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Academic achievement is determined by two factors, namely internal factors originating from within the individual in this case students and external factors that come from outside the individual or things that are influenced by the environment. There are many ways to find an academic achievement, one of which uses data mining which aims to predict or classify data using a classification algorithm. This study aims to 1) find out how to apply the Naive Bayes algorithm to student achievement, and 2) see the accuracy of the Naive Bayes algorithm to student achievement. This type of research is secondary data in the form of student data obtained from the information technology center and the Ar-Raniry UIN database. This research uses Naive Bayes algorithm and random forest algorithm. The results obtained from this study indicate the highest correlation value in the initial IP variable of r = 0.783 and the leave variable has a very weak correlation level of r = 0.054. The accuracy value of Naive Bayes algorithm after cleaning is 78.0% and Random Forest algorithm variable is 76.7%.

The supporting documents for accreditation forms are the most important part in the accreditation submission process. Otherwise the process of finding and collecting documents supporting accreditation forms are still done manually. This study aims to design the information systems which supports web-based accreditation in Department of Information Technology UIN Ar-Raniry. The designing process refered to regulation of National Accreditation Board for Higher Education and followed the latest instrument in 4.0 version. The application has namely SIDASI 4.0. The research method of this study is the Rapid Application Development (RAD) process which uses the PHP programming language and MYSQL as database engine. SIDASI 4.0 can only be accessed by users who have already registered and obtained an account as username and password. The SIDASI 4.0 have support the accreditation submission phase as the digital documentation forms.

Background: This study aimed to compare the chondroprotective efficacy and mechanism of annatto tocotrienol (AnTT) and palm tocotrienol-rich fraction (PT3) using SW1353 chondrocytes treated with monosodium iodoacetate (MIA). Methods: The chondrocytes were incubated with AnTT or PT3 in advance or concurrently with MIA for 24 h. The viability of the cells was tested with an MTT assay. The 8-isoprostane F2-α, extracellular matrix proteins, metalloproteinase and sex-determining region Y box protein 9 (SOX9) levels were determined using immunoassays. Results: AnTT and PT3 reversed an MIA-induced decrease in chondrocyte viability when incubated together with MIA (p < 0.05). Prior incubation with both mixtures did not produce the same effects. AnTT and PT3 cotreatment could suppress 8-isoprostane F2-α level in chondrocytes exposed to MIA (p < 0.01). Co-exposure to tocotrienols and MIA increased the type II collagen/type I collagen ratio in chondrocytes (p < 0.01). In addition, the co-exposure of AnTT and MIA for 24 h significantly upregulated SOX9, type II collagen and aggrecan levels (p < 0.05), which was not observed with co-exposure of PT3 and MIA, AnTT or PT3 exposure alone. Conclusion: AnTT and PT3 could prevent a reduction in chondrocyte viability following MIA exposure by reducing oxidative stress. In addition, AnTT might induce self-repair and anabolic activities in chondrocytes challenged with MIA.

Background: Osteoarthritis is a degenerative joint disease lacking disease-modifying therapeutic agents. This study aimed to compare the effects of palm tocotrienol-rich fraction (TRF), glucosamine sulphate, and both agents combined in rats with osteoarthritis induced by monosodium iodoacetate (MIA). Methods: Thirty adult male rats were randomized into normal control, and osteoarthritis groups were treated orally daily with vehicle, palm TRF (100 mg/kg), glucosamine sulphate (250 mg/kg), and both agents combined for 4 weeks. Body weight and grip strength were measured weekly. After being sacrificed, the joints and blood were harvested for histology and serum cartilage oligomeric matrix protein (COMP) levels. Results: The body weight of the rats receiving treatment rebounded significantly after an initial reduction (vs osteoarthritic control, p < 0.05). The rats receiving combined treatments showed significantly better grip strength than the osteoarthritic control and individual treatment groups (p < 0.05). The serum COMP level was lower in all the treated groups (vs osteoarthritic control, p < 0.05). Cartilage histology of the treated rats was not significantly improved (vs osteoarthritic control, p > 0.05). Conclusion: The combination of palm TRF and glucosamine sulphate was more effective than individual agents in improving the grip strength of the rats, but the cartilage damage might need more time to heal.

This paper examines the role of demand response aggregators in minimizing the cost of electricity generation by distribution utilities in a day-ahead electricity market. In this paper, 2500 standard South African homes are considered as end users. Five clusters (and aggregators) are considered with 500 homes in each cluster. Two cases are analysed: (1) Utilization of renewable energy sources (RES) is implemented by the distribution supply operator (DSO), where it meets excess demand for end users during peak hours by purchasing electricity from the renewable sources of the energy market, and (2) Utilization of RES is implemented by end users alone, and it is assumed that every household has one plug-in electric vehicle (PEV). The aggregators then compete with each other for the most cost-effective energy usage profile; the aggregator with the least energy demand wins the bid. In both cases, energy pricing is estimated according to the day-ahead energy market. A typical day during winter in Johannesburg is considered for the simulation using a genetic algorithm (GA). Results obtained demonstrate the effectiveness of demand response aggregators in maximizing the benefits on both sides of the electricity supply chain.

The prevalence of osteoporosis is forecasted to escalate in Malaysia with an increasing elderly population. This study aimed to analyze the prevalence and the risk factors of osteoporosis among middle-aged and elderly Chinese Malaysians. Three hundred sixty seven Malaysian Chinese aged ≥40 years in Klang Valley, Malaysia, were recruited. All subjects completed a structured questionnaire comprised of demographic details, medical history, diet, and lifestyle practices. Body anthropometry and bone mineral density measurements were also performed. The relationship between bone health status and risk factors was determined using multivariate logistic regression. Fifteen-point-three percent of the overall study population and 32.6% of those aged ≥71 years had osteoporosis. The prevalence of osteoporosis among women (18.9%) was higher than men (11.5%). The significant predictors of osteoporosis were age, body weight, and low monthly income. Lean mass, low education level, and being underweight predicted osteoporosis in women. Lean mass was the only significant predictor of osteoporosis in men. Overall, 15.3% of the Malaysian Chinese aged ≥40 years from Klang Valley, Malaysia, had osteoporosis. Osteoporosis was associated positively with increased age and low monthly income and negatively with body weight. Therefore, osteoporosis preventive strategies targeting Chinese elderly from a low socioeconomic background is necessary.

This paper investigates the effect of selected strategies of distributed energy resources (DER) on an energy cost function that optimizes the distribution of distributed energy resources for a mid-rise apartment building. This is achieved through comparing parameter optimization results for both a high-level and low-level optimizer, respectively. The optimization process is carried out using the following approach: (1) a two-objective function is constructed with one objective function similar to that of the high-level optimizer (DER-CAM); (2) an evolutionary algorithm (EA) with modified selection capability is used to optimize the two-objective function problem in (1) for four selected cases of DER utilization that were previously optimized in DER-CAM; and (3) the optimization results of the low-level optimizer are compared with the outcome of DER-CAM optimization for the four selected cases. This is done to establish the capability of DER-CAM as an effective tool for optimal distributed energy resource allocation. Results obtained reveal the effect of load shifting and solar photovoltaic (PV) panels with power exporting capability on the optimization of the cost function. The Pareto-based MOEA approach has also proved to be effective in observing the interactions between objective function parameters. Mean inverted generational distance (MIGD) values obtained over 10 runs for each of the four cases that were considered show that a DER combination of PV panel, battery storage, heat pump, and load shifting outperforms the other strategies in 70% of the total simulation runs.

Recent advancements in technology and information have profoundly influenced the realm of education, prompting a transformation in the learning process. This shift has become imperative due to the impact of the pandemic a few years ago, which necessitated the utilization of online technology for education. Given this situation, the conventional, monotonous learning approach now requires diversification to cater to various learning preferences. This evolution aligns with the need to tailor learning methods to students' current interests and hobbies. Consequently, the adoption of educational technology based on gamification in the era of Industry 4.0 emerges as a solution to address these educational demands. There are several strategies to implement gamification effectively, aimed at fostering students' enthusiasm for learning. In essence, Industry 4.0 has ushered in profound changes across all sectors by optimizing resources and production costs. The research method employed here is gamification, which revolves around the concept of game-based learning. The outcome of this research is an interactive monopoly game designed for integration into the learning process. [ABSTRACT FROM AUTHOR]

In the observation period from 2018 to 2021, the growth of outstanding financing only grew 6.5% per year and the number of outstanding customers grew 4.91% per year, while the number of stagnant micro waqf banks did not grow at 62. Therefore, it is urgent to measure the efficiency and productivity of micro waqf banks so that the source of the causes of the suboptimal distribution of micro waqf bank financing to the community around the pesantren can be evaluated. Technical analysis with DEA and Malmquist Index was used to measure the efficiency and productivity. The input data used are the number of outstanding customers, the number of Kumpi, and the number of micro waqf bank offices. Output data uses the amount of outstanding financing. Micro waqf banks that have consistently been inefficient from 2018 to 2021 are waqf banks in Riau and West Java Provinces. The inefficient waqf bank according to this approach is only 27.75. Overall, micro waqf banks have very low technological innovation. Optimization of micro waqf bank financing can still be done by optimizing the function of micro waqf banks and activating micro waqf bank partners who have been passive. [ABSTRACT FROM AUTHOR]

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Discharge routing is a key method for predicting downstream hydrograph in canals or rivers. Predicting the downstream hydrograph using the upstream hydrograph can significantly decrease the flood damages. A combination of a large dataset and Muskingum model can enhance river discharge routing reliability. However, calibrating the Muskingum model for large datasets requires powerful optimization algorithms. Metaheuristic Optimization Algorithms (MOAs) can accurately calibrate the Muskingum model, but their performance may vary. Hence, in the present study, a new technique is introduced for discharge routing based on the large discharge dataset: Muskingum model, MOAs, and Multi-criteria Decision-Making (MCDM). Different MOAs, including a Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Firefly Algorithm (FFA), Cuckoo Search (CS), Bat Algorithm (BA), Shark Smell Optimization (SSO), Whale Optimization Algorithm (WOA), Harris Hawk''s Optimization (HHO), and hybrid of WOA and CS (WOA_CS), were developed for Muskingum calibration. The Mollasani-Ahvaz, Harmaleh-Bamdej river reaches Karun basin, and Lighvan-Heravi in the Urmia basin are considered case studies. Results of discharge routing based on the evaluation criteria in the training period showed MOAs were trained with high accuracy and reliability. While in the testing period, each MOA achieves better results in some evaluation criteria. Considering all evaluation criteria using the MCDM showed that WOA_CS, WOA, and FFA were placed in the first, second, and third rankings, respectively. The MCDM scores for WOA_CS, WOA, and FFA were equal to 0.960, 0.913, and 0.907, respectively. The developed method in this study has a good potential for discharge routing in various river reaches.

Several bridges in Indonesia are designed using elastomeric bearing (ERB) with a low capability of reducing seismic responses. This results in a significant demand for larger pier cross-sectional dimensions and a greater number of reinforcements, necessitating the consideration of seismic isolation devices to optimize the pier configuration. Lead rubber bearing (LRB) has been widely used as a seismic isolation device due to the natural period shifting and sufficient energy dissipation, but it costs a lot. A shear panel damper equipped with a gap (SPDG) was proposed regarding its capability to provide high damping at low cost as an alternative device to LRB. This study compared the seismic performance of three structural systems of simply supported prestressed concrete (PSC) box girder bridges. Those were analyzed using Nonlinear Time History Analysis (NLTHA) with the OpenSees software. As a result, both SPDG and LRB increased the structural flexibility and generated similar relative pier responses to the conventional bridge with ERB. For example, SPDG generated the relative responses of the top pier displacement, base shear, and bending moment up to 64.76%, 83.55%, and 65.66%, while LRB was 64.92%, 83.39%, and 66.89%, respectively. Meanwhile, the bridge’s structural performance equipped with LRB and SPDG showed a fully operational and operational limit, while the one with ERB reached the life safety limit due to the longitudinal earthquake. The life safety limit due to the transverse earthquake was also observed in the three bridge models. In conclusion, SPDG is applicable in the seismic isolation system as it has a similar performance to the LRB.

Fiber-reinforced composites can be classified into two parts, namely short fiber composites and long fiber composites. Long fibers are generally stronger than short fibers. Long fiber (continuous fiber) is more efficient in laying than short fiber but short fiber is easier to lay than long fiber. Fiber length affects the processability of the fiber composite. Judging from the theory, long fibers can transfer the load and stress from the stress point to the other fiber. In this research, we simulated the effects of volume fraction and fiber orientation in glass fiber-reinforced polyester composites on bending strength to examine the effect of each parameter on the mechanical properties of glass fiber composites. The mechanical properties of the composite were tested using the three-point bending and tensile testing methods. The study expects to find variations in mechanical properties with changes in the glass fiber volume fraction and fiber orientation. In this study, it is planned to function in a relevant environment, the components in this study must be able to operate properly and have been well integrated with prototype manufacturing that has been tested as a test tool function. Notably, the pinnacle of the bending test, measuring 170.41 MPa, was achieved at the specific combination of a 0.5 Fiber Volume Fraction and the 0-90o fiber orientation.

Red ginger is a rhizome plant that has the benefit of increasing the body's immune system. BUMDesma Sari Bumi, located in Pule Village, Trenggalek Regency, focuses on processing fresh red ginger into dry simplisia. This is done to extend the shelf life of red ginger. The problem encountered so far is the accumulation of subgrade red ginger in the BUMDesma Sari Bumi warehouse. The solution offered to increase the selling value of subgrade red ginger is to diversify the product into red ginger drink premix powder. The premix powdered beverage product consists of two variants namely red ginger coffee and red ginger chocolate. The approach taken in the empowerment activities of BUMDesma Sari Bumi is Participatory Rural Appraisal which emphasizes direct community involvement as subjects and objects as well as overall activities. Activities carried out include module preparation, training and mentoring on how to make Pemix beverage products, and product manufacturing demonstrations. The resulting assistance activity is a red ginger premix product that has obtained a P-IRT number so that it can increase the selling value of subgrade simplistic.

This study thoroughly investigates the enhancement of power system stability through the coordinated design of Static VAR Compensator (SVC) and Multi-Band Power System Stabilizer type 3C (MB-PSS3C). An Improved Mayfly Algorithm (IMA) is employed to optimize the placement and tuning of SVC and MB-PSS3C within the Southern Sulawesi (Sulbagsel) integrated Wind Power Plant (WPP) electricity system. Mayfly intelligence is inspired by the mating and flight behaviors of adult mayflies. In its standard form, this algorithm cannot be applied to high-dimensional, nonlinear, and complex space problems. This study proposes an Exponent Decreasing Inertia Weight (EDIW) strategy to enhance the performance of the standard Mayfly Algorithm (MA). The IMA demonstrates superior performance compared to other methods, achieving a minimum fitness function value of 80.0582 and converging rapidly by 8th iteration. For SVC, the optimization analysis involved reviewing the voltage profile at each bus and analyzing Optimal Power Flow (OPF). For MB-PSS3C, the optimization focused on evaluating system stability through damping analysis, time-domain simulations, and eigenvalue visualization. The application of IMA-based SVC improved reactive power management in transmission systems, resulting in enhanced voltage profiles, OPF, and reduced transmission losses. Specifically, transmission losses decreased by 3.12% in the normal system case study and by 2.88% in the N-1 contingency case study. Meanwhile, the IMA-based MB-PSS3C enhanced system stability by increasing system damping, reducing generator oscillation overshoot, and improving system eigenvalues. The implementation of MB-PSS3C using IMA across 14 generators achieved the highest damping ratio of 0.7375, compared to 0.7274 for MB-PSS3C with MA.

Current agricultural energy management methods do not sufficiently account for the environmental impacts related to farm size. This gap limits the ability to accurately predict the ecological effects of sugar beet cultivation, particularly regarding climate change, eutrophication, acidification, human toxicity, and terrestrial and aquatic ecotoxicity. Moreover, optimizing agricultural productivity and integrating artificial intelligence (AI) are necessary to improve energy management and anticipate future consumption patterns, considering current constraints. Our innovative solution combines LCA with AI to better forecast the ecological impacts of sugar beet cultivation across farms of different sizes. By using historical data and advanced neural models, this method anticipates future energy consumption while integrating environmental and energy constraints. The study highlights that medium-sized farms (MF) show the lowest environmental impacts, according to the CML and USEtox methodologies, unlike small (SF) and large farms (LF), which do not significantly differ in their contribution to pollutants. The average energy consumption for beet production was 1,092,000 MJ/ha on small farms, 1,126,100 MJ/ha on medium farms, and 1,107,080 MJ/ha on large farms. The key critical points identified were related to the use of diesel, machinery, seeds, and pesticides. In terms of energy distribution, chemical fertilizers accounted for 41 % of the total energy used, followed by fuel (24 %), pesticides (18 %), and machinery (7 %). Neural models revealed correlation coefficients (R2) ranging from 0.634 to 0.945 for testing, 0.861 to 0.967 for validation, and 0.944 to 0.982 for training. These results suggest good predictive accuracy for our AI-based approach. Finally, to improve energy efficiency, the study proposes modernizing agricultural equipment and developing more sustainable technologies, such as hybrid tractors, robotic and automated weeding systems, and cultivation techniques that reduce carbon emissions.

Optimizing Earth observation nanosatellite missions requires careful orbit selection to ensure global coverage and high image quality. The main challenge is to define an optimal orbit that maximizes image quality while achieving comprehensive Earth coverage. To address this, an orbit definition model that accounts for the J2 perturbation was developed, evaluating key parameters such as swath width, spatial resolution, time resolution, and tilt angle. The model was applied within the context of a 3U university nanosatellite equipped with the Gecko imager. The analysis identified 551 km as the most suitable altitude for achieving comprehensive coverage. At this altitude, the swath width is 88.16 km, and the distance between adjacent ground tracks is 86.33 km, satisfying the condition for global coverage with nadir pointing. The revisit time is 31 days, ensuring regular coverage of the Earth's surface. Results were validated through Systems Tool Kit simulations, showing minimal differences compared to the MATLAB model, underscoring the model's accuracy and reliability. To further optimize the mission's effectiveness and bandwidth utilization, images captured in this orbit can be processed onboard to ensure that only relevant and useful data are transmitted to the ground station. To support this, a dataset of 900 images was generated at the optimal orbit altitude, specifically tailored for a cloud detection application using the Gecko imager. The findings validate the effectiveness of the proposed orbit design model and demonstrate the feasibility of pre-launch dataset preparation, paving the way for future onboard implementations and in-orbit real-time applications.

Nowadays, researchers are interested in surface modification of bearings by adding texture to increase bearing performance. Regrettably, modifications in surface roughness can induce cavitation. Conversely, slip engineering can enhance pressure to effectively eliminate cavitation. The objective of this work is to present a 3D design for thrust bearing that integrates pockets and slip engineering techniques in order to improve the load carrying capacity of the bearing and reduce cavitation. The originality of this study on thrust bearing is in the integration of surface texture, slip engineering, and cavitation. A CFD modeling approach is employed to simulate the slip formation in the design. Implementing slip induces hydrophobicity on the surface by introducing velocity to the fluid at the contact with the bearing pad. The simulation involves two types of bearings: radial pocket and multitexture, each with various slip positions. The important study's findings indicate that the radial pocket design, which incorporates slip formations both between and inside the pocket, can enhance the load carrying capacity by as much as 259.2 % compared to the absence of slip formation. When using the multitexture type, the load bearing capability can rise by up to 179 %. In the multitexture configuration, pressure is uniformly distributed throughout the bearing surface. To enhance the comprehensiveness of the research, the findings of this study are also compared to those of earlier conducted studies on circumferential pocket and closed pocket types. Arranging slip correctly has been empirically shown to enhance the load carrying capability of bearings.

Global Positioning System (GPS) is widely used in a variety of fields, including aircraft applications and rockets where speeds exceed standards. Signal simulators, which are relatively expensive, are required to test commercial GPS devices and GPS signal processing methods. The methodology for verifying GPS receiver specifications using a Software Defined Radio (SDR)-based simulator is described in this paper. Simulator tests are run in both static and dynamic modes. The test findings demonstrate that the system can measure altitude and speed, but only up to 50 km and 500 m/s, respectively, according to commercial GPS specifications. This simulator system can be used to evaluate future GPS processing algorithms in highly dynamic environments.

The objective of this paper is to propose an offline signature verification system (OSVS) designed for writer-independent applications. The system shall differentiate between original and forged signatures. The system encompasses key stages such as pre-processing, feature extraction, and model training and testing, employing the Support Vector Machine algorithm for classification. One challenge in creating a good OSVS is to find proper signature features to be used in the training/classification phases. In this research, global and local features are utilized. This includes signature area, mean, standard deviation, perimeter, number of connected components, number of vertical and horizontal edges, number of end points, number of branch points, and number of lines. The contributions of this paper are on several aspects of the offline signature verification process. Investigation in this study include data pre-processing techniques (normalization vs. standardization), kernel selection (Poly vs. RBF), dataset distribution for training and testing (80%-20% vs. 5-fold), and variations of the C and gamma parameters (C=1, 10, 100 and gamma=1, 10, 100). Improving the recognition rate involves removing of features with little or bad effect on the recognition rate. An algorithm for model-agnostic feature importance is executed, revealing that the most crucial features in the classification process are mean, standard deviation, perimeter, number of connected components, and number of end points. Signatures are classified as either original or forgery, and the model's performance is assessed on the CEDAR dataset. Experimental results shows a 95.65% accuracy of the proposed system when utilizing standardization with the RBF kernel.

An efficient room-temperature self-powered, broadband (300 ​nm–1100 nm) photodetector based on a CuO–TiO2/TiO2/p-Si(100) heterostructure is demonstrated. The CuO–TiO2 nanocomposites were grown in a two-zone horizontal tube furnace on a 40 ​nm TiO2 thin film deposited on a p-type Si(100) substrate. The CuO–TiO2/TiO2/p-Si(100) devices exhibited excellent rectification characteristics under dark and individual photo-illumination conditions. The devices showed remarkable photo-response under broadband (300–1100 ​nm) light illumination at zero bias voltage, indicating the achievement of highly sensitive self-powered photodetectors at visible and near-infrared light illuminations. The maximum response of the devices is observed at 300 ​nm for an illumination power of 10 ​W. The response and recovery times were calculated as 86 ​ms and 78 ​ms, respectively. Moreover, under a small bias, the devices showed a prompt binary response by altering the current from positive to negative under illumination conditions. The main reason behind this binary response is the low turn-on voltage and photovoltaic characteristics of the devices. Under illumination conditions, the generation of photocurrent is due to the separation of photogenerated electron-hole pairs within the built-in electric field at the CuO–TiO2/TiO2 interface. These characteristics make the CuO–TiO2/TiO2 broadband photodetectors suitable for applications that require high response speeds and self-sufficient functionality.

In this study, multi-structured coatings of biocompatible and antibacterial materials on a 316L stainless steel (SS) implant have been electrophoretically deposited for orthopedic applications. Two layers of composite coatings have been deposited; the first layer of composite coating biopolymer layer consists of (chitosan with Gelatin) and the second composite coating layer of bio-ceramic comprises Hydroxyapatite with chitosan. The first layer was deposited at (3 g/L concentration of Gelatin, 20 voltage, and 3 min and 0.5 g/L chitosan) parameters, and the second layer was deposited at (6 g/L, 40 voltage, 1 min, and 0.5 g/L chitosan) parameters. In this study, the stability of the suspension was evaluated using the Zeta potential test, which manifested good stability. The adhesion strength between the composite coatings' first layer and the 316L stainless steel substrate, as well as between the coating layers themselves, was determined using the Tape test, and the removal area for the first layer was 8.06% while for the second layer was 6.01%. The wettability test elucidated for the 316L stainless steel and first coating layer and the second coating layer, the second composite coating layer became super hydrophilic, and a very high wettability was exhibited with multilayer coating. Scanning electron microscopy showed that the surface's topology revealed a homogenous and defect-free composite coating. The result of the antibacterial efficiency of the 316L SS substrate improved as the number of coating composite layers increased, and the resistance to corrosion of 316L SS enhanced as the number of coating composite layers increased.

Traditional recurrent neural networks (RNNs) encounter difficulty in capturing long-term temporal dependencies. However, lightweight recurrent models for speech enhancement are important to improve noisy speech, while being computationally efficient and able to capture long-term temporal dependencies efficiently. This study proposes a lightweight hourglass-shaped model for speech enhancement (SE) and automatic speech recognition (ASR). Simple recurrent units (SRU) with skip connections are implemented where attention gates are added to the skip connections, highlighting the important features and spectral regions. The model operates without relying on future information that is well-suited for real-time processing. Combined acoustic features and two training objectives are estimated. Experimental evaluations using the short time speech intelligibility (STOI), perceptual evaluation of speech quality (PESQ), and word error rates (WERs) indicate better intelligibility, perceptual quality, and word recognition rates. The composite measures further confirm the performance of residual noise and speech distortion. With the TIMIT database, the proposed model improves the STOI and PESQ by 16.21% and 0.69 (31.1%) whereas with the LibriSpeech database, the model improves STOI by 16.41% and PESQ by 0.71 (32.9%) over the noisy speech. Further, our model outperforms other deep neural networks (DNNs) in seen and unseen conditions. The ASR performance is measured using the Kaldi toolkit and achieves 15.13% WERs in noisy backgrounds.

Abstract The global scientific community is deeply concerned about the deterioration of water quality resulting from the release of industrial effluents. This issue is of utmost importance as it serves to safeguard the environment and combat water pollution. The objective of this work is to elaborate a biomaterial of vegetable origin, based on the twigs of Aleppo pine, and to use it as an abundant and less expensive material for the treatment of wastewater. For this reason, the twigs were treated physically to get the powder called biomaterial FPA (Aleppo pine fiber), which was characterized by physicochemical, and spectroscopic analyses namely scanning electron microscopy (SEM), Fourier transform infrared spectroscopy (FTIR) and X-ray diffraction (XRD). The crystallinity index of FPA was evaluated by the peak height method. The findings indicate that the FPA powder has an acidic nature, exhibiting a porous structure that promotes the adsorption and binding of molecules. Additionally, it has a zero charge point of 5.8 and a specific surface area of 384 m2.g−1. It is primarily composed of hydroxyl, carboxyl, and amine functional groups, along with mineral compounds and organic compounds, including cellulose and other mineral elements such as Ca, Mg, Fe, Na, P, Al, K, Ni, and Mo. Combining these characteristics, FPA biomaterial has considerable potential for use as an effective adsorbent biomaterial for various wastewater pollutants. Its abundance and relatively low cost make it an attractive solution to the growing challenges of water pollution worldwide. Graphical Abstract

This research aims to optimize the trajectory of a low-thrust spacecraft carrying biological cargo. The objective is to minimize the radiation exposure from the Van Allen belts, the optimal criterion for the control problem concerning orbital transfers from low orbits to high orbits. Since the minimum radiation stress criterion introduced in this article is unconventional, solving this optimal control problem is complex, necessitating using the honey bee optimization algorithm.The trajectory optimization in this study involves rewriting the motion equations based on the control variables and solving these newly defined equations using the honey bee optimization method. The primary advantage of this approach is its integration of optimal control theory with population-based optimization methods, employing a global approach. In the novel method presented, the optimal control problem is simplified by redefining the differential equation system, and the results demonstrate both accuracy and ease of solution.Based on the results obtained from the comparison between the optimal criterion of minimum time and minimum radiation stresses presented in this article, the minimum radiation stress criterion causes an increase of 8.89% in transfer time. However, this criterion significantly reduces exposure to magnetic radiation, which is crucial in the transfer to high orbits.

Installation of the sideboard assemblies on a 300 feet barge requires an estimate of the need for welding wire, besides that an estimate of the need for oxygen and LPG gas is also required for the work of cutting the old sideboard. This is so that the repair work does not experience errors in estimating the amount of consumables used so as not to result in losses for the shipyard company. The objectives obtained during the research were to determine the need for welding wire in the welding process of the 300 feet barge sideboard. The design of this study uses a quantitative approach and there are several variables used including independent variables such as the thickness of the material, the length of the welding, and cutting areas. Dependent variables such as the number of electrodes, oxygen, and LPG gas used. Control variables such as the SMAW welding process using AWS A5.1 E6013 electrodes and cutting using an oxygen and LPG gas cutting blender. The data collection techniques in this study were literature study, observation, and interviews. Based on the calculation, the total need for welding wire for the installation of the 300 feet sideboard assembly is 95.87 kg total oxygen requirement of 241.51 kg and LPG gas of 177.35 kg for cutting the old sideboard.

Water crisis ahead Water, although plentiful on Earth, poses a paradox of scarcity due to its uneven distribution and limited accessibility. Earth contains around 326 million trillion gallons of water, but the majority of it, roughly 97% is saline, and another 2% is trapped in polar ice caps. This means that humanity depends on only 1% of Earth's water for survival for life. This small proportion is primarily located underground, which is challenging and expensive to reach, influencing the development of human settlements and economic activity towards more easily accessible surface waters such as rivers and lakes.Throughout history, societies who efficiently controlled and utilized water resources thrived, whilst those that failed to do so experienced a fall. Contemporary instances, such as Cape Town, which came close to being the first big metropolis to experience water scarcity, emphasize the ongoing challenge. The experience of this city serves as a cautionary tale for other global cities, such as Sao Paulo, Melbourne, Jakarta, and Beijing, who may face "Day Zeroes" in the future. These "Day Zeroes" refer to the complete depletion of water supplies in metropolitan centers. To avoid this issue, dramatic adjustments in water usage must be implemented.Water usage patterns expose other disparities, whereas human activities such as drinking, cooking, and hygiene only account for a small 8% of the available freshwater, the bulk is dedicated to agricultural and industrial purposes. This distribution not only emphasizes the unequal utilization of water, but also sheds light on the economic undervaluation of this important resource. Water is often underpriced in various areas, which means its actual cost is not accurately reflected. This leads to excessive use and wastefulness, which is not sustainable in the long run. Economic activity frequently worsens local water shortages. For instance, Mexico City, although it receives a greater amount of rainfall compared to London, experiences significant water scarcity as a result of the previous drainage of lakes and excessive use of groundwater. The city is experiencing subsidence, causing certain portions to sink at a rate of up to 20 cm per year. Comparable difficulties are apparent in areas such as northern India, where underground water reservoirs are disappearing rapidly, posing a threat to the water security of millions. The participation of the private sector in water management is on the rise, as institutions such as hedge funds are acquiring water rights. This has raised concerns about the potential commercialization and exploitation of water scarcity for financial gain. The implementation of this method has sparked ethical questions over the fair distribution of water resources, specifically the effect of pricing techniques on low-income communities who are disproportionately impacted by rising costs. Climate change exacerbates the burden on water resources by making precipitation patterns more unpredictable and less consistent. The lack of freshwater is leading to an increase in conflict, as seen by ongoing disagreements and violence in regions such as Darfur and northeastern Nigeria. The growing crisis has stimulated interest in technology solutions such as desalination, which has experienced a more than twofold increase in capacity in the last decade. Nevertheless, desalination is characterized by high energy consumption and significant expenses, which renders it a less feasible choice for wider implementation considering the present economic assessments of water. Innovation for water sustainability management Different models have been tried by many towns and countries to tackle these difficulties. In 2017, Philadelphia implemented a system where water costs are modified according to income levels. The objective is to protect essential human requirements while avoiding additional financial strain on those with lower incomes. This paradigm exemplifies an increasing acknowledgment of the necessity to oversee water not just as a commodity with economic value but also as an essential entitlement of every human being. The experience of Cape Town facing Day Zero in 2018 demonstrates both the possibility of a crisis and the ability of the community to respond collectively. The implementation of stringent water restrictions and active participation from the public in conservation endeavors successfully averted the predicted catastrophe, showcasing the efficacy of immediate collaborative measures. However, depending on crisis-driven conservation is not a sustainable approach, it is crucial to take proactive actions instead. Accurate assessment and control of water require acknowledging its crucial significance in worldwide sustainability and the preservation of human life. Investments in infrastructure to mitigate leakages, which can constitute as much as 42% of the municipal water supply in areas such as Mexico City, are important. In addition, the implementation of water-efficient farming practices, enhancement of industrial water usage, and promotion of responsible consumption patterns might help alleviate some of the most urgent challenges. With the ongoing increase in global populations and the changing climate conditions, the significance of water management becomes increasingly evident. With the intention of fostering global cooperation and tangible progress, the 10th World Water Forum in 2024, held in Bali, Indonesia, seeks to serve as a crucial milestone in bringing nations and people together to pledge to concrete and effective measures in ensuring universal access to clean water and proper sanitation. The experiences of places such as Cape Town (Capital of South Africa) and the current efforts being made globally offer both warnings and models for achieving a sustainable water future. Ultimately, addressing the worldwide water crisis necessitates a comprehensive approach that encompasses policy restructuring, technical advancement, and active involvement of communities. By assigning the proper value to water and making investments in sustainable management practices, humanity can protect this essential resource for future generations, guaranteeing its ability to support life and prosperity on our planet.

Incorporating sustainability into Concrete-Filled Steel Tubular (CFST) columns' optimization can enhance efficiency and sustainability in construction. Discrepancies in international standards for ultimate load capacity computation in compact CFST columns under eccentric loading, particularly with lightweight high-strength concrete, pose challenges. This research compile a dataset of compact CFST columns, evaluating design codes (AISC 360-16, Eurocode 4) against experimental results. Besides, a comprehensive finite-element model predicts compact CFST column performance, investigating axial force-moment (P-M) interaction behavior with respect to the material strength ratio (fy/fc′). In the second phase of the study, an ANN model, incorporating input parameters, estimates axial load capacity, facilitating multi-objective optimization for optimal CFST column geometry. The results confirmed that Eurocode 4 outperforms AISC 360-16 in experimental axial capacity predictions (Nuc/Nuc,theoretical) where, the mean and standard deviation for Eurocode 4 were estimated at 1.07 and 0.22, respectively, compared to 1.21 and 0.29 for AISC 360-16. Besides, statistical metrics confirm the precision of the ANN model, particularly with high-strength concrete, promising efficiency in future computational intelligence-based structural design platforms.

Honeypots have become invaluable tools in the field of cybersecurity, allowing researchers to gain insights into attacker behaviour, collect data on malicious activities, and develop effective defence strategies. Traditionally, honeypots relied on rule-based approaches or signature-based detection to identify and categorise attacks. However, with the growing complexity and diversity of cyber threats, these methods often struggle to keep pace with evolving attack techniques. Modern honeypots, such as T-Pot, have become multi-faceted systems that provide researchers with a wealth of data. They could emulate different vulnerabilities and services, thus attracting a wide array of cyberattacks. This ability to simulate real-world systems and networks allowed for a detailed analysis of attack methodologies and helped to understand the evolving nature of cyber threats. As attacks became more sophisticated, so did the strategies to combat them. This included understanding the landscape of cyber threats, anticipating potential vulnerabilities, and staying ahead of the attackers. Thus, this project aims to implement a complex honeypot system with capabilities to detect and prevent cyberattacks. The project will involve designing the honeypot infrastructure, collecting data on attacks, integrating the model into the honeypot system for real-time analysis, generating reports and alerts based on the analysis, and continuously improving the system's defences. The tests revealed that honeypots can perform real cyberattacks, as well as detect and warn about threats. This project used Nmap, Hydra, and Hping3 to pretend to be attackers and show that the honeypot could fake network resources and attract them, which makes it a smart network intrusion detection system. There was a lot of experimental data on how well the honeypot could find things. Each test checked how well the honeypot could find threats on the network. In conclusion, these tests proved that the honeypot's methods for finding threats are correct, which means it can indeed find network breaches.

Rapid floods usually occur in an uncertain and unpredictable time, and it will cause major disaster to environment and humanity especially at recreational areas. An IoT early warning system using Arduino technology is proposed to cater this problem. The system includes sensors for temperature, humidity, water flow, and ultrasonic measurements used for further analysis. This research aims to develop a system to monitor and detect water flow activity, such as water level and velocity, and notify of potential rapid floods earlier than estimated occurring time. The current temperature and humidity of the areas also can be recorded with the proposed device The System Development Life Cycle (SDLC) methodology was adapted for implementation of the project. Field testing at Puncak Janing Waterfall in Kedah State was chosen as test site for sensor functionality evaluation. Data is stored in a Firebase database, with an ESP32 Lo-Ra used for connectivity suitable for remote area and coding was done in Arduino IDE tool. The project successfully monitors and stores water level and velocity data and the data can be use as benchmarking the time rapid flood will occur.

Badminton is a thrilling and competitive sport that has gained popularity across the world, attracting players of all ages. The sport fosters social interaction among players which helps create friendships and engagement within a community. Although many local sport centres provide badminton courts, some facilities lack a systematic way to handle bookings. The booking process can be frustrating and time-consuming. This may result in delayed replies, miscommunication and conflicting booking information. In this paper, we present iSmashON, a mobile application developed to simplify the booking process by providing systematic way of booking badminton courts, chat with other players and manage bookings. It features real-time availability updates and personalized notifications. The development encompasses four phases which are requirement analysis, design and development, testing and refinement. The evaluation of the developed mobile application is done using Technology Acceptance Model (TAM) that considers four components which are Perceived Ease of Use (PEU), Perceived Usefulness (PU), Attitude (ATT) and Behavioural Intention (BI). Questionnaire with 13 questions is prepared and distributed over 30 respondents with at least one-year experience of playing badminton. Results from the TAM evaluation shows a favourable attitude among respondents towards using the mobile application as a medium for booking badminton courts. This is drawn by the highest mean score obtained from ATT component. It is believed that the iSmashON application is expected to enhance the booking experience, providing more efficient and user-friendly process that will be benefited by badminton community.

The advancement of future technology seeks to create wireless systems capable of achieving faster data transfer rates. One solution to confront this challenge is by employing Multiple-Input Multiple-Output (MIMO) systems. Nonetheless, the primary obstacle in assessing MIMO performance revolves around the size and spacing of the antennas to maintain isolation among the ports. Consequently, it is essential to devise an approach to tackle these challenges, with the utilization of a DGS as a viable and promising approach. Hence, in this study, we developed a dual-frequency MIMO system featuring two linear array antennas with two elements each and configured to function at 128 GHz and 178 GHz. Initially, this design is conceived as a single-element structure before evolving into a two-element MIMO antenna array configuration. The antenna's physical dimensions are 2.7 × 3.65 × 0.15 mm3, and it consists of two linear array nodes positioned at a separation distance of 0.18 mm. Additionally, the antenna delivers peak gains of 5.16 dB and 6.24 dB at frequencies of 128 GHz and 178 GHz, respectively. The values of various MIMO performance indicators meet stringent standards with ECC values below 0.012 and a DG of approximately 10 dB. Furthermore, the TARC remains below 0 dB. The aim of this research is to create a novel design for a THz antenna that can achieve a wide bandwidth and a high gain while maintaining the standard compact antenna size necessary for THz applications.

The high demand for drinking water has increased the by-products from treatment plants, notably during coagulation-flocculation, leading to substantial sludges' accumulation. This study aims to focus on the characterization of drinking water treatment sludge (DWTS) batches from the three last years and statically studying it's uniformity, as exploring potential repurposing options. The samples were characterized using X-Ray Fluorescence (XRF), X-Ray Diffraction (XRD), Fourier Transform Infrared Spectroscopy (FTIR), Thermogravimetric Analysis (TGA), Inductively Coupled Plasma Optical Emission Spectroscopy (ICP-OES), Loss On Ignition (LOI), and Granulometric analysis. The results evinced that DWTS have a sandy nature, the organic fraction takes third of the DWTS’ chemical composition, the amount of Al2O3 + SiO2 + Fe2O3 is slightly different, with 55.84, 57.47, and 56.14 % for DWTS21, DWTS22, and DWTS23 respectively, as per ANOVA results, which exhibited the consistency sludge composition among the samples, providing valuable insights into repurposing DWTS, and contributing to sustainable and eco-friendly practices in the construction industry especially DWTS21, which presents higher content of CaO with 2.95 %.

Fuel additives are recognized for their potential to make diesel engines cleaner and more efficient. However, the use of Pure Palm Oil (PPaO) as a fuel faces challenges due to its inherent combustion inefficiencies. This study explores how Acetylene Black (AB), as an additive, affects the combustion performance and spray characteristics of a diesel engine. Observations of spray phenomena were conducted using a High-Speed Camera Phantom C110. The addition of AB to PPaO resulted in an increase in engine power and torque by 11.43 % at a 60 % load, averaging a 2.97 % improvement over diesel fuel (DF). PPaO demonstrated the most significant improvement in Specific Fuel Consumption (SFC) by 17.2 % at a 40 % load, averaging an 8.2 % increase. For PPaO with AB (PPaOAB), the greatest enhancement in SFC occurred at a 10 % load, showing a 9.2 % improvement over DF, with an average increase of 2.1 %. The study also found that thermal efficiency was optimally enhanced with PPaOAB at a 20 % load, achieving a remarkable 17.7 % improvement compared to DF. Notably, the inclusion of PPaOAB significantly reduced the thickness of carbon deposits on the pistons and cylinder head by 51.8 % and 3.9 %, respectively, and improved spray angle and penetration compared to PPaO. These findings suggest that AB could be a promising additive for PPaO, sparking further research interest in the application of AB in other fuels.

This research explores how technology has changed teaching methods in the classroom through a blended learning approach. The research method used in this study is literature studies, which look for references appropriate to the research context. The study's findings suggest that technology can increase student engagement, facilitate access to a wider range of learning activities, and improve learning outcomes through more interactive and flexible methods. When strategically integrated into the learning process, technology improves teaching efficiency and prepares students with the skills needed for the digital age. The implications of these findings are important for education stakeholders in designing and implementing teaching practices that harness the potential of technology to improve the learning process in the classroom. Further research is suggested to conduct empirical studies involving experiments in real classrooms to measure the direct impact of technology and blended learning approaches on student engagement and learning outcomes.

Lulus tepat waktu adalah pencapaian paripurna ingin dicapai oleh semua mahasiswa atau calon mahasiswa. Kelulusan memiliki 2 klasifikasi seperti lulus tidak tepat waktu dan lulus tepat waktu. Kelulusan menjadi suatu penilaian akreditasi universitas dan penilaian oleh masyarakat secara luas. Perguruan tinggi meluluskan mahasiswa-mahasiwa dengan beberapa kriteria standar yang harus dimiliki. Diharapkan mahasiswa lulus memenuhi syarat standar kelulusan dalam waktu maksimal 4 tahun masa studi. Evaluasi dan pemantauan kelulusan sangat penting dilakukan, salah satunya dengan mempelajari data history mahasiswa yang telah lulus sebagai upaya mahasiswa lulus tidak melebihi waktu standar yang telah ditetapkan. Prediksi kelulusan yang dilakukan pada riset menggunakan kaidah klasifikasi K-Nearest Neighbor dengan objek penelitian yaitu mahasiswa. Atribut yang dipakai dalam penelitian metode klasifikasi yaitu nama, asal SMA/SMK, wilayah asal SMA/SMK, nilai rata-rata matematika dan lama studi. Tahapan penelitian diawali dengan pengumpulan data, pemilihan atribut, pembersihan data, transformasi data, pemilihan data testing dan data training. Pengujian akurasi yang didapatkan pada penelitian metode klasifikasi dengan klaster data k=1, k=2, k=3, k=4, k=5, k=6 dan k=7 menghasilkan klaster dengan nilai k=3 paling tinggi. Hasil pengujian akurasi prediksi penelitian menggunakan confusion matrix menghasilkan akurasi paling besar sesuai target yaitu mencapai 78% menggunakan objek penelitian sebanyak 93 data mahasiswa terdiri dari 78 training dan 12 data testing. Hasil pengujian point k=1 sampai point k=7, k=3 merupakan nilai akurasi prediksi yang paling tinggi sehingga hasil penelitian menjadi sumber pengetahuan untuk fakultas dalam prediksi kelulusan mahasiswa. Abstract Graduating on time is a plenary achievement to be achieved by all students or prospective students. Graduation has 2 classifications such as graduating not on time and graduating on time. Graduation becomes an assessment of university accreditation and an assessment by the wider community. Universities graduate students with several standard criteria that must be possessed. It is expected that graduating students meet the graduation standard requirements within a maximum of 4 years of study period. Evaluation and monitoring of graduation is very important to do, one of which is by studying the history data of students who have graduated as an effort for students to graduate not to exceed the standard time that has been set. The graduation predictions carried out in research use the K-Nearest Neighbor classification rules with the research object being students. The attributes used in the research classification method are name, high school/vocational high school origin, high school/vocational high school origin, average grade in mathematics and length of study. The research phase begins with data collection, attribute selection, data cleaning, data transformation, selection of testing data and training data. The accuracy test obtained in the classification method research with data clusters k = 1, k = 2, k = 3, k = 4, k = 5, k = 6 and k = 7 produces a cluster with the highest k = 3 value. The results of testing the accuracy of research predictions using the confusion matrix produced the greatest accuracy according to the target, reaching 78% using a research object of 93 student data consisting of 78 training and 12 testing data. The test results point k=1 to point k=7, k=3 is the highest prediction accuracy value so that the research results become a source of knowledge for the faculty in predicting student graduation.

Klasifikasi aktivitas manusia merupakan salah satu topik penelitian yang penting karena dapat diterapkan pada berbagai bidang. Penelitian mengenai klasifikasi aktivitas manusia sebelumnya telah banyak dikembangkan dengan menerapkan dataset public HAR Repository yang telah tersedia. Namun dataset tersebut memiliki hasil dari ekstraksi fitur keluaran nilai sensor memiliki dimensi yang tinggi. Tingginya dimensi fitur dapat menyebabkan penurunan akurasi, untuk itu pada penelitian ini diusulkan penerapan dataset primer tanpa ekstraksi fitur. Selain tingginya dimensi, pada penelitian sebelumnya, banyaknya jumlah label dengan menerapkan machine learning tradisional tidak mampu melebihi akurasi 88%. Sehingga pada penelitian ini menerapkan dataset primer dengan menggunakan label kelas sebanyak 16 sehingga diusulkan metode deep learning Long Short Term Memory (LSTM). Proses penelitian dimulai dari pengambilan data, preprocessing data, modelling dan perbandingan algoritma deep learning LSTM dan machine learning KNN. Berdasarkan hasil pengujian perbandingan kedua algoritma tersebut dengan implementasi dataset yang sama, algoritma terbaik yaitu LSTM dengan nilai akurasi sebesar 0.94 dan KNN dengan nilai akurasi sebesar 0.71.

Based on information from several official government websites and national television media, at the beginning of November 2021, there was a decrease in the spread of the COVID-19 virus. At the end of November 2022, a new type of virus was discovered, namely SARS-CoV-2, also known as the Omicron variant, resulting in additional cases. Therefore, researchers conducted research with the aim of providing information about the spread of the virus with a mapping system down to the RT level, so that the public gets detailed and real-time information about the area based on mapping. The urgency of this research is that, with the results of this research, the public can take better care of and be able to provide self-awareness regarding health protocols. The public can have access to detailed information and mapping about the spread of the virus. This research adopts changes in the design science research methodology proposed by Hevner. The results of the research are an Information System for Mapping the Distribution of COVID-19 Cases and Their Danger Level with 5 calculation categories, namely class I with a range of 66–80 has very high danger level criteria, class II with a range of 51–65 has high danger level criteria, class III with a range of 36–50 has medium level criteria, class IV with a range of 36–50 has criteria for a low level of danger, and class V with a range of 5–20 has a very low level of danger.

Cardiovascular disease is one of the leading causes of death in the world. Heart failure is a cardiovascular disease in which the heart is unable to pump sufficient blood to fulfill the body’s requirements and can lead to fluid overload. Traditional solutions are not adequate to address the progression of heart failure. Herein, we report a body-mounted wearable sensor to monitor the parameters related to heart failure. These include heart rate, blood oxygen saturation, thoracic impedance, and activity status. The device is compact and wearable and measures the parameters continuously in real time. The device is an Internet of Things (IoT) device connected with a cloud-based database enabling the parameters to be visualized on a mobile application.

Abstract The increasing trend of energy generation and management systems towards decentralized structures such as using renewable energy resources makes it necessary to use digital and smart platforms for exchanging information and even conducting financial transactions in a decentralized manner, known as the peer‐to‐peer model. The decentralized transaction verification of cryptocurrencies makes it possible to use these encrypted currencies and decentralized blockchain networks in energy management systems and carry out financial transactions related to carbon trading. Carbon and other greenhouse gas (GHG) emission trading systems reduce the competitiveness of fossil fuel projects in the market and accelerate investment in low‐carbon energy sources such as wind and photovoltaic power generation units. This market mechanism allows large entities such as countries and companies that emit GHGs into the atmosphere to buy and sell these gases. This paper reviews the blockchain solutions developed for carbon markets. Studies related to the design of smart contracts in the platform of blockchain are investigated. Special cryptocurrencies that are used in the field of green energy transactions and carbon trading are introduced. In addition, the application of artificial intelligence and game theory in energy trading is stated. The study of different blockchain frameworks for carbon trading shows that the use of decentralized platforms in carbon trading can have a significant impact on the trend towards low‐carbon measures and achieving the goals of the Kyoto Treaty, increasing the value of green cryptocurrencies and the volume of transactions. These technologies offer a promising avenue for creating a more decentralized, efficient, and environmentally conscious energy ecosystem.

Inundation and flooding are problems that often occur in many cities worldwide, including university campuses. This research aims to examine the existing drainage conditions on the Universitas Islam Riau (UIR) campus and identify solutions to overcome inundation and flooding that probably occur on the UIR campus in extreme conditions. The method in this study uses the EPA's SWMM (Storm Water Management Model) 5.2 software simulation program. The results indicate that the current drainage system is unable to handle rain discharge during extreme conditions. In this study, three improvements are set up to analyze hydrological characteristics. The first improvement is the traditional improvement by changing the existing channel dimensions. The second improvement is using Low Impact Development (LID) technology, in which the combination of 14 infiltration wells and 7 Biopore Infiltration Holeare applied. The third improvement is using the combination of 3 LID: Infiltration well, Biopore infiltration Hole, and Rain Water Harvesting (RWH). From the results, runoff in the improvement one was reduced by 21.5%. The second improvement, where 2 LID was used, reduced the runoff by 51.5%; finally, the third improvement reduced the runoff by 57.6%. So, the scenario that is most effective in overcoming inundation or flooding is the third scenario. Furthermore, the improvement is in line with the water conservation strategy. The findings of this research can serve as a valuable resource for future studies, contributing to the advancement of SDGs 6, 9, and 11, specifically targeting better water management, resilient infrastructures, and sustainable cities and communities. Moreover, the result can be a guide for the decision maker, especially on the UIR Campus and Campus worldwide, as an effective strategy for water conservation and to prevent flooding in similar universities.

Solar electric and solar thermal energies are often considered as part of the solution to the current energy emergency. The pipes of flat plate solar devices are normally heated by their upper surfaces giving rise to an asymmetric temperature field in the bulk of the fluid, which influences the heat transfer process. In the present work, a study of the characteristic length of tubes, or most efficient distance at which heat transfer occurs, in flat photovoltaic-thermal (PVT) hybrid solar devices has been carried out using three heat transfer fluids: water, [Emim]Ac ionic liquid and ionanofluid of graphene nanoparticles suspended in the former ionic liquid. The mean objective of the study was to know whether the heat transfer occurs in optimal conditions. Experimental measurements have been made on a commercial PVT device, and numerical simulations have been performed using the HEATT® platform to determine the characteristic length of the process. The tests conducted showed a clear improvement in the temperature jump of the fluid inside the collector when INF is used compared to water and ionic liquid and even a higher overall energy efficiency. Electricity generation is not greatly affected by the fluid used, although it is slightly higher when water is used. Slower fluid velocities are recommended if high fluid outlet temperatures are the goal of the application, but this penalizes the overall thermal energy production. The characteristic process length is not typically achieved in parallel tube PVT collectors with ordinary flow rates, which would require a speed, and consequently, a flow rate, about 10 times lower, which penalizes the performance (up to four times), although it increases the fluid outlet temperature by 234%, which can be very interesting in certain applications. Ionanofluids may in the medium term become an alternative to water in flat plates or vacuum solar collectors for applications with temperatures close to or above 100 °C, when their costs will hopefully fall. The results and methodology developed in this work are applicable to solar thermal collectors other than PVT collectors.

This study focuses on microgrid systems incorporating hybrid renewable energy sources (HRESs) with battery energy storage (BES), both essential for ensuring reliable and consistent operation in off-grid standalone systems. The proposed system includes solar energy, a wind energy source with a synchronous turbine, and BES. Hybrid particle swarm optimizer (PSO) and a genetic algorithm (GA) combined with active disturbance rejection control (ADRC) (PSO-GA-ADRC) are developed to regulate both the frequency and amplitude of the AC bus voltage via a load-side converter (LSC) under various operating conditions. This approach further enables efficient management of accessible generation and general consumption through a bidirectional battery-side converter (BSC). Additionally, the proposed method also enhances power quality across the AC link via mentoring the photovoltaic (PV) inverter to function as shunt active power filter (SAPF), providing the desired harmonic-current element to nonlinear local loads as well. Equipped with an extended state observer (ESO), the hybrid PSO-GA-ADRC provides efficient estimation of and compensation for disturbances such as modeling errors and parameter fluctuations, providing a stable control solution for interior voltage and current control loops. The positive results from hardware-in-the-loop (HIL) experimental results confirm the effectiveness and robustness of this control strategy in maintaining stable voltage and current in real-world scenarios.

The advancement of technology has led to the creation of numerous platforms that could potentially be used for remote education. For example, the recent development of the English Diploma Programme at a top university in the Kingdom of Saudi Arabia (KSA) deploys a new platform known as Area9 Lyceum (Area9). Because the English Diploma Programme is a recent development, and especially given its use of a new platform, this proposed research will investigate learners’ attitudes to and acceptance of using the platform. Furthermore, it will look at how other universities could benefit from this experience to develop their own English as a Foreign Language (EFL) programmes along technological lines, specifically by deploying a survey tool based on the unified theory of acceptance and use of technology (UTAUT). The results reflect the positive attitude of the participants. Recommendations can be drawn from this study to help persuade stakeholders in higher education to adopt such platforms in the teaching of EFL or English as a Second Language (ESL).

The advancement to Urban 4.0 requires urban digitization and predictive maintenance of infrastructure to improve efficiency, durability, and quality of life. This study aims to integrate intelligent technologies for the predictive maintenance of photovoltaic panel systems, which serve as essential smart city renewable energy sources. In addition, we employ vision transformers (ViT), a deep learning architecture devoted to evolving image analysis, to detect anomalies in PV systems. The ViT model is pre-trained on ImageNet to exploit a comprehensive set of relevant visual features from the PV images and classify the input PV panel. Furthermore, the developed system was integrated into a web application that allows users to upload PV images, automatically detect anomalies, and provide detailed panel information, such as PV panel type, defect probability, and anomaly status. A comparative study using several convolutional neural network architectures (VGG, ResNet, and AlexNet) and the ViT transformer was conducted. Therefore, the adopted ViT model performs excellently in anomaly detection, where the ViT achieves an AUC of 0.96. Finally, the proposed approach excels at the prompt identification of potential defects detection, reducing maintenance costs, advancing equipment lifetime, and optimizing PV system implementation.

During a crown generation procedure, dental technicians depend on commercial software to generate a margin line to define the design boundary for the crown. The margin line generation remains a non-reproducible, inconsistent, and challenging procedure. In this work, we propose to generate margin line points on prepared teeth meshes using adaptive point learning inspired by the AdaPointTr model. We extracted ground truth margin lines as point clouds from the prepared teeth and crown bottom meshes. The chamfer distance (CD) and infoCD loss functions were used for training a supervised deep learning model that outputs a margin line as a point cloud. To enhance the generation results, the deep learning model was trained based on three different resolutions of the target margin lines, which were used to back-propagate the losses. Five folds were trained and an ensemble model was constructed. The training and test sets contained 913 and 134 samples, respectively, covering all teeth positions. Intraoral scanning was used to collect all samples. Our post-processing involves removing outlier points based on local point density and principal component analysis (PCA) followed by a spline prediction. Comparing our final spline predictions with the ground truth margin line using CD, we achieved a median distance of 0.137 mm. The median Hausdorff distance was 0.242 mm. We also propose a novel confidence metric for uncertainty quantification of generated margin lines during deployment. The metric was defined based on the percentage of removed outliers during the post-processing stage. The proposed end-to-end framework helps dental professionals in generating and evaluating margin lines consistently. The findings underscore the potential of deep learning to revolutionize the detection and extraction of 3D landmarks, offering personalized and robust methods to meet the increasing demands for precision and efficiency in the medical field.

Usually, hyperspectral data captured from an airborne UAV or satellite contain some noise that can be severe in some channels. Often, channels that are badly affected by the noise are discarded. This is because the corrupted channels cannot be reclaimed by common filtering techniques, making important information in the affected channels different from those of field spectroscopy of similar wavelengths. In this study, a fast-denoising method is implemented on some channels of oil palm hyperspectral data that are badly affected by noise. The amount of noise is unknown, and it varies across the noisy channels from bad to severe. This is different from the data normally used by many studies, which are essentially clean data spiked with mild noise of known variance. The process starts by identifying which noisy channels to filter based on the level of the estimated noise in them. Then, filtering is conducted within each channel and across channels. Once the noise is removed, the improvement in signal-to-noise ratio (SNR) is calculated for each channel. The performance of Kalman, Wiener, Savitzky–Golay, wavelet, and cosine filters is tested in the same framework and the results are compared in terms of execution time, signal-to-noise ratio, and visual quality. The results show that the Kalman filter slightly outperformed the other filters. The proposed scheme was implemented using MATLAB R2023b running on an Intel i7 processor, and the average execution time was less than 1 second for each channel. To the best of our knowledge, this is the first attempt to filter real oil palm hyperspectral data containing speckle noise using a Kalman filter. This technique can be a useful tool to those working in the oil palm industry.

This study examines the potential for the smart integration of waste and renewable energy sources to supply industrial heat at temperatures between 150 °C and 250 °C, aiming to decarbonize heat demand in European industry. This work is part of a European project (SUSHEAT) which focuses on developing a novel technology that integrates several innovative components: a Stirling cycle high-temperature heat pump (HTHP), a bio-inspired phase change material (PCM) thermal energy storage (TES) system, and a control and integration twin (CIT) system based on smart decision-making algorithms. The objective is to develop highly efficient industrial heat upgrading systems for industrial applications using renewable energy sources and waste heat recovery. To achieve this, the specific heat requirements of different European industries were analyzed. The findings indicate that industrial sectors such as food and beverages, plastics, desalination, textiles, ceramics, pulp and paper, wood products, canned food, agricultural products, mining, and chemicals, typically require process heat at temperatures below 250 °C under conditions well within the range of the SUSHEAT system. Moreover, two case studies, namely the Pelagia and Mandrekas companies, were conducted to validate the effectiveness of the system. An analysis of the annual European heat demand by sector and temperature demonstrated that the theoretical potential heat demand that could be met by the SUSHEAT system is 134.92 TWh annually. Furthermore, an environmental impact assessment estimated an annual significant reduction of 19.40 million tonnes of CO2 emissions. These findings underscore the significant potential of the SUSHEAT system to contribute to the decarbonization of European industry by efficiently meeting heat demand and substantially reducing carbon emissions.

This paper addresses the practical challenge of detecting tomato plant diseases using a hybrid lightweight model that combines a Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN). Traditional image classification models demand substantial computational resources, limiting their practicality. This study aimed to develop a model that can be easily implemented on low-cost IoT devices while maintaining high accuracy with real-world images. The methodology leverages a CNN for extracting high-level image features and an RNN for capturing temporal relationships, thereby enhancing model performance. The proposed model incorporates a Closed-form Continuous-time Neural Network, a lightweight variant of liquid time-constant networks, and integrates Neural Circuit Policy to capture long-term dependencies in image patterns, reducing overfitting. Augmentation techniques such as random rotation and brightness adjustments were applied to the training data to improve generalization. The results demonstrate that the hybrid models outperform their single pre-trained CNN counterparts in both accuracy and computational cost, achieving a 97.15% accuracy on the test set with the proposed model, compared to around 94% for state-of-the-art pre-trained models. This study provides evidence of the effectiveness of hybrid CNN-RNN models in improving accuracy without increasing computational cost and highlights the potential of liquid neural networks in such applications.