This pilot study evaluated the visionMC system, a low-cost artificial intelligence system integrating HOG-based facial recognition and voice notifications, for workflow optimization in a family medicine practice. Implemented on a Raspberry Pi 4, the system was tested over two weeks with 50 patients. It achieved 85% recognition accuracy and an average detection time of 3.4 s. Compared with baseline, patient waiting times showed a substantial reduction in waiting time and administrative workload, and the administrative workload decreased by 5–7 min per patient. A satisfaction survey (N = 35) indicated high acceptance, with all scores above 4.5/5, particularly for usefulness and waiting time reduction. These results suggest that visionMC can improve efficiency and enhance patient experience with minimal financial and technical requirements. Larger multicenter studies are warranted to confirm scalability and generalizability. visionMC demonstrates that effective AI integration in small practices is feasible with minimal resources, supporting scalable digital health transformation. Beyond biometric identification, the system’s primary contribution is streamlining practice management by instantly displaying the arriving patient and enabling rapid chart preparation. Personalized greetings enhance patient experience, while email alerts on motion events provide a secondary security benefit. These combined effects drove the observed reductions in waiting and administrative times.
Engineering machinery, tools, and implements, Technological innovations. Automation
The rapid development of large language models is transforming software development. Beyond serving as code auto-completion tools in integrated development environments, large language models increasingly function as foundation models within coding agents in vibe-coding scenarios. In such settings, prompts play a central role in agent-based intelligent software development, as they not only guide the behavior of large language models but also serve as carriers of user requirements. Under the dominant conversational paradigm, prompts are typically divided into system prompts and user prompts. System prompts provide high-level instructions to steer model behavior and establish conversational context, while user prompts represent inputs and requirements provided by human users. Despite their importance, designing effective prompts remains challenging, as it requires expertise in both prompt engineering and software engineering, particularly requirements engineering. To reduce the burden of manual prompt construction, numerous automated prompt engineering methods have been proposed. However, most existing approaches neglect the methodological principles of requirements engineering, limiting their ability to generate artifacts that conform to formal requirement specifications in realistic software development scenarios. To address this gap, we propose REprompt, a multi-agent prompt optimization framework guided by requirements engineering. Experiment results demonstrate that REprompt effectively optimizes both system and user prompts by grounding prompt generation in requirements engineering principles.
Dhiraj Neupane, Richard Dazeley, Mohamed Reda Bouadjenek
et al.
Reinforcement learning (RL) offers significant promise for machinery fault detection (MFD). However, most existing RL-based MFD approaches do not fully exploit RL's sequential decision-making strengths, often treating MFD as a simple guessing game (Contextual Bandits). To bridge this gap, we formulate MFD as an offline inverse reinforcement learning problem, where the agent learns the reward dynamics directly from healthy operational sequences, thereby bypassing the need for manual reward engineering and fault labels. Our framework employs Adversarial Inverse Reinforcement Learning to train a discriminator that distinguishes between normal (expert) and policy-generated transitions. The discriminator's learned reward serves as an anomaly score, indicating deviations from normal operating behaviour. When evaluated on three run-to-failure benchmark datasets (HUMS2023, IMS, and XJTU-SY), the model consistently assigns low anomaly scores to normal samples and high scores to faulty ones, enabling early and robust fault detection. By aligning RL's sequential reasoning with MFD's temporal structure, this work opens a path toward RL-based diagnostics in data-driven industrial settings.
The escalating demand for sustainable development in the built environment necessitates the integration of innovative, system-based assessment tools. This study investigates the role of energy efficiency (EE) within a nature-inspired sustainability assessment framework, drawing from biomimicry principles to evaluate green building practices in South Africa. Grounded in the ethos of nature’s efficiency, such as closed-loop energy systems, passive energy use, efficiency through form and function, and decentralised and localised energy generation, this study identifies and prioritises key EE criteria, including efficient energy management, renewable energy optimisation, passive heating, ventilation and air conditioning (HVAC) systems, and energy-saving technologies. Using the Analytic Hierarchy Process (AHP), this research engaged 38 highly experienced, practising, and registered construction professionals to perform pairwise comparisons of EE criteria. Results revealed that efficient energy management (29.8%) emerged as the most significant factor, followed closely by energy-saving equipment (26.4%), with strong expert consensus (consistency ratio = 0.03). The findings reflect a convergence of ecological wisdom and industry expertise, suggesting that nature’s design strategies offer a compelling roadmap for achieving sustainable energy performance in buildings. This study reinforces the applicability of biomimicry in shaping context-specific sustainability metrics and informs the development of adaptive, ecologically aligned certification frameworks. This study recommends the integration of these EE criteria into building rating systems, fostering interdisciplinary collaboration, and scaling nature-based frameworks to inform global sustainability practice. By bridging theory and application, this study advances a regenerative approach to construction that aligns with the UN Sustainable Development Goals and long-term environmental resilience.
S. Jacob, Mohd Majid, S. C. V. Ramana Murty Naidu
et al.
Cashew nut shell liquid (CNSL) is a byproduct of cashew processing that has largely been overlooked as a biomass resource for biodiesel production. While some research has been conducted on CNSL in diesel engines, there remains a lack of studies on using processed CNSL with industrial waste catalysts for diesel engines. This study focuses on the performance and emissions of catalytically cracked CNSL (CC-CNSL) created with fly ash as a catalyst. Blends of 25%, 50%, 75%, and 100% CC-CNSL-diesel were used as a fuel in a single-cylinder diesel engine under different load conditions. The CC-CNSL25 blend, which contains 25% CC-CNSL, outperformed the others with a 2% increase in brake thermal efficiency. Additionally, it showed substantial reductions in emissions, i.e., 11.76% less carbon monoxide, 9.09% reduced smoke density, 8.57% lower hydrocarbon emissions, and 5.27% decreased specific fuel consumption compared to conventional diesel at full load. This research highlights fly ash-catalyzed CNSL processing as an effective method for converting agricultural waste into high-quality biodiesel. It offers a dual advantage as a sustainable fuel source while addressing waste management challenges.
This paper investigates the economic and technical feasibility of integrating Vehicle-to-Grid (V2G) technology in the Non-Road Mobile Machinery (NRMM) sector. These often-idling assets, with their substantial battery capacities, present a unique opportunity to participate in energy markets, providing grid services and generating additional revenue. A novel methodology is introduced that integrates Bayesian Optimization (BO) to optimize the energy infrastructure together with an operating strategy optimization to reduce the electricity costs while enhancing grid interaction. While the focus lies on the methodology, the financial opportunities for the use-case of an electric NRMM rental service will be presented. However, the study is limited by the availability of real-world data on the usage of electric NRMM and does not address regulatory challenges of V2G. Further research is needed to extend the model accuracy and validate these findings.
Walter Vargas, Jackeline Abad, Christian Tipantuña
et al.
The XXXII Electrical and Electronic Engineering Conference (XXXII JIEE-2024) is an annual event organized by the Faculty of Electrical and Electronic Engineering at the Escuela Politécnica Nacional, Quito, Ecuador [...]
To combat the environmental issues posed by global warming and the depletion of natural resources, the emission of greenhouse gases (GHG) and the consumption of natural resources must be economically reduced. Upgrading and remanufacturing are expected to play important roles in dealing with both issues since they can save the production of virgin materials associated with GHG emissions for assembled products by component reuse and material recycling. Although upgrading components can add additional values and avoid their value obsolescence, composing reused components with shorter physical or value lifetime in a remanufactured product leads to decreasing revenue from selling the upgrade-remanufactured product. Hence, in these cases, component reuse and material recycling can be more economical life cycle option than upgrading and remanufacturing. Furthermore, disassembly is an essential process for recovery options such as upgrading, remanufacturing, components reuse, and material recycling, and tends costly due to the labor costs of manual disassembly. Disposing without disassembly may be a better life cycle option. Therefore, life cycle options, including upgrading, remanufacturing, reusing, recycling and disposal should be suitably selected for each component based on an additional value by upgrading, physical and value lifetimes for each component. This study proposes an upgrade-remanufacturing decision method to maximize GHG saving rate and profit using 0-1 integer programming with ε constraint method. The numerical experiments are conducted using the laptop consisted of 34 components. The results in the laptop indicate that the selling price of upgrade-remanufactured product should be set to more than 2,000 Yen, and the achievement of much higher GHG saving rate such as 99% would lead to negative earnings. Additionally, the bi-objective model is expanded to multi-objective for profit, GHG saving, and recovery rates for investigation of the profit and the selected life cycle options for each component.
Engineering machinery, tools, and implements, Mechanical engineering and machinery
This paper presents ongoing research in our project software engineering with process algebra. In this project we have developed among others a reimplementation of the simulator from the PSF Toolkit, a set of tools for the Process Specification formalism (PSF). This new simulator uses the ToolBus, a tool coordination architecture based on process algebra. We now developed new tool coordination architectures based on this ToolBus. We implement the primitives of the ToolBus in the programming languages Raku and Go. Both these languages have support for concurrency and communication between concurrent entities in the form of channels. We apply these tool coorination architectures on a small example. And we give implementations for the simulator in the PSF Toolkit based on the tool coordination architectures in Raku and Go.
Neural backdoors represent insidious cybersecurity loopholes that render learning machinery vulnerable to unauthorised manipulations, potentially enabling the weaponisation of artificial intelligence with catastrophic consequences. A backdoor attack involves the clandestine infiltration of a trigger during the learning process, metaphorically analogous to hypnopaedia, where ideas are implanted into a subject's subconscious mind under the state of hypnosis or unconsciousness. When activated by a sensory stimulus, the trigger evokes conditioned reflex that directs a machine to mount a predetermined response. In this study, we propose a cybernetic framework for constant surveillance of backdoors threats, driven by the dynamic nature of untrustworthy data sources. We develop a self-aware unlearning mechanism to autonomously detach a machine's behaviour from the backdoor trigger. Through reverse engineering and statistical inference, we detect deceptive patterns and estimate the likelihood of backdoor infection. We employ model inversion to elicit artificial mental imagery, using stochastic processes to disrupt optimisation pathways and avoid convergent but potentially flawed patterns. This is followed by hypothesis analysis, which estimates the likelihood of each potentially malicious pattern being the true trigger and infers the probability of infection. The primary objective of this study is to maintain a stable state of equilibrium between knowledge fidelity and backdoor vulnerability.
Metal complexes are widely used in pharmaceutics, cosmetics, electronics, casting printing and power generation. One of the major challenges due to their long-term use as medicines is their accumulation in the body. This issue needs to be resolved to achieve better results of metal complexes as medicines. The use of metal-nanoparticles (MNPs) can be expected to reduce the toxicity of metals and their accumulation in the body. The aim of this paper is to give an insight into the variation induced in the cytotoxic activity of MNP–ligand complexes by replacing the respective heavy metals with their nanoparticles (NPs).
Nicky Andre Prabatama, Pierre Hornych, Stefano Mariani
et al.
Safety related to pavement ageing is a major issue, as cracks and holes in the road surface can lead to severe accidents. Although pavement maintenance is extremely costly, detecting a deterioration before its surface becomes completely damaged remains a challenge. Current approaches still use wired sensors, which consume a lot of energy and are expensive; further to that, wired sensors may become damaged during installation. To avoid the use of cables, in this work, a prototype of a Zigbee-based wireless sensor network for pavement monitoring was developed and tested in the laboratory. The system consists of a slave sensor and a roadside unit; the slave sensor sends wireless acceleration data to the master, and the master saves the received acceleration dataset in a csv file. Further data processing can be performed in the master on this acceleration dataset. Two laboratory tests were performed for dynamic calibration and simulating five-axle truck pavement displacement. The preliminary results showed that the Zigbee-based wireless sensor network is capable of capturing the required ranges of displacement, acceleration, and frequency. The ADXL354 sensor was found to be the most appropriate accelerometer for this application, with as small as 155 uA power consumption.
Amalia Moutsopoulou, Georgios E. Stavroulakis, Markos Petousis
et al.
During the past few years, there has been a notable surge of interest in the field of smart structures. An intelligent structure is one that automatically responds to mechanical disturbances by minimizing oscillations after intelligently detecting them. In this study, a smart design that contains integrated actuators and sensors that can dampen oscillations is shown. A finite element analysis is used in conjunction with the application of dynamic loads such as wind force. The dynamic-loading-induced vibration of the intelligent piezoelectric structure is aimed to be mitigated using a <i>μ</i>-controller. The controller’s robustness against uncertainties in the parameters to address vibration-related concerns is showcased. This article offers a thorough depiction of the benefits stemming from <i>μ</i>-analysis and active vibration control in the behavior of intelligent structures. The gradual surmounting of these challenges is attributed to the increasing affordability and enhanced capability of electronic components used for control implementation. The advancement of <i>μ</i>-analysis and robust control for vibration reduction in intelligent structures is amply demonstrated in this study.
Engineering machinery, tools, and implements, Technological innovations. Automation
Ambe Harrison, Njimboh Henry Alombah, Salah Kamel
et al.
This paper outlines the development of a high-performance maximum power point tracking (MPPT)-based solar irradiance estimator for photovoltaic (PV) systems. The suggested estimator is constructed around a simple current–voltage-based algebraic equation that hinges on the operation of the PV system at its maximum power point (MPP). In the realm of MPP operation, the overall system is driven by a nonlinear MPPT controller. To achieve this function, we integrated a hybrid incremental conductance integral backstepping (H-INC-IBS) controller to effectively regulate the PV system. This controller was specially chosen for its powerful potency in maximizing the dynamics of the PV system, leading to heightened robustness against changing environmental conditions. The simulation results are provided to showcase the suitability of the proposed estimator. Furthermore, the estimator was verified under experimental conditions, highlighting its soundness and practicality. Through evaluations and comparisons with the conventional irradiance estimator, this paper aimed to emphasize the superiority of the proposed solar irradiance estimator in providing more accurate estimations of solar irradiance for PV systems operating under MPPT supervision.
Svetlana Morozkina, Petr Snetkov, Mayya Uspenskaya
Amyloidosis is a systemic disease, leading to the disfunction of many organs. There are several clinical and morphological forms of amyloidosis based on the organ-specific nature of amyloid fibril deposition, which is found in the heart, brain, kidneys, spleen, liver, pancreas, thyroid glands, bone marrow and intestines. The nature of organ damage correlates with the types of amyloid fibrils. Thus, damage to the tissues of the heart and kidneys are the most significant factors affecting mortality. The complexity of drug molecule discovery against amyloidosis is connected with the fact that more than 30 proteins are involved in fibril formation. The fact that only two small molecules, namely diflunisal and tafamidis, are clinically used nowadays underlines the complexity in this field of research. The mechanism of action for both drugs include the stabilization of the tetrameric form of transthyretin. The crucial approach for the discovery of drug molecules against cardiac amyloidosis requires the use of predictive models. The main restrictions of most developed in vivo models, however, are related to their reproducibility and cost. Therefore, an in silico approach may be a relatively effective procedure to minimize time and difficulty during the drug discovery process. In this paper, we collected key information which highlights the scope and limitations of the development of an in silico approach.
So far, the relationship between open science and software engineering expertise has largely focused on the open release of software engineering research insights and reproducible artifacts, in the form of open-access papers, open data, and open-source tools and libraries. In this position paper, we draw attention to another perspective: scientific insight itself is a complex and collaborative artifact under continuous development and in need of continuous quality assurance, and as such, has many parallels to software artifacts. Considering current calls for more open, collaborative and reproducible science; increasing demands for public accountability on matters of scientific integrity and credibility; methodological challenges coming with transdisciplinary science; political and communication tensions when scientific insight on societally relevant topics is to be translated to policy; and struggles to incentivize and reward academics who truly want to move into these directions beyond traditional publishing habits and cultures, we make the parallels between the emerging open science requirements and concepts already well-known in (open-source) software engineering research more explicit. We argue that the societal impact of software engineering expertise can reach far beyond the software engineering research community, and call upon the community members to pro-actively help driving the necessary systems and cultural changes towards more open and accountable research.
Power diode with multi-layer compound passivation and dual-P typed diffusion are studied and manufactured. Combine multiple advantages, a power diode with reverse breakdown voltage higher than 2200V, static on-resistance 1.11Ω and leakage current of 8.3mA can be obtained on silicon with a drift layer thickness of 280um and a resistivity of 60Ωcm. Simulation shows multilayer passivation layer with polysilicon contact can effectively change the edge carrier distribution, thus reducing the transverse current generation and improving the voltage withstand. Inserting a light and deep aluminum doped layer into PIN diode can increase the reverse withstand voltage by more than 16% through expanding depletion layer. And the moat structure of the device can further reduce the edge peak electric field, thus reducing the risk of breakdown. This device can be used in switching power supply and inverter.
Diesel engine is a complex nonlinear system. In view of the uncertainty of the model in the process of linearization of diesel engine model and the insufficient robustness of the existing PID controller, the robust control theory is applied to the speed control of diesel engine. Firstly, the quasi-steady state model of diesel engine is locally linearized to establish its state space model. Then, by analyzing the robust stability under friction torque uncertainty, the diesel engine speed H∞ controller is established, and the control law is finally obtained by solving the LMI method. Finally, a simulation experiment is designed in the MATLAB/Simulink environment. The simulation results show that the designed speed controller has strong model fault tolerance, good resistance to external disturbance, and the speed regulation is obviously better than the traditional PID speed controller.
Ultrasoinc acoustic emissions (UAEs) can be used as index of plant water deficit. In order to avoid background noise interference, the UAEs detecting frequency is more than 100 KHz. The relationships between the conduits embolism and transpiration can describe the mathematical relationship of embolism degree and the plant physiologies, namely transpiration-embolism curve (TEC). The plant physiological activities are closely related with body water reserving from 8:00 to 14:00 when it is sunny, light adequate. The UAEs grow rapidly from 9:00 to 11:00, the concave and convex characteristics of Logistic curve are not obvious, the same to inflection point. In general, the maximum of transpiration rate occurs from 9:00 to 11:00, while the UAEs reach the maximum 1h later. Before irrigation, the plant is in the serious water stress conditions, and the soil water content is low, the UAEs are below 50 times before 7:00 and after 18:00, and long duration. After irrigation, the UAEs do not appear the peak, and the UAEs will decrease or disappear, while UAEs appear a small peak 1h or 2h later, the physiological activities will lag a short time, the length of time has the strong relationship with the water stress and embolism degree. Studies have shown that the embolism is sensitive to soil water content conditions, in other words, the potted plant shows the sensitivity of transpiration, and high soil water content can improve the plant embolism vulnerability.
This paper investigated the effect of the modification of 5A zeolite by KCl solution for eliminating Cu2+ from aqueous solutions. The results show that the adsorbent modified with 0.5 M KCl exhibited the best performance, and the removal rate is 82.12%, the adsorption capacity is 53.1 mg/g, when the initial concentration of Cu(II) is 63.4 mg/L, In addition, the extension of the adsorption time will also increase the adsorption capacity. This indicates that KCl can effectively increase the adsorption capacity of Cu2+. It is hoped that this method can be applied to wastewater treatment.