Human Capabilities Research Articles

Uncovering Salafi jihadist terror activity through advanced technological tools

ABSTRACT This study investigates the evolving challenges intelligence and law enforcement agencies face in countering Salafi jihadist terrorist activities across digital platforms, focusing on integrating artificial intelligence (AI) and data fusion technologies in open-source intelligence (OSINT) methodologies. Through an analysis of case studies involving ISIS and other non-state actors, the study examines how terrorist organisations adapt their digital strategies to evade detection while exploiting social media platforms, instant messaging applications and websites for recruitment, propaganda, and operational planning. The study demonstrates the necessity of developing advanced technological solutions such as the integration of AI-enhanced OSINT capabilities, combined with data fusion approaches, that can improve analysts’ ability to process multilingual content, identify threat actors who use online anonymity, and detect patterns of terrorist activity across multiple network layers. However, the study also reveals critical ethical and operational challenges in balancing effective counter-terrorism measures with privacy rights and civil liberties. The study concludes that while OSINT, AI and data fusion tools can substantially augment human analysis capabilities, successful counter-terrorism efforts require a carefully calibrated approach combining technological innovation and human expertise.

The Role of Contractual Budgeting as a Tool for Planning and Control in Investment Budgeting: A Field Study in Iraqi Universities

This study aimed to identify the extent of the possibility of developing the state's general budget by stating the availability of the necessary components for the contractual budget method in the investment aspect of the budgets of Iraqi universities. And to determine the importance and advantages of the contractual budget method as a tool for planning and controlling the investment budget in Iraqi universities in the opinion of the sample members studied. The study methodology relied on the descriptive and analytical approach. The researcher used the questionnaire as the main tool for this study. The study community consisted of employees of Iraqi universities, as the number of Iraqi universities reached (36) universities. A sample consisting of workers in planning and budgeting, the financial manager, the investment accountant, and the internal control manager was selected to conduct the study on them. A sample of the study community, which numbered (194) people, was taken, and this type of community was tested for their specialization in the contractual budget. The questionnaire was distributed to the sample members, as (188) questionnaires were retrieved, and after examining them, (8) questionnaires were excluded for their unsuitability for analysis purposes. Thus, the number of questionnaires valid for analysis reached (180), using the statistical program (SPSS). The results showed that there are advanced legislative and regulatory capabilities to apply the contractual budgeting method when preparing and implementing the budgets of Iraqi universities. There are also planning and contractual capabilities applied when setting the estimated budgets for investment projects when preparing and implementing the budgets of Iraqi universities. There are advanced accounting and control capabilities to apply the contractual budgeting method when preparing and implementing the budgets of Iraqi universities. There are also advanced human and material capabilities to apply the contractual budgeting method when preparing and implementing the budgets of Iraqi universities. The study recommended the necessity of using the contractual budget in preparing and implementing the investment budget of Iraqi universities due to its advantages and the importance of qualifying and training workers in the field of planning, finance and budgets on modern methods in preparing the contractual budget.

MEASURING INFORMATION TECHNOLOGY GOVERNANCE USING COBIT 2019 FRAMEWORK AT TOURISM INDUSTRY

Although PT XYZ has adopted information technology, it has not formally assessed its governance, leading to persistent issues in IT management, human resource capabilities, and alignment with business processes. This study evaluates IT governance at PT XYZ, a company in the travel and tourism industry, where rapid technological advancements have impacted operations. Using the COBIT 2019 framework, the study assessed IT governance through interviews and literature review, focusing on the domains APO04 – Managed Innovation, BAI02 – Managed Requirements Definition, BAI03 – Managed Solution Identification & Build, and BAI05 – Managed Organizational Change. The results indicate that these domains are at level 2, "Largely Achieved," highlighting areas of improvement. This benchmark provides practical recommendations to enhance IT governance and improve integration between IT and business functions. The findings offer PT XYZ actionable steps to strengthen governance practices, improve organizational performance, and better align technology with strategic business goals.

Progress towards multidimensionally scalable assisted and/or automated ship navigation and control - part I: reliable and autonomous guidance - a contradiction?

The automation of vessels is becoming increasingly important, developing from human dominated command structures through partial automation to the long-term goal of fully autonomous ship command and control. If fully autonomous vessels are realised, the guarantee of at least equally reliable and safe ship guidance is crucial. Part I of the paper addresses key questions: How can the safety and reliability of autonomous navigation be improved through online accessible methods? Can established risk assessment methods be extended to maintain functionality without compromising safety? New ideas for improved guidance are proposed, using a recently published method for assessing reliability based on probability of detection given the basis for developing a reliable guidance unit that balances adaptive behaviour with safety goals. Also addressed are the limitations of object detection approaches, especially in terms of practical applicability. The solution idea presented includes a reliability- and task-based functional degradation to guarantee safety despite functional restrictions. Overall, the contribution demonstrates improvements showing new methods in the development of highly automated ship guidance systems, which have the potential to be widely used through certified capabilities, while part II addresses progress in integrating human capabilities into automated and assisted systems.

Transparency in AI for emergency management: building trust and accountability

Abstract Artificial intelligence (AI) stands at the forefront of transforming emergency management, offering unprecedented capabilities in disaster preparedness and response. Recent implementations demonstrate this shift from reactive to proactive approaches, particularly through flood prediction algorithms and maritime search-and-rescue optimization systems that integrate real-time vessel locations and weather data. However, the current landscape reveals a critical challenge: the opacity of AI systems creates a significant trust deficit among emergency responders and communities. Research findings paint a concerning picture of this transparency gap. A comprehensive survey of emergency management AI systems reveals striking statistics: 68% lack adequate documentation of their data sources, while 42% fail to provide clear justifications for their recommendations. This “black box” phenomenon carries serious implications, particularly when flood prediction models disproportionately affect vulnerable populations or when opaque decision-making processes lead to suboptimal resource allocation during critical rescue operations. Analysis of real-world applications in flood preparedness and search-and-rescue operations exposes systematic communication deficiencies within these essential emergency response frameworks. The research examines how varying levels of AI transparency directly influence emergency responders' decision-making during crises, exploring the delicate balance between operational openness and security considerations. These findings highlight an urgent need for robust oversight mechanisms and context-specific transparency protocols to ensure ethical AI deployment in emergency management. The evidence points toward a clear solution: developing human-centric approaches that enhance rather than replace human capabilities in emergency response. This strategy requires establishing tailored transparency guidelines and monitoring systems that address current challenges while facilitating effective AI integration. By prioritizing both technological advancement and human oversight, emergency management systems can better serve their critical public safety mission.

Deep-learning real-time phase retrieval of imperfect diffraction patterns from X-ray free-electron lasers

Machine learning is attracting surging interest across nearly all scientific areas by enabling the analysis of large datasets and the extraction of scientific information from incomplete data. Data-driven science is rapidly growing, especially in X-ray methodologies, where advanced light sources and detection technologies produce vast amounts of data that exceed meticulous human inspection capabilities. Despite the increasing demands, the full application of machine learning has been hindered by the need for data-specific optimizations. In this study, we introduce a new deep-learning-based phase retrieval method for imperfect diffraction data. This method provides robust phase retrieval for simulated data and performs well on partially damaged and noisy single-pulse diffraction data from X-ray free-electron lasers. Moreover, the method significantly reduces data processing time, facilitating real-time image reconstructions that are crucial for high-repetition-rate data acquisition. This approach offers a reliable solution to the phase problem to be widely adopted across various research areas confronting the inverse problem.

Kant and Nāgārjuna: A hermeneutic inquiry into transcendental aesthetics

The Madhyamaka tradition of Buddhism, with development of śūnyatā philosophy, was widely considered as founded by Nāgārjuna (ca 150–250 CE) who is best known as one of most important philosophers in the history of Buddhism. Critics do not generally regard Nāgārjuna’s philosophy of emptiness, Madhyamaka, as a theory of nonexistence or nihilism. Its acceptance of absolute non-being leads to recognition of a hyperousiological (hyper-Being that mystically surpasses Being) ontotheological superbeing, which is a priori and that it has similarities with transcendental aesthetics where Kant deals with nature of sensibility. Hyperousiology denotes a "Being beyond being" and predominantly used in negative theology. Negative theology is heavily relied on “denegations” that lead to a hyper-theology affirming God as hyperousious, as defined by Pseudo-Dionysius, knowing God by unknowing or “denominating” God. Such mystical theology is a “theology of absence”, a higher apophasis that overcomes both affirmation and negation to sustain a hyperessence. Kant’s transcendental aesthetics is considered as the investigation of a priori elements which enter into Perception. Kant toiled hard to understand human cognition and human judgment; his focus was on innate human capability of making judgments by a cognitive faculty. Kant claims the matter of all appearance is only given to us a posteriori, but its form must all lie ready for it in the mind a priori and can therefore be considered separately from all sensation. The foundational idea of Madhyamaka school, śūnyatā, the set of ultimately existent things, is an empty set that transcends epistemic foundations in linguistic and conceptual conventions. Accordingly, Madhyamaka school has a two-truth doctrine: ultimate truth (paramārtha-satya) and relative truth (saṃvṛti-satya). Like a priori sensibility, it is argued, ultimate truth (paramārtha-satya) is separate from all sensation as it transcends conceptual conventions. The major research problem investigated in this study is whether the ultimate truth (paramārtha-satya) of Madhyamaka school acts as a priori sensibility and creates a synthesis with relative truth (saṃvṛti-satya) making cognition transcendentally ideal and empirically real? This inquiry delves into investigating interconnections between transcendental ideals and empirical reals. Hermeneutics has been employed as the principal research method to interpret both Kant and Nāgārjuna on aesthetic judgment.

The Role of AI in Predictive Database Performance Tuning

The integration of artificial intelligence into database performance tuning marks a pivotal evolution in data management practices. As traditional manual approaches by Database Administrators give way to predictive and autonomous systems, organizations are experiencing transformative benefits across multiple dimensions of database operations. AI technologies now enable workload prediction, automated indexing, anomaly detection, and resource optimization that far exceed human capabilities in both accuracy and efficiency. While challenges exist in implementing these systems—particularly regarding continuous learning requirements and legacy database integration—the trajectory toward fully autonomous database management continues to accelerate. This advancement fundamentally shifts the role of database professionals from routine maintenance to strategic data architecture and innovation, ultimately promising a future where databases self-optimize with minimal human oversight while delivering superior performance and reliability.

Guest Editorial: 2025 Perspective: Digital Transformation in Oil and Gas—From Evolution to Shockwave

_ In June 2019, before the seismic shift brought by the OpenAI revolution, I authored a JPT guest editorial on the oil and gas industry's digitalization journey. I emphasized the transformative roles of the industrial internet of things (IIoT), cloud computing, and artificial intelligence (AI). Now, reflecting on that editorial from the vantage point of 2025, it is remarkable to witness how many predictions have materialized and how the industry has evolved beyond expectations. A Sudden Acceleration: The AI Shockwave in Oil and Gas For nearly 3 decades, technological advancements in the oil and gas industry followed an evolutionary trajectory. Each breakthrough—2D and 3D seismic imaging, horizontal and directional drilling, semisubmersibles, and permanent sensors such as microelectromechanical systems (MEMS) and nanotechnology—built upon prior successes, driving incremental gains in efficiency and recovery. The ability to acquire real-time downhole data has ushered in the "sense-compute-act" era, fundamentally transforming reservoir management. However, these advances followed a measured pace, allowing engineers time to integrate new methods with traditional workflows. Then, in a span of just 20 months, the industry was hit by a technological shockwave. The AI revolution—accelerated by OpenAI’s breakthroughs—upended long-held paradigms. No longer do companies rely solely on downhole measurements from oilfield service providers. Instead, they now train AI models on decades of historical data to predict maximum efficient rate (MER) and expected ultimate recovery (EUR). The “old world” of deterministic modeling is rapidly giving way to probabilistic AI-driven decision making. This abrupt shift is not just about automation; it is redefining the fundamental principles of reservoir management. The industry finds itself grappling with uncertainties inherent in AI predictions, challenging engineers to place trust in models that are not explicitly programmed but instead learn patterns from vast data sets. The rate of technological change is outpacing our ability to fully comprehend its implications—a stark contrast to the controlled evolution of past innovations. From Data Collection to Intelligent Systems: The Rise of Metaknowledge In 2019, I introduced the concept of metaknowledge—understanding what we know and don’t know—as more critical than primary knowledge—the ability to discern what we know and what we do not. Today, this has become an essential component of digital transformation. AI no longer just processes data—it determines which data is meaningful, distinguishing signal from noise within the vast repositories of information. Where traditional methods relied on human-driven interpretations, AI models now identify patterns and anomalies at scales beyond human capability. The ability to extract actionable insights from billions of data points has eliminated many inefficiencies in field operations. More importantly, it has reshaped the role of engineers, shifting them from manual interpreters to strategic AI curators who validate and refine machine-generated insights.

NASA’s Top Human System Research and Technology Needs for Mars

NASA’s Top Human System Research and Technology Needs for Mars

Relevance of Probabilistic Reversal Learning for Adolescent Drinking Trajectories.

One of the many human capabilities acquired during adolescence is the adaptivity in changing environments. In this longitudinal study, we investigated this adaptivity, as measured by probabilistic reversal learning (PReL) tasks, in N = 143 adolescents at ages 14, 16 and 18. Computational modelling and functional magnetic resonance imaging were applied to identify the neurocognitive processes underlying reversal learning and its development. Previous studies have demonstrated a correlation between heavy alcohol use and impaired reversal learning. Our hypothesis was that PReL is negatively associated with current and future alcohol use and that alcohol use impairs PReL by altering neurocognitive processes. Behaviourally, PReL performance improved, which was associated with a lower probability of switching choices and was considered an adaptive process. Computationally, this was accounted for by higher learning rates, enhanced sensitivity to wins and reduced sensitivity to losses in older adolescents. Alcohol consumption increased but remained at a low level for most participants. More risky drinking was associated with less medial frontal activity elicited by reward prediction errors. These findings suggest that reversal learning may be more relevant for the maintenance or escalation of risky than for low-level drinking. Challenges and potential solutions for longitudinal studies such as reliability are discussed.

Cartographic representation of human capabilities for rural development in Sarkapkan district

Demonstrating and researching human capabilities is no less valuable than natural capabilities In many places, human resources are the basis of current spatial differences Therefore, rural geography research is trying to increase its research in this field and link it to rural development. Human resources are an essential element in achieving sustainable development, especially in rural areas that rely heavily on local capabilities to achieve social and economic development. In this context, the cartographic representation of human resources is gaining importance as an effective tool for analyzing and understanding the distribution of the population, their capabilities and needs, which contributes to formulating integrated and effective development plans Serkabkan area, As a rural area with environmental and social characteristics, it faces many challenges related to the optimal use of human resources to achieve balanced and sustainable development. Therefore, human resource maps can provide a comprehensive vision that enhances understanding of the current reality of the region and helps identify opportunities and address development challenges. Purpose of the study It is an attempt to provide a unified basis for how to use cartographic capabilities as a tool for displaying, analyzing and planning human resources in the Sarkabkan area, which serves the comprehensive development of villagers and enhances the interaction between human capabilities and development efforts. The importance of this study The main role of mapping in showing the human potential for rural development is clear. It also highlights the human potential in the region and how it can be used to achieve development, focusing on strategies that support improving living standards and narrowing development gaps. In this study, many modern maps were used using geographic information systems, and finally we reached several important conclusions and recommendations.

Development of post-quantum cryptosystems based on the Rao-Nam scheme

The object of the research is the process of ensuring the protection of data transmission in communication channels of critical infrastructure objects based on mobile and smart technologies. The development of quantum computing technologies based on Grover and Shor algorithms provides practical cracking of symmetric and asymmetric cryptosystems in polynomial time. The emergence of systems based on artificial intelligence allows creating hybrid systems for detecting weaknesses (critical points) in security systems not only on critical infrastructure objects. In addition, a full-scale quantum computer will open a new era of implementing post-quantum cryptography algorithms. Among the winners of post-quantum algorithms, the crypto-code constructions (CCC) of McEliece and Niederreiter are separately highlighted, which allow to provide the required level of protection and the required level of reliability of information transmission in an integrated manner. But a significant drawback is the possibility of cracking such systems on linear codes, as well as the need to build them on the Galois field 210–213, which significantly reduces their use in low-capacity systems based on smart and mobile technologies. To solve this drawback, the work proposes the use of a symmetric CCC based on the Rao-Nam scheme on algebrogeometric and flawed codes, which provides the possibility of significantly reducing the volume of key data (construction of CCC over the Galois field 24–26). When using the Rao-Nam CCC, a quantum symmetric algorithm is formed, which ensures the preservation of the level of stability and reliability of information transmission (safe time 1025–1035). This approach provides the possibility of forming intelligent information protection systems (IIPS). The given structural scheme of the IIPS construction ensures timely detection of threats with an assessment of the computational and financial and human capabilities of attackers, as well as the use of the necessary CCC/algebraic (flawed) codes to ensure the required level of security

The need for age-inclusive and gender-sensitive social policies for India’s longevity society

The demographic transition is leading to the rapid ageing of India’s population and significant changes in the social structures, thereby posing socioeconomic and policy challenges for older care. Drawing on data from the Longitudinal Ageing Study of India (LASI) and other sources, this study reviews the current social and health policies for older adults. The exploration has reviewed the current state of healthcare infrastructure, pension coverage, social security schemes and their utilization, health insurance coverage, challenges of older women and labour force participation among older adults. The findings indicate several critical gaps in the existing policy context and implementation of schemes and thereby highlights the urgent need for targeted interventions that are age-inclusive and gender sensitive. We recommend enhancing and stabilizing financial security, providing basic housing and environmental facilities, increasing the availability of health services, and expanding welfare measures for older adults. We also emphasize the significance of building institutional and human resource capabilities to meet the needs for elder care-inclusive public participation. Increased sensitization about the rights of the elderly and promoting a more age-inclusive and gender-sensitive approach toward aging are recommended. We offer policy suggestions to create a supportive age-friendly living environment for India's ageing population aimed at a more accessible and inclusive society that prioritizes the well-being of older adults.

The Entangled Nature of First Language Learning, Education, and Literacy

The entangled relationship between first language acquisition, education, and literacy challenges conventional linguistic paradigms. This paper explores recent findings revealing the intricate interplay of these factors, particularly emphasizing the impact of education and literacy on grammatical knowledge. Drawing on studies conducted in diverse linguistic contexts, this paper argues against the universal applicability of conventional metrics such as the High Academic Attainment/Low Academic Attainment (HAA/LAA) cut-off to investigate the role of reading, advocating for a more nuanced understanding of linguistic development that considers local educational dynamics. Additionally, the paper discusses methodological challenges in studying illiterate populations and proposes alternative measures to capture the cumulative effects of language experience. Ultimately, the paper underscores the importance of interdisciplinary collaboration in developing inclusive research methodologies and educational interventions that address the diverse needs of learners worldwide. By embracing the complexities of language learning, education, and literacy, linguists can advance our understanding of human language capabilities and promote more equitable opportunities for linguistic development.

EXPLORING THE IMPACT OF DYNAMIC MANAGERIAL CAPABILITIES ON INNOVATION: A TYPOLOGY-BASED APPROACH

This study examines the interplay between Dynamic Managerial Capabilities (DMCs) and innovation performance in Iran’s industrial sector. We propose a novel framework that categorises managers based on three DMC components — managerial human capital, social capital, and cognitive capabilities — and investigates their combined effects on innovation. Using a sample of 344 managers, data were analysed through Structural Equation Modeling (SEM) and Analysis of Variance (ANOVA). Findings reveal that specific DMC profiles, particularly those that align with the firm’s strategic context, significantly enhance innovation outcomes. The results underscore the importance of tailored managerial development strategies in fostering innovation within dynamic and complex environments. This study contributes to the strategic management literature by offering practical insights for optimising organisational performance through the strategic configuration of managerial capabilities.

Humanity and AI: Collaborating for a Flourishing Planet Through Wise Decision-Making

Our world now faces significant challenges including complex issues arising from human–machine interaction. This paper explores how we could address these challenges using a new approach called Cybernetics 3.0. This approach highlights human agency and the co-evolution of humans and machines, particularly regarding how we make decisions. Rather than viewing machines as separate from people, Cybernetics 3.0 views machines as extensions of human action, which is vital in our complex world. This paper argues that when this approach is combined with advanced Web 3.0 technologies, we can create better decision-making systems that integrate both human values and machine capabilities. This paper uses diagrams to illustrate how human and machine decision-making are interconnected and provides a healthcare example to demonstrate the practical value of this framework. Through a holistic and systems thinking approach, we can foster sustainable and wise decisions that will promote human flourishing and planetary health. The discipline of cybernetics is a promising way to better understand the interaction between humans and technology via its focus on systems of control, human–AI learning and communication, feedback loops, self-regulating systems, and knowledge enhancement in humans and machines. Ultimately this approach could help us to address critical issues such as inequality, climate change, and other complex threats to our planet and society.

AI Empowerment of Asian-Australian Migrant Workers: Progress, Potentials, and Patterns

As Artificial Intelligence (AI), particularly generative AI, becomes integral to organizational practices, its capacity to augment human capabilities presents opportunities and challenges. We focus on the integration of AI into the workplace, emphasizing its impact on Asian-Australian migrant workers—a group frequently transitioning from developing to developed economies and facing unique workplace challenges. We explore how AI may not only enhance job performance and integration by overcoming cultural and linguistic barriers but also influences perceptions of overqualification among these workers. Employing psychological empowerment theory and the information systems fusion framework, alongside time-lag surveys and K-means clustering, we introduce a novel AI empowerment scale and investigate the nuanced effects of AI on immigrant workers. Our findings reveal that while AI-driven psychological empowerment increases technology infusion use and overall job performance, it also underscores significant variations in how different demographic groups experience these benefits, offering new insights into the complex interplay between AI empowerment and employee perceptions. The study advances psychological empowerment and perceived overqualification research by revealing AI's varied impacts across workforce clusters. It underscores the need to manage AI carefully to avoid workplace inequalities and calls for further exploration of AI's dynamics in diverse settings.

The use of artificial intelligence in the preparation of an administrative act: benefits and risks

The article is devoted to the study of the role of artificial intelligence (AI) in the legal sphere, in particular, its application in the administrative process. The author analyzes various scientific approaches to defining the concept and functions of AI, in particular, as a computer program for data analysis, an intelligent system that surpasses human capabilities, and a cybernetic approach that includes an algorithmic model of cognitive functions. The article examines the legal status of AI in Ukraine, the peculiarities of its regulation under national legislation, and the problems associated with legal adaptation to international standards. The article also discusses the prospects of using AI to automate administrative processes, in particular in the preparation of administrative acts, improvement of the motivational part and checking documents for errors. The authors emphasize the need to improve legislation and technical support to minimize the risks associated with the use of AI. According to the study, the introduction of AI into administrative procedures has significant potential to increase efficiency, transparency and reduce administrative burdens on public authorities. Key words: administrative process, artificial intelligence, administrative act, administrative proceedings.

Human-AI Collaboration in Workflow Optimization: A Framework for Hybrid Decision Systems in Automation-Heavy Industries

This article presents a comprehensive framework for human-AI collaborative workflow optimization in automation-heavy industries, addressing the limitations of fully automated approaches while leveraging the complementary strengths of human judgment and artificial intelligence. We introduce the Collaborative Workflow Intelligence Framework (CWIF), which establishes structured information flows and decision authority boundaries between human operators and AI components across manufacturing, logistics, and financial services domains. Through industry-specific applications, we demonstrate how this collaborative approach enhances production scheduling, quality control, supply chain efficiency, transportation optimization, and financial risk assessment while maintaining appropriate human oversight. Our methodology provides practical guidance for system architecture design, data integration, performance evaluation, and phased implementation, with particular attention to ethical considerations including worker autonomy and skills development. The framework balances operational efficiency with human expertise, creating systems that suggest process improvements and identify inefficiencies while preserving human decision authority in complex and consequential domains. This collaborative paradigm represents a significant advance over traditional automation approaches, offering organizations a path to workflow optimization that enhances rather than replaces human capabilities while addressing the technical, organizational, and ethical challenges of AI implementation.