A simple wideband magneto-electric (ME) dipole antenna is presented in this letter. In particular, two pairs of L-shaped slots with different sizes are successively etched out of the pair of horizontal patches of electric dipole, which can enlarge the bandwidth of ME dipole antenna without increasing the antenna size. Two new resonant frequency points are generated respectively at the lower and upper frequency region. To improve the impedance matching characteristics, two vertical rectangular patches are loaded in the middle of the horizontal portion of the Г-shaped feed structure. A prototype is fabricated and measured, and an overlapped bandwidth of 101.3% (from 1.36 GHz to 4.15 GHz) in terms of VSWR≤2 is achieved, which can simultaneously cover the n50 (1.432GHz-1.517GHz), n51(1.427GHz-1.432GHz), and n78 (3.300GHz to 3.800GHz) bands of sub-6G. It should be highlighted that other excellent properties of ME dipole antenna, such as stable radiation pattern, high gain, and low cross-polarization level (CRPL), are maintained, which makes the proposed ME dipole antenna a good candidate for sub-6G applications.

This study utilizes plant-derived fibers including Mikania, wool, kapok, sugarcane and pineapple as strengthening additions to fabricated and tested reinforced polyester mixtures. This study aimed to examine the mechanical qualities and potential applications of these natural materials in boosting the effectiveness of polyester composites. The composites were formed by including natural fibers into a polyester base using a hands-on preparation method. The finished samples received a complete assessment, covering tensile and impact testing as well as SEM analysis. The findings showed that incorporating natural materials like Mikania vines, wool, Kapok fuzz, sugarcane fibers, and pineapple leaves had a notably positive effect on the mechanical features of the plastic composites. The tensile strength of the material combined pineapple was particularly high, achieving a maximum stress of 42.4822 N/mm 2 and a maximum stretch of 4.27950%. Similarly, that same composite exhibited the greatest maximum stretch of 3.75753% and the highest maximum stress of 77.4922 N/mm 2 during the bending experiment. The substances exhibiting the strongest impact endurance, as determined by the impact assessment, were the ones including Kapok, Mikania, wool, pineapple, and sugarcane. The kapok provided an impact toughness of 1.2 Newton meters, which was somewhat elevated. This research offers a precious understanding of using natural fibers as strengthening additives in polyester composites, demonstrating their flexible perspective for many uses. The discoveries present hopeful possibilities for additional examination and progression in the area of composite materials, consequently advancing the development of sustainable materials.

The research proposes a hybrid algorithm model that combines model-driven and data-driven approaches for the direct application of bridge health monitoring technology in bridge management. This comprehensive study encompasses a series of analytical techniques and methodologies to build a multi-objective optimization model for bridge performance assessment and prediction. It focuses on the processing of multi-source heterogeneous data, selection of key sub-parameters using Principal Component Analysis (PCA), enhanced K-means clustering analysis, determination of structural component target thresholds, time-dependent survival probability analysis, regression fitting, and timing prediction of the bridge system for both the components of double-layer truss arch bridge and the bridge system. The initial phase of the study concentrates on the diversification and decentralization of monitored data from various sources, integrating and cleaning data obtained from different sources to ensure data quality and consistency. PCA technique is applied to identify key sub-parameters that have significant impacts on the performance of structural components. Enhanced K-means clustering analysis is carried out to effectively group and classify the identified key sub-parameters. Numerical simulations, including structural nonlinear analysis, are conducted to determine the target thresholds of bridge structure, providing important benchmarks for performance evaluation. Finally, a multi-parameter regression model is used to evaluate and update the performance of the bridge structure, taking into account survival probability (using the Kaplan-Meier method), maintenance history, and material deterioration to estimate the most critical time for the bridge system. A case study is conducted to validate the suggested comprehensive algorithms for a double-layer truss arch combination bridge, which contributes to enhancing performance evaluation and predicting the most critical time for structural components and the bridge system in the bridge management and maintenance practices. It should not be ignored that, the accuracy and reasonability of bridge structure system performance evaluation and prediction depend largely on the selection of target thresholds.

Bipolar plates in the fuel cells are mechanical components chiefly responsible for power variation and even distribution of fuel and oxidant at the membrane exchange assembly. To ensure the fuel cell optimal performance and longevity of the membrane the flow field pressures need to be in the optimum range. In this study, a combination of existing and new designs of bipolar plate flow Fields for 100 × 100mm area membrane exchange assembly Size have been modelled and studied with the help of computational fluid dynamics. A total of six variant designs have been analyzed. An attempt is made to understand the most efficient channel design which results in a good flow field & low-pressure drop. The simulation studies indicate that the serpentine channel with two channels-opposite inlets-outlets provides the most uniform pressure drop and the best fuel distribution and is 10% better than the serpentine conventional single-channel bipolar plate Flow fields.

The identification of the critical quality attributes (CQAs) of monoclonal antibodies (mAbs) is a key component of quality by design. Traditional detection methods for the identification of mAbs, such as peptide mapping and weak cation-exchange chromatography (WCX-HPLC), require sophisticated equipment, experienced staff, and a considerable amount of time. In this study, the novel identification of mAbs was performed using Raman spectroscopy, combined with the evaluation of other CQAs, such as the appearance and purity of size-exclusion high-performance liquid chromatography(SEC-HPLC), charge heterogeneity of WCX-HPLC, and bioassay. Raman spectroscopy achieved comparable results to the conventional, but complex, approach to identifying anti-vascular endothelial growth factor antibodies. Raman spectroscopy was used to identify and distinguish between different antibody types. Additionally, the Raman technique with principal component analysis of multivariate algorithms is rapid, efficient, and accurate for mAb identification; this technique has great potential to support biopharmaceutical development and counterfeiters.

Nonlinear systems; vector-valued differential equations; higher order equivalent differential equations; semi-analytical solutions

The present study proposes an exposure model for indoor PM2.5 levels during cooking activities in poorly ventilated Kazakh houses with high emission levels. It aims to identify influencing factors of PM2.5 concentration patterns during cooking and explain the mechanisms underlying the build-up and downtrend of PM2.5 concentrations. The methodology integrates PM2.5 sampling, monitoring, and modeling to predict household PM2.5 levels and estimate daily concentrations, employing USEPA’s IAQX v1.1 for simulating the one-zone concept for cooking-related PM2.5 concentrations in multiple households. During cooking, PM2.5 concentrations varied between 13 and 266 µg/m3. Kitchen size, air exchange, type of food, and cooking methods were key factors influencing the observed concentrations. The model demonstrated high accuracy (R>0.9). The contribution of cooking to household air pollutant (HAP) PM2.5 levels ranged from 9% to 94%. This impact was more pronounced in warmer months. In colder months, outdoor PM levels and household ventilation were the primary factors regulating indoor air concentrations. The present study is among the first attempts to assess exposure to HAP in Central Asia, providing foundational insights into the poorly understood indoor air quality of Kazakh houses. Future research should refine models to account for individual behaviors and house types, improving accuracy and representativeness.

Textures in Molybdenum-Rhenium alloys are inevitable during thermal fabrication. [110] and [100] are common orientations in Molybdenum-Rhenium alloys and effect mechanical responses. However, orientation dependence of mechanical responses in Molybdenum-Rhenium alloys is not quite clear yet. To clarity this problem, micro-pillar compression tests are conducted in grains with orientation [100] and [110] separately. Orientation-dependent compressive properties are found in Mo-14Re and Mo-42Re, but are not found in Mo and Mo-5Re, which may be attributed to activated muiti-slip planes as increased Re. Solid solution effect of Re not only rely on orientations, but also on Re content. Softening effect occurs in both [100] and [110] Mo-5Re. while, strong strengthening effect happens in [110] Mo-14Re and Mo-42Re. Our research clarifies that Mo-Re alloys with [110] orientation / texture could be preferred to obtain good strengthening effect.

This paper develops a technical framework for the next-generation power grid transformer (NGPGT) for grid renewables. This framework is structured to overcome the environmen-tal challenges produced due to the explosive use of non-renewable base energy generation sources. The use of these sources cannot meet the required electricity for the world’s growing community due to their availability, cost, and lack of flexibility. However, modern energy systems focus on the use of renewable energy sources where the grid transformer’s interaction acts the essential role in their generation, transmission, and distribution. The lack of central-ization, local monitoring, interoperability, authenticity, and precise bi-directional flow may limit the application of current framework power grid transformers in grid renewables. In this paper, a new technical framework, called NGPGT, is developed by introducing some ex-tended features for addressing the challenges shown in the current-generation transformers. This is structured by enabling some advanced technical features with the existing frame-work which includes automatic condition monitoring, intelligent inverters, edge computing, automatic controlling, and intelligent management. This paper also illustrates the benefits and scopes of the NGPGT compared to the existing transformer by assembling essential re-quirements and obligatory components. Additionally, this paper highlights a few difficulties of implementing NGPGT in terms of operational, communication, energy management, and economic point of view which may enable further research scopes for the researchers.

Air quality is an important part of environmental health, having serious consequences for human health and well-being. The Air Quality Index (AQI) is a frequently used metric for assessing air quality in various areas and at different times. However, AQI data, like many other types of environmental data, can contain outliers - data points that deviate significantly from other observations, indicating exceptionally good or poor air quality, a critical step in identifying and understanding extreme pollution episodes that can have serious environmental and public health consequences. These outliers can be caused by a variety of variables, including measurement mistakes, odd meteorological circumstances, and pollution occurrences. While outliers can occasionally give useful information about these unusual conditions, they can also skew studies and models if they are not adequately accounted for. This paper describes a hybrid method for detecting outliers in data, AQI data are used in this study. The model uses a stacked machine learning model that incorporates K-means clustering, Random Forest (RF), and Gradient Boosting Classifier (GBC). K-means is used for initial categorization, followed by RF model training, and ultimately, the RF output is used as input for the GBC to generate the final classification. The performance of this stacked machine learning model is examined and compared to single models using the Accuracy measure. The findings show that the suggested technique is efficient, with an accuracy of 0.99, showing its potential for effective outlier detection in data.

This study develops a methodology to create detailed visual Digital Twins of large-scale structures with their realistic damages detected from visual inspection or nondestructive testing (NDT). The methodology is demonstrated with a transition piece of an offshore wind turbine and a composite rotor blade, with surface paint damage and subsurface delamination damage, respectively. Artificial Intelligence and color threshold segmentation are used to classify and localize damages from optical images taken by drones. These damages are digitalized and mapped to a three-dimensional geometry reconstruction of the large-scale structure or a CAD model of the structure. To map the images from 2D to 3D, metadata information is combined with the geo placement of the large-scale structure’s 3D model. After mapping the damage, the Digital Twin gives an accurate representation of the structure. The location, shape, and size of the damage are visible on the Digital Twin. The demonstrated methodology can be applied to industrial sectors such as wind energy, the oil and gas industry, marine and aerospace to facilitate asset management.

This paper proposes a new distribution named “The Generalized Alpha Power Exponentiated Inverse Exponential (GAPEIEx for short) distribution” with four parameters, from which one (1) scale and three (3) shape parameters and the statistical properties such as Survival function, Hazard function, Quantile function, r^(th) Moment, Rényi Entropy, and Order Statistics of the new distribution are derived. The method of maximum likelihood estimation (MLE) is used to estimate the parameters of the distribution. The performance of the estimators is assessed through simulation, which shows that the maximum likelihood method works well in estimating the parameters. The GAPEIEx distribution was applied to simulated and real data in order to access the flexibility and adaptability of the distribution, and it happens to perform better than its submodels.

A document by Imtiaz Ahmed. Click on the document to view its contents.

Silver sulfide (Ag2S) could be a viable chalcogenide absorber layer in the applications of thin film solar cells due to its optimum band gap of 1.1 eV and high absorption coefficient. Here, we propose a novel Ag2S-based double-heterojunction n-CdS/p-Ag2S/p+-SnS solar cell structure. The numerical analysis has been performed with SCAPS-1D (Solar Cell Capacitance Simulator). In the case of single heterojunction, n-CdS/p-Ag2S manifests the efficiency of 19.75% where the VOC= 0.66 V, the JSC=36.99 mA/cm2, FF=81.50%. However, Ag2S-based double-heterojunction with optimized structure provides efficiency of 29.51% wherein VOC = 0.81 V, JSC = 42.81 mA/cm2 and FF = 85.24%. The noteworthy augmentation of VOC and JSC in double heterojunction results from the reduction in surface recombination velocity and rise in built-in voltage in the p-Ag2S/p+-SnS heterostructure.

Modelers typically produce different system dynamics models for the same problem, depending on each modeler’s perspective, leading to reduced stakeholder confidence. Validation of system dynamics can increase stakeholder confidence. This research proposes the use of Fuzzy-set Qualitative Comparative Analysis (fsQCA), based on the set theory method, as a method to validate causal relationships between entities in Causal Loop Diagram (CLD) models. The problem of mobile network operators in Indonesia with small sample data characteristics is used as a case study to demonstrate the use of fsQCA. The fsQCA method is used after the CLD model is built in the system dynamics methodology. The fsQCA method is used to test causal relationships between entities in the CLD that need to be validated. The results can be used to improve the previously created CLD model. The Fuzzy-set Qualitative Comparative Analysis (fsQCA) method, which combines QCA with fuzzy set theory that allows partial membership, can be used to test whether or not there is a causal relationship between entities in the CLD model. The fsQCA method can be used to test causal relationships between entities in the CLD model with small sample data and increase stakeholder confidence in the CLD model.

Bioactive coatings on metallic ‘titanium’ or ceramic ‘sapphire’ implants are known for their potential to promote bone biocompatibility, osseointegration, and long-term survival. In this study, we have investigated the effect of the surface topography of titanium and sapphire at different temperatures on the chemistry, morphology, organization, and coverage of the synthesized apatite coatings. We use a wet chemical method with a fluoride rich calcium-phosphate solution, to induce bioactive coatings onto etched titanium, non-etched titanium, polished and unpolished sapphire at 37, 70, and 90˚C. Fluoridated hydroxyapatite formation is detected across all temperatures using FTIR, synchrotron XRD, and MAS-NMR. Surface topography and temperature changes play a crucial role in the organization and coverage of the apatite crystals. Well-defined hexagonal nanocrystals are observed across each of the conditions, in the range between 35-81 nm. At lower temperatures, self-assembled organized nanocrystals appear to grow out from spherical structures, creating highly-ordered apatite architectures. However, at 90˚C, the nanocrystals seem to lack the hierarchical organization and appear to be arranged randomly. This work demonstrates a promising avenue for modifying implant surfaces with highly-ordered apatite-based coatings at physiological conditions.

The Enhance Low Dose Rate Sensitivity (ELDRS) of bipolar devices is one of the important effects on space electronics systems that could be impact on its high reliability and long life. It is important to ensure radiation reliability for space applications by evaluating ELDRS of bipolar devices. However, the low dose rate irradiation test has a long period and high cost, so accelerated testing methods are required. In this paper, low dose rate, high dose rate and hydrogen soaking accelerated irradiation experiments are carried out on the operational amplifier circuit (XJ139), and the experimental data of high and low dose rate of the circuit are analyzed, the conclusion that the circuit has a ELDRS effect is given. Experiments on circuit irradiation effects under different hydrogen concentrations and immersion time were carried out. The degradation data of irradiation-sensitive parameters under different hydrogen soaking pretreatment conditions were analyzed. The optimal combination conditions for hydrogen soaking are given based on this circuit, and verified by experiments. At last, the degradation equivalence under accelerated hydrogen soaking and low dose rate irradiation test results is analyzed and discussed. The results show that the degradation degree of the samples after hydrogenation pretreatment after high dose rate irradiation is comparable to that of low dose rate irradiation. The research results can be used to guide the development of low dose rate accelerated tests and engineering applications.

Dry ice is one of the world’s most in-demand commodities for cold chain distribution of temperature sensitive products in insulated shippers. A rule of thumb for packaging dry ice has been that it sublimates about 2.3 to 4.5 kg every 24 hours, without any consideration for specific dry ice type, size or geometry [(CISA, 2020)](#ref-0004). This study explored sublimation rates of dry ice composition (binding agent and density), age, and geometry (size and shape). Dry ice block manufacturing often involves dosing propylene glycol-water “binder” solution to liquid carbon dioxide. Tests performed in this study revealed that the binding agent did not influence the dry ice sublimation rate. Dry ice density was found to impact sublimation rate. Fresh and aged blocks of similar dimensions and weight were found to have similar performance outcomes. Weight loss curves for pellets and various sized dry ice blocks showed a dependence on surface area and volume. Chunked blocks and pellets with and without binding agent sublimated at similar rates of 2.84 % h -1 and 2.53 % h -1 (w/w), respectively, whereas block varieties with significantly lower surface area to volume ratios had lower sublimation rates of 0.98-1.60 % h -1.