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Harmonic distortion in power systems is a significant problem, and it is thus necessary to mitigate critical harmonics. This study proposes an optimal method for designing passive power filters (PPFs) to suppress these harmonics. The design of a PPF involves multi-objective optimization. A multi-objective bee swarm optimization (MOBSO) with Pareto optimality is implemented, and an external archive is used to store the non-dominated solutions obtained. The minimum Manhattan distance strategy was used to select the most balanced solution in the Pareto solution set. A series of case studies are presented to demonstrate the efficiency and superiority of the proposed method. Therefore, the proposed method has a very promising future not only in filter design but also in solving other multi-objective optimization problems.

The Tor browser is widely used for anonymity, providing layered encryption for enhanced privacy. Besides its positive uses, it is also popular among cybercriminals for illegal activities such as trafficking, smuggling, betting, and illicit trade. There is a need for Tor Browser forensics to identify its use in unlawful activities and explore its consequences. This research analyzes artifacts generated by Tor on Windows-based systems. The methodology integrates forensic techniques into incident responses per NIST SP (800-86), exploring areas such as registry, storage, network, and memory using tools like bulk-extractor, autopsy, and regshot. We propose an automated PowerShell script that detects Tor usage and retrieves artifacts with minimal user interaction. Finally, this research performs timeline analysis and artifact correlation for a contextual understanding of event sequences in memory and network domains, ultimately contributing to improved incident response and accountability.

Passive power filters (PPFs) are most effective in mitigating harmonic pollution from power systems; however, the design of PPFs involves several objectives, which makes them a complex multiple-objective optimization problem. This study proposes a method to achieve an optimal design of PPFs. We have developed a new multi-objective optimization method based on an artificial bee colony (ABC) algorithm with a minimum Manhattan distance. Four different types of PPFs, namely, single-tuned, second-order damped, third-order damped, and C-type damped order filters, and their characteristics were considered in this study. A series of case studies have been presented to prove the efficiency and better performance of the proposed method over previous well-known algorithms.

Dental caries is a biofilm-mediated, sugar-driven, multifactorial, dynamic disease that results in the phasic demineralization and remineralization of dental hard tissues. Despite scientific advances in cariology, dental caries remains a severe global concern. The aim of this study was to determine the optimization of microbial and molecular techniques for the detection of cariogenic pathogens in dental caries patients, the prevalence of cariogenic bacteria on the basis of socioeconomic, climatological, and hygienic factors, and in vitro evaluation of the antimicrobial activity of selected synthetic antibiotics and herbal extracts. In this study, oral samples were collected from 900 patients for bacterial strain screening on a biochemical and molecular basis. Plant extracts, such as ginger, garlic, neem, tulsi, amla, and aloe vera, were used to check the antimicrobial activity against the isolated strains. Synthetic antimicrobial agents, such as penicillin, amoxicillin, erythromycin, clindamycin, metronidazole, doxycycline, ceftazidime, levofloxacin, and ciprofloxacin, were also used to access the antimicrobial activity. Among 900 patients, 63% were males and 37% were females, patients aged between 36 and 58 (45.7%) years were prone to disease, and the most common symptom was toothache (61%). For oral diseases, 21% used herbs, 36% used antibiotics, and 48% were self-medicated, owing to sweets consumption (60.66%) and fizzy drinks and fast food (51.56%). Staphylococcus mutans (29.11%) and Streptococcus sobrinus (28.11%) were found as the most abundant strains. Seven bacterial strains were successfully screened and predicted to be closely related to genera S. sobrinus, S. mutans, Actinomyces naeslundii, Lactobacillus acidophilus, Eubacterium nodatum, Propionibacterium acidifaciens, and Treponema Pallidum. Among plant extracts, the maximum zone of inhibition was recorded by ginger (22.36 mm) and amla (20.01 mm), while among synthetic antibiotics, ciprofloxacin and levofloxacin were most effective against all microbes. This study concluded that phyto extracts of ginger and amla were considered suitable alternatives to synthetic antibiotics to treat dental diseases.

The mobile network operators (MNOs) need to efficiently utilize spectrum resources to meet increasing user demands for massive and ubiquitous connectivity. The licensed spectrum resources are scarce, costly and difficult to acquire. Consequently, the available bandwidth becomes a challenge. In this paper, a deep reinforcement learning (DRL)–based method has been utilized to share the spectrum in a multi–channels multi–operator environment. To intelligently and dynamically assign suitable channels, the proposed DRL model implemented at each MNO takes the load on the gNodeBs (gNBs), such as, the number of packets in the gNB queue and resource requirements of user equipments, such as, achievable data rate of users, into account to estimate the suitable channel selections. The scheduler then utilizes this channel information for efficient channel allocations. The performance of the proposed DRL–based spectrum sharing scheme has been compared with the conventional scheduling–based spectrum allocation scheme using extensive simulations. Results indicate that the dynamicity in network environment and traffic demands can be reasonably handled by the proposed DRL–based multi–channel spectrum sharing scheme, since it adapts feasibly to the varying number of channels, number of UEs, and network traffic conditions, compared to those of the conventional scheme. Furthermore, the proposed scheme shows superior performance gains in terms of throughput, resource utilization, delay, transmission time, and packet drop rates, compared to those of the conventional scheme.

Automatic Student performance prediction is a crucial job due to the large volume of data in educational databases. This job is being addressed by educational data mining (EDM). EDM develop methods for discovering data that is derived from educational environment. These methods are used for understanding student and their learning environment. The educational institutions are often curious that how many students will be pass/fail for necessary arrangements. In previous studies, it has been observed that many researchers have intension on the selection of appropriate algorithm for just classification and ignores the solutions of the problems which comes during data mining phases such as data high dimensionality ,class imbalance and classification error etc. Such types of problems reduced the accuracy of the model. Several well-known classification algorithms are applied in this domain but this paper proposed a student performance prediction model based on supervised learning decision tree classifier. In addition, an ensemble method is applied to improve the performance of the classifier. Ensemble methods approach is designed to solve classification, predictions problems. This study proves the importance of data preprocessing and algorithms fine-tuning tasks to resolve the data quality issues. The experimental dataset used in this work belongs to Alentejo region of Portugal which is obtained from UCI Machine Learning Repository. Three supervised learning algorithms (J48, NNge and MLP) are employed in this study for experimental purposes. The results showed that J48 achieved highest accuracy 95.78% among others.

The agriculture sector in semi-arid regions of the world is highly vulnerable to climate change. People in Potohar Plateau, a rainfed semi-arid region in Pakistan, largely depends on locally grown crops for their staple food requirements. Abrupt changes in climate patterns lead to crop failures and adversely impact the livelihood of local communities. Future climate change is expected to further amplify food and livelihood insecurities. This study analyzes the past (1981–2010) temperature and precipitation data and project future (2021–2100) changes in temperature and precipitation using CMIP5 models under both RCP 4.5 and 8.5 emission scenarios. The study area was splited into two sub-regions: The eastern Potohar region (Islamabad, Rawalpindi, and Jhelum) and the western Potohar region (Attock and Chakwal). Mann–Kendall and Sen’s slope tests were applied to check the significance and rate of trends in temperature and precipitation. Signficant rising temperature trends were simulated for the both sub-regions. Models under RCP 4.5 emission scenario projected average increase of 3.2 °C and 2.8 °C in eastern and western Potohar regions, respectively. Under business as usual RCP 8.5 emission scenario models projected increase of 3.9 °C and 4.0 °C in eastern and western Potohar region, respectively. On contrary, models projected large variations in precipitation with no distinct trends in both eastern and western Potohar regions. Rising temperatures and high rainfall variability is an alarming sign for agriculture sector and food security of the region.

Hydropericardium syndrome (HPS) is common even in the commercial broilers that are vaccinated using locally prepared HPS virus infected liver homogenate (HPS-LH) vaccine. In the HPS-LH vaccine production process, some of the in process quality control factors mitigate its efficacy. The HPS-LH vaccine containing more than 10(4.6) units of bird lethal dose 50 (LD50) when injected to 14 days old broilers and 21 days post vaccination given challenge with infection dose (100 units of LD50 of HPS-LH) showed 100 per cent protection. Moreover, less than 25 doses of the vaccine prepared from one gram of the HPS-LH induced 90 per cent protection in the vaccinated birds. Addition of adjuvant such as oil base (Montanide ISA 70) or aluminum hydroxide gel (AHG) in the vaccine showed additive effect on its efficacy. The birds vaccinated with montanide based HPS-LH vaccine (10(4.6) units of LD50: 25 doses/gram) showed 100 percent protection to challenge infection. The montanide is more effective adjuvant as compared to AHG. Infectivity titer (LD50) of HPS virus infected chicken embryo hepatocyte homogenate (CEHH) was 100 times less 10(2.5) units than that of HPS-LH. Gel based HPS-CEHH vaccine (LD50 titer 10(2.5) units showed poor response in the vaccinated birds (40 per cent protection) as compared to that of gel based HPS-LH vaccine (90 per cent). It is concluded that gel based vaccine prepared from fresh HPS-LH is more effective and economical as compared to that of HPS-CEHH vaccine.

The energy sector has become the largest contributor to greenhouse gas (GHG) emissions. Among these GHG emissions, most threatening is CO2 emission which comes from the consumption of fossil fuels. This empirical work analyzes the roles of renewable energy consumption and non-renewable energy consumption in CO2 emissions in Pakistan. The empirical evidence is based on an auto-regressive distributive lag (ARDL) model of data from 1970 to 2016. The disaggregate analysis reveals that renewable energy consumption has an insignificant impact on CO2 emission in Pakistan and that, in the non-renewable energy model, natural gas and coal are the main contributors to the level of pollution in Pakistan. Economic growth positively contributes to CO2 emission in the renewable energy model but not in the non-renewable energy model. Policies that emphasize the contribution of renewable energy to economic growth and that add more clean energy into the energy mix are suggested.