Genome data, characterized by its high dimensionality and complexity, presents significant challenges for computational analysis and biological interpretation. Feature selection plays a crucial role in reducing dimensionality, improving model interpretability, and enhancing predictive performance by identifying the most informative genomic attributes. In this study, we construct a robust, generalisable ensemble framework for the feature selection and ML classification of metagenomic data. The framework incorporates six different feature selection algorithms of different types working in an ensemble. We comprehensively assess four ML classifiers to pair with them and three aggregation methods to combine their results, testing numerous configurations to find which ones perform best. Our result shows that Random Forest is a general and reliable algorithm for metagenomic datasests and consistent with the literature, we found that feature selection universally improves classification performance, though this improvement varies per dataset and, on non-wrapper methods, depends on choosing the right subset size. When looking at their best scores, the six FS algorithms performed broadly similarly across the data, with the largest differences being on the hardest-to-classify datasets, where mRMR and Boruta edged out the others.
The dam repair process entails the usage of several vital equipment and techniques. There are many problems that dam structures can encounter. Hence, repair is necessary. The paper looks into how different materials and substances are utilized in fixing processes by examining scientific literature and real-world examples. The main aim of this research is to understand how these parts work together to maintain the integrity and functionality of dams and also prolong their lifespan and safety. The study of the properties of materials and their practical applications is vital in the field of dam engineering and maintenance. The data presented here can aid in enhancing methods of construction and maintenance of dams.
In the history of digital ecosystems, security has always been and will continue to be a primary issue. Newer fields of study, such as blockchain and wireless networks, continue to confront the difficulties that are exclusive to them despite technological breakthroughs. This article provides an assessment of these two important domains, diving into their relevance and fundamental security methodologies, primary research, unsolved challenges, and prospective future paths. This article covers the key components of blockchain security and emphasizes the importance of wireless network security. A comparative study of the security of blockchain technology and wireless networks elucidates the distinctive qualities, difficulties, and solutions that each one presents. This paper also explores the synergistic integration of AI and blockchain to address the multifaceted security challenges in modern wireless networks.
Utilizing available renewable energy resources has been characterized as a reliable indicator to mitigate energy dependency in countries as well as securing the supplying of energy-based needs in the future. This research explores the impact of renewable energy as trustworthy resources in mitigating energy imports and how accurately predicting the energy consumption can lead to better examination of energy dependency. A system dynamics model with special aim on the role of renewable energy resources on decreasing energy dependency has been constructed. By analyzing the dynamics of the model, different scenarios of renewable energy policies are employed as interventions to be implemented and assessed in the model while investigating the applicability of renewable energies to manage national energy supply sustainably. To illustrate the benefits of renewable energy utilization, the proposed model is applied to a case study to analyze the decrease in imported energy resources from external sources. The results indicate that the system dynamics approach outperforms in predicting energy demand compared to the most commonly used techniques in energy forecasting studies and under which policies the desired level of energy dependency will be sustainably achieved.
This study critically assesses the current state, global positioning, and future prospects of Bangladesh's shipbuilding industry. Using a mixed-methods approach that combines global benchmarking, policy analysis, focus group discussions, and statistical forecasting (ARIMA and Holt-Winters models), the paper identifies key structural deficiencies and emerging opportunities. Although Bangladesh offers one of the world’s lowest labor costs, it significantly lags in productivity, technological integration, and coordinated policy support compared to global leaders such as Japan, South Korea, and China. The analysis finds that Bangladesh is well-positioned to target niche markets for small- and medium-sized vessels (1,000–10,000 DWT), which remain underserved globally. Forecast models suggest that Bangladesh could achieve a 2% global market share by 2033, translating to over USD 4 billion in annual export earnings. To realize this potential, the study recommends a comprehensive national industrial policy, strategic investment in automation and R&D, and dedicated export financing mechanisms. Limitations include outdated productivity data and limited access to primary financial records from local shipyards. Future research should address green shipbuilding, institutional capacity building, and innovation ecosystems.
Ports plays a vital role of global economy by transporting 80% of trade and commerce of globe by value pass through them. The economic benefits are often less directly tied to port activities and more linked to the dynamics of the supporting supply chains. This operational support becomes beneficial and effective, playing a crucial role in enhancing national competitiveness. Smart ports are modern and highly facilitated port which uses digital, automated and other smart technologies to enhance efficiency, accountability, sustainability, competitiveness, monitoring, operations, and security. Smart ports frequently utilize digital tools such as sensors, data analytics, augmented reality, big data, digital twins, and automation to improve cargo movement, minimize waste and emissions, and provide superior services to shippers, shipping companies, customs authorities, local communities, and stakeholders. They may also feature renewable energy sources, electric charging stations, onshore power supply, sustainable to climate change, natural calamities, and smart infrastructure for better logistics and optimum transportation. This analytical paper will investigate the future development of port in contrast of history, global demand and advanced technology.
In this article we measure the surface potential of the polycarbonate track etched membrane having multiple nanopores and graphene/copper membrane having multiple nanopores. We use potassium chloride elec trolyte solution (KCl) with 0.6 mol in the high concentration inlet source reservoir. We use 0.6 mmol potassium chloride electrolyte solution in the low concentration outlet sink reservoir. We provide theory to match the surface potential of the membranes. We develop model to understand the potassium ion transference number inside the multiple nanopores for our membranes. We develop GUI simulation to understand the concentration of the potassium chloride electrolyte solution inside single nanochannel, single nanopore for different surface potential of the membranes. We use Gouy-Chapman equation to calculate the concentration and Smolu chowski model is used to calculate the velocity of the potassium chloride electrolyte solution. Here we present a detailed comparative analysis of the structural, transport mechanisms of nanochannels, nanopores to ex plain the several orders of magnitude difference in the current inside the nanochannel and the nanopore. The volume of the single nanochannel is larger than the volume of the single nanopore resulting in larger charge of the potassium ions, chlorine ions and potassium chloride electrolyte solution inside the single nanochannel compared to the single nanopore resulting in the significant difference in the current. We develop GUI based simulation of nanofluidics electronics calculator. Each button of the calculator is related to their own current of the potassium chloride electrolyte solution inside the nanochannel and nanopore, respectively.
By utilizing the sensory channels, the psyche of biological organisms forms a mirror-like symmetrical information space of the reflected external environment. The symmetry line is the polymorphic sensory field of the organism's extra/interoception. The fundamental principle of functioning of the psyche of biological organisms is the creation and maintenance of a systemic homeostatic/informational equilibrium. The HS psyche has evolutionarily formed mental regulatory algorithms, which have acquired the ability to translate/interfere properties and meanings between phenomena/objects of various species, expanding the communicative and intellectual potential of HS, in comparison with other biological species. Algorithmically repeating organized frequency patterns of the external environment, perceived outside the focus of voluntary attention and creating an arsenal of "hidden regulatory algorithms" (HRAs). Upon updating HRAs are perceived as manifestations of “intuition” or “emerging abilities.” Extrapolation of the AIarchitecture to the psyche of Homo Sapiens with autism spectrum disorders (ASD) has shown significant similarity in the structural principles of these intelligent systems. One of the main problems of ASD/AI is the impossibility of constructing mental RAs of communicative/social vectors with "blurred" parameters of the goal image. Under the traditional approach, this problem is also relevant for AGI programming. We consider the use of structural blocks of hieroglyphic thinking to be a promising direction for integrative AGI architecture. Originating from pictographic writing, the hieroglyph graphically (visually) conveys the general, complete idea ofan object/phenomenon/concept. The constructive difference between hieroglyphic thinking and conceptual thinking consists in the reduction of abstraction, metaphor, and uncertainty in the mental regulatory algorithms of the communicative and social spectra, which implies effectiveness in AGI architecture.
The convergence of enterprise mobility, quantum cryptography, and virtual invisible network technologies presents new opportunities for secure communications, albeit with complex integration challenges. This analysis explores the transformative impact of quantum key encryption (QKE) on modern cryptographic systems, platforming an examination of the benefits and technical feasibility of combining a single, abstracted mobile application that houses unified communication functions with quantum key encryption, deployed over virtual invisible networks (VINs). While RSA and elliptic curve cryptography (ECC) have served reliably for decades, their impending obsolescence necessitates urgent investment in quantum-safe infrastructure. The analysis draws on contemporary peer-reviewed research, industry standards, and technical implementations to evaluate security enhancements, operational gains, and deployment challenges. Key findings highlight the security advantages of quantum-resistant encryption, network obfuscation, and unified enterprise communications, while identifying critical challenges in scalability, implementation complexity, and resource requirements. Recommendations for staged deployment, hybrid security models, and standardized integration frameworks are presented to facilitate the practical adoption of these emerging technologies. Through a synthesis of contemporary research, the paper argues that quantum-resistant cryptographic protocols must be urgently developed and adopted to preserve information security in the quantum computing era.
In the $1 trillion freight brokerage industry, securing the right truck at the right time and price depends on labour-intensive carrier outreach, where reps send daily bid emails to 1,000–3,000 carriers in a "spray and pray" approach, yielding only 10–15% booking rates after 4–6 hours of manual effort (Transport Topics, 2023). This paper investigates how AI agents, powered by Large Language Models (LLMs), can enhance this process by up to 80% in efficiency. These agents automate bid distribution by generating personalized emails using TMS data, parse carrier replies with 95% accuracy (NLP Benchmarks, 2024), respond to 80% of queries instantly, and negotiate rates in real time against $150 billion in DAT benchmarks (DAT, 2024), integrating seamlessly with load boards. Simulations of 20 loads across 2,000 carriers show response rates rising from 10% to 20%, bookings from 15% to 25%, and time dropping to 48–72 minutes daily, saving $50–$100/load ($1,000–$2,000 daily) based on $3.50/gallon fuel costs (EIA, 2024). In this automated world, brokers cover loads 25% faster, while carriers receive tailored bids, cutting outreach effort by 50%. Despite 6–12 month integration challenges (Gartner, 2024), AI agents transform carrier outreach into a proactive, data-driven ecosystem, optimizing the 20% of freight on spot markets (DAT, 2024) and beyond in a competitive landscape.
This paper presents a survey of state-of-the-art trust models for Vehicular Ad Hoc Networks (VANETs). Trust management plays an essential role in isolating malicious insider attacks in VANETs which traditional security approaches fail to thwart. To this end, many trust models are presented; some of them only address trust management, while others address security and privacy aspects besides trust management. This paper first reviews, classifies, and summarizes state-of-the-art trust models, and then compares their achievements. From this literature survey, our reader will easily identify two broad classes of trust models that exist in literature, differing primarily in their evaluation point. For example, most trust models follow receiver-side trust evaluation and to the best of our knowledge, there is only one trust model for VANETs which evaluates trust at the sender-side unless a dispute arises. In the presence of a dispute, a Roadside Unit (RSU) rules on the validity of an event. In receiver-side trust models, each receiver becomes busy while computing the trust of a sender and its messages upon the messages' arrival. Conversely, in the sender-side class, receivers are free from any kind of computation as the trust is verified at the time the message is announced. Also, vehicles can quickly act on the information, such as taking a detour to an alternate route, as it supports fast decision-making. We provide a comparison between these two evaluation techniques using a sequence diagram. We then conclude the survey by suggesting future work for sender-side evaluation of trust in VANETs. Additionally, the challenges (real-time constraints and efficiency) are emphasized whilst considering the deployment of a trust model in VANETs
This paper presents two simple topologies of novel grounded lossy series and parallel RL circuit employing VDVTA. Inductor and resistor are simulated employing one VDVTA and one passive component. Both the proposed topologies for the immittance simulation have been realized using a simple circuit. There is little deviation in the values of theoretically calculated and practically simulated series and parallel RL circuit. To study the performance of the realized grounded lossy series and parallel RL circuit, a second order high pass filter has been constructed using one of the circuits. To verify the presented theoretical analysis, the PSPICE simulation results are given using CMOS technology.
Artificial intelligence (AI) is transforming the hospitality and multifamily real estate sectors, enhancing revenue management, customer experience, and operational efficiency. This paper examines how AI-driven technologies are reshaping these industries, highlighting their applications, benefits, and challenges. In the hospitality sector, AI optimizes revenue through dynamic pricing, enhances guest experiences with intelligent automation, and reduces operational costs via predictive maintenance. Meanwhile, in the multifamily sector, AI streamlines tenant screening, automates leasing, improves resident engagement, and integrates smart home technologies to enhance security and efficiency. AI-powered market analysis, rent optimization, and predictive maintenance further improve asset performance in both sectors. Despite its transformative potential, AI adoption presents challenges such as high initial costs, algorithmic bias in tenant screening and pricing models, and data privacy concerns. The integration of AI with emerging technologies like blockchain and IoT is also explored as a potential solution to enhance security, transparency, and efficiency in real estate operations. By analyzing real-world case studies and industry data in hospitality and multifamily sectors, this study explores best practices for AI implementation, provides a glimpse into what the future could hold for real estate with AI, and offers strategic recommendations for stakeholders looking to maximize efficiency, profitability, and long-term sustainability in an AI-driven real estate landscape.
In contrast to conventional power plants, which are based on large synchronous generators with large inertia capabilities to dampen sudden disturbances, renewable energy sources, such as solar and wind, connected to the grid through power electronics converters, display low system inertia and overload limiting capabilities. Additionally, because they lack primary frequency regulation capabilities, they are unable to actively respond to the frequency response of the system. This research investigates the contribution of droop control strategies to grid resilience by focusing on their ability to maintain frequency stability during grid disturbances. The study employs a simulation-based approach using MATLAB/Simulink to model the Djoum power plant in Cameroun and implement droop control algorithm. The methodology involves designing and analyzing the system's response under sudden load changes using droop and supervisory control strategies. Parameters such as droop coefficients and control bandwidths were systematically varied to analyze their impact on frequency regulation and grid resilience. Results show that droop control maintains system operation by adjusting frequency and voltage under disturbance, while supervisory control acts as a secondary layer to fully restore parameters to their reference values thereby ensuring reliable operation during grid disturbances.
Currently, cybersecurity threats, particularly cyber-attacks, are a growing concern. As time goes on, it becomes increasingly challenging to hinder these attacks. Nevertheless, a new participant has entered the arena, known as artificial intelligence (AI). AI offers a way for cybersecurity experts to counteract the ever-evolving attacks. By utilizing techniques such as identifying threats and automated responses to incidents, organizations can enhance their security measures and safeguard confidential data. Despite the numerous advantages of adopting AI, it is equally important to remain vigilant about potential risks. In recent years, the rapid growth in cybersecurity threats has necessitated the development of more effective measures to protect sensitive information and systems. This paper delves into the ethical concerns of AI in cybersecurity, stressing the crucial balance between technological innovation and maintaining ethical standards.
This is a simple geometric modification to the currently recorded sizes of the sun and moon. The concept is based on the well-known observation that when looking at an object, it appears to progressively taper off toward its distant end. Spherical objects are no exception, even though their apparent tapering is not readily noticeable due to their unique geometry. As shown in this study, when looking at the solar disc or at the moon, we are actually looking at the base side of a seemingly slightly egg-shaped sun or moon. Given that the sun and moon are spherical in reality, the geometrically adjusted sizes show that each is about 1.4% bigger in volume than currently thought.
In data science and analytics, the driving force is not on how to perform analytics tasks or how to use advanced technology in analytics projects. Business problems and goals should always drive the overall approaches. Projects and applications in data science and analytics should serve business goals and help business decision making. In this paper, a case study that serves various directions in answering business questions is presented.
Agile methodologies have gained widespread adoption in software development due to their flexibility, iterative approach, and focus on collaboration. However, their effectiveness in addressing critical software engineering domains, particularly the Software Requirements and Software Construction Knowledge Areas (KAs), remains an open question. This study systematically evaluates five agile methodologies—Extreme Programming (XP), Scrum, Feature-Driven Development (FDD), Rapid Application Development (RAD), and Kanban—using a comparative framework built upon key attributes from the Software Engineering Body of Knowledge (SWEBOK). The assessment considers how each methodology approaches requirements gathering, change management, software design, coding practices, and overall project adaptability. By analyzing these methodologies through the lens of SWEBOK Knowledge Areas, this research aims to identify their strengths, limitations, and applicability to different types of software projects. The findings provide valuable insights for software practitioners, project managers, and educators in selecting the most appropriate agile methodology based on project complexity, team structure, and development constraints. Additionally, this study highlights potential gaps in agile methodologies concerning software engineering best practices, paving the way for future research and methodological enhancements. Ultimately, this research contributes to a deeper understanding of how agile practices align with formal software engineering principles, aiding organizations in making more informed decisions when adopting agile frameworks.
With the continuous expansion of the scale of educational engineering projects and the increasing requirements for quality, cost, and efficiency, the traditional single-phase audit method can no longer meet the needs of full-cycle project management. Lifecycle Audit (LCA), as a comprehensive system evaluation tool, can effectively promote the sustainable development of a project by reviewing each phase of the project. This paper takes the construction project of a comprehensive teaching building for an educational institution as a case study, combines the lifecycle audit model, and explores the audit contents and application practices during the design, implementation, operation, and maintenance stages. The research shows that the implementation of the LCA model not only improves project management efficiency but also helps project managers better address potential challenges that may arise during the project, thus providing theoretical and practical references for similar projects.
Crimebots are fueling the cybercrime pandemic by exploiting artificial intelligence (AI) to facilitate crimes such as fraud, misrepresentation, extortion, blackmail, identity theft, and security breaches. These AI-driven criminal activities pose a significant threat to individuals, businesses, online transactions, and even the integrity of the legal system. Crimebots enable unjust exonerations and wrongful convictions by fabricating evidence, creating deepfake alibis, and generating misleading crime reconstructions. In response, lawbots have emerged as a counterforce, designed to uphold justice. Legal professionals use lawbots to collect and analyze evidence, streamline legal processes, and enhance the administration of justice. To mitigate the risks posed by both crimebots and lawbots, many jurisdictions have established ethical guidelines promoting the responsible use of AI by lawyers and clients. Approximately 1.34% of lawyers have been involved in AI-related legal disputes, often revolving around issues such as fees, conflicts of interest, negligence, ethical violations, evidence tampering, and discrimination. Additional concerns include fraud, confidentiality breaches, harassment, and the misuse of AI for criminal purposes. For lawbots to succeed in the ongoing battle against crimebots, strict adherence to complex AI regulations is essential. Ensuring compliance with these guidelines minimizes malpractice risks, prevents professional sanctions, preserves client trust, and upholds the ethical and legal professional standards of excellence.
