As global demand for REEs continues to rise due to their strategic role in clean energy technologies, geothermal systems offer an emerging unconventional source. Although data on Indonesian geothermal REEs remain limited, geochemical signals from Mount Slamet provide valuable insights when interpreted alongside global analogs. This study investigates the occurrence, mobility, and potential of rare earth elements (REEs) in the active geothermal system of Mount Slamet, Central Java, Indonesia, with a focus on the hot spring manifestations in Baturraden and Guci using a systematic literature review method based on published research. Baturraden exhibits neutral fluids (pH 6–7) with elevated chloride levels, suggesting enhanced REEs mobilization and strong positive europium (Eu) anomalies under deeper reducing conditions. In contrast, Guci displays more alkaline fluids (pH ~8) with lower chloride content, indicating possible meteoric water dilution and lower REEs transport efficiency. These contrasting hydrochemical profiles highlight diverse water-rock interaction mechanisms and fluid pathways. The findings highlight Mount Slamet as a promising candidate for REEs exploration in a volcanic-related geothermal system. This study underscores the urgent need for systematic research on REEs geochemistry in Indonesian geothermal fields to support mineral diversification and sustainable energy transitions.

The evolution of cybercrime poses significant threats to global security, with perpetrators exploiting digital vulnerabilities using increasingly sophisticated techniques. Profiling such offenders demands innovative approaches beyond traditional methods. This paper explores the integration of Artificial Intelligence (AI) in enhancing cybercrime profiling by identifying behavioral patterns among online offenders. Leveraging Natural Language Processing (NLP), Machine Learning (ML), and neural networks, AI systems can detect suspicious activities, predict potential threats, and analyze digital footprints to classify offenders into behavioral archetypes. The study discusses various AI models used for behavioral profiling, such as decision trees, clustering algorithms, and recurrent neural networks (RNNs), and evaluates their accuracy and limitations. Through a systematic literature review and case study analysis, this research highlights the role of AI in early detection, real-time monitoring, and predictive profiling. It also outlines the ethical considerations and challenges in adopting AI-driven profiling systems in law enforcement. The findings support the conclusion that AI-enhanced behavioral profiling is a promising tool for modern cybersecurity and forensic intelligence.

Cognitive warfare constitutes a rapidly emerging domain in modern military strategy, wherein the battlefield transcends physical spaces to target the cognitive faculties of individuals and societies. This study explores the strategic mechanisms and ramifications of cognitive warfare, with particular attention to psychological operations, information manipulation, and neuropsychological tactics that shape perception, decision-making, and behavior. Through the examination of contemporary literature and illustrative case studies, the paper offers a comprehensive analysis of cognitive warfare’s growing influence in geopolitical conflict and its associated ethical dilemmas. Ultimately, this work highlights the pressing need to protect mental sovereignty amid the rise of digital and psychological manipulation.

La migración genera en las personas un cambio radical en sus vidas, optar por emigrar supone un trascendental cambio para el individuo, la familia y por ende conlleva un proceso migratorio con riesgos y repercusiones a nivel físico y psicológico. Como consecuencia de lo antes expuesto se pone énfasis sobre la relación existente entre la salud mental y la migración de las personas, enfocándose en el Síndrome de Ulises que denota las consecuencias, repercusiones que conlleva el proceso migratorio de los individuos una vez se encuentran en el país de acogida (Achotegui, 2017).

Artificial Neural Networks (ANNs) are widely used for modelling nonlinear systems due to their adaptive learning capabilities. In this work, different combinations of ANN functions such as transfer functions, learning rules, and training algorithms are examined to determine their impact on prediction accuracy for solar photovoltaic (PV) modules. A dataset comprising input parameters including maximum power (Pm), open-circuit voltage (Voc), short-circuit current (Isc), irradiance (Ir), temperature (T), and fill factor (FF) from 34 different PV modules is used to predict output parameters: current (Im) and voltage (Vm) at peak power. We've developed a total of 84 ANN models by bringing together a variety of functions. Each model is trained using mean squared error (MSE), along with a set number of epochs and performance indicators. To compare the models effectively, we use a weighted ranking system. This approach helps to pinpoint the best ANN setup for accurately modeling how photovoltaic output behaves.

In the forging process, if the shape of the final part is complex, the raw material cannot be transformed into the shape of the final part in one forging step; therefore, the use of preform molds is essential. An optimal preform mold is a mold that can meet the design criteria. Design criteria include producing a defect-free part with the least volume of raw material, the least plastic strain, the least force required to perform the process, and also completely filling the final mold. In this research, using the ability of the continuous genetic algorithm to generate Cartesian paths, first several different preform mold shapes are produced using mathematical functions. Then, using finite element simulation of the process, the optimal mold selection criteria are calculated. From the information obtained from the simulation, the artificial neural network is trained so that it can predict the results of the simulation process. This network and design criteria are used for the objective function in the continuous genetic algorithm. Finally, the best shape of the preform mold is calculated by the continuous genetic algorithm, which is a mathematical function and is obtained by plotting this function in the Cartesian coordinates of the mold shape. Next, this method is used for a part with an H-shaped cross-section to examine its efficiency. The optimal preform mold for the part is calculated and its forging results are extracted by the continuous genetic algorithm. Also, finite element simulation is performed for the optimal mold to compare its results with the results obtained from the continuous genetic algorithm. Finally, the success of using this method was proven.

The construction of the National Capital City (IKN) is currently being carried out in East Kalimantan, resulting in increased demand for construction materials. Basalt is among the rocks that can be used as construction material in IKN. This rock has a high economic value due to its high demand but limited quantity. Basalt exploration around IKN is carried out to acquire rock resources and reduce construction costs due to its proximity to the site. This study aims to determine the distribution of basalt in the Kuaro area, East Kalimantan. The geology of the research area is composed of the Ultramafic Complex Formation, which comprises gabbro, basalt, serpentinite, and harzburgite. This research uses the Wenner-Alpha configuration geoelectric method to identify the distribution of basalt in the subsurface. Geoelectric measurements were carried out on six measurement lines, each line length of 470 m and electrode spacing of 10 m. The results of geoelectric data analysis show that the basalt has a specific resistivity value ranging from 200–4022 Ωm. Basalts were identified at depths of 10–75 m with 145 m, 325 m, 165 m and 183 m thicknesses in lines 1, 2, 5 and 6, respectively. The presence of basalt in lines 3 and 4 is at a depth of 10–75 m with an average thickness of 153 m and 125 m, respectively. Economically, basalt rocks in this area are classified as medium to highly recommendable for exploitation.

Scoria cones are a typical product of volcanic activity constructed by the bomb and lapilli-sized pyroclasts formed by Strombolian eruption. Mount Pengki is a scoria cone found in Miocene Dago Volcano, West Java. Mount Pengki was a remnant of a Miocene volcano that was exceptionally well preserved and exposed. This scoria cone contains layers of scoria beds and a lava flow unit. The study aims to characterize the exposed scoria bed deposits and investigate the eruptive history and degradation process of Mount Pengki. Field observation, including measured sections and detailed characterizations of the Mount Pengki quarry, allows us to observe its volcanic sequence from its internal structure toward the surface. Morphometric analysis of Mount Pengki can describe the degradation process undergone by the scoria cone. The early phase deposits were characterized by massive to weakly bedded, poorly sorted, clast-supported beds mainly composed of coarse lapilli to bombs/blocks scoria grain. The middle phase deposit typically shows well-stratified, well-sorted, clast-supported scoria beds with coarse ash to coarse lapilli grain size. The late phase deposit is similar to the middle phase deposit, with additional features of coarser-grain, reverse grading, and clast-supported lenticular beds. Eruptive mechanisms involved in the formation of Mount Pengki include ballistic transport of clasts, fallout deposition, and grain avalanching process. The degradation process was likely influenced by prolonged exposure to weathering, cone rim collapse, and regional deformation processes.

The tectonic setting of Sumatra Island is strongly influenced by the oblique subduction of the Indo-Australian Plate, which subducts the Eurasian Plate at a speed of 52–60 mm/year. The movement of these plates resulted in the Northern Sumatra region having seismic sources from tectonic and volcanic activity. The data used in this study is in the form of seismic wave travel-time recorded by numerous seismic stations in the research area from January 2012 to December 2020. The data comes from 5,003 earthquakes recorded by the BMKG seismic network. The inversion is a simultaneous inversion between seismic velocity models (Vp and Vs) and hypocenter parameters by applying a double-difference seismic tomography algorithm. Tomogram results in parts of Aceh (Singkil and Subulussalam) and North Sumatra (Pakpak Bharat and Dairi) at a depth of 0 km show negative perturbations in Vp and Vs values and high Vp/Vs values. The anomaly is most likely related to cracks in fluid-saturated rocks. The tomograms in the south of Lake Toba at depths of 30 km and 40 km have high Vp and Vs perturbation values and low Vp/Vs values. This anomaly indicates a magma supply line that is no longer active or has cooled for a long time. Based on the seismic tomography modeling results, the subducted Indo-Australian Plate to the Eurasian Plate is visible in the study area.