibility and effectiveness of this method.

Polyphenols and fermentable fibers have shown favorable effects on gut microbiota composition and metabolic function. However, few studies have investigated whether combining multiple fermentable fibers or polyphenols may have additive beneficial effects on gut microbial states. Here, an in vitro fer-mentation model, seeded with human stool combined from 30 healthy volunteers, was supplemented with blends of polyphenols (PP), dietary fibers (FB) or their combination (PPFB) to determine influence on gut bacteria growth dynamics and select metabolite changes. PP and FB blends independently lead to significant increases in the absolute abundance of select beneficial taxa, namely Ruminococcus bromii, Bifidobacterium spp., Lactobacillus spp. and Dorea spp. Total short chain fatty acid concentrations, relative to non-supplemented control (F), increased significantly with PPFB and FB supplementation, but not PP. Indole and ammonia concentrations decreased with FB and PPFB supplementation, but not PP alone while increased antioxidant capacity was only evident with both PP and PPFB. These findings demonstrated that, while the independent blends displayed selective positive impacts on gut states, combination of both blends provided an additive effect. The work outlines the potential of mixed substrate blends to elicit a broader positive influence on gut microbial composition and function to build resiliency toward dysbiosis.

The rheological properties of a polyamide (PA) resin with low crystallinity were modified by melt-mixing it with a small amount of an alternative a-olefin–maleic anhydride copolymer as a reactive compound. Because PA has a low melting point, rheological characterization was performed over a wide temperature range. Owing to the reaction between PA and the alternative a-olefin–maleic anhydride copolymer, the blend sample behaved as a long-chain branched polymer in the molten state. The thermo-rheological complexity was obvious owing to large flow activation energy values in the low modulus region. The primary normal stress difference under steady shear was greatly increased in the wide shear rate range, leading to a large swell ratio at capillary extrusion. Furthermore, strain hardening in the transient elongational viscosity, which is responsible for favorable processability, was clear. Because this is a simple modification method, it will be widely employed to modify the rheological properties of various polyamide resins.

The performance of OpenFOAM in strong scaling tests on HPC systems with AMD EPYC processors exhibits a pronounced superlinear speedup. Simple test cases show superlinear speedups of over 300%, which significantly impacts the efficient use of computing resources. With the last generation of HPC architectures, a superlinear speedup of about 10% to 20% was well expected and accepted by CFD users. The measured superlinear speedup is much more pronounced and withstands the communication overhead to even larger scales. A detailed performance analysis of OpenFOAM follows, employing various High-Performance Computing (HPC) architectures, including AMD, ARM and Intel systems. The performance metric FVOPS (Finite VOlumes solved Per Second) is introduced to compare the performance of Computational Fluid Dynamics (CFD) applications when varying the grid size, as occurs in a strong scaling test. The achievable FVOPS depends on various factors, including the simulation type, boundary conditions, and especially the grid size of a use case. Analysing FVOPS on a single node level with varying grid size shows a significant difference in performance and cache utilization, which explains the large superlinear speedups seen in the strong scaling tests. Furthermore, FVOPS can be used as a simple benchmark to determine the optimal number of grid elements per rank to simulate a given use case at peak efficiency on a given platform, resulting in time, energy, and cost savings. The FVOPS metric also facilitates the direct comparison between different HPC architectures. The tests using AMD, ARM, and Intel processors show a peak in performance when employing around 10,000 grid elements per core. The presence of a large L3 cache on AMD processors is particularly advantageous, as indicated by L3 cache miss rates observed on AMD EPYC processors. Our results suggest that future HPC architectures with larger caches and higher memory bandwidth would benefit the CFD community.

Mutual exclusion algorithms are at the heart of concurrent, distributed/parallel, real-time and operating systems. It is well-known that such algorithms are very difficult to analyze and in the literature different conjectures about starvation-freedom and the number of by-passes (also said the overtaking factor), which in turn affects the (hopefully) bounded waiting time that a process competing for entering the critical section has to suffer before accessing the shared resource, have been formulated for specific algorithms. This paper proposes a novel modelling approach based on Timed Automata and the Uppaal toolset, which proves effective for studying all the properties of a mutual exclusion algorithm for N≥2 processes, by exhaustive model checking. Although the approach, as already confirmed by similar experiments reported in the literature, is not scalable due to state explosion problems, and can be practically applied until N≤5, it is of great value for revealing true properties of analyzed algorithms. For dimensions N>5 the statistical model checker of Uppaal can be used which, although based on simulations, can confirm properties by estimations and probabilities. The paper describes the proposed modelling and verification method and applies it to several mutual exclusion algorithms, thus retrieving known properties but also showing new results about properties often studied only by peer-review and intuitive reasoning.

Soil and Water Assessment Tool (SWAT) was used to simulate five glacierized river basins that are global in coverage and vary in climate. The river basins included the Narayani (Nepal), Vakhsh (Central Asia), Rhone (Switzerland), Mendoza (Central Andes, Argentina), and Central Dry Andes (Chile) with a total area of 85,000 km2. A modified SWAT snow algorithm was applied in order to consider spatial variation of associated snow melt /accumulation by elevation band across each subbasin. In the previous studies, melt rates varied as a function of elevation resulting from an air temperature gradient while the snow parameters were constant throughout the entire basin. A major improvement of the new snow algorithm is separating the glaciers from seasonal snow based on their characteristics. Two SWAT snow algorithms were evaluated in simulation of monthly runoff from glaciered watershed: 1) the snow parameters are lumped (i.e. constant throughout the entire basin) and 2) the snow parameters are spatially variable based on elevation band-subbasin (i.e. modified snow algorithm). Applying the distributed SWAT snow algorithm improved the model performance in simulation of monthly runoff with snow-glacial regime, so that mean RSR decreased to 0.49 from 0.55 and NSE increased to 0.75 from 0.69. Improvement of model performance was negligible in simulation of monthly runoff from the basins with monsoon runoff regime.

Objectives: To compare the effects of high-velocity resistance training to slow-velocity re-sistance training on neuromuscular and functional parameters and to analyze the relationship between changes in walking speed and improvements in neuromuscular parameters following intervetions. Methods: 40 participants were randomly assigned to either a high-speed resistance training group (GHS, n = 18; age = 80.41 ± 10.12 years; BMI = 23.81 ± 3.45 kg/m²) or a low-speed resistance training group (GLS, n = 22; age = 82.89 ± 5.32 years; BMI = 23.81 ± 3.45 kg/m²). Before and after the interventions, gait speed (m/s) was assessed using a 10-minute walking test, and relative maximal force (Relative F max, N/kg) was evaluated during maximal voluntary isometric con-traction of the plantar flexors. From the force-time curve, early (0 – 50 ms) and late (100 – 200 ms) rates of force development (RFD) were extracted from the linear slopes (Δ force / Δ time). Results: Gait speed significantly improved in both groups (p < 0.05). However, the improvement was more pronounced in the GHS compared to the GLS (p < 0.05). Relative Fmax showed a more significant increase in the GLS than in the GHS (p < 0.05). Moreover, a significant 10% increase in early RFD in the GLS and a 20.1% increase in the GHS were observed (p < 0.05). The improvement in early RFD was greater in the GHS (p < 0.05). Additionally, late RFD improved significantly only in the GHS (+20.4%, p < 0.05). Conclusion: High-velocity resistance training appears particularly effective in improving the ability to rapidly generate force, which is essential for many daily activities re-quiring explosive movements and quick responses.

As a representative of Exponential Organizations, MI went from obscurity to the Fortune 500 in just ten years. Mi's success is inextricably linked to its outstanding business model. This paper summarizes the elements of MI's business model from four perspectives: value proposition, value creation, value delivery, and value capture, as well as its measures for developing into an exponential organization. Expecting to provide theoretical references for the transformation and upgrading of manufacturing organizations.

Accurate measurement of pedaling kinetics and kinematics is vital for optimizing rehabilitation, exercise training, and understanding musculoskeletal biomechanics. Pedal reaction force, the main external force in cycling, is essential for musculoskeletal modeling and closely correlates with lower limb muscle activity and joint reaction forces. However, sensor instrumentation like 3-axis pedal force sensors is costly and requires extensive post-processing. Recent advancements in machine learning (ML), particularly neural network (NN) models, provide promising solutions for kinetic analyses. In this study, an NN model was developed to predict radial and mediolateral forces, providing a low-cost solution to study pedaling biomechanics with stationary cycling ergometers. Fifteen healthy individuals performed a 2-minute pedaling task at two different self-selected (58±5 rpm) and higher (72±7 rpm) cadences. Pedal forces were recorded using a 3-axis force system. The dataset included pedal force, crank angle, cadence, power, and participants' weight and height. The NN model achieved an inter-subject normalized root mean square error (nRMSE) of 0.15±0.02 and 0.26±0.05 for radial and mediolateral forces at high cadence, respectively, and 0.20±0.04 and 0.22±0.04 at self-selected cadence. The NN model's low computational time suits real-time pedal force predictions, matching the accuracy of previous ML algorithms for estimating ground reaction forces in gait.

Sustainable water resource management in Bangladesh's Gumti River Basin faces challenges from climate change, urbanization, and population growth. This study aimed to calibrate and validate the HEC-HMS model for the Gumti River to enhance flood prediction and water resource planning. The model was set up using precipitation, temperature, and discharge data from 2019-2021, with SRTM elevation data for catchment delineation. Manual calibration was performed for 2019-2020, followed by validation for 2021, using parameters such as Muskingum routing and SCS Curve Number. Model performance was evaluated using R², Nash-Sutcliffe efficiency (NSE), RSR, and PBIAS metrics. Results showed satisfactory to good performance, with R² values of 0.64 and 0.68 for calibration and validation, respectively. NSE values were within the satisfactory range, while PBIAS indicated very good performance with slight underestimation. The study concludes that the calibrated HEC-HMS model provides a reliable tool for simulating the Gumti River's hydrology, though there's room for improvement. Integration with flood frequency analysis, water quality monitoring, and climate change projections is recommended for comprehensive water resource management, alongside the implementation of Sustainable Water Resource Management principles and community engagement strategies.

Distributed/semi-distributed models are considered to be sensitive to the spatial resolution of the data input. In this paper, we take a small catchment in high urbanized Yangtze River Delta, Qinhuai catchment as study area, to analyze the impact of spatial resolution of precipitation and the potential evapotranspiration (PET) on the long-term runoff and flood runoff process. The data source includes the TRMM precipitation data, FEWS download PET data, and the interpolated metrological station data. GIS/RS technique was used to collect and pre-process the geographical, precipitation and PET series, which were then served as the input of CREST (Coupled Routing and Excess Storage) model to simulate the runoff process. The results clearly showed that, the CREST model is applicable to the Qinhuai catchment; the spatial resolution of precipitation had strong influence on the modelled runoff results and the metrological precipitation data cannot be substituted by the TRMM data in small catchment; the CREST model was not sensitive to the spatial resolution of the PET data, while the estimation fourmula of the PET data was correlated with the model quality. This paper focused on the small urbanized catchment, suggesting the influential explanatory variables for the model performance, and providing reliable reference for the study in similar area.

This study analyzes future hydrological droughts in LCKW using the SWAT model and data from 2010-2021, evaluating the Streamflow Drought Index (SDI) for 5, 10, 25, and 50-year return periods (RP) for 2029 and 2039 under RCP4.5 and RCP8.5 scenarios. The model shows high accuracy (R² = 0.82, NSE = 0.78). Under RCP4.5, streamflow may increase by 34.74% and 18.74%, while RCP8.5 predicts significant decreases of 37.06% and 55.84%. Historical data reveal frequent short-term droughts during SDI-3, notably in 2014–2016 and 2020–2021, with SDI-6 indicating less frequent but severe droughts, especially in 2015 and 2020. Future projections under RCP8.5 suggest wors-ening drought conditions in 2029 and 2039, with critical drought periods from January to April and November. Wavelet analysis indicates a high risk of severe hydrological drought in LCKW due to low annual recurrence rates, with the highest drought severity over a 50-year RP. The study high-lights an increasing likelihood of frequent and severe droughts, particularly under high emission scenarios such as RCP8.5, which could result in significant water shortages and ecosystem disrup-tions. It emphasizes the significance of adaptive water resource management strategies in mitigating negative impacts on water security and ecosystem health.

Aim of the study was to determine the influence of climatic and hydrological changes on the growth of Scotch pine (Pinus sylvestris L.) and Black alder (Alnus glutinosa (L.) Gaertn) in the Narew National Park over past 50 years. The researches were based on tree increment cores extracted from 42 pine trees and 57 alder trees ranging in age from 25 to 88 years and climatic (temperature and precipitation for Bialystok) and hydrological (water level of the Narew River) data. Standardized data (5-year index) were analysed by species, generation (Y - 21-40 years, M - 41-60, O - 61-90) and decade using correlation analysis. The results of the study show that pine responded more strongly to changes in air temperature and changes in precipitation totals, while alder growth was more strongly related to fluctuations in the water level of the Narew River. The most variable climatic and hydrological conditions and the most difficult conditions for tree growth prevailed in the first and last decades (1971-1980, 2011-2020). The decrease in the water level of the Narew River that occurred over the last 50 years did not adversely affect the condition of the trees. Analysis of DBH growth rates showed that the younger generations (Y, M) grow faster than the older generation (O). One of the reasons for the accelerated tree growth and increased resistance to the decline in the water level of the Narew River may be the increase in CO2 concentration in the atmosphere. Accelerated tree growth and increased resistance to drought may have certain consequences like increased damage from wind as a result of the decrease in wood density or decline in the sensitivity of trees to climate and hydrological changes. However, less sensitivity to changes in climatic conditions may only apply to older trees.

The effects of rare earth Ce on the microstructure and properties of cast pure copper were investigated through thermodynamic calculations, XRD analysis, mechanical testing, metallographic microscopy, and scanning electron microscopy (SEM). The experimental results demonstrate that the reaction between rare earth Ce and oxygen as well as sulfur in copper exhibits a significantly negative Gibbs free energy value, indicating a strong thermodynamic driving force for deoxidation and desulfurization reactions. Ce is capable of removing trace amounts of O and S from copper. Moreover, the maximum solid solubility of Ce in Cu falls within the range of 0.009% to 0.01%. Furthermore, Ce can refine columnar grains while enlarging equiaxed grains in as-cast copper.Upon addition of rare earth Ce, the tensile strength increased by 8.45%, elongation increased by 12.1%, and microhardness rose from 73.5HV to 81.2HV-an increase of 10.5%. Overall, rare earth Ce has been found to enhance both the microstructure and mechanical properties of cast pure copper.

(1) Background: The aim of the research was to present and emphasize the importance of magnetic resonance imaging (MRI) in diagnosing Achilles tendon (AT) ruptures by presenting case reports. (2) Materials and Methods: The sample of respondents included 7 patients (4 males and 3 females, 2 athletes and 5 non-athletes), aged 25 to 58 years, with AT injuries, who were examined and treated at the Institute for Treatment and Rehabilitation "Niška Banja", Niš, Serbia. Ankle examinations with MRI were conducted using the Esaote O-scan 0.3 T machine. The examinations were performed without the application of a paramagnetic contrast agent and without the use of anesthesia. Results: The MRI method's sensitivity and overall diagnostic accuracy were excellent, with 100% true positives. In our study, the method accurately detected the presence, location, and severity of AT injuries in all cases. MRI is an important and reliable method for detecting AT injuries, classifying them, monitoring the course of recovery, and assessing the effects of therapy. Conclusions: An unrecognized or delayed diagnosis of an AT injury can lead to chronic changes. Timely diagnosis (clinical examination, ultrasonography, magnetic resonance imaging) and appropriate treatment (conservative or operative) enable patients to return to normal activities, including sports. Unfortunately, the use of MRI is limited by restricted access and the cost of the examination.

Climate change is a global issue that draws widespread attention from the international society. As an important component of the climate system, the water cycle is directly affected by climate change. Thus, it is very important to study the influences of climate change on the basin water cycle with respect to maintenance of healthy rivers, sustainable use of water resources, and sustainable socioeconomic development in the basin. In this study, by assessing the suitability of multiple General Circulation Models (GCMs) recommended by the Intergovernmental Panel on Climate Change, Statistical Downscaling Model (SDSM) and Automated Statistical Downscaling model (ASD) were used to generate future climate change scenarios. These were then used to drive distributed hydrologic models (Variable Infiltration Capacity, Soil and Water Assessment Tool) for hydrological simulation of the Yangtze River and Yellow River basins, thereby quantifying the effects of climate change on the basin water cycle. The results showed that suitability assessment adopted in this study could effectively reduce the uncertainty of GCMs, and that statistical downscaling was able to greatly improve precipitation and temperature outputs in global climate mode. Compared to a baseline period (1961–1990), projected future periods (2046–2065 and 2081–2100) had a slightly decreasing tendency of runoff in the lower reaches of the Yangtze River basin. In particular, a significant increase in runoff was observed during flood seasons in the southeast part. However, runoff of the upper Yellow River basin decreased continuously. The results provide a reference for studying climate change in major river basins of China.

Globally, bridges fail mostly due to hydrological causes such as scour or flooding. Therefore, under a hydrological approach, this study proposes a methodology that contributes to prioritize the intervention of bridges to prevent their collapse. Through an exhaustive literature review, an evaluation matrix subdivided into 4 dimensions was developed and a total of 18 evaluation parameters were considered, distributed as follows: 4 environmental, 6 technical, 4 social and 4 economic. This matrix was applied to eight bridges with a history of hydrological problems in the same river and validated through semi-structured interviews with specialists. Data were collected through field visits, journalistic information, review of the gauged basin historical hydrological flow rates and consultations with the population. Then, the modeling, which considered the influence of gullies that discharge additional flow, was carried out using HEC-HMS and HEC-RAS and subsequently calibrated. The application of the matrix revealed that five bridges have a high vulnerability, and three bridges have a medium vulnerability. The multidimensional approach can be adapted for studies of other riverine bridges.

Variable flow emitters are a new type of emitter for underground drip irrigation that are used in locations where soil moisture is difficult to transport to the surface, which leads to crop emer-gence difficulties. The flow channel structure parameters of emitters affect the hydraulic per-formance of the emitter and its anticlogging ability. In this study, the variable flow emitter at the conventional low–flow water supply stage is taken as the research object. A combination of CFD numerical simulation of a two–phase flow of water and sand and clear water testing of test samples is applied to study the influence laws of 10 groups of variable flow emitter structural combinations on the effluent flow rate (q), flow index (x), particle passage rate and flow field inside the flow channel. The results show that the Realizable k–ε turbulence model can be used to simulate the flow field inside the variable flow emitter flow channel. The nRMSE between the measured and simulated values of q is 11.23%, and the relative error between the measured and simulated values of x is 4.66%, which gives a high simulation accuracy. The polar analysis shows that the tooth angle A has the smallest effect on the effluent flow rate q0.1 under 0.1 MPa, x, and particle passage rate of the variable flow emitter. The depth of the flow channel D, the spacing of the teeth B, and the height of the teeth E have a different order of precedence in the influence of the three indices, which are D > B > E > A, B > E > D > A, and E > B > D > A, respectively. The value of particle passage rate is positively correlated with the mean flow velocity (v) and the mean turbulent kinetic energy (k) in the flow channel, and particle passage rate tends to increase and then decrease with the increase of x. The retention time (t) of the particles in the flow channel is closely related to the magnitude of v and k. Three multivariate linear regression equations (R2 = 0.883 ~ 0.995) were constructed for q0.1, x, and particle passage rate versus the flow channel structural parameters. Using the scipy.optimize.minimize function in Python, we de-termined the three optimal combinations of flow channel structural parameters corresponding to better hydraulic performance: q0.1 is 1.5 or 2 L/h (objective function y = min x), better clogging resistance (y = max particle passage rate), and both (y = min (0.5 x - 0.5 particle passage rate)). The research results can provide a ref-erence for the optimal design of variable flow emitters.

The effective utilization of medium-high temperature geothermal energy is pivotal in reducing carbon emissions and plays a crucial role in developing clean energy technologies. The MiDu ge-othermal field, situated in the southeastern region of Dali Prefecture, Yunnan Province, lies within the Mediterranean-Himalayan high-temperature geothermal belt and is characterized by abundant geothermal resources. However, due to its considerable depth, exploration poses significant risks, resulting in a total utilization rate of less than 0.5% of the total reserves. This study employs natural seismic data to perform a tomographic analysis of the geothermal system in the Midu basin. By examining the P-wave velocity (Vp) and velocity ratio of P-wave and S-wave (Vp/Vs) at various depths, the findings reveal that the basin comprises two distinct structural layers: the thrust basement of the Mesozoic and Paleozoic eras and the strike-slip extensional sedimentary layer of the Cenozoic era. A low-velocity anomaly in the central basin corresponds to the loose Cenozoic sedimentary layer. In contrast, high-velocity anomalies at the basin edges correlate with boundary faults and the Mesozoic-Paleozoic strata. Below a depth of 4 km, the Red River Fault and MiDu Fault continue to dominate the basin's structure, whereas the influence of the Malipo Fault dimin-ishes. The MiDu Fault exhibits higher thermal conductivity than the Yinjie Fault. It interfaces with multiple carbonate and basalt formations characterized by well-developed pores and fractures, making it a crucial conduit for water and a control point for geothermal storage. Consequently, the existence of high temperature geothermal resources for power generation should be concentrated around the Midu fault on the western side of the basin, while the Yinjie fault area is more favorable for advancements in heating and wellness.

The one-dimensional MoS₂/carbon nanofibers (1D MoS₂/CNFs) are synthesized by electrospinning using exfoliated MoS₂ nanosheets and polyacrylonitrile as raw materials. The exfoliated MoS₂ nanosheets with size of about 150 nm are encapsulated in carbon nanofibers, and the free-standing MoS₂/CNFs can be easily cut into flexible tablet and directly used as binder-free anode for lithium storage. The resultant 1D MoS₂/CNFs exhibit a very high reversible capacity of 700 mAh g^-1 at 100 mA g^-1 after 50 cycles, high rate capacity (450 mAh g^-1 at 1000 mA g^-1 after 200 cycles) and good cycle stability.

Glass cleaning is one of the major cleaning tasks in malls, offices, and modern households where the human workforce is still used for cleaning. Manual cleaning on façade glasses is a less safe, tedious, ineffective, and complex process. With the advancement of robot technology, it is possible to use robots for glass cleaning to facilitate mankind and improve cleaning efficiency. This work presents the development of a vacuum-type glass cleaning robot for façade cleaning and its performance analysis. The developed square-shaped robot is controlled by an Arduino Mega microcontroller, equipped with vacuuming and cleaning technology. An adhesion mechanism on the wheels makes the robot move forward and backward on the glass. The robot runs on a 24-volt DC with a power rating of 75 watts. There are ultrasonic sensors to detect obstacles and help the robot navigate. The robot uses a random-walk mechanism and works continuously for 90 minutes effectively. The performance of the robot has been analyzed in terms of coverage area, repeatability, and dust collection efficiency.

Currently, the use of recycled waste plastic in road construction is not just considered ecofriendly but also considered advantageous for improving road performance over time. This research explores using recycled plastic bags, made of low-density polyethylene (LDPE), in asphalt mixtures to improve road performance and manage waste. The study aims to assess the effects of incorporating different amounts of shredded waste plastic into asphalt mixes and determine the optimal percentage. The aggregate used was obtained from Sargodha quarry Pakistan and asphalt used was 60/70 grade obtained from national refinery limited Pakistan. Using the Marshall mix design, the optimum bitumen content (OBC) was found to be 4.5% of the asphalt mix weight. Thirty samples were tested with waste plastic proportions of 6%, 8%, 10%, 12%, and 14% of OBC weight, along with a standard mix. Tests on stability, bulk density, flow, and air voids showed that a 10% plastic content significantly enhances asphalt performance, increasing stability by around 25%. The study recommends using 10% plastic by OBC weight in asphalt mixes for better performance and suggests further research on optimal incorporation techniques of waste materials in asphalt mixtures.

Hydrological processes are complex to compute on hilly areas when compared to the plain areas. Most of the hydrological model do not take into account the critical rainfall-runoff generation processes such as subsurface storm flow, saturation excess flow, overland flow, return flow and pipe storage. The simulations of the above processes in the soil matrix requires detailed hillslope hydrological modelling. In present study, a hillslope experimental plot is designed to study the runoff generation processes on the plot scale. The setup is designed keeping in view the natural hillslope conditions prevailing in the north western Himalayas, India where high intensity storm event occurs frequently. Using the experimental data and the developed conceptual model, the overland flow and the subsurface flow through macropore dominated area has been estimated/analyzed on the pixel basis. Over the experimental hillslope plot, a rainfall simulator was installed to generate the rainfall intensity in the range of 15 to 150 mm/hr which represented the dominating rainfall intensity range in the region. Soil moisture sensors were also installed at 100 mm and 300 mm depth at different locations of the plot to observe soil moisture variations. It was found that once the soil is saturated, it remains in the field capacity for next 24-36 hours. Such antecedent moisture conditions are most favorable for the generation of rapid stormflow from hillslopes. Dye infiltration test was also performed on the undisturbed soil column to observe the macropore fraction variability over the vegetated hillslopes. The surface runoff predicted using the developed hillslope hydrological model compared well with the observed surface runoff under high intensity rainfall conditions.

Athlete’s heart is characterised by structural, functional, and electrical remodelling. During an exercise session, the heart increases the use of oxygen, which is associated with decreased vascular resistance in the lungs’ circulation. Left ventricular hypertrophy is an echocardiographic modification especially met in endurance athlete’s heart. Moreover, left atrial dilatation represents another important characteristic of endurance athletes. Electrical modulation in the athletic heart induces neurological modifications leading to sinus arrhythmia, bradycardia, early repolarisation, and first-grade atrioventricular (AV) blocks. Athletes' nutritional status involves the assimilation of macronutrients such as proteins, carbohydrates, and fats. Depending on the duration of physical exercise, ATP can be obtained in aerobic or anaerobic conditions. This systematic review aims to find correlations between different evaluative methods of cardiac adaptation and present the metabolism in performance athletes.

A binary biomass-based solid electrolyte film (LSE) was successfully synthesized through the incorporation of lignin nanoparticles (LNP) with sodium alginate (SA). The impact of the mass ratio of SA to LNP on the microstructure, porosity, electrolyte absorption capacity, ionic conductivity, and electrochemical properties of the LSE was thoroughly investigated. The results indicated that as the proportion of SA increased from 5% to 15% LNP, the pore structure of the LSE became increasingly uniform and abundant. Consequently, enhancements were observed in porosity, liquid absorption capacity, ionic conductivity, and overall electrochemical performance. Notably, at a SA amount of 15% LNP, the ionic conductivity of resultant LSE-15 was recorded at 14.10 mS cm1, with porosity and liquid absorption rates reaching 308% and 58.4%, respectively. LSE-15 was employed as a solid electrolyte, while LNP-based carbon aerogel (LCA) served as the two electrodes in the construction of a symmetric all-solid-state supercapacitor (SSC). The SSC device demonstrated exceptional electrochemical storage capacity, achieving a specific capacitance of 197 F g1 at 0.5 A g1, along with maximum energy and power density of 27.33 W h kg1 and 4998 W kg1, respectively. Furthermore, the SSC device exhibited highly stable electrochemical performance under extreme conditions, including compression, bending, and both series and parallel connections. Therefore, the development and application of the binary biomass-based solid electrolyte film in supercapacitors represent a promising strategy for harnessing high-value biomass resources in the field of energy storage.

This study assessed the hydrological performance and runoff water quality of 12 green roof (GR) modular systems located at the Universidad de los Andes campus (Bogotá, Colombia). Based on 223 rainfall events spanning a 3-year period, average rainfall retention was 85% (SD = 25%). T-tests, Welch Test, multiple linear regressions and correlation analysis were performed in order to assess the potential effect of air temperature, substrate type, vegetation cover, relative humidity, antecedent dry weather period (ADWP), rainfall duration and rainfall maximum intensity. In some cases, GR design variables (i.e. growing media and type of vegetation) were found to be significant for describing rainfall retention efficiencies and, depending on the GR type, some hydrological variables were also correlated with the rainfall retention. Rainfall and GR runoff were monitored for Total Kjeldahl Nitrogen (TKN), Nitrates, Nitrites, Ammonia, Total Phosphorus (TP), Phosphates, pH, Total Dissolved Solids (TDS), Total Suspended Solids (TSS), Color, Turbidity, Biological Oxygen Demand (BOD), Chemical Oxygen Demand (COD), Total Coliforms, metals and Poliaromatic Hydrocarbons (PAHs). The results obtained confirmed that GR systems have the ability to neutralize pH, but are source of the rest of the aforementioned parameters, excluding PAHs (with concentrations below detection limits), Ammonia, TSS, Se and Li, where differences with reference values (rainfall and plastic panel runoff) were not statistically significant. Substrate type, event size and rainfall regime are relevant variables for explaining runoff water quality.

The extensive utilization of drones has led to numerous scenarios that encompass both advantageous and perilous outcomes. By using deep learning techniques, it is aimed to reduce the dangerous effects of drone use through early detection of drones.The state of the art of the study is evaluation of deep learning approaches such as pre trained YOLOv8 drone detection for security issues. In this study, dataset collected by Mehdi Özel for a UAV competition are sourced from GitHub. These images are labeled using Roboflow, and the model is trained on Google Colab. To enhance model performance, dataset augmentation techniques including rotation and blurring are implemented. Following these steps, a significant precision value of 0.946, a notable recall value of 0.9605 and a considerable precision-recall curve value of 0.978 are achieved, surpassing many popular models such as Mask CNN, CNN, and YOLOv5.

Accurate streamflow forecasting is crucial for effectively managing water resources, particularly in countries like Colombia, where hydroelectric power generation significantly contributes to the national energy grid. Although highly interpretable, traditional deterministic, physically-driven models often suffer from complexity and require extensive parameterization. Data-driven models like Linear Autoregressive (LAR) and Long Short-Term Memory (LSTM) networks offer simplicity and performance but cannot quantify uncertainty. This work introduces Sparse Variational Gaussian Processes (SVGPs) for forecasting streamflow contributions. The proposed SVGP model reduces computational complexity compared to traditional Gaussian Processes, making it highly scalable for large datasets. The methodology employs optimal hyperparameters and shared inducing points to capture short-term and long-term relationships among reservoirs. Training, validation, and analysis of the proposed approach consider the streamflow dataset from 23 geographically dispersed reservoirs recorded during twelve years in Colombia. Performance assessment reveals that the proposal outperforms baseline Linear Autoregressive (LAR) and Long Short-Term Memory (LSTM) models in three key aspects: adaptability to changing dynamics, provision of informative confidence intervals through Bayesian inference, and enhanced forecasting accuracy. Therefore, the SVGP-based forecasting methodology offers a scalable and interpretable solution for multi-output streamflow forecasting, thereby contributing to more effective water resource management and hydroelectric planning.

This study investigates water consumption in two areas with limited water resources – the Salar de Atacama and Salar de Atacama-Vertiente Pacifico basins in Chile's Antofagasta Region – with the aim of developing strategies that incorporate social and environmental aspects into water management. A qualitative approach was employed that involved a focus group with four water management representatives and surveys of the general population (156 responses). Additionally, the current state of water rights in the basins was examined and the feasibility of the proposed strategies was assessed. The findings reveal that the mining industry's development approach is viewed negatively, mainly due to inadequate community engagement, confidential consumption data, and limited government oversight. Adopting a comprehensive water stewardship approach that considers social and environmental factors would help mitigate the adverse effects of mining on communities. Therefore, improved management and transparent collaboration among stakeholders are essential for responsible water resource use in mining.

Dams design and their operation cause strong environmental alteration and therefore a long-term debate is ongoing for the scale of these projects. At the same time, the concept of Environmental Flow Assessment (EFA) is a crucial element of modified ecosystems featuring large infrastructure such as dams and reservoirs for mitigating potential environmental degradation while they operate. Nowadays, integrated scientific frameworks are required to quantify the risks caused by large infrastructure. Through the use of stochastic analysis, it is possible to quantify these uncertainties, and present a solution that incorporates long-term persistence and environmental sustainability into a balanced reservoir simulation model. In this work, an attempt is made to determine a benchmark reservoir size incorporating hydrological and ecological criteria though stochastic analysis. The primary goal is to ensure the best possible conditions for the ecosystem, and then secondarily to allow a steady supply of water for other uses. Using a synthetic timeseries based on historical inputs, it is possible to determine and preserve essential statistical characteristics of a river’s streamflow, and use these to detect the optimal reservoir capacity that maximizes environmental and local water demand reliability.

The strong competition in the automotive industry has required manufacturers to implement lean production, both with methods and techniques specific to Industry 4.0. At the same time, universities must provide graduates with specific skills for applying these new production methods and techniques. In this context, a Lean Learning Factory was developed in the Pitesti University Center, which allows students to learn, experiment, and research, in an environment similar to that of enterprises, the new Lean Manufacturing methods and techniques, and Industry 4.0. The research presented in this study had as its objective identifying the minimum number of repetitions necessary to train operators who serve two differently organized workstations, one classic, the other including digital techniques, but on which the same assembly operation is performed. Several indicators were considered in the analysis, such as the number of errors, the number of stops, the effective duration of the work cycle, and the percentage ratio between the standard duration of cyclical activities and the effective duration of the work cycle. The evolution of these indicators was mathematically modelled by regression functions, using the least squares method. The obtained results also highlight the usefulness of applying the DOJO method as a Lean manufacturing-specific learning technique and the efficiency of implementing digital techniques in work organization.

The amount of solar energy produced is directly proportional to the surface area of solar panels exposed to sun rays. To increase the productivity of solar panels, a number of techniques are proposed by curving the surface or protruding the panel surface to maximise the surface area, e.g. Protruding panel surfaces with shapes such as pyramidal, cone, spherical cap, sinusoidal wavy curve, etc. These techniques are analysed to determine whether the panel surface area increases. The geometrical design analysis showed that, depending on the chosen surface curving method, the panel can achieve a remarkable increase in surface area.The system advisor model was used to assess a sample of solar cells with standard measurements of 6 by 6 inches that were organised in a zigzag pattern with different angles of inclination (10°, 15°, 20°, 25°, 30°, and 35°). Overall, the maximum power pump and annual AC energy performance increase by 1.4%, 4.2%, 6.8%, 10.8%, 14.9%, and 21.6%, respectively, over a one-year period of productivity. However, by applying the concept of solar panel efficiency degradation due to tilting solar panels at an angle by Mamun et al. (2022), for each 5° increase in tilt angle beyond the ideal 10°, the efficiency decreases by approximately 0.76%. In conclusion, the overall efficiency is greater than that of standard flat solar panels, provided the tilt angle is within a reasonable range.

Dipole antennas, fundamental components in wireless communication systems, have garnered significant attention due to their versatility and efficiency across various applications. This literature review explores the performance analysis of dipole antennas, focusing on recent advancements and methodologies from 2019 to 2024. Employing a systematic approach, the review synthesizes findings from multiple databases, emphasizing peer-reviewed studies that delve into dipole antenna designs and applications. Through meticulous data extraction, key parameters influencing dipole antenna performance, such as material properties and operating frequency, are identified and analyzed. Results highlight the widespread utilization of dipole antennas in wireless communication systems, including indoor positioning, mobile communication, and antenna calibration, among others. Discussions underscore the evolution of dipole antennas in base station environments and their role in filtering applications. The review concludes by emphasizing the continued relevance and evolution of dipole antennas, showcasing their adaptability and potential across diverse wireless communication contexts.

Precision agriculture is a farm management technology that involves sensing and then responding to the observed variability in the field. Remote sensing is one of the tools of precision agriculture. The emergence of small unmanned aerial vehicles (sUAV) have paved the way to accessible remote sensing tools for farmers. This paper describes the comparison of two popular off-the-shelf sUAVs: 3DR Iris and DJI Phantom 2. Both units are equipped with a camera gimbal attached with a GoPro camera. The comparison of the two sUAV involves a hovering test and a rectilinear motion test. In the hovering test, the sUAV was allowed to hover over a known object and images were taken every second for two minutes. The position of the object in the images was measured and this was used to assess the stability of the sUAV while hovering. In the rectilinear test, the sUAV was allowed to follow a straight path and images of a lined track were acquired. The lines on the images were then measured on how accurate the sUAV followed the path. Results showed that both sUAV performed well in both the hovering test and the rectilinear motion test. This demonstrates that both sUAVs can be used for agricultural monitoring.

South rear-edge populations of widely distributed temperate and boreal tree species such as birches (Betula pubescens and B. pendula) are considered particularly vulnerable to climate warming, and at the same time they constitute genetic reservoirs of drought-adapted ecotypes. Here, we compared radial growth patterns and responses to climate, river or dam flows and a drought index of rear-edge (southernmost) populations (Toledo Mountains, central-southern Spain) with populations located in northern Spain of B. pubescens and B. pendula. Then, we per-formed a comparative analysis across Europe of B. pendula populations. The main climatic con-straint of birch growth was high summer water deficit, although the effect of local hydrological conditions was particularly important in rear-edge populations. We found declining growth trends in rear-edge stands since the early 21st century, related to decreasing water availability and increasing aridity. Our results also suggested distinct growth patterns and climate-growth associations of B. pendula across Europe that show how populations further south and in warmer locations were more sensitive to the drought. In conclusion, drought-induced growth decline can be exacerbated by local human land uses leading to reduced river inflow, thus endangering birch populations at their southern distribution limit. Protection of endangered rear-edge birch populations requires adequate management of local water resources.

Ammonium ion is a chemical species that is found in abundance in natural waters, whether underground or surface, but also in wastewater resulting from agricultural and industrial activities. Even if the removal of the ammonium ion from water has been studied for a very long time, it has been found that its removal is far from being solved. In this study, we evaluated the performance of the ammonium ions adsorption process on two adsorbents, zeolite clinoptilolite, ZR, (manufacturer Zeolite Develop-ment SRL, Rupea, Brasov, Romania) and the other granular activated carbon type Norit GAC 830 W. In the first step the paper presents the mineralogical (XRD) and structural (SEM and EDX) characterization of the ZR and determination of the pH zero-point charge, pHZPC, for all the adsorbents. Studies were carried out in equilibrium and kinetic conditions. The efficiency of the adsorbent was investigated in different experimental conditions by varying the initial concentration, particle size, temperature, pH, ionic strength and contact time. The mathematical models and parameters specific to the adsorption isotherms that best describe the experimental results were identified. Langmuir, Freundlich, Temkin and Dubinin-Radushkevich mathematical models were used for comparison. The Langmuir isotherm proved to be the most appropriate to describe the adsorption of ammonium ions on all types of adsorbents used. The adsorption capacity of ammonium ion from synthetic solutions, at 20 °C, pH = 6.09, for the range of initial concentrations 0 - 50 mg/L for Rupea Zeolite is in the range of 10.46 mg/g -12.34 mg/g and for granular activated carbon GAC W830 is 16.64 mg/g. It was found that the increase in temperature and pH have a positive effect on the adsorption capacity of the ammonium ion on the studied adsorbents, and the ionic strength determines the decrease in the adsorption capacity. Kinetic models were also identified that best describe the experimental processes. In this sense, pseudo first order, pseudo second order, intraparticle diffusion and the Elovich model were used. The results of the investigation showed that second-order kinetics governs the adsorption process on ZR zeolites and pseudo-first order for activated carbon.

The Brazilian water legislation advocates that some uses have priority over others, but this aspect has never been clearly addressed, generating conflicts. Water authorities usually refer to hydrological models to justify their decisions on water allocation. However, a significant group of stakeholders does not feel qualified to discuss these models and is, therefore, excluded from the decision process. We hereby propose a hydrologically robust method to correlate water uses with their respective reservoir alert volumes, which should empower the less formally educated stakeholders. The method consists of: (i) generating the water discharge versus reliability curve, using a stochastic approach; (ii) generating the withdrawal discharge versus alert volume family of curves, using a water-balance approach; (iii) calibrating the key parameter T using field data; and (iv) associating each water use with its alert volume. We have applied the method to four of the largest reservoirs (2.10³ - 2.10² hm³) in the semi-arid Ceará State. The results indicate that low-priority water uses should be rationalized when the reservoir volume is below 20%; whereas uses with very high priority should start rationalization when it is below 11%. These hydrological guidelines should help enhance water governance among non-specialist stakeholders in water-scarce and reservoir-dependent regions.

In recent years, there has been an increasing interest in adopting novel sensing technologies for the continuous monitoring of structural systems. In this respect, Micro-Electro-Mechanical System (MEMS) sensors are widely used in several applications, including Structural Health Monitoring (SHM). Thanks to their significantly lower cost, ease of installation on the structure, and lower power consumption, they enable extensive, pervasive, and battery-less monitoring systems. This paper presents an innovative high-performance device for SHM applications, based on a low-noise triaxial MEMS accelerometer, providing a guideline and insightful results about the opportunities and capabilities of these devices. Sensor nodes have been designed, developed, and calibrated to meet structural vibration monitoring and modal identification requirements. These components include a protocol for reliable command dissemination through the network and data collection, and improvements to software components for data pipelining, jitter control, and high-frequency sampling. Devices were tested in the lab using shaker excitation. Results demonstrate that MEMS-based accelerometers are a feasible solution to replace expensive piezo-based accelerometers. Deploying MEMS is promising to minimize sensor node energy consumption. Time and frequency domain analysis show that MEMS can correctly detect modal frequencies, useful parameters for damage detection. The acquired data from the test bed were used to examine the functioning of the network, data transmission, and data quality. The proposed architecture has been successfully deployed in a real case study to monitor the structural health of the Marcus Aurelius Exedra Hall within the Capitoline Museum of Rome. The performance robustness was demonstrated and the results showed that the wired sensor network provides dense and accurate vibration data for structural continuous monitoring.

Artificial intelligence methods and application have recently shown great contribution in modeling and prediction of the hydrological processes, climate change, and earth systems. Among them, deep learning and machine learning methods mainly have reported being essential for achieving higher accuracy, robustness, efficiency, computation cost, and overall model performance. This paper presents the state of the art of machine learning and deep learning methods and applications in this realm and the current state, and future trends are discussed. The survey of the advances in machine learning and deep learning are presented through a novel classification of methods. The paper concludes that deep learning is still in the first stages of development, and the research is still progressing. On the other hand, machine learning methods are already established in the fields, and novel methods with higher performance are emerging through ensemble techniques and hybridization.

Forecasting streamflow accurately is essential to achieve an efficient integrated water resources management strategy and provide consistent support to water decision-makers. We present a simple, low-cost and robust approach for forecasting monthly and yearly streamflow during the hydrological year in course, applicable to headwater catchments. It combines the use of regression analysis techniques, the two-parameter Gamma continuous cumulative probability distribution function and the Monte Carlo method. It is based on a probabilistic comparison of the progression of the current hydrological year with the historic observed series. The methodology has been successfully applied to two headwater reservoirs within the Guadalquivir River Basin in southern Spain. The root-mean-square error and correlation coefficient were used to measure the accuracy of the model and the results showed good levels of reliability. The outputs are the probabilistic monthly and yearly streamflow and 80% confidence interval. Further reductions in prediction errors may be achieved from increasing the number of observed years. These risk-based predictions are of great value, especially, before the intensive irrigation campaign starts (usually in April), when Water Authorities are to take responsible management decisions about the best allocation of the available water volume between the different water users and environmental needs.

Seasonal snowpacks, characterized by their snow water equivalent (SWE), play a major role in the hydrological cycle, snow melt contributions to floods and the subsequent availability of water resources downstream. Accurately estimating SWE and understanding its spatial and temporal variations presents a considerable challenge, particularly within mountainous regions characterized by complex terrain and limited observational data. Seeking to enhance the performance of the widely used Soil and Water Assessment Tool (SWAT), we report a new approach characterising snowpack behaviour incorporating both modelled and remotely sensed derived SWE calibration data. We focus on the Chenab River Basin (CRB) a headwater catchment of the Indus Basin, globally significant in terms of human inhabitants and intensifying flood risk due to climate change. We conducted a thorough assessment of five satellite-derived and reanalysis-based precipitation datasets: ERA5-Land, CMORPH, TRMM, APHRODITE, and CPC UPP. This reveals significant levels of uncertainty in global precipitation products when compared to reference data from observed stations as well as in the resulting simulated streamflow from the SWAT model. Subsequently, we expanded the scope of the SWAT model to encompass the spatial and temporal simulation of SWE. This was achieved by incorporating information from remotely sensed and modelled SWE products, manually adjusting snow parameters in R-SWAT for both the main basin and at sub-basin scales. Integrating SWE from reference snow products into the calibration process, alongside streamflow data, substantially enhanced modelling accuracy to simulate SWE compared to the conventional auto-calibration and single-variable approaches reliant solely on streamflow data. This approach results in considerable improvement in SWE predictions and to some extent in streamflow simulation in catchments dominated by snow. This research highlights the potential of remote sensing and modelled SWE parameterisation in the absence of in-situ snowpack data in high-altitude environments. An improved understanding of SWE behaviour is vital for predicting hydrological responses spanning hazards to water resources in the populous downstream regions of the Indus Basin, especially in the face of climate change.

Drought events are evident effects of climate change around the globe and yield several socio-economic impacts. Such effects are even more relevant for South America (SA) since different activities essential for the continent, like agriculture and energy generation, depend highly on water resources. Thus, this study aimed to evaluate future changes in precipitation and hydro-logical droughts occurrence in SA through climate projections from eight global climate models (GCMs) of CMIP6. To this end, statistical downscaling was applied to the projections with the Quantile Delta Mapping technique, and the method proved to be efficient in reducing systematic biases and preserving GCMs’ trends. For the following decades, the results show considerable and statistically significant reductions in precipitation over most of SA, especially during the austral spring, with the most intense signal under the SSP5-8.5 forcing scenario. Furthermore, GCMs showed mixed signals about projections of the frequency and intensity of drought events. Still, they indicated agreement regarding the increase in duration and severity of events over all of SA and a substantial proportion of moderate and severe events over most of Brazil during the 21st century. These results can be helpful for better management of water resources by deci-sion-makers and energy planners.

The large areas being targeted for tropical forest restoration as part of the UN Decade on Ecosystem Restoration will have major consequences for the flow of water through landscapes. Whilst the prevailing mantra that ‘more forest implies less streamflow’ remains true in terms of annual water yields, we demonstrate that opportunities for increased tree cover to improve seasonal flow regimes of streams, particularly baseflows, are important. We discuss several potential positive feedbacks of forest restoration on hydrological processes at various scales, including ‘trade-offs’ between changes in vegetation water use and infiltration after foresting degraded land; the recovery of the capacity of vegetation to capture ‘occult’ precipitation in specific coastal and montane settings; and enhanced moisture recycling and transport at various scales. Modelled changes in baseflow after foresting all degraded land climatically capable of carrying forest in the tropics suggested a positive effect in 10% of the land. For an additional 8%, the effect was predicted to be about neutral (<2 mm/y). We conclude that a more positive narrative regarding the relationship between tropical forestation and water availability is justified. It is time for greater involvement of hydrologists and atmospheric scientists in the development and assessment of forest landscape restoration efforts.

Research within the field of hydrology often focuses on comparing stochastic to machine learning (ML) forecasting methods. The comparisons performed are all based on case studies, while an extensive study aiming to provide generalized results on the subject is missing. Herein, we compare 11 stochastic and 9 ML methods regarding their multi-step ahead forecasting properties by conducting 12 large-scale computational experiments based on simulations. Each of these experiments uses 2 000 time series generated by linear stationary stochastic processes. We conduct each simulation experiment twice; the first time using time series of 100 values and the second time using time series of 300 values. Additionally, we conduct a real-world experiment using 405 mean annual river discharge time series of 100 values. We quantify the performance of the methods using 18 metrics. The results indicate that stochastic and ML methods perform equally well.

Passive microwave measurements have been available on satellites dating back to the 1970s on research satellites flown by the National Aeronautics and Space Administration (NASA). Since then, several other sensors have been flown to retrieve hydrological products for both operational weather applications (e.g., the Special Sensor Microwave/Imager–SSM/I; the Advanced Microwave Sounding Unit–AMSU) and climate applications (e.g., the Advanced Microwave Scanning Radiometer–AMSR; the Tropical Rainfall Measurement Mission Microwave Imager–TMI; the Global Precipitation Mission Microwave Imager–GMI). Here the focus is on measurements from the AMSU-A, AMSU-B and Microwave Humidity Sounder (MHS). These sensors have been in operation since 1998 with the launch of NOAA-15, and are also on board NOAA-16, -17, -18, -19 and the MetOp-A and -B satellites. A data set called the “Hydrological Bundle” is a Climate Data Record (CDR) that utilizes brightness temperatures from Fundamental CDRs to generate Thematic CDRs (TCDR). The TCDR’s include: Total Precipitable Water (TPW), Cloud Liquid Water (CLW), Sea-Ice concentration (SIC), Land surface temperature (LST), Land surface emissivity (LSE) for 23, 31, 50 GHz, rain rate (RR), snow cover (SC), ice water path (IWP), and snow water equivalent (SWE). The TCDR’s are shown to be in general good agreement with similar products from other sources such as the Global Precipitation Climatology Project (GPCP) and the Modern-Era Retrospective Analysis for Research and Applications (MERRA-2). Because of the careful intercalibration of the FCDR’s, little bias is found among the different TCDR’s produced from individual NOAA and MetOp satellites, except for normal diurnal cycle differences.

This paper proposes a method to utilize weather and land cover models to generate future environmental scenarios, and presents the watershed models to simulate the hydrological impact on watershed-scale hydrology. The Weather Generator model and General Circulation Model were applied to produce rainfall and local temperature under different climate conditions, and the Conservation and Land Use and its Effects model was incorporated to simulate future land cover variability. The circumstances of future climate and land cover changes were used as inputs to drive the HEC-HMS rainfall runoff model for obtaining surface runoff in a mountainous area. The WASH123D model was then utilized for the entire watershed simulation. Modeling results were then examined to discuss hydrological impacts on three different time periods: near future (2020-2039), future (2050-2069), and distant future (2080-2099). The Fengshan Creek basin in northern Taiwan was selected as study site. Simulations results indicated that the influence of climate change revealed more relevant effects when compared to local land cover changes. The ground water levels tended to diminish as the land cover area changed. In addition, both river and groundwater levels reveal that it is drier in dry season and wetter in wet season in future.

The purpose of this study is to examine the match-related performance (scores and penalties) of the athletes in the Junior EJC (European Judo Championship) and U23 EJC categories before and immediately after the Covid-19 quarantine, according to gender and weight category groups. Our sample was based on the junior and U23, male and females, 2019-2020 Junior European Judo Championships and 2019-2020 U23 European Judo Championships. A total of 1431 matches in these tournaments were analyzed: male (n=812) and female (n=619). The main findings of this study showed that most matches in both the male and female categories resulted in ippon (2019-2020 average: 64,35% for Junior male, 70,55% for U23 male and 69,3% for Junior female, 64,4% for U23 female). No significant differences (p &gt; 0.05) were observed between gender and age groups concerning the match scores. In the formation phase of the technique, considerations of high- and low-intensity actions, and analysis of the speed of athletes during matches in official competitions can help to better understand the dynamics of the variability of movements in judo matches. In addition, suggestions are presented to be applied to athletes in different age categories who are on the way to become Olympic athletes.

This paper aims to assess whether the financial performance of Portuguese companies that publish sustainability reports (SR) differs from the financial performance of companies that do not publish SR. We use two methodological procedures. First, we conducted a univariate analysis to test the differences in the financial performance according to the disclosure of SR. Second, we conducted a multivariate analysis using a multiple linear regression that explains financial performance by the disclosure of SR, as well as control variables such as sector, size, leverage, growth, and liquidity. Findings indicate that financial performance of companies that disclose SR does not significantly differ from the financial performance of companies that do not disclose SR. The results are robust to both methodological procedures, as well as to the sample split by sectors.

The existing methods of oil palm Fresh Fruit Bunch (FFB) evacuation from in-field to temporary collection point which typically apply medium to large tractors, often result in unexpected soil compaction. Meanwhile, smaller size equipment indicates low working capacity and unable to perform direct unloading to collection bin. This paper addresses the challenge of FFB evacuation by focusing on preventing soil compaction, increasing working capacity and enabling direct unloading to collection bin. For this purpose, a 6x6WD transporter prototype with a scissor lift mechanism was developed. With the design, the transporter could evacuate FFB in both steep mineral terrain and low ground pressure peatland, able to carry FFB with capacity of 750 kg and provide direct unloading into collection bin. The average field working capacity of the transporter was found to be 15 tons day-1. This research offers insights for future designs of medium-sized FFB transporters with advanced unloading mechanisms.

Continuous-flow microwave heating stands out for its ability to rapidly and uniformly heat substances, making it widely applicable in chemical production. However, in practical applications, the permittivity of the heated liquid changes dramatically as the reaction progresses, affecting the efficiency and uniformity of continuous-flow heating. Herein, this work presents a novel microwave heating device based on coaxial structure for high-performance heating. Our approach commenced with the development of a multi-physical field model, incorporating a spiral polytetrafluoroethylene (PTFE) as a water channel and the coaxial waveguide as a container was established. The analysis shows that the uniform distribution of the sectional electric field of electromagnetic waves in the TEM mode within the coaxial structure can enhance heating uniformity. Then, a continuous-flow microwave heating system for different liquid loads was established, and experimental measurements were conducted. The heating efficiency for all loads exceeded 90%, which basely matched the simulation results, validating the accuracy of the model. Finally, the heating efficiency and uniformity under different permittivity loads were analyzed, as well as the impact of channel radius on heating efficiency. The device demonstrated high heating efficiency and stable heating uniformity across different loads. This continuous-flow microwave device is suitable for chemical research and production, contributing to the promotion of microwave energy applications in the chemical industry.

Understanding the evolving hydrological characteristics of landslide-induced barrier lakes is crucial for flood control, forecasting, early warning, and safety measures in the reservoir area. This study examines the changes in hydrological characteristics of the Attabad landslide-dammed lake over the past decade after the occurrence of the landslide, focusing on lake area dynamics and sediment concentration. High-resolution satellite images from QuickBird, Pleiades, and WorldView2 over seven periods were analyzed. The findings indicate that the lake area has gradually decreased, with the center of mass shifting towards the lake dam, indicating a trend towards stability. The suspended sediment in the barrier lake is distributed in a strip running from north to south, then northeast to southwest, with sediment concentration decreasing from the lake entrance to the dam and from the lake bank to the center. Over time, the average sediment concentration has decreased from 2010 to 2020, with higher concentrations in summer than in winter. Notably, during the 2017-2020 period, the lower-middle parts of the lake experienced higher sediment concentration, while the dam area witnessed lower concentrations, thereby reducing the sediment impact on the dam.

During the dry season, glaciers contribute significantly to the water supply downstream, especially in areas with seasonal rainfall. It is important to have physical-based glacier models, which are more advanced in quantifying the future dynamics of glaciers and melt distribution. Toward this purpose, an investigation of different numerical modeling tools and their approach is conducted and planned further to incorporate a glacier module into a spatially distributed model called mHM (Mesoscale Hydrological Model, mhm-ufz.org). An open-source tool called MATILDA (Modeling Water Resources in Glacierized Catchments) is used beforehand to implement the glacier entities to the application catchment Maipo Basin in Chile. Due to limited time availability and source of data, the glacier melt of the MATILDA model is integrated into the mHM model to see what the implications would be if the glacier module was implemented from scratch in mHM. The analysis of the glacier hydrology resulting from the Maipo-Glacier model was carried out by comparing the modeled glacier flows with the total observed flows. To assess the credibility of the models, the results are compared based on three goodness-fit measures, specifically r^2Coefficient of Determination), NSE (Nash-Sutcliffe Efficiency), and KGE (Kling-Gupta Efficiency). Application of the mHM model in the Maipo basin in Central Chile is performed by using prepared input datasets, showing that the approach is viable as evidenced by a Kling-Gupta Efficiency of 0.80 and Nash-Sutcliffe Efficiency of 0.70 from the starting periods of 1950 to 2020. Overestimation by the mHM model occurs during the late summer season, particularly when temperature decreases. The MATILDA model is used on glacier melt distribution, forcing data is prepared from regional datasets, and a glacier profile is constructed from ice thickness datasets. According to the model, the average KGE result for the years 2012–14 and 2015–18 was respectively 0.44 and 0.41. Glacier melt produced by Matilda is in the range of 58-68 % of the total runoff and the glacier area decreased by 22-23% during the simulation period from 2012-14 and 2016-2018. A pilot sub-basin Olivares is used in the coupling mHM model with initializing MATILDA glacier melt. The new model is underestimated during the seasonal transition in the initial days of the simulation. A considerable portion of the ablation period might be missed as a result of the initial storage quality, true glacier extent, and height distribution being understated. As a conclusion, the mHM Model was capable of predicting runoff well in a mountainous region and could also serve as a monitoring tool for watershed management. There are differences in results between the two models because of the different spatial resolutions and methodologies. MATILDA have a glacier retreat routine that gives better result for the glacier characteristics such as. A new glacier dynamics model needs to be implemented in the future to develop intermediate complexity, bridge catchment, and glacier scales after using the mHM, MATILDA, and mHM with MATIDLA model for analysis of glacier and highlighting the model's deficiencies in the initial period of simulative assessments.

Spatial pattern-oriented evaluations of distributed hydrological models have contributed towards an improved realism of hydrological simulations. This advancement was supported by the broad range of readily available satellite-based datasets of key hydrological variables, such as evapotranspiration (ET). At larger scale, spatial patterns of ET are often characterized by an underlying climate gradient, and with this study, we argue that gradient dominated patterns may hamper the potential of spatial pattern-oriented evaluation frameworks. We hypothesize that the climate control of spatial patterns of ET overshadows the effect model parameters have on the simulated variability. To solve this limitation, we propose a climate normalization strategy. This is demonstrated for the Senegal River basin as modeling case study, where the dominant north-south precipitation gradient is the main driver of the observed hydrological variability. Two multi-objective calibration experiments investigate the effect of climate normalization. Both calibrations utilize observed discharge (Q) in combination with remote sensing ET data, where one is based on the original ET pattern and the other utilizes the normalized ET pattern. We identify parameter sets that balance the tradeoffs between the two independent observations and find that the calibration using the normalized ET pattern does not compromise the spatial patern performance of the original pattern. However, vice versa, this is not necessarily the case, since the calibration using the original ET pattern showed a poorer performance for the normalized pattern. Both calibrations reached comparable performance of Q. With this study, we identified a general shortcoming of spatial pattern-oriented model evaluations using ET in basins dominated by a climate gradient, but we argue that this also applies to other variables such as, soil moisture or land surface temperature.

Mediterranean catchments are characterized by significant spatial and temporal hydrological variability caused by the interaction of natural as well human-induced abiotic and biotic factors. This study investigates the (non-)linearity rainfall-runoff relationship at multiple temporal scales in representative small Mediterranean catchments (i.e., < 10 km2) to achieve a better understanding of the hydrological response. Rainfall-runoff relationship was evaluated in 44 catchments at annual and event –203 events in 12 of these 44 catchments– scales. A linear rainfall-runoff relation was observed at annual scale with higher scatter in pervious than impervious catchments. Larger scattering was observed at event scale, although pervious lithology and agricultural land use promoted significant rainfall-runoff linear relations in winter and spring. These relationships were particularly analysed during five hydrological years in Es Fangar catchment (3.35 km2; Mallorca, Spain) as a temporal downscaling to assess intra-annual variability in which antecedent wetness conditions played a significant role in runoff generation.

Recently, the environment is being put under constantly increasing pressure. Globally, water shortage is considered as one of the most serious environmental problems which affect human life and plant wealth. Iraq is highly affected by water deficit in many regions. In particular, Al-Najaf region is selected to be under evaluation for the current and future water resources shortage. This study is based on the collected data for rainfall, evaporation, flow-rate, groundwater, water needed for irrigation, and daily uses for the period between 2000 and 2018. The Artificial Neural Network, normal distribution, and lognormal distribution type III are applied for analyzing the collected data in addition to predict the water shortage for year 2050. Results show a water shortage in years 2002, 2004, 2005, and 2017 only for the selected period. A Simulink model is constructed using Matlab to increase the credibility of the estimated results and gives accurate results for the groundwater and surface water needed. Where in 2050, it is found that it needs to use the groundwater source by 0.024 x 10⁹ m³ to support the surface water source which is represented by the Euphrates River. The study shows the extent of inefficient management of water resources in Al-Najaf region.

The review scrutinizes the currently performed research on the new methods for enhancing bituminous binder performance through radiation and radical grafting of polymer modifers of bitumen. It investigates innovative methods, including using waste polymers as modifiers, and applying radiation for polymer grafting, to overcome challenges like high costs, low aging resistance and storage stability issues, of which separation of phases polymer/bitumen is the most significant obstacle. These advanced modification techniques promise sustainability through the decrease of the carbon footprint of transportation systems by improving the properties and durability of binders. Additionally, the review discusses the parameters and mechanistic aspects from a scientific perspective, shedding light on the underlying processes that contribute to the improved performance of modified bituminous binders.

In 2004, first absolute gravity (AG) measurements were performed on the mountain tops of Mt. Zugspitze (2 sites) and Mt. Wank (1 site), and at the Wank foot (1 site). Wank (summit height 1780 m) and Zugspitze (2960 m) are about 20 km apart from each other and belong geologically to different parts of the Northern Limestone Alps. Bridging a time span of 15 years, the deduced gravity variations for Zugspitze are in the order of 0.30 μm/s² with a standard uncertainty of 0.04 μm/s². The Wank stations (foot and top) show no significant gravity variation. The vertical stability of Wank summit is also confirmed by results of continuous GNSS recordings. Because an Alpine mountain uplift of 1 or 2 mm/yr cannot explain the obtained gravity decline at Zugspitze, the dominating geophysical contributions are assumed to be due to the diminishing glaciers in the vicinity. The modelled gravity trend caused by glacier retreat between epochs 1999 and 2018 amounts to -0.012 μm/s²/yr at both Zugspitze AG sites. This explains more than half of the observed gravity decrease. Long-term variations on inter-annual and climate-relevant decadal scale will be investigated in the future using as a supplement superconducting gravimetry (installed in 2019) and GNSS equipment (since 2018).

Sediment transport is highly sensitive to flow conditions, showing significant increase during flood events. Based on this principle, this study set out to rank flood events occurring along river Trotuș (Romania) based on the amount of transported sediment and event duration. The 77 flood events recorded from 2000 to 2017 were ranked into 4 classes: type A (4%); type B (16%), type C (14%) and type D (66%). The sediment transport specific for the 4 types of flood events was related to the flow discharge (sediment rating curve and hysteresis effect), the specific stream power and the energy expenditure of these events. More than 60% of the hysteresis loops typical for flood events were clockwise, thus singling out the channel as the main sediment source. Ca. 74% of the total sediment yield was transported at stream power values higher than the 300 Wm-2 threshold, which was exceeded in less than 1% of the investigated timeframe. The changes occurring in the sediment transport rates after major floods show that these events are significant thresholds in the hydrogeomorphic evolution of river channels.

Seedling establishment is often a critical bottleneck to revegetation of mine tailings and similar substrates with a low capacity to retain water and plant nutrients. Biochar and deactivated yeast are potential sustainable, low-cost materials with high nutrient- and water-holding capacity that could be used in this context as seed coatings to aid in seedling establishment on challenging sub-strates. We conducted a greenhouse study to assess the effectiveness of biochar and deactivated yeast, applied alone in a factorial combination, as seed coatings, on the germination, establish-ment, and early growth of white clover (Trifolium repens) and purple prairie clover (Dalea pur-purea). Coated seeds were applied to a mine tailing, a coarse granitic sand, and potting soil mix substates; seedling establishment and growth were monitored over 75 days. Results indicate strong interactive effects of seed coatings with species and substrate. Biochar coatings enhanced seedling establishment of Trifolium, with biochar or biochar plus yeast coatings giving the best results. In some cases, these effects persisted throughout the experiment: biochar coatings resulted in a ~5-fold increase in Trifolium biomass at harvest for plants in the potting soil mix but had neutral effects on sand or tailings. Biochar seed coatings also enhanced Dalea germination in some cases, but benefits did not persist. Our results indicate that biochar-based seed coatings can have lasting effects on plant growth well beyond germination, but also emphasize highly spe-cies-specific responses that highlight the need for further study of broader patterns and mecha-nisms.

Emotional intelligence may help nurses to cope with demanding work environments. Although it is well known that emotional intelligence is positively related to work performance literature on nurses is limited. The aim of our study was to examine the impact of emotional intelligence on work performance in a sample of nurses in Greece. We conducted a cross-sectional study in Greece. We collected data from a convenience sample of nurses during January 2024. We measured emotional intelligence with the Trait Emotional Intelligence Questionnaire-Short Form (TEIQue-SF), and work performance with the Individual Work Performance Questionnaire (IWPQ). We created multivariable linear regression models adjusted for sex, age, educational level, and work experience. We found that the four streams of emotional intelligence (i.e. well-being, self-control, emotionality, sociability) increases nurses’ work performance. In particular, we found a positive relationship between well-being and task performance and contextual performance. Similarly, there was a positive relationship between self-control and task performance. Additionally, sociability increased task performance and contextual performance. Moreover, emotionality and sociability reduced counterproductive work behavior. Our multivariable models identified a positive impact of emotional intelligence on nurses’ work performance. Nurses managers and healthcare organizations should adopt appropriate interventions to improve nurses’ emotional intelligence.

The human eye is a vital source of collecting information worldwide via daily observation. The computer science area recognizes it in research as a human-computer interaction. Human blink is an integral part of this observation as a research topic. Several studies have been conducted on eye blinking and left-right eye movements. Previous studies conducted on the eye blink consider the availability of hardware devices high in the budget, high Precision, and low light expo-sure, unlike our study, which uses low-budget devices as simple as a webcam and more versatile usable techniques, incredibly both dim and high light to name a few. This study offers the observational approach via ResNet101v2, VGG-19, and Convolution Neural Network (CNN) architectures. Outperforming both VGG-19 and CNN in this study, ResNet101v2 achieves an impressive accuracy of 98.2%. In contemporary times, deep learning in AI is taking an advanced form. This study attempts to provide a new insight into the real-world implementation of the eye blink.

High dimensional datasets are highly likely to have redundant, irrelevant, and noisy features that negatively affect the performance of the classification algorithms. Selecting the most relevant features and reducing the dimensions of datasets by removing the undesired features is a dimensional reduction technique called Feature Selection (FS). In this paper, we propose an FS approach based on the Black Hole Algorithms (BHO) with a mutation technique called MBHO. Generally, BHO contains two major phases. At the exploitation phase, a set of stars are modified based on some rule and according to some objective function, the best star is selected as the black hole which attracts other stars. Furthermore, when a star gets closer to the event horizon, it will be swallowed and a new one will be randomly generated in the search space which thus is the exploration phase. However, randomness may cause the algorithm to fall into the trap of local optima, and to overcome such complications, inversion mutation is used. Furthermore, we modify a widely utilized objective function in most of the proposed works for wrapper feature selection by combining two new terms that are based on the correlation among the selected subset of features and the features and the classification label. We also utilize a transfer function, known as the V2 transfer function, to convert continuous values into discrete ones to enhance search. We assess our approach via extensive evaluation experiments using fourteen benchmark datasets. We benchmark the performance of a wrapper FS approach called Binary Cuckoo Search (BCS), and three filter-based FS approaches (namely Mutual Information Maximisation (MIM), Joint Mutual Information (JMI), and minimum Redundancy Maximum Relevance (mRMR)). Our evaluation has shown that the proposed model is an effective approach for FS, in selecting better features that enhance the performance metrics on the classifiers. Thus, MBHO can be utilized as one alternative to the existing state-of-art-approaches. We release the source codes of our implementation for the community to build on with new methods and datasets.

The emerging wireless energy transfer technology enables sensor nodes to maintain perpetual operation. However, maximizing the network performance while preserving short charging delay is a great challenge. In this work, a Wireless Mobile Charger (WMC) and a Directional Charger (DC) are deployed to transmit wireless energy to the sensor node to improve the network's throughput. To the best of our knowledge, this is the first work to optimize the data sensing rate and charging delay by joint scheduling of MC and DC. We prove we can transmit maximum energy to each sensor node to obtain our optimization objective. In our proposed work, a DC selects a total horizon of equipped with DCs, which selects the horizon of each specific area with based on its antenna orientation. DC's orientation is scheduled for each time slot. Furthermore, multiple MCs are used to transmit energy for sensor nodes which cannot be covered by DCs. We divided the Rechargeable Wireless Sensor Network into several zones via the Voronoi diagram. We deployed a static DC and one MC charging location in each zone to provide wireless charging service jointly. We obtain the optimal charging location of MCs in each zone by solving Mix Integral Programming for energy transmission. The optimization objective of our proposed research is to sense maximum data from each sensor node with the help of maximum energy. The lifetime of each sensor network can increase, and the end delay can be maximized with joint energy transmission. Extensive simulation results demonstrate that our RWSNs are designed significantly to improve network lifetime over the baseline method.

Tipping bucket rain gauges (TBRs) have been, and apparently will continue to be one of the most widely used pieces of equipment for rainfall monitoring, being frequently used for the calibration, validation and downscaling of radar and remote sensing data, due to their major advantages–low cost, simplicity, and low energy consumption. Thus, many works have focused and continue to focus on their main disadvantage–measurement biases (mainly in wind and mechanical underestimations). However, despite arduous scientific effort, calibration methodologies are not frequently implemented by monitoring networks operators or data users, propagating bias in databases and in the different applications of such data, causing uncertainty in the modeling, management, and forecasting in hydrological research, mainly due to a lack of knowledge. Within this context, this work presents a review of the scientific advances in TBR measurement uncertainties, calibration, and error reduction strategies from a hydrological point of view, by describing different rainfall monitoring techniques in Section 2, summarizing TBR measurement uncertainties in Section 3, focusing on calibration, and error reduction strategies in Section 4, a discussion and perspectives in Section 5, and conclusions in Section 6, providing an overview of the of the state of the art and future perspectives of the technology.

Multisource meteorological re-analyses are the most reliable forcing data for driving hydrological models to simulate streamflow. We aimed to assess the different hydrological responses through hydrological modeling in the upper Lancang-Mekong River Basin (LMRB) using the two gridded meteorological datasets, climate forecast system reanalysis (CFSR) and the China Meteorological Assimilation Driving Datasets for the SWAT model (CMADS). We selected the Pearson’s correlation coefficient (R), percent bias (PBIAS), and root mean square error (RMSE) indices to compare the six meteorological variables of the two datasets. The spatial distributions of the statistical indicators in the CFSR and CMADS, namely, the R, PBIAS, and RMSE values, were different. Furthermore, the soil and water assessment tool plus (SWAT+) model was used to do hydrological modeling based on CFSR and CMADS meteorological re-analyses in the upper LMRB. Different meteorological datasets resulted in significant differences of hydrological responses, which reflected by different sensitive parameters and their optimal value. These different calibrated optimal values of sensitive parameters further lead to the different simulated water balance components between CFSR- and CMADS-based SWAT+ model. These findings can help in a better understanding of the strengths and weaknesses of different meteorological re-analysis datasets and the roles on the hydrological modeling.

Precipitation and human activities are two essential forcing dynamics that influence hydrological processes. To investigate those impacts, the Zuli River Basin (ZRB, a typical tributary basin of the Yellow River in China) was chosen to identify the impact of precipitation and human activities on runoff and sediment discharge. A double mass curve (DMC) analysis and the test methods, including accumulated variance analysis, sequential cluster, Lee-Heghnian, and moving t-test methods was utilized to determine the abrupt change point based on data from 1956 to 2015. Correlation formulas and multiple regression methods were used to calculate the runoff and sediment discharge reduction effects of soil conservation measures and to estimate the contribution rate of precipitation and soil conservation measures to runoff and sediment discharge. Our results show that the runoff reduction effect of soil conservation measures (45%) is greater than the sediment discharge reduction effect (32%). Soil conservation measures were the main factor controlling the 74.5% and 75.0% decrease in runoff and sediment discharge, respectively. Additionally, the contribution rate of vegetation measures was higher than that of engineering measures. This study provides scientific strategies for water resource management and soil conservation planning at catchment scale to face future hydrological variability.

Shape memory alloys (SMA) are becoming a more important factor in actuation technology. Due to their unique features, they bring the potential to safe weight, installation space and reduce energy consumption. The system integration of the generally small diameter NiTi wires is an important cornerstone for the emerging technology. Crimping, a common method for the mechanical and electrical connection of SMA wires, has several drawbacks when it comes to miniaturization and high force outputs. For high force applications for example, multiple SMA wires in parallel are needed to keep actuation frequencies high, while scaling up the actuation force. To meet these challenges, the proposed study deals with the development of a resistance welding process for manufacturing NiTi wire bundles. The wires are welded to sheet metal substrate, resulting in promising functional properties and high joint strengths. The welding process benefits from low cost, easy to control parameters and good automation potential. A method for evaluating the resistance welding process parameters is presented. With these parameters in place, a manufacturing process for bundled wire actuators is discussed and implemented. The welded joints are examined by peel tests, microscopy, and fatigue experiments. The performance of the manufactured bundle actuators is demonstrated by comparison to a single wire with the same accumulated cross-sectional area.

A novel nano-laminated GdB2C2 material was successfully synthesized using GdH2, B4C, and C via an in-situ solid state reaction approach for the first time. The formation mechanism of GdB2C2 was revealed based on the microstructure and phase evolution investigation. A purity of 96.4 wt. % GdB2C2 was obtained at a low temperature of 1500 ℃, while a near full pure GdB2C2 can be obtained at a temperature over 1700 ℃. The as-obtained GdB2C2 presented excellent thermal stability at high temperature of 2100 ℃ in Ar atmosphere due to the stable frame work formed by the high covalent four-members and eight-members B-C rings in GdB2C2. The minimum reflection loss value (RLmin) of -47.01 dB with an effective absorption bandwidth (EAB) of 1.76 GHz at a thickness of 3.44 mm was obtained for the GdB2C2 synthesized at 1500 ℃. The possible EMWA mechanism could be ascribed to the nano-laminated structure and appropriate electrical conductivity, which facilitated to the good impedance matching, remarkable conduction loss, interfacial polarization as well as multiple interface reflection and scattering. The as-obtained GdB2C2, with excellent EMWA performance as well as remarkable ultra-high temperature thermal stability, could be a promising candidate for the EMWA materials applications in extreme ultra-high temperatures.

Rapid urbanisation and city expansion worldwide have significantly increased impervious surfaces such as roads, buildings, and pavements. These impervious surfaces impede water infiltration into the ground, resulting in escalated surface runoff during rainfall. This has been exacerbated by climate change and heightened precipitation levels, resulting in recurrent annual flooding. Conventional drainage systems are now insufficient to cope with the amplified surface runoff, contributing to the escalating flood occurrences. Sustainable urban drainage systems such as green roofs, swales, and permeable pavements were developed as modern flood control techniques to replace the traditional drainage system approach by optimising resource utilisation and developing novel and more productive technologies. While most SUD techniques are sustainable in design, construction, and operation, permeable pavements are considered one of the least sustainable SUD techniques. This has garnered considerable attention from researchers who have been studying the design of a more sustainable permeable pavement system by utilising sustainable and recycled materials. This paper reviews the performance of permeable pavements systems using findings from extensive reviews and analyses of scientific publications on permeable pavements. It introduces a future solution and design of permeable pavements using recycled materials. The study will consist of two permeable pavement systems, one incorporating recycled materials in its layers and the other using traditional construction materials. The data collected from both systems will be compared to determine their performance.

Circadian rhythm (CR) influences various physiological functions, including physical and cognitive performance, which fluctuate throughout the day. The present study aimed to investigate the combined and separate effects of CR and physical fatigue on cognitive and physical performance. A sample of 18 amateur athletes was subjected to a series of tests at three different times of the day: morning, afternoon, and evening. Fatigue was induced following isokinetic concentric exercise combined with a 20-minute treadmill run, followed by assessments of selected physical and cognitive tasks. Repeated measures ANOVA did not reveal an interaction between CR and fatigue in cognitive performance (p &gt; 0.05). However, a significant main effect of fatigue was observed in visual reaction time (VisRT) across all three time points. Moreover, peak torque (PT) and peak torque fatigue index (PTFI) showed significant differences between the three times of the day, peaking in the evening. Although we found no interaction between CR and physical fatigue state on selected cognitive parameters at the three times of day, a separate effect of fatigue on cognitive performance was identified. Additionally, physical parameters exhibited peak values occurring in the evening hours. Future research should further explore underlying mechanisms that potentially influence cognitive performance at different times of the day.

A lead zirconate titanate [PZT;Pb(Zr_0.52Ti_0.48)O₃] layer embedded infrared (IR) detector decorated with wavelength-selective plasmonic crystals has been investigated for high-performance non-dispersive infrared (NDIR) spectroscopy. A plasmonic IR detector with an enhanced IR absorption band has been designed based on numerical simulations, fabricated by conventional microfabrication techniques, and characterized with a broadly tunable quantum cascade laser. The enhanced responsivity of the plasmonic IR detector at specific wavelength band has improved the performance of NDIR spectroscopy and pushed the limit of detection (LOD) by an order of magnitude. In this paper, a 13 fold enhancement in the LOD of a methane gas sensing using NDIR spectroscopy is demonstrated with the plasmonic IR detector.

Three-dimensional V₂O₅ hollow structures have been prepared through a simple synthesis strategy combining solvothermal treatment and a subsequent thermal annealing. The V₂O₅ materials are composed of microspheres 2–3 μm in diameter and with a distinct hollow interior. The as-synthesized V₂O₅ hollow microspheres, when evaluated as a cathode material for lithium-ion batteries, can deliver a specific capacity as high as 273 mAh·g⁻¹ at 0.2 C. Benefiting from the hollow structures that afford fast electrolyte transport and volume accommodation, the V₂O₅ cathode also exhibits a superior rate capability and excellent cycling stability. The good Li-ion storage performance demonstrates the great potential of this unique V₂O₅ hollow material as a high-performance cathode for lithium-ion batteries.

Global concerns of decreasing water availability and food security are being exacerbated by the increasing demand for food, water quality degradation, and climate change impacts. Rice is an essential crop for food security with paddy rice cultivation largely practiced using traditional flooding conditions. Water regimes and components of the soil water balance in paddy rice catchments affect uptake of water by rice roots. Paddy management, especially puddling, significantly affects soil hydrological properties, such as hydraulic conductivity, percolation, and seepage in the paddy rhizosphere. Improved knowledge of these interactions is vital for developing sustainable water management strategies in paddy fields. Alternate Wetting and Drying (AWD) practice contributes to sustainable water management, but little is known about effects of soil wetting and drying duration on gravitational soil water flow, moisture dynamics, and hydraulic conductivity in the rice paddy rhizosphere. These factors depend on soil type, rice variety, rooting conditions, and environmental factors, such as solar radiation and temperature. Here we examine the paddy cultivation environment and soil properties under traditional continuous flooding and AWD practice. Some studies indicate that the duration and frequency of wetting and soil drying under AWD practice in paddy fields affects soil hydraulic conductivity that alters the next cycle of water application. During droughts and short rainy seasons, rainfall, soil properties, and atmospheric conditions control soil moisture dynamics in paddy fields. Limitations and challenges of applying AWD in paddy soils, including the variation of hydrological properties and the need for frequent field manual monitoring of water level (WL) head, are discussed. Implications of AWD practice on yield, water use efficiency, soil hydrology, and reduction of methane emissions, are outlined along with opportunities to improve AWD integration within government policies, irrigation schemes, and adoption by farmers. Coupled crop models can provide important insights for sustainable agricultural water management.

Land cover and climate changes greatly influence hydrologic responses of a basin. However, the response vary from basin to basin depending on the nature and severity of the changes and basin characteristics. Moreover, the combined impacts of the changes affect hydrologic responses of a basin in an offsetting or synergistic manner. This study quantified the separate and combined impacts, and the relative contributions of land cover and climate changes on multiple hydrological regimes (i.e., surface runoff, streamflow, groundwater recharge evapotranspiration) for the Dhidhessa Subbasin. Land cover and climate change data were obtained from a recent study completed for the basin. Calibrated Soil and Water Analysis Tool (SWAT) was used to quantify the impacts. The result showed that SWAT model performed well for the Dhidhessa Subbasin in predicting the water balance components. Substantial land cover change as well as an increasing temperature and rainfall trends were reported in the river basin during the past three decades. In response to these changes, surface runoff, streamflow and actual evapotranspiration (AET) increased while groundwater recharge declined. Surface runoff was more sensitive to land cover than to climate changes whereas streamflow and AET were more sensitive to climate change than to land cover change. The combined impacts played offsetting effect on groundwater recharge and AET while inconsistent effects within study periods for other hydrologic responses. Overall, the predicted hydrologic responses will have negative impacts on agricultural production and water resources availability. Therefore, the implementation of integrated watershed management strategies such as soil and water conservation and afforestation could reverse the negative impacts.

Soil moisture modeling is necessary for many hydrometeorological and agricultural applications. One of the ways in which modeling of soil moisture (SM) can be improved is by assimilating SM observations to update the model states. Remotely sensed SM observations are prone to being riddled with data discontinuities, namely in the horizontal and vertical spatial, and temporal dimensions. A set of synthetic experiments were designed in this study to assess how much impact each of these individual components of spatiotemporal gaps can have on the modeling performance of SM as well as streamflow. Results show that not having root-zone SM estimates from satellite derived observations is most impactful in terms of modeling performance. Having temporal gaps and horizontal spatial gaps in the satellite SM data also impacts modeling performance, but to a lesser degree. Real-data experiments with the remotely sensed Soil Moisture Active Passive (SMAP) product generally brought improvements to the SM modeling performance in the upper soil layers, but not so much in the bottom soil layer. The updating of model SM states with observations also resulted in some improvements in the streamflow modeling performance during the synthetic experiments, but not during the real-data experiments.

Land use land cover (LULC) changes are highly pronounced in African countries, as they are characterized by an agriculture-based economy and a rapidly growing population. Understanding how land use/cover change (LULCC) influence watershed hydrology will enable local governments and policymakers to formulate and implement effective and appropriate response strategies to minimize the undesirable effects of future land use/cover change or modification and sustain the local socio-economic situation. The hydrological response of the Ethiopia Fincha’a watershed to LULCC happened during the last 30 years was investigated comparing the situation in three reference years: 1994, 2004 and 2018. The information was derived from Landsat sensors, respectively Landsat 5 TM, Landsat 7 ETM and Landsat 8 OLI/TIRS. The various LULC classes were derived via ArcGIS using a supervised classification system, and the accuracy assessment was done using confusion matrixes. For all the years investigated the overall accuracies and the kappa coefficients were higher than 80%, with 2018 as the more accurate year. The analysis of LULCC revealed that forest decreased by 19.99% between the years 1994-2004, and it decreased by 11.85% in the following period 2004-2018. Such decline in areas covered by forest is correlated to an expansion of cultivated land by 16.4% and 10.81%, respectively. After having evaluated the LULCC at the basin scale, the watershed was divided into 18 sub-watersheds, which contained 176 Hydrologic Response Units (HRUs), having a specific LULC. Accounting for such a detailed subdivision of the Fincha’a watershed, the SWAT model was firstly calibrated and validated on past data, and then applied to infer information on the hydrological response of each HRU on LULCC. The modelling results pointed out a general increase of average water flow, both during dry and wet periods, as a consequence of a shift of land coverage from forest and grass towards settlements and build-up areas. The present analysis pointed out the need of accounting for past and future LULCC in modelling the hydrological responses of rivers at the watershed scale.

Hydrochemical and geophysical data collected during a hydrological survey in September 2017, reveal patterns of small-scale hydrological connectivity in a small water track catchment, north-European Arctic. Elevated tundra patches underlain by sands were disconnected from the stream and stored precipitation water from previous months. At the catchment surface and in the water track thalweg, some circular hollows, from 0.2 to 0.4 m in diameter, acted as evaporative basins with low d-excess values, from 2 to 4‰. Other hollows were connected to shallow subsurface runoff, yielding d-excess values between 12 and 14‰. ‘Connected’ hollows yielded a 50% higher dissolved organic carbon (DOC) content, 17.5±5.3 mg/L, than the ‘disconnected hollows, 11.8±1.7 mg/L. Permafrost distribution across the landscape is continuous, but highly variable. Open taliks exist under fens and small hummocky depressions, as revealed by electric resistivity tomography surveys. Isotopic evidence supports upward subpermafrost groundwater migration through open taliks under water tracks and fens/bogs/depressions, and its supply to streams via shallow sub-surface compartment. Temporal variability of isotopic composition and DOC in water track and a major river system, the Vorkuta R., evidence the widespread occurrence of the described processes in the large river basin. Water tracks effectively drain the tundra terrain and maintain xeric veg-etation over the elevated inter-track tundra patches.

A low-cost bio-mass-derived carbon substrate has been employed to synthesize MoS₂@carbon composites through a hydrothermal method. Carbon fibers derived from natural cotton provide a three-dimensional and open framework for the uniform growth of MoS₂ nanosheets, thus constructing hierarchically coaxial architecture. The unique structure could synergistically benefit fast Li-ion and electron transport from the conductive carbon scaffold and porous MoS₂ nanostructures. As a result, the MoS₂@carbon composites, when served as anodes for Li-ion batteries, exhibit a high reversible specific capacity of 820 mAh g^-1, high-rate capability (457 mAh g^-1 at 2 A g^-1), and excellent cycling stability. The superior electrochemical performance makes the MoS₂@carbon composites to be low-cost and promising anode materials for Li-ion batteries.

Background/Aim: Stool gluten immunogenic peptides (GIP) determination is now considered a valuable tool for detecting dietary lapses in treated celiac disease (CeD) patients. Our study aims to explore the diagnostic performance of a qualitative lateral flow immunoassay point-of-care test (LFIA/PoCT) compared with a qualitative enzyme-linked immunosorbent assay (ELISA) for detection of stool GIP. Methods: The study is a post hoc analysis of stool samples collected in the real-life conditions from a cohort of gluten-free diet-treated CeD patients. Stool GIP was measured in µg/g of stool using an ELISA kit (iVYLISA GIP S®, Biomedal SL). For the qualitative evaluation of stool GIP, a LFIA/PoCT test (GlutenDetect®; Biomedal S.L) was employed, which reported results as positive or negative. The LFIA/PoCT performance was evaluated against ELISA, considered as the gold standard. GIP concentrations

As the demand of heating and air conditioning is increasing day by day due to getting a comfortable life, for that reason, the market for heat pump and air conditioning systems or both, is seeing a steady increase in low global warming potential. All researchers on heat pump and air conditioning systems are searching for environmentally friendly refrigerants. However, a significant number of contemporary refrigerants (HCFCs) have the potential to cause global warming and ozone depletion, such as those found in heat pump air conditioning systems. Using more environmentally friendly refrigerants, including hydrocarbon and blend refrigerant mixtures, is one way to lower GWP and Ozone depletion potential. This study examines the performance of several alternative refrigerants, including R32, R134A, R410A, R22, and R290. In order to examine the performance of R290, theoretical and simulation results are compared on various refrigerants while taking into account a number of performance parameters, including the refrigerant energy efficiency ratio, mass flow rate, coefficient of performance, cooling capacity, and compressor work for one ton of refrigeration (1TR) air conditioners. One player with a very low GWP is R290. However, the only drawback is that it can catch fire and is hazardous to use in split-type commercial heat pump air conditioning applications. The environmental and thermo physical properties of HC-290 are significantly better than those of other materials, according to theoretical and simulation studies utilizing Cool Pack software, hence it is a viable replacement.

Yoga promotes acceptance, compassion, physicality, active postures, mental and emotional awareness, and spiritual benefits. It involves breath techniques, postures, and body locks, maintaining balance and strength. It's based on ethical principles. Beyond simple performance, swimming is a catalyst for overall well-being. Complementarily, yoga serves as an invaluable adjunct for swimmers, enhancing flexibility, strength, and body awareness - a triad essential to aquatic excellence. The confluence of yoga and swimming, particularly through the practice of aqua yoga asanas, presents a new paradigm for both athletes and non-performers alike. Athletes who embrace the harmonious fusion of swimming and yoga unearth the potential for achieving not just athletic mastery but also a more balanced and fulfilling lifestyle. This blend provides a route not only to enhance performance in the water but also to elevate the overall quality of life. We examined the feasibility of incorporating yoga and swimming practice into Romanian subjects' lifestyles, designing a factor analysis in SmartPLS software, based on an online survey. The study assesses the participants' knowledge of yoga's theory and philosophy, as well as their perceptions of the swimming practice's benefits for social and health issues. According to our study, Romanian participants practice yoga and swimming as often as possible to reduce stress, improve concentration for work-related tasks, and improve joint elasticity, balance, and muscular tone. Thus, Yoga and swimming are substitutes for other approaches in prevention and therapy.

The performance of the presurgical test (preSurg) for temporal lobe epilepsy (TLE) was evaluated via video-electroencephalography (VEEG), electroencephalography (EEG), 99mTc-HmPAO single-photon emission tomography (SPECT) and 1.5 T magnetic resonance imaging (MRI) in a group of 112 men (37.0 ± 1.1 years) and 106 women (39.7 ± 1.1 years) operated on for TLE. The epileptic zone (EZ) was adequately identified to determine whether the patient reached an Engel I grade (EI) at least one year postop. Accuracy was evaluated by the coefficient α, ranging from 3 (when result = EZ) to 2 (result in the same hemisphere as the EZ), 1 (noninformative result) or 0 (EZ in the contralateral hemisphere). The simplicity of diagnosis was defined as the number of preSurg surgeries needed to identify the EZ. EI was obtained in 85.8% of patients even though 42.2% of patients had noninformative MRI results. For preSurg α was (mean ± SEM) = VEEG (2.83 ± 0.04) &gt; MRI (2.14 ± 0.08) &gt; EEG (1.87 ± 0.09) &gt; SPECT (1.71 ± 0.09). The accuracy (combination of sensitivity and specificity) was calculated as follows: VEEG (0.797) &gt; MRI (0.518) &gt; EEG (0.446) &gt; SPECT (0.360). The likelihood positive ratio was greater, and the likelihood negative ratio was lower for VEEG. The most relevant factor for the regression model was VEEG (3.106) &gt; MRI (2.558) &gt; EEG (1.905), and the factors were not significantly related to SPECT. EZs in EI patients with low simplicity were identified mainly by VEEG. A very good postoperative outcome can be obtained even in TLE patients with no lesions on MRI. The VEEG is the most reliable preSurg test and may be the only reliable test for patients with very low simplicity (i.e., difficult).single paragraph of about 200 words maximum. For research articles, abstracts should give a pertinent overview of the work. We strongly encourage authors to use the following style of structured abstracts, but without headings: (1) Background: Place the question addressed in a broad context and highlight the purpose of the study; (2) Methods: briefly describe the main methods or treatments applied; (3) Results: summarize the article’s main findings; (4) Conclusions: indicate the main conclusions or interpretations. The abstract should be an objective representation of the article and it must not contain results that are not presented and substantiated in the main text and should not exaggerate the main conclusions.

Using density functional theory (DFT), the density of states of NH4V3O8 (NVO) was analyzed pre and post-oxygen defects (Od) formation. The findings revealed a reduced bandgap in NVO after Od introduction, emphasizing the role of Od in enhancing conductivity of the material, thus improving its electrochemical attributes. Through the water bath method, both NVO and its oxygen-deficient counterpart, (NH4)2V10O25⸱8H2O (NVOd), were synthesized as potential cathode materials for aqueous zinc-ion batteries (AZIBs). Experimental outcomes resonated with DFT predictions, highlighting the beneficial role of oxygen defects in boosting electrical conductivity. Notably, the refined material displayed a remarkable capacity of 479.3 mAh g-1 at 0.1 A g-1, underscoring its promise for advanced energy storage solutions.

The relationship between the structure characteristics and performances of coal based hydrogenation isomeric base oil (CTL) and metallocene catalyzed coal based polyalphaolefin (mPAO) base oil was clarified here. CTL and mPAO were compared with typical petroleum based and natural gas based commercial API Class III and IV base oils. Pressurized differential scanning calorimetry (PDSC), Rotary Bomb Oxidation Test (RBOT) and four-balls friction tester were used to evaluate the oxidation stability and lubrication performance of base oils under different working conditions. The sensitivity of different base oils to typical antioxidants and extreme pressure antiwear agents was compared. Specially, the composition and structure of CTL base oil are obviously different from GTL and mineral base oil. The coal-based CTL and mPAO base oils exhibit commendable viscosity-temperature properties, characteristics, coupled with low-temperature fluidity, fire safety, and minimal evaporation loss. The lubricating properties, oxidation stability and the sensitivity to extreme pressure antiwear agents of CTL are close to those of similar base oils. But the sensitivity of CTL to typical antioxidants is relatively poor. Besides, compared with commercial PAO base oil, mPAO has lower isomerization degree and fewer isomerization types. The oxidation stability and sensitivity to typical antioxidants of mPAO base oil are comparable with those of commercial PAO base oil, while its lubrication performance and sensitivity to typical extreme pressure antiwear agents are significantly better than those of commercial PAO base oil.

The aim was to identify if surface electromyography (sEMG) parameters are indicative of post-activation potentiation (PAP)/post-activation performance enhancement (PAPE), in terms of twitch potentiation and voluntary performance. Three databases were used in March 2024, with the following inclusion criteria: (a) original research, assessed in healthy human adults, (b) sEMG parameters were measured. The exclusion criteria were: (a) studies with no PAP/PAPE-protocol, (b) non-randomized control trials. The following data were extracted: study characteristics/demographics, PAP/PAPE-protocols, sEMG parameters, twitch/performance outcomes, and study findings. A modified physiotherapy evidence database (PEDro) scale was used for quality assessment. Fifteen randomized controlled trials (RCTs), with a total of 199 subjects were included. The M-wave amplitude (combined with a twitch torque outcome) was shown to generally be indicative of PAP. The sEMG amplitude (in some muscles) were found to be indicative of PAPE during ballistic movements, while a small decrease in the MdF (in certain muscles) were shown to reflect PAPE. Changes in the Hmax/Mmax ratio were found to contribute (temporally) to PAP, while the H-reflex amplitude was shown to be neither indicative of PAP nor PAPE. This review provides preliminarily findings suggesting that certain sEMG parameters could be indicative of PAP/PAPE. However, future research is warranted.