This paper proposes a Kalman-gain-driven neural Kalman filtering (KF) defense framework, termed KFDBP, for secure state estimation in cyber–physical systems (CPSs) under denial-of-service (DoS), spoofing, and replay attacks. Unlike end-to-end neural filtering approaches such as KalmanNet that directly learn state estimators or implicitly approximate the Kalman gain using deep recurrent architectures, the proposed method employs a lightweight back-propagation (BP) neural network to adaptively regulate the Kalman gain online, while strictly preserving the classical Kalman filter prediction–correction recursion. By formulating an innovation-oriented Kalman gain learning objective, KFDBP explicitly addresses attack-induced observation uncertainty and non-Gaussian measurement corruption without requiring prior knowledge of attack timing, attack type, or attack probability during online estimation. The bounded gain regulation mechanism enhances estimation stability and interpretability, which are critical for safety-sensitive CPS applications, while significantly reducing computational complexity compared with deep neural network–based filters. Extensive Monte Carlo simulations under single and hybrid attack scenarios demonstrate that KFDBP consistently achieves lower estimation error and improved robustness than the conventional Kalman filter and KalmanNet under different attack probabilities, making it suitable for real-time and resource-constrained CPS applications.
We introduce a framework for Foundational Analysis of Safety Engineering Requirements (SAFER), a model-driven methodology supported by Generative AI to improve the generation and analysis of safety requirements for complex safety-critical systems. Safety requirements are often specified by multiple stakeholders with uncoordinated objectives, leading to gaps, duplications, and contradictions that jeopardize system safety and compliance. Existing approaches are largely informal and insufficient for addressing these challenges. SAFER enhances Model-Based Systems Engineering (MBSE) by consuming requirement specification models and generating the following results: (1) mapping requirements to system functions, (2) identifying functions with insufficient requirement specifications, (3) detecting duplicate requirements, and (4) identifying contradictions within requirement sets. SAFER provides structured analysis, reporting, and decision support for safety engineers. We demonstrate SAFER on an autonomous drone system, significantly improving the detection of requirement inconsistencies, enhancing both efficiency and reliability of the safety engineering process. We show that Generative AI must be augmented by formal models and queried systematically, to provide meaningful early-stage safety requirement specifications and robust safety architectures.
The rapid rise of LLMs over the last few years has promoted growing experimentation with LLM-driven AI tutors. However, the details of implementation, as well as the benefit in a teaching environment, are still in the early days of exploration. This article addresses these issues in the context of implementation of an AI Teaching Assistant (AI-TA) using Retrieval Augmented Generation (RAG) for Trinity College Dublin's Master's Motion Picture Engineering (MPE) course. We provide details of our implementation (including the prompt to the LLM, and code), and highlight how we designed and tuned our RAG pipeline to meet course needs. We describe our survey instrument and report on the impact of the AI-TA through a number of quantitative metrics. The scale of our experiment (43 students, 296 sessions, 1,889 queries over 7 weeks) was sufficient to have confidence in our findings. Unlike previous studies, we experimented with allowing the use of the AI-TA in open-book examinations. Statistical analysis across three exams showed no performance differences regardless of AI-TA access (p > 0.05), demonstrating that thoughtfully designed assessments can maintain academic validity. Student feedback revealed that the AI-TA was beneficial (mean = 4.22/5), while students had mixed feelings about preferring it over human tutoring (mean = 2.78/5).
Magezi K. Mabaso, Evans M. Chirwa, Shepherd M. Tichapondwa
The high concentration of heavy metals in wastewater highlights the urgent need to explore alternative treatment methods. Partially treated wastewater with elevated heavy metal levels can have severe environmental consequences, ultimately affecting the food chain. This study evaluates the effectiveness of bio-phytoremediation in treating heavy metal-contaminated wastewater using perennial grasses. The research analyzed one-year average effluent results for Pb and Cd, comparing their removal efficiencies at an initial concentration of 10ppm after introducing Vetiver grass (Chrysopogon zizanioides) and Elephant grass (Pennisetum purpurem). The compliance levels of different remediation approaches were assessed against South African wastewater discharge limits and World Health Organization (WHO) guidelines. Various remediation methods were considered, with a particular focus on bio-phytoremediation using selected grass species to remove heavy metals from contaminated wastewater. The findings indicated that Vetiver grass demonstrated a higher removal efficiency for Pb compared to Cd.
Chemical engineering, Computer engineering. Computer hardware
Carbon emission flow (CEF) is a promising approach for assessing both generation-and consumption-side carbon footprints in the power system sector. In this study, we propose a carbon-aware mobile energy storage system (MESS) scheduling framework that reduces the total carbon emissions via the CEF approach while ensuring the economical and robust operation of coupled power distribution and transportation systems under uncertainties. To manage uncertainties in photovoltaic (PV) generation outputs and traffic user equilibrium model-based traffic flow demands, the proposed framework is formulated as a distributionally robust optimization (DRO) problem using the Wasserstein metric. Thus, the Wasserstein-based DRO problem is reformulated as a tractable deterministic optimization problem by calculating distributionally robust bounds of the uncertainties. The proposed framework was tested on coupled IEEE 33-bus power distribution and 24-node Sioux Falls transportation systems, including three carbon-free PV systems, three gas-turbine generators, and three stationary energy storage systems (SESSs) or MESSs. The results show that MESSs decrease system-wide carbon emissions significantly in the CEF model and, compared to SESSs, further reduce them at the cost of a slight increment in real power losses due to the mobility characteristics of MESSs.
Vincenzo De Martino, Mohammad Amin Zadenoori, Xavier Franch
et al.
Language Models are increasingly applied in software engineering, yet their inference raises growing environmental concerns. Prior work has examined hardware choices and prompt length, but little attention has been paid to linguistic complexity as a sustainability factor. This paper introduces Green Prompt Engineering, framing linguistic complexity as a design dimension that can influence energy consumption and performance. We conduct an empirical study on requirement classification using open-source Small Language Models, varying the readability of prompts. Our results reveal that readability affects environmental sustainability and performance, exposing trade-offs between them. For practitioners, simpler prompts can reduce energy costs without a significant F1-score loss; for researchers, it opens a path toward guidelines and studies on sustainable prompt design within the Green AI agenda.
Larissa Barbosa, Sávio Freire, Rita S. P. Maciel
et al.
[Context and Motivation] Several studies have investigated attributes of great software practitioners. However, the investigation of such attributes is still missing in Requirements Engineering (RE). The current knowledge on attributes of great software practitioners might not be easily translated to the context of RE because its activities are, usually, less technical and more human-centered than other software engineering activities. [Question/Problem] This work aims to investigate which are the attributes of great requirements engineers, the relationship between them, and strategies that can be employed to obtain these attributes. We follow a method composed of a survey with 18 practitioners and follow up interviews with 11 of them. [Principal Ideas/Results] Investigative ability in talking to stakeholders, judicious, and understand the business are the most commonly mentioned attributes amongst the set of 22 attributes identified, which were grouped into four categories. We also found 38 strategies to improve RE skills. Examples are training, talking to all stakeholders, and acquiring domain knowledge. [Contribution] The attributes, their categories, and relationships are organized into a map. The relations between attributes and strategies are represented in a Sankey diagram. Software practitioners can use our findings to improve their understanding about the role and responsibilities of requirements engineers.
Prompt design and engineering has rapidly become essential for maximizing the potential of large language models. In this paper, we introduce core concepts, advanced techniques like Chain-of-Thought and Reflection, and the principles behind building LLM-based agents. Finally, we provide a survey of tools for prompt engineers.
The overcomplete convolutional structure for biological images and volume segmentation is an excellent solution to the problem in which traditional codec methods cannot accurately segment the boundary regions. Although such methods perform well, the drawback that convolutional operations do not effectively learn global and remote semantic information interactions must be addressed. Accordingly, a new image segmentation network, KTU-Net, is proposed for the medical image segmentation of liver tumors. The network structure constitutes three branches: 1)Kite-Net, which is an overcomplete convolutional network that learns to capture input details and precise edges; 2)U-Net, which learns high-level features; 3)Transformer, which learns sequential representations of input bodies and efficiently captures global multiscale information. KTU-Net is designed for both early and late fusion, and a hybrid loss function is adopted to guide network training to achieve increased stability. From extensive experimental results regarding the LiTS liver tumor segmentation dataset, KTU-Net achieves higher or similar segmentation accuracy than other advanced 3D medical image segmentation methods such as KiU-Net, TransBTS, and UNETR. Fusing the three branching features, the average Dice scores of liver tumors are improved by 0.7% and 2.1%, achieving increased quality of features learned by the network and more accurate segmentation results of liver tumors, thus providing a reliable basis for doctors to perform precise liver tumor cell assessments and treatment plans.
The new and problematic age of greatest disparity in Agricultural sector turnover residential (from this point forward called applications) in light of the Internet of Things (IoT) is essentially controlled and dispersed. Along these lines, atthis glance, an outline is done to delineate the greatest losses in Agriculture. In this paper, we have a pattern to audit a few deals with savvy farms utilizing IoT as of late. Web of Things (IoT) is a rising development that is making our world increasingly savvy. The associated world can't be imagined without IoT. An IoT-based Automatic Plant Irrigation system is one such model. IoT-engaged Smart Farms condition various things. For instance, Temperature, Resistance, Moisture Sensing nodes, Protection Relays, etc. all are related to each other. The Internet is empowering our Modern people to screen and control things offering little appreciation for time and regional necessity. This paper delineates making Automated Farming a Practice of the present and Coming Future. This paper looks at components of the basics of Electrical Engineering and IoT-based Electronics concerning our proposed structure. The proposed system shown in this paper is used for checking and controlling Water Irrigation conditions on Fields. This is the pinnacle point that needs to be renovated with the latest available technology Concerning our farmers, the quality of their crops and the very growth of our Nation itself, this thought can be suitably combined to make it progressively astute, increasingly automated and robotized. This Research paper adventure is based on building a sharp remotely operated project. The Automated Plant Irrigation system works hand-in-hand with our farmers to reduce their fieldwork and effectively reduce Man-Power. Also, the comparable can in like manner be utilized for home automation by using a comparative course of action of sensors. One of the most widely recognized goals of distributing this paper is to utilize sensors, IC’s, Protection Relays and Timers to machine correspondence in our savvy farm frameworks, which depends on the Internet of Things (IoT) and utilizing a sort of confirmation for making the brilliant innovations available to all.
Anh Nguyen-Duc, Beatriz Cabrero-Daniel, Adam Przybylek
et al.
Generative Artificial Intelligence (GenAI) tools have become increasingly prevalent in software development, offering assistance to various managerial and technical project activities. Notable examples of these tools include OpenAIs ChatGPT, GitHub Copilot, and Amazon CodeWhisperer. Although many recent publications have explored and evaluated the application of GenAI, a comprehensive understanding of the current development, applications, limitations, and open challenges remains unclear to many. Particularly, we do not have an overall picture of the current state of GenAI technology in practical software engineering usage scenarios. We conducted a literature review and focus groups for a duration of five months to develop a research agenda on GenAI for Software Engineering. We identified 78 open Research Questions (RQs) in 11 areas of Software Engineering. Our results show that it is possible to explore the adoption of GenAI in partial automation and support decision-making in all software development activities. While the current literature is skewed toward software implementation, quality assurance and software maintenance, other areas, such as requirements engineering, software design, and software engineering education, would need further research attention. Common considerations when implementing GenAI include industry-level assessment, dependability and accuracy, data accessibility, transparency, and sustainability aspects associated with the technology. GenAI is bringing significant changes to the field of software engineering. Nevertheless, the state of research on the topic still remains immature. We believe that this research agenda holds significance and practical value for informing both researchers and practitioners about current applications and guiding future research.
β -titanium alloys are essential in many applications, particularly biomedical applications. Ti-14Mn β -type alloy was produced using an electric arc furnace from raw alloying elements in an inert atmosphere. The alloy was homogenized at 1000 °C for 8 h to ensure the complete composition distribution, followed by solution treatment at 900 °C, then quenched in ice water. The alloy was subjected to cold deformation via cold rolling with different ratios: 10, 30, and 90%. The phases change, microstructure, mechanical properties, and corrosion resistance of Ti-14Mn alloys were evaluated before and after cold rolling. The results showed that the β -phase is the only existed phase even after a high degree of deformation. The microstructure shows a combination of twinning and slipping deformation mechanisms in the deformed alloy. Microhardness values indicated a linear increase equal to 30% by increasing the ratio of cold deformation due to the strain hardening effect. The corrosion resistance of Ti-14Mn alloy was doubled after 90% cold rolling.
Materials of engineering and construction. Mechanics of materials, Chemical technology
Abstract This work presents the operation and control of a pico‐hydro‐solar photovoltaic (PV)‐battery energy storage (BES)‐based isolated renewable energy system (RES) feeding 3‐phase 4‐wire loads. For voltage regulation, to maintain frequency, and power quality improvement in this system, a 4‐leg VSC is used. The BES is connected to the DC‐link of the voltage source converter (VSC) through a bidirectional converter (BDC), which regulates the DC‐link voltage and controls the charging and discharging current of the battery. An advanced perturb and observe (AP&O)‐based MPPT control technique with drift free operation and capability to operate in the derated mode is adapted in this work. The VSC connected to PCC, injects or absorbs power from this system based on the difference of power between generation and the load. The modified complex co‐efficient filter (MCCF)‐based control technique monitors the power quality of this RES system and 4 leg VSC provides the source neutral current compensation. This control algorithm is used to extract the amplitude of the fundamental load current component with improved dynamic response, DC offset elimination and higher order harmonics removal capability. The ability of the presented control strategy for power quality improvement, power management, load balancing and neutral current compensation is reported in this work.
Production of electric energy or power. Powerplants. Central stations, Energy industries. Energy policy. Fuel trade
The dispersion and orientation of three different montmorillonite clay nanoparticles embedded in nitrile-based nanocomposites were examined in the current study. Maleic anhydride was grafted onto a nitrile structure for the purpose of enhancing compatibility, and the resulting nanocomposites were investigated. The grafting of maleic anhydride seemed to have a pronounced effect, leading the structure to a near-exfoliation state. Using energy dispersive x-ray spectrometer, the state of distribution of layered silicate clusters in the nanocomposite was assessed, and it was observed that maleic anhydride provided a reduction in the size of agglomerations and enhanced the homogeneity of the system. The intercalation and delamination of the layered silicates over grafting were validated by transmission electron microscopy. Inter-lamellar spacing measurements were found to correlate perfectly with x-ray data. On the other hand, the alignment of the clay nanoparticles was examined by small angle x-ray scattering. A 3D-orientation approach was developed based on the scattering stereographs.
Materials of engineering and construction. Mechanics of materials, Chemical technology
Marie-Eve Yvenat, Benoit Chavillon, Eric Mayousse
et al.
Hybrid supercapacitors have been developed in the pursuit of increasing the energy density of conventional supercapacitors without affecting the power density or the lifespan. Potassium-ion hybrid supercapacitors (KIC) consist of an activated carbon capacitor-type positive electrode and a graphitic battery-type negative one working in an electrolyte based on potassium salt. Overcoming the inherent potassium problems (irreversible capacity, extensive volume expansion, dendrites formation), the non-reproducibility of the results was a major obstacle to the development of this KIC technology. To remedy this, the development of an adequate formation protocol was necessary. However, this revealed a cell-swelling phenomenon, a well-known issue whether for supercapacitors or Li-ion batteries. This phenomenon in the case of the KIC technology has been investigated through constant voltage (CV) tests and volume measurements. The responsible phenomena seem to be the solid electrolyte interphase (SEI) formation at the negative electrode during the first use of the system and the perpetual decomposition of the electrolyte solvent at high voltage. Thanks to these results, a proper formation protocol for KICs, which offers good energy density (14 Wh·kg<sub>electrochemical core</sub><sup>−1</sup>) with an excellent stability at fast charging rate, was developed.
Production of electric energy or power. Powerplants. Central stations, Industrial electrochemistry
The year 2023 represents a milestone for our country, our academy, and for our journal. The Republic of Türkiye will celebrate its centennial, the Turkish Naval Academy (TNA) will celebrate its quarter-millennial, and the Journal of Naval Sciences and Engineering (JNSE) will celebrate its vicennial in 2023. Undoubtedly, the historical intersection is a source of praise and pride for our academy and for our journal. 99 years ago, the Turkish nation created an epic of independence under the leadership of Gazi Mustafa Kemal Atatürk and his comrades in the War of Independence. The 100th anniversary of the Republic will be celebrated in 2023 to cherish the deep-rooted memory of our struggle for independence. Founded in 1773 by Mustafa III, the 26th Sultan of the Ottoman Empire, the Imperial School of Naval Engineering (Mühendishane-i Bahr-i Hümâyûn), considered the first engineering school of the Empire, became the foundation of both the TNA and the Istanbul Technical University (ITU). TNA and ITU have special positions in terms of Turkish modernization history as the institutions where engineering and technical education have continued since the Ottoman period with their 249 years of distinguished history. 2023 will mark these exceptional institutions’ 250th anniversary. 2023 will also mark the 20th anniversary of the international journal of the TNA, i.e., JNSE. The journal aims to provide a scientific contribution to the theory and applications of naval sciences and engineering and share knowledge in relevant fields since its first issue in 2003. As we are entering this historically significant year, it is an honor and privilege for me to have been appointed as the Editor-in-Chief (EIC) of the scientific journal of our prestigious 250-year-old academy. As an Editorial Board Member since 2013, as well as an Editor in both 2018 and 2019, and as an Administrative Board Member of the Naval Sciences and Engineering Institute from 2013 to 2022, I have seen the continued improvement that JNSE has experienced. I inherit the role of Editor-in-Chief of the JNSE at a special time when our journal has been included within the scope of the ULAKBIM TR Index. The journal must, first and foremost, continue to provide a contribution to science and engineering in fields aligned with the areas of interest of the JNSE, e.g., electrical and electronics engineering, naval/mechanical engineering, naval architecture and marine engineering, industrial engineering, computer science and engineering, and basic/social sciences. In the meantime, we will do our best to make our journal ready to be a candidate to be indexed in Science Citation Indexes. Raising the quality of the accepted papers by introducing strict review criteria will be instrumental in achieving this goal by increasing the impact of the journal. To this end, we recently applied to join Crossref as a member under the sponsorship of TUBITAK ULAKBIM DergiPark, and consequently, the JNSE will be able to provide a DOI (Digital Object Identifier) for accepted papers submitted after 1 September 2022 via the 'DergiPark' online system. With the new structure of JNSE’s Editorial Board, when a manuscript is submitted, the EIC assigns it to the appropriate Editorial Board Member based on the main topic area of the manuscript. The assigned Member should assign a minimum of two reviewers to evaluate the manuscript. He/she is expected to make his/her own independent review of the manuscript and, once the reviewers have completed their evaluation, he/she submits a recommendation to the EIC. The EIC checks for consistency among the recommendations, typically accepts the recommendation of the assigned Editorial Board Member and then communicates to the corresponding author the final decision together with the comments of the reviewers. We are therefore starting with a smaller, more flexible, and resourceful Editorial Board, dedicated to this purpose. I hope that with the other editors, we can push JNSE to a more competitive position within the field; a position that it rightly deserves. I also take this opportunity to introduce Onur USTA who will be working with me as the Assistant Editor-in-Chief. I would like to thank all the editorial team members and look forward to working with them. The success of any journal is built on the support of the contributors, the reviewers, the editors, and the publication staff. I look forward to receiving your ideas for making JNSE more valuable for our community. We welcome your original contributions to our issues that will be published in this historical intersection, where the centennial of the Republic, the sestercentennial of the TNA, and the twentieth year of the JNSE meet.
Telescope Array Collaboration R.U. Abbasi, M. Abe, T. Abu-Zayyad
et al.
Telescope Array (TA) is the largest ultrahigh energy cosmic-ray (UHECR) observatory in the Northern Hemisphere. It explores the origin of UHECRs by measuring their energy spectrum, arrival-direction distribution, and mass composition using a surface detector (SD) array covering approximately 700 km and fluorescence detector (FD) stations. TA has found evidence for a cluster of cosmic rays with energies greater than 57 EeV. In order to confirm this evidence with more data, it is necessary to increase the data collection rate. We have begun building an expansion of TA that we call TAx4. In this paper, we explain the motivation, design, technical features, and expected performance of the TAx4 SD. We also present TAx4’s current status and examples of the data that have already been collected.