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This study aims to observe the coloring efficacy of graphite (G) and nano bentonite clay (BCNPs) on the adsorption of Basic Blue 5 dye from residual dye bath solution.

Some factors that affected the adsorption processes were examined and found to have significant impacts on the adsorption capacity such as the initial concentration of G and/or BCNPs (Co: 40–2,320 mg/L), adsorbent bath pH (4–9), shaking time (30–150 min.) and initial dye concentration (40–200 mg/L). The adsorption mechanism of dye by using G and/or BCNPs was studied using two different models (first-pseudo order and second-pseudo order diffusion models). The equilibrium adsorption data for the dye understudy was analyzed by using four different models (Langmuir, Freundlich, Temkin modle and Dubinin–Radushkevich) models.

It has been found that the adsorption kinetics follow rather a pseudo-first-order kinetic model with a determination coefficient (R²) of 0.99117 for G and 0.98665 for BCNPs. The results indicate that the Freundlich model provides the best correlation for G with capacities q_max = 2.33116535 mg/g and R² = 0.99588, while the Langmuir model provides the best correlation for BCNPs with R² = 0.99074. The adsorbent elaborated from BCNPs was found to be efficient and suitable for removing basic dyes rather than G from aqueous solutions due to its availability, good adsorption capability, as well as low-cost preparation.

There is no research limitation for this work. Basic Blue 5 dye graphite (G) and nano bentonite clay (BCNPs) were used.

This work has practical applications for the textile industry. It is concluded that using graphite and nano bentonite clay can be a possible alternative to adsorb residual dye from dye bath solution and can make the process greener.

Socially, it has a good impact on the ecosystem and global community because the residual dye does not contain any carcinogenic materials.

The work is original and contains value-added products for the textile industry and other confederate fields.

This study aims to model and investigate Basic Yellow 28 (BY28) adsorption onto activated carbon in batch and continuous process.

Batch adsorption experiments were carried out at 25 °C with 50 mg/L BY28 solution at pH 6 with different amounts of activated carbon. Freundlich and Langmuir adsorption isotherm models were used to model batch data. Pseudo-first-order and pseudo-second-order kinetic models were applied with linear regression. The changes of the breakthrough curve with the column height, flow rate, column diameter and adsorbent amount were examined in fixed bed column at room temperature. BY28 adsorption data were modelled by using different adsorption column models (Adams & Bohart, Thomas, Yoon & Nelson, Clark and modified dose–response) with non-linear regression.

Freundlich model and pseudo-second-order kinetic model expressed the experimental data with high compatibility. Modified dose-response model corresponded to the fixed bed column data very well.

Adsorption of Basic Yellow 28 on activated carbon in a fixed bed column was studied for the first time. Continuous adsorption process was modelled with theoretical adsorption models using non-linear regression.

Drawing on the resource-based view (RBV), this paper aims to analyse the relevance of Industry 4.0 (I4.0) technologies and participation in global value chains (GVC) and the effects of the complementarity between both in the adoption of circular economy (CE) actions by small and medium-sized enterprises (SME).

This paper analysed a large-scale international sample by employing logistic and linear regression models to test the research hypotheses on the effects of I4.0 technologies, GVC participation, and the interaction on CE actions (recycling or reusing materials, reducing the consumption and impact of natural resources, saving energy and/or switching to sustainable energy sources, developing sustainable products or services).

The evidence suggests that I4.0 technologies already represent important resources for CE adoption and SME participating in GVC display a greater likelihood of adopting CE actions. From the perspective of resource complementarity, by interacting the two factors viewed as resources in this article, results may report that adopting I4.0 technologies and simultaneously participating in GVC may turn out detrimental to SME undertaking CE actions, specifically as regards recycling and reusing materials, saving energy or switching to sustainable energy sources and in cases of widely adopting CE practices.

The paper returns novel insights into the adoption of CE practices by presenting evidence that I4.0 technologies and participation in GVC may be successful means for SME seeking to implement CE actions but must be combined carefully. This paper also provides theoretical and practical implications for SME managers, firms participating in GVCs and policy makers, and shedding light on new research avenues.

Adoption of integrated MOS based pseudo-resistor (PR) structures instead of using off-chip passive poly resistors for analog circuits in complementary metal oxide semiconductor technology (CMOS) is an area-efficient way for realizing larger time constants. However, issue of common-mode voltage shifting and excess dependency on the process and temperature variations introduce nonlinearity in such structures. So there is dire need to not only closely look for the origin of the problem with the help of a thorough mathematical analysis but also suggest the most suitable PR structure for the purpose catering broadly to biomedical analog circuit applications.

In this work, incremental resistance (IR) expressions and IR range for balanced PR (BPR) structures operating in the subthreshold region have been closely analyzed for broader range of process-voltage-temperature variations. All the post-layout simulations have been obtained using BSIM3V3 device models in 0.18 µm standard CMOS process.

The obtained results show that the pertinent problem of common-mode voltage shifting in such PR structures is completely resolved in scaled gate linearization and bulk-driven quasi-floating gate (BDQFG) BPR structures. Among all BPR structures, BDQFG BPR remarkably shows constant IR value of 1 TΩ over −1 V to 1 V voltage swing for wider process and temperature variations.

Various balanced PR design techniques reported in this work will help the research community in implementing larger time constants for analog-mixed signal circuits.

The PR design techniques presented in the present piece of work is expected to be used in developing tunable and accurate biomedical prosthetics.

The BPR structures thoroughly analyzed and reported in this work may be useful in the design of analog circuits specifically for applications such as neural signal recording, cardiac electrical impedance tomography and other low-frequency biomedical applications.

This study aims to examine the perceptions and opinions of relevant stakeholders regarding entrepreneurship opportunities and growth in the Gilgit-Baltistan (GB) province of Pakistan. Specifically, it focuses on the role of special economic zones (SEZs), such as Maqpondass SEZ and the China–Pakistan Economic Corridor (CPEC), in fostering nascent entrepreneurship (NE) and promoting regional development.

The study employs ordered logistic regression to estimate the relationship between various independent variables and nascent entrepreneurship (NE). The independent variables include awareness of CPEC (AAC), awareness of Maqpondass SEZ (AAMEZ), SEZ incentives (SEZInc), regional market competitiveness (RMC), loan availability (LA) and education and experience (EE).

The findings indicate a robust positive relationship between SEZ-based industries and the growth of local small businesses and enterprises in Gilgit-Baltistan. Furthermore, the study suggests that government incentives, access to finance, skill development, relevant knowledge, and connections with local businesses facilitate the establishment of new ventures.

The study underscores the importance of focusing on human capital development, providing financial assistance, and creating incentives for adopting advanced technology to foster the growth of local businesses in Gilgit-Baltistan through SEZs. It emphasizes the need for policymakers and stakeholders to prioritize initiatives that support entrepreneurship and innovation in the region.

This study contributes to the existing literature by providing novel insights into the perceptions of entrepreneurship development in Gilgit-Baltistan, particularly concerning the influence of natural resources and SEZs. It fills a gap in the research by offering valuable implications for policymakers, researchers, and practitioners seeking to promote sustainable economic development in the region.

The adverse interactions between disruptions can increase the supply chain's vulnerability. Accordingly, establishing supply chain resilience to deal with disruptions and employing business continuity planning to preserve risk management achievements is of considerable importance. The aforementioned idea is discussed in this study.

This study proposes a multi-objective optimization model for employing business continuity management and organizational resilience in a supply chain for responding to multiple interrelated disruptions. The improved augmented e-constraint and the scenario-based robust optimization methods are adopted for multi-objective programming and dealing with uncertainty, respectively. A case study of the automotive battery manufacturing industry is also considered to ensure real-world conformity of the model.

The results indicate that interactions between disruptions remarkably increase the supply chain's vulnerability. Choosing a higher fortification level for the supply chain and foreign suppliers reduces disruption impacts on resources and improves the supply chain's resilience and business continuity. Facilities dispersion, fortification of facilities, lateral transshipment, order deferral policy, dynamic capacity planning and direct transportation of products to markets are the most efficient resilience strategies in the under-study industry.

Applying resource allocation planning and portfolio selection to adopt preventive and reactive resilience strategies simultaneously to manage multiple interrelated disruptions in a real-world automotive battery manufacturing industry, maintaining the long-term achievements of supply chain resilience using business continuity management and dynamic capacity planning are the main contributions of the presented paper.

This study is based on the consumer purchase intention (CPI) in real estate. The purpose of this study is to investigate the link between CPI and preferred individual investment capacity in buying real estate properties. This study investigates if commonly known factors of CPI such as attitude, social power or subjective norms, perceived behavior power or control, location, surrounding environment and socialization can influence a consumer’s preferred investment amount when buying a house, either for own use or for rental purpose.

A total of 334 respondents participated in this study. The survey data was analyzed using factor analysis technique, ordinary least square technique and Poisson pseudo maximum likelihood technique.

This study finds that location, surrounding environment, property/construction papers, roads, mosque/temple and fire services significantly influence the preferred investment amount of a real estate investor.

This study suggests that a link exists between CPI and real estate investment decision through factors such as location, surrounding environment, legal documentation and communication facility. These identified CPI factors require serious consideration by the real estate developers and their financing partners.

This study empirically identifies household demographic and socioeconomic characteristics as well as restaurant characteristics that affect the probability of households choosing an ethnic restaurant (American, Asian, European, Mexican and other) in the USA.

A multinomial logistic regression model is applied using the data derived from the information from the National Household Food Acquisition and Purchase Survey conducted between April 2012 and January 2013.

The empirical findings suggest that such factors as the unit cost on away-from-home food items (i.e. price), region of residence, primary respondent's ethnicity, race, education level, marital status and employment status as well as such restaurant characteristics as availability of loyalty program and presence of coupons significantly affect the probability of households choosing a particular ethnic restaurant in the USA.

The original dataset employed in this study does not permit the quantification of information associated with size, location, and number of years in operation for the ethnic restaurants considered. Also, the dataset does not permit the classification of the ethnic restaurants included in the “other” category.

To the best of the authors' knowledge, there has been no empirical micro-level analysis associated with determining factors impacting households' choice of ethnic restaurants using a polytomous logistic regression model allowing for a wide range of ethnic restaurants and covering the entire USA, based on an extensive set of household demographic and socioeconomic factors and restaurants characteristics. As such, the current study plugs this research gap, with the empirical findings furnished by this study being of importance to ethnic restaurant operators (owners) in the operators' effort to develop effective marketing strategies.

Accurate and rapid tracking and counting of building materials are crucial in managing on-site construction processes and evaluating their progress. Such processes are typically conducted by visual inspection, making them time-consuming and error prone. This paper aims to propose a video-based deep-learning approach to the automated detection and counting of building materials.

A framework for accurately counting building materials at indoor construction sites with low light levels was developed using state-of-the-art deep learning methods. An existing object-detection model, the You Only Look Once version 4 (YOLO v4) algorithm, was adapted to achieve rapid convergence and accurate detection of materials and site operatives. Then, DenseNet was deployed to recognise these objects. Finally, a material-counting module based on morphology operations and the Hough transform was applied to automatically count stacks of building materials.

The proposed approach was tested by counting site operatives and stacks of elevated floor tiles in video footage from a real indoor construction site. The proposed YOLO v4 object-detection system provided higher average accuracy within a shorter time than the traditional YOLO v4 approach.

The proposed framework makes it feasible to separately monitor stockpiled, installed and waste materials in low-light construction environments. The improved YOLO v4 detection method is superior to the current YOLO v4 approach and advances the existing object detection algorithm. This framework can potentially reduce the time required to track construction progress and count materials, thereby increasing the efficiency of work-in-progress evaluation. It also exhibits great potential for developing a more reliable system for monitoring construction materials and activities.

Accurate prediction of the structural condition of urban critical infrastructure is crucial for predictive maintenance. However, the existing prediction methods lack precision due to limitations in utilizing heterogeneous sensing data and domain knowledge as well as insufficient generalizability resulting from limited data samples. This paper integrates implicit and qualitative expert knowledge into quantifiable values in tunnel condition assessment and proposes a tunnel structure prediction algorithm that augments a state-of-the-art attention-based long short-term memory (LSTM) model with expert rating knowledge to achieve robust prediction results to reasonably allocate maintenance resources.

Through formalizing domain experts' knowledge into quantitative tunnel condition index (TCI) with analytic hierarchy process (AHP), a fusion approach using sequence smoothing and sliding time window techniques is applied to the TCI and time-series sensing data. By incorporating both sensing data and expert ratings, an attention-based LSTM model is developed to improve prediction accuracy and reduce the uncertainty of structural influencing factors.

The empirical experiment in Dalian Road Tunnel in Shanghai, China showcases the effectiveness of the proposed method, which can comprehensively evaluate the tunnel structure condition and significantly improve prediction performance.

This study proposes a novel structure condition prediction algorithm that augments a state-of-the-art attention-based LSTM model with expert rating knowledge for robust prediction of structure condition of complex projects.