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Defining and validating a map of related technologies is critical for managers, investors and inventors. Because of the increase in the applications of and demand for semiconductor lasers, analyzing the technological position of developers has become increasingly critical. Therefore, the purpose of this study is to adopt the technological position analysis to identify mainstream technologies and developments relevant to semiconductor lasers.

Correspondence analysis and k-means cluster analysis, which are data mining techniques, are used to reveal strategic groups of major competitors in the semiconductor laser market according to their Patent Cooperation Treaty (PCT) patent applications.

The results of this study reveal that PCT patent applications are generally obtained for masers, optical elements, semiconductor devices and methods for measuring and that technology developers have varying technological positions.

Through position analysis, this study identifies the technological focuses of different manufacturers to obtain information that can guide the allocation of research and development resources.

Renewable energy alternatives and nanoscale materials have gained huge attention in recent years due to the problems associated with fossil fuels. The recyclable battery is one of the recent developments to address the energy requirement issues. In this work, the development of nanoscale materials is focused on using green synthesis methods to address the energy requirements of hybrid electric vehicles.

The current research focuses on developing metal oxide nanoscale materials (NANO-SMs). The Zno-Aloe vera NANO-SM is prepared using the green synthesis method. The developed nanoscale materials are characterized using analysis methods like FESEM, TEM, XRD and FTIR.

The average size of ZnO-Aloe vera mono-crystalline was recorded as 60–70 nm/Hexagonal shape. The nanoscale materials are used for the detection of LPG gases. The sensitivity observed was 48%. The response time and recovery time were recorded as 8–10 s and 230–250 s, respectively. The average size of SnO₂-green papaya leaves poly-crystalline was recorded as 10–20 nm/powder form.

Nanoscale materials are developed using green synthesis methods for hybrid vehicle applications. The nanoscale materials are used for the detection of harmful gases in hybrid vehicles.

The purpose of this study is to introduce a new type of sensor which uses microwave metamaterials and direct-coupled split-ring resonators (DC-SRRs) to measure the dielectric properties of solid materials in real time. The sensor uses a transmission line with a bridge-type structure to measure the differential frequency, which can be used to calculate the dielectric constant of the material being tested. The study aims to establish an empirical relationship between the dielectric properties of the material and the frequency measurements obtained from the sensor.

In the proposed design, the opposite arm of the bridge transmission line is loaded by DC-SRRs, and the distance between DC-SRRs is optimized to minimize the mutual coupling between them. The DC-SRRs are loaded with the material under test (MUT) to perform differential permittivity sensing. When identical MUT is placed on both resonators, a single transmission zero (notch) is obtained, but non-identical MUTs exhibit two split notches. For the design of differential sensors and comparators based on symmetry disruption, frequency splitting is highly useful.

The proposed structure is demonstrated using electromagnetic simulation, and a prototype of the proposed sensor is fabricated and experimentally validated to prove the differential sensing principle. Here, the sensor is analyzed for sensitivity by using different MUTs with relative permittivity ranges from 1.006 to 10 and with a fixed dimension of 9 mm × 10 mm ×1.2 mm. It shows a very good average frequency deviation per unit change in permittivity of the MUTs, which is around 743 MHz, and it also exhibits a very high average relative sensitivity and quality factor of around 11.5% and 323, respectively.

The proposed sensor can be used for differential characterization of permittivity and also as a comparator to test the purity of solid dielectric samples. This sensor most importantly strengthens robustness to environmental conditions that cause cross-sensitivity or miscalibration. The accuracy of the measurement is enhanced as compared to conventional single- and double-notch metamaterial-based sensors.

High packaging density in the present VLSI era builds an acute power crisis, which limits the use of MOSFET device as a constituent block in CMOS technology. This leads researchers in looking for alternative devices, which can replace the MOSFET in CMOS VLSI logic design. In a quest for alternative devices, tunnel field effect transistor emerged as a potential alternative in recent times. The purpose of this study is to enhance the performances of the proposed device structure and make it compatible with circuit implementation. Finally, the performances of that circuit are compared with CMOS circuit and a comparative study is made to find the superiority of the proposed circuit with respect to conventional CMOS circuit.

Silicon–germanium heterostructure is currently one of the most promising architectures for semiconductor devices such as tunnel field effect transistor. Analytical modeling is computed and programmed with MATLAB software. Two-dimensional device simulation is performed by using Silvaco TCAD (ATLAS). The modeled results are validated through the ATLAS simulation data. Therefore, an inverter circuit is implemented with the proposed device. The circuit is simulated with the Tanner EDA tool to evaluate its performances.

The proposed optimized device geometry delivers exceptionally low OFF current (order of 10^−18 A/um), fairly high ON current (5x10^−5 A/um) and a steep subthreshold slope (20 mV/decade) followed by excellent ON–OFF current ratio (order of 10^13) compared to the similar kind of heterostructures. With a very low threshold voltage, even lesser than 0.1 V, the proposed device emerged as a good replacement of MOSFET in CMOS-like digital circuits. Hence, the device is implemented to construct a resistive inverter to study the circuit performances. The resistive inverter circuit is compared with a resistive CMOS inverter circuit. Both the circuit performances are analyzed and compared in terms of power dissipation, propagation delay and power-delay product. The outcomes of the experiments prove that the performance matrices of heterojunction Tunnel FET (HTFET)-based inverter are way ahead of that of CMOS-based inverter.

Germanium–silicon HTFET with stack gate oxide is analytically modeled and optimized in terms of performance matrices. The device performances are appreciable in comparison with the device structures published in contemporary literature. CMOS-like resistive inverter circuit, implemented with this proposed device, performs well and outruns the circuit performances of the conventional CMOS circuit at 45-nm technological node.

The aim of this study is to determine the fracture behavior of wool felt and fabric based epoxy composites and their responses to electromagnetic waves.

Notched and unnotched tensile tests of composites made of wool only and hybridized with a glass fiber layer were carried out, and fracture behavior and toughness at macro scale were determined. They were exposed to electromagnetic waves between 8 and 18 GHz frequencies using two horn antennas.

The keratin and lignin layer on the surface of the wool felt caused lower values to be obtained compared to the mechanical values given by pure epoxy. However, the use of wool felt in the symmetry layer of the laminated composite material provided higher mechanical values than the composite with glass fiber in the symmetry layer due to the mechanical interlocking it created. The use of wool in fabric form resulted in an increase in the modulus of elasticity, but no change in fracture toughness was observed. As a result of the electromagnetic analysis, it was also seen in the electromagnetic analysis that the transmittance of the materials was high, and the reflectance was low throughout the applied frequency range. Hence, it was concluded that all of the manufactured materials could be used as radome material over a wide band.

Sheep wool is an easy-to-supply and low-cost material. In this paper, it is presented that sheep wool can be evaluated as a biocomposite material and used for radome applications.

The combined evaluation of felt and fabric forms of a natural and inexpensive reinforcing element such as sheep wool and the combined evaluation of fracture mechanics and electromagnetic absorption properties will contribute to the evaluation of biocomposites in aviation.

This study aims to synthesize and analyze research on the Supply Chain Management Practices (SCMPs)-performance nexus, examine current knowledge, identify emerging trends, and provide plausible suggestions for future research engagements in the manufacturing sector in the context of Developing and Emerging Economies (DEEs).

Following a systematic review approach, this study analyzed 20 peer-reviewed scientific journal articles published between 2007 and 2021. The study sample was systematically selected from the Web of Science (WoS) and Google Scholar databases, following strict evaluation and selection criteria.

Numerous dimensions of SCMPs have been considered in the extant literature; however, six have stood out as the most common. In addition, operational performance stood out as the most widely investigated measure in the SCM literature. Moreover, SCMPs have predominantly shown positive effects on performance outcomes. Methodological issues that future studies should consider are suggested.

The sample size was not sufficiently large relative to the rule of thumb set in the literature because of the scarcity of studies in the manufacturing sector in the DEEs context. Despite these limitations, the results of this study provide crucial insights into knowledge and practice.

This review is the first of its kind to examine the SCMPs-performance nexus in the context of DEEs. Based on the findings of this study, future research directions are proposed.

The purpose of this paper is to illuminate the ethical concerns associated with the use of artificial intelligence (AI) in the medical sector and to provide solutions that allow deriving maximum benefits from this technology without compromising ethical principles.

This paper provides a comprehensive overview of AI in medicine, exploring its technical capabilities, practical applications, and ethical implications. Based on our expertise, we offer insights from both technical and practical perspectives.

The study identifies several advantages of AI in medicine, including its ability to improve diagnostic accuracy, enhance surgical outcomes, and optimize healthcare delivery. However, there are pending ethical issues such as algorithmic bias, lack of transparency, data privacy issues, and the potential for AI to deskill healthcare professionals and erode humanistic values in patient care. Therefore, it is important to address these issues as promptly as possible to make sure that we benefit from the AI’s implementation without causing any serious drawbacks.

This paper gains its value from the combined practical experience of Professor Elhassan gained through his practice at top hospitals worldwide, and the theoretical expertise of Dr. Arabi acquired from international institutes. The shared experiences of the authors provide valuable insights that are beneficial for raising awareness and guiding action in addressing the ethical concerns associated with the integration of artificial intelligence in medicine.

The COVID-19 pandemic has caused significant disruptions to global supply chains (SCs), affecting the production, distribution, and transportation of goods and services. To mitigate these disruptions, it is essential to identify the barriers that have impeded the seamless operation of SCs. This study identifies these barriers and assesses their impact on supply chain network (SCN).

To determine the relative importance of different barriers and rank the affected industries, a hybrid approach was employed, combining the best-worst method (BWM) and the technique for order preference by similarity to an ideal solution (TOPSIS). To accommodate the inherent uncertainties associated with the pandemic, a triangular fuzzy TOPSIS was used to represent the linguistic variable ratings provided by decision-makers.

The study found that the airlines and hospitality industry was the most affected by the barriers, accounting for 46% of the total, followed by the healthcare industry (23%), the manufacturing industry (19%), and finally the consumer and retail industry (17%).

This study is limited to the four critical industries and nine identified barriers. Other industries and barriers may have different weights and rankings. Nevertheless, the findings offer valuable insights for decision-makers in SC management, aiding them in mitigating the impact of COVID-19 on their operations and enhancing their resilience against future disruptions.

This study enhances understanding of COVID-19’s impact on SCN and provides a framework for assessing disruptions using multi-criteria decision-making processes. The hybrid approach of BWM and TOPSIS in a fuzzy environment is unique and offers potential applicability in various evaluation contexts.

This paper aims to provide insight into the evolving relationship between humans and machines, understanding its multifaceted impact on our lifestyle and landscape in the past as well as in the present, with implications for the near future. It uses bibliometric analysis combined with a systematic literature review to identify themes, trace historical developments and offer a direction for future human–machine interactions (HMIs).

To provide thorough coverage of publications from the previous four decades, the first section presents a text-based cluster bibliometric analysis based on 305 articles from 2,293 initial papers in the Scopus and Web of Science databases produced between 1984 and 2022. The authors used VOS viewer software to identify the most prominent themes through cluster identification. This paper presents a systematic literature review of 63 qualified papers using the PRISMA framework.

Next, the systematic literature review and bibliometric analysis revealed four major historical themes and future directions. The results highlight four major research themes for the future: from Taylorism to advanced technologies; machine learning and innovation; Industry 4.0, Society 5.0 and cyber–physical system; and psychology and emotions.

There is growing anxiety among humankind that in the future, machines will overtake humans to replace them in various roles. The current study investigates the evolution of HMIs from their historical roots to Society 5.0, which is understood to be a human-centred society. It balances economic advancement with the resolution of social problems through a system that radically integrates cyberspace and physical space. This paper contributes to research and current limited knowledge by identifying relevant themes and offering scope for future research directions. A close look at the analysis posits that humans and machines complement each other in various roles. Machines reduce the mechanical work of human beings, bringing the elements of humanism and compassion to mechanical tasks. However, in the future, smart innovations may yield machines with unmatched dexterity and capability unthinkable today.

This paper attempts to explore the ambiguous and dynamic relationships between humans and machines. The present study combines systematic review and bibliometric analysis to identify prominent trends and themes. This provides a more robust and systematic encapsulation of this evolution and interaction, from Taylorism to Society 5.0. The principles of Taylorism are extended and redefined in the context of HMIs, especially advanced technologies.

Advancement in optimization of VLSI circuits involves reduction in chip size from micrometer to nanometer level as well as fabrication of a billions of transistors in a single die where global routing problem remains significant with a trade-off of power dissipation and interconnect delay. This paper aims to solve the increased complexity in VLSI chip by minimization of the wire length in VLSI circuits using a new approach based on nature-inspired meta-heuristic, invasive weed optimization (IWO). Further, this paper aims to achieve maximum circuit optimization using IWO hybridized with particle swarm optimization (PSO).

This paper projects the complexities of global routing process of VLSI circuit design in mapping it with a well-known NP-complete problem, the minimum rectilinear Steiner tree (MRST) problem. IWO meta-heuristic algorithm is proposed to meet the MRST problem more efficiently and thereby reducing the overall wire-length of interconnected nodes. Further, the proposed approach is hybridized with PSO, and a comparative analysis is performed with geosteiner 5.0.1 and existing PSO technique over minimization, consistency and convergence against available benchmark.

This paper provides high performance–enhanced IWO algorithm, which keeps in generating low MRST value, thereby successful wire length reduction of VLSI circuits is significantly achieved as evident from the experimental results as compared to PSO algorithm and also generates value nearer to geosteiner 5.0.1 benchmark. Even with big VLSI instances, hybrid IWO with PSO establishes its robustness over achieving improved optimization of overall wire length of VLSI circuits.

This paper includes implications in the areas of optimization of VLSI circuit design specifically in the arena of VLSI routing and the recent developments in routing optimization using meta-heuristic algorithms.

This paper fulfills an identified need to study optimization of VLSI circuits where minimization of overall interconnected wire length in global routing plays a significant role. Use of nature-based meta-heuristics in solving the global routing problem is projected to be an alternative approach other than conventional method.