The focal objective of optimizing drilling processes is to mitigate challenges tied to the operation. However, the triumph of mineral drilling relies on the availability of pertinent data to ensure effectiveness. For efficient and successful drilling, an optimization approach necessitates access to pertinent data, especially concerning the physicochemical properties of the rock and operational parameters of the machine. In this study, our focus is on optimizing specific energy, a critical metric for assessing mining drilling efficiency. This measure evaluates the energy used during drilling per unit volume of rock extracted. Considering the complexity of factors involved, treating the selection of the operational mode governing specific energy as a form of multi-criteria decision-making is justifiable. This method involves an in-depth analysis of the problem's underlying structure. Experimental measures were used to validate the proposed optimization approach. The paper delves into evaluating the differences in rankings derived from the TOPSIS and VIKOR methods. A ranking similarity coefficient is employed to compare the rankings against experimental values. Ultimately, the available choices are prioritized, and the most suitable operating mode for the drilling machine is determined. The study's comparative analysis using TOPSIS and VIKOR methodologies leads to the discovery of the best operational modes for drilling machines, highlighting the subtle differences in how well the two methods work. By using a ranking similarity coefficient, this study not only shows what each method's rankings mean in real life compared to experimental values, but it also gives a plan for improving the efficiency of drilling machines by carefully adjusting their parameters. Such insights contribute significantly to the field of drilling optimization, showcasing a methodical approach to energy conservation and operational efficiency.

Enhancing performance standards by judiciously fusing established methods with innovative strategies. This paper aims to combine the existing YOLOv5 algorithm, which is well-known for its object identification abilities, with new models, such as the Autoencoder-CNN (Convolutional Neural Network), Autoencoder-LSTM (Long Short-Term Memory), and Recurrent Neural Network (RNN) frameworks, in order to improve its performance. Through combining these disparate methods, the study seeks to use each of their unique advantages, ultimately resulting in a thorough comparison study that reveals their separate effects on precision and productivity. This methodical assessment, characterized by rigorous optimization and careful testing, not only improves traffic sign recognition systems' accuracy but also reveals useful connections between the suggested and known methods. The main goal of this endeavor is to unravel how these seemingly unrelated components, when brought together, can potentially usher in a new age of higher performance standards. This study aims to pave the way for the development of more sophisticated, flexible, and well-tuned traffic sign detection and identification systems by bridging the gap between the established and the cutting edge. The ramifications of this work encompass a wide range of real-world applications. Robust optimization and experimentation not only improve traffic sign recognition systems' accuracy but also reveal useful connections between the suggested and proven methods.

In the pursuit of optimizing automotive suspension systems, a semi-active suspension system (SASS) utilizing continuous skyhook control has been developed to enhance vehicle ride comfort and handling. This system is specifically engineered to mitigate vibrations stemming from high-frequency road excitations. Central to this advancement is the introduction of an electrohydraulic (EH) damper, which is uniquely characterized by solenoid valves capable of adjusting the orifice size to modify damping characteristics. By tuning the damping ratio, the system effectively minimizes the positional oscillations of the sprung mass in response to road irregularities. The dynamic behavior of this damper is comprehensively modeled through a boundary model approach, ensuring precise simulation and prediction of performance. A full-scale quarter-car test platform was constructed to evaluate the dynamic response and the efficacy of various control strategies implemented within the SASS. The performance assessments were conducted using MATLAB Simulink to simulate the behavior of the system under skyhook control algorithms, which aim to maintain the chassis’s vertical stability during disturbances. Comparative tests involving a single EH damper have demonstrated a high level of correlation with the simulated models, achieving a 95% agreement level. These findings underscore the capability of the SASS to surpass traditional hydraulic dampers in terms of performance, cost-efficiency, and versatility in testing applications. The insights garnered from this study not only validate the functionality of the proposed system but also contribute significantly to the body of knowledge in vehicle dynamics and control. This research provides a foundational framework for future exploration and potential implementation of advanced damping systems in the automotive industry.

Batu City, a premier tourist destination in Indonesia, has experienced a significant influx of tourists, leading to an upsurge in vehicular traffic. This increase in vehicular load has precipitated premature deterioration of the city's road pavements. A systematic approach to addressing this degradation is imperative for the refinement of road planning strategies, tailored to the pavement's lifespan, and for the development of a holistic road construction policy that aligns with the actual traffic load. This study employs a systematic literature review (SLR) to investigate the effects of vehicle overloading on the structural longevity of road pavements in Batu City. A keyword-driven search was conducted, resulting in the selection of 50 pertinent articles which were scrutinized to determine the extent of the impact that overloaded vehicles have on road infrastructure within tourist-heavy urban centers and to identify effective management solutions. The findings from the SLR indicate that excessive vehicle axle loads, or the presence of cities with high vehicular traffic, considerably expedite pavement damage and diminish the structural lifespan, as supported by evidence from 48% of the analyzed journals. These insights have practical implications for the assessment of road geometric designs, the examination of construction techniques and materials, and the formulation of models or policies that are congruent with the functional requirements of the city.

The global shift towards renewable energy sources is driven by the desire for a sustainable energy future. Integrating intermittent renewable sources and maintaining grid stability are obstacles that must be overcome to achieve this goal, which is why grid stability and energy storage systems are being investigated in this study using Energy PLAN simulation. This study conducts a thorough analysis of energy storage solutions necessary to support Japan’s energy landscape shift to renewable electricity. It offers a comprehensive analysis considering technological, environmental, and policy aspects to evaluate the applicability, difficulties, and potential of renewable electricity. Technical factors emphasize how critical it is to maintain grid balance and consider scalability and technology compatibility with Japan’s distinct energy infrastructure. Economic analyses examine revenue streams, levelized storage prices, investment needs, and cost-benefit evaluations to shed light on the viability and appeal of technologies for storing energy from an economic standpoint. The goal of synthesizing these several characteristics is to provide policymakers, and energy stakeholders participating in Japan’s ambitious journey towards renewable electricity with strategic insights, practical recommendations, and a roadmap. This study aims to steer Japan’s energy landscape towards resilience, sustainability, and diversification by establishing links between imperatives, economic feasibility, and technical breakthroughs.

This paper is the result of the electric vehicle (EV) powertrain and fuel powered vehicle analysis conducted by using the Ipoh driving cycle (IDC) in Simulink. This thorough analysis is on studying the effectiveness of fuel-powered vehicle and EV on the IDC which involves a several main components of an EV which includes motor and controller subsystem, battery system, driver system and the parameter calculations. The model also involves a dashboard in which all parameters are viewed in it. Several parameters were chosen for this analysis, which is time, distance travelled, average speed, average running speed, average acceleration, average deceleration, acceleration percentage, deceleration percentage, idling percentage, cruising percentage, kWh and fuel costing, battery voltage, current, state-of-charge (SOC) and power. This paper involves two major methods which are EV modelling and EV analysis. Several parameters are considered during the modelling process; aerodynamic drag force, rolling resistance force, gravitational force and cumulative tractive force which impacts the efficiency of the EV. EV is proven to be more efficient in which the cost of travelling with an EV on IDC is 60% lower compared to a fuel powered vehicle.

The forest fire phenomenon is severe and requires appropriate handling. This analysis aims to understand legal developments, strict liability, and compensation in the context of civil environmental law enforcement. This research employs a normative juridical legal approach. Data were obtained from the texts of court decisions, including the Pangkalan Bun District Court Decision, Palangkaraya High Court Decision, Supreme Court Cassation Decision, and Supreme Court Judicial Review Decision. Data are analyzed through selecting relevant decisions, analyzing legal texts, and comparing these decisions. The research results illustrate the progression of the PT. Kumai Sentosa case through a series of judicial decisions. The Pangkalan Bun District Court’s decision initially determined strict liability for PT. Kumai Sentosa. However, the Palangkaraya High Court decision annulled the decision because PT. Kumai Sentosa was not responsible for the land fire. The Supreme Court Cassation Decision confirmed this decision. However, in the Judicial Review Decision of the Supreme Court, this case was re-examined and again determined the absolute responsibility of PT. Kumai Sentosa. This Judicial Review Decision confirms that PT. Kumai Sentosa is responsible for the land fire and must pay material compensation. Affirming strict liability in the Supreme Court’s Judicial Review Decision has significant implications for enforcing civil environmental law in Indonesia. This case shows the importance of accountability in cases of environmental damage and compensation as an incentive for companies to be more careful in protecting the environment.

This study presents a new radiative cooling method to mitigate the radiation effect on a photovoltaic panel using a glass cover consisting of two glass panels. The gap between the perfectly sealed glass layers is filled with Argon gas as a heat suppressant. The assessment of the new proposed idea is performed experimentally and numerically. An experimental setup has been designed and fabricated to measure the required parameters for the system performance evaluation. The research parameters included design parameters such as the gap height between the glass layers at 10, 15, 20 and 25 mm. The tests were performed on various solar irradiances over the day. The output voltage, current, and temperatures at various locations were also recorded to permit performance evaluation. The investigations were extended by computational simulation to visualize the thermal situation at various design parameters. The results showed that there was a decrease in the panel surface temperature of the photovoltaic panels after adding the glass cover. By installation of the double glassing cover with a 20 mm gap, the surface temperature was reduced by between 5℃–9℃. Such temperature reduction demonstrates the success of the novel idea of an Argon-filled double-glassing cover. The maximum efficiency was increased to 14.2% for a panel with the added radiative cooler compared to 12.1% for a regular panel without cover under the same operating conditions.

A geoelectrical imaging survey, employing resistivity and induced polarization (IP) methodologies, was executed on Dala Hill, Kano, Nigeria, positioned between latitudes 12.008611°N and 12.009722°N, and longitudes 8.505833°E and 8.507222°E. The objective was to assess and compare findings with prior ground magnetic studies to delineate subsurface geological structures. The survey utilized an ABEM Terrameter SAS 1000 for data acquisition along three distinct profiles encompassing the hill and adjacent areas, with electrode separations fixed at 10 meters. Data processing was conducted using RES2DINV software, revealing resistivity profiles that identified three stratified layers with resistivity values ranging from 300Ωm to 6798Ωm for the first layer, 128Ωm to 744Ωm for the second, and 4Ωm to 127Ωm for the third. IP profiles identified zones of varying chargeability, from -3.44 msec to 19.6 msec. Analysis indicated a consistent positive correlation between zones of high resistivity and low chargeability. For instance, a zone along Profile 1 demonstrated high resistivity values (2142Ωm - 6798Ωm) between 60m and 190m, coinciding with a low chargeability zone (0.506 msec to 2.43 msec) observed from 20m to 100m along the profile, equating to depths of 10m to 39.6m. Similar correlations were observed in the subsequent profiles, with significant intersections between high resistivity and low chargeability zones. These areas were interpreted as being rich in iron ore minerals, predominantly magnetite, based on the comparative analysis with standard values of rocks and minerals. The presence of magnetite, known for its high iron content and magnetic properties, underscores the area's potential for steel production. Moreover, the identification of a dyke within the study area corroborates findings from earlier magnetic studies, further validating the geophysical methodology's effectiveness in revealing the shallow subsurface structural settings. This alignment not only substantiates the layered configurations deduced from magnetic studies but also highlights the geoelectrical survey's capability in providing a comprehensive understanding of subsurface geology.

The productivity of sandy tropical soils may be enhanced through the application of composted organic materials. This study investigates the effects of composted mixtures of brewery spent grain (BSG) and animal manures on the growth of green amaranth (Amaranthus caudatus L) under field conditions. Composted treatments included BSG mixed with poultry droppings (PD) or cattle dung (CD) in volumetric ratios of 1:1 and 2:1 prior to composting, resulting in four compost variants: BSG+PD_(1:1), BSG+PD_(2:1), BSG+CD_(1:1), and BSG+CD_(2:1). Additionally, composted BSG alone served as a reference. Each amendment was added at 20 t ha^–1, alongside an un-amended control for comparative purposes. Growth and yield assessments conducted four weeks post-sowing revealed that not all amended treatments outperformed the control. Notably, the BSG+PD_(1:1) treatment consistently increased total biomass (fresh and dry matter yields by approximately 143% and 58%, respectively) as immediate effects, and significantly more (184% and 108%, respectively) as residual effects when compared to the control. Leaf yields under this treatment showed increases of 173-177% (immediate effects) and 122-125% (residual effects). These variations in amaranth growth and yield were primarily attributed to improvements in soil exchangeable calcium (Ca) and total nitrogen (N) content due to the compost application. The findings suggest that a composted equal-volume mix of BSG and PD may serve as an effective organic amendment for enhancing the yield of short-duration leafy vegetables like amaranth in coarse-textured soils of the humid tropics.

The aim of the attempt is to build a mechanism for objective evaluation of the autonomous nervous system (ANS) for disease diagnosis at an early stage. With the experience of data collection from various control subjects, BARC has identified eight different pulse morphologies. A Peripheral Pulse Analyser (PPA) measures peripheral blood flow. Blood flow was measured in control subjects (100) and patients (100). The morphology of a person's pulse changes throughout time. Pulse morphologies vary according to age, disease, and other parameters. More than 8500 signals from 200 humans were tested. Various pattern-matching and classification techniques are given in this research to detect the existence of specific pulse shapes in obtained PPA signals. Peaks of PPA blood flow patterns are detected, and features are extracted from the sample pattern. Various machine learning (ML) algorithms are used to identify various pulse shapes depending on the parameters of extracted features. We observed that in one PPA signal of the duration of 300 seconds, 3 to 4 defined pulse morphologies out of 8 are available. Every pulse morphology is different from the others. After training, the system was able to detect pulse shapes to assess the ANS of the subject with more than 94% to 97% accuracy. The proposed system will assist the doctor in making a decision quickly based on a few processed parameters rather than assessing several individual parameters at a crucial time. The output of the system is the assessment report of ANS. This is an attempt to replace traditional Ayurvedic pulse examination methos for disease detection.

When wireless nodes communicate without the use of infrastructure, the network is subject to security breaches. Mobile Adhoc Network (MANET) is one of the most vulnerable wireless networks in terms of security breaches. The most common types of security breaches are intruders and attackers, whose tasks are to reduce the internal performance of the network. Many research studies are focused on detecting and preventing these two security threads. This article focuses on intruder and black hole attackers and their communication. Several techniques were proposed for thwart the intruders and attackers in the Mobile Adhoc Network communication by using the modern technologies which are an additional load to the nodes operation and these techniques could not be able to predict the attacker before it was done. To achieve this goal, this article proposed the Watch Dog approach involves routing protocol to monitoring the forwarding time of all nodes in the transmission. Delays in forwarded time nodes could indicate an intruder, while discarding the forwarded node could indicate a black hole attacker. The proposed Watch Dog routing algorithm with classification technique was implemented with a network simulator with Adhoc On Demand Vector protocol named as WD-AODV, and the simulation results were compared to a modern techniques of Fuzzy Logic based AODV (FL-AODV), Machine Learning-based AODV (ML-AODV) and Artificial Intelligence based AODV (AI-AODV) routing protocol. The compared results of attack rates, attack detection time, Packet delivery ratio and End to End delay showed that the Watch Dog-based attacker and intruder detection methods perform better by more than 59%, with excellent performance factors of 69%.

The study was initiated focusing on the rising concerns of the fisher community and environmental activists that construction of 600×2 MW Matarbari Ultra Super Critical Coal-fired power project and construction of 6.5km access road over the foreshore is filling the riverbed and impacting to the aquatic ecosystem of the Kohelia River. The study was carried out to identify the impact issues, to assess the project’s wastewater discharge quality, surface water quality, primary productivity status, and fishery status during dry season. From the primary drain, labor camp wastewater samples were collected, and overflow dredging water samples were taken from the immediate discharge point. Three sites in the Kohelia River had their surface water quality examined between January and March of 2022. Samples were collected by using statistical sampling techniques, stored, and transported to a facility for analysis. By evaluating numerous physicochemical and biological indicators, this monitoring attempts to shed light on the environmental circumstances surrounding the river. Standard procedures were adopted to conduct the Fish Catch Assessment survey to find out the status of fish diversity and catch composition of dry season. Study found that concentrations of COD and Fecal Coliform in labor camp wastewater were significantly higher than national standards. On the other hand, discharged overflow dredging water contained high load of turbidity and TSS that caused significant increase in turbidity and TSS levels of surface water. Compared to the baseline data (EIA 2013), noticeable decline in water level and increase in salinity level of surface water also were observed. Due to wastewater discharge, no variations were observed for other surface water quality parameters i.e. pH, temperature, DO, BOD, COD, Nitrate, Oil and grease, and Fecal Coliform. The study on biological characteristics of the Kohelia River found both abundance and species diversity of phytoplankton, zooplankton and benthos community recorded higher than baseline study (EIA 2013) period.

Applying solar radiation as a renewable energy source using photovoltaic panels has problems, such as work efficiency decreasing when the photovoltaic cell temperature is above the working temperature, thus requiring a cooling method. This research examines the cooling effect of photovoltaic panels using water spray with various types and diameters to reduce the temperature and performance of photovoltaic panels, which was carried out experimentally with solar radiation at 08:00-15:00 local time. The research results show that the water spray cooling system can reduce the temperature of the photovoltaic panel from 61.96 to 36.51℃ and increase efficiency from 10.98 to 14.47% with variations in the full cone nozzle with a hole diameter of 2 mm. Full cone nozzles can provide the best cooling performance compared to hollow cone nozzles and flat fan nozzles due to the more even distribution of water spray on the surface of the photovoltaic panel. Using different nozzle diameters also influences cooling. Based on the research results, the water spray cooling system effectively increases the work efficiency of photovoltaic panels with a 2 mm total cone nozzle variation, producing the highest efficiency.

A problem of heat transfer by conduction, convection, and radiation has been studied for both steady and unsteady states. A numerical technique based on the finite difference method was adopted to solve the mathematical boundary value problem, which was created under some conditions with different values of physical parameters. The solution started with an unsteady state, reaching a steady state after many iterations. The effect of various parameters has been discussed for different temperatures of the parallel walls, and the governing equations have been established, which appear to be of the parabolic type. They were treated numerically using the Alternating Direction Implicit Method, which is considered good in stability with acceptable accuracy. Both cases for the steady and unsteady state, which usually arise in the discussion of fluid flow or heat transfer problems, are treated in this paper as one case dissimilar to the previous works, and this is the main goal of the present article.

Unmanned Aerial Vehicles (UAVs), have recently sparked attention due to its versatility in a wide range of real-life uses. They require to be controlled so as to conduct different operations and widen their typical roles. This study proposes an optimal robust deadbeat controller for the roll angle motion of tail-sitter vertically take-off and land vehicles, taking into consideration the systems’ intrinsic sensitivity to outside influences and fluctuation of their dynamics. Primarily, several assumptions are used to develop an appropriate transfer function that reflects the system physical attributes. The suggested controller is then formed in two sections: the first section addresses the nominal system’s unstable dynamics, and the second element imposes the desired deadbeat performance and robustness. The control system variables are optimized using the creative and efficient Incomprehensible but Time-Intelligible Logics optimization technique, ensuring that the specified robustness demand is satisfied correctly. Finally, simulation is used to evaluate the developed controller effectiveness, revealing beneficial stability and performance indicators for both nominal and uncertain regulated system featuring uniform, bounded, and feasible closed-loop outputs. The control unit performs well, with a rising time of 0.0965 seconds, a settling time of 0.1134 seconds, and an overshoot of 0.167%.

Limitations inherent in conventional rule generation methodologies, particularly concerning knowledge redundancy and efficiency in product design, are addressed through the adoption of a rough set-based approach in this study. An enhancement to the Ant Colony Optimization (ACO) algorithm's information gain ratio is introduced by integrating a redundancy detection mechanism, which notably accelerates the convergence process. Furthermore, the application of a classification consistency algorithm effectively minimizes the number of attributes, facilitating the extraction of potential associative rules. Comparative validation performed on a public dataset demonstrates that the proposed attribute reduction approach surpasses existing methods in terms of attribute count reduction, reduction rate, and execution time. When applied to an automotive design case study, the approach yields rules with 100% coverage and accuracy, characterized by a reduced average number of attributes per rule. These findings underscore the superiority of the rough set-based methodology in generating product design rules, providing a robust framework that enhances both the precision and applicability of the design process.

In the automated production line for suspended insulators, precise alignment of the U-shaped notch in iron caps is crucial for effective gluing. This study introduces a system based on machine vision that automates the alignment process. The system initially preprocesses the images of iron caps to segment the U-shaped contour. It utilizes the method of quadratic maximum contour connectivity domain to accurately identify the target U-shaped region. The alignment process involves calculating the coordinates of the largest external rectangle's longest edge and the external circle's center point. These coordinates are instrumental in determining the necessary rotation angle for proper notch alignment. The fixture then adjusts the iron cap based on this calculated angle, ensuring precise alignment. Experimental validations of this system have demonstrated a notch alignment error within 0.5 degrees with 96.51% accuracy and an error within 1 degree with 100% accuracy. The algorithm's execution time is a swift 0.034 seconds. Both the error margins and operational speed satisfy the stringent requirements of the automatic production line.