Wrocław University of Environmental and Life Sciences

Urban landscapes increasingly experience intensified heat, escalating cooling demand and associated energy costs. Allotment gardens, as a multifunctional landscape form, offer various ecosystem services, including local microclimate regulation. However, no quantitative evidence of their economic benefits through reduced cooling demand has been presented, hindering their integration into urban planning. We address this gap by assessing the economic impact of allotment gardens on cooling energy savings in Wrocław, Poland. Using high-resolution hourly air temperature (T-air) data from 2014 to 2024, cooled zones of allotment gardens were delineated using a watershed algorithm. Then, the Cooling Degree Days (CDDs) of buildings within these zones were compared with those of reference buildings, considering residential and non‑residential buildings. Based on the CDD–annual cooling demand relationship, potential electricity savings were estimated. The results show that buildings adjacent to allotment gardens exhibit lower T-air by a median of 0.34°C (max 2.2°C), reducing annual total CDD by a median of 14.6 per building. This translates to a median annual cooling demand reduction of 11.1 kWh/m2 and median annual cost savings of €0.23–0.52/m2 for air‑conditioned buildings (up to €15,673 for a single allotment complex). Cumulatively, allotment gardens potentially saved €211,220–486,723 in cooling costs (10‑year average electricity price: €0.1059/kWh), with savings increasing over time. Importantly, cooling benefits depended not only on the garden’s presence but also on its spatial context. This study provides quantifiable evidence for urban planners on the economic value of allotment gardens to support climate adaptation strategies.

Human mobility analysis increasingly relies on large-scale trajectory data, yet results remain highly sensitive to preprocessing choices that are rarely applied in a consistent or well-documented manner. Stop detection, which transforms raw location traces into sequences of visited places, is a particularly critical and bias-prone step, with parameter choices often guided by dataset-specific conventions. The absence of systematic and transparent parameter-selection practices risks introducing methodological bias and limits the comparability and reproducibility of mobility studies. Here, we introduce OptimalStop, a model-agnostic optimisation framework for stop detection that exploits a predictability–complexity trade-off in human mobility trajectories to guide preprocessing. OptimalStop operates as a higher-level tuning layer applicable to any method that produces symbolic mobility sequences. By framing stop detection as a multi-objective optimisation problem, the framework identifies compact Pareto-optimal sets of parameter configurations that balance predictability and structural complexity, avoiding over-fragmented or over-aggregated representations. We evaluate OptimalStop on both real-world and synthetic mobility datasets using multiple stop detection algorithms, and confirm through controlled synthetic experiments that the predictability–complexity trade-off is structurally grounded in recurrent return-to-place behaviour rather than being a mathematical artefact of discretisation. The results show that OptimalStop consistently retains near-optimal solutions while substantially reducing the effective parameter search space within a reasonable computational time. An optional automatic selection strategy further demonstrates that high-quality stop detection can be obtained without ground truth, outperforming or matching commonly used sensitivity-based tuning approaches. We release OptimalStop under an open licence, providing a transparent and reproducible framework for parameter selection that supports more structured mobility data preprocessing. While demonstrated here for stop detection, the underlying predictability–complexity balance offers a general perspective for mitigating methodological bias in mobility analytics.

Extracellular vesicles (EVs) are lipid bilayer-enclosed particles released by both eukaryotic and prokaryotic cells and represent an evolutionarily conserved system of intercellular communication. By transporting bioactive cargo, including proteins, lipids, microRNAs, EVs enable the transfer of molecular signals between cells, thereby regulating immune homeostasis and inflammatory responses. In allergic diseases, EVs have emerged as key mediators linking epithelial barriers, immune cells, and the microbiome. EVs derived from epithelial, immune, and microbiota-associated cells may contribute to the initiation, amplification, and persistence of allergic inflammation by modulating barrier integrity, immune cell polarization, and cytokine signaling pathways. Disease-specific alterations in EV cargo reflect underlying pathogenic mechanisms, positioning EVs as promising non-invasive biomarkers for disease diagnosis, stratification, and monitoring. In parallel, accumulating experimental evidence highlights the therapeutic potential of EVs as cell-free immunomodulatory agents capable of suppressing allergic inflammation and promoting immune tolerance. This review synthesizes current knowledge on extracellular vesicles across three major allergic diseases: asthma, atopic dermatitis, and food allergy, integrating mechanistic insights with diagnostic and therapeutic advances. By incorporating highly recent literature and covering a broad spectrum of EV sources and engineered vesicle-based strategies, the review provides a comprehensive overview of how EV-mediated cellular communication translates into clinically relevant applications in allergy.

Most of the geodetic satellites observed with Satellite Laser Ranging (SLR) are placed in near-circular orbits. This paper uses simulations to discuss the advantages of launching a satellite into an eccentric orbit from the perspective of the quality of geodetic parameters. For the first time, we evaluate how eccentricity, inclination, and semi-major axis affect the accuracy of determining SLR-based parameters, including station coordinates, Earth rotation parameters, geocenter coordinates, and low-degree Earth's gravity field coefficients. We found that eccentric orbits substantially improve gravity field recovery, especially for low-degree zonal, tesseral, and sectorial coefficients. The greatest impact is observed for the odd-zonal terms: the formal errors for C30 and C50 decrease by over 90

This study investigated the influence of rice husk biochar (RHBC) prepared at 300, 500, 700, and 900 degrees C (BC300, BC500, BC700, and BC900) on the anaerobic digestion (AD) of food waste. The properties of RHBC related to AD, including high heating value, specific surface, porosity, elemental mapping through scanning electron microscopy - energy dispersive X-ray spectroscopy (SEM-EDX), thermal decomposition, electrical conductivity (EC), elemental composition, functional groups, and mcrA gene copy numbers, were investigated. The specific surface area and mcrA gene copy numbers are listed as a property of the RHBC relating to AD. Parameters investigated during the AD process were biomethane potential, biomethane production rate, biodegradability, variation in the total volatile fatty acids, specific volatile fatty acids, pH, EC, and chemical oxygen demand, functional groups, changes in the nitrite, nitrate, ammonia, and nitrogen concentration. The study show that increase in the pyrolysis temperature (300 to 900 degrees C) resulted to the devolatilization and heteroatom removal in the RHBCs, resulting in decreased concentrations of H, N, and S, while the C content was relatively consistent across pyrolysis temperatures (38.09% to 39.62%). The reactors dopped with BC300 had the highest cumulative BMP at 335.76 mL/g-VS followed by BC500, BC700, and BC900 at 325.63, 316.76, and 311.36 mL/g-VS, respectively. These findings suggest that, compared to highly carbonized biochars made at higher temperatures, lowertemperature biochars, which retain more functional groups and labile components, offer better conditions for the production of biomethane.