Crystal structure of catena-poly[[tri-methyltin(IV)]-μ-2-(2-nitrophenyl)-acetato-κ2O:O′]

The authors acknowledge the provision of funds for the purchase of a diffractometer and encouragement by Dr Muhammad Akram Chaudhary, Vice Chancellor, University of Sargodha, Pakistan.Peer reviewedPublisher PD

Enhancing Cyber Security through Predictive Analytics: Real-Time Threat Detection and Response

This research paper aims to examine the applicability of predictive analytics to improve the real-time identification and response to cyber-attacks. Today, threats in cyberspace have evolved to a level where conventional methods of defense are usually inadequate. This paper highlights the significance of predictive analytics and demonstrates its potential in enhancing cyber security frameworks. This research integrates literature on using big data analytics for predictive analytics in cyber security, noting that such systems could outperform conventional methods in identifying advanced cyber threats. This review can be used as a framework for future research on predictive models and the possibilities of implementing them into the cyber security frameworks. The study uses quantitative research, using a dataset from Kaggle with 2000 instances of network traffic and security events. Logistic regression and cluster analysis were used to analyze the data, with statistical tests conducted using SPSS. The findings show that predictive analytics enhance the vigilance of threats and response time. This paper advocates for predictive analytics as an essential component for developing preventative cyber security strategies, improving threat identification, and aiding decision-making processes. The practical implications and potential real-world applications of the findings are also discussed.Comment: 10 pages and 6 tables. Files available at https://github.com/cs-maestro/predictive-analytic

Development of mixed metal metal-organic polyhedra networks, colloids, and MOFs and their pharmacokinetic applications

The coordination networking of discrete metal-organic polyhedra (MOPs) involving different ligands as well as metals is a challenging task due to the features of limited solubility and chemical stability of these polyhedra. An unusual approach, ligand-oriented polyhedral networking via click chemistry and further metal coordination is reported here. An alkyne decorated Cu(II)-MOP self-catalyzes the regioselective click reaction (1,3-dipolar cycloaddition) using azide-functionalized ligands under unconventional reaction conditions. Introducing new metal ions, M(II), interlinks the carboxylic groups on the MOP surfaces creating coordination networks. On the other hand, exposure of the respective individual ligand components in the presence of Cu(II) promotes an in-situ click reaction along with metal coordination generating a new 3D-framework. These materials demonstrated a high drug hosting potential exhibiting a controlled progressive release of anticancer (5-flourouracil) and stimulant (caffeine) drugs in physiological saline at 37 degrees C. These innovative and unconventional MOP networks provide a significant conceptual advance in understanding

Integrating supply chain management into circular economy practices for HVAC industry

This thesis critically examines the incorporation of Circular Economy (CE) principles within the supply chain management framework of Mecatech Pvt. Ltd., a prominent HVAC service provider in Pakistan. It specifically addresses the adaptation of reuse, remanufacturing, and recycling strategies to transition from traditional linear models to a regenerative economic model. Comparative analysis with leading U.S. HVAC firms that have integrated CE practices illuminates a pathway for Mecatech to realize environmental, operational, and economic enhancements. Employing a robust mixed-methods approach, this study gauges the systemic barriers and enablers influencing CE adoption through qualitative interviews and quantitative data analysis. The research articulates strategic interventions necessary to mitigate technological shortcomings, regulatory insufficiencies, and stakeholder reluctances. It also delineates the comprehensive benefits accruing from CE, such as operational cost reductions, improved resource sustainability, and fortified market positioning. Findings have widened discourse on sustainable supply chain practices in developing contexts and clearly outline actionable strategies for Mecatech's journey towards sustainability. This thesis presents a substantial contribution to the academic and practical knowledge of embedding CE into high growth markets and develops a scalable model for industry wide adoption of CE

Relationship between Child Immunization and Household Socio-Demographic Characteristic in Pakistan

The purpose of this study is to document the child immunization and its association with the household, socio demographic characteristics which effect child immunization of children aged 12 to 23 months Pakistan.The analysis in this study is based on the Household level data taken from the Pakistan Social and Living Standard Measurement Survey (PSLM) 2010 - 2011 carried out by the Federal Bureau of Statistic, Government of Pakistan. Chi-square test and logistic regression is used for the analysis of data. The results indicate that in case of child immunization, not only child s age, but also child’s gender,resident of the child and he/she parents education, household income and family size plays a significant role. The gender differentials are more prominent in rural areas where negative impact on child immunization also exists due to the higher income inequality, among, household.The analysis of socio demographic characteristic provides the researchers, educationists and policy makers with a critical review of the issues at hand, so that appropriate policies and programmers can be designed for increasing child immunization in the country. Keywords: Child Immunization, householdsocio-demographic characteristic, Logistic regression, Chi Square, Pakista

POWER ALLOCATION ALGORITHM FOR MIMO BASED MULTI-HOP COOPERATIVE SENSOR NETWORK

Cooperative transmission is a new breed of wireless communication systems that enables the cooperating node in a wireless sensor network to share their radio resources by employing a distributed transmission and processing operation. This new technique offers substantial spatial diversity gains as the cooperating nodes help one another to send data over several independent paths to the destination node. In recent times, an extensive effort has been made to incorporate these systems in the future wireless networks like LTE (Long Term Evolution), IEEE 802.16j (Mobile Multi-hop Relay (MMR) Networks) and IEEE 802.16m (Mobile WiMAX Release 2 or WirelessMAN-Advanced). But, there are few technical issues which need to be addressed before this promising technique is integrated into future wireless networks. Among them, managing transmission power is a critical issue, which needs to be resolved to fully exploit the benefits of cooperative relaying. Optimal Power Allocation, is one such technique that optimally distributes the total transmission power between the source and relaying nodes thus saving a lot of power while maintaining the link quality. In the first part of the thesis, mathematical expressions of the received signals have been derived for different phases of cooperative transmission. Average-Bit-error-rate (ABER), has been taken as a performance metric to show the efficiency of cooperative relaying protocols. In the second part of this Chapter, a multi-hop framework has been presented for the power allocation algorithm with Amplify-and-Forward relaying protocol. The efficiency of the power allocation algorithm has been discussed with different scenarios i.e. First for a three node (2-Hop) wireless network configuration and then for a four node (3-Hop) wireless network configuration. The transmission scenarios (2-Hop and 3-Hop) have been further categorized into multiple cases on the basis of channel quality between source-to-destination, source-to-relay, relay-to-relay and relay-to-destination links.fi=Opinnäytetyö kokotekstinä PDF-muodossa.|en=Thesis fulltext in PDF format.|sv=Lärdomsprov tillgängligt som fulltext i PDF-format

A STUDY OF LONG-TERM SUNSPOTS AND K-INDEX GEOMETRIC CYCLES USING PROBABILISTIC MODELING

The research work done in this paper comprises the application of different well-known probability distribution models. This includes the understanding of the behavior and dynamics of 24 sunspot cycles with total data. The time-series data sets were selected from 1749 to 2014. To observe the solar activity effects on K-index activity the double cycles from 1932 to 2014 were also incorporated in the study. The comparative study is useful to observe the long-term solar-terrestrial connection. The magnetic field of the sun reverses its polarity after every 11 years of the cycle. So after every 22 years, the north pole becomes again north pole. By using the two well-known tests Kolmogorov-Smirnov (KST) and Anderson-Darling test (ADT) the probability distribution models were obtained for each sunspot cycles and compare. The significant probability models for all the sunspot cycles have been obtained. The fitted probability distribution models on selected data sets may be useful to understand the trend of solar and geomagnetic activity

COVID-19 Outbreak: Consumer Impulsive Buying Behavior towards Personal Safety and Healthcare Products

The global escalation of COVID-19 in 2020 has altered the consumption patterns of consumers. The research on impulsive buying during a pandemic is understudied and requires more scholarly intentions. This study addresses this gap by utilizing the reflections of well-known theoretical lenses: affect theory, the health belief model, and social exchange theory in the context of impulsive buying attitudes toward personal safety and healthcare products. The research model hypothesized the positive association between emotional, cognitive, and behavioral aspects. A total of 407 online buyers were recruited through an online cross-sectional survey. The empirical model was examined by using a method of covariance-based structural-equation modeling. Data was analyzed by using SPSS 26 and AMOS. The study findings showed a significant positive association between emotional, cognitive, and behavioral aspects. Negative and positive appeals significantly drove consumer perceptions about threats, benefits, and costs. The study results supported that cognitive aspects were associated considerably with impulsive buying behavior toward safety care products. The study findings significantly affect regulators, academics, and practitioners interested in developing regulations and strategies during pandemic situations