Search

Due to the significance and broad utilization of adders in computing systems, the design of low-power approximate adders (LPAAs) has received a significant amount of attention from the system design community. However, the selection and deployment of appropriate approximate modules require a thorough design space exploration, which is (in general) an extremely time-consuming process. Toward reducing the exploration time, different error estimation techniques have been proposed in the literature for evaluating the quality metrics of approximate adders. However, most of them are based on certain assumptions that limit the usability of such techniques for real-world settings. In this work, we highlight the impact of those assumptions on the quality of error estimates provided by the state-of-the-art techniques and how they limit the use of such techniques for real-world settings. Moreover, we highlight the significance of considering input data characteristics to improve the quality of error estimation. Based on our analysis, we propose a systematic data-driven error estimation methodology, DREAMx, for adders composed of cascaded approximate units, which covers a predominant set of LPAAs. DREAMx in principle factors in the dependence between input bits based on the given input distribution to compute the probability mass function (PMF) of error value at the output of an approximate adder. It achieves improved results compared to the state-of-the-art techniques while offering a substantial decrease in the overall execution(/exploration) time compared to exhaustive simulations. Our results further show that there exists a delicate tradeoff between the achievable quality of error estimates and the overall execution time.

Aim:To determine the efficacy of manuka honey against multidrug-resistant (MDR) and extensively drug-resistant (XDR) clinical strains of SalmonellaTyphi.Materials & methods:Clinical isolates were processed using the Bactec blood culture system, identification and antibiogram by Vitek 2 and antibiotic resistance genes through polymerase chain reaction (PCR). Microbroth dilution assays evaluated the antibacterial activity of manuka honey.Results:MDR and XDR-S.Typhi was susceptible to azithromycin. These strains carried the H58, gyrA, gyrB, blaCTX-M-15 , and blaTEM-1genes. At 100% honey, the zone of inhibition for MDR (15–23 mm) and XDR (15–24 mm) strains. 18/50 MDR and 14/50 XDR strains inhibited at 3.125 v/v% killed at 6.25 v/v% concentration respectively.Conclusion:Manuka honey could be an alternative option for treating S.Typhi infections.

The present study endeavored to design and develop a self-nanoemulsifying drug delivery system to improve the solubility and dermatological absorption of curcumin and naringin. Curcumin and naringin-loaded self-nanoemulsifying drug delivery system formulations were developed using aqueous phase titration. Phase diagrams were used to pinpoint the self-nanoemulsifying drug delivery system zones. Tween 80 and Labrasol (surfactants), Transcutol (cosurfactant), and cinnamon oil were chosen from a large pool of surfactants, cosurfactants, and oils based on their solubility and greatest nano-emulsion region. Fourier transform infrared spectroscopy, zeta sizer, and atomic force microscopy were used to characterize the optimized formulations and test for dilution and thermodynamic stability. The optimized curcumin-naringin-self-nanoemulsifying drug delivery system demonstrated the following characteristics: polydispersity index (0.412 ± 0.03), % transmittance (97%), particle size (212.5 ± 05 nm), zeta potential (− 25.7 ± 1.80 mV) and having a smooth and spherical droplet shape, as shown by atomic force microscopy. The ability of their combined formulation to cure wounds was tested in comparison to pure curcumin suspension, empty self-nanoemulsifying drug delivery system, and standard fusidic acid. Upon topical administration, the optimized curcumin-naringin-self-nanoemulsifying drug delivery system demonstrated significant wound healing activity in comparison with a pure curcumin suspension, empty self-nanoemulsifying drug delivery system, and standard fusidic acid. Based upon this result, we assume that skin penetration was increased by using the optimized curcumin-naringin-self-nanoemulsifying drug delivery system with enhanced solubility. [ABSTRACT FROM AUTHOR]

Organometallic species, such as organoferrate ions, are prototypical nucleophiles prone to reacting with a wide range of electrophiles, including proton donors. In solution, the operation of dynamic equilibria and the simultaneous presence of several organometallic species severely complicate the analysis of these fundamentally important reactions. This can be overcome by gas-phase experiments on mass-selected ions, which allow for the determination of the microscopic reactivity of the target species. In this contribution, we focus on the reactivity of a series of trisarylferrate complexes toward 2,2,2-trifluoroethanol and 2,2-difluoroethanol. By means of mass-spectrometric measurements, we determined the experimental bimolecular rate constants kexpof the gas-phase protolysis reactions of the trisarylferrate anions FePh3–and FeMes3–with the aforementioned acids. Based on these experiments, we carried out a dual blind challenge, inviting theoretical groups to submit their best predictions for the activation barriers and/or theoretical rate constants ktheo. This provides a unique opportunity to evaluate different computational protocols under minimal bias and sets the stage for further benchmarking of quantum chemical methods and data-driven approaches in the future.

This article looks at how noise impacts correct classification using artificial neural networks, ANNs. Topics include limitations in analyzing noise in ANNs, identification of weak areas of these neural networks regarding noise and specific investigative methods-- linear programming and Satisfiability solving. Ultimately the Formal Analysis Framework for Neural Networks, FANNet, is used to investigate the total impact of noise on ANNs using a real-world Leukemia dataset.

Graph neural networks (GNNs) have shown great success in detecting intellectual property (IP) piracy and hardware Trojans (HTs). However, the machine learning community has demonstrated that GNNs are susceptible to data poisoning attacks, which result in GNNs performing abnormally on graphs with pre-defined backdoor triggers (realized using crafted subgraphs). Thus, it is imperative to ensure that the adoption of GNNs should not introduce security vulnerabilities in critical security frameworks. Existing backdoor attacks on GNNs generate random subgraphs with specific sizes/densities to act as backdoor triggers. However, for Boolean circuits, backdoor triggers cannot be randomized since the added structures should not affect the functionality of a design. We explore this threat and develop PoisonedGNN as the first backdoor attack on GNNs in the context of hardware design. We design and inject backdoor triggers into the register-transfer- or the gate-level representation of a given design without affecting the functionality to evade some GNN-based detection procedures. To demonstrate the effectiveness of PoisonedGNN, we consider two case studies: (i) Hiding HTs and (ii) IP piracy. Our experiments on TrustHub datasets demonstrate that PoisonedGNN can hide HTs and IP piracy from advanced GNN-based detection platforms with an attack success rate of up to 100%.

The high-frequency standard magnetic links were recently considered viable candidates for construction of the medium-voltage power converters, rather than link with the common dc specialized magnetic materials, like nano-crystalline and the amorphous materials. This provides a new route of portable and lightweight incorporation of renewable systems into the direct grid, stepping-up transformer. The main objective is to propose an ideal design approach for magnetic links and to validate their viability by the evaluation of a sample. This will discuss a specific approach to design, research, and implementation. The predicted result is that the proposed architecture strategy will be implemented effectively in the future with clean energy generation schemes and intelligent grids. The current approaches have some critical disadvantages, including voltage mismatch, minimum overall power extraction, and general mode problems because of several direct grid and common dc connections. With emphasis on implementing renewable energies, exploring different forms of configuring structures for efficiency and output is becoming increasingly relevant. Both technologies employ grid-based voltage converters, but conventional equipment poses several problems in terms of voltage balance and maximum power output. Magnetic connections are a viable solution to minimize the costs, reliability, and consistency of the network. Instead of several dc links, these can be solved. Thus, the plan will depend on its optimal design to monitor system costs while improving efficiency by adding a new magnet-powered voltage converter. Particularly the voltage mismatch and typical mode problems of converter systems are minimized. The electromagnetically designed common magnetic links are, however, a multi-physics challenge, affecting device performance and costs. The paper objectives are to propose an ideal design approach for magnetic links and to validate their viability by the evaluation of a sample.

The rapid increase in the application of carbon fiber-reinforced polymer composites in the fabrication and development of modern industrial products is attributed to their lightweight nature, excellent mechanical properties, and corrosion resistance, among other factors. Additive manufacturing of recycled carbon fiber (rCF), which has garnered significant attention in recent years owing to the massive potential waste obtained from carbon fiber-reinforced polymer composites (CFRPC) and the manufacturing of rCF after the surface treatment produced the parts with excellent properties comparable to virgin carbon fiber (vCF). Additive manufacturing of rCF obtained after recycling has received much interest over the past few years because of the massive potential waste of the carbon fiber and the excellent properties after the surface treatment of the rCF. This research examines additive manufacturing of the rCF, surface treatment for enhancing the properties of the printed specimens, the potential waste of the carbon fiber obtained from the different sectors, and applications of reclaimed carbon fiber composites in manufacturing various products. Specifically, the mechanical characteristics of the printed specimens using rCF and the different percentages of the rCF reinforcement in the other polymer's matrix composites are discussed. This work demonstrates that an additive manufacturing-based recycling approach can recycle carbon fiber-reinforced polymer composites (rCFRPC) waste and create high-performance engineering parts with complicated geometries that are both cost-effective and environmentally acceptable. This review also defines the significant challenges and outlook for future developments in manufacturing of rCFRPC.

While high-level synthesis (HLS) tools offer faster design of hardware accelerators with different area versus delay tradeoffs, HLS-based delay estimates often deviate significantly from results obtained from ASIC logic synthesis (LS) tools. Current HLS tools rely on simple additive delay models which fail to capture the downstream optimizations performed during LS and technology mapping. Inaccurate delay estimates prevent fast and accurate design-space exploration without performing time-consuming LS tasks. In this work, we exploit different machine learning models which automatically learn to map the different downstream optimizations onto the HLS critical paths. In particular, we compare graph-based and nongraph-based learning models to investigate their efficacy, devise hybrid models to get the best of the both worlds. To carry out our learning-assisted methodology, we create a dataset of different HLS benchmarks and develop an automated framework, which extends a commercial HLS toolchain, to extract essential information from LS critical path and automatically matches this information to HLS path. This is a nontrivial task to perform manually due to difference in level of abstractions. Finally, we train the proposed hybrid models through inductive learning and integrate them in the commercial HLS toolchain to improve delay prediction accuracy. Experimental results demonstrate significant improvements in delay estimation accuracy across a wide variety of benchmark designs. We demonstrate that the graph-based models can infer essential structural features from the input design, while incorporating them into traditional nongraph-based models can significantly improve the model accuracy. Such “hybrid” models can improve delay prediction accuracy by 93% compared to simple additive models and provide $175\times $ speedup compared to LS. Furthermore, we discuss key insights from our experiments, identifying the influence of different HLS features on model performance.

Triple modular redundancy (TMR) is one of the most common techniques in fault-tolerant systems, in which the output is determined by a majority voter. However, the design diversity of replicated modules and/or soft errors that are more likely to happen in the nanoscale era may affect the majority voting scheme. Besides, the significant overheads of the TMR scheme may limit its usage in energy consumption and area-constrained critical systems. However, for most inherently error-resilient applications such as image processing and vision deployed in critical systems (such as autonomous vehicles and robotics), achieving a given level of reliability has more priority than precise results. Therefore, these applications can benefit from the approximate computing paradigm to achieve higher energy efficiency and a lower area. This article proposes an energy-efficient approximate reliability (X-Rel) framework to overcome the aforementioned challenges of the TMR systems and get the full potential of approximate computing without sacrificing the desired reliability constraint and output quality. The X-Rel framework relies on relaxing the precision of the voter based on a systematical error bounding method that leverages user-defined quality and reliability constraints. Afterward, the size of the achieved voter is used to approximate the TMR modules such that the overall area and energy consumption are minimized. The effectiveness of employing the proposed X-Rel technique in a TMR structure, for different quality constraints as well as with various reliability bounds, is evaluated in a 15-nm FinFET technology. The results of the X-Rel voter show delay, area, and energy consumption reductions of up to 86%, 87%, and 98%, respectively, when compared to those of the state-of-the-art approximate TMR voters. Also, the effectiveness of the proposed X-Rel-based TMR structure is assessed in four benchmark applications from different domains. For these benchmarks, the results show $1.59\times $ , $2.35\times $ , and $3.39\times $ energy-delay-area-product (EDAP) reduction for less than 1%, 5%, and 10% output quality degradations, respectively. Finally, an image processing application is benchmarked to evaluate the X-Rel framework efficacy in the presence of errors, where the results show up to a $4.78\times $ higher output image quality in comparison with the typical TMR voters.

A comprehensive novel framework for retrofitting frequency-selective surface (FSS) shields to any glass surface using low-cost copper foils has been proposed in this work. The realization technique is demonstrated through a novel triband FSS shield. Global System for Mobile Communication (GSM)-1800, Wi-Fi 2.5 GHz, and wireless local area network (WLAN) 5.6 GHz spectrum, with–10 dB bandwidth of 680, 190, and 1300 MHz, respectively, are suppressed by the shield. The unit cell is made up of a combination of square and circular concentric loops with some alterations. The design is entirely polarization-insensitive and has a steady frequency response for oblique angles up to 60°. The unit element is highly miniaturized for triband operation measuring $\lambda _{0}$ /9 at 1.8 GHz frequency. An equivalent circuit model (ECM) is also derived to better elaborate the working mechanism of the shield. The pattern is chemically etched on low-cost copper adhesive foil and affixed to ordinary glass as a sticker. The design retains the visual transparency of 72% allowing enough light to enter through windows while rejecting the intended frequencies by at least 28 dB or more in all three bands. The design exhibits a very high shielding efficiency of above 98% for all three bands. Experimental demonstration for single-layer glass, sandwiched glass, as well as air-filled double-glazed glass is carried out. The thermal stability of the proposed design is measured and found intact up to the ambient temperature of 50 °C.

Background and Objective Pancreatic cancer (PC) is a significant cause of cancer-related mortality, with limited curative options and high rates of cachexia, a debilitating syndrome associated with poor prognosis. While previous research has linked sarcopenia to poor outcomes in PC, the correlation between cachexia and treatment outcomes remains underexplored. This meta-analysis aims to investigate the association between cachexia and overall survival and time to treatment failure in advanced PC patients undergoing first-line chemotherapy.Method A systematic search of electronic databases was conducted following PRISMA guidelines. Eligible studies compared cachexic and non-cachexic PC patients, reporting outcomes of observed survival or time to treatment failure. Data extraction and analysis were performed using Comprehensive Meta-Analysis Version 3.3, employing random-effects models and sensitivity analyses to assess heterogeneity and bias.Results Seven observational studies involving 2834 PC patients were included. The incidence of cachexia was 45% (95% CI: 0.27-0.65), with a higher prevalence in East Asian populations. Cachexic patients experienced significantly earlier treatment failure (SDM: −2.22, 95% CI: −2.6 to −1.7, P= 0.0001) and higher mortality risk (HR: 2.02, 95% CI: 1.17-3.48, P= 0.011) compared to non-cachexic patients. Overall survival was lower in cachexic patients (SDM: −2.34, 95% CI: −3.7 to −0.90, P= 0.001), with considerable heterogeneity across studies. Meta-regression analysis revealed significant differences between countries but insignificant correlations with age.Conclusion Cachexia is associated with reduced overall survival, early chemotherapy failure, and elevated mortality in advanced PC patients undergoing first-line chemotherapy. Recognition and management of cachexia are crucial for optimizing treatment outcomes and improving patient survival. Future research should focus on prospective studies to better understand the impact of cachexia on treatment response and develop tailored interventions to mitigate its adverse effects.

Background: Non-alcoholic fatty liver disease (NAFLD) is a common liver condition affecting 25%–40% of the worldwide population. NAFLD is traditionally related to obesity and metabolic disorders. NAFLD can also affect non-obese individuals, termed “lean NAFLD” (LN), who exhibit a paradoxical combination of physical leanness and metabolic obesity. Factors contributing to LN remain unclear, necessitating further research. This analysis aims to understand LN’s prevalence and metabolic characteristics compared to obese NAFLD (ON) populations.Methods: This meta-analysis searched various databases until August 1, 2023. Inclusion criteria involved observational studies comparing LN with overweight/obese NAFLD. Data extraction included baseline characteristics, disease occurrence, metabolic profile, and clinical parameters—statistical analysis employed calculating risk ratios (RR) and standard mean differences.Results: Twenty-five studies were analyzed. LN is associated with lower prevalence in both NAFLD (RR 0.27, 95% confidence interval (CI) 0.14–0.52, p= <0.0001) and total (RR 0.27, 95% CI 0.15–0.51, p< 0.0001) population. LN had lower diabetes mellitus (RR 0.78, 95% CI 0.71–0.87, p< 0.00001), dyslipidemia (RR 0.87, 95% CI 0.79–0.95, p= 0.002), hypertension (RR 0.80, 95% CI 0.74–0.87, p< 0.00001), and metabolic syndrome (RR 0.45, 95% CI 0.31–0.64, p< 0.00001) compared to those with ON. The LN group’s lipid profile, blood pressure, and other clinical parameters were favorable compared to ON.Conclusion: The prevalence of NAFLD among lean and non-lean individuals varies by region. Our analysis revealed that LN is associated with lower metabolic diseases, fasting blood sugar, blood pressure, and a more favorable lipid profile compared to ON.

The voltage and current ratings of the voltage source converter (VSC) based high voltage direct current (HVDC) transmission line projects have increased over the years. It testifies the increasing popularity of VSC-HVDC technology. Since the long-distance, VSC-HVDC transmission lines are more exposed to commonly appearing short circuit faults; thus, protection equipment with increased fault-current handling capability is required. The DC circuit breakers (DCCBs), with or without fault current limiters (FCLs), can effectively handle the fault-current. The variations in VSC-HVDC projects’ ratings and their impact for DCCB with FCL and without FCL are thoroughly analysed in this paper. The paper further includes an in-depth discussion on DCCBs with FCL and without FCL. An equivalent circuit modelling approach is used for simulation analysis of three case studies under the influence of these breakers to analyse current behaviour; the case studies include the Zhoushan HVDC Project, Xiamen HVDC Project, and Zhangbei-I HVDC Project. All these projects have different capacities in terms of their power, voltage, and current ratings. For DCCBs, this paper includes a detailed analytical model to understand the fluctuations in the size of fault-current limiting components (in DCCBs) for increased fault-current handling requirements. The simulation results using PSCAD/EMTDC and MATLAB are presented to verify different aspects of this study. A small scale laboratory prototype is also developed to validate the performance of DCCB with and without FCL. Based on theoretical, simulation, and experimental validations, it is substantiated that for increased VSC-HVDC projects’ ratings, DCCB with FCL can perform better compared to conventional DCCB without FCL.

Bioenergy from energy plants, an alternative fuel, is projected to increasingly meet future worldwide energy demands as well as those of a developing nation like Pakistan. Jatropha curcas(JC) has been found to possess many desirable qualities, including high oil content seeds (27–40 %), rapid growth, ease of growing, tolerance to drought, ability to grow on poor soil and wasteland, needing less nutrient input and management, and not interfering with current food crops, insects, or pest resistance. Hence, growing JCin highly appropriate barren lands could reduce the global reliance on fossil fuels. However, an evaluation of the suitability of the land would be required for tree establishment to be effective. In this study, we integrated an Analytical Hierarchy Process (AHP) with the ArcGIS tool for assessing suitable available sites to cultivate JCacross Pakistan. Using geospatial technologies, this study intended to assess and map possible areas appropriate for JCbioenergy plantations with the necessary climate, soil type, and topography factors. Pakistan was classified into 3 levels of suitability for JCplantations based on the results of a spatial analysis that integrated various data sets and varied evaluation criteria: Most Suitable, Moderately Suitable, and Less Suitable. Our results showed that out of the total area of Pakistan, around 54,392,075 hectares (57 %), 22,516,700 hectares (23 %), and 19,594,100 hectares (20 %) were identified as highly suitable, moderately suitable, and less suitable, respectively, for JC bioenergy plantations. We conclude that the methods used in this study provided a considerably reliable estimate of suitable sites for JCproduction in Pakistan. It can assist smallholder-based initiatives to promote JCcultivation on farmer-owned to enhance their living circumstances. Multi-criteria decision analysis (MCDA) was found to be a beneficial tool in breaking down the issues of identifying and classification of the locations for afforestation in the management of forests.

Background: Trifarotene is effective for treating acne and other skin issues. To ensure its quality and meet regulatory standards, a reverse phase liquid chromatography (RP-LC) stability-indicating method was developed and validated for its quantification in pure and topical dosage forms. An isocratic elution chromatographic method was employed, using an octadecylsilyl silica gel-packed column (150 mm × 4.6 mm, 3 µm particle size). The mobile phase was a mixture of phosphate buffer and acetonitrile (40:60 v/v). Chromatographic conditions included a flow rate of 0.5 mL/min, column temperature of 40 °C, detection at 265 nm, and injection volume of 20 µL. Results: The developed analytical method reports the retention time of trifarotene 11.2 min, higher theoretical plate count, asymmetric peak, and good resolution between the peaks of trifarotene, phenoxyethanol, and environmentally generated impurities. Conclusion: The analytical method has been found to be linear, accurate, robust, specific, and selective for impurities produced during forced degradation studies. The proposed analytical method can be utilized for routine pharmaceutical analysis of trifarotene to judge its quality and safety.

Consumers like to try innovative technologies when they perceive them to be approachable, convenient, and entertaining. The purpose of this study was to explore the adoption of AI voice recognition avatar in the gameplay. This study's interesting insights show us that gamers are keen and interested in voice recognition technology. The study augments value to TAM theories, perceived value theory, and the flow theory with localization's moderating role. The 218 respondents from China provided useful insights. In China, gaming is a vast industry, and such gaming options can attract current and new gamers. Thus, AI can benefit gamers in this regard and give them the freedom to interact with their avatar through the power of voice recognition.

AI is playing a disruptive role in many industrial sectors and also in the banking industry. The purpose of this research study was to empirically test consumer willingness to use AI-powered financial investment robo-advisors. Analysis of data from 500 survey responses collected from m-banking users performed using Smart Pls showed that there was clear intention on the part of m-banking consumers in Pakistan to use AI-powered financial investment robo-advisors. The results reinforce the findings of earlier studies.

Aim:To determine the prevalence of multidrug (MDR) and extensively drug-resistant (XDR) pathogens from pediatric blood samples Methods:In total, 4543 children's blood samples were processed in the BacT/ALERT system. Confirmation of the isolates and MIC was determined in VITEK®2 system. Molecular identification of blaIMP, blaVIMand blaOXA-48was done by PCR. Results:Of 4543 blood cultures, 458 (10%) were positive for bacterial growth and SalmonellaTyphi (415; 90%) remained the primary pathogens. Antibiogram revealed 208 (50.1%) and 137 (33%) were MDR and XDR S. Typhi, respectively. Klebsiella pneumoniaedisplayed 46% resistance to imipenem. One hundred twelve (81.7%) XDR Typhi were positive for blaCTXM, whereas 14 (66.6%) blaVIMwere found in carbapenem-resistant bacteria. Conclusion:A high prevalence of MDR and XDR pathogens was found in peads blood culture.

The acme of this paper was to examine the QR code payment services adoption by retailers in Pakistan. An all-inclusive review of the literature and data analysis has justified the purpose to use this inventive technology. The study established that innovation diffusion theory (IDT) dimensions and convenience impacted the usefulness of QR code payment services which are well-thought-out critical antecedents of accepting future technologies. The inferences of this research work provide valuable acumens for banks, financial institutions, and the government to develop a platform about digital payment for the retailers, wholesalers, and the end consumers. Further, the study approves that QR code payment services will aid to advance the business operations in Pakistan in the COVID-19 pandemic.

The evolution of antimicrobial-resistant Gram-negative pathogens is a substantial menace to public health sectors, notably in developing countries because of the scarcity of healthcare facilities. New Delhi metallo-β-lactamase (NDM) is a potent β-lactam enzyme able to hydrolyze several available antibiotics. NDM was identified from the clinical isolates of Klebsiella pneumoniaeand Escherichia colifrom a Swedish patient in New Delhi, India. This enzyme horizontally passed on to various Gram-negative bacteria developing resistance against a variety of antibiotics which cause treatment crucial. These bacteria increase fatality rates and play an integral role in the economic burden. The efficient management of NDM-producing isolates requires the coordination between each healthcare setting in a region. In this review, we present the prevalence of NDM in children, fatality and the economic burden of resistant bacteria, the clonal spread of NDM harboring bacteria and modern techniques for the detection of NDM producing pathogens.

AbstractThis study examines the short- and long-run of the causal correlation between economic growth (G.R.) and renewable electricity generation sources for a panel of 25 developing nations over the period 1990–2017. To do so, second-generation cross-sectional dependence (C.D.) test Im, K.S., Pesaran and Augmented Dickey-Fuller panel unit root test, panel cointegration, autoregressive distributed lag in view of the pooled mean group estimation and panel heterogeneous Dumitrescu Hurlin (2012) causality methods are utilised. The main findings indicate that the positive and significant impact of renewable electricity generation on G.R. shows that renewable electricity generation sources stimulate G.R. in the long run for these selected countries. It is also demonstrated that there is bidirectional causality between renewable electricity generation and G.R. both in the short run and long run. Based on our findings, the feedback hypothesis is valid for developing countries.

The prevalence of modern techniques and industrialized materials has resulted in environmental contamination. Therefore, prioritizing the use of sustainable materials with minimal CO2emissions should be the fundamental principle guiding future developments in construction projects. Rammed earth construction, a traditional method in Latin America, is valued for its sustainable, environmentally friendly properties. The research examines integrating Building Information Modeling (BIM) and Life Cycle Assessment (LCA) in Colombia, a region with limited exploration of BIM-LCA interoperability on earth-based materials. This study applies LCA (Cradle-to-Gate) to assess material use, and transportation, aiming to reduce construction carbon emissions. This study compares the traditional and sustianble building environmental impacts through the BIM (using Autodesk Revit) and carbon footpring. Findings show that BIM-based models with traditional materials have significantly higher carbon footprints (171.93 kg CO2eq per square meter) than sustainable models (62.25 kg CO2eq per square meter). This research highlights the importance of low carbon materials for the carbon reduction to meet net zero target in construction industry.

On-ramp merging zones have long been accident-prone spots as well as major triggers of traffic congestion in freeway networks. This paper proposes a collision-free merging control for connected and autonomous vehicles based on trajectory optimization to facilitate traffic safety and efficiency. The optimization is embedded in a model predictive control algorithm with the aim of maximizing the average travel speeds of all vehicles in the planning horizon. To guarantee collision-free merging, the merging control allows only one vehicle to pass the merging zone at a time. Safety constraints, including minimum time gaps and minimum distances between vehicles, are also considered. A rolling horizon control framework is established to enable dynamic traffic control; specifically, generating optimal acceleration decisions for each time step. The optimization problem is formulated as mixed-integer linear programming to enable efficient computation. Numerical studies and sensitivity analysis were conducted to validate the effectiveness and applicability of the proposed method. The results show that the introduced safety constraints can guarantee collision-free merging. Compared to the traditional method, Ih is built on the intelligent driver model and the minimizing overall braking induced by lane-change model, the proposed optimization-based approach results in 5.0%–22.2% improvements with respect to average travel speeds during the merging process. The safety and efficiency improvements are more significant when dealing with higher demand levels. Notably, compared to the difference in initial speeds between the mainline and on-ramp vehicles, the length of the acceleration lane has a greater impact on the control performance.

As global concerns about climate change and environmental sustainability escalate, the construction industry is shifting significantly towards eco-resilient and sustainable building practices. This paper comprehensively explores integrating Building Information Modelling (BIM) with eco-resilient principles to create a framework for designing and constructing sustainable buildings. This research introduced a practical approach for evaluating the embodied carbon from building construction, drawing insights from a residential building in Yangon City, Myanmar. The method developed in this study aims to assess the carbon footprints associated with distinct stages of the construction process, including raw material production and material transportation. Additionally, the proposed method compares the material embodied carbon, transportation embodied carbon, and total cost in the residential building, considering two cases of traditional and low-carbon materials. The results indicate that material embodied carbon contributes merely 84% to the overall, while material transportation makes up the remaining 16% for both cases. Utilising low-carbon materials yields remarkable reductions, with a 40% decrease in material embodied carbon and a 39% decrease in transportation carbon footprint compared to conventional materials. However, adopting low-carbon materials incurs a modest increase of approximately 6.7% in total cost. This study underscores the imperative of integrating low-carbon materials into the design of future passive buildings, advancing the pursuit of a net-zero strategy. This research underlines the potential for BIM-driven eco-resilient practices in mitigating carbon emissions and the need for continued innovation and collaboration in sustainable building design and construction.

The United Nation's adaptation of the 2030 sustainable Development Goals (SDG) agenda has swung the attention toward sustainability. However, in achieving SDGs, this study investigates the nexus between agricultural productivity, renewable energy, ICT, human capital, CO2emissions, and natural resources in a panel of ten European Union (EU) countries from 1996 to 2019. Our study uses the panel autoregressive distributed lag (ARDL) model of the Pool Mean Group (PMG) to estimate the long-run and short-run effects and heterogeneous causality approach. The empirical results ratify that a 1 % upsurge in the coefficient of renewable energy, ICT, and human capital increases agricultural productivity by 0.174 %, 0.030 %, and 2.158 % in the long run. Whereas, CO2emission and natural resources decrease agricultural productivity. Finally, various causality exists among variables for EU countries. Our empirical results for the EU countries are notionally reliable and offer important policy implications accordingly.

In recent decades, the planet has been negatively impacted by the construction industry due to the development of various infrastructures. Thus, it became one of the most polluting sectors that have required increasing the environmental awareness of public and private entities. Under these circumstances, there are urgent efforts required to mitigate these negative impacts. Different approaches have been made for sustainable buildings based on construction technologies, where sustainability must be included as the main premise from the initial stage of the project. Through a systematic literature review, different methodologies are analyzed such as Building Information Modeling (BIM), Life Cycle Assessment (LCA), and Building Energy Modeling (BEM) based on the existing regulations and standards of each country. The existing digital tools allow for the development of various low-carbon technologies, including sustainable materials and new integrations between them to find sustainable solutions and reduce carbon emissions from buildings. Several limitations exist as Building Information Modeling (BIM) software does not have all the green applications. Therefore, clear results are needed for effective indicators to help interested parties appropriate these technologies and invest time and money in projects based on Building Information Modeling (BIM) and learn a useful lesson for future projects from these technological tools.

This article examines factors that can be argued to influence the academician's behavioral intentions in using open government data (OGD). Policy-makers and practitioners will determine policy instruments in increasing acceptance and use of OGD by maintaining a good understanding of these factors. In this article, Rogers' Diffusion of Innovations (DOI) theory has been proposed and used in order to empirically examine these factors taking perceived characteristics of innovations. Relevant hypotheses have been developed through the literature review, forming a preliminary research model, while respective influences of the factors on the behavioral intention to use open government data have been statistically tested. Results have shown that compatibility and voluntariness have had a strong influence on behavioral intention, whereas a 66.2% variance has been found in academicians' behavioral intentions to use open government data.

Comparative cross sectional study was conducted on blood samples (n = 231) collected from children of 1 to 10 years of age in Punjab Pakistan through convenient sampling method. Indirect haemagglutination assay (IHA) was standardized and used for serodiagnosis and evaluation of humoral immunity against measles. Associated risk factors including age, gender, locale, and vaccination status were analyzed. Geometric mean titre (GMT) of vaccinated individuals was significantly higher (p < 0.001) than that of non-vaccinated individuals showing that IHA titre of vaccinated individuals was a measure of humoral immune response; whereas, in case of non-vaccinated individuals an indicative of exposure to the measles infection.

Green storm water infrastructure (GSI) or low impact development (LID) is an alternative land development approach for managing storm water close to the source that has been recommended instead of the traditional storm water design. The main purpose of LID is to reduce the impact of development on water-related problems through the use of GSI practices such as bioretention, green roofs, grass swales, and permeable pavements that infiltrate, evaporate, or harvest and use stormwater on the site where it falls. In recent years, more research has been carried out on GSI practices and the use of these practices has shown magnificent benefits in stormwater management. LID techniques have been successfully used to manage stormwater runoff, improve water quality, and protect environmental and hydrological aspects of the developed areas. Bioretention cells have been effectively used in retaining large volumes of runoff and capturing pollutants on site. Pervious pavements have been extremely effective and efficient at infiltrating stormwater on site and storing large quantities of rainwater. Sand ditches are a new water harvesting technique that is being used to significantly reduce runoff, soil loss and sediment loss and to increase infiltration. This paper highlights evidence in the literature regarding the beneficial uses of LID practices and encourages adopting these practices for environmental friendly construction and sustainable development in the world. In the end, some of recommendations for the implementation of LID practices to achieve multiple benefits are given.

Green blue roof has the potential to reduce the surface temperature of the building in the urban areas. Green blue roof is a new innovative low impact development (LID) practice that has exhibited an option to mitigate the heat island phenomena in urban area. This is the modified form of green roof that has ability to store rainwater in vegetation, soil layer and increases the evapotranspiration rate which decreases the temperature of an area. For this purpose, green blue roof is installed at the Cheong-un middle school building roof, Seoul, Korea. During the different time scenarios the surface temperature from the green blue roof and control roof were analyzed and their results were compared with each other. The results revealed that the surface temperature of green blue roof was much less as compared to control roof under same climatic conditions. From the results it is also concluded that surface temperature value of green blue roof was less than 5°C to 9°C as compared to control roof.

Recently, a lot of studies have been focused on the use of smartphones for automatic detection of transportation mode. This task is made easy by the availability of sensors like accelerometer and GPS in modern smartphones. The advantages include the increased accuracy which was partially lost due to underreporting in case of conventional travel surveys. In this paper Probe Person data collected by 46 participants in three different cities of Japan, namely Niigata, Gifu and Matsuyama, was used. Although the data, comprising of acceleration and GPS information, was collected by a wearable device but the same can be achieved very easily with the help of smartphones. In order to address the most important problem of continuously changing position of smartphone during the trip, only resultant acceleration was taken. In addition, personal characteristics like age and gender were also included. Regarding GIS information, distance and time calculated by Google Maps for both driving and walking was introduced to increase the prediction accuracy. Random Forest was applied for the purpose of prediction. 70% of the data was randomly selected to train the algorithm and rest 30% was used to test it. Prediction was done among four different modes; walk, bicycle, car and train. The results are quite promising with an overall prediction accuracy of more than 99.6% for all three cities. A slight improvement in the prediction accuracy is achieved by selecting the best features for the classification purpose.

Bioretention basins/rain garden is a very suitable low-impact development (LID) practice for storm water management around the globe. By using this practice in urban areas, flash flooding problems can be decreased and the environment of an area can be improved. The concept of bioretention was introduced a few decades ago and has been proven to be the best management practice (BMP) for storm water in urban areas. Due to urbanisation, natural surface areas are converted into hard surfaces such as roads, through which water cannot infiltrate into the ground. Due to this, infiltration decreases and surface run-off increases, which causes depletion of ground water continuously. In this study, we mainly explain the bioretention concept and its function as derived from different studies. This review includes different scientists’ results for the performance of the bioretention system at different locations. A summary of the research findings by different scientists on the performance of bioretention systems is also provided, including the hydrologic and water quality performances. Finally, future work necessary to enhance the performance and widespread use of bioretention systems is also explained.

A low impact development (LID) is an alternative land development approach for managing stormwater that has been recommended instead of the traditional stormwater design. The main purpose of LID is to reduce the impact of development on water related problems through the use of stormwater management practices that infiltrate, evaporate, or harvest and use stormwater on the site where it falls. In recent years, more research has been carried out on the individual practice of LID such as bioretention, pervious pavements, rain garden and grassed swales. Nowadays LID practices have been successfully used to manage stormwater runoff, improve water quality, protect the environmental and hydrological aspects of the developed areas. Bioretention cells have been effectively used in retaining large volumes of runoff and pollutants on site. Pervious pavements have been extremely effective practice in infiltrating stormwater runoff as early as possible as rain fall on site and store a large quantity of water. Nowadays, sand ditch a new water harvesting technique is used that significantly reduces runoff and sediment losses and increases infiltration and soil loss. This paper highlights evidence in the literature regarding the beneficial uses of LID practices and encourage to adopt these practices for environmental friendly construction and sustainable development in the world.

This paper presents a multi-layer software reliability approach that leverages multiple software layers (e. g., programming language, compiler, and operating system) to improve the overall system reliability considering unreliable or partly-reliable hardware. We present a comprehensive design flow that integrates multiple software layers while accounting for the knowledge from lower hardware layers. We show how multiple software layers synergistically operate to achieve a high degree of reliability.

Household trip data collection is essential for design and construction of transportation infrastructure. Conventionally, this information is collected by travel surveys, which require the respondents to answer a list of questions targeting their daily travelling. As the responses depend on the memory of the respondents, inaccuracies usually occur during the reporting process. To improve the accuracy of the collected data, a lot of research is currently being focused on inferring the important information from data collected automatically with the help of devices like smartphones. The current study proposes a new method for identifying the travel mode, by applying the binomial logistic regression in a hierarchical manner, using the data collected by the accelerometer of the smartphone. Three methods of application are discussed, namely ranking, one against rest and one against all. Apart from train, all the other modes are successfully modelled with goodness of fit approaching to 1. Low goodness of fit in case of train is due to the wide range of accelerations recorded. Although, all the three methods exhibit good outcomes, one against all method provides relatively better results.

The aggregation of CuS nanoparticles during synthesis by a hassle-free aqueous route under microwave irradiation gave remarkable spherical shape, utilizing Cu(CH3COO)2·H2O as the source of copper and Na2S2O3·5H2O, as sulfur source; these materials were used without assistance of any surfactant or template. An X-ray powder diffraction pattern proved that the product is hexagonal CuS phase. The morphologies of the product were observed by scanning electron microscopy. Thermal behavior, different solid state, and chemical conversion in CuS with respect to temperature were studied by DSC/TGA, which confirmed the thermal oxidation of CuS and its conversion into CuO then to Cu2O.

Pure ZnO and Al-doped ZnO nanomaterial have been successfully fabricated using zinc acetate dihydrate in a basic aqueous solution of KOH through solution precipitation method then treated at 600°C in air. The XRD analysis confirms the Wurtzite hexagonal crystal structure of the product with crystallite size in 32–53 nm range. The morphology of the product has been studied under scanning electron microscopy (SEM). The simultaneous differential scanning calorimetry and thermogravimetric analyses were used to investigate thermal decomposition temperature and different phase transitions up to 800°C. The optical properties and variation in band gap of ZnO by Al doping were investigated by ultraviolet–visible spectroscopy.

Controlled conduction of magnetic spins is desired for data processing in modern spintronic devices. Transition metal-doped ZnO is a potential candidate for this purpose. We studied the effects of cobalt doping on structural, absorbance, and magnetic properties of ZnO nano-particles. Different compositions (Zn0.99Co0.1O, Zn0.97Co0.3O, and Zn0.95Co0.5O) of cobalt-doped ZnO were fabricated using metallic chlorides by co-precipitation method. XRD revealed standard ZnO wurtzite crystal structure without lattice distortion due to impurities but showed presence of additional phases at higher doping ratios. Fourier transformed infrared spectroscopy also confirmed the standard ZnO profiles at lower doping ratios but additional phases at higher doping. Vibrating sample magnetometer showed soft ferromagnetic behavior for low impurity samples and harder ferromagnetic behavior for higher doping at room temperature. A simultaneous differential scanning calorimetry/thermo gravimetric analysis was performed to study the phase variations during crystallization.

This study analyzes the impact of renewable electricity consumption on economic growth for the panel of 10 newly industrialized countries from 1990 to 2015. For this, panel unit root tests, panel heterogeneous co-integration method, panel Fully Modified Ordinary Least Square and the Granger causality method are employed. The primary outcomes of this study are as follows: (1) panel unit root test and panel co-integration method confirm that the economic growth, renewable electricity consumption, non-renewable electricity consumption, gross capital formation, labor force and trade openness are co-integrated, (2) panel Fully Modified Ordinary Least Square confirms that all studied variables have a positive long-run effect on economic growth, a 1% increase in renewable and non-renewable electricity consumption increased economic growth by 0.095% and 0.017% respectively, (3) the outcomes from Granger causality test also illustrates that there is bidirectional causation between renewable electricity consumption and economic growth in both the short-run and long-run that supports the feedback hypothesis. From the empirical findings, the feedback hypothesis is valid for newly industrialized countries.

At present existence OTDR based techniques have become a standard practice for measuring chromatic dispersion distribution along an optical fiber transmission link. A constructive measurement technique has been offered in this paper, in which a four wavelength bidirectional optical time domain reflectometer (OTDR) has been used to compute the chromatic dispersion allocation beside an optical fiber transmission system. To improve the correction factor a novel formulation has been developed, which leads to an enhanced and defined measurement. The investigational outcomes obtained are in good harmony.