Search Results (2,513)

This article presents a technical and economic analysis of a photovoltaic system operating in conjunction with a heat pump in a single-family home. The aim of this study was to compare the cost-effectiveness of two prosumer billing systems currently in use in Poland, net metering and net billing, implemented in accordance with the provisions of the Renewable Energy Sources (RES) Act and the Energy Law and to assess the effectiveness of a proprietary algorithm for managing surplus electricity produced by the photovoltaic system. The energy performance of the facility was determined using ArCADia Termo 11.1 software, while energy and economic calculations were performed using Microsoft Excel 365 and a developed heat pump control algorithm. The algorithm is based on an analysis of the building’s energy balance with a 15 min resolution and utilizes data on outdoor temperature, energy production from the PV system, building heat loss, heat pump operating parameters, and energy self-consumption. Its goal was to maximize the use of energy produced for the building’s own needs by appropriately controlling the heat pump and storing surplus energy as heat stored in domestic hot-water tanks. The annual electricity consumption of the analyzed building was 6902.18 kWh, of which 3724.13 kWh was for heating and domestic hot water provided by the heat pump. The algorithm reduced grid energy consumption by approximately 900 kWh per year and achieved a self-consumption level of 12.73 (%). Full article

Road traffic surveys require sensing systems that can be deployed rapidly without modifying the road surface or requiring a permanent power connection. This paper presents the design, embedded implementation, and evaluation of a low-power roadside magnetic sensor that performs vehicle-event detection and classification directly at the edge. The sensing node integrates two RM3100 three-axis magnetometers (PNI Sensor, Santa Rosa, CA, USA) with an NXP MK22FN512VLH12 microcontroller (NXP Semiconductors N.V., Eindhoven, The Netherlands) based on a 120 MHz Arm Cortex-M4F core with 512 kB Flash and 128 kB SRAM. Magnetic-field data are acquired at 250 Hz and processed locally using baseline removal, low-pass filtering, signal-energy calculation, and peak-based event detection. Detected magnetic signatures are classified using an integer-quantised one-dimensional convolutional neural network implemented directly on the microcontroller. The model processes four synchronised 512-sample channels representing the three magnetic-field axes and their combined signal energy. Model development was supported by approximately 50,000 annotated events obtained from 36 h of real-world traffic measurements at eight locations. The selected model achieved an overall classification accuracy of 91.1% for the considered operational categories. The implemented network requires 288,128 multiply–accumulate operations per inference, while its quantised weights and biases occupy approximately 23 kB of Flash memory. Complete three-axis event signatures are stored locally for subsequent verification, whereas only the timestamp and predicted vehicle category are transmitted through the wireless interface. Based on the capacity of the applied LiFePO₄ battery and the estimated consumption of the implemented hardware, the expected autonomous operating period is approximately 41 days. The results demonstrate the feasibility of integrating magnetic sensing, embedded signal processing, and Edge AI on a conventional resource-constrained Cortex-M4 platform for non-invasive road traffic monitoring. Full article

Construction work zones are a difficult case for open-vocabulary semantic segmentation. Their layouts are temporary, safety-relevant objects that are often small and long-tailed, and generic models readily confuse them with background. We address these failures inside an LoRA adapter space rather than retraining the backbone. Using only ROADWork training data, we audit a CAT-Seg RoadWork LoRA for false-positive- and recall-dominated cases and pair them with anchor images to train a residual adapter. Path-conditioned per-layer LoRA fusion (PC-PLF) then distributes a global correction budget across adapted layers using each layer’s first-order tangent magnitude along the stored factor path. Under group-disjoint out-of-fold evaluation on ROADWork, the complete method raises the mIoU from 61.72 for the RoadWork LoRA baseline to 62.29, with a shared-budget allocation gain of 0.33 mIoU over uniform fusion. Most of the total improvement appears before per-layer allocation. Uniform residual fusion contributes 0.69 points over the baseline, confirming that failure-driven residual training supplies the larger share; PC-PLF contributes a smaller allocation effect when tested on the same trained base-residual pair. The allocation effect is reproducible across four curation rules but near zero under SAN architecture transfer and MUSES second-target-domain evaluation. Three-group and text-weighted controls do not recover the full gain. Improvements concentrate in several long-tail safety classes. Cross-architecture, cross-dataset, and calibration audits define the operating regime rather than universal advantage. Residual curation supplies the larger share of the improvement; layer-wise allocation contributes a smaller, pair-specific gain. Full article

Biometric recognition systems are fundamental to modern identity management and access control. However, the security and privacy of these systems are severely compromised when adversaries gain access to stored biometric templates, given the immutable link between individuals and their biometric traits. This study presents a benchmark analysis of the trade-offs between dimensionality and system performance in the context of securing iris biometric templates using Fully Homomorphic Encryption (FHE). We examine the impact of Principal Component Analysis (PCA) as a dimensionality reduction technique to enable encrypted-domain computation while maintaining recognition accuracy and reducing computational overhead. In lieu of introducing a new dimensionality reduction technique, this study rigorously benchmarks the applicability of PCA in balancing computational efficiency and recognition accuracy for biometric systems operating under Fully Homomorphic Encryption (FHE) constraints. We evaluate our method on various iris databases, including CASIA-V1, CASIA-V3, UBATH, and IITD. Our approach achieves a $100\%$ True Acceptance Rate (TAR) on CASIA-V1, CASIA-V3, and UBATH and a $99.38\%$ TAR on the IITD database at a $0.1\%$ False Accept Rate (FAR), with a feature dimensionality of 250. This work advances iris recognition security by combining privacy measures with dimensionality reduction for improved authentication accuracy. Full article

In this paper, a pixel architecture that can be re-programmed to favor low noise, high speed, high dynamic range, or other characteristics desired in scientific imaging is proposed. Termed a programmable readout (PRO) pixel, this novel design combines a pinned photodiode, a deep-buried-channel CCD router, and multiple in-pixel amplifiers of different characteristics. By clocking the CCD router with different sequences, photogenerated charge can be steered to an appropriate readout amplifier given the application. The CCD router can be further programmed to store charge from multiple frames. The concept of such a pixel and its operation are described in detail and TCAD simulations aid in this discussion. To demonstrate this concept of programmability, a test chip is made with a CCD router and two in-pixel amplifiers: a floating diffusion amplifier (FDA) for fast readout and high-illumination imaging and a floating gate amplifier (FGA) for a low-noise Skipper-in-CMOS readout for low-light imaging. The test chip contains a 36 × 94 array of 20 µm pixels and contains 9 different pixel variants for experimental analysis. At the end of paper, preliminary images captured from the different amplifiers are presented which demonstrate the ability to move charge to the selected readout while showcasing the different output characteristics of the two readout amplifiers. Furthermore, the output image from the Skipper-in-CMOS is compared across different numbers of non-destructive samples to demonstrate its noise-reduction capability. Full article

As the global energy sector and maritime industry transition toward carbon neutrality, Carnot batteries have emerged as a critical technology for flexible, long-duration energy management. Carnot batteries, which operate on a Power-to-Heat-to-Power principle, present a highly promising alternative to conventional electrochemical batteries. The present study provides a review of Carnot battery architectures, systematically evaluating their thermodynamic cycles, working fluids, Thermal Energy Storage media and key turbomachinery components. A comparative assessment of the current literature illustrates that system selection requires balancing round-trip efficiency, Energy Storage Density and Technology Readiness Level. According to the findings of the present study, high-temperature Brayton cycles offer robust baseline efficiencies of 60–80% whereas subcritical Rankine cycles benefit from commercial maturity and can achieve efficiencies exceeding 200% when integrated with cryogenic heat sinks like LNG. It should be clarified that efficiency values exceeding 100% represent “Apparent Round-Trip-Efficiencies (RTE)” resulting from the thermodynamic contribution of external exergy streams, such as LNG cryogenic cold, rather than standalone cycle efficiencies, which are strictly below 100%. In addition, volumetric energy density varies drastically based on the physical phase of the storage medium, scaling from under 1 kWh/m³ for unpressurized water to over 385 kWh/m³ for advanced thermochemical systems. Although most configurations currently remain in the prototyping phase, the technology holds transformative potential for the maritime sector. Carnot batteries can deliver a self-contained, zero-emission electrical power supply to cover the vessel’s electrical load requirements during harbor stays and transit within Emission Control Areas (ECAs) by dynamically capturing and storing shipboard waste heat during open sea transit. Full article

Risk registers remain static governance artefacts, manually maintained and weakly coupled to operational evidence. While organisations generate continuous security telemetry from vulnerability scanners, incident reports, and audit findings, this evidence is rarely synthesised into coherent, evolving risk structures. Existing approaches address fragments of the problem: SIEM systems correlate events but do not construct risk registers; GRC platforms manage risk documentation but depend on manual entry; and LLM applications assist with summarisation but introduce non-determinism incompatible with governance requirements. This paper presents a hybrid architecture that separates stochastic LLM-based extraction from deterministic risk correlation and aggregation. The system ingests heterogeneous evidence, extracts structured claims via schema-bounded LLM processing, and correlates events into stable risk trees using anchor-based tiered matching. All correlation and projection operations are deterministic and replayable. The contribution is an architectural design pattern for integrating probabilistic inference into governance systems without compromising auditability. The walkthroughs run on a reference prototype. Replaying the stored evidence three times rebuilt the same register state, and admission scores matched the values the rules predict. An injected malformed extraction was quarantined; the register did not change. Full article

The MENRO: Monitoring and Management System is a digital solution developed to modernize the manual operations of the Municipal Environment and Natural Resources Office (MENRO). The system provides a secure and user-friendly platform designed to streamline document handling, automate monitoring tasks, and ensure accurate environmental data management. This project specifically aims to: (1) develop a user-friendly interface that enables seamless document and record management, allowing administrators to effortlessly add, edit, update, and store essential information, and (2) design a system that is capable of automatically generating comprehensive lists of apprehension receipts, including violations and penalties issued to establishments under MENRO’s jurisdiction. These functions address long-standing inefficiencies in manual monitoring, recordkeeping, and compliance tracking. The system was evaluated using the ISO/IEC 25010:2011 software quality standards, focusing on functional suitability, performance efficiency, reliability, usability, compatibility, maintainability, portability, and security. Results show high usability and strong security features, ensuring that the platform meets operational needs while safeguarding sensitive environmental and legal records. By integrating real-time data access, automated reporting, and digitized forms used in environmental compliance checks, the system enhances MENRO’s capability to uphold environmental governance with greater transparency, accuracy, and accountability. Full article

Long-term dry storage is a key stage in managing spent nuclear fuel (SNF) from sodium-cooled fast reactors, for which monitoring supports aging management and future fuel-cycle decisions. This study presents a monitoring-based condition assessment of BN-350 SNF stored in 60 UKKh-123 metal–concrete casks at the dedicated long-term container storage site of the «Baikal-1» research reactor complex. The casks were transported to and placed at this site in 2010, and their condition has been monitored annually. The assessment integrates dosimetric records, full-container campaigns from 2018 and 2023, and detailed inspection data for selected packages. The monitored indicators included external gamma dose equivalent rate, surface contamination, external surface temperature, visual condition, and leak-tightness of detachable sealing connections. For all 60 packages, the maximum gamma dose equivalent rates were 5.8–25.7 μSv/h in 2018 and 2.0–20.3 μSv/h in 2023, far below the nominal 500 μSv/h value specified for normal operation by the technical operating conditions. Surface contamination remained within applicable limits, and external surface temperatures were below the nominal 84 °C operating limit. A detailed inspection confirmed low gamma dose rates and satisfactory leak-tightness. The results support continued controlled storage with periodic monitoring and safety reassessment. Full article

Social commerce platforms increasingly serve as ecosystem orchestrators, coordinating technological infrastructures, financial systems, governance mechanisms, and algorithmic resource allocation. While prior research has predominantly focused on consumer behavior, limited attention has been given to merchant vulnerabilities arising from platform dependency. This study investigates operational vulnerabilities within the TikTok Shop ecosystem using 8993 negative merchant reviews collected from the Google Play Store. BERTopic, a transformer-based contextual topic modeling approach, was employed to identify latent vulnerability themes embedded in merchant complaint narratives. The results revealed 28 interpretable topics, which were aggregated into six higher-order vulnerability dimensions: Infrastructural Instability, Ecosystem Integration Vulnerability, Financial Vulnerability, Algorithmic Dependency, Coordination Breakdown, and Governance Asymmetry. Among these dimensions, Infrastructural Instability (13.59%) and Ecosystem Integration Vulnerability (13.44%) emerged as the most prominent sources of merchant dissatisfaction. The findings indicate that merchant vulnerability extends beyond isolated operational issues and is embedded within interconnected technological, financial, governance, algorithmic, and coordination structures. This study contributes to platform ecosystem research by providing empirical evidence and a structured interpretation of how platform-controlled resource orchestration may be associated with merchant vulnerability alongside value creation, while highlighting ecosystem integration as a significant source of risk during platform transformation. Full article

Implementation of real-scale systems for rainwater harvesting, treatment and reuse of greywater in residential areas is challenging because several factors should be considered for full adoption and satisfaction of decision-makers, urban developers and users. Technological, construction, operational, social (acceptance), impact on water resources, regulatory, and economic factors are involved. This study presents the implementation of a full-scale hybrid system for rainwater harvesting, treatment and reuse of greywater in a residential building located in Monterrey, Nuevo León, Mexico. The study included intervening in the hydraulic infrastructure of an already constructed residential building for collecting greywater, harvesting and collecting rainwater, designing and constructing an 80 m² controlled natural soil treatment system (CNSTS) and a 65 m³ storage tank for treating and storing rain and greywater. Furthermore, the full-scale hybrid system was monitored under real operating conditions for a two-month period to assess its performance. Results showed that the CNSTS has the potential to replace up to 2835 m³ year⁻¹ of potable water, equivalent to 65% of the building’s annual water consumption. The CNSTS achieved removal efficiencies of up to ~90% for Chemical Oxygen Demand, 90% for surfactants, and 50% for total nitrogen. Most of the measured parameters complied with the corresponding limits established by the Mexican standards NOM-003-SEMARNAT-1997 for non-potable water reuse, NOM-001-SEMARNAT-2021 for wastewater discharges, and NOM-127-SSA1-2021 for potable water with the exception of methylene blue active substances (surfactants), which exceeded the permissible limit during the initial monitoring stage, highlighting the need for further optimization of the system’s vegetative cover. Based on these findings, conceptual designs and preliminary evaluations were conducted for additional buildings, resulting in potable water substitution rates above 90% with investment payback periods of 2 to 5 years, depending on the water demand and the water catchment potential. Full article

Risk assessment of Unmanned Aerial Vehicle (UAV) path planning is a crucial step towards ensuring a safe UAV operation. However, achieving a reasonable risk assessment of UAV flight paths remains challenging because it involves multiple responsibilities and processes that extend beyond a single agency or organization. Additionally, it must balance various complex factors and data from social, technical, political, and economic sources. Most existing works on flight path planning evaluate flight risks at a global level and generalized geometric representations of the UAV operating environment with respect to the current applicable UAV regulations. However, geometric information alone does not provide sufficient insight for comprehensive and safe path planning. Therefore, there are other ideas and concepts for using semantic data to characterize objects, obstacles and actions in the UAV environment. Incorporating semantic information into path planning enables more meaningful scene descriptions and a better representation of relevant constraints, obstacles within UAV environment that may influence the safety level of UAV operation, and the relevant risk assessment process. In this paper, we propose the development of methods and frameworks integrated into a flight planning prototype designed to generate safe two-dimensional UAV routes within a local, fine-grained planning context. The prototype incorporates safety considerations to enable a comprehensive assessment of UAV operational risks. It leverages both geometric and semantic datasets to characterize objects and obstacles within the UAV environment. These datasets are processed and stored in a relational database to support structured access and long-term usability. All concepts and experiments were implemented using datasets from a study area in the city of Brunswick, Germany. Full article

Background: Tetranectin (TN) is a protein that plays a role in tissue remodeling. In adults with heart failure, TN has a better prognostic value compared to B-type natriuretic peptide (BNP). We aimed to examine changes in TN in neonates following the Norwood operation and explore its prognostic value. Methods: Retrospective observational pilot cohort study in a tertiary pediatric cardiac intensive care unit. Neonates who underwent the Norwood procedure and had stored plasma samples in our biorepository were screened for inclusion. TN and BNP levels were measured by ELISA at five different time points in relation to cardiopulmonary bypass (CPB) in 26 neonates who met the inclusion criteria. The primary outcomes were time to lactate clearance and duration of inotropic support. Results: Univariate analysis showed that higher TN levels at 0 and 4 h were associated with a longer duration of inotropic support. Logistic regression analyses comparing TN levels at different time points with duration of inotropic support showed that TN level at 4 h post-CPB was a significant predictor (p = 0.04) for a longer duration of inotropic support with an AUC of 0.876. Conclusions: In this pilot cohort, higher observed TN concentrations in the early post-operative period were associated with longer duration of inotropic support. TN was not found to be significantly associated with time to lactate clearance. These exploratory findings require validation in larger prospective studies before TN can be considered for clinical prognostication. Full article

Hydrogen production via polymer electrolyte membrane (PEM) water electrolysis has attracted considerable attention as a promising technology to store renewable electricity and combat global warming. Although PEM water electrolyzers can produce high-purity hydrogen at high current densities, the use of low-purity water leads to device degradation because metal ions from the water are deposited on the membrane, thereby increasing its resistance and operating voltage. In this study, an in-situ recovery method was developed, in which degraded PEM water electrolysis cells were chemically treated without disassembly. Cells after degradation were subjected to a 24-h chemical treatment with either a strong acid (1.0 mol/L nitric acid) or a weak acid (12.9 and 1.0 mol/L phosphoric acid), followed by the supply of ultrapure water for 72 h. Recovery was evaluated using cell voltage measurements, while scanning electron microscopy (SEM)-dispersive X-ray spectroscopy (EDX) and inductively coupled plasma (ICP) analyses were performed to investigate membrane morphology, elemental distributions, and metal ion removal. Among the tested acids, 12.9 mol/L phosphoric acid showed the highest voltage recovery performance, achieving a 90% recovery ratio immediately after treatment (0 h). Moreover, a comparison of the voltage recovery ratios at 1 h post-immersion suggests that higher hydrogen ion concentrations are more effective for the recovery of degraded PEMs. These findings demonstrate that in-situ acid treatment can restore the performance of contaminated PEM water electrolyzers without disassembly and may provide a practical approach for extending cell lifetime and reducing maintenance requirements. Full article

Symbolic regression and symbolic classification generate explicit mathematical expressions that combine predictive modelling with direct model interpretability. However, symbolic learning based on genetic programming can be computationally expensive because large populations of candidate expressions must be repeatedly evaluated over multiple generations. This paper presents FastSymbolicGP, a lightweight Python library for symbolic regression, binary classification, and multiclass classification through a compact, scikit-learn-compatible interface. The library implements tree-based genetic programming, protected mathematical operators, tournament selection, subtree crossover, subtree, hoist, and point mutation, elitism, validation-aware model selection, adaptive parsimony, expression complexity analysis, and Numba-compiled postfix evaluation. FastSymbolicGP was evaluated through 940 successful benchmark runs covering real-world scientific regression, binary and multiclass classification, physical law recovery, dynamical system identification, parameter sensitivity, ablation, and scalability. Across four real-world scientific regression datasets, FastSymbolicGP achieved the highest mean test $R^2$ on every dataset and obtained significantly better pooled paired results than gplearn and PySR under the evaluated configurations. These results are specific to the selected hyperparameters, primitive sets, stopping criteria, and computational budgets, and should not be interpreted as evidence of universal superiority. In binary classification, it achieved a mean balanced accuracy of 0.8507, compared with 0.7458 for gplearn, while validation-based threshold optimization and class weighting further improved performance under severe class imbalance. FastSymbolicGP also achieved competitive multiclass and dynamical system results while generally producing substantially simpler models than gplearn. In the scalability experiment with 50,000 samples, FastSymbolicGP was approximately 6.20 times faster than PySR and 1.63 times faster than gplearn, while obtaining predictive performance nearly identical to PySR. physical law experiments showed that nondimensionalization increased the dimensional validity rate of recovered FastSymbolicGP expressions from 24% to 96%, while reducing runtime and expression complexity. Analysis of the stored equation pools further showed that near-optimal model selection reduced symbolic complexity by an average of 28.6% when a simpler candidate was available, with negligible predictive degradation. These results indicate that FastSymbolicGP provides a practical balance of predictive performance, computational efficiency, task coverage, and symbolic interpretability for reproducible scientific and machine learning applications. Full article