This paper evaluates the technical, economic, and environmental feasibility of residential prosumer implementation in Sarajevo, Bosnia and Herzegovina, through the integration of photovoltaic (PV) generation and battery energy storage. A simulation‑based assessment was performed in HOMER Pro using a measured 12‑month household load profile with an average daily consumption of 11.25 kWh and a peak demand of 2.85 kW. Six system configurations were investigated, consisting of 3 kW, 4 kW, and 5 kW PV installations, each analyzed with and without a 5.04 kWh lithium‑ion battery, over a 25‑year project lifetime under a zero‑credit export scheme. Among the analyzed configurations, the 3 kW PV system without battery storage achieved the lowest Net Present Cost (NPC) and Levelized Cost of Energy (LCOE) of 0.137 BAM/kWh, representing a 28% reduction compared to the grid tariff, with a simple payback period of 17 years and the highest Internal Rate of Return (IRR) of 3.8%. The 4 kW PV system provided the most balanced operational performance, generating 4,954 kWh annually with imports and exports nearly equal, yielding an LCOE of 0.139 BAM/kWh and a 20‑year payback. The 5 kW PV system generates highest total renewable energy and full annual carbon neutrality, but its surplus generation provided limited additional financial benefit under the current net‑metering framework, which only offsets future bills rather than generating revenue. Battery storage integration substantially enhanced energy autonomy by increasing self‑consumption and renewable fraction (from 52% to 61% for the 3 kW system, and from 57% to 68% for the 4 kW system), but it raised LCOE to 0.198–0.202 BAM/kWh due to higher capital investment. The environmental assessment demonstrated that PV adoption significantly reduces greenhouse gas emissions, with the 3 kW system lowering annual CO₂ emissions by ~76%, the 4 kW system by ~92%, and the 5 kW system achieving net‑negative emissions. The results demonstrate that under zero‑sellback regulatory frameworks, PV sizing must prioritize direct self‑consumption matching. Moderate‑capacity PV installations therefore represent the most economically viable investments, while battery storage serves primarily as a driver of energy independence rather than immediate financial gain.

Fuzzy inference systems have demonstrated considerable promise for EEG-based cognitive state monitoring in neurodegenerative conditions. However, two design decisions significantly influence system performance and clinical applicability: the choice of inference architecture (Takagi-Sugeno vs Mamdani) and the method of rule and membership function generation (manual expert-driven vs data-driven automated). This paper presents a comparative analysis of both dimensions in the context of an EEG-based Alzheimer’s disease monitoring system operating on the ds004504 OpenNeuro dataset (88 subjects: 36 AD, 23 FTD, 29 HC). A Takagi-Sugeno system, implemented as a hybrid FSM-Fuzzy architecture, is compared against a Mamdani equivalent across four axes: inference transparency, output interpretability, computational complexity, and suitability for FPGA deployment. The limitations of manual membership function tuning are then examined through failure case analysis, and an Adaptive Neuro-Fuzzy Inference System (ANFIS) proof-of-concept is presented, demonstrating automated rule and MF generation from the delta/alpha ratio (DAR) biomarker. ANFIS training on 88-subject DAR data (70 training, 18 test) achieved minimal training RMSE of 0.392 and validation RMSE of 0.391. Data-driven MF centers differ substantially from manually designed centers — shifts of 2.754σ, 0.455σ, and 2.055σ for LOW, MEDIUM, and HIGH MFs respectively — quantifying the representational gap that expert tuning introduces. These findings motivate a hybrid design strategy: ANFIS for offline MF calibration, TS for online FPGA inference, and Mamdani for clinical output presentation.

The present work aspires to contribute to the discussion on the relationship between macroscopic measurements and microstructure, helping establish a methodology that will allow the quantitative assessment of the effect of strain on magnetic properties in the plastic deformation regime. In particular, we study the effect of strain on the magnetization process as a result of varying the anisotropy profile at the grain level. Results on micromagnetic calculations of hysteresis loops for various configurations of magnetic anisotropy are shown and discussed against the interplay between the energy terms involved in the calculations, namely anisotropy, demagnetizing, and exchange. The results are in line with previously obtained results using vector Preisach modeling with the Stoner–Wohlfarth model acting both as a switching and rotation mechanism. The hysteresis loop phenomenology is consistent with the emergence of a hard phase in the form of a boundary around soft grains which is assumed to be the result of the onset of compressive stresses in the plastic region. Future research will be oriented toward the study of the effect of the secondary peak in differential permeability, which is observed experimentally in the plastic deformation region, and its dependence on the angle of misalignment between the hard boundary and the soft grain.

Solar exposure of streets and parking spaces in dense urban areas varies significantly due to the infrastructure: buildings, parks, tunnels, multistorey car parks. This variability leaves space for both real-time and offline route and parking optimization for solar-powered vehicles. In this chapter we present Solar Car Optimized Route Estimation (SCORE), our optimization system based on historic and current solar radiance measurements. In addition to the comprehensive review of SCORE, we offer a new perspective on it by embedding it in the bigger picture of smart cities (SC): we analyze SCORE's relationship with the smart power generation and distribution systems (smart grid), novel transportation paradigms and communication advancements. While the previous work on SCORE was focused on technical challenges which are described in the first part of this chapter (optimization, communication, sensor data collection and fusion), here we proceed with a systemic approach and observe a SCORE-equipped unit in the near-future society, examine the sustainability of the model and possible business models based on it. We consider the problem of vehicle routing and congestion avoidance using incentives for users on non-critical journeys and co-existence of SCORE and non-SCORE using vehicles. Realistic pointers for SCORE-aware design of infrastructure are also given, both for improved data collection and improved solar exposure while considering trade-offs for non-SCORE users.

Abstract Poorly regulated and insufficiently supervised medical devices (MDs) carry high risk of performance accuracy and safety deviations effecting the clinical accuracy and efficiency of patient diagnosis and treatments. Even with the increase of technological sophistication of devices, incidents involving defibrillator malfunction are unfortunately not rare. To address this, we have developed an automated system based on machine learning algorithms that can predict performance of defibrillators and possible performance failures of the device which can affect performance. To develop an automated system, with high accuracy, overall dataset containing safety and performance measurements data was acquired from periodical safety and performance inspections of 1221 defibrillator. These inspections were carried out in period 2015–2017 in private and public healthcare institutions in Bosnia and Herzegovina by ISO 17,020 accredited laboratory. Out of overall number of samples, 974 of them were used during system development and 247 samples were used for subsequent validation of system performance. During system development, 5 different machine learning algorithms were used, and resulting systems were compared by obtained performance. The results of this study demonstrate that clinical engineering and health technology management benefit from application of machine learning in terms of cost optimization and medical device management. Automated systems, based on machine learning algorithms, can predict defibrillator performance with high accuracy. Systems based on Random Forest classifier with Genetic Algorithm feature selection yielded highest accuracy among other machine learning systems. Adoption of such systems will help in overcoming challenges of adapting maintenance and medical device supervision mechanism protocols to rapid technological development of these devices. Due to increased complexity of healthcare institution environment and increased technological complexity of medical devices, performing maintenance strategies in traditional manner is causing a lot of difficulties.

Noninvasive load monitoring have been investigated by researchers for decades due to its cost-effective benefits. Upon introduction of smart meters, obtaining data about power consumption of households became easier. Numerous different techniques have been applied on the power consumption data to gain useful information out of it. This study applies machine learning techniques (Bayes network, random forest and rotational forest) to determine the operation state of households, where households are assumed to be either in ON or OFF state. Tracebase power consumption signature repository was used to train and test proposed machine learning models. Tracebase dataset was preprocessed to generate 4 different datasets. Test results have shown that these machine learning algorithms are able to estimate operation state with high accuracy and Bayes network shows outstanding performance among them with overall accuracy of 95%. Proposed method is extremely cost-effective for load monitoring and could replace some of the physical sensors in the smart houses.