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Identification of Influential nodes in complex networks is challenging due to the largely scaled data and network sizes, and frequently changing behaviors of the current topologies. Various application scenarios like disease transmission and immunization, software virus infection and disinfection, increased product exposure and rumor suppression, etc., are applicable domains in the corresponding networks where identification of influential nodes is crucial. Though a lot of approaches are proposed to address the challenges, most of the relevant research concentrates only on single and limited aspects of the problem. Therefore, we propose Global Structure Model (GSM) for influential nodes identification that considers self-influence as well as emphasizes on global influence of the node in the network. We applied GSM and utilized Susceptible Infected Recovered model to evaluate its efficiency. Moreover, various standard algorithms such as Betweenness Centrality, Profit Leader, H-Index, Closeness Centrality, Hyperlink Induced Topic Search, Improved K-shell Hybrid, Density Centrality, Extended Cluster Coefficient Ranking Measure, and Gravity Index Centrality are employed as baseline benchmarks to evaluate the performance of GSM. Similarly, we used seven real-world and two synthetic multi-typed complex networks along-with different well-known datasets for experiments. Results analysis indicates that GSM outperformed the baseline algorithms in identification of influential node(s).

Contemporary Smart Power Systems (SPNs) depend on Cyber-Physical Systems (CPSs) to connect physical devices and control tools. Developing a robust privacy-conserving intrusion detection method involves network and physical data regarding the setups, such as Supervisory Control and Data Acquisition (SCADA), for defending real data and recognizing cyber-attacks. A key issue in the implementation of SPNs is the security against cyber-attacks, targeting to interrupt SCADA operations and violate data privacy over the usage of penetration and data poisoning attacks. In this paper, a privacy-conserving framework, so-called PC-IDS, is proposed for realizing the privacy and safety features of SPNs through hybrid machine learning approach. The framework includes two key components. Primarily, a data pre-processing component is proposed for cleaning and transforming actual data into a different layout that accomplishes the aim of privacy conservation. Then, an intrusion detection component is proposed using a particle swarm optimization-based probabilistic neural network for the identification and recognition of malicious events. The performance of PC-IDS framework is evaluated by means of two commonly available datasets, i.e. the Power System and UNSW-NB15 datasets. The experimental outcomes highlight that the framework can proficiently protect data of SPNs and determine anomalous behaviours compared to numerous recent compelling state-of-the-art methods with respect to false positive rate (FPR), detection rate (DR) and computational processing time (CPT) by achieving 96.03% of DR, 0.18% FPR for Power System dataset and 95.91% of DR, 0.14% FPR for UNSW-NB15 dataset.

Computer networks face vulnerability to numerous attacks, which pose significant threats to our data security and the freedom of communication. This paper introduces a novel intrusion detection technique that diverges from traditional methods by leveraging Recurrent Neural Networks (RNNs) for both data preprocessing and feature extraction. The proposed process is based on the following steps: (1) training the data using RNNs, (2) extracting features from their hidden layers, and (3) applying various classification algorithms. This methodology offers significant advantages and greatly differs from existing intrusion detection practices. The effectiveness of our method is demonstrated through trials on the Network Security Laboratory (NSL) and Canadian Institute for Cybersecurity (CIC) 2017 datasets, where the application of RNNs for intrusion detection shows substantial practical implications. Specifically, we achieved accuracy scores of 99.6% with Decision Tree, Random Forest, and CatBoost classifiers on the NSL dataset, and 99.8% and 99.9%, respectively, on the CIC 2017 dataset. By reversing the conventional sequence of training data with RNNs and then extracting features before applying classification algorithms, our approach provides a major shift in intrusion detection methodologies. This modification in the pipeline underscores the benefits of utilizing RNNs for feature extraction and data preprocessing, meeting the critical need to safeguard data security and communication freedom against ever-evolving network threats.

The existing control chart for monitoring the blood components by attribute is designed using classical statistics. The existing attribute control chart can be applied only when the experimenter is sure about the proportion of defective or all the observations in the sample are determined. In this paper, new attribute control charts for monitoring the blood components under the neutrosophic statistics will be presented. The design of the proposed control chart is given under the neutrosophic statistical interval method. The applications of these control charts demonstrate that the proposed control charts are quite effective, adequate, flexible, and informative for monitoring the blood components under uncertain environment.

The Hindu Kush Himalaya region is experiencing rapid climate change with adverse impacts in multiple sectors. To put recent climatic changes into a long-term context, here we reconstructed the region’s climate history using tree-ring width chronologies of climate-sensitive Cedrus deodara and Pinus gerardiana . Growth-climate analysis reveals that the species tree-growth is primarily limited by moisture stress during or preceding the growing season, as indicated by a positive relationship between the chronology and precipitation and scPDSI, and a negative one with temperature. We have reconstructed 635 years (1384–2018 CE) of February–June precipitation using a robust climate reconstruction model that explains about 53% variance of the measured precipitation data. Our reconstruction shows several dry and wet episodes over the reconstruction period along with an increase in extreme precipitation events during recent centuries or years. Long, very wet periods were observed during the following years: 1392–1393, 1430–1433, 1456–1461, 1523–1526, 1685–1690, 1715–1719, 1744–1748, 1763–1767, 1803–1806, 1843–1846, 1850–1855, 1874–1876, 1885–1887, 1907–1909, 1921–1925, 1939–1944, and 1990–1992, while long dry periods were observed during the following years: 1398–1399, 1464–1472, 1480–1484, 1645–1649, 1724–1727, 1782–1786, 1810–1814, 1831–1835, 1879–1881, 1912–1918, 1981–1986, 1998–2003, and 2016–2018 CE. We found predominantly short-term periodicity cycles of 2.0, 2.2, 2.3, 2.4, 2.6–2.7, 2.9, 3.3, 4.8, 8.1–8.3, and 9.4–9.6 years in our reconstruction. Spatial correlation analyses reveal that our reconstruction is an effective representation of the precipitation variability in the westerly climate-dominated areas of Pakistan and adjacent regions. In addition to the influence of regional circulation systems like western disturbances, we found possible teleconnections between the precipitation variability in northern Pakistan and broader-scale climate modes or phases like AMO and ENSO. The study also highlights the prospects of tree-ring application to explore linkages between western disturbance, increasing intensity and frequency of extreme climate events, and analysis of long-term atmospheric circulation over the western Himalayan region.

Efficient identification of influential nodes is one of the essential aspects in the field of complex networks, which has excellent theoretical and practical significance in the real world. A valuable number of approaches have been developed and deployed in these areas where just a few have used centrality measures along with their concerning deficiencies and limitations in their studies. Therefore, to resolve these challenging issues, we propose a novel effective distance-based centrality (EDBC) algorithm for the identification of influential nodes in concerning networks. EDBC algorithm comprises factors such as the power of K-shell, degree nodes, effective distance, and numerous levels of neighbor’s influence or neighborhood potential. The performance of the proposed algorithm is evaluated on nine real-world networks, where a susceptible infected recovered (SIR) epidemic model is employed to examine the spreading dynamics of each node. Simulation results demonstrate that the proposed algorithm outperforms the existing techniques such as eigenvector, betweenness, closeness centralities, hyperlink-induced topic search, H-index, K-shell, page rank, profit leader, and gravity over a valuable margin.

In the present paper, we propose a control chart based on extended exponentially weighted moving average (EEWMA) statistic to detect a quick shift in the mean. The mean and variance expression of the proposed EEWMA statistic are derived. The proposed EEWMA statistic is unbiased and simulation results show a smaller variance as compared to the traditional EWMA. The performance of the proposed control chart with the existing chart based on the EWMA statistic is evaluated in terms of average run length (ARL). Various tables were constructed for different values of parameters. The comparison of the EEWMA control chart with the traditional EWMA and Shewhart control charts illustrates that the proposed control chart performs better in terms of quick detection of the shift. The working procedure of the proposed control chart was also illustrated by simulated and application data.

Migratory birds globally face escalating anthropogenic threats, with crane species being particularly vulnerable. This study assessed human-perceived threats to cranes (Grus virgo & Grus grus) along Pakistan’s vital Indus Flyway using 400 stakeholder questionnaires across eight districts (2021–2024). We quantified perceived threat based on frequency (1 = Very Rare; 5 = Very Frequent) and severity (1 = Not Severe; 5 = Extremely Severe), revealing poaching (illegal killing) as the dominant threat (frequency = 4.9; severity = 4.8), followed by illegal wildlife trade (4.7; 4.5) and taming (4.6; 4.3). Spatial analysis showed strikingly higher perceived threats in southern Pakistan (Rajanpur: frequency = 4.88, severity = 4.82) versus central regions (Khushab: 3.76, 4.02; p < 0.001), with riverbanks identified as high-risk poaching zones (42 incidents). Cluster analysis also confirmed Rajanpur as a critical hotspot within three distinct threat tiers. Critically, analysis of socio-demographic drivers revealed threat type (frequency: F = 104.92, p < 0.001; severity: F = 153.64, p < 0.001) and poaching method (frequency: F = 10.14, p < 0.001; severity: F = 15.43, p < 0.001) as significant perception-shapers, while education, occupation, and crane species preference (frequency: F = 1.17, p = 0.310) exerted a non-significant influence. These results highlight that individual backgrounds minimally modulate threat perceptions. The study aligns with global evidence of uniform crane threats demanding the following urgent conservation action: region-specific enforcement (especially southern hotspots), community-led anti-poaching initiatives, and targeted awareness programs to shift high-threat communities toward crane-friendly coexistence practices.

This manuscript presents the development of an attribute control chart (ACC) designed to monitor the number of defective items in manufacturing processes. The charts are specifically tailored using time-truncated life test (TTLT) for two lifetime data distributions: the half-normal distribution (HND) and the half-exponential power distribution (HEPD) under a repetitive sampling scheme (RSS). To assess the effectiveness of the proposed control charts, both in-control (IC) and out-of-control (OOC) scenarios are considered by deriving the average run length (ARL). Various factors, including sample sizes, control coefficients, and truncated constants for shifted phases, are taken into account to evaluate the performance of the charts in terms of ARL. The behavior of ARLs is analyzed in the shifted process by introducing shifts in its parameters. The superiority of the HEPD-based chart is highlighted by comparing it with both the HND-based ACC and the ACC based on the Exponential distribution (ED) under TTLT using RSS. The results showcase the superior performance of the proposed HEPD-based chart, indicated by smaller ARL values. Additionally, the benefits of another proposed ACC using HND are compared with the ED-based ACC under RSS, further confirming the effectiveness of the HND-based approach through smaller ARLs Finally, the proposed control charts are evaluated through simulation testing and real-life implementation, emphasizing their practical applicability in real-world manufacturing settings.

In this manuscript, we construct an attribute control chart (ACC) for the number of defective items using time-truncated life tests (TTLT) when the lifetime of a manufacturing item follows two lifetime data distributions: the half-normal distribution (HND) and the half-exponential power distribution (HEPD). To assess the potential of the proposed charts, necessary derivations are made to obtain the value of the average run length (ARL) when the production process is in-control and out-of-control. The performance of the presented charts is evaluated for different sample sizes, control coefficients, and truncated constants for shifted phases in terms of ARL. The behavior of ARLs is studied for the shifted process by introducing shifts in its parameters. The advantages of the proposed HEPD-based chart are discussed in the form of ARLs with HND and Exponential Distribution (ED) based ACCs under TTLT, showing the excellent assessment of the proposed chart. Additionally, the advantages of another proposed ACC using HND are compared with ED-based ACC, and the findings support the HND in the form of smaller ARLs. Finally, simulation testing and real-life implementation are also discussed for functional purposes.