Identification and characterization of antimicrobial peptides derived from venom gland transcriptome of scorpion Superstitionia donensis.

Rental housing plays an increasingly vital role in urban and regional housing systems worldwide, particularly amid growing affordability pressures and declining homeownership rates. In Australia, the private rental sector now accommodates nearly one-quarter of all households, reflecting demographic shifts and sustained market constraints. Yet despite its scale and importance, the quality and safety of rental housing remain uneven, with mounting concerns about compliance with legal minimum standards. Ensuring rental housing quality not only improves living conditions but also contributes to property value and broader community well-being. However, persistent non-compliance continues to undermine regulatory efforts to provide safe and fair housing. In this study, we examine the 14 minimum standards defined by the Victorian State Government for rental property management through a socio-spatial lens. Using audit data from more than 11,000 property inspections across Victoria, we integrate compliance records with geographic and socio-economic indexes to assess spatial and social disparities across suburbs. Our findings show that overall compliance has declined in recent years, falling below 40% in early 2024. We also identify significant variation in compliance by location and agency, with suburbs in lower socio-economic areas less likely to meet critical standards. Through exploratory data analytics, we demonstrate how integrated datasets can support evidence-based regulation, promote transparency, and guide targeted policy interventions. Our results highlight the need for a data-driven, equity-focused approach to rental housing governance, especially in under-resourced and socioeconomically disadvantaged communities. • Rental compliance in Victoria fell from 76% to 40% between 2021 and 2024. • Compliance rates vary widely by region, socio-economic status, and agency. • Probit models show agency scale and SEIFA strongly affect compliance. • Results support equity-focused reforms to improve rental housing quality

Efficient identification of high-value molecules under limited experimental budgets remains a central challenge in automated chemical design. In Level 4 automation settings, where chemists define candidate spaces and machine learning models guide experimental selection, pool-based active learning has emerged as a practical framework for prioritizing compounds. However, conventional approaches primarily optimize predictive accuracy of surrogate structure-function models, which may not directly align with the objective of maximizing search efficiency toward identifying the molecule with the most favorable property value within a predefined candidate pool. We propose a policy-based active learning framework that reformulates molecular pool selection as a sequential decision-making problem. The iterative selection process is modeled as a Markov decision process, and a policy network is trained to optimize cumulative search performance under constrained evaluation budgets. Property prediction models are incorporated as contextual signals rather than optimization targets, enabling direct optimization of search efficiency. We construct 1409 molecular identification tasks derived from MoleculeNet and ChEMBL. In addition, six literature-curated in vivo tasks are constructed to assess performance. Across both benchmark and in vivo settings, the framework demonstrates improved efficiency in identifying optimal molecules within limited evaluation cycles. Case studies further illustrate the strengths and limitations of the framework. These results highlight the potential of policy-driven active learning to enhance molecular identification efficiency in predefined candidate pools, offering a generalizable strategy for budget-constrained chemical discovery.

Purpose With the boom of shale oil and gas in the USA as the context, the quick expansion of pipeline networks has garnered public concern regarding the economic consequences of pipeline failures. Design/methodology/approach The survey examined 426 gas pipeline accidents in the USA from 2010 to 2020 and examined their impact on housing prices. Findings It was found that only a very serious accident on the ground pipeline (for example, an explosion, a fire and someone else was injured, which we called a “high exposure events”), reduce the price of a neighboring house, and other types of accidents did not cause any major changes in housing prices. Further analysis also showed that high exposure events would reduce housing prices within 1 billion meters of the pipeline by 8.2%, and this poor effect lasted an average of about 8 years. Originality/value In addition, we found that at the beginning of the business, the volume of the real estate transaction fell slightly, which shows that the power side of demand responds quickly to such things. Compared to the apparent impact of the pipeline accident, the impact of the pipeline installation on housing prices is not great, and statistically little meaning. The study also showed that different types of things are very different, so it is impossible to summarize all the situations with the results of the same thing.

ABSTRACT This paper examines the displacement of craft businesses from inner-city areas in five cities in North Rhine-Westphalia, Germany. Drawing on expert interviews with representatives from urban planning, economic development, and chambers of crafts, it identifies a complex interplay of highly context-related factors driving this process – ranging from land-use conversion and rising property values to emission conflicts and limited expansion opportunities. Urban policies have often tolerated or indirectly facilitated the marginalization of the craft sector. Recent planning initiatives show growing awareness of the issue, offering prospects for safeguarding productive uses and preserving mixed-use urban structures that are central to sustainable and resilient city economies.

Purpose There is a growing need for private sector participation (PSP) in the provision of water infrastructure to boost availability and efficiency, especially in developing economies. This study investigates users' preference for PSP in water infrastructure provision in Lagos, Nigeria. Design/methodology/approach Using a multistage sampling, the study selected 57, 172 and 296 buildings in Shomolu, Surulere and Ikeja local government areas, respectively, in Lagos. Closed-ended questionnaires were administered to one adult resident in each building. From a total of 525 questionnaires, only 450 (85.71%) were returned and analyzed using descriptive and inferential statistical techniques. Findings The analysis revealed that users had a higher level of awareness of PSP in water infrastructure provision, though preference for PSP was slightly lower. Three key determinants of PSP acceptance were identified: effective management/policy framework, service competitiveness/effectiveness and price increase. While users recognize the advantages of improved service delivery, the positive attitudes are offset by concerns of increased costs. Anticipated price increases and reduced affordability diminish perceived behavioural control and present compatibility challenges. Regression analysis confirms that while governance and efficiency drive acceptance, affordability is a factor limiting support. Thus, users' preferences rely not solely on efficiency, but on equitable pricing, regulatory transparency and socioeconomic factors. Practical implications Evaluating end-users’ perspectives could encourage PSP in water infrastructure provision, thereby limiting environmental hazards, guaranteeing quality control and reducing individual expenses through economies of scale. Originality/value Quality water supply infrastructure significantly influences housing quality and property values. However, empirical research on the determinants of user preferences for PSP in developing countries remains scarce.

Prostate Cancer Detection on Micro-Ultrasound Raw Data Using a Deep Learning Neural Network.

ABSTRACT As climate resilience becomes central to urban planning, cities increasingly use urban greening to mitigate climate risks. Integrating nature into urban environments – especially through public green spaces – provides substantial environmental and health benefits. However, these interventions also reshape urban space and property values, often reinforcing socio-spatial inequalities by favouring affluent areas or displacing vulnerable communities. While scholars highlight the role of real estate dynamics in these processes, little is known about how local governments address them. This study examines how municipalities account for real estate market dynamics in the planning and management of green spaces, drawing on case studies from three Spanish cities: Bilbao, Valladolid, and Málaga. Using policy document analysis and interviews with urban planners and policymakers, we identify three key challenges to just urban greening: (1) siloed and reactive planning focused narrowly on redistributing green cover (organizational challenge); (2) funding models dependent on adjacent real estate development, tying green space provision to market dynamics (financial challenge); and (3) the alignment of greening initiatives with urban competitiveness agendas, often prioritizing economic gains over citizen well-being (strategic challenge). By situating urban greening within critical political ecology debates, this study highlights governance gaps undermining socially just climate adaptation and outlines pathways toward more equitable urban greening practices.

Accurate evaluation of house valuation is crucial. Misestimation of house prices creates serious consequences for a variety of stakeholders. House prices can be modeled as a function of their constituent attributes. House prices are fuzzy due to negotiation and unpredictable market conditions. This study aims to develop rules to predict triangular fuzzy numbers of price for a given new house by implementing locally weighted KNN-based fuzzy regression, and to compare its performance with possibilistic fuzzy regression. The dataset used is the house valuation dataset. Data is examined using the modified Cheng and Lee k-nearest neighbor fuzzy regression and Tanaka’s possibilistic fuzzy regression. It is found that the modified Cheng and Lee k-nearest neighbor fuzzy regression outperforms possibilistic fuzzy regression in predicting the triangular fuzzy number of the house prices. The best performance is achieved when data is trained using the modified Cheng and Lee k-nearest neighbor fuzzy regression using: = 29 nearest neighbors, Minkowski distance with exponent parameter = 1.6 and an unequal weighting scheme with r = 1.

The studied sediments of the Tyumen suite can be characterized by difficulties in confirming oil saturation coefficients derived from well testing data, including the electrical resistivity. Alternative estimation of oil saturation using a capillary model is complicated. This is because the above-mentioned sediments have a complex deposit structure. In some instances, these deposits are limited by a conventionally accepted depth of the lower oil-saturated interval, without justifying the free water level. For this reason, we must estimate deposit heights indirectly using only core analysis data. This article aims to develop indirect methods for evaluating deposit height within the constraints of geological models. To achieve this aim, we first generalized and analyzed capillary properties under both atmospheric and thermobaric conditions. We utilized direct indicators of oil saturation from core fluorescence under ultraviolet light, to determine boundary values for reservoir properties in the zone of maximum saturation. We then selected appropriate values for residual water saturation. Using these values, we compared total porosity with effective and dynamic porosity and obtained consistent boundary values. Based on the established values for residual water saturation and available capillary pressure curves, we calculated capillary pressures and formation heights. As a result, the results show that capillary pressures ranging from 7 atm to 12 atm are sufficient to establish residual water saturation in the Tyumen suite deposits within the studied areas. These pressures correlate to reservoir heights between 90 to 160 m. This approach facilitates the estimation of oil saturation coefficients using the capillary model in reservoirs with uncertain fluid contacts. It can also serve as supplementary tool for justifying individual lenses and blocks within geological models.

Performance valuation of the gel properties of starch-based hydrogel beads: Understanding the interaction between amylose and sodium alginate and the formation mechanism of the double network structure

ABSTRACTA study of burn thresholds from superficially penetrating radio‐frequency (RF) energy at 5.6 GHz for swine skin was conducted. The study estimated the thresholds for superficial, partial‐thickness, and full‐thickness burn severities after 20 s of exposure at power densities of 4–8 W/cm2. Biopsies were collected from each burn site at 1, 24, 72, and 168 h post exposure. Each sample was assessed by a burn pathologist against 20 histological factors to characterize the damage resulting from these RF overexposures. There were significant differences in the burn thresholds at the different severities compared to previous thresholds found at 8.2 and 95 GHz. A one‐dimensional, layered digital phantom that utilized realistic values for dielectric and thermal properties was consistent with the observed damage trends and empirical dosimetry values. The results of the heating and cooling response of the animal model, and histology scores of each exposure are provided to establish damage thresholds and support development of simulation tools to predict parameters associated with RF hazards.

Although magnetic nanoparticles have been widely studied, limited research has compared different ferrite types and synthesis routes for use in hyperthermia-based bioactive glass applications. This study aims to synthesize magnetic materials from two types of ferrites: magnesium ferrite (MgFe₂O₄) and zinc ferrite (ZnFe₂O₄). These ferrite nanoparticles were synthesized using two distinct methods; the conventional solid-state reaction and the co-precipitation method in order to identify the optimal synthesis route and the most suitable type of magnetic material for hyperthermia treatment. The data demonstrated that MgFe₂O₄ powder with synthesis by using the solid-state method consistently presented higher value of magnetic properties compared to those synthesized by co-precipitation method under higher calcination temperature. Moreover, ZnFe₂O₄ powder was found to be unsuitable for use as a precursor in hyperthermia treatment because of its structure typically leads to antiferromagnetic or superparamagnetic behavior. The effect of MgFe₂O₄ containing in bioactive glass was investigated. The oxide precursors of bioactive glass were mixed with varying amounts of MgFe₂O₄ and subsequently melted to form glass at 1400 °C. The phase formation presented SiO₂ was the dominant phase and coexisted with Na₂CO₃, MgSiO₃, Fe₃O₄, Na₂Ca (PO₄)₂SiO₄, Ca₂SiO₄, and Na₄Ca₄Si₆O₁₈. However, the MgFe₂O₄ phase was not observed in all of glass-ceramic samples. This may be due to MgFe₂O₄ decomposed during the high-temperature melting process at 1400 °C. Nevertheless, these magnetic bioactive glass ceramic samples exhibited magnetic properties, which were attributed primarily to the presence of Fe₃O₄.

This study reports the development, characterization, and certification of QS-CRM-23 , the first Moroccan quinoa seed powder Certified Reference Material (CRM) intended for reliable multi-element food composition analysis. Quinoa seeds collected from the Rehamna and Berrechid regions were processed under ISO 17034:2016 requirements, including drying, milling, sieving, homogenization, and bottling to ensure material uniformity. Homogeneity and stability assessments were performed according to ISO 33405:2024 using one-way ANOVA and linear regression models. Macronutrients and micronutrients (N, P, K, Ca, Mg, Cu, Mn, Fe, Zn, B) were quantified using validated ICP-OES and Kjeldahl methods. Certified values were established through an interlaboratory comparison involving eight ISO/IEC 17025-accredited laboratories and calculated in accordance with ISO 13528:2022 Algorithm A. QS-CRM-23 exhibited excellent within- and between-bottle homogeneity and demonstrated long-term (ambient, 24 months) and short-term (50 °C, 24 days) stability. All certified property values were assigned with expanded uncertainties (k = 2) below 15%. QS-CRM-23 provides a robust metrological tool to support calibration, method validation, and proficiency testing in food and nutritional analysis laboratories. Its availability contributes to improving data comparability, traceability, and measurement reliability in agro-food composition studies. • QS-CRM-23 is the first Moroccan quinoa seed powder Certified Reference Material. • Produced in accordance with ISO 17034, with homogeneity and stability assessed following ISO 33405:2024. • Macro- and micronutrients were determined using validated ICP-OES and Kjeldahl methods. • Certified values were assigned through an interlaboratory study applying ISO 13528 Algorithm A. • All certified analytes exhibit expanded uncertainties (k = 2) below 15%. • QS-CRM-23 provides a reliable metrological tool for calibration, method validation, and proficiency testing in food analysis

Low sulphur content is essential for high‐quality steels and can be achieved using desulphurization slag. During the desulphurization process the chemical composition of the slag changes leading to alteration in its thermophysical properties, such as surface tension, viscosity, and density. However, the influence of CaS on these thermophysical properties is poorly investigated. In this study, the influence of various CaS content (0–10 wt%) and CaO/Al 2 O 3 ratio (1.87–1.36) on the viscosity, surface tension, and density of desulphurization slags is investigated. Measurements of surface tension and density are carried out using the maximum bubble pressure method, while viscosity is determined using the rotating bob method. The obtained results show that a decrease in viscosity value is observed with an increase in CaS content only up to 6 wt%; however, further addition of CaS sharply increases viscosity. In case of surface tension and density measurements, an increase in CaS content up to 10 wt% results in a reduction of both property values. Additionally, to study microstructure differences of the investigated slags, they were quenched and examined using scanning electron microscopy (SEM) and Energy‐dispersive X‐ray spectroscopy (EDX).

In Quantitative Structure Property Relationship research, eccentricity-based topological indices are an effective molecular descriptor. The goal of this study is to establish predictive models that correlate the physicochemical properties of critical type II antidiabetic drugs. Linear regression is applied to validate the relation between six physicochemical properties and twelve topological indices. The resulting analysis demonstrated a high degree of correlation between the calculated topological indices and the experimental property values of the drugs.

Purpose This study investigates the anchoring effect in property valuation among students from different educational backgrounds, aiming to determine whether an engineering education mitigates cognitive bias compared to a social science education. Design/methodology/approach An experimental design was implemented during the final examinations in property valuation courses at two Swedish universities. Students from three educational programs – civil engineering in surveying, real estate brokerage, and property management – were randomly assigned either a low or high anchor value and tasked with appraising a property using the comparable sales method. Statistical analyses, including ANOVA and Scheffé’s multiple comparison tests, were conducted to assess the significance of anchoring effects across groups. Findings The results confirm a statistically significant anchoring effect among students with a social science background, while no significant effect was found among engineering students. These findings support the hypothesis that engineering education, with its emphasis on model-driven thinking, reduces vulnerability to anchoring bias. Practical implications The study highlights the importance of educational framing in valuation training. It suggests that incorporating model-thinking and quantitative reasoning into curricula may help mitigate cognitive biases in professional practice. Originality/value By comparing students from distinct educational traditions within a controlled experimental setting, this study contributes novel insights into how cognitive biases manifest in property valuation and how educational background influences susceptibility to anchoring.

Purpose The study arose because of myriad objections and consequent appeals to the tribunals regarding the municipal valuation estimates. This paper assesses valuation accuracy and uniformity levels relating to the City of Johannesburg's (CoJ's) general valuation roll (GVR) of 2018. Design/methodology/approach The study adopts a quantitative approach underpinned by descriptive and inferential statistics. These statistical tools, including the Assessment Sales Ratio (ASR), Mean Absolute Percentage Error (MAPE), and Root Mean Squared Error (RMSE), were applied to a sample of secondary data on property assessments and sales obtained from the CoJ. The Price Related Differential (PRD), Price Related Bias (PRB), and Coefficient of Dispersion (COD) were used to test uniformity. Findings The results reveal the Median ASR, MAPE, and RMSE at 0.90, 19.05%, and R123,514.66, respectively. The uniformity measures for PRD, PRB, and COD are 1.02, −0.01, and 18.87%, respectively. In keeping with the IAAO standards, these results suggest an acceptable degree of accuracy and uniformity, which is fair to the rate payers of the low-valued properties. The results also underscore the critical need for the post-valuation date ratio analysis in South African municipalities, a step that cannot be delayed. Research limitations/implications The study relates to the compelling need for post-valuation independent ratio studies conducted on the GVRs, with specific consideration given to the assessed values. After the valuation date, the property sales were not sufficiently large for the sample; hence, remedial measures were taken to improve the sample's representativeness. Originality/value This is the first time a post-valuation date study on accuracy and uniformity of value estimates has been done in South Africa, thus proving an independent audit on the GVRs. Extending such practice to all local property rates and taxes municipalities in South Africa is imperative, as it would enhance public confidence in municipal valuation.

Carbonate mound–shoal complexes, despite their complex pore structures and pronounced heterogeneity, represent one of the most productive reservoir units within carbonate formations. Accurately predicting key physical properties—such as porosity, permeability, and flow zone index—from well log data remains a significant challenge for conventional empirical methods. This study investigates the application of machine learning algorithms for optimizing the prediction of reservoir properties in hill-and-plain carbonate bodies. Six machine learning approaches—Support Vector Machines (SVM), Backpropagation Neural Networks (BPNN), Long Short-Term Memory Networks (LSTM), K-Nearest Neighbors (KNN), Random Forests (RF), and Gaussian Process Regression (GPR)—are systematically evaluated and compared. The analysis employed flow zone indices, geological data, and well log curves to classify porosity–permeability types. Seven logging parameters were used as input features: spectral gamma ray (SGR), uranium-free gamma ray (CGR), photoelectric absorption cross-section index (PE), bulk density (RHOB), acoustic travel time (DT), neutron porosity (NPHI), and true resistivity (RT). These features were paired with measured physical property values to train and validate the predictive models. Results demonstrate distinct algorithmic advantages for specific properties. The RF model achieved superior performance in permeability prediction, yielding an R2 of 0.6824, whereas the GPR model provided the highest accuracy for porosity estimation, with an R2 of 0.7342 and an Accuracy Index (ACI) of 0.9699. Despite these improvements, machine learning models still face limitations in accurately characterizing low-permeability zones within highly heterogeneous hill–terrace reservoirs. To address this challenge, the study integrates geological prior knowledge into the machine learning framework and applies cross-validation techniques to optimize model parameters, thereby providing a practical and robust approach for detailed assessment of mound–hoal carbonate reservoirs.

The issue of determining the fair value of shares has become particularly significant in Ukraine since 2017, following the large-scale implementation of mandatory buyout procedures of minority shareholders’ shares by majority shareholders (squeeze-out). In practice, these procedures have generated numerous disputes due to the substantial undervaluation of buyout prices. Such problems are largely caused by imperfections in national legislation and by the possibility of manipulating valuation methodologies in favor of controlling shareholders. According to Ukrainian national valuation standards, three main methodological approaches are used in property valuation: the asset-based approach, the income approach, and the market (comparative) approach. However, in practical valuation reports prepared for squeeze-out procedures, the asset-based approach is often disregarded, typically justified by the alleged lack of necessary accounting information or by claims regarding the priority of the income approach. This practice creates opportunities for significant underestimation of the fair value of shares. An analysis of judicial practice, including decisions of the Supreme Court of Ukraine, demonstrates that the asset-based approach should be applied alongside other valuation methods. Within this framework, the fair value of a share may be determined based on the company’s net asset value divided by the total number of outstanding shares. Empirical comparisons between actual squeeze-out prices and the net asset value per share reveal considerable discrepancies, confirming systematic undervaluation in a number of cases. Furthermore, large joint-stock companies in Ukraine prepare financial statements in accordance with International Financial Reporting Standards (IFRS), which require periodic revaluation of assets at fair value. Consequently, the necessary data for applying the asset-based approach are generally available in financial reporting. It is also important to emphasize that international valuation standards cannot substitute national valuation regulations, which remain legally binding for valuation activities in Ukraine. In judicial disputes related to squeeze-out procedures, courts rely primarily on forensic economic examinations rather than valuation reports commissioned by majority shareholders. Such examinations must be conducted in accordance with national valuation standards and relevant methodological guidelines for determining the value of shares. Therefore, the application of the asset-based approach in the valuation of shares during mandatory buyout procedures is essential for ensuring a fair price and protecting the rights of minority shareholders. The final valuation should be determined in accordance with the principle of the highest and best use of the enterprise as an integrated property complex.