STS International

Data engineering underpins the design of scalable analytics pipelines, yet most books, courses, and tutorials on data engineering ignore essential foundational topics. An appreciation of these topics enhances proficiency in data engineering, improves the quality of deployed pipelines, and enables engineers to avoid implementing common data problems. A detailed overview is provided of fundamental concepts in data engineering—not explanations of the internals of a cloud provider, the machine-learning pipeline, a specific tool, or a programming framework—but topics that every data engineer should know about, regardless of how they are assigned the title. Some solutions to these problems are also proposed, based on foundational concepts in data engineering. At a high level, data engineering is about building scalable analytics pipelines that meet business requirements. The analytics pipeline encompasses data acquisition, ETL, data modeling, and the consumption of data by data science, business intelligence, and operational teams for analysis, reporting, and operational decision-making. The pipeline is usually orchestrated with a high-level tools like Apache NiFi or AWS Glue. The emphasis on scalability arises from the reality that data pipelines and the associated analytics are often processing large volumes and varieties of data. Additionally, the solutions are specific to the fundamental concepts in data engineering enumerated next: data modeling and storage; ingestion, cleansing, and validation; architectural patterns for scalability; data processing frameworks and tools; data quality; and orchestration and scheduling.

The rapid emergence and adoption of the Internet of Things, Edge Computing, Cyber-Physical Systems, Digital Twins, Artificial Intelligence, Blockchains, 5G Telecommunications, Quantum Computing, such as yet other radically new technology trends manifesting their effectiveness and potential to disrupt traditional approaches, is gradually reshaping the fabric of modern industries all over the globe. All these advancements are enabling the development of tailored systems fulfilling specific requirements, whether in manufacturing or energy, transportation or healthcare, environment or safety, security or finance or any other sector. The gradual implementation of such tailored systems constitutes what many refer to as the Industry 4.0 revolution paradigm shift. These trends and developments are presenting novel opportunities yet also introducing unprecedented challenges when it comes to implementing integrated systems that allow diverse technologies, plants, and services to work cohesively and appear as a single powerful facility to customers, for example. To serve specialized, niche purposes yet work collectively as a more complex Super-System in a decentralized manner whenever possible, the industry-focused systems are best understood as a specific instance of What System of Systems encompass: a collection of independent and using different styles in terms of technology, infrastructure, and system integration approach to fulfil dedicated function(s) that can combine efforts at certain times yet also be utilized separately, when needed, in a truly scalable and flexible way. As such, the more special cases of theme-based industries represent then a resolution to the two-size-fit-all dilemma by allowing the deployment of specialized systems tailored for niche areas while also providing integration patterns that permit combining resources across the broader landscape when needed.

The importance of advancing intelligence into operational systems—such as intelligent transportation, smart energy, and intelligent supply chain systems—cannot be overstated. Such systems manage demanding physical processes for real-time operations, yet their intelligence has typically been limited to business systems that operate in time frames of seconds to days. Intelligent Operational Systems aim to combine event-driven, real-time data processing with external AI and machine learning for high-frequency, real-time decisions and high-sensitivity supervised learning. To date, the concept has largely been explored using on-premises, private-cloud infrastructure. The analysis here focuses on Cloud Platforms for Intelligent Operational Systems, with an emphasis on definitions, architectural patterns, supporting cloud platform paradigms, enabling data provisioning and processing, cloud support for AI and ML, and key components of reliability, scalability, and availability. The term cloud provides strong intuitive guidance about what is possible from a cloud platform. That is, the service abstraction of Infrastructure as a Service (IaaS) enables the hosting of compute and storage resources in the cloud, which can then be capitalized, instrumented, and scaled to improve developer productivity and reliability. When resources are hosted on IaaS, the Cloud provider is responsible for deploying, managing, and operating the software packages. Platform as a Service (PaaS) provides an even more compelling operational product abstraction with benefits from both Execution as a Service (EaaS) and Software as a Service (SaaS)—lower operational costs and reduced software management overhead.

Trafficking in Persons (TIP) for the purposes of exploitation is believed to be one of the most prolific areas of international criminal activity and is of significant concern to the United States and the international community. According to Department of State estimates, roughly 800,000 people are trafficked across borders each year. If trafficking within countries is included in the total world figures, official U.S. estimates indicate that some 2 to 4 million people are trafficked annually. As many as 17,500 people are believed to be trafficked into the United States each year and some have estimated that 100,000 U.S. citizen (USC) children are victims of trafficking within the United States.. Since enactment of the Victims of Trafficking and Violence Protection Act of 2000 (TVPA, P.L. 106—386), the Administration and Congress have aimed to address TIP by authorizing new programs and reauthorizing existing ones, appropriating funds, creating new criminal laws, and conducting oversight on the effectiveness and implications of U.S. anti-TIP policy. Most recently, the TVPA was reauthorized through FY2011 in the William Wilberforce Trafficking Victims Protection Reauthorization Act of 2008 (PL. 110-A5T). Obligations for global vol2:68and domestic antiTIP programs, not including operations and law enforcement investigations, totaled approximately $103.5 million in FY2009. Activity on combating TIP may continue into the 112th Congress, particularly related to efforts to reauthorize the TVPA. Ongoing international policy issues include how to measure the effectiveness of the U.S. and international responses to TIP, including the State Department’s annual TIP rankings and the use of unilateral sanctions; and how to prevent known sex offenders from engaging in child sex tourism. Domestic issues that may arise include whether there is equal treatment of all victims—both foreign nationals and U.S. citizens, as well as victims of labor and sex trafficking; and whether current law and services are adequate to deal with the emerging issue of domestic minor sex trafficking (i.e., the prostitution of children in the United States). Other issues are whether to include all forms of prostitution (i.e., children and adults) in the definition of TIP, and whether sufficient efforts are applied to addressing all forms of TIP, including not only sexual exploitation, but also forced labor and child soldiers. On June 14, 2010, the State Department issued its 10th annual, congressionally mandated report on human trafficking. In addition to outlining major trends and ongoing challenges in combating TIP, the report provides a country-by-country analysis and ranking, based on what progress foreign countries have made in their efforts to prosecute traffickers, protect victims, and prevent TIP. For the first time, the United States was included as one of the ranked countries. The report categorizes countries into four tiers according to the government’s efforts to combat trafficking. Those countries that do not cooperate in the fight against trafficking (Tier 3) may be subject to U.S. foreign assistance sanctions. On September 13, 2010, President Barack Obama determined that two Tier 3 countries will be sanctioned for FY2011 without exemption (Eritrea and North Korea). In addition, he determined that four Tier 3 countries will be partially sanctioned (Burma, Cuba, Iran, and Zimbabwe). The 2010 TIP report also included for the first time, a list of six countries that recruit, use, or harbor child soldiers. Inclusion on this list subjects these countries to possible U.S. assistance sanctions.

The rapidly increasing volume of healthcare data, including Electronic Health Records (EHR), medical imaging, and sensor data from wearables, presents significant opportunities to improve patient outcomes. This paper introduces an AI-powered healthcare analytics framework that integrates predictive modeling and multisource medical records, enabling early disease detection, personalized treatment, and proactive decision support. The framework leverages advanced machine learning techniques, including ensemble learning and deep learning, to derive actionable insights from both structured and unstructured big data. Key outcomes of the study demonstrate that incorporating clinical, genomic, and real-time sensor data enhances the accuracy and speed of diagnoses, resulting in improved prediction rates and reduced latency in patient care. This AI-assisted system has the potential to revolutionize healthcare delivery by supporting value-based care and personalized patient management.

Abstract Accurate prediction of the Remaining Useful Life (RUL) of lead-acid batteries is critical for enhancing the reliability and efficiency of electric vehicles (EVs) and energy storage systems. This research focuses on machine learning (ML) models to improve the accuracy of RUL estimation by leveraging feature engineering and model ensemble techniques. A comparative analysis of various ML algorithms, including Random Forest with metric variables, using battery characteristics or indicators such as voltage, current, Charge-Discharge Cycles, and Temperature Variations to track battery health. applying time-series analysis and feature selection to validate the best model that gives optimal and best prediction based on the dataset. The experimental results demonstrate that optimized ML models significantly outperform traditional methods, providing high-precision RUL estimations with reduced error margins. The findings contribute to advancing predictive maintenance strategies, minimizing unexpected battery failures, and extending the operational life of lead-acid batteries in EV applications.

This paper analyses the data trends of start-ups growth in India over the past two years, exploring sectoral distribution, government initiatives, investment patterns, human resources expectations, and the forecast for the next five years. It also contrasts Indian start-ups dynamics with ecosystems in Silicon Valley and Europe, aiming to highlight opportunities, risks, and returns in the evolving Indian start-up ecosystem. The study reveals a shift toward purpose-driven sectors like Agritech, Cleantech, and SaaS, reflecting India's changing innovation landscape. By benchmarking global ecosystems, it identifies key challenges such as funding gaps and talent retention, while emphasizing India's potential for sustainable entrepreneurial growth.

ABSTRACT In this paper, we advocate a trans-theoretical approach to understanding the liminal space between conscious and unconscious processes, expanding the binary logic of Western psychology with the meta-reductive scientific perspectives and the indigenous Andean medicine tradition. A meta-theory description, inclusive of findings in quantum information, complexity, and fractal sciences combined with the ancestral Incan codes practiced by indigenous Andean medicine people (paqos) will be provided to map the uncharted dimensions of psychic spaces crossing temporal/atemporal boundaries in psychotherapy practice. Weaving together mind, brain, and psychophysical “uncanny” domains that happen within a clinical setting may further scientific and clinical understanding of the fractal patterning inherent within all living systems, and enhance the clinician’s capacity for therapeutic engagement in more profound and attuned ways. A systemic approach to formulating a deeper conceptualization of what constitutes the Self and boundaries between conscious and unconscious processes will be presented. It is the authors’ premise that psychological healing and emergence of new relational organization takes place in the fractal seams between conscious and unconscious self-states, where “uncanny” synchronistic connections bring new information and ways of being with each other into conscious awareness.