Formation Of Intrusions Research Articles

A Review of the Mineral Chemistry and Crystallization Conditions of Ediacaran–Cambrian A-Type Granites in the Central Subprovince of the Borborema Province, Northeastern Brazil

Ediacaran–Cambrian magmatism in the Central Subprovince (Borborema Province, NE Brazil) generated abundant A-type granites. This study reviews published whole-rock and mineral chemistry data from thirteen Ediacaran–Cambrian A-type intrusions and a related dike swarm. It also presents new mineral chemistry and whole-rock data for one of these intrusions, along with zircon trace element data for five of the intrusions. Geochronological data from the literature indicate the formation of these A-type intrusions during a 55 Myr interval (580–525 Ma), succeeding the post-collisional high-K magmatism in the region at c. 590–580 Ma. The studied plutons intruded Paleoproterozoic basement gneisses or Neoproterozoic supracrustal rocks. They are ferroan, metaluminous to peraluminous and mostly alkalic–calcic. The crystallization parameters show pressure estimates mainly from 4 to 7 kbar, corresponding to crustal depths of 12 to 21 km, and temperatures ranging from 1160 to 650 °C in granitoids containing mafic enclaves, and from 990 to 680 °C in those lacking or containing only rare mafic enclaves. The presence of Fe-rich mineral assemblages including ilmenite indicates that the A-type granites crystallized under low ƒO2 conditions. Zircon trace element analyses suggest post-magmatic hydrothermal processes, interpreted to be associated with shear zone reactivation. Whole-rock geochemical characteristics, the chemistry of the Fe-rich mafic mineral assemblages, and zircon trace elements in the studied granitoids share important similarities with A2-type granites worldwide.

IOT NETWORK INTRUSION DETECTION USING MACHINE LEARNING ON UNSW-NB15 DATASET

This research presents a comprehensive investigation into the application of machine learning techniques for addressing the pervasive security challenges within Internet of Things (IoT) networks. With the exponential growth of interconnected devices, ensuring the integrity and confidentiality of data transmissions has become increasingly critical. In this study, we deploy and evaluate seven distinct machine learning methods tailored to the IoT network intrusion detection problem. Leveraging the rich and diverse UNSW-NB15 dataset, encompassing real-world network traffic scenarios, our analysis encompasses a thorough examination of both traditional and state-of-the-art algorithms. Through rigorous experimentation and performance evaluation, we assess the efficacy of these methods in accurately detecting and classifying various forms of network intrusions. Our findings provide valuable insights into the strengths and limitations of different machine learning approaches for enhancing the security posture of IoT environments, thereby facilitating informed decision-making for network administrators and cybersecurity practitioners.

Discovery of Late Cretaceous basalts in the Asuo area, Central Tibet: Implications for orogenic root removal beneath the Lhasa-Qiangtang orogenic belt

Post-collisional mafic rocks provide valuable insights into the mantle properties and evolution of orogenic belts. However, their origins remain unknown. In this study, comprehensive age, elemental, and zircon Lu–Hf isotopic data are presented for mafic dikes from the Asuo area of the Central Tibetan Plateau. The Asuo mafic dikes exhibit typical arc-related characteristics such as enrichment in large-ion lithophile elements and depletion in high-field-strength elements. This sodium-rich calc-alkaline mafic rocks has low K2O contents (0.98–1.26 wt%) and high Na2O/K2O ratios (2.55–2.97). The presence of xenolith zircons and varying zircon Hf isotopic composition [εHf(t): 1.33–8.32] indicate that the mafic magma assimilated juvenile crust. Zircon U-Pb dating results suggest that the Asuo mafic rocks were emplaced contemporaneously with the adakite-like rocks during ∼ 95–75 Ma. However, no genetic link between Asuo mafic rocks and the adakite-like rocks in the Lhasa–Qiangtang Orogenic belt was observed. The primary contribution of mantle-derived mafic magmas to the formation of coeval intermediate–felsic magmas is the provision of additional heat. The mass exchange between them may be limited. We suggest a geodynamic scenario in which the orogenic root may have been removed during the post-collisional period via repeated and localized lithospheric dripping. In this model, the formation of mafic and adakite-like intrusions occurred in two stages, that is, (1) partial melting of metasomatized lithospheric mantle generated mafic melts, and (2) underplating of these mafic melts beneath the thickened juvenile lower crust, which resulted in partial melting of the juvenile mafic lower crust and generation of adakite-like melts. Our results provide new insights into the magmatism in the terminal stage of an orogenic system.

Cost-Efficient Automated Intrusion Detection and Reporting System for Homes in Nigeria

Substantial investments are made to mitigate the persistent threat to lives and assets posed by various forms of intrusion. Automated security systems have emerged as crucial tools for safeguarding homes against intrusions in recent times. A major advantage of this is its independent ability to report human activities around homes without direct observation. However, the cost of implementing the system is not pocket-friendly to an average Nigerian given the prevailing economic situation. This research therefore seeks to develop and implement an intrusion detection and reporting system using low cost materials while maintaining a balance between quality and cost at the same time. The system architecture employs the passive infrared sensor (PIR) for motion detection, the ultrasonic sensor measures intruders’ location from the home entrance while the NodeMCU ESP8266 microcontroller is responsible for coordinating the reception and relaying of signals within the system. The system was implemented with varying distances of human presence from the security device. Reports of potential intrusion alerts were obtained within reasonable time frames (between 11 seconds and 49 seconds) on the homeowner’s mobile phone via the Blynk application. This performance demonstrates the reliability of the system for home security. The system is equally cost-efficient relative to most similar state-of-the art IoT based home security systems considered in this work. Our work therefore contributes to knowledge by proposing an affordable home security solution for a low budget Nigerian user.

Neural processing of audiovisual and painful analogue trauma and its relationship with subsequent audiovisual and pain intrusions

ABSTRACT Background: Posttraumatic stress disorder and medically unexplained pain frequently co-occur. While pain is common during traumatic events, the processing of pain during trauma and its relation to audiovisual and pain intrusions is poorly understood. Objective: Here we investigate neural activations during painful analogue trauma, focusing on areas that have been related to threat and pain processing, and how they predict intrusion formation. We also examine the moderating role of cumulative lifetime adversity. Methods: Sixty-five healthy women were assessed using functional magnetic resonance imaging. An analogue trauma was induced by an adaptation of the trauma-film paradigm extended by painful electrical stimulation in a 2 (film: aversive, neutral) x 2 (pain: pain, no-pain) design, followed by 7-day audiovisual and pain intrusion assessment using event-based ecological momentary assessment. Intrusions were fitted with Bayesian multilevel regression and a hurdle lognormal distribution. Results: Conjunction analysis confirmed a wide network including anterior insula (AI) and dorsal anterior cingulate cortex (dACC) being active both, during aversive films and pain. Pain resulted in activation in areas amongst posterior insula and deactivation in a network around ventromedial prefrontal cortex (VMPFC). Higher AI and dACC activity during aversive>neutral film predicted greater audiovisual intrusion probability over time and predicted greater audiovisual intrusion frequency particularly for participants with high lifetime adversity. Lower AI, dACC, hippocampus, and VMPFC activity during pain>no-pain predicted greater pain intrusion probability particularly for participants with high lifetime adversity. Weak regulatory VMPFC activation was associated with both increased audiovisual and pain intrusion frequency. Conclusions: Enhanced AI and dACC processing during aversive films, poor pain vs. no-pain discrimination in AI and dACC, as well as weak regulatory VMPFC processing may be driving factors for intrusion formation, particularly in combination with high lifetime adversity. Results shed light on a potential path for the etiology of PTSD and medically unexplained pain.

Distributed fiber optic sensing signal recognition based on class-incremental learning

Distributed fiber optic sensing (DFOS) based on phase-sensitive optical time-domain reflectance (φ-OTDR) technology has outstanding performance in pipeline safety monitoring and perimeter security detection. Accurate identification of new events remains challenging due to environmental variability and emerging forms of intrusions. In order to solve the problem of failing to accurately identify new events due to the inability to obtain all samples at once in real-time monitoring, this paper proposes an incremental learning network framework for distributed fiber-optic sensing signal recognition. This framework integrates an optimized Learning without Memorizing (LwM) algorithm with an improved ConvNeXt network for dynamic training of new events. An improved Efficient Channel Attention (HECA) is incorporated to thoroughly extract the spatio-temporal features of the intrusion signals collected by the DFOS. The forgetting problem is mitigated during incremental learning using knowledge distillation and optimized Gradient Weighted Class Activation Mapping to generate an attention map. A linear correction layer is added after the output layer to correct the bias towards new classes by rebalancing the information between new and old classes. Experimental comparisons show that the recognition rate for 10 different intrusion signals exceeds 93 %, while the forgetting rate is reduced from a peak of 41.44 % to 5.25 %. The time required to process and train incremental learning for 1000 samples in real time on an edge device (NVIDIA 3050 GPU) is approximately 1060 s, its ability to demonstrating its suitability for deployment in resource-constrained.

Enhancing Tennis Practice: Sensor Fusion and Pose Estimation with a Smart Tennis Ball.

This article demonstrates the integration of sensor fusion for pose estimation and data collection in tennis balls, aiming to create a smaller, less intrusive form factor for use in progressive learning during tennis practice. The study outlines the design and implementation of the Bosch BNO055 smart sensor, which features built-in managed sensor fusion capabilities. The article also discusses deriving additional data using various mathematical and simulation methods to present relevant orientation information from the sensor in Unity. Embedded within a Vermont practice foam tennis ball, the final prototype product communicates with Unity on a laptop via Bluetooth. The Unity interface effectively visualizes the ball's rotation, the resultant acceleration direction, rotations per minute (RPM), and the orientation relative to gravity. The system successfully demonstrates accurate RPM measurement, provides real-time visualization of ball spin and offers a pathway for innovative applications in tennis training technology.

Can Listening to a Verbal Trauma Report Induce Intrusions? – Replication of a Randomized Trial

ABSTRACT Recent evidence suggests that indirect verbal exposure to traumatic events can be sufficient to cause intrusions and other posttraumatic stress symptoms. In this study, we used a verbal trauma report to experimentally induce intrusions and a tactile task to manipulate the putative processes underlying intrusion development. Our findings indicate that the verbal report indeed induced intrusive recall. Moreover, the verbal report induced negative mood, state anxiety, and state dissociation, with mood and state dissociation predicting intrusion development. Additionally, the tactile task interfered with intrusion formation as indicated by the primary diary measure, but not the retrospective self-report. However, these results await further replication as this and previous trials suffered from limited statistical power. The findings support the reports by trauma therapists who experience secondary traumatization. They also challenge the assumption that all intrusions develop bottom-up from low-level sensory input via sensory representations. Future studies should explore differential processes for intrusion development based on their modality.

Submesoscale Processes in the Kuroshio Loop Current: Roles in Energy Cascade and Salt and Heat Transports

AbstractThe Kuroshio Loop Current (KLC) is an important form of Kuroshio intrusion into the northeastern South China Sea (NESCS), which has significant influences on dynamical and biogeochemical processes in the NESCS. Recent studies suggested that the KLC is a hot spot of submesoscale processes (submesoscales) with spatiotemporal scales of O(1–10) km and O(1–10) days, but submesoscales' roles in energy cascade and salt and heat transports remain obscure. Here, we investigate this issue through analyzing outputs from a 1/48° simulation. The kinetic energy exchange rate between submesoscale and larger‐scale processes (KER) is overall positive in the KLC region, which suggests the dominance of forward cascade. The magnitude of KER is comparable with the temporal change rate of larger‐scale kinetic energy in the upper 200 m. We also find that magnitude and direction of KER are closely associated with strain rate and horizontal divergence of background flows, respectively. In addition, the KLC region shows elevated submesoscale salinity and heat diffusivities with magnitudes reaching O(102) m2 s−1. During the KLC period, horizontal mixing by submesoscales can transport 0.90 × 1013 kg salt and 0.71 × 1020 J heat westward into the NESCS interior, which are an order of magnitude larger than those caused by the mesoscale eddy shedding from the KLC. These results suggest that submesoscales play important roles not only in energy cascade but also in salt and heat transports in the KLC region. Therefore, the roles of submesoscales should be taken into account when studying energy, salt, and heat budgets in the NESCS.

Experimental Constraints on the Storage Conditions and Differentiation of High-Ti Basalts from the Panzhihua and Hongge Layered Intrusions, SW China

Abstract High-Ti basalts are commonly believed to represent parental magmas leading to the formation of mafic-ultramafic layered intrusions, such as Late Permian Panzhihua and Hongge in the Emeishan Large Igneous Province (SW China). Consequently, elucidation of the crystallisation and crustal differentiation of high-Ti basalts is critical for our understanding of the petrogenesis of these layered intrusions and the associated oxide ore mineralisation. Here, we present the results of crystallisation experiments carried out in internally heated pressure vessels using a primitive high-Ti basaltic composition. The experiments were conducted at 100 and 300 MPa, in the temperature interval of 950–1200 °C and with water activities (aH2O) from 0 to 1. The oxygen fugacity (fO2) was controlled and varied from FMQ −1 to FMQ +3.3 log units (FMQ corresponds to fayalite-quartz-magnetite buffer). The main mineral phases are olivine, clinopyroxene and plagioclase, accompanied by Cr-Fe-Ti-oxides, orthopyroxene, apatite and amphibole, depending on the conditions. Redox conditions primarily influence the stability fields of Cr-Fe-Ti oxides. Clinopyroxene, orthopyroxene and amphibole are pressure-dependent and have larger stability fields under high pressure conditions. The olivine→orthopyroxene and olivine→amphibole peritectic reactions are observed. Comparisons of phase equilibria between this study and experiments conducted with parental magma of Skaergaard layered intrusion demonstrate the effect of bulk system composition. For instance, ilmenite crystallisation is determined not only by intrinsic parameters such as fO2 but also by additional compositional parameters (e.g. melt Ti, Fe, Al and Mg content). Although COMAGMAT and MELTS modelling results generally reproduce the crystallisation sequence, only the stability field of clinopyroxene and its composition are perfectly modelled. The comparison of experimental results with the rocks from the lowest units of Panzhihua and Hongge layered intrusions are applied to constrain storage conditions in the magma reservoirs. Compared to Hongge, we conclude that the Panzhihua magma chamber was probably located at a shallower depth (~3–6 km), that magma crystallisation started at lower temperatures (~1125–1100 °C), higher fO2 (~FMQ + 1 to FMQ + 2) and that its initial melt H2O content was lower (~0.5–1 wt. %).

More severe surface relief but stronger fatigue resistance at small scales: Vacancy-assisted fatigue damage mechanism

Surface relief in forms of extrusions and intrusions as the substantial feature of early fatigue damage is one of the most important phenomena studied in metal fatigue. The most common surface relief models in bulk metals are agreed to be correlated with the formation of typical dislocation patterns as persistent slip bands (PSBs), while little is known about the fundamental mechanisms at submicron and even nanometer scales where dislocation pattern formation is fully inhibited. Here, as exampled with thin Au films, the underlying fatigue damage mechanism at small scales is investigated through the quantitative characterization of fatigue damages. Continuous generation and migration of vacancies is found to be crucial for the shape of extrusion/intrusions and kinetics of their growth at submicron and even nanometer scales. Due to the degraded dislocation interaction and intensified vacancy diffusion, the delayed vacancy accumulation in the small-scale metal interior suppresses the extrusion and interface void formation in thinner films, which finally leads to the superior ability to support tremendous surface relief and strong fatigue resistance. The finding of the vacancy-dominated fatigue mechanism at small scales extends our understanding of the metal fatigue mechanisms down to the submicron and even nanometer scales and suggests a novel interface engineering strategy by vacancy behavior modulation for fatigue-tolerance material design.

Incivility is systematically associated with indicators of health, stress, well-being, and the psychosocial work environment

Abstract Aim Incivility is one of the most intrusive forms of antisocial behavior in the workplace. Using a newly constructed, single-item measure of incivility, the aim of the current study was to assess the possible prevalence of incivility in a sample from the Swedish retail industry, and to investigate possible associations between prevalence of incivility and a multitude of indicators of health, stress, well-being, and the psychosocial work environment. Subject and methods A total of 1014 (20%) individuals enrolled in the cross-sectional study and responded to a questionnaire, partially or fully. We conducted t-tests to assess possible differences in the indicators between those responding “Yes” versus “No” regarding prevalence of incivility at their workplace. Results Incivility at work was related to statistically significant differences in mean values in 32 out of 33 key indicators of health, well-being, stress, recovery, and the psychosocial work environment. Those reporting a prevalence of incivility systematically exhibited worse ratings. Conclusion The findings are in line with previous research. Although this study cannot infer causality, the profoundly systematic results indicate that the prevalence of workplace incivility is a bothersome problem that should be addressed. A single item about the prevalence of workplace incivility seems to be a feasible indicator of associations to a wide range of health-, stress-, well-being-, and psychosocially oriented work-related variables.

Electrical characteristics of ore-forming intrusion No. 2 in the Xiarihamu magmatic Ni-Cu sulfide deposit in East Kunlun Orogenic Belt

The Xiarihamu magmatic Ni-Cu sulfide deposit is situated within the East Kunlun Orogenic Belt and consists of four intrusions. While the estimated Ni resources of intrusion No. 1 exceed 1.2 million tons, the resource potential of other intrusions remains unknown. One significant factor contributing to this knowledge gap is the lack of effective detection and localization methods for concealed intrusions. To address this issue, we conducted an electromagnetic study focusing on intrusion No. 2. Due to the substantial topographic relief in the survey area, the conventional artificial-source electromagnetic method presented limitations in data acquisition. Consequently, we employed the short-offset transient electromagnetic method for data acquisition. A group-network observation mode was utilized to reconstruct the electrical structure of intrusion No. 2 through data inversion. Our results reveal a predominant high-resistivity background within the survey area. Rocks including granite, biotite gneiss, gabbro, and pyroxenite exhibit high resistivities, whereas mineralized ultramafic rocks show lower resistivities due to the presence of sulfides. The detection of concealed conductive bodies serves as a crucial indicator for deep mineral exploration. The distribution characteristics of low-resistivity anomalies indicate that the mafic bodies extend deeper towards the southwest, with mineralized pyroxenites observed beneath the gabbros. The results also suggest a gradual weakening of mineralization from the northeast to the southwest, with conductive mineralized anomalies existing at considerable depths beneath the known exposed intrusion. These findings highlight promising prospecting potential and provide valuable insights into the formation of intrusions within the Xiarihamu deposit, supporting resource exploration in similar deposits.

Detection of Cyber Attacks and Network Attacks using Machine Learning Algorithms

With the escalating sophistication of cyber threats and the increasing complexity of network infrastructures, traditional rule-based intrusion detection systems (IDS) are proving inadequate in safeguarding against modern cyber attacks. Consequently, the integration of machine learning (ML) algorithms has emerged as a promising approach to fortify network security by enabling proactive detection and mitigation of cyber threats. This review paper comprehensively explores the application of ML algorithms in detecting various forms of cyber-attacks and network intrusions. The review begins by outlining the fundamental concepts of cyber attacks and network intrusions, providing context for the subsequent discussion on ML-based detection methodologies. It surveys the landscape of ML algorithms employed in cybersecurity, ranging from classical techniques like Support Vector Machines (SVM) and Random Forests to more advanced methods such as deep learning and ensemble models. Furthermore, the paper explains the diverse datasets utilized for training and evaluating ML-based intrusion detection systems, highlighting their significance in ensuring robust and generalizable models. Additionally, it examines the challenges and limitations associated with ML-driven detection, including issues of data scarcity, adversarial attacks, and model interpretability

Detecting IoT Anomalies Using Fuzzy Subspace Clustering Algorithms

There are many applications of anomaly detection in the Internet of Things domain. IoT technology consists of a large number of interconnecting digital devices not only generating huge data continuously but also making real-time computations. Since IoT devices are highly exposed due to the Internet, they frequently meet with the challenges of illegitimate access in the form of intrusions, anomalies, fraud, etc. Identifying these illegitimate accesses can be an exciting research problem. In numerous applications, either fuzzy clustering or rough set theory or both have been successfully employed. As the data generated in IoT domains are high-dimensional, the clustering methods used for lower-dimensional data cannot be efficiently applied. Also, very few methods were proposed for such applications until today with limited efficacies. So, there is a need to address the problem. In this article, mixed approaches consisting of nano topology and fuzzy clustering techniques have been proposed for anomaly detection in the IoT domain. The methods first use nano topology of rough set theory to generate CORE as a subspace and then employ a couple of well-known fuzzy clustering techniques on it for the detection of anomalies. As the anomalies are detected in the lower dimensional space, and fuzzy clustering algorithms are involved in the methods, the performances of the proposed approaches improve comparatively. The effectiveness of the methods is evaluated using time-complexity analysis and experimental studies with a synthetic dataset and a real-life dataset. Experimentally, it has been found that the proposed approaches outperform the traditional fuzzy clustering algorithms in terms of detection rates, accuracy rates, false alarm rates and computation times. Furthermore, nano topological and common Mahalanobis distance-based fuzzy c-means algorithm (NT-CM-FCM) is the best among all traditional or nano topology-based algorithms, as it has accuracy rates of 84.02% and 83.21%, detection rates of 80.54% and 75.37%, and false alarm rates of 7.89% and 9.09% with the KDDCup’99 dataset and Kitsune Network Attack Dataset, respectively.

A case study on the implementation of location tracking technologies for productivity monitoring: understanding workers’ acceptance and socio-technical implications

Automated real-time data collection is becoming more prevalent in construction, with workers’ location data being a pivotal component in detecting poor logistics and inefficient construction flows. However, the collection of location data for productivity monitoring raises significant concerns about privacy and wellbeing implications for workers. Implementing such technological solutions requires an understanding of how humans may respond to sensor-based automated data collection, making this a socio-technical issue. This study identifies the drivers of construction workers’ acceptance of radio-based location tracking technology for productivity measurement using a modified Technology Acceptance Model (TAM) and offers a sociotechnical understanding of technology acceptance with implications for managing how new technologies are introduced on construction projects. Using a large residential project in Lima, Peru as a case study, construction workers were monitored using Bluetooth Low Energy (BLE) technology, and data were gathered using mixed methods. A k-means clustering analysis showed two forms of acceptance among workers: supporters (37%) and acceptance with reservations (63%). Partial least squares Structural Equation Modelling (PLS-SEM) results showed that perceived usefulness and perceived stress underpinned workers’ attitudes and intention to accept the technology. Perceived privacy risk, however, emerged as the sole most significant predictor at the end of the monitoring process. Findings further suggest that workers’ acceptance of the technology is influenced by the perception that it is also beneficial for safety management. Building on the preceding, the paper highlights the need for employee orientation focused on addressing perceived privacy concerns by leveraging positive perceptions about using monitoring technologies for improving onsite safety, logistics and productivity. This requires management of construction firms to develop narratives that reflect their goals for rolling out technologies in ways that ensure workers’ buy-in, and a re-focus on problem framing and collective approaches to identifying functional and less intrusive forms of monitoring technologies.

MODELS FOR THE FORMATION OF POLYPHASE GABBRO-MONZODIORITE MASSIFS OF THE WESTERN SANGILEN IN THE COLLISIONAL AND TRANSTENSIONAL-SHEAR SETTINGS

A model for the formation of intrusions of the collision stage of 525–490 Ma and a model of magmatism of the transtensional shear stage of 465–440 Ma within the Mugur-Chinchilig and Erzin-Naryn blocks of Western Sangilen (Tuva) have been developed to describe the process of crust-mantle interaction. Model experiments confirm petrological data on the presence of multi-level chambers during the formation of the Pravotarlashkinsky and Bashkymugur massifs. The proposed model describes the migration of mantle magmas above the head of the mantle plume at the collision stage and assumes the rise of magmas along a permeable tectonic zone in the mantle lithosphere and crust at the transtensional-shear stage. The modeling results allow us to establish that material from the magma chamber can reach depths of the upper crust in the volume ratio of gabbroids to diorites from 1 : 2 to 3 : 4 and additionally introduce about 5 % of the volume fraction of lower crustal material.The physical parameters of the primary magmas (viscosity, solidus and liquidus temperatures, degree of melting depending on temperature and composition, change in density) were calculated taking into account the real geochemical characteristics of igneous rocks from the polyphase massifs of Western Sangilen.

Nature of magma and ore-forming conditions at the Randu Kuning porphyry Cu-Au deposit, Indonesia: a comparative study with other Cu-Au deposits in the region

ABSTRACT The subduction-related Sunda-Banda arc hosts several giant and major Cu–Au–Mo porphyry and epithermal Au systems, making it one of the most important magmatic-related metallogenic belts in Indonesia. They are accompanied by a number of smaller systems, one of which is the recently discovered Randu-Kuning porphyry Cu-Au deposit on Java Island. The present study investigates the geochemical characteristics of this deposit through whole-rock and mineral geochemistry and mineral geothermobarometry, focusing on three sequential dioritic intrusions that make up the host igneous complex, two barren and the third mineralized. Moreover, a comparative analysis is performed between other porphyry deposits in the Sunda-Banda arc and with Grasberg in Papua. This study shows that the older porphyry deposits have mostly similar geochemical characteristics to the younger deposits, suggesting the same magma source, which is mantle-derived oxidized I-type magma. Amphibole geothermobarometry shows that the formation of syn-ore intrusion in the older porphyry was higher in temperature and pressure than those at the younger porphyry. The halogen and oxygen fugacities inferred from biotite and amphibole chemistry indicate an oxidized, water- and halogen-rich melt. Our study shows that combining amphibole and biotite chemistry and whole-rock geochemical data is useful to fingerprint the magmatic-hydrothermal processes, especially where multiple magmatic phases occur. This research also provides valuable insights into the magmatic-hydrothermal physiochemical conditions during the formation of the mineralized system.

M-MultiSVM: An efficient feature selection assisted network intrusion detection system using machine learning

The intrusions are increasing daily, so there is a huge amount of privacy violations, financial loss, illegal transferring of information, etc. Various forms of intrusion occur in networks, such as menacing networks, computer resources and network information. Each type of intrusion focuses on specified tasks, whereas the hackers may focus on stealing confidential data, industrial secrets and personal information, which is then leaked to others for illegal gains. Due to the false detection of attacks in the security and changing environmental fields, limitations like data lagging on actual attacks and sustaining financial harms occur. To resolve this, automatic abnormality detection systems are required to secure the required computing ability and to analyze the attacks. Hence, an efficient automated intrusion detection system using machine learning methodology is proposed in this research paper. Initially, the data are gathered from CSE-CIC-IDS 2018 and UNSW-NB15 datasets. The acquired data are pre-processed using Null value handling and Min-Max normalization. Null value handling is used to remove missing values and irrelevant parameters. Min-Max normalization adjusted the unnormalized data in the pre-processing stage. After pre-processing, the class imbalance problem is reduced by using the Advanced Synthetic Minority Oversampling Technique (ASmoT). ASmoT aims to balance the class and reduce imbalance class problems and overfitting issues. The next phase is feature extraction, which is performed by Modified Singular Value Decomposition (M-SvD). M-SvD extracts essential features such as basic features, content features and traffic features from the input. The extracted features are optimized by the Opposition-based Northern Goshawk Optimization algorithm (ONgO). These optimal features are able to produce optimal output. After feature selection, the different types of attacks are classified by a hybrid machine learning model called Mud Ring assisted multilayer support vector machine (M-MultiSVM) and finally, the hyperparameters are tuned by the Mud Ring optimization algorithm. Thus, the proposed M-MultiSVM model can efficiently detect intrusion in the network. The performance metrics show that the proposed system achieved 99.89 % accuracy by using the CSE-CIC-IDS 2018 dataset; also, the proposed system achieved 97.535 % accuracy by using the UNSW-NB15 dataset.

Strontianite from the Korkodinskoye deposit of jewelery andradite (Middle Urals)

Te relevance of the work is due to the need to study the mineralogy of the Ural gems, in this case deposits of jewelry andradite (demantoid and topazolite). Te purpose of the work – description and detailed instrumental study of strontianite from paragenesis with gem topazolite, which was established at the Korkodinskoye deposit. Research methodology. Strontianite was studied at the Institute of Geology and Geochemistry named afer academician A. N. Zavaritsky, Ural Branch of the Russian Academy of Sciences (Ekaterinburg), the Institute of Mineralogy of the Ural Branch of the Russian Academy of Sciences (Miass) and the Ural State Mining University (Ekaterinburg). For its study, the whole complex of modern research methods was used. Chemical, geochemical, X-ray and X-ray spectral studies were carried out, and the method of IR spectrometry was also used. Results and conclusions. Carbonate was found in a fssure-vein zone in association with garnet (topazolite) among tectonized serpentinites at the Korkodinskoe deposit of gem andradite. According to the chemical composition, strontianite is extremely calcium. Crystal chemical formula of the carbonate: (Sr0,48Ca0,45Ba0,04Mg0,03)1,00CO3 . In terms of chemical composition, the carbonate of the Karkodinskoe deposit is very similar to the strontianite of the Buldymsky massif; apparently, this is a typochemical feature of all strontium carbonates from ultramafc bodies. For strontianite, the results of powder difractometry and IR spectroscopy are given, which are very close to the reference data. Strontianite belongs to the post-garnet stage of mineral formation, and the source of strontium for carbonate was small bodies of quartz-plagioclase metasomatites occurring at the site. Te results obtained do not agree with the latest model of the formation of the Korkodinsky ultramafc massif proposed by A. Yu. Kisin with co-authors. Te discovery of strontianite in a fssure-vein zone with andradite (topazolite) is another evidence of the formation of demantoid-bearing bodies afer the formation of gabbro and granitoid intrusions.