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Comprehensive Analysis of Thermal–Electrical Models for PV Module: A Review of Current Approaches and Challenges
No full textThe independent application of conventional electrical or thermal models is, generally,
not adequate to model the interdependence between temperature distribution, heat transfer
mechanisms, and the electrical performance of Photovoltaic (PV) generators. In this
context, coupled thermal–electrical modeling approaches have recently gained increasing
importance to accurately simulate the PV performance. This work presents a comprehensive
and systematic analysis of electrical, thermal, and coupled thermal–electrical models
developed for PV modules. Electrical models are classified into analytical/physical, semiempirical,
and empirical classes, highlighting their assumptions, parameter requirements,
computational complexity, and applicability at cell, module, and system levels. Thermal
modeling approaches are reviewed by distinguishing lumped parameter and thermal network
models from spatially distributed numerical methods. Particular emphasis is placed on
the ability of these models to represent non-uniform temperature distributions and transient
operating conditions. Furthermore, this review critically examines state-of-the-art coupled
thermo-electrical models, focusing on different coupling strategies, feedback mechanisms,
and levels of spatial resolution. The advantages and limitations of each modeling approach
are discussed in relation to accuracy, computational cost, and suitability for performance prediction,
fault analysis, and reliability assessment. Finally, current research gaps and future directions
are identified, providing a structured framework to guide the selection of the most
appropriate model and the development of more accurate and physically consistent PV modeling
strategies under complex and realistic operating conditions
Organic Amendments for Sustainable Agriculture: Effects on Soil Function, Crop Productivity and Carbon Sequestration Under Variable Contexts
No full textSoil amendments play a critical role in improving soil health and supporting sustainable crop production, especially under declining soil fertility and climate-related stress. However, their impact varies because each amendment influences the soil through different biogeochemical processes rather than a single universal mechanism. This review synthesizes current knowledge on a wide range of soil amendments, including compost, biosolids, green and animal manure, biochar, hydrochar, bagasse, humic substances, algae extracts, chitosan, and newer engineered options such as metal–organic framework (MOF) composites, highlighting their underlying principles, modes of action, and contributions to soil function, crop productivity, and soil carbon dynamics. Across the literature, three main themes emerge: improvement of soil physicochemical properties, enhancement of nutrient cycling and nutrient-use efficiency, and reinforcement of plant resilience to biotic and abiotic stresses. Organic nutrient-based amendments mainly enrich the soil and build organic matter, influencing soil carbon inputs and short- to medium-term increases in soil organic carbon stocks. Biochar, hydrochar, and related materials act mainly as soil conditioners that improve structure, water retention, and soil function. Biostimulant-type amendments, such as algae extracts and chitosan, influence plant physiological responses and stress tolerance. Humic substances exhibit multifunctional effects at the soil–root interface, contributing to improved nutrient efficiency and, in some systems, enhanced carbon retention. The review highlights that no single amendment is universally superior, with outcomes governed by soil–crop context. Its novelty lies in its mechanism-based, cross-amendment synthesis that frames both yield and carbon outcomes as context-dependent rather than universally transferable. Within this framework, humic substances and carbon-rich materials show potential for climate-smart soil management, but long-term carbon sequestration effects remain uncertain and context-dependent
Linking Precipitation Deficits to Reservoir Storage: Robust Statistical Analyses in the Monte Cotugno Catchment (Sinni Basin, Italy)
No full textThis study examines the hydroclimatic controls on reservoir storage dynamics in the Sinni River basin (southern Italy), with a specific focus on the Monte Cotugno dam—the largest earth-fill reservoir in Europe. Using monthly precipitation data (2000–2024) from eight
gauges and standardized indicators (SPI at multiple timescales and SRI for storage), we apply robust trend, correlation, autocorrelation, and causality analyses, supported by advanced preprocessing (TFPW), to disentangle climatic influences from anthropogenic
pressures. Results show a statistically significant and persistent decline in the SRI series, indicating progressive storage depletion, despite stationary or slightly positive trends in precipitation at annual and hydrologically relevant timescales. These findings highlight the dominant role of cumulative operational losses and systemic inefficiencies—rather than sustained climatic drying—as primary drivers of reservoir decline. Granger causality
and lagged-correlation analyses reveal that multi-month to annual precipitation anomalies (SPI-3, SPI-6, SPI-12) exert the strongest influence on storage variations, yet the basin’s ability to convert rainfall into effective reservoir supply is severely constrained by in-
frastructural and management limitations. The study underscores the urgent need to integrate climate-based monitoring with infrastructural modernization and governance reforms to address the combined climatic and anthropogenic pressures increasingly affecting Mediterranean water systems
Darunavir analog precursors target mitochondrial metabolism in multiple myeloma and CLL.
No full textBackground: Multiple myeloma (MM) and chronic lymphocytic leukemia (CLL) are hematological malignancies with poor prognosis due to drug resistance, with no effective therapies. Drug resistance occurs from alterations in microenvironment and metabolism. Impaired mitochondrial metabolism underlies drug resistance in MM and CLL, and drugs targeting mitochondrial respiration show cytotoxicity and increase chemotherapy sensitivity. HIV protease inhibitors like nelfinavir show antitumor activity and resensitize drug-resistant cells to bortezomib and venetoclax. We evaluated effects of two HIV protease inhibitor precursors, BupM-NH2 and BnpM-NH2, on MM and CLL cells and their mechanism.
Methods: The cytotoxicity of the compounds was evaluated in MM (MM1S and RPMI-8226) and CLL (HG3) cell lines, peripheral blood mononuclear cells (PBMCs) and in patient-derived MM cells using MTS assays. Apoptosis was assessed by Annexin V staining and cell cycle analysis using flow cytometry. Western blotting was employed to assess protein expression to investigate the effects of the tested compounds on autophagy, endoplasmic reticulum stress, and respiratory chain function. Cellular metabolic activity was measured using Seahorse analyzer, whereas mitochondrial function and ROS generation were quantified via flow cytometry with MitoSOX and MitoTracker staining.Results BupM-NH2 and BnpM-NH2 at 50 μM decreased MM and CLL cell viability by 60% and 90%, while reducing PBMC viability by 40% and 50%. These compounds inhibited autophagy and mitochondrial respiration, reducing ATP production by 70% in MM, 30–60% in CLL, and 50% in PBMCs. BnpM-NH2 decreased the viability of cells from newly diagnosed multiple myeloma (NDMM) patients by 60% but showed a non-significant 15% reduction in relapsed/refractory multiple myeloma (RRMM) patients, whereas BupM-NH2 had no significant impact. Both compounds induced stress in respiratory chain and mitochondria that may help resensitize drug-resistant MM and CLL
Renewable Energy and Landscape Transformation: A GIS-Based and Ecosystem Services Assessment
No full textAlthough increasing the use of renewable energy is a crucial step in reducing the effects of climate change, assessments of sustainable energy can be supported by the lens of ecosystem services to guarantee the sustainability of renewable energy production at the regional level. Land use policy plays a crucial role in this context, as it has the primary power to transform land from the planning stage to implementation. Furthermore, an environmental policy-based analysis supports urban planners in proposing more sustainable and innovative interventions. Compared to traditional planning approaches, these interventions are relatively new, and there is limited time for trial and error due to the urgency of addressing climate change. Ecosystem service indicators can support urban planners, policymakers, and other stakeholders in fostering win-win collaborations. In this study, we investigate renewable energy sources as a phenomenon that has gained significant importance in urban and energy planning. To geographically examine the integration of renewable energy with habitat quality—one of the primary components of ecosystem services—this article suggests a GIS-based methodology by considering Basilicata region as the case study. Three major sections comprise the methodological approach: First, the regional energy plan’s specified policy limits. Second, the sprinkling index was used to geographically analyze the energy landscape fragmentation to evaluate how land use changes were affected by renewable energy installations. Lastly, the ecosystem services lens will guarantee the sustainability of renewable energy production at the regional level
Role of IoT for Energy Monitoring and Energy Management System in Smart Renewable Energy Community: Concept, Need and Proposed Architecture
No full textThe Renewable energy community has emerged in European countries due to many benefits like social, economic, and environmental. Renewable energy communities contain energy technologies like renewable energy sources, energy storage systems and electric vehicles. It is well known that the utilization of these energy technologies can help to achieve the energy transition, however managing the renewable energy community needs an innovative design and technologies as it undergoes significant and drastic structure. Moreover, there is the main issue of monitoring and energy management system in these communities to check the energy flow and exchanges and associated benefits. In this instance, a fundamental and important requirement for renewable energy communities’ operation is the integration of the current electrical infrastructure enabling information communication and technologies. Information communication and technologies include many diverse areas, but our research work is on the Internet of Things for the energy sector particularly for the renewable energy communities. Internet of Things is a vast and an emerging area, which has become dominant in many areas due to many advantages. Enabling technologies like Internet of Things in renewable energy communities facilitate to check and monitor the data through the Internet of Things enabled components which are necessary to guarantee the operational viability of these renewable energy communities for the energy management and the monitoring system. This would help to analyze the data and associated incentives like for energy self-consumption and energy sharing with RESs and other energy technologies and a consumption profile could be changed after monitoring the data for profit. This article also focusses on the background study relevant to renewable energy communities and Internet of Things, discuss the components for Internet of Things and propose its architecture for the renewable energy communities. This architecture would help the researcher, participants and stakeholders to check, monitor and control the energy flows and the benefits
PAV-2: a new mock-up to investigate niobium membrane-PAV performances optimization in PbLi systems
No full textAdaptive Detection Schemes for Multistatic/Polarimetric Radar Networks
No full textThis paper addresses the design of bespoke adaptive detectors for point-like targets embedded in a sea-clutter-dominated environment using a multistatic/polarimetric radar network. The system consists of one monostatic as well as two co-located and cross-polarized bistatic sensors. The detector design accounts for possible range domain heterogeneity in sea-clutter backscattering, as well as potential functional relationships between the covariance matrices that characterize clutter returns across the bistatic polarimetric nodes. Accordingly, suitable estimates of the nuisance parameters for both monostatic and bistatic measurements are employed to develop adaptive decision rules based on the two-step Generalized Likelihood Ratio Test (GLRT) design criterion. The performance of the proposed receivers is assessed using simultaneously recorded monostatic and cross-polarized bistatic returns collected with the Netted RADar (NetRAD) system. The analysis assesses both the Constant False Alarm Rate (CFAR) behavior and the detection capability. The results show that, despite minor deviations from ideal CFAR behavior when near zero Doppler cells are tested, all the proposed decision rules maintain an overall robust CFAR behavior with respect to the nuisance parameters. In terms of detection capability, the proposed strategies outperform those relying solely on monostatic measurements and demonstrate comparable, or slightly improved, performance with respect to a competing approach confirming the effectiveness and robustness of the devised techniques
Morphometric Analysis and Evolutionary Implications of Badland Basins in Southern Italy
No full textThis study introduces the Badland Dissection Index (BDI), a new morphometric parameter
that quantifies the internal dissection and drainage maturity of badland basins. The index
was applied to 87 calanchi basins developed on marine clays in the Ionian sector of Basilicata
(southern Italy). BDI values range from 0.13 to 0.62, with approximately 65% of the
basins exhibiting values lower than 0.30, indicating mature geomorphic stages dominated
by organized fluvial incision. Pearson correlation analysis shows that BDI is strongly
correlated with compactness and shape indices (r =−0.71 with circularity ratio, r = 0.74
with Gravelius compactness index, GCI), and moderately with relief (r = 0.46 with Melton
ratio), highlighting the primary control exerted by basin geometry on badland dissection. A
principal component analysis shows that compactness-related variables and BDI dominate
the first component, which explains 38.6% of the variance, while hydrological indices define
an independent second component; together the first two components account for 57.4% of
total variance. A multiple regression model confirms GCI as the dominant predictor of BDI
(R2 = 0.58), with relief variables playing a secondary role. Owing to its simplicity, limited
data requirements and clear geomorphic meaning, BDI provides a robust and scalable
tool for comparing badland morphodynamics across semiarid settings and for monitoring
landscape evolution where only medium-resolution topographic data are available
Multi-Decadal Coastal Erosion Assessment and Machine Learning-Based Forecasts from Multi-Mission Satellites: Application to the Ionian Coast of Basilicata (1984–2050)
No full textCoastal erosion is a growing concern along many Mediterranean sandy coasts, particularly where reduced fluvial sediment supply, relative sea-level rise and coastal development coincide. This study uses multi-mission Landsat 5/7/8/9 and Sentinel-2 data in Google Earth Engine to extract long-term shoreline series (1984–2025) from MNDWI-based composites. DSAS-style metrics quantify multi-decadal change, while a supervised linear regression forecasting model—validated against a 2013 orthophoto and an independent 2017–2025 test set using an RMSE-based acceptance criterion—is employed to forecast shoreline positions up to 2050. Using this framework, we reconstruct and forecast shoreline evolution along the ~38 km Ionian coast of Basilicata (southern Italy), a microtidal, sediment-starved littoral that has been affected by significant erosion over the past few decades, threatening natural habitats, infrastructure and economic activities. Results show pervasive erosion over the last four decades, with an average shoreline retreat of ≈47 m along the entire coast, and localized retreats exceeding 400 m, particularly at the mouths of the Agri and Sinni rivers and near the Metaponto sector. Forecasts, under linearity and trend-persistence assumptions, indicate further substantial retreat by 2050 in already critical sectors. Methodologically, this work provides a reproducible framework to inform scenario-based coastal planning in similar Mediterranean environments and the first multi-decadal, spatially continuous satellite-based analysis and machine learning-supported forecast for the Basilicata coast, offering a robust basis for regional coastal management