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Online learning has become increasingly prevalent in the era of digitalization, impacted by both internet penetration and the COVID-19 pandemic. Classroom interaction, as a key factor in evaluating students' learning experience in online settings, has been identified to be associated with their academic achievement. While previous research has underscored the significance of classroom interaction in language learning settings, there is relatively limited research on its relationships with student satisfaction and learning. This research intends to explore classroom interaction and its mediating role in the correlation between student satisfaction and perceived online learning in online contexts. Given this, the paper reports a quantitative-method study that examined how Chinese EFL tertiary students perceived three types of classroom interaction (i.e., learner-learner, learner-instructor, and learner-content), their satisfaction, and its influences on their perceived online learning. Data was collected from 319 Chinese university students through convenience sampling and a self-designed questionnaire, and then analyzed with the help of SPSS. The results showed that: 1) the students had moderately positive perceptions of classroom interaction and online learning, but were neutral about their satisfaction; 2) positive correlations were observed between classroom interaction, student satisfaction, and online learning; and 3) student satisfaction predicted perceived online learning, with classroom interaction partially mediating this relationship. The findings of this research have implications for strengthening the effectiveness of online language teaching in the post-COVID-19 era.

Precision irrigation systems have become indispensable tools in modern agriculture due to their ability to address water management challenges and optimize bamboo productivity. This paper presents a novel innovation called AquaBamboo, a next-generation precision irrigation system designed to revolutionize sustainable bamboo water management. The paper outlines the shortcomings of traditional irrigation methods, highlighting their inefficient water management for bamboo, lack of real-time data integration, and limited precision. This suggested approach overcomes these challenges by integrating IT sensors, advanced analytics, and predictive modelling to deliver water precisely tailored to each bamboo species' requirements. The system's dynamic adaptability to changing environmental factors ensures optimal water delivery, enhancing bamboo health and yield while minimizing water waste. AquaBamboo's holistic approach not only improves resource efficiency but also contributes to sustainable agriculture practices, aligning with the principles of responsible water management. The significance of this approach lies in its potential to transform conventional irrigation practices, optimize water utilization, promote bamboo species' health, and advance the sustainability of all bamboo agricultural operations. This paper offers a comprehensive understanding of AquaBamboo's features, advantages, and innovative contributions to precision bamboo agriculture management.

The World Health Organization has reported that over one million individuals drink contaminated water, resulting in over 30,000 deaths daily. Every year, over 7×107 tons of synthetic dyes are produced globally, with the textile industry utilizing over 104 tons of such materials. To protect freshwater from pollutants, wastewater treatment methods such as permeable reactive barrier disinfection use activated carbon. Bamboo charcoal is an emerging substance with great potential in this area. Bamboos represent a renewable biological resource for long-term development. This paper reviews the dye removal processes that can be utilized to avoid water pollution, using bamboo charcoal as a natural adsorbent. A literature search using used various keywords from several bibliographic databases yielded seven studies that supported the adsorptive properties of bamboo charcoal. These investigations used FTIR, EDS, SEM and XRD to demonstrate the porous nature of bamboo charcoal, activated bamboo charcoal and/or modified bamboo charcoal, as well as showing the effects of pH and temperature on dye removal. The best-fitting kinetic model and isotherm data were identified. Bamboo charcoal was an effective adsorbent, while modified or activated bamboo charcoal rapidly increased absorptive capacity, indicating the future potential of bamboo charcoal in the cleaning up of water pollutants and contributing to a clean environment.

Bamboo, a member of the grass family, is a fast growing and high yielding renewable resource. The popularity of bamboo has risen in recent times owing to its multifaceted and myriad of practical applications. There are unlimited uses of bamboo, therefore proper utilization of this beneficial resource will be useful to the industrial sector. This review provides a comprehensive summary regarding the potential of bamboo as a vital non-wood fibrous raw material for pulp, paper and other allied industries owing to its ability to conserve forests and foster sustainable economic development. Bamboo's abundance, renewability, mechanical strength, and other functional features make it an appealing and potential building biomaterial in the pursuit of sustainable raw materials needed for industrial development. This review provides an in-depth summary and unique perspective on the application of bamboo at a commercial scale in various sectors, thereby encouraging the utilization of this potential material towards environmental sustainability and economic growth.

Bamboo is gaining more and more attention in the field of building materials thanks to its unique characteristics, such as resistance, lightness, sustainability, and flexibility. However, the widespread adoption of bamboo as a building material presents some challenges, especially in relation to the durability of the material. In fact, being a natural material, it requires protection from atmospheric agents, such as rain and sun, as well as from attacks by insects and animals. Ensuring the longevity of bamboo involves implementing tailored treatments, precise precautions, and regular structural inspections to detect any potential degradation over time. In this context, the article focused on the monitoring of a structure made of bamboo and on the testing of an innovative system to identify the structural critical issues present, in the context of a FISR project which envisaged a cultural exchange between China and Italy. Starting from the survey of the structure carried out with a drone, a 3D model was then built using a commercial software. For the monitoring phase of the structure, an innovative system was used that exploits the YOLO v5s6 algorithm to identify structural critical issues, supported by a Virtual/Augmented/Mixed reality app developed by the authors. This app is useful not only during the monitoring phase, but also in the subsequent phase of disseminating information relating to the potential of the use of bamboo in the field of structural engineering. The tested and proposed methodologies have proven to be particularly useful and high-performance, especially regarding the monitoring of bamboo structures, in relation to the resolution of problems linked to their possible fragility and limitations in certain application areas. The contribution provided in this paper by Geomatics methodologies is evident to better highlight the potential of this eco-friendly material for its desirable ever-increasing use in the construction sector by becoming aware of its potential.

Computerized Numeric Control (CNC) plays an essential role in highly autonomous manufacturing systems for interlinked process chains for machine tools. NC-programs are mostly written in standardized G-code. Evaluating CNC-controlled manufacturing processes before their real application is advantageous due to resource efficiency. One dimension is the estimation of the energy demand of a part manufactured by an NC-program, e.g. to discover optimization potentials. In this context, this paper presents a Machine Learning (ML) approach to assess G-code for CNC-milling processes from the perspective of the energy demand of basic G-commands. We propose Latin Hypercube Sampling as an efficient method of Design of Experiments to train the ML model with minimum experimental effort to avoid costly setup and implementation time of the model training and deployment.

Material extrusion (MEX) of metallic components is an indirect additive manufacturing (AM) process that is recently gaining a lot of attention in the industry. This multi-step process with debinding and sintering, provides an inexpensive safe alternative, that is effective, flexible and office-friendly for several corporations compared to other metal AM techniques. However, optimizing the manufacturing parameters of the MEX process is still challenging due to the lack of research on their impact on the mechanical and surface properties of the fabricated materials.For this purpose, this paper investigates how various processing parameters impact the mechanical properties and surface roughness of 17-4 PH stainless steel parts produced by MEX. The parameters analyzed include layer thickness, build orientation, number of contours, and aging thermal treatment for 1 h at 482°C (H900). A Taguchi design of experiments (DoE) was employed to conduct the parametric analysis and the results were post-evaluated via the analysis of variance (ANOVA). The experimental results show that H900 treatment increases the micro-hardness by ∼50 HV0.3 and contributes in augmenting the ultimate tensile strength (UTS) by ∼200 MPa. The build orientation and its interaction with the layer thickness have the highest impact on the surface roughness. Moreover, the amount of enclosed porosity is higher in the samples with lower layer thickness. The absorbed impact energy (Wabs) is relatively low due to the enclosed porosity content and is not linked to the analyzed processing parameters. The best mechanical properties were obtained for parts built with solid infills, 0° build orientation, 0.125 mm layer thickness, two contours, and H900 as a post-treatment.

The manufacturing of three-dimensional sheet metal components, such as car body parts, heavily relies on deep drawing. With the increasing demand for lightweight measures in the automotive industry due to CO2 limitation requirements, various methods are currently employed to reduce the weight of deep drawn components. However, these methods often overlook the potential for influencing the stress-strain dependent damage state within the component, even though it can greatly affect its performance in subsequent applications, and thus offers lightweight potentials. Friction between the deep drawing tools and the sheet metal is a key factor influencing the stress-strain state, and hence, represents a lever that can be utilized to manipulate the damage state in the component. This paper focuses on the numerical analysis of the dependence of damage on friction during deep drawing. Therefore, a rectangular geometry with an asymmetrical material flow is numerically investigated. A friction ratio is introduced with different constant friction coefficients for the corner of the tools and the straight sides. The established load paths at a chosen reference point are then considered in the form of selected stress and strain characteristics and the numerically predicted damage state is compared. The results are used to derive a recommended friction ratio that leads to less damage in the corner of the geometry. Afterwards strip drawing tool surfaces are modified to manipulate their friction properties using machine hammer peening. Subsequently, the influence of the structures of the strip drawing tool surfaces is quantified using strip drawing tests. The identified contact normal stress dependent friction coefficients are then implemented in the numerical simulation and the established numerical predicted damage state is examined to gain a more comprehensive understanding of how the friction ratio and the structure of the tool surfaces influence the damage state.

The paper proposes a method and technique that allow, using a minimum sample (3 units) from a batch (30 units) of real gears, to obtain stochastic information about the actual distribution of the gear grinding allowance both on the left and right flanks of the gear gaps for all gears in the batch. The proposed gear grinding allowance stochastic model is based on the representation of the distribution of the gear grinding allowance along the gear periphery as a superposition of a sinusoidal component with a random amplitude and a random component of the white noise type. The conditions for the equivalence of the stochastic characteristics of real (measured) and virtual (not actually measured) gears are formulated, namely the ratio between the amplitude of the sinusoidal component and the average amplitude of the high-frequency harmonic components of the gear grinding allowance distribution along the gear periphery. The gear grinding allowance stochastic model adjusted according to the proposed method makes it possible to predict the distribution of the gear grinding allowance on the left and right flanks of the gear gaps for a batch of gears and, taking into account the information obtained (replacing the experimental data), both evaluate the existing technology and optimize the gear grinding allowance value. As a result, it becomes possible to assess the quality of the technological process of manufacturing the gear, taking into account the effect random deformations to gear grinding allowance at the stage of their heat treatment (hardening), which is performed before the gear grinding operation. In turn, optimization of the grinding allowance (based on its actual distribution along the periphery of the gear) makes it possible to reduce both defects in burns (with increased allowance) and defects in unground teeth (with insufficient allowance).

This paper introduces a novel sheet test specimen that plastically deforms under biaxial stretching when subjected to uniaxial loading in conventional testing machines. The specimen was designed using finite element simulation and consists of a two-dimensional reticular structure with interconnected arms in the form of triangles that converge at the center and are connected to full-width sheets at the specimen's ends. Accumulation of damage at the center is ensured through spherical cups that are milled at opposite sheet surfaces. Experimental strain loading paths obtained by digital image correlation confirm the capability of the novel sheet test specimen to provide biaxial stretching strain paths. This is achieved under friction-independent conditions and without requiring complicated multiaxial setups and machines, provided appropriate values of the inner and outer angles of the interconnected arms are used.

The Internet of Things (IoT) has experienced significant growth in recent years and has emerged as a very dynamic sector in the worldwide market. Being an open-source platform with a substantial user base, Android has not only been a driving force in the swift advancement of the IoT but has also garnered attention from malicious actors, leading to malware attacks. Given the rapid proliferation of Android malware in recent times, there is an urgent requirement to introduce practical techniques for the detection of such malware. While current machine learning-based Android malware detection approaches have shown promising results, the majority of these methods demand extensive time and effort from malware analysts to construct dynamic or static features. Consequently, the practical application of these methods becomes challenging. Therefore, this paper presents an Android malware detection system characterized by its lightweight design and reliance on explainable machine-learning techniques. The system uses features extracted from mobile applications (apps) to distinguish between malicious and benign apps. Through extensive testing, it has exhibited exceptional accuracy and an F1-score surpassing 0.99 while utilizing minimal device resources and presenting negligible false positive and false negative rates. Furthermore, the classifier model's transparency and comprehensibility are significantly augmented through the application of Shapley's additive explanation scores, enhancing the overall interpretability of the system.

Some sensitive industries with precise specifications in their manufacturing process, such as aircraft engine parts, impose 100% inspection processes in their inventory-manufacturing system. In some cases, a lot of small dimensions are measured and compared with strict acceptable tolerances during these inspection processes, causing manufacturing process times (machine time speed) to be less than inspection times. This study is relevant to manufacturing enterprises that implement comprehensive 100% inspection protocols within their production procedures, encompassing sectors such as pharmaceuticals, aerospace, electronics, medical devices, and textiles. In this paper, such a phenomenon is investigated, as an inventory system with a strict inspection model is deployed under three conditions: a) an inventory-manufacturing system with scrapped items without shortage, b) an inventory-manufacturing system with reworked items without shortage, and c) an inventory-manufacturing system with scrapped and reworkable items with backorder. The aim is to minimize total cost by considering strict inspection during production process. These proposed models are suitable for managing systems where quality control is a bottleneck due to its speed such as high-precision manufacturing systems. The optimal solutions for each model are obtained as a closed-form expression. In models 1 and 2, where the shortage is not allowed, the closed-form expression is focused on determining the economic production quantity. However, in model 3, there are two expressions of significance. First, it addresses the economic production quantity, like models 1 and 2. Additionally, it provides an optimal value for the shortage in each cycle, as it considers a scenario where shortages are permitted within the inventory-manufacturing system. This study has included a numerical example to illustrate the practical applicability of each model. Based on the optimal solutions obtained, it is evident that Model 3 stands out as the superior choice. This outcome aligns with expectations, as Model 3 encompasses and comprehensively addresses the scenarios presented in the two other models. Finally, a sensitivity analysis is performed to investigate the effects of key parameters on the optimal solutions of each model, and the results are summarized to present a comparison between them.

Pattern-based building envelope is a modern style of contemporary facades. Architects may rely on imitating known practices without knowing the processes involved in their design. Therefore, this paper seeks to identify the algorithmic steps implemented in the derivation of contemporary façade. The design algorithms of pattern-based building envelopes are classified into generative algorithms of the basic pattern and elements, and transformation algorithms. An experimentation method was applied using Grasshopper for Rhino to simulate the formation of nine contemporary pattern-based envelopes. The finding reveals that several approaches are implemented such as elements are extruded from the basic pattern or separated from it. The envelope pattern may result from repeting the elements, or the envelope pattern may be devoid of elements. Modification techniques can manipulate the basic pattern, the elements, or both patterns and elements. They act on the patterns and elements simultaneously in the same way, or separately using different ways.

Effectively managing healthcare data is crucial for accurate diagnosis and personalized patient care. As the utilization of healthcare data grows for personalized care, concerns about reliability, privacy, and security have emerged. To address these issues, this research explores the fusion of analytical techniques with interactive visual representations, known as visual analytics, as a promising solution. The focus is on evaluating the trustworthiness of healthcare data in Kingdom of Saudi Arabia, particularly the capability of visual analytics tools in facilitating accurate and secure healthcare data analysis. This study tackles challenges such as the absence of specific evaluation criteria, the need to process vast healthcare datasets, the establishment of trust, and the necessity for automation. In response, a hybrid medical decision support system is introduced, leveraging hesitant fuzzy decision systems. The primary objective is to evaluate trustworthiness of visual analytics tools for disease diagnosis within healthcare data. Within the framework of hesitant fuzzy logic, the paper employs a medical multi-criteria decision-making system that integrates the analytic network process and the technique for order of preference by similarity to an ideal solution. Rigorous validation ensures the accuracy and reliability of the findings. The research not only provides valuable insights but also conducts comparative analyses of the proposed models against existing ones, demonstrating the practicality of optimal decision-making in Saudi Arabia environment of healthcare scenarios. Several popular alternatives of healthcare based tools have been used in this study such as Tableau, JupyteR, Zoho Reports, QlikView, Visual.ly, DOMO BI, SAS Visual Analytics. From the results achieved DOMO BI visual analytics tool is found to be most secure and robust tool for healthcare professionals. This effort aims to enhance patient care and outcomes, ultimately contributing to the improvement of the overall healthcare landscape in Saudi Arabia.

This paper discusses one method of prevention of capillary water rising through the walls, i.e., making horizontal damp-proof courses by chemical injection. The aim of the tests was to find out which materials and groups of injection methods give the opportunity to create the most effective secondary horizontal damp proof course of underground parts of walls. The tests were carried out on large samples, i.e., walls built under ideal laboratory conditions of water capillary rise, i.e., with the repetitive properties of the materials used to build the test’s walls, without its additional salinity. The nine randomly selected injection products were tested as two product groups: silanes and silicates. Tested silanes and/or siloxanes products were in the form of cream or paste, silicates were in the form of emulsions or dispersions. They were injected into walls with both methods, i.e. gravity and low pressure. Visible effects of the action of the damp-proof courses usually occur after 90 days after injection of products into the wall. A faster increase in efficiency was found for single-row injection than for double-row injection but with higher values after the end of the study for double injection. According to the density of injection products, the highest efficiency was found for products with a density range of 0.92 kg/dm3 up to 0.96 kg/dm3, for silanes products and with a density less than 1.2 kg/dm3 for silicate products.

Discrete orthogonal polynomials (DOPs) have gained significant research attention owing to their crucial role in digital signal processing applications such as computer vision, pattern recognition, and compression. However, the computation of DOP coefficients often incurs a substantial computational burden, exacerbating for higher-degree moments along with the resulting numerical errors. To address this challenge, this paper exploits the inherent parallelism in Charlier polynomial coefficient calculations to achieve enhanced polynomial performance. Independent calculations are distributed among threads, making efficient use of the available processing resources. Two algorithms are presented, the first algorithm evenly distributes the rows in a sequential manner (straightforward). Additionally, to achieve a more equitable distribution of coefficient calculations, this paper proposes alternative distribution approaches, aimed at balancing processing load among threads. Through extensive comparative experiments, we have confirmed that the proposed approaches achieved high performance across different degrees (1540 to 7370) and at different numbers of threads (2 to 256). The results show processing time in the multithreaded case is improved by up to 9.1 times with respect to the unthreaded case. Furthermore, by increasing the number of threads from 2 to 256, the trend indicates that the most significant improvement occurs in the range of 32 to 128 threads, confirming the robustness of the proposed algorithm. These findings signify the importance of this paper.

During passive solar design of greenhouses, engineers usually encounter issues such as building form parameter selection. Suitable parameters can help to reduce energy losses related to interior temperature control and relatively intensive crop production. However, by using bibliometric analyses, no existing review works provide concise parameter selection lists. To fill in this gap, this paper compares and evaluates various passive technologies for greenhouse design in five areas: (1) orientation, (2) building structures, (3) envelope materials, (4) heat storage options, and (5) numerical modeling. First, the orientation of a passive solar greenhouse significantly influences its performance. Second, greenhouses exhibit various architectural shapes, including single- and multispan, with transparent and opaque envelopes. Third, greenhouses usually include envelopes constructed from transparent materials, opaque materials, and movable insulation materials. Fourth, most passive greenhouses provide daily energy storage systems equipped with storage media, including water, soil, rock, brick, and phase change material (PCM). Finally, this paper reviews numerical modeling and performance evaluations for passive greenhouses.

In Dubai, 38.9 % of the total energy consumption is related to buildings, and the high-rise building sector is key to energy efficiency. BIPV (Building Integrated Photovoltaic) can be a very efficient alternative in Dubai because of building load reduction and power generation. This paper aims to investigate energy efficiency according to the number of floors with BIPV application. As a methodology, an analysis model for office use was used with the curtain wall with a floor height of 3.6 m, floor area of 400 m2, and a window area ratio to wall area ratio of 80 %. Energy Plus Version 9.0 and TRNSYS were used as evaluation tools. The physical properties of windows and doors in the analysis model were assumed to be low-E double-glazed glass with a Solar Radiation Gain Coefficient (SHGC) of 0.564, a visible light transmittance of 47.2 %, and a thermal transmittance of 1.760 W/m2K. The result showed that when the alternatives of the window replacement type, exterior wall finishing type, and hybrid type BIPV system are applied, heating and cooling energy consumption is reduced by 13.2 % to 32.8 %. It was proven that applying BIPV was effective in high-rise office buildings. It is practical to replace windows and apply light-transmitting amorphous thin film Photovoltaic (PV) double-layered, double-layered Low-E, or triple Low-E windows, replace exterior wall finishing materials with crystalline PV, or use a mixture of both. The simulation results show that applying a roof-top BAPV (Building-Applied Photovoltaic) system is only practical for low-rise buildings.

Drug usage has always been a top concern for parents and government officials, harming younger people's lives in circumstances that cannot be undone. This paper considers a modified ABC-fractional-order Ice-smoking dynamical system for the theoretical and numerical results. In the theoretical aspect, we study the solution existence, uniqueness work, and Hyers-Ulam (HU) stability of the presumed extended fractional order dynamical system of Ice smoking. Recursive sequences that follow one another are designed to verify the existence of a solution to the provided Ice-smoking model. Functional analysis principles and findings are applied to showcase the uniqueness of the solution and the stability under the Hyers-Ulam (HU) framework. We offer simulations and a comparison by graphs by using Lagrange polynomials. The simulations demonstrate the scheme's applicability, future forecasting, and validations of the results of modified ABC-fractional orders.

Efficient control of automotive engine idle speed is crucial for achieving better fuel economy and smoother engine running. This paper presents a comparison between proportional-integral-derivative (PID) control and Reinforcement Learning (RL) using the Deep Q-Network (DQN) algorithm as a high-level control method for minimizing idle speed fluctuations caused by changes in engine irregularities, and the response time and accuracy of the throttle control mechanism. In addition to low-level PID control for the throttle valve position, MATLAB/Simulink was employed to build the simulation environment, incorporating an engine model and an electronic throttle body model, and observing the engine's current speed. The results demonstrated the superiority of RL-based control over PID in reducing idle speed fluctuations and enhancing engine performance in simulations and real-world experiments. This study advances automotive engine control strategies.

The advent of the Fourth Industrial Revolution (4IR) has spurred the development of innovative products, significantly impacting industries reliant on technology-oriented machinery and equipment, notably the construction sector. In response, the Ministry of Works, through CIDB Malaysia, has implemented the Construction Strategy Plan 4.0 (2021 – –2025), grounded in 4IR principles, to facilitate the construction industry's gradual adaptation to technological advancements. Despite its introduction in 2016 to enhance project delivery efficiency and effectiveness, the extent of adoption of advanced 4IR-related technologies and the industry players' awareness of the importance of the Construction 4.0 strategic plan remains largely unexplored. Therefore, this paper examines the awareness and adoption level of CR 4.0 technologies in the Malaysian construction industry through a structured questionnaire survey with all G1-G7 contractors that registered with CIDB Malaysia. Based on 912 valid questionnaire responses received from these contractor grades, it has been identified that the awareness and adoption of most of the contractors in Malaysia on construction 4.0 technologies are predominantly at a semi-automation level. Most of the contractors in Malaysia especially lower grade contractors (

In this paper, a 2-bit optimized coding based metasurface absorber (CMA) is designed for electromagnetic interference (EMI) shielding and radar cross section (RCS) reduction. The 2-bit CMA comprises four digital elements using triple circular ring resonators to represent ‘00′, ‘01′, ‘10′ and ‘11′, through optimized coding sequences obtained via particle swarm optimization (PSO) algorithm applied to array theory. The simulation results demonstrate that optimized pattern performs better, achieving an RCS reduction exceeding −10 dB across 7.5 to 17.5 GHz frequency range, with additional evidence from 3D scattering patterns at 9.76, 11.90, 13.84, and 16.80 GHz confirming its remarkable ability to suppress bistatic RCS. The optimized pattern also demonstrates high shielding effectiveness with near unity absorption. A prototype is fabricated and experimentally tested to validate the optimized pattern performance, with both simulation and experimental results confirming the optimized pattern as a viable option for achieving enhanced EMI shielding and RCS reduction.

Purpose: The paper discusses the critical importance of understanding and managing risks associated with the construction of pre-stressed girder bridges. As a result, it aims to assess, analyze, and propose strategies to mitigate various risk factors contributing to delays and schedule overruns in bridge construction projects. Methodology: These risk risks through literature review and interviews with stakeholders engaged in bridge projects in Pakistan, highlighting the need for activity-specific risk analysis rather than general assumptions. Results: The findings show that 74% of the identified risks can be effectively managed through suitable risk management strategies. Additionally, it was suggested that 17% of the risks should be transferred, while 9% of the identified risks must be retained without any mitigation measures. This survey not only confirmed risks already identified in existing literature but also brought to light new risks previously unnoticed. Moreover, the results highlighted significant differences in risk severity among various project activities. This underscores the necessity for conducting activity-specific risk analysis instead of depending on generalized assumptions for the entire bridge construction project. Conclusions: Among all bridge construction activities, piling emerged as the most high-risk activity, while girder installation was deemed the least risky. Based on this assessment, a set of guidelines aim to mitigate these risks and serve as a reference for minimizing schedule overruns in bridge construction, providing valuable insights for bridge constructors to effectively manage risks.

In this paper, we establish a novel identity by leveraging fractional integral operators of Riemann-type. Subsequently, we employ this identity to investigate Bullen's fractional integral inequalities within the framework of classical convex functions. The validity of these innovative inequalities is verified through the visualization of 2D and 3D graphs. Furthermore, we obtain certain well-known results as special cases to our main findings and express them as remarks. Lastly, we derive differences in mean estimates, which serve as valuable tools for representing complex information, facilitating decision-making and aiding analysis in various fields.

The main objective of this paper is to analyze the Montgomery identities and Ostrowski like inequalities, within the framework of quantum calculus. The study utilizes qϖ3 and qϖ4 differentiable functions to establish two new Montgomery identities, which are essential for the development of our main results which are new quantum estimates of general Ostrowski type inequality. The study involves numerous techniques, including q-identities, Hölder-like inequalities, and the Jensen-Mercer inequality for convex mappings, to derive the main outcomes of the paper. Additionally, the study presents special cases, numerical validation, and graphical illustrations to support the main results.

This paper presents a Super-Twisting Algorithm (STA) sliding mode control technique as an alternative to the conventional Proportional-Integral (PI) control system. This application is specifically in the context of a nonlinear Archimedes Wave Swing (AWS) device, which functions as a wave energy converter system (WECS) connected to the power grid. The main goal is dynamic performance enhancement of such wave energy systems under network disturbances. Two STA controllers are essential for the rectifier control system in the grid-connected system to capture the most power from waves while limiting losses in the AWS linear generator. In addition, four STA controllers are incorporated into the inverter control loop to preserve the DC link and common coupling voltages at the preset values. The Honey-Badger Algorithm (HBA) is applied for the optimization of gain parameters of the STA controllers, which are compared to the PI gains adjusted using the coot, the hybrid augmented grey wolf optimizer and cuckoo, and PSO search techniques to assess the worthiness of adopting this new control approach. The grid-connected AWS experiences a variety of faults, including symmetrical and unsymmetrical faults with successful and unsuccessful breaker reclosures. Finally, the system is experimentally validated using the OP4510 real-time simulator. The experimental results reveal that the HBA-STA controllers outperform PI controllers in omitting fluctuations in the regulated variables, making the STA a strong contender as a control method.

The penetration of increasing solar gain towards the building deteriorates the building's thermal quality and raises cooling demand. In particular, the roof receives the most solar gain. The roof system reformation is urged to improve the roof system's heat capacity and conserve building energy. This paper introduced a novel cool roofing system to replace the traditional roof system. The proposed novel cool roofing applications merged: (i) lightweight foam concrete, (ii) hollow concrete slab, (iii) white cooling paint and (iv) active reflective insulation system powered by solar energy. The discovery of the proposed system was evaluated with experimental chambers exposed to the outdoor environment. Under intense sunlight, the internal roof surface was 30.6 % cooler (50.5 °C to 35.0 °C). The mean attic temperature achieved was 33.9 °C under a field where the daily maximum recorded surrounding temperature is 43.0 °C. In contrast with a bare roof at 41.9 °C, the proposed design demonstrated a 19.2 % lower solar gain, achieving a sub-ambient structure. Besides that, the cooling demand simulation generated similar cooling demand savings (20 %). Significantly, this comprehensive cool roofing system exhibited a considerable and constructive outcome in cooling the buildings by achieving sustainable development to create a better and more environmental future for all.

This paper introduces a novel approach to enhance the control algorithm for a single-phase shunt active power filter(SAPF) by integrating a new technique into a 5-level cascaded multilevel inverter (MLI) with Photo Voltaic (PV) array integration. Due to the integration of non-linear loads in the grid, such as computers, variable speed drives, and other solid-state equipment, non-sinusoidal currents are drawn, introducing harmonics that distort the voltage and current waveforms. It is essential to mitigate these harmonics, and integrating Shunt Active Power Filters (SAPFs) gives the grid the ability to inject active power. In order to improve power quality and achieve active and reactive power balance, this integration requires an imperative control method. This work extracts the fundamental component of the load current and efficiently handles grid distortions such as DC offset, phase shift, and harmonics using a phase lock loop (PLL) based on a Modified Notch Filter Second Order Generalised Integrator (MNFSOGI). Using MATLAB/Simulink software modelling, experimental validation utilising the OPAL-RT real-time data simulator verifies the proposed system's operation. The control algorithm, which has been painstakingly designed and verified, allows feed-forward current estimate from the PV array, synchronization template obtained from the grid voltage signal, and estimation of both fundamental and non-linear load currents. The performance of the MNFSOGI-PLL is additionally compared with that of the conventional E-PLL, MAF-PLL and DSOGI-PLL techniques. By attaining power balance in multilevel inverters under distorted grid conditions, this research substantially contributes to improving power quality. According to simulation and experimental results, the Total Harmonic Distortion (THD) of the grid current is found to be 1.50% in normal grid and 3.96% in distorted grid which is less than 5%, by IEEE-1547 standards. Moreover, the limitations of the presented multilevel inverter system are highlighted by the disadvantages of traditional 2-level inverters, which include increased harmonics, restricted voltage levels, and poorer power quality.

In the present paper, the combination of mixed interpolation of tensorial components technique of triangular elements (MITC3) based upon the first-order shear deformation theory (FSDT) is developed to research the free and forced vibration of the unsymmetrical functionally graded porous (UFGP) folded sandwich plates. The MITC3 element still uses the triangular mesh and avoids the shear-locking phenomena that occur when using the classical triangular elements. Based on the basic formula of the MITC3 element for analyzing plates, the twisted angle (φz) is added to the node displacement vector to solve for folded plates. Material properties of folded sandwich plates are unsymmetric with an FGP core layer, a metal bottom layer, and a ceramic top layer. The motion equations for UFGP folded sandwich plates are derived from Hamilton's principle. Some examples are conducted to verify the accuracy and reliability of the present method. Then, the new numerical results on the free and forced vibrations of UFGP folded sandwich plates are provided.

An intelligent detection of the communication channel by the transceiver is what is known as cognitive radio (CR), a sort of radio communication. Avoid using channels that are already in use to reduce interference and swiftly direct traffic to open channels. In cognitive wireless networks, spectrum allocation has long been a research hotspot as one of the key problems to increase spectrum efficiency. This article examines the issue of spectrum sharing, in which one primary user (PU) might rent out portions of their spectrum to numerous secondary users (SU) in order to share it. The suggested Fuzzy Ant Colony Optimization based Multiple Scheduling Resource Selection Algorithm (FAMSRSA) chooses the best network while maximising data rate and minimising interference and cost. Here, Nash's balanced multi-antenna-based spectrum sharing model (NE-MASS) is used to optimize and share the spectrum. In this paper, a mathematical model optimizes multiple network selection goals to maximize the overall bandwidth, and the total cost and idle spectrum assignment are determined to a minimum in the context of a comprehensive consideration of differences in the spectral resource characteristics of primary networks that differ in network domain and diversify the user's secondary requirements and analyze the complexity of the problem in the user domain. This approach provides an efficient way to access the spectrum available to both primary and secondary users. As a result of the experiment, this algorithm shows the algorithm's efficiency better than the four conventional algorithms in terms of performance metrics like Maximized Minimum Bandwidth, Maximized Network Efficiency, Maximized Proportion Fairness, Relative error and Spectrum Utilization Efficiency. The performance of the NE spectrum allocation algorithm was evaluated by extensive simulation. The proposed spectrum allocation algorithm has shown superior channel allocation performance than other conventional literature algorithms.

Angle estimation of signals received by antenna arrays is a key step in adaptive beamforming, used to detect and locate targets in space. It is desirable to reduce costs by employing a smaller number of antennas while maintaining efficiency and degrees of freedom (DOF) of the system at the same time. Co-prime antenna arrays not only circumvent the operational constraints of cost and hardware complexity, but also boost the DOF by utilizing far fewer antennas as compared to traditional uniform linear arrays. In this paper, meta-heuristic computing paradigm for estimating arrival angle of targets with co-prime arrays incorporated in monostatic radar is proposed. The system's autocorrelation and Mean Square Error (MSE) are used for fitness function optimized by global/local search with Genetic Algorithm (GA) and Pattern Search (PS). Monte Carlo simulations are performed to evaluate the performance via estimation accuracy, MSE, and computational cost for varying numbers of impinging waves.

Geopolymer concrete is excellent for thermal insulation because of its low thermal conductivity, it may be utilized as a suitable insulation for building facades. Furthermore, it may lessen the impact of fire on the reinforcement for geopolymer constructions. Precise forecast of temperature increase during fire in buildings is crucial to evaluate efficacy of insulation and the performance of structures when exposed to high temperature. This paper introduced simple equations to predict the thermal properties (density, thermal conductivity, and specific heat) of normal and light weight GC when exposed to increased temperatures. Additionally, numerical investigation using the Ansys workbench software programme was performed to explore the temperature distribution in geopolymer concrete slabs when exposed to increased temperatures according to ISO 834 fire curve. Simple equations were created using multiple regression analysis for estimating the temperature distribution in geopolymer concrete slabs. Additionally, the predicted equations for thermal properties and the temperature rise predicted from the numerical study were experimentally validated.

Graphene has appeared as one of the most promising nanomaterials with unique thermal, electronic and mechanical properties which make it suitable for many applications in materials science, energy storage, electronics, water filtration and medicine. In this paper, we have studied the (2+1)-dimensional graphene sheets (GS) equation with variable heat transmission thermophoretic motion by using both Bäcklund transformation and similarity reduction techniques. As a result, from Bäcklund transformation one soliton solution was obtained and from similarity reduction technique the GS equation is reduced to a nonlinear ordinary differential equation which can be solved using the trial equation method; where many novel wave solutions were obtained. As an additional remark, the Bäcklund transformation obtained can be found by the traditional Painléve test. Finally, some graphs are given to illustrate the effect of the variable coefficients on the behavior of the thermal wave transmission.

In this paper, the enhancement of the perovskite solar cells (PSCs) efficiency through the implementation of a half-tandem structure featuring two absorbent layers of MAPbI3 and MoTe2 with a V-shaped nanostructured grating was investigated. MoTe2 was utilized as the second lower absorbent with a smaller bandgap than the first and upper absorbent, MAPbI3, to cover the incident light absorption in the infrared wavelength range. Additionally, the structure was also optimized by introducing a nanostructured V-shaped grating to increase the light propagation path and improve the absorption in the active layers. Also, the critical role of the electron transfer layer (ETL) and hole transfer layer (HTL) in carrier transfer and prevention of charge leakage was examined for materials including TiO2, TiO2-Gr (5%) nanocomposite, and Al-doped ZnO (AZO) as ETLs, and Cu2FeSnS4 (CFTS) and reduced Graphene Oxide (rGO) as HTLs in four distinct cell configurations. In this study, the optical and electrical models were solved to determine the optimal solar cell structure and materials through a comparative analysis of structures. Our findings revealed substantial improvements in short-circuit current density (Jsc), open-circuit voltage (Voc), and power conversion efficiency (PCE), where they increased from 15.27 to 31.55 mA/cm2, 0.91 to 1.30 V, and 11.78 to 27.63%, respectively. This resulted in a 2.35 times efficiency enhancement for the nanostructured V-shaped half-tandem solar cell with MAPbI3 (250 nm) and MoTe2 (150 nm) as absorbent layers, AZO (40 nm) as ETL and rGo (40 nm) as HTL compared to the planar non-tandem solar cell with MAPbI3 (250 nm) as absorbent layer, TiO2 (40 nm) as ETL and CFTS (40 nm) as HTL.

The integration of multiterminal hybrid HVDC grids connecting LCC- and VSC-based networks faces several technical challenges such as DC fault isolation, ensuring multi-vendor interoperability, managing high DC voltage levels, and facilitating high-speed power reversal without interruptions. The two-stage DC-DC converter emerges as a key solution to address these challenges. By implementing the modular multilevel converter (MMC) structure, the converter's basic topology includes half-bridge sub-modules on the VSC side and full-bridge sub-modules on the LCC side. However, while this topology has been discussed in the literature, its connection to an LCC-based network with controlled current magnitude lacks detailed analysis regarding operational challenges, control strategies under various scenarios, and design considerations. This paper fills this gap by providing comprehensive mathematical analysis, design insights, and control strategies for the modular DC-DC converter to regulate DC voltage on the LCC-HVDC side. Additionally, the proposed control scheme minimizes the interfacing inductor between the two bridges, ensuring uninterrupted power flow during reversal and effective handling of DC faults. Validation through Control-Hardware-in-the-Loop testing across diverse operational and fault scenarios, along with a comparative analysis of different converters, further strengthens the findings.© 2017 Elsevier Inc. All rights reserved.

Developing a dynamic, personalized user interface that changes in real-time in response to user behavior is the goal. This paper supplies a modern method to beautify consumers enjoy using Reinforcement Learning (RL) and a Deep Q Network (DQN). Through support examination, the task objectives are to upgrade buyer connections and increment commitment, delight, and undertaking of consummation rates. Users who utilize traditional user interfaces get a common experience because they're impersonal and unflexible. The potential for higher engagement and happiness levels is limited in the absence of real-time changes based on individual preferences and behaviors. To overcome this problem, the study suggests a cunning technique for a getting-to-comprehend layout that may constantly analyze and modify patron communications. This evaluation is new as it provides a blended RL and DQN framework that modifies person interfaces grade by grade. Dissimilar to conventional methodologies, the proposed form adjusts the utilization of well-known, over-the-top prize moves with the development of the most recent ones through an investigation double-dealing system. EventType, contentId, personId, sensorId, and timestamp are instances of timestamped insights handles that give a thorough skill of client conduct and license planned and nuanced changes.

In today's modern world, marketing is a fruitful source to grow a business by reaching a larger audience. This paper considers a popular social media source called LinkedIn.com as a way of marketing or reaching a larger audience. We consider a regression approach to explore the statistically significant role of the LinkedIn medium as a tool for reaching a larger audience. In addition to statistically exploring the role of the LinkedIn medium in the financial sector, we also introduce a new Weibull-based probability distribution, namely, the exponential TX inverse Weibull (for short'ETXI-Weibull') distribution. The ETXI-Weibull distribution is specially designed for analyzing LinkedIn marketing data. The beauty of the ETXI-Weibull distribution is that it satisfies the heavy-tailed characteristics, which are explored visually and mathematically. For the ETXI-Weibull distribution, the estimators are derived whose performances are evaluated visually and numerically through a simulation study. The ETXI-Weibull distribution is applied practically by considering the LinkedIn marketing data set as an example. Using certain established statistical tests, we observe that the ETXI-Weibull distribution provides an optimal fit to the LinkedIn marketing data set. Furthermore, a bivariate extension of the exponential TX inverse Weibull model is also introduced. Finally, the bivariate extension of the exponential TX inverse Weibull model is illustrated by modeling the LinkedIn marketing and sales data.

The utilization of solar radiation by converting them into thermal energy is discussed in this paper. Nanoparticles improve the ability of heat transfer therefore, it is beneficial in the use of solar thermal systems and energy storage devices. The novel mixture of nanoparticles Graphene and Polythiophene in base fluid, which has high thermodynamic properties for the improvement of thermal effect with electromagnetic effect by using Maxwell fluid model is discussed. Polyvinyl alcohol water is taken as base fluid flowing through a moveable flat plat. The governing partial differential equations are transformed into ordinary differential equations. The semi-analytical technique, homotopy analysis method is used to obtain the solution of the ordinary differential equations. The velocity is enhanced with magnetic and electric field strength. The increase of the Prandtl number, Eckert number and chemical reaction parameter, exceeds the thermal effect which produces more entropy generation and heat enhancement. The results show that the hybrid nanofluid with this Novel mixture is highly thermodynamic with higher entropy and rapid thermal augmentation which can be used in energy production and energy storage devices. A novel intelligent numerical computing technique multi-layer perceptron with feed-forward back-propagation, an artificial neural networking method with the Levenberg-Marquard algorithm is used in this model. The data is gathered for the neural networking method training, validation, and testing. The efficiency of the model is obtained and mean square error is obtained by artificial neural networking.

This paper presents a type of A-stable spectral deferred correction (SDC) method. The scheme is initiated by the first-order backward Euler method. We adopt the linear stabilization approach for the Allen-Cahn model to get the linear semi-implicit SDC scheme. This is done by the addition and subtraction of the linear stabilization operators that have been provided for the Allen-Cahn problem. The ranges of the stabilization factors are then given. The aim is to achieve A-stability and error estimates for the implicit part of the scheme. Finally, the semi-implicit SDC scheme coupled with the Fourier spectral method is used to simulate the Allen-Cahn problem, which is also suitable for the highly nonlinear problem. Numerical experiments are given to numerically demonstrate the high-order accuracy and the energy decay property of the scheme with A-stability parameters.

In recent years, image semantic segmentation technology has developed rapidly, but image annotation usually requires a significant amount of human and financial resources, especially for remote sensing image annotation, which can be expensive and sometimes even unaffordable. To address this issue, this paper integrates the idea of curriculum learning into the self-training method and screens reliable pseudo-labels through computing image-level confidence, significantly reducing the confirmation error problem. Furthermore, the semi-supervised model in this paper combines implicit semantic enhancement with strong data augmentation, which can reduce the coupling between the teacher model and the student model’s prediction distribution and enhance the model’s robustness. Finally, the proposed semi-supervised method is experimentally verified using the ISPRS competition dataset and compared with existing state-of-the-art (SOTA) methods. Experimental results show that the proposed semi-supervised segmentation method achieves higher segmentation accuracy compared to self-training methods. Moreover, despite not using iterative training to simplify the training process, the proposed method still yields satisfactory segmentation results.

This paper addresses some classes of combined and separate imputation methods (CSIMs) of the population mean under stratified simple random sampling (SSRS) along with their characteristics. To the best of our knowledge, these imputation methods (IMs) have yet not been studied by any author under SSRS, hence these IMs are called ‘novel’. In addition, the existing CSIMs are distinguished as the members of the suggested CSIMs, respectively. The theoretical conditions under which the proposed IMs perform better are obtained by comparing the proposed IMs with the existing IMs. To validate the theoretical findings, the numerical and simulation studies are conducted on real and artificial populations, respectively.

In light of fractional theory, this paper presents several new effective solitonic formulations for the Langmuir and ion sound wave equations. Prior to this study, no previous research has presented the comparision and obtained the generalized fractional soliton solutions of this kind with power law kernel and Mittag-Leffler kernel. The ion sound and Langmuir wave equations are essential in plasma physics, offering insights into the collective behavior of charged particles in plasmas and enabling diagnostics and control of these complex, ionized gas systems. The two distinct fractional order differential operators are substituted for the traditional order derivative to reshape the examined model. The Atangana-Baleanu non-singular and non-local operator and conformable fractional operator are the fractional-order operators that are used to create the fractional complex system equations for Langmuir waves and ion sound. A constructive approach new auxiliary equation method utilizes to obtain the exact analytical soliton solutions for ion sound and Langmuir wave equation. A wide range of soliton solutions is obtained, including mixed complex solitary shock solutions, singular solutions, mixed shock singular solutions, mixed trigonometric solutions, mixed singular solutions, exact solutions, mixed periodic solutions, and mixed hyperbolic solutions, dark soliton, bright soliton, trigonometric solutions, periodic results, and hyperbolic results. The solitons solution of the ion sound and Langmuir wave equations lies in their ability to maintain wave stability, their role in modeling wave propagation and nonlinear effects, their potential use as diagnostic tools, and their relevance in wave-particle interactions in plasma physics. The solitons provide a valuable framework for understanding the behavior of waves in plasmas and offer insights into the complex dynamics of these charged particle systems. A graphical comparison analysis of a few solutions is also shown here, taking into account appropriate parametric values through the use of the software package. Moreover, the results of this study have important implications for Hamilton's equations and generalized momentum, where solitons are employed in long-range interactions.

Design of efficient test plans to assessing product reliability rapidly, is a critical step to obtain product reliability accurately. In this paper, we are adopted the problem of statistical inference of two generalized inverted exponential populations in a competing duration. To save the balance between the optimal test time and the number of failures needing for statistical inference, joint adaptive type-II hybrid progressive censoring scheme is applied. The model parameters are estimated with classical methods (maximum likelihood and bootstrap) for point estimate and the corresponding confidence intervals. Also, Bayes estimators of model parameters are computed under importance sample technique with the corresponding Bayes credible intervals. A real data sets are analyzed for illustrating purposes. The results are compared and assessed through Monte Carlo simulation study.

In this paper, we consider the evolution of a new probability distribution called the exponential flexible Weibull distribution. The proposed exponential flexible Weibull distribution is obtained by combining the flexible Weibull extension with the exponential T-X strategy. For the exponential flexible Weibull, the estimators of the model parameters are derived mathematically. The appraisal of these parameters is accomplished through a simulation study. The applicability and virtuoso of the exponential flexible Weibull distribution are exemplified via two data sets. Furthermore, we implement the state-of-the-art deep learning algorithms, specifically Artificial Neural Networks and Extreme Gradient Boosting, which are widely employed in real-world applications. A comprehensive comparative analysis is conducted to assess the relative performance of these methodologies.

World Heritage sites face many challenges that affect the quality of life (QOL), it is thus empirical that more research studies shed light upon this important topic. This research examines the most important problems of Egyptian World Heritage Sites, with a focus on the Memphis site due to its outstanding universal value (OUV) and the posed threats that may affect its global value. The necessity of maintaining world heritage sites in accordance with UNESCO's guidelines has recently come to the state's attention so a supreme committee was formed, according to a presidential decree. Thus, this paper aims to present an applied strategy for managing Memphis site in an integrated manner to contribute to raising the QOL indicators while maintaining the OUV of the site through a multiphase research approach; first the theoretical approach, where a literature review and the basic concepts; second, an empirical desk-based literature review of similar sites. This in turn yielded quantitative and qualitative results and allowed for the measurement of the link between QOL and heritage sites management. Then a third phase which consisted of devising an applicable strategy management plan for Memphis site. The strategy has been proposed in light of the country’s vision, strategy for sustainable development: Egypt vision 2030, and the strategic plan for Greater Cairo Region 2050.

This paper investigates the behavior of magnetohydrodynamic (MHD) peristaltic flow with electroosmosis. The Jeffrey fluid in microchannel under the influence of homogeneous-heterogeneous chemical reaction has been taken. The heat absorption and nonlinear radiation are also scrutinized here. This study contributes to the fundamental understanding of complex fluid dynamics in microscale systems and offer valuable insights for designing and optimizing microfluidic devices. In the framework of mathematical simulation, the relevant dimensional nonlinear equations are reduced into dimensionless equations by using linear transformations. Thus Debye-Hückel linearization has been employed. The streamline approach with mathematical modelling has been performed within the limits of δ≪1 and Re→0. The appropriate equations for temperature and concentration with boundary conditions are solved by using perturbation approach whereas, the exact solution is found for velocity equation. A graphical depiction of crucial physical characteristics on velocity, temperature, concentration and streamlines are reported in the last section. It is observed that the velocity distribution decreases for the higher value of M (0.5≤M≤2) however, it increases for the escalating values of Uhs (−0.5≤Uhs≤1.5). When Rn(0.1≤Rn≤0.6) gets stronger then temperature profile decreases. It is noticed that the concentration profile decays owing to enhancement in Sc(−0.5≤Sc≤1.5). As the M (0.5≤M≤2) increases, the size of trapped bolus decreases. Escalating values of temperature ratio parameterθw(1.0≤θw≤1.6)enhances the Nusselt number. The novelty of this study lies in the comprehensive analysis of multiple complex phenomena in a single framework. The interplay between MHD electroosmotic flow, homogeneous heterogeneous chemical reactions, peristalsis, nonlinear thermal radiation, heat absorption and no-slip condition has not been explored in previous research.

This research stems from the urgent need to address the complex challenges faced in multimodal transportation within the cargo sector, particularly concerning the reduction of transportation costs and CO2 emissions. Existing transportation systems often struggle to efficiently balance economic and environmental concerns, necessitating innovative solutions. In this paper, we have introduced a novel mathematical model that integrates Singular Value Decomposition (SVD), Genetic Algorithms (GA), and the Adaptive Neural Fuzzy Inference System (ANFIS). The synergy of GA and the ANFIS forms a dynamic learning system that continually refines solutions by adapting to historical decisions and aligning with supply-demand dynamics. This integrated approach aims to optimize cost allocation and resource utilization while simultaneously minimizing CO2 emissions. Deploying the ANFIS+GA+SVD model in multimodal scenarios yields impressive results, with a minimal average percentage error (MAPE) of 0.28% for transportation costs and a remarkable coefficient of determination R2 of 0.992 for the Truck + Ship scenario. Similarly, a MAPE of 0.11% for CO2 emissions, supported by an impressive R2 of 0.989, underscores the model's accuracy. Observations of people's transportation choices in response to emission price changes reveal a significant decrease in truck usage, plummeting from 84% to below 50%. Alternative transportation methods, especially in the 3S-3 M-3 C scenario, show a substantial surge, with railways leading at a remarkable 30% increase in utilization. The combined quantity and cost comparison, utilizing the ANFIS+GA+SVD model, emphasizes the suitability of the Truck + Ship scenario for Iraq's transportation network, reinforcing the case for sustainable and efficient multimodal transportation systems.

In this work, we developed and analyzed a biphasic calcium phosphate (BCP) bioceramic for bone regeneration using stereolithography (SLA). The SLA method is a promising additive manufacturing (AM) technique capable of creating BCp parts with high accuracy and efficiency. However, the ceramic suspension used in SLA exhibits significantly higher viscosity and is not environmentally friendly. Therefore, adequate preparation of a suspension with low viscosity and high solid loading is essential. In this paper, we optimized the effects of surfactant doses and solid loading on the BCp slurry, and initially examined the process parameters of photocuring, debinding, and sintering. The utilization of 9 wt % Disperbyk (BYK) with a 40 vol % loading of BCp bioceramics exhibited a reasonably low viscosity of 8.9 mPa·s at a shear level of 46.5 s−1. Functional and structural analyses confirmed that BCp was retained after photocuring and subsequent treatment, which were incorporated into the BYK dispersion. The 3D printed objects with different sintered temperatures, specifically at 1100 °C, 1200 °C, and 1300 °C, were further optimized. Additionally, the surface roughness, porosity, and mechanical properties of BCp green parts were systematically investigated. Most importantly, in vitro analysis of cell attachment, differentiation, and red alizarin analysis could support the application of bone regeneration.

This paper shows that local institutional shareholders tend to improve firm performance through corporate social investments. Using an extensive U.S. mutual fund-firm dataset, we find that local mutual funds tend to promote corporate social responsibility (CSR). In addition, the social investments are positively associated with firm performance. Finally, it is evident that CSR mediates the relation between local ownership and firm performance. Consistent with instrumental stakeholder theory, our findings suggest that local shareholders help firms develop reputational and relationship capital through CSR and lead to higher firm performance.

Gonadal soma-derived factor (Gsdf) is a member of the transforming growth factor beta (TGF-β) superfamily and a teleost-specific gene. Early studies suggested that Gsdf promotes spermatogonial proliferation; however recent reports have shed light on its role in sex differentiation based on expression profiles and gain- and loss-of-function studies. This paper discusses the involvement of Gsdf in gonadal sex differentiation and sex changes in teleosts.