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With revolutions in the telecommunication sector having led to wide unprecedented consequences in all facets of human life, this paper aims to examine the relationship between cell phone tower base stations (CPTBSs) and residential property prices within the City of Johannesburg (CoJ), South Africa.

The authors align their work with global literature and assess how the impact of CPTBSs influences residential property values in South Africa. The authors use a semi-log hedonic pricing model to test the hypothesis that proximity of CPTBSs to residential properties does not account for any variation in residential property prices.

The results show a significant impact that proximity of CPTBS has on residential property sale prices. However, the impact of CTPBSs’ proximity on residential property prices depends on their distance from the residential properties. The closer a residential property is to the CTPBS, the greater the impact that the CTPBS will have on the selling price of the residential property.

With international studies offering mixed findings on the impact of CPTBSs on residential property values, there is limited research on their impact in South Africa. The findings of this study offer crucial insights for the real estate practitioners, property owners, telecommunications companies and the public, providing a nuanced understanding of the relationship between CPTBSs and property values. This research helps property owners understand the effects of CPTBSs on their properties, and it assists property valuers in gauging the impact of CPTBSs on property values.

Petroleum hydrocarbons are naturally occurring flammable fossil fuels used as conventional energy sources. It has carcinogenic, mutagenic properties and is considered a hazardous pollutant. Soil contaminated with petroleum hydrocarbons adversely affects the properties of soil. This paper aim to remove pollutants from the environment is an urgent need of the hour to maintain the proper functioning of soil ecosystems.

The ability of micro-organisms to degrade petroleum hydrocarbons makes it possible to use these microorganisms to clean the environment from petroleum pollution. For preparing this review, research papers and review articles related to petroleum hydrocarbons degradation by micro-organisms were collected from journals and various search engines.

Various physical and chemical methods are used for remediation of petroleum hydrocarbons contaminants. However, these methods have several disadvantages. This paper will discuss a novel understanding of petroleum hydrocarbons degradation and how micro-organisms help in petroleum-contaminated soil restoration. Bioremediation is recognized as the most environment-friendly technique for remediation. The research studies demonstrated that bacterial consortium have high biodegradation rate of petroleum hydrocarbons ranging from 83% to 89%.

Proper management of petroleum hydrocarbons pollutants from the environment is necessary because of their toxicity effects on human and environmental health.

This paper discussed novel mechanisms adopted by bacteria for biodegradation of petroleum hydrocarbons, aerobic and anaerobic biodegradation pathways, genes and enzymes involved in petroleum hydrocarbons biodegradation.

Some studies have been reported in the past on diaphragmatic hernia (DH) surgery techniques using additive manufacturing (AM) technologies, symptoms of a hernia and post-surgery complications. But hitherto little has been reported on bibliographic analysis (BA) for health monitoring of bovine post-DH surgery for long-term management. Based on BA, this study aims to explore the sensor fabrication integrated with innovative AM technologies for health monitoring assistance of bovines post-DH surgery.

A BA based on the data extracted through the Web of Science database was performed using bibliometric tools (R-Studio and Biblioshiny).

After going through the BA and a case study, this review provides information on various 3D-printed meshes used over the sutured site and available Internet of Things-based solutions to prevent the recurrence of DH.

Research gaps exist for 3D-printed conformal sensors for health monitoring of bovine post-DH surgery.

Surface acoustic wave (SAW) sensors have attracted great attention worldwide for a variety of applications in measuring physical, chemical and biological parameters. However, stability has been one of the key issues which have limited their effective commercial applications. To fully understand this challenge of operation stability, this paper aims to systematically review mechanisms, stability issues and future challenges of SAW sensors for various applications.

This review paper starts with different types of SAWs, advantages and disadvantages of different types of SAW sensors and then the stability issues of SAW sensors. Subsequently, recent efforts made by researchers for improving working stability of SAW sensors are reviewed. Finally, it discusses the existing challenges and future prospects of SAW sensors in the rapidly growing Internet of Things-enabled application market.

A large number of scientific articles related to SAW technologies were found, and a number of opportunities for future researchers were identified. Over the past 20 years, SAW-related research has gained a growing interest of researchers. SAW sensors have attracted more and more researchers worldwide over the years, but the research topics of SAW sensor stability only own an extremely poor percentage in the total researc topics of SAWs or SAW sensors.

Although SAW sensors have been attracting researchers worldwide for decades, researchers mainly focused on the new materials and design strategies for SAW sensors to achieve good sensitivity and selectivity, and little work can be found on the stability issues of SAW sensors, which are so important for SAW sensor industries and one of the key factors to be mature products. Therefore, this paper systematically reviewed the SAW sensors from their fundamental mechanisms to stability issues and indicated their future challenges for various applications.

This paper aims to present the design development and measurement of two aerodynamic slotted X-bands back-to-back planer substrate-integrated rectangular waveguide (SIRWG/SIW) to Microstrip (MS) line transition for satellite and RADAR applications. It facilitates the realization of nonplanar (waveguide-based) circuits into planar form for easy integration with other planar (microstrip) devices, circuits and systems. This paper describes the design of a SIW to microstrip transition. The transition is broadband covering the frequency range of 8–12 GHz. The design and interconnection of microwave components like filters, power dividers, resonators, satellite dishes, sensors, transmitters and transponders are further aided by these transitions. A common planar interconnect is designed with better reflection coefficient/return loss (RL) (S₁₁/S₂₂ ≤ 10 dB), transmission coefficient/insertion loss (IL) (S₁₂/S₂₁: 0–3.0 dB) and ultra-wideband bandwidth on low profile FR-4 substrate for X-band and Ku-band functioning to interconnect modern era MIC/MMIC circuits, components and devices.

Two series of metal via (6 via/row) have been used so that all surface current and electric field vectors are confined within the metallic via-wall in SIW length. Introduced aerodynamic slots in tapered portions achieve excellent impedance matching and tapered junctions with SIW are mitered for fine tuning to achieve minimum reflections and improved transmissions at X-band center frequency.

Using this method, the measured IL and RLs are found in concord with simulated results in full X-band (8.22–12.4 GHz). RLC T-equivalent and p-equivalent electrical circuits of the proposed design are presented at the end.

The measurement of the prototype has been carried out by an available low-cost X-band microwave bench and with a Keysight E4416A power meter in the microwave laboratory.

The transition is fabricated on FR-4 substrate with compact size 14 mm × 21.35 mm × 1.6 mm and hence economical with IL lie within limits 0.6–1 dB and RL is lower than −10 dB in bandwidth 7.05–17.10 GHz. Because of such outstanding fractional bandwidth (FBW: 100.5%), the transition could also be useful for Ku-band with IL close to 1.6 dB.

At the moment, in terms of both research and commercial products, smart shoe technology and applications seem not to attract the same magnitude of attention compared to smart garments and other smart wearables such as wrist watches and wrist bands. The purpose of this study is to fill this knowledge gap by discussing issues regarding smart shoe sensing technologies, smart shoe sensor placements, factors that affect sensor placements and finally the areas of smart shoe applications.

Through a review of relevant literature, this study first and foremost attempts to explain what constitutes a smart shoe and subsequently discusses the current trends in smart shoe applications. Discussed in this study are relevant sensing technologies, sensor placement and areas of smart shoe applications.

This study outlined 13 important areas of smart shoe applications. It also uncovered that majority of smart shoe functionality are physical activity tracking, health rehabilitation and ambulation assistance for the blind. Also highlighted in this review are some of the bottlenecks of smart shoe development.

To the best of the authors’ knowledge, this is the first comprehensive review paper focused on smart shoe applications, and therefore serves as an apt reference for researchers within the field of smart footwear.

Agriculture is the backbone of a country, contributing more than half of the sector of economy throughout the world. The need for precision agriculture is essential in evaluating the conditions of the crops with the aim of determining the proper selection of pesticides. The conventional method of pest detection fails to be stable and provides limited accuracy in the prediction. This paper aims to propose an automatic pest detection module for the accurate detection of pests using the hybrid optimization controlled deep learning model.

The paper proposes an advanced pest detection strategy based on deep learning strategy through wireless sensor network (WSN) in the agricultural fields. Initially, the WSN consisting of number of nodes and a sink are clustered as number of clusters. Each cluster comprises a cluster head (CH) and a number of nodes, where the CH involves in the transfer of data to the sink node of the WSN and the CH is selected using the fractional ant bee colony optimization (FABC) algorithm. The routing process is executed using the protruder optimization algorithm that helps in the transfer of image data to the sink node through the optimal CH. The sink node acts as the data aggregator and the collection of image data thus obtained acts as the input database to be processed to find the type of pest in the agricultural field. The image data is pre-processed to remove the artifacts present in the image and the pre-processed image is then subjected to feature extraction process, through which the significant local directional pattern, local binary pattern, local optimal-oriented pattern (LOOP) and local ternary pattern (LTP) features are extracted. The extracted features are then fed to the deep-convolutional neural network (CNN) in such a way to detect the type of pests in the agricultural field. The weights of the deep-CNN are tuned optimally using the proposed MFGHO optimization algorithm that is developed with the combined characteristics of navigating search agents and the swarming search agents.

The analysis using insect identification from habitus image Database based on the performance metrics, such as accuracy, specificity and sensitivity, reveals the effectiveness of the proposed MFGHO-based deep-CNN in detecting the pests in crops. The analysis proves that the proposed classifier using the FABC+protruder optimization-based data aggregation strategy obtains an accuracy of 94.3482%, sensitivity of 93.3247% and the specificity of 94.5263%, which is high as compared to the existing methods.

The proposed MFGHO optimization-based deep-CNN is used for the detection of pest in the crop fields to ensure the better selection of proper cost-effective pesticides for the crop fields in such a way to increase the production. The proposed MFGHO algorithm is developed with the integrated characteristic features of navigating search agents and the swarming search agents in such a way to facilitate the optimal tuning of the hyperparameters in the deep-CNN classifier for the detection of pests in the crop fields.

How can large international financial firms go green in authentic ways? What enhances ‘Net Zero action’? Changes in global banks, fund managers, and insurance firms are at the heart of green finance. External change pressures – combined with problematic firm predispositions – exacerbate barriers to change and promote scepticism about authentic Net Zero change. Field research reveals main elements, connections, and interactions of this question by considering financial firms as complex socio-technical systems (Mitleton-Kelly, 2003). An interdisciplinary/holistic narrative approach (De Bakker et al., 2019) is adopted to design a conceptual framework that can support a green ‘behavioural theory of the financial firm’ (green BTFF). The BTFF presents an international version (Peng, 2001) of the resource-based view (RBV) of the firm (Barney, 1991; Hart, 1995; Teece et al., 1997).

The approach of this chapter is aimed at closing knowledge gaps and realign values in financial markets and society. By raising awareness about organised hypocrisy and facades (Brunsson, 1993; Cho et al., 2015; Schoeneborn et al., 2020) in financial firms the chapter aims at overcoming the gap between ‘talking’ and ‘walking’ in the financial sector. The chapter defines testable firm-level hypotheses for ‘Green Finance’ (Poterba, 2021) as well as – by implication – tests for ‘greenwashing’.

The purpose of this paper is to investigate how different manufacturing technologies are bundled together and how these bundles influence operations performance and, indirectly, business performance. With the emergence of Industry 4.0 (I4.0) technologies, manufacturing companies can use a wide variety of advanced manufacturing technologies (AMT) to build an efficient and effective production system. Nevertheless, the literature offers little guidance on how these technologies, including novel I4.0 technologies, should be combined in practice and how these combinations might have a different impact on performance.

Using a survey study of 165 manufacturing plants from 11 different countries, we use factor analysis to empirically derive three distinct manufacturing technology bundles and structural equation modeling to quantify their relationship with operations and business performance.

Our findings support an evolutionary rather than a revolutionary perspective. I4.0 technologies build on traditional manufacturing technologies and do not constitute a separate direction that would point towards a fundamental digital transformation of companies within our sample. Performance effects are rather weak: out of the three technology bundles identified, only “automation and robotization” have a positive influence on cost efficiency, while “base technologies” and “data-enabled technologies” do not offer a competitive advantage, neither in terms of cost nor in terms of differentiation. Furthermore, while the business performance impact is positive, it is quite weak, suggesting that financial returns on technology investments might require longer time periods.

Relying on a complementarity approach, our research offers a novel perspective on technology implementation in the I4.0 era by investigating novel and traditional manufacturing technologies together.

Scholarship on America’s K-12 economics curriculum reveals an inattention to many harmful economic realities, specifically wealth inequality. Critics of the present curriculum posit that its emphasis on out-dated concepts and models ignores crucial elements of reality that impact economic interaction and identities. In response to the dominant economic paradigm and methods, this practitioner-focused paper discusses an economically pluralist, pedagogically critical approach to interrogating destructive economic realities. It details how three social studies classroom simulations based on the board game Monopoly may be integrated with certain informational texts to explore economic factors that contribute to America’s unique form of wealth inequality.

This paper describes wealth inequality in America and rationalizes the need to make this social problem a focus of study in the secondary social studies classroom. First, I survey the present curricular apparatus of K-12 economics education and then argue for a pluralist approach that expands the curriculum’s dominant neoclassical paradigm. Connecting economic pluralism to critical citizen education, I draw upon emerging critical economic citizen education scholarship to explain attendant pedagogical and instructional approaches. The described lesson builds upon a tradition of Monopoly simulations, is rooted in critical citizen education pedagogy and aligns with Soroko’s (2023) critical economic literacy framework.

This paper progresses the curricular movement of economic pluralism through its critique of America’s current K-12 economics curriculum that does not focus on immediate, lived social problems. It further defines critical economics, citizenship and pedagogy, then details an instructional practice that employs critical disciplinary tools to investigate contributing factors of American wealth inequality.

This paper contributes to the growing field of pluralist economic perspectives and pedagogies. Specifically, it enriches understanding of critical economics citizenship education by further defining attendant pedagogy and explaining Monopoly as an instructional tool for critical economics citizen education. Previous works have discussed Monopoly’s utility for teaching various concepts within the social studies disciplines. This simulation lesson is unique in its instructional approach that merges simulation experiences with certain informational texts to cultivate critical economic knowledge of American wealth inequality and critical economic skills for critiquing and transforming oppressive economic realities.