Search

Anaerobic digestion technology is a well-established technology for energy generation as well as an efficient and economical way of waste disposal. The research was focused on the design, development, and commissioning of a continuous stirring batch reactor to calculate the biodegradability and potential of biomethane production through biowaste (molasses). The reactor was designed using Solid Work software, and fabricated using locally available market components. The continuous stirring batch reactor (CSBR) operation was analyzed through a 30-day batch digestion experiment to estimate the methane percentage and biodegradability of the molasses at 37 °C. The maximum biomethane production was recorded as 52% in the biogas mixture produced from the anaerobic digestion of molasses with a 42% biodegradability of the feedstock. The performance of the CSBR experiment was then compared with the biogas simulator (AMPTS II) experiment using the same kind of cow dung inoculum and de-sugar molasses. The maximum biomethane potential was observed as 221L CH4 kg VS−1 produced using the bio-simulator. In contrast, a lower biomethane potential of 210L CH4 kg VS−1 was recorded using CSBR by adding VS-based 40-mL molasses. [ABSTRACT FROM AUTHOR]

This paper examines sustainable development, which employs an integrated approach to tackle environmental, social, and economic challenges. It provides a theoretical underpinning by examining sustainable development's inception, fundamental tenets, and conceptual structures. This study highlights the interdependence of social equity, economic prosperity, and environmental conservation, emphasizing the need for a comprehensive approach. Quantitative methodology is utilized in this study, and the dependent variable is sustainable development. Financial risk, green growth, technological innovation, renewable energy, financial inclusion, and soft infrastructure are all independent variables. The analysis is predicated on secondary data from the Organization for Economic Cooperation and Development and World Development Indicators databases spanning 2004 to 2019. An entropy-weighted method used for the green growth index is a metric that enhances the precision of variable indicators. Cointegration, correlation, VIF, cross-sectional dependency, and stationarity tests are among the diagnostic tests that inform the selection of methods for the panel data set. It is determined that fully modified ordinary least squares is the suitable technique. The findings suggest statistically significant positive correlations among greenhouse gases, financial inclusion, and soft infrastructure. Conversely, significant negative correlations exist between financial risk, green growth, renewable energy, and technological innovation. An estimated 55% long-run variance is present. The study's key finding is that financial risk has an adverse effect on sustainable development, while an impactful relationship where increased green growth is linked to decreased GHG emissions. This association is notably significant. Results show that renewable energy has a negative coefficient and significant negative impact on greenhouse gases, showing an active relation to enhancing sustainable development. In contrast, financial inclusion has a significant positive effect on sustainable development. The implications imply that providing incentives to institutions engaged in alternative energy, precisely renewable sources, could positively impact the environment. Government policies and funding regulations oriented toward sustainable development are indispensable for environmental sustainability. Government policies and incentives are pivotal in advancing an environmentally conscious and sustainable future. This study's contribution lies in elucidating the positive correlation between government interventions and promoting renewable energy adoption, thereby paving the way for a greener tomorrow. [ABSTRACT FROM AUTHOR]

The study focuses on the relationship between leader-member exchange and innovative work behavior of project employees with mediating role of employee engagement and moderating role of self-efficacy. This study was conducted in the IT sector of Pakistan. Questionnaires were used to collect data from 230 employees of IT projects. The research findings highlight the actions that need to be taken to increase the innovative work behavior of project employees. Results indicate that leader-member exchange is positively linked with innovative work behavior while employee engagement mediates the relationship between leader-member exchange and innovative work behavior. In addition, the results confirmed the moderating role of Self-Efficacy between employee engagement and innovative work behavior. The positive exchange between leader and member can contribute positively toward the outcomes, especially in project-based organizations where every project is unique and requires the exchange of knowledge and ideas. [ABSTRACT FROM AUTHOR]

In the present study, Pb1−xMnxS (0, 0.04, and 0.06) nanoparticles (NPs) were prepared by chemical co-precipitation process. The effects of Mn doping on the structural, morphological, vibrational, optical, and electrical characteristics of the PbS nanoparticles were investigated by X-ray diffraction (XRD), scanning electron microscopy (SEM), Raman spectroscopy, UV–Vis spectroscopy, and I–V characteristics respectively. XRD patterns confirm the face-centered cubic (fcc) structure without impurity phases. The average crystalline size, unit cell constant, and porosity were found in the ranges of 11.93–14.33 nm, 5.7250–5.6888 Å, and 4.80–2.35%, respectively. SEM analysis revealed the flower-like, stone-like, and rode-like shapes of the Pb1−xMnxS (0, 0.04, and 0.06) nanoparticles. SEM images show that the PbS with Mn doping has flower, stone, and rod-like shapes for the samples doped with 0%, 4%, and 6%, respectively, Raman spectra depicted that the 6% Mn-doped PbS nanoparticles have stronger, and sharper peak intensity and bands as compared to PbS nanoparticles doped with 0 and 4%. The energy band gap decreased from 1.48 to 1.42 eV by increasing Mn concentration as compared to that of pure PbS nanoparticles. The electrical studies show that the increase in Mn-doping contents enhanced the electrical characteristics of the PbS nanoparticles without any structural distortions. The resistivity of the Pb1-xMnxS (0, 0.04, and 0.06) nanoparticles reduced from 1.3 × 106 to 2.1 × 103 Ω cm and conductivity increased from 7.69 × 10−7 to 4.76 × 10−4 Ʊ cm−1 with Mn content. Due to extraordinary characteristics and narrow band gaps, pure PbS and Mn-doped PbS nanoparticles are suitable for electronic devices operated at low temperatures. Highlights: Pure and Mn-PbS nanoparticles (NPs) were successfully synthesized by a chemical co-precipitation route. The crystalline size, lattice constant and porosity were 10.35–12.09 nm, 5.7250–5.6888 Å and 4.80–2.35%. SEM images confirmed the varied morphology and increasing grain size. Raman analysis exhibited the high intensity of bands with increasing the Mn-doping content. The energy band gap decreased from 1.48 to 1.42 eV by increasing Mn content. [ABSTRACT FROM AUTHOR]

The agricultural applicability of biogas digestate from cattle manure is based upon its effects on soil fertility and the content of different elements in the grown crop. The aim of this study was to evaluate digestate characteristics obtained from cattle manure and its application on soil fertility and growth of crops (Sorghum bicolor (L.) Moench, Saccharum officinarum L., Gossypium hirsutum L., Zea mays L., Triticum aestivum L.) in field-scale experimentation. Samples of digestate, soil, and crops were collected and analyzed according to standard methods. Afterward, accumulation factor (AF), sodium absorption ratio (SAR), magnesium absorption ratio (MAR), and health index (HI) were determined. The results showed that macro and micronutrients (N, P, K, Fe, Zn, Cu, and Pb) in the digestate group were below the permissible limits and also have irrigational value (SAR > 10) as compared to the control group. Available P and K contents were high in digestate irrigated soil and crop as compared to the control group. The correlation analysis showed that digestate irrigated crops has a strong correlation with high R2 values (0.90) than control soil crops. Accumulation factor was found highest in G. hirsutum (1.48) in Fe. Health index value in adults was within the permissible limit (HI > 1) but the value in children was near to threshold level in T. aestivum. crop as compared to control. It may be concluded that the use of digestate will become a valuable additional source in the enhancement of soil fertility and crop growth. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) is a latest concept of machine-to-machine communication, that also gave birth to several information security problems. Many traditional software solutions fail to address these security issues such as trustworthiness of remote entities. Remote attestation is a technique given by  Trusted Computing Group (TCG) to monitor and verify this trustworthiness. In this regard, various remote validation methods have been proposed. However, static techniques cannot provide resistance to recent attacks e.g. the latest Heartbleed bug, and the recent high profile glibc attack on Linux operating system. In this research, we have designed and implemented a lightweight Linux kernel security module for IoT devices that is  scalable enough to monitor multiple applications in the kernel space. The newly built technique can measure and report multiple application’s static and dynamic behavior simultaneously. Verification of behavior of applications is performed via machine learning techniques. The result shows that deviating behavior can be detected successfully by the verifier.The Internet of Things (IoT) is a latest concept of machine-to-machine communication, that also gave birth to several information security problems. Many traditional software solutions fail to address these security issues such as trustworthiness of remote entities. Remote attestation is a technique given by  Trusted Computing Group (TCG) to monitor and verify this trustworthiness. In this regard, various remote validation methods have been proposed. However, static techniques cannot provide resistance to recent attacks e.g. the latest Heartbleed bug, and the recent high profile glibc attack on Linux operating system. In this research, we have designed and implemented a lightweight Linux kernel security module for IoT devices that is  scalable enough to monitor multiple applications in the kernel space. The newly built technique can measure and report multiple application’s static and dynamic behavior simultaneously. Verification of behavior of applications is performed via machine learning techniques. The result shows that deviating behavior can be detected successfully by the verifier.The Internet of Things (IoT) is a latest concept of machine-to-machine communication, that also gave birth to several information security problems. Many traditional software solutions fail to address these security issues such as trustworthiness of remote entities. Remote attestation is a technique given by  Trusted Computing Group (TCG) to monitor and verify this trustworthiness. In this regard, various remote validation methods have been proposed. However, static techniques cannot provide resistance to recent attacks e.g. the latest Heartbleed bug, and the recent high profile glibc attack on Linux operating system. In this research, we have designed and implemented a lightweight Linux kernel security module for IoT devices that is  scalable enough to monitor multiple applications in the kernel space. The newly built technique can measure and report multiple application’s static and dynamic behavior simultaneously. Verification of behavior of applications is performed via machine learning techniques. The result shows that deviating behavior can be detected successfully by the verifier.The Internet of Things (IoT) is a latest concept of machine-to-machine communication, that also gave birth to several information security problems. Many traditional software solutions fail to address these security issues such as trustworthiness of remote entities. Remote attestation is a technique given by  Trusted Computing Group (TCG) to monitor and verify this trustworthiness. In this regard, various remote validation methods have been proposed. However, static techniques cannot provide resistance to recent attacks e.g. the latest Heartbleed bug, and the recent high profile glibc attack on Linux operating system. In this research, we have designed and implemented a lightweight Linux kernel security module for IoT devices that is  scalable enough to monitor multiple applications in the kernel space. The newly built technique can measure and report multiple application’s static and dynamic behavior simultaneously. Verification of behavior of applications is performed via machine learning techniques. The result shows that deviating behavior can be detected successfully by the verifier.The Internet of Things (IoT) is a latest concept of machine-to-machine communication, that also gave birth to several information security problems. Many traditional software solutions fail to address these security issues such as trustworthiness of remote entities. Remote attestation is a technique given by  Trusted Computing Group (TCG) to monitor and verify this trustworthiness. In this regard, various remote validation methods have been proposed. However, static techniques cannot provide resistance to recent attacks e.g. the latest Heartbleed bug, and the recent high profile glibc attack on Linux operating system. In this research, we have designed and implemented a lightweight Linux kernel security module for IoT devices that is  scalable enough to monitor multiple applications in the kernel space. The newly built technique can measure and report multiple application’s static and dynamic behavior simultaneously. Verification of behavior of applications is performed via machine learning techniques. The result shows that deviating behavior can be detected successfully by the verifier.The Internet of Things (IoT) is a latest concept of machine-to-machine communication, that also gave birth to several information security problems. Many traditional software solutions fail to address these security issues such as trustworthiness of remote entities. Remote attestation is a technique given by  Trusted Computing Group (TCG) to monitor and verify this trustworthiness. In this regard, various remote validation methods have been proposed. However, static techniques cannot provide resistance to recent attacks e.g. the latest Heartbleed bug, and the recent high profile glibc attack on Linux operating system. In this research, we have designed and implemented a lightweight Linux kernel security module for IoT devices that is  scalable enough to monitor multiple applications in the kernel space. The newly built technique can measure and report multiple application’s static and dynamic behavior simultaneously. Verification of behavior of applications is performed via machine learning techniques. The result shows that deviating behavior can be detected successfully by the verifier.The Internet of Things (IoT) is a latest concept of machine-to-machine communication, that also gave birth to several information security problems. Many traditional software solutions fail to address these security issues such as trustworthiness of remote entities. Remote attestation is a technique given by  Trusted Computing Group (TCG) to monitor and verify this trustworthiness. In this regard, various remote validation methods have been proposed. However, static techniques cannot provide resistance to recent attacks e.g. the latest Heartbleed bug, and the recent high profile glibc attack on Linux operating system. In this research, we have designed and implemented a lightweight Linux kernel security module for IoT devices that is  scalable enough to monitor multiple applications in the kernel space. The newly built technique can measure and report multiple application’s static and dynamic behavior simultaneously. Verification of behavior of applications is performed via machine learning techniques. The result shows that deviating behavior can be detected successfully by the verifier.The Internet of Things (IoT) is a latest concept of machine-to-machine communication, that also gave birth to several information security problems. Many traditional software solutions fail to address these security issues such as trustworthiness of remote entities. Remote attestation is a technique given by  Trusted Computing Group (TCG) to monitor and verify this trustworthiness. In this regard, various remote validation methods have been proposed. However, static techniques cannot provide resistance to recent attacks e.g. the latest Heartbleed bug, and the recent high profile glibc attack on Linux operating system. In this research, we have designed and implemented a lightweight Linux kernel security module for IoT devices that is  scalable enough to monitor multiple applications in the kernel space. The newly built technique can measure and report multiple application’s static and dynamic behavior simultaneously. Verification of behavior of applications is performed via machine learning techniques. The result shows that deviating behavior can be detected successfully by the verifier.The Internet of Things (IoT) is a latest concept of machine-to-machine communication, that also gave birth to several information security problems. Many traditional software solutions fail to address these security issues such as trustworthiness of remote entities. Remote attestation is a technique given by  Trusted Computing Group (TCG) to monitor and verify this trustworthiness. In this regard, various remote validation methods have been proposed. However, static techniques cannot provide resistance to recent attacks e.g. the latest Heartbleed bug, and the recent high profile glibc attack on Linux operating system. In this research, we have designed and implemented a lightweight Linux kernel security module for IoT devices that is  scalable enough to monitor multiple applications in the kernel space. The newly built technique can measure and report multiple application’s static and dynamic behavior simultaneously. Verification of behavior of applications is performed via machine learning techniques. The result shows that deviating behavior can be detected successfully by the verifier. [ABSTRACT FROM AUTHOR]