Explore the vast range of titles available.

The evolution of intelligent manufacturing has had a profound and lasting effect on the future of global manufacturing. Industry 4.0 based smart factories merge physical and cyber technologies, making the involved technologies more intricate and accurate; improving the performance, quality, controllability, management, and transparency of manufacturing processes in the era of the internet-of-things (IoT). Advanced low-cost sensor technologies are essential for gathering data and utilizing it for effective performance by manufacturing companies and supply chains. Different types of low power/low cost sensors allow for greatly expanded data collection on different devices across the manufacturing processes. While a lot of research has been carried out with a focus on analyzing the performance, processes, and implementation of smart factories, most firms still lack in-depth insight into the difference between traditional and smart factory systems, as well as the wide set of different sensor technologies associated with Industry 4.0. This paper identifies the different available sensor technologies of Industry 4.0, and identifies the differences between traditional and smart factories. In addition, this paper reviews existing research that has been done on the smart factory; and therefore provides a broad overview of the extant literature on smart factories, summarizes the variations between traditional and smart factories, outlines different types of sensors used in a smart factory, and creates an agenda for future research that encompasses the vigorous evolution of Industry 4.0 based smart factories.

This study focuses on estimating a finite population cumulative distribution function (CDF) using two-stage and three-stage cluster sampling under non-response. This work is then extended to estimate the finite population CDF under non-response using stratified two-stage and three-stage cluster sampling. We propose two distinct families of CDF estimators, specifically designed for these complex surveys, namely classical ratio/product-type and exponential ratio/product-type. Furthermore, we introduce a difference estimator for the CDF under non-response, utilizing ancillary information about the variances and covariances of the estimators under these complex schemes. We provide mathematical expressions for the biases and mean squared errors of the proposed CDF estimators, based on first-order approximation. To evaluate the performance of the proposed estimators, we conduct extensive simulations and assess their efficiency. The simulation results demonstrate that the proposed families of estimators perform well under different sampling scenarios. Our findings indicate that difference CDF estimators are more explicit than the other estimators discussed. We support our theoretical claims by analyzing real datasets.

The Internet of Things (IoT) and its benefits and challenges are the most emergent research topics among academics and practitioners. With supply chains (SCs) gaining rapid complexity, having high supply chain visibility (SCV) would help companies ease the processes and reduce complexity by improving inaccuracies. Extant literature has given attention to the organisation’s capability to collect and evaluate information to balance between strategy and goals. The majority of studies focus on investigating IoT’s impact on different areas such as sustainability, organisational structure, lean manufacturing, product development, and strategic management. However, research investigating the relationships and impact of IoT on SCV is minimal. This study closes this gap using a structured literature review to critically analyse existing literature to synthesise the use of IoT applications in SCs to gain visibility, and the SC. We found key IoT technologies that help SCs gain visibility, and seven benefits and three key challenges of these technologies. We also found the concept of Supply 4.0 that grasps the element of Industry 4.0 within the SC context. This paper contributes by combining IoT application synthesis, enablers, and challenges in SCV by highlighting key IoT technologies used in the SCs to gain visibility. Finally, the authors propose an empirical research agenda to address the identified gaps.

Childhood morbidity and mortality are key indicators of human development, particularly reflecting poor health conditions in children. In Pakistan, child mortality remains a serious problem despite efforts to reduce it. One factor that may be associated with child mortality is an undesired pregnancy, whether unwanted (the parents did not want more children) or mistimed (the pregnancy occurred earlier than desired). Unwanted pregnancies and births are psychological factors that negatively impact children’s nutritional health. The main objective of the study is to measure the impact of mothers’ aspired status on child mortality and morbidity in Pakistan. We limited our analysis to children under 5 before the survey and used Pakistan demographic health survey conducted in 2017-2018, a national representative cross-sectional survey. We were able to predict the unwanted state (excess in boys, girls, both, and parity) by subtracting the ideal number of children from total live births. Morbidity (fever, diarrhea, cough, acute respiration infection, and Short rapid breathing), nutritional status, and mortality were also evaluated. We perform machine learning techniques such as random forest (RF) and neural network (NN) in the analysis of the data. The findings revealed that the overall percentage of the undesired child was 8%, 4%, 15%, and 27% for boys, girls, parity, and dual excess, respectively. Finally, we perform multivariate analysis following the principal component analysis (PCA) to study the relationship between variables. All the variables were associated with the unwanted child. Child morbidity, fever, and cough were higher among the undesired children. We found evidence that undesired children have acute respiration infection and that an unwanted child has a significant impact on childhood diseases. The ratio of child mortality was lower for boys but higher for girls.

Accurate estimation of the finite population mean is a fundamental challenge in survey sampling, especially when dealing with large or complex populations. Traditional methods like simple random sampling may not always provide reliable or efficient estimates in such cases. Motivated by this, the current study explores complex sampling techniques to improve the precision and accuracy of mean estimators. Specifically, we employ two-stage and three-stage cluster sampling methods to develop unbiased estimators for the finite population mean. Building upon these, the next phase of the study formulates unbiased mean estimators using stratified two- and three-stage cluster sampling. To further enhance the precision of these estimators, a ranked-set sampling strategy is applied to the secondary and tertiary sampling stages. Additionally, unbiased variance estimators corresponding to the proposed mean estimators are derived. Real-world datasets are utilized to demonstrate the application of these complex survey sampling methodologies, with results showing that the mean estimates derived using ranked set sampling are more accurate than those obtained via simple random sampling.

The Internet of Things (IoT) has realised the fourth industrial revolution concept; however, its applications in the manufacturing industry are relatively sparse and primarily investigated without contextual peculiarities. Our research undertakes an intricate critical review to investigate significant aspects of IoT applications in the manufacturing Industry 4.0 perspective to address this gap. We adopt a systematic literature review approach by Denyer and Tranfield (2009) to carry out critical analyses that help develop future research domains based on empirical studies. We describe key knowledge gaps in the existing literature and empirical studies by exploring the main contribution categories and finding six critical differences between traditional and manufacturing Industry 4.0 and 10 enablers and 11 challenges of IoT applications. Finally, an agenda for future research is proposed with 11 research domains to focus on the recognised gaps.

Industry 4.0 is a new paradigm of digitalization and automation that demands high data rates and real-time ultra-reliable agile communication. Industrial communication at sub-6 GHz industrial, scientific, and medical (ISM) bands has some serious impediments, such as interference, spectral congestion, and limited bandwidth. These limitations hinder the high throughput and reliability requirements of modern industrial applications and mission-critical scenarios. In this paper, we critically assess the potential of the 60 GHz millimeter-wave (mmWave) ISM band as an enabler for ultra-reliable low-latency communication (URLLC) in smart manufacturing, smart factories, and mission-critical operations in Industry 4.0 and beyond. A holistic overview of 60 GHz wireless standards and key performance indicators are discussed. Then the review of 60 GHz smart antenna systems facilitating agile communication for Industry 4.0 and beyond is presented. We envisage that the use of 60 GHz communication and smart antenna systems are crucial for modern industrial communication so that URLLC in Industry 4.0 and beyond could soar to its full potential.

Some soil microbes, with their diverse inhabitance, biologically active metabolites, and endospore formation, gave them characteristic predominance and recognition among other microbial communities. The present study collected ten soil samples from green land, agricultural and marshy soil sites of Khyber Pakhtunkhwa, Pakistan. After culturing on described media, the bacterial isolates were identified through phenotypic, biochemical and phylogenetic analysis. Our phylogenetic analysis revealed three bacterial isolates, A6S7, A1S6, and A1S10, showing 99% nucleotides sequence similarity with Brevibacillus formosus, Bacillus Subtilis and Paenibacillus dendritiformis. The crude extract was prepared from bacterial isolates to assess the anti-bacterial potential against various targeted multidrug-resistant strains (MDRS), including Acinetobacter baumannii (ATCC 19606), Methicillin-resistant Staphylococcus aureus (MRSA) (BAA-1683), Klebsiella pneumoniae (ATCC 13883), Pseudomonas aeruginosa (BAA-2108), Staphylococcus aureus (ATCC 292013), Escherichia coli (ATCC25922) and Salmonella typhi (ATCC 14028). Our analysis revealed that all bacterial extracts possess activity against Gram-negative and Gram-positive bacteria at a concentration of 5 mg/mL, efficiently restricting the growth of E. coli compared with positive control ciprofloxacin. The study concluded that the identified species have the potential to produce antimicrobial compounds which can be used to control different microbial infections, especially MDRS. Moreover, the analysis of the bacterial extracts through GC-MS indicated the presence of different antimicrobial compounds such as propanoic acid, oxalic acid, phenol and hexadecanoic acid.

Independent control of electromagnetic (EM) waves by metasurfaces for multiple tasks are highly desired and is the recent hot topic of research. In this work we contribute a polarization insensitive frequency multiplexed 2-bit coding metasurface to control the Terahertz (THz) waves in the two operating bands independently. In this regard, as a first step a cascaded meta-atom composed of square rings and/or square metallic patches separated by two polyimide substrates is designed and optimized that provides sixteen independent distinct discrete phases in the reflection geometry. These meta-atoms are then distributed with distinct coding sequences in the two-dimensional spatial plane to realize various bi-functional metasurfaces. As a proof of the concept various full structures are designed and simulated to realize a series of bi-functionalities including anomalous reflection/beam shaping, beam shaping/anomalous reflection, beam deflection/Orbital angular momentum (OAM) beam generation with distinct modes and propagating wave to surface wave (PW–SW) conversion/PW beam manipulation in the lower and higher THz bands, respectively. All the simulation results are in excellent agreement with their theoretical equivalents. We envision that the proposed meta-designs have potential applications for the multi-spectral control of EM waves in THz band. The idea can be further extended to design frequency dependent tri-functional and multi-functional THz meta-devices.

Chalcogenide phase-change materials (PCMs) are widely applied in electronic and photonic applications, such as non-volatile memory and neuro-inspired computing. Doped Sb2Te alloys are now gaining increasing attention for on-chip photonic applications, due to their growth-driven crystallization features. However, it remains unknown whether Sb2Te also forms a metastable crystalline phase upon nanoseconds crystallization in devices, similar to the case of nucleation-driven Ge-Sb-Te alloys. Here, we carry out ab initio simulations to understand the changes in optical properties of amorphous Sb2Te upon crystallization and post annealing. During the continuous transformation process, changes in the dielectric function are highly wavelength-dependent from the visible-light range towards the telecommunication band. Our finite-difference time-domain simulations based on the ab initio input reveal key differences in device output for color display and photonic memory applications upon tellurium ordering. Our work serves as an example of how multiscale simulations of materials can guide practical photonic phase-change applications.