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Post-COVID-19 working conditions have been the primary reason behind increased stress among business owners. There is an ever-rising need for entrepreneurial work engagement in their jobs to mitigate the increased entrepreneurial work pressures caused by uncontrolled digitization, enhanced consumer power, and brutal competition. Therefore, this study intends to respond to the existing practical and empirical gaps by investigating the relationships between entrepreneurial job demands (EJD), work-related stress, entrepreneurial job resources (EJR), and entrepreneurial work engagement (WE) for their role in generating entrepreneurial success (ES), especially in the Chinese context. It also tested the mediating role of work-related stress and entrepreneurial work engagement on the relationship between job demands, job resources, and entrepreneurial success. Structured questionnaires were circulated among the targeted respondents (i.e., business owners across China) using quantitative techniques, followed by PLS-SEM for data analysis, as these are the best-suited techniques, considering the context and time constraints. The results verified the impact of job demands on work-related strain, followed by the inverse direct impact of work-related strain on entrepreneurial success. This study found the significant impact of entrepreneurial job resources on entrepreneurial work engagement, followed by the positive impact of WE on entrepreneurial success. Likewise, work engagement's mediating role was validated, while work-related strain could only negatively mediate the relationship between EJD and ES. Likewise, this study has practical and empirical implications for practitioners and researchers to be mindful of their employees' emotional states by providing sufficient resources and psychological interventions to ensure business success. [ABSTRACT FROM AUTHOR]

The creation of novel anticancer treatments for a variety of human illnesses, including different malignancies and dangerous microbes, also potentially depends on nanoparticles including silver. Recently, it has been successful to biologically synthesize metal nanoparticles using plant extracts. The natural flavonoid 3,3′, 4′, 5,5′, and 7 hexahydroxyflavon (myricetin) has anticancer properties. There is not much known about the regulatory effects of myricetin on the possible cell fate-determination mechanisms (such as apoptosis/proliferation) in colorectal cancer. Because the majority of investigations related to the anticancer activity of myricetin have dominantly focused on the enhancement of tumor cell uncontrolled growth (i.e., apoptosis). Thus, we have decided to explore the potential myricetin interactors and the associated biological functions by using an in-silico approach. Then, we focused on the main goal of the work which involved the synthesis of silver nanoparticles and the labeling of myricetin with it. The synthesized silver nanoparticles were examined using UV-visible spectroscopy, dynamic light scattering spectroscopy, Fourier transform infrared spectroscopy, and scanning electron microscopy. In this study, we have investigated the effects of myricetin on colorectal cancer where numerous techniques were used to show myricetin's effect on colon cancer cells. Transmission Electron Microscopy was employed to monitor morphological changes. Furthermore, we have combined the results of the colorectal cancer gene expression dataset with those of the myricetin interactors and pathways. Based on the results, we conclude that myricetin is able to efficiently kill human colorectal cancer cell lines. Since, it shares important biological roles and possible route components and this myricetin may be a promising herbal treatment for colorectal cancer as per an in-silico analysis of the TCGA dataset. [ABSTRACT FROM AUTHOR]

The purpose of this study is to identify the leading causes of slope instability along a local highway in Anhui, People's Republic of China. As part of the east expansion project, the mountain range will be excavated to create a two-way, nearly 30 m wide highway. The site's topography consists of a hill with palm-shaped faces carved from limestone running along its sides. The geological characteristics and slope stability of the research area highlight the possibility of slope failure along both sides of the roadway. Slope stability analysis was performed in order to determine the failure mechanism and create a stable slope. Initial slope characterization and shear properties of the rock were determined by means of fieldwork and laboratory analysis. By causing wedging failure and toppling collapse, the bedding joints and discontinuity orientations increase instability, as determined by a kinematic analysis performed with DIP.6 software. The Limit Equilibrium Method (LEM) of analysis is presented in the software Slide 6.020 to illustrate the instability of the slope. The unstable condition of the slopes was determined using empirical methods that were validated and enhanced by limit equilibrium analysis. [ABSTRACT FROM AUTHOR]

In this article, the compact, ultra-wideband and high-gain MIMO antenna is presented for future 5G devices operating over 28 GHz and 38 GHz. The presented antenna is designed over substrate material Roger RT/Duroid 6002 with a thickness of 1.52 mm. The suggested design has dimensions of 15 mm × 10 mm and consists of stubs with loaded rectangular patch. The various stubs are loaded to antenna to improve impedance bandwidth and obtain ultra-wideband. The resultant antenna operates over a broadband of 26.5–43.7 GHz, with a peak value of gain >8 dBi. A four-port MIMO configuration is achieved to present the proposed antenna for future high data rate devices. The MIMO antenna offers isolation <−30 dB with ECC of <0.0001. The antenna offers good results in terms of gain, radiation efficiency, envelop correlation coefficient (ECC), mean effective gain (MEG), diversity gain (DG), channel capacity loss (CCL), and isolation. The antenna hardware prototype is fabricated to validate the performance of the suggested design of the antenna achieved from software tools, and good correlation between measured and simulated results is observed. Moreover, the proposed work performance is also differentiated with literature work, which verifies that the suggested work is a potential applicant for future 5G compact devices operating over wideband and high gain. [ABSTRACT FROM AUTHOR]

Over the most recent couple of years, the Internet of Things and other empowering innovations have been logically utilized for digitizing the vegetable supply chain (VSC). Background: The unpredictable examples and complexity inserted in enormous data dimensions present a test for an orderly human master examination. Hence in an information-driven setting, soft computing (SC) has accomplished critical energy to investigate, mine, and concentrate confidential information data, or tackle complex improvement issues, finding some harmony between good productivity and maintainability of vegetable supply frameworks. Methods: This paper presents a new and diverse scientific classification of VSC issues from the SC methodology. It characterizes VSC issues and sorts them in light of how they be demonstrated according to the SC perspective. Moreover, we examine the SC methodologies commonly utilized in each phase of the VSC and their related classes of issues. Accordingly, there is an issue in distinguishing and characterizing VSC issues according to a more extensive point of view, enveloping the different SC strategies that can apply in various phases (from creation to retailing), and recognizing the issues that emerge in these phases according to the SC viewpoint. Results: We likewise acquaint some rules with the assistance of VSC analysts and specialists to settle on appropriate strategies while resolving specific issues they could experience. Even though a few latest examinations have arranged the SC writing in this field, they are situated towards a solitary group of SC strategies (a gathering of techniques that share standard qualities) and survey their application in VSC phases. Conclusions: We have suggested a novel approach and complete scientific classification of vegetable supply chain concerns about soft computing. We present a view of three delegate supply chains: cruciferous vegetables, dark green leafy vegetables, and tomatoes. We assembled the scientific type in light of different parts to arrange vegetable supply chain issues as per how they can be demonstrated utilizing soft computing methodologies. [ABSTRACT FROM AUTHOR]

Personalized travel recommendations propose locations of interest (LOIs) for users. The LOI sequence suggestion is more complicated than a single LOI recommendation. Only a few studies have considered LOI sequence recommendations. Creating a reliable succession of LOIs is difficult. The two LOIs that follow each other should not be identical or from the same category. It is vital to examine the types of subsequent LOIs when designing a sequence of LOIs. Another issue is that providing precise and accurate location recommendations bores users. It can be tedious and monotonous to look at the same types of LOIs repeatedly. Users may want to change their plans in the middle of a trip. The trip must be dynamic rather than static. To address these concerns in the recommendations, organize a customized journey by looking for continuity, implications, innovation, and surprising (i.e., high levels of amusement) LOIs. We use LOI-likeness and category differences between subsequent LOIs to build sequential LOIs. In our travel recommendations, we leveraged luck and dynamicity. We suggest a fortunate refining trip recommendation (FRTR) to address the issues of identifying and rating user pleasure. An algorithm oof compelling recommendation should offer what we are likely to enjoy and provide spontaneous yet objective components to maintain an open doorway to new worlds and discoveries. In addition, two advanced novel estimations are presented to examine the recommended precision of a sequence of LOIs: regulated precision (RP) and pattern precision (PP). They consider the consistency and order of the LOIs. We tested our strategy using data from a real-world dataset and user journey records from Foursquare dataset. We show that our system outperforms other recommendation algorithms to meet the travel interests of users. [ABSTRACT FROM AUTHOR]

Cache-enabled networks suffer hugely from the challenge of content caching and content delivery. In this regard, cache-enabled device-to-device (D2D) assisted multitier cellular networks are expected to relieve the network data pressure and effectively solve the problem of content placement and content delivery. Consequently, the user can have a better opportunity to get their favored contents from nearby cache-enabled transmitters (CETs) through reliable and good-quality links; however, as expected, designing an effective caching policy is a challenging task due to the limited cache memory of CETs and uncertainty in user preferences. In this article, we introduce a joint content placement and content delivery technique for D2D assisted multitier cellular networks (D2DMCN). A support vector machine (SVM) is employed to predict the content popularity to determine which content is to be cached and where it is to be cached, thereby increasing the overall cache hit ratio (CHR). The content request is satisfied either by the neighboring node through the D2D link or by the cache-enabled base stations (BSs) of the multitier cellular networks (MCNs). Similarly, to solve the problem of optimal content delivery, the Hungarian algorithm is employed aiming to improve the quality of satisfaction. The simulation results indicate that the proposed content placement strategy effectively optimizes the overall cache hit ratio of the system. Similarly, an effective content delivery approach reduces the request content delivery delay and power consumption. [ABSTRACT FROM AUTHOR]

Precipitation elasticity provides a basic estimate of the sensitivity of long-term streamflow to changes in long-term precipitation, and it is especially useful as the first assessment of climate change impact in land and water resource projects. This study estimated and compared the precipitation elasticity (εp) of streamflow in 86 catchments within Pakistan over 50 major rivers using three widely used analytical models: bivariate nonparametric (NP) estimator, multivariate NP analysis, and multivariate double logarithm (DL) model. All the three models gave similar values of elasticity in the range of 0.1–3.5 for over 70–75% of the catchments. This signifies that a 1% change in the annual mean precipitation compared to the long-term historic mean annual precipitation will amplify the streamflow by 0.1–3.5%. In addition, the results suggested that elasticity estimation of streamflow sensitivity using the multivariate DL model is more reliable and realistic. Precipitation elasticity of streamflow is observed high at altitudes ranging between 250 m and 1000 m while the longitudinal and latitudinal pattern of εp shows higher values in the range of 70–75 and 32–36 decimal degrees, respectively. The εp values were found to have a direct relationship with the mean annual precipitation and an inverse relationship with the catchment areas. Likewise, high εp values were noticed in areas where the mean annual temperature ranges between 15 and 24 °C. [ABSTRACT FROM AUTHOR]

Unbalanced load condition is one of the major issues of all commercial, industrial and residential sectors. Unbalanced load means that, when different loads are distributed on a three-phase four-wire system, unequal currents pass through the three phases. Due to it, a heavy current flows in the neutral wire, which not only adds the losses, but also puts constraints on three phases' loads. In this paper, we have presented a practical approach for load balancing. First, we have considered the existing three-phase load system where the supply is a three-phase unbalanced supply. Before balancing the load, it is necessary to compensate the current in neutral wire. A nature-inspired moth–flame optimization (MFO) algorithm is used to propose a scheme for balancing of current in neutral wire. The information of a distributed single-phase load was used to balance the currents in a three-phase system. The feeder phase and load profiles of each single-phase load are used to reconfigure the network using an optimization process. By balancing the current of three phases, the current of the neutral conductor in substation transformers was reduced to almost zero. [ABSTRACT FROM AUTHOR]

The limited caching capacity of the local cache enabled Base station (BS) decreases the cache hit ratio (CHR) and user satisfaction ratio (USR). However, Cache enabled multi-tier cellular networks have been presented as a promising candidate for fifth generation networks to achieve higher CHR and USR through densification of networks. In addition to this, the cooperation among the BSs of various tiers for cached data transfer, intensify its significance many folds. Therefore, in this paper, we consider maximization of CHR and USR in a multi-tier cellular network. We formulate a CHR and USR problem for multi-tier cellular networks while putting major constraints on caching space of BSs of each tier. The unsupervised learning algorithms such as K-mean clustering and collaborative filtering have been used for clustering the similar BSs in each tier and estimating the content popularity respectively. A novel scheme such as cluster average popularity based collaborative filtering (CAP-CF) algorithm is employed to cache popular data and hence maximizing the CHR in each tier. Similarly, two novel methods such as intra-tier and cross-tier cooperation (ITCTC) and modified ITCTC algorithms have been employed in order to optimize the USR. Simulations results witness, that the proposed schemes yield significant performance in terms of average cache hit ratio and user satisfaction ratio compared to other conventional approaches. [ABSTRACT FROM AUTHOR]

Due to the rapid increase in human population, the use of energy in daily life is increasing day by day. One solution is to increase the power generation in the same ratio as the human population increase. However, that is usually not possible practically. Thus, in order to use the existing resources of energy efficiently, smart grids play a significant role. They minimize electricity consumption and their resultant cost through demand side management (DSM). Universities and similar organizations consume a significant portion of the total generated energy; therefore, in this work, using DSM, we scheduled different appliances of a university campus to reduce the consumed energy cost and the probable peak to average power ratio. We have proposed two nature-inspired algorithms, namely, the multi-verse optimization (MVO) algorithm and the sine-cosine algorithm (SCA), to solve the energy optimization problem. The proposed schemes are implemented on a university campus load, which is divided into two portions, morning session and evening session. Both sessions contain different shiftable and non-shiftable appliances. After scheduling of shiftable appliances using both MVO and SCA techniques, the simulations showed very useful results in terms of energy cost and peak to average ratio reduction, maintaining the desired threshold level between electricity cost and user waiting time. [ABSTRACT FROM AUTHOR]

The past few decades have witnessed significant advances in the development of functionalized metal/metal oxide nanoparticles including those of inorganic noble metals and magnetic materials stabilized by various polymeric ligands. Recent applications of such functionalized nanoparticles, including those in bio-imaging, sensing, catalysis, drug delivery, and other biomedical applications have triggered the need for their facile and reproducible preparation with a better control over their size, shape, and surface chemistry. In this perspective, the multidentate polymer ligands containing functional groups like thiol, thioether, and ester are important surface ligands for designing and synthesizing stable nanoparticles (NPs) of metals or their oxides with reproducibility and high yield. These ligands have offered an unprecedented control over the particle size of both nanoparticles and nanoclusters with enhanced colloidal stability, having tunable solubility in aqueous and organic media, and tunable optical, magnetic, and fluorescent properties. This review summarizes the synthetic methodologies and stability of nanoparticles and fluorescent nanoclusters of metals (Au, Ag, Cu, Pt, and other transition metal oxides) prepared by using thioether based ligands and highlights their applications in bio-imaging, sensing, drug delivery, magnetic resonance imaging (MRI), and catalysis. The future applications of fluorescent metal NPs like thermal gradient optical imaging, single molecule optoelectronics, sensors, and optical components of the detector are also envisaged. [ABSTRACT FROM AUTHOR]

The increase in population growth and demand is rapidly depleting natural resources. Irrigation plays a vital role in the productivity and growth of agriculture, consuming no less than 75% of fresh water utilization globally. Irrigation, being the largest consumer of water across the globe, needs refinements in its process, and because it is implemented by individuals (farmers), the use of water for irrigation is not effective. To enhance irrigation management, farmers need to keep track of information such as soil type, climatic conditions, available water resources, soil pH, soil nutrients, and soil moisture to make decisions that resolve or prevent agricultural complexity. Irrigation, a data-driven technology, requires the integration of emerging technologies and modern methodologies to provide solutions to the complex problems faced by agriculture. The paper is an overview of IoT-enabled modern technologies through which irrigation management can be elevated. This paper presents the evolution of irrigation and IoT, factors to be considered for effective irrigation, the need for effective irrigation optimization, and how dynamic irrigation optimization would help reduce water use. The paper also discusses the different IoT architecture and deployment models, sensors, and controllers used in the agriculture field, available cloud platforms for IoT, prominent tools or software used for irrigation scheduling and water need prediction, and machine learning and neural network models for irrigation. Convergence of the tools, technologies and approaches helps in the development of better irrigation management applications. Access to real-time data, such as weather, plant and soil data, must be enhanced for the development of effective irrigation management applications. [ABSTRACT FROM AUTHOR]

Decoupled data and control planes in Software Defined Networks (SDN) allow them to handle an increasing number of threats by limiting harmful network links at the switching stage. As storage, high-end servers, and network devices, Network Function Virtualization (NFV) is designed to replace purpose-built network elements with VNFs (Virtualized Network Functions). A Software Defined Network Function Virtualization (SDNFV) network is designed in this paper to boost network performance. Stateful firewall services are deployed as VNFs in the SDN network in this article to offer security and boost network scalability. The SDN controller's role is to develop a set of guidelines and rules to avoid hazardous network connectivity. Intruder assaults that employ numerous socket addresses cannot be adequately protected by these strategies. Machine learning algorithms are trained using traditional network threat intelligence data to identify potentially malicious linkages and probable attack targets. Based on conventional network data (DT), Bayesian Network (BayesNet), Naive-Bayes, C4.5, and Decision Table (DT) algorithms are used to predict the target host that will be attacked. The experimental results shows that the Bayesian Network algorithm achieved an average prediction accuracy of 92.87%, Native–Bayes Algorithm achieved an average prediction accuracy of 87.81%, C4.5 Algorithm achieved an average prediction accuracy of 84.92%, and the Decision Tree algorithm achieved an average prediction accuracy of 83.18%. There were 451 k login attempts from 178 different countries, with over 70 k source IP addresses and 40 k source port addresses recorded in a large dataset from nine honeypot servers. [ABSTRACT FROM AUTHOR]

Uncertainties are the most significant challenges in the smart power system, necessitating the use of precise techniques to deal with them properly. Such problems could be effectively solved using a probabilistic optimization strategy. It is further divided into stochastic, robust, distributionally robust, and chance-constrained optimizations. The topics of probabilistic optimization in smart power systems are covered in this review paper. In order to account for uncertainty in optimization processes, stochastic optimization is essential. Robust optimization is the most advanced approach to optimize a system under uncertainty, in which a deterministic, set-based uncertainty model is used instead of a stochastic one. The computational complexity of stochastic programming and the conservativeness of robust optimization are both reduced by distributionally robust optimization.Chance constrained algorithms help in solving the constraints optimization problems, where finite probability get violated. This review paper discusses microgrid and home energy management, demand-side management, unit commitment, microgrid integration, and economic dispatch as examples of applications of these techniques in smart power systems. Probabilistic mathematical models of different scenarios, for which deterministic approaches have been used in the literature, are also presented. Future research directions in a variety of smart power system domains are also presented. [ABSTRACT FROM AUTHOR]

The paper demonstrates symmetric integral operator and interpolation based numerical approximations for linear and nonlinear ordinary differential equations (ODEs) with oscillatory factor x ′ = ψ (x) + χ ω (t) , where the function χ ω (t) is an oscillatory forcing term. These equations appear in a variety of computational problems occurring in Fourier analysis, computational harmonic analysis, fluid dynamics, electromagnetics, and quantum mechanics. Classical methods such as Runge–Kutta methods etc. fail to approximate the oscillatory ODEs due the existence of oscillatory term χ ω (t) . Two types of methods are presented to approximate highly oscillatory ODEs. The first method uses radial basis function interpolation, and then quadrature rules are used to evaluate the integral part of the solution equation. The second approach is more generic and can approximate highly oscillatory and nonoscillatory initial value problems. Accordingly, the first-order initial value problem with oscillatory forcing term is transformed into highly oscillatory integral equation. The transformed symmetric oscillatory integral equation is then evaluated numerically by the Levin collocation method. Finally, the nonlinear form of the initial value problems with an oscillatory forcing term is converted into a linear form using Bernoulli's transformation. The resulting linear oscillatory problem is then computed by the Levin method. Results of the proposed methods are more reliable and accurate than some state-of-the-art methods such as asymptotic method, etc. The improved results are shown in the numerical section. [ABSTRACT FROM AUTHOR]

Metallic nanoparticles, such as gold nanoparticles (AuNPs), have been extensively studied as drug delivery systems for various therapeutic applications. However, drug-loaded-AuNPs have been rarely explored in vivo for their effect on bacteria residing inside tissues. Ciprofloxacin (CIP) is a second-generation fluoroquinolone with a broad-spectrum of antibiotic properties devoid of developing bacteria resistance. This research is focused on the synthesis and physical characterization of Ciprofloxacin-loaded gold nanoparticles (CIP-AuNPs) and their effect on the colonization of Enterococcus faecalis in the liver and kidneys of mice. The successfully prepared CIP-AuNPs were stable and exerted enhanced in vitro antibacterial activity against E. faecalis compared with free CIP. The optimized CIP-AuNPs were administered (500 µg/Kg) once a day via tail vein to infected mice for eight days and were found to be effective in eradicating E. faecalis from the host tissues. Moreover, unlike CIP, CIP-AuNPs were non-hemolytic. In summary, this study demonstrated that CIP-AuNPs are promising and biocompatible alternative therapeutics for E.-faecalis-induced infections resistant to conventional drugs (e.g., beta-lactams and vancomycin) and should be further investigated. [ABSTRACT FROM AUTHOR]

The green synthesis of nanoparticles has emerged as a simple, safe, sustainable, reliable and eco-friendly protocol. Among different types of NPs, green-synthesized zinc oxide NPs (ZnONPs) show various promising biological uses due to their interesting magnetic, electrical, optical and chemical characteristics. Keeping in view the dependence of the therapeutic efficacy of NPs on their physico-chemical characteristics, the green synthesis of ZnONPs using Casuarina equisetifolia leaf extract under UV-A and UV-C light was carried out in this study. UV-irradiation helped to control the size and morphology of ZnONPs by exciting the electrons in the photoactive compounds of plant extracts to enhance the bio-reduction of ZnO into ZnONPs. C. equisetifolia leaf extract was found enriched with phenolic (2.47 ± 0.12 mg GAE/g DW) and flavonoid content (0.88 ± 0.28 mg QE/g DW) contributing to its 74.33% free-radical scavenging activity. FTIR spectra showed the involvement of polyphenols in the bio-reduction, stabilization and capping of ZnONPs. Moreover, SEM-EDX and XRD analyses showed great potential of UV-C light in yielding smaller (34–39 nm) oval-shaped ZnONPs, whereas UV-A irradiation resulted in the formation of fairly spherical 67–71 nm ZnONPs and control ZnONPs were of mixed shape and even larger size (84–89 nm). Green-synthesized ZnONPs, notably CE-UV-C-ZnONPs, showed promising anti-bacterial activities against Bacillus subtilis, Pseudomonas fluorescens and Pseudomonas aeruginosa. Moreover, ZnONPs also enhanced ROS production which led to a significant loss of mitochondrial membrane potential and activated caspase-3 gene expression and caspase-3/7 activity in human hepatocellular carcinoma (HepG2) cells. CE-UV-C-ZnONP treatment reduced HepG2 cell viability to as low as 36.97% owing to their unique shape and smaller size. Lastly, ZnONPs were found to be highly biocompatible towards brine shrimp and human red blood cells suggesting their bio-safe nature. This research study sheds light on the plausible role of UV radiation in the green synthesis of ZnONPs with reasonable control over their size and morphology, thus improving their biological efficacy. [ABSTRACT FROM AUTHOR]

As the world population and its dependency on energy is growing exponentially day by day, the existing energy generating resources are not enough to fulfill their needs. In the conventional grid system, most of the generated energy is wasted because of improper demand side management (DSM). This leads to a difficulty in keeping the equilibrium between the user need and electric power production. To overcome these difficulties, smart grid (SG) is introduced, which is composed of the integration of two-way communication between the user and utility. To utilize the existing energy resources in a better way, SG is the best option since a large portion of the generated energy is consumed by the educational institutes. Such institutes also need un-interrupted power supply at the lowest cost. Therefore, in this paper, we have taken a university campus load. We have not only applied two bio-inspired heuristic algorithms for energy scheduling—namely, the Firefly Algorithm (FA) and the Lion Algorithm (LA)—but also proposed a hybrid version, FLA, for more optimal results. Our main objectives are a reduction in both, that is, the cost of energy and the waiting time of consumers or end users. For this purpose, in our proposed model, we have divided all appliances into two categories—shiftable appliances and non-shiftable appliances. Shiftable appliances are feasible to be used in any of the time slots and can be planned according to the day-ahead pricing signal (DAP), provided by the utility, while non-shiftable appliances can be used for a specified duration and cannot be planned with the respective DAP signal. So, we have scheduled shiftable appliances only. We have also used renewable energy sources (RES) for achieving maximum end user benefits. The simulation results show that our proposed hybrid algorithm, FLA, has reduced the cost excellently. We have also taken into consideration the consumers' waiting times, due to scheduling of appliances. [ABSTRACT FROM AUTHOR]

Whitlockite, being the second most abundant bio-mineral in living bone, finds huge applications in tissue regeneration and implants and its synthesis into its pure form has remained a challenge. Although precipitation of whitlockite phase has been reported recently in many publications, effects of various parameters to control such phase as well as conditions for the bulk preparation of this extremely important bio-mineral have not been investigated so far. In this work, we report the precipitation of pure whitlockite phase using common precursors. As reported in the literature, whitlockite is stable in a narrow pH range, therefore; optimization of pH for the stabilization of whitlockite phase has been investigated. Additionally, in order to narrow down the optimum conditions for the whitlockite precipitation, effect of temperature and heating conditions has also been studied. The obtained solids were characterized using powder X-ray diffraction (PXRD), Fourier transform infrared spectroscopy (FTIR), Raman spectroscopy, scanning electron microscopy (SEM) and thermogravimetric analysis (TGA). From PXRD analysis, it was observed that heating the precursor's mixture at 100 °C with subsequent aging at the optimized pH resulted in the precipitation of pure whitlockite phase. These results were further confirmed by TGA, SEM and Raman spectroscopy analysis and it was confirmed that the conditions reported here favor whitlockite precipitation without formation of any secondary phase. These reaction conditions were further confirmed by changing all the parameters like aging, heating time, feed rate of precursors one by one. From PXRD analysis of these samples, it was concluded that not only pH but temperature, heating time, aging time and feed rate effect simultaneously on the precipitation of pure whitlockite phase and a subtle change in any of these parameters could lead to the formation of undesired stable secondary calcium phosphate phases. [ABSTRACT FROM AUTHOR]

Multidrug resistance (MDR) has been a potentiator for the exploration of antibiotics. Nano drug delivery systems have opened new avenues to overcome this challenge. Although antibacterial nanocarriers are extensively realized, their effect on the bacteria residing inside the tissues and their toxicity is rarely explored. This study investigated the effects of flavonoid coated gold nanoparticles (FAuNPs) on the colonization of Enterococcus faecalis in the mouse liver and kidneys. Flavonoids were extracted from the leaves of Berberis lycium Royle and used to stabilize gold following a green synthesis approach. FAuNPs were characterized by ultraviolet-visible (UV-Vis) spectroscopy, Fourier-transform infrared spectroscopy (FTIR), scanning transmission electron microscopy (STEM), X-ray powder diffraction (XRD), and energy-dispersive X-ray spectroscopy (EDS). FAuNPs showed significantly higher reduction in bacterial counts in in-vitro and in-vivo in mice organs as compared to the free flavonoids owing to their biocompatibility and effectiveness. [ABSTRACT FROM AUTHOR]

Copper nanoclusters (Cu NCs) are generally formed by several to dozens of atoms. Because of wide range of raw materials and cheap prices, Cu NCs have attracted scientists' special attention. However, Cu NCs tend to undergo oxidation easily. Thus, there is a dire need to develop a synthetic protocol for preparing fluorescent Cu NCs with high QY and better stability. Herein, we report a one-step method for preparing stable blue-green fluorescent copper nanoclusters using glutathione (GSH) as both a reducing agent and a stabilizing agent. High-resolution transmission electron microscopy (HRTEM), X-ray photoelectron spectroscopy (XPS) and electrospray ionization mass spectrometer (ESI-MS) were used to characterize the resulting Cu NCs. The as-prepared Cu NCs@GSH possess an ultrasmall size (2.3 ± 0.4 nm), blue-green fluorescence with decent quantum yield (6.2%) and good stability. MTT results clearly suggest that the Cu NCs@GSH are biocompatible. After incubated with EB-labeled HEK293T cells, the Cu NCs mainly accumulated in nuclei of the cells, suggesting that the as-prepared Cu NCs could potentially be used as the fluorescent probe for applications in cellular imaging. [ABSTRACT FROM AUTHOR]

Faults and failures are familiar case studies in centralized and decentralized tracking systems. The processing of sensor data becomes more severe in the presence of faults/failures and/or noise. Effective schemes have been presented for decentralized systems, in the presence of faults only. In some practical scenarios of systems, there are certain interruptions in addition to these faults. These interruptions may occur in the form of noise. However it is expected that the decision about the sensor data is difficult in the presence of noise. This is because the noise adversely affects the communication amongst sensors and the processing unit. More complexity is expected when there are faults and noise simultaneously. To deal with this problem, in addition to existing fault detection and isolation schemes, the Kalman filter is employed. Here, a generic discussion is provided, which is equally applicable to other situations. This work addresses various faults in the presence of noise for decentralized tracking systems. Local single faults and multiple faults in the presence of noise are the core issues addressed in this paper. The proposed work is comprised of a general scenario for a decentralized tracking system followed by a case study of a target tracking scenario with and without noise. The presented schemes are also tested for different types of faults. The proposed work presents effective tracking in the presence of noise and faults. The results obtained demonstrate the acceptable performance of the scheme of this work. [ABSTRACT FROM AUTHOR]

Due to the exponential increase in the human population of this bio-sphere, energy resources are becoming scarce. Because of the traditional methods, most of the generated energy is wasted every year in the distribution network and demand side. Therefore, researchers all over the world have taken a keen interest in this issue and finally introduced the concept of the smart grid. Smart grid is an ultimate solution to all of the energy related problems of today's modern world. In this paper, we have proposed a meta-heuristic optimization technique called the dragonfly algorithm (DA). The proposed algorithm is to a real-world problem of single and multiple smart homes. In our system model, two classes of appliances are considered; Shiftable appliances and Non-shiftable appliances. Shiftable appliances play a significant role in demand side load management because they can be scheduled according to real time pricing (RTP) signal from utility, while non-shiftable appliances are not much important in load management, as these appliances are fixed and cannot be scheduled according to RTP. On behalf of our simulation results, it can be concluded that our proposed algorithm DA has achieved minimum electricity cost with a tolerable waiting time. There is a trade-off between electricity cost and waiting time because, with a decrease in electricity cost, waiting time increases and vice versa. This trade-off is also obtained by our proposed algorithm DA. The stability of the grid is also maintained by our proposed algorithm DA because stability of the grid depends on peak-to-average ratio (PAR), while PAR is reduced by DA in comparison with an unscheduled case. [ABSTRACT FROM AUTHOR]

Industries are consuming more than 27% of the total generated energy in the world, out of which 50% is used by different machines for processing, producing, and assembling various goods. Energy shortage is a major issue of this biosphere. To overcome energy scarcity, a challenging task is to have optimal use of existing energy resources. An efficient and effective mechanism is essential to optimally schedule the load units to achieve three objectives: minimization of the consumed energy cost, peak-to-average power ratio, and consumer waiting time due to scheduling of the load. To achieve the aforementioned objectives, two bio-inspired heuristic techniques—Grasshopper-Optimization Algorithm and Cuckoo Search Optimization Algorithm—are analyzed and simulated for efficient energy use in an industry. We considered a woolen mill as a case study, and applied our algorithms on its different load units according to their routine functionality. Then we scheduled these load units by proposing an efficient energy management system (EMS). We assumed automatic operating machines and day-ahead pricing schemes in our EMS. [ABSTRACT FROM AUTHOR]

An adaptive splitting algorithm was implemented for numerical evaluation of Fourier-type highly oscillatory integrals involving stationary point. Accordingly, a modified Levin collocation method was coupled with multi-resolution quadratures in order to tackle the stationary point and irregular oscillations of the integrand caused by ω. Some test problems are included to verify the accuracy of the proposed methods. [ABSTRACT FROM AUTHOR]