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Abstract Background Rockfalls pose a serious threat along the main road network, representing a major hazard in mountainous territory and causing damage and victims. Currently, susceptibility mapping represents a starting point to identify areas more susceptible to rockfall occurrence, a key approach in land use planning and risk management. Despite the extensive use of these maps by decision makers and administrators, the usability of such maps over time and their reliability represent a poorly discussed and examined feature. Methods Here, we proposed a-posteriori analysis of a three-year-old rockfall susceptibility map, generated along the main road network of the Aosta Valley, an alpine region of north-western Italy. To verify map consistency over time, we implemented a dual-analysis in GIS-environment and by text mining, to respectively analyse the geocoded data and textual information derivable from the regional landslide inventory. The first one allowed us to extract rockfall events occurred after the susceptibility map generation. By this way, we operated to spatially and temporally verify the map consistency. Jointly, the textual information reported in the Event Description Form, linked to each geocoded event, are being exploited. This allowed us to derive relevant information about occurred damage and their degree, presence of protective measures or secondary roads, i.e. involvement of, farm or forestry, road. Results The implemented approach allowed us to prove the quality of the previous map in terms of reliability, robustness and degree of fitting respect to the succession of rockfall occurrence over time. After only three years as many as 198 rockfall events have been occurred and collected since the map was generated. Particularly, 80% of rockfall fit with “high” and “very high” susceptibility classes, and pointed out large involvement of main roads in rockfall occurrence, representing the most affected target with damage to road pavement and vehicles, as well as a relevant involvement of existing rockfall barriers and of the dense network of forestry roads and footpaths that characterize this alpine region. Results The proposed approach representing a starting point in landslide susceptibility map verification and usability as valid instrument for a reliable land planning.

Abstract Background Liquid biopsy based on cell-free DNA (cfDNA) analysis holds significant promise as a minimally invasive approach for the diagnosis, genotyping, and monitoring of solid malignancies. Human tumors release cfDNA in the bloodstream through a combination of events, including cell death, active and passive release. However, the precise mechanisms leading to cfDNA shedding remain to be characterized. Addressing this question in patients is confounded by several factors, such as tumor burden extent, anatomical and vasculature barriers, and release of nucleic acids from normal cells. In this work, we exploited cancer models to dissect basic mechanisms of DNA release. Methods We measured cell loss ratio, doubling time, and cfDNA release in the supernatant of a colorectal cancer (CRC) cell line collection (N = 76) representative of the molecular subtypes previously identified in cancer patients. Association analyses between quantitative parameters of cfDNA release, cell proliferation, and molecular features were evaluated. Functional experiments were performed to test the impact of modulating DNA methylation on cfDNA release. Results Higher levels of supernatant cfDNA were significantly associated with slower cell cycling and increased cell death. In addition, a higher cfDNA shedding was found in non-CpG Island Methylator Phenotype (CIMP) models. These results indicate a positive correlation between lower methylation and increased cfDNA levels. To explore this further, we exploited methylation microarrays to identify a subset of probes significantly associated with cfDNA shedding and derive a methylation signature capable of discriminating high from low cfDNA releasers. We applied this signature to an independent set of 176 CRC cell lines and patient derived organoids to select 14 models predicted to be low or high releasers. The methylation profile successfully predicted the amount of cfDNA released in the supernatant. At the functional level, genetic ablation of DNA methyl-transferases increased chromatin accessibility and DNA fragmentation, leading to increased cfDNA release in isogenic CRC cell lines. Furthermore, in vitro treatment of five low releaser CRC cells with a demethylating agent was able to induce a significant increase in cfDNA shedding. Conclusions Methylation status of cancer cell lines contributes to the variability of cfDNA shedding in vitro. Changes in methylation pattern are associated with cfDNA release levels and might be exploited to increase sensitivity of liquid biopsy assays.

In the aftermath of the COVID-19 pandemic, we continue to take stock of student learning. Although the "crisis-context" (Gacs et al., 2020) move to fully online instruction may be over, a complete understanding of how student outcomes have been impacted remains. The present study focuses on how students' Spanish language proficiency, as measured by the STAMP test, was affected by moving all on-ground language courses online in March 2020 at a small public university in the northeastern US. Comparing overall student Spanish language proficiency as well as reading, writing, speaking, and listening skills across 30 sections of a third-semester Spanish course before and during the pandemic, we examined student learning outcomes based upon the modality of instruction. Results revealed a significant increase in students' overall Spanish language proficiency and significant increases in sub-level proficiencies in three out of the four skills in the online modality. Thus, in spite of the many changes that took place as a result of the COVID-19 pandemic, students' Spanish language proficiencies were either positively impacted or unimpacted. We discuss the implications of these results and pose questions for on-ground and online language courses moving forward.

Abstract Background Aggressive malignancies, such as pancreatic cancer, are increasingly impacting young, female populations. Our investigation centered on whether the observed trends in cancer incidence were unique to pancreatic cancer or indicative of a broader trend across various cancer types. To delve deeper into this phenomenon, we analyzed cancer incidence trends across different age and sex groups. Furthermore, we explored differences in cancer incidence within specific young subgroups aged 18 to 26 and 27 to 34, to better understand the emerging incidence trend among young individuals. Methods This study collected cancer incidence data from one of the Surveillance, Epidemiology, and End Results cancer registry databases (SEER22), with 10,183,928 total cases from 2000 to 2020. Data were analyzed through Joinpoint trend analysis approach to evaluate sex- and age-specific trends in cancer incidence. Exposure rates were reported as Average Annual Percentage Changes (AAPCs). Results The analysis revealed significant age and sex-specific disparities, particularly among individuals aged 18–26 and 27–34. Pancreatic cancer incidence rates increased more in females aged 18–26 (AAPC, 9.37% [95% CI, 7.36–11.41%]; p

Abstract Aim Functional Management and Recovery is a standardized Psychoeducational Intervention, derived from “Integro”, an effective salutogenic-psychoeducational intervention for people in recovery journey, designed to improve recovery and functioning of individuals with psychotic disorders in Psychiatric Residential Facilities (PRFs). The aim of this study is to evaluate the primary and secondary outcomes of this intervention elaborated specifically for PRFs where evidence based structured interventions seem rare and desirable. Methods 66 individuals with psychotic disorders were recruited in 9 PRFs dislocated in the North, Center and South Italy and 63 underwent a multicenter follow-up study with a two time-point evaluation (t0, pre-treatment and t1, 6 months; ). At each time point, social functioning was assessed as primary outcome by the Personal and Social Performance scale (PSP); furthermore, psychopathological status was assessed by Brief Psychiatric Rating Scale (BPRS), Recovery by Recovery Assessment Scale (RAS), Cognitive Functioning by Repeatable Battery for the Assessment of Neuropsychological Status (RBANS), Stress management by Stress-Scale, Cognitive Flexibility by Modified Five-Point Test (M-FPT), Emotional Intelligence by Emotional Intelligence Index (EI-I), the PRF Atmosphere and the Opinion of users about the PFR by an ad hoc questionnaire. The Abilities Knowledge, the Utility and Pleasantness of sessions were measured by an ad hoc list of items. Results 63 individuals out of 66, 52 (82,5%) affected by schizophrenia and 11 (17,5%) by bipolar I disorder with psychotic symptoms according to DSM-5-TR completed the study. At the end of the study, 43 (68,3%) were male, 57 (90.5%) were single, 5 (7.9%) engaged, 1 (1.6%) married; 45 (71.4%) unemployed. The total scores of PSP, RAS, BPRS, BANS, Stress management, Abilities Knowledge, Utility and Pleasantness of sessions showed a statistically significant improvement at t1 vs. t0. Two sub-scales out of 5 of M-FPT showed a statistically significant improvement. The Emotional Intelligence, the Unit Atmosphere and the Opinion of Users about PFR improved without statistical significance. Six months after the end of the follow-up study 22 individuals of the sample were dismissed with a very high turnover. Conclusions After a six-month follow-up (a short period of time), these results showed improvement in functioning, the primary outcome, as well as in the following secondary outcome variables: RAS, BPRS, BANS, Stress management, Abilities Knowledge, two sub-scales out of 5 of M-FPT, Utility and Pleasantness of sessions. Overall, a remarkable impact of psychoeducational structured intervention on the key Recovery variables is observed. Further studies are needed to address extent and duration of these improvements.

Sleep quality (SQ) is a crucial aspect of overall health. Poor sleep quality may cause cognitive impairment, mood disturbances, and an increased risk of chronic diseases. Therefore, assessing sleep quality helps identify individuals at risk and develop effective interventions. SQ has been demonstrated to affect heart rate variability (HRV) and skin temperature even during wakefulness. In this perspective, using wearables and contactless technologies to continuously monitor HR and skin temperature is highly suited for assessing objective SQ. However, studies modeling the relationship linking HRV and skin temperature metrics evaluated during wakefulness to predict SQ are lacking. This study aims to develop machine learning models based on HRV and skin temperature that estimate SQ as assessed by the Pittsburgh Sleep Quality Index (PSQI). HRV was measured with a wearable sensor, and facial skin temperature was measured by infrared thermal imaging. Classification models based on unimodal and multimodal HRV and skin temperature were developed. A Support Vector Machine applied to multimodal HRV and skin temperature delivered the best classification accuracy, 83.4%. This study can pave the way for the employment of wearable and contactless technologies to monitor SQ for ergonomic applications. The proposed method significantly advances the field by achieving a higher classification accuracy than existing state-of-the-art methods. Our multimodal approach leverages the synergistic effects of HRV and skin temperature metrics, thus providing a more comprehensive assessment of SQ. Quantitative performance indicators, such as the 83.4% classification accuracy, underscore the robustness and potential of our method in accurately predicting sleep quality using non-intrusive measurements taken during wakefulness.

Abstract Background The European Regulation on Health Technology Assessment (EU HTA R), effective since January 2022, aims to harmonize and improve the efficiency of common HTA across Member States (MS), with a phased implementation from January 2025. At “midterms” of the preparation phase for the implementation of the Regulation our aim was to identify and prioritize tangible action points to move forward. Methods During the 2023 Spring Convention of the European Access Academy (EAA), participants from different nationalities and stakeholder backgrounds discussed readiness and remaining challenges for the Regulation’s implementation and identified and prioritized action points. For this purpose, participants were assigned to four working groups: (i) Health Policy Challenges, (ii) Stakeholder Readiness, (iii) Approach to Uncertainty and (iv) Challenges regarding Methodology. Top four action points for each working group were identified and subsequently ranked by all participants during the final plenary session. Results Overall “readiness” for the Regulation was perceived as neutral. Prioritized action points included the following: Health Policy, i.e. assess adjustability of MS laws and health policy processes; Stakeholders, i.e. capacity building; Uncertainty, i.e. implement HTA guidelines as living documents; Methodology, i.e. clarify the Population, Intervention, Comparator(s), Outcomes (PICO) identification process. Conclusions At “midterms” of the preparation phase, the focus for the months to come is on executing the tangible action points identified at EAA’s Spring Convention. All action points centre around three overarching themes: harmonization and standardization, capacity building and collaboration, uncertainty management and robust data. These themes will ultimately determine the success of the EU HTA R in the long run.

The construction industry is rapidly adopting Industry 4.0 technologies, creating new opportunities to address persistent environmental and operational challenges. This review focuses on how Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL) are being leveraged to tackle these issues. It specifically explores AI’s role in predicting air pollution, improving material quality, monitoring worker health and safety, and enhancing Cyber-Physical Systems (CPS) for construction. This study evaluates various AI and ML models, including Artificial Neural Networks (ANNs) and Support Vector Machines SVMs, as well as optimization techniques like whale and moth flame optimization. These tools are assessed for their ability to predict air pollutant levels, improve concrete quality, and monitor worker safety in real time. Research papers were also reviewed to understand AI’s application in predicting the compressive strength of materials like cement mortar, fly ash, and stabilized clay soil. The performance of these models is measured using metrics such as coefficient of determination (R2), Root Mean Squared Error (RMSE) and Mean Absolute Error (MAE). Furthermore, AI has shown promise in predicting and reducing emissions of air pollutants such as PM2.5, PM10, NO2, CO, SO2, and O3. In addition, it improves construction material quality and ensures worker safety by monitoring health indicators like standing postures, electrocardiogram, and galvanic skin response. It is also concluded that AI technologies, including Explainable AI and Petri Nets, are also making advancements in CPS for the construction industry. The models’ performance metrics indicate they are well-suited for real-time construction operations. The study highlights the adaptability and effectiveness of these technologies in meeting current and future construction needs. However, gaps remain in certain areas of research, such as broader AI integration across diverse construction environments and the need for further validation of models in real-world applications. Finally, this research underscores the potential of AI and ML to revolutionize the construction industry by promoting sustainable practices, improving operational efficiency, and addressing safety concerns. It also provides a roadmap for future research, offering valuable insights for industry stakeholders interested in adopting AI technologies.

BackgroundPatients receiving chemotherapy require ongoing symptom monitoring and management to optimize their outcomes. In recent years, digital remote monitoring interventions have emerged to provide enhanced cancer care delivery experiences to patients and clinicians. However, patient and clinician experiential evaluations of these technologies are rare. Therefore, we explored user experiences and perceptions of one such intervention—Advanced Symptom Management System (ASyMS)—after its scaled deployment in the context of the Electronic Symptom Management System Remote Technology (eSMART) trial. The eSMART trial was a large, multicenter randomized controlled trial to evaluate the efficacy of ASyMS in 12 clinical sites in 5 European countries. ObjectiveIn this qualitative study, both patients’ and clinicians’ experiences of using ASyMS for up to 6 cycles of chemotherapy were explored to understand the impact of ASyMS on patients’ experiences, clinical practice, and supportive care delivery. MethodsFor this analysis, individual, semistructured, one-to-one interviews with 29 patients with breast, colorectal, and hematological cancers and 18 clinicians from Austria, Greece, Ireland, Norway, and the United Kingdom were conducted. Interviews focused on patients’ and clinicians’ experiences of using ASyMS, care organization and changes in practice following the introduction of ASyMS, perceived changes in care associated with the use of ASyMS, and its potential for future integration into routine chemotherapy care pathways. ResultsThematic analysis identified several themes that describe patients’ and clinicians’ experiences using ASyMS. One central orienting theme—ASyMS as a facilitator of change—was supported by 5 key themes associated with human and technology monitoring: reassurance, enhanced communications and relationships, knowing what is “normal” and what is to be expected, enhancing cancer care experiences, and informing future cancer care. ConclusionsThis study is the first to evaluate both patients’ and clinicians’ experiences of using a digital health intervention to remotely monitor chemotherapy symptoms across 5 countries. Experiences with ASyMS were positive from both patients’ and clinicians’ perspectives, although some improvements to support the wider-scale rollout and sustained implementation were identified. Overall, this study demonstrates that real-time remote monitoring systems can help patients feel more reassured during their chemotherapy treatments and can help clinicians provide the right care, at the right time, and in the right place. Trial RegistrationClinicalTrials.gov NCT02356081; https://clinicaltrials.gov/study/NCT02356081 International Registered Report Identifier (IRRID)RR2-10.1136/bmjopen-2016-015016

Background: Human-Machine Interaction (HMI) has been an important field of research in recent years, since machines will continue to be embedded in many human actvities in several contexts, such as industry and healthcare. Monitoring in an ecological mannerthe cognitive workload (CW) of users, who interact with machines, is crucial to assess their level of engagement in activities and the required effort, with the goal of preventing stressful circumstances. This study provides a comprehensive analysis of the assessment of CW using wearable sensors in HMI. Methods: this narrative review explores several techniques and procedures for collecting physiological data through wearable sensors with the possibility to integrate these multiple physiological signals, providing a multimodal monitoring of the individuals’CW. Finally, it focuses on the impact of artificial intelligence methods in the physiological signals data analysis to provide models of the CW to be exploited in HMI. Results: the review provided a comprehensive evaluation of the wearables, physiological signals, and methods of data analysis for CW evaluation in HMI. Conclusion: the literature highlighted the feasibility of employing wearable sensors to collect physiological signals for an ecological CW monitoring in HMI scenarios. However, challenges remain in standardizing these measures across different populations and contexts.

Introducción: La metatarsalgia central es una causa frecuente de dolor de antepié. La osteotomía de Weil es el tratamiento quirúrgico más popular y la osteotomía metatarsiana distal percutánea (OMDP) es la técnica percutánea más utilizada. La principal desventaja de estas técnicas es la aparición de dedo flotante que es aún mayor cuando se la asocia a artrodesis interfalángica proximal (AIFP). En esta serie de casos, se combinó la OMDP y la osteosíntesis con clavija de Kirschner para elevar el centro de rotación de la cabeza del metatarsiano con el objetivo de disminuir la presencia de dedos flotantes. Nuestra principal hipótesis fue que esta técnica generará menos dedos flotantes en los pacientes con diagnóstico de metatarsalgia mecánica y dedo en martillo rígido, comparada con la osteotomía de Weil. Materiales y Métodos: Se realizó un estudio retrospectivo en pacientes adultos con diagnóstico de metatarsalgia mecánica y dedo en martillo rígido. Se los sometió a una OMDP más fijación con clavija de Kirschner en combinación con AIFP. Finalmente, se comparó la presencia de dedos flotantes con un grupo de pacientes operados con la técnica de Weil y AIFP. Resultados: Se realizaron 39 OMDP más AIFP. La tasa de dedos flotantes fue del 31%. No hubo una diferencia estadísticamente significativa comparada con la técnica de Weil (36%, p = 0,634). Conclusión: La OMDP con elevación del centro de rotación asociada con AIFP no proporcionó una menor incidencia de dedos flotantes en comparación con la osteotomía de Weil.

Introducción: Las osteotomías supramaleolares son una opción terapéutica para la artrosis asimétrica de tobillo. El objetivo de este artículo es describir los resultados clínicos, radiográficos y subjetivos obtenidos en pacientes con artrosis de tobillo sometidos a una osteotomía supramaleolar. Materiales y Métodos: Estudio observacional descriptivo retrospectivo de pacientes sometidos a una osteotomía supramaleolar por artrosis de tobillo entre enero de 2010 y julio de 2017. Se analizaron las historias clínicas para recabar datos clínicos y radiográficos preoperatorios y posoperatorios. Resultados: Se incluyó a 13 pacientes: 8 con una desalineación del tobillo y retropié en valgo (61,5%) y 5 (38,5%) en varo. El puntaje medio posoperatorio de la escala analógica visual del grupo con deformidad en varo disminuyó de 9 ± 0,45 a 3 ± 1 (p

Objectives: Stakeholder involvement has long been considered a success factor for a joint European health technology assessment (HTA) process, and its relevance is now anchored in the EU HTA Regulation’s (EU HTAR) legislative wording. Therefore, we aimed to explore the roles, challenges, and most important activities to increase the level of involvement per stakeholder group. Methods: At the 2022 Fall Convention of the European Access Academy (EAA), working groups addressed the involvement of patients, clinicians, regulators, health technology developers (HTD), and national HTA bodies and payers within the EU HTA process. Each working group revisited the pre-convention survey results, determined key role characteristics for each stakeholder, and agreed on the most important activities to fulfill the role profile. Finally, the activities suggested per group were prioritized by plenary group. Results: The prioritized actions for patients included training and capacity building, the establishment of a patient involvement committee, and the establishment of a patient unit at the EC secretariat. For clinicians, it included alignment on evidence assessment from a clinical vs. HTA point of view, capacity building, and standardization of processes. The most important actions for regulators are to develop joint regulatory-HTA guidance documents, align processes and interfaces under the regulation, and share discussions on post-licensing evidence generation. HTDs prioritized scientific advice capacity and the review of the scoping process, and further development of the scope of the assessment report fact checks. The top three actions for national HTA bodies and payers included clarification on the early HTD dialogue process, political support and commitment, and clarification on financial support. Conclusions: Addressing the activities identified as the most important for stakeholders/collaborators in the EU HTA process (e.g., in the implementation of the EU HTA Stakeholder Network and of the guidance documents developed by the EUnetHTA 21 consortium) will be key to starting an “inclusive civil society dialogue”, as suggested by the European Commission’s Pharmaceutical Strategy.

Abstract Allopregnanolone (ALLO) is a known neurosteroid and a progesterone metabolite synthesized in the ovary, CNS, PNS, adrenals and placenta. Its role in the neuroendocrine control of ovarian physiology has been studied, but its in situ ovarian effects are still largely unknown. The aims of this work were to characterize the effects of intrabursal ALLO administration on different ovarian parameters, and the probable mechanism of action. ALLO administration increased serum progesterone concentration and ovarian 3β-HSD2 while decreasing 20α-HSD mRNA expression. ALLO increased the number of atretic follicles and the number of positive TUNEL granulosa and theca cells, while decreasing positive PCNA immunostaining. On the other hand, there was an increase in corpora lutea diameter and PCNA immunostaining, whereas the count of TUNEL-positive luteal cells decreased. Ovarian angiogenesis and the immunohistochemical expression of GABAA receptor increased after ALLO treatment. To evaluate if the ovarian GABAA receptor was involved in these effects, we conducted a functional experiment with a specific antagonist, bicuculline. The administration of bicuculline restored the number of atretic follicles and the diameter of corpora lutea to normal values. These results show the actions of ALLO on the ovarian physiology of the female rat during the follicular phase, some of them through the GABAA receptor. Intrabursal ALLO administration alters several processes of the ovarian morpho-physiology of the female rat, related to fertility and oocyte quality.

Background: The integration of edge computing into smart healthcare systems requires the development of computationally efficient models and methodologies for monitoring and detecting patients’ healthcare statuses. In this context, mobile devices, such as smartphones, are increasingly employed for the purpose of aiding diagnosis, treatment, and monitoring. Notably, smartphones are widely pervasive and readily accessible to a significant portion of the population. These devices empower individuals to conveniently record and submit voice samples, thereby potentially facilitating the early detection of vocal irregularities or changes. This research focuses on the creation of diverse machine learning frameworks based on vocal samples captured by smartphones to distinguish between pathological and healthy voices. Methods: The investigation leverages the publicly available VOICED dataset, comprising 58 healthy voice samples and 150 samples from voices exhibiting pathological conditions, and machine learning techniques for the classification of healthy and diseased patients through the employment of Mel-frequency cepstral coefficients. Results: Through cross-validated two-class classification, the fine k-nearest neighbor exhibited the highest performance, achieving an accuracy rate of 98.3% in identifying healthy and pathological voices. Conclusions: This study holds promise for enabling smartphones to effectively identify vocal disorders, offering a multitude of advantages for both individuals and healthcare systems, encompassing heightened accessibility, early detection, and continuous monitoring.

Abstract Mounting evidence recognizes structural variations (SVs) and repetitive DNA sequences as crucial players in shaping the existing grape phenotypic diversity at intra- and inter-species levels. To deepen our understanding on the abundance, diversity, and distribution of SVs and repetitive DNAs, including transposable elements (TEs) and tandemly repeated satellite DNA (satDNAs), we re-sequenced the genomes of the ancient grapes Aglianico and Falanghina. The analysis of large copy number variants (CNVs) detected candidate polymorphic genes that are involved in the enological features of these varieties. In a comparative analysis of Aglianico and Falanghina sequences with 21 publicly available genomes of cultivated grapes, we provided a genome-wide annotation of grape TEs at the lineage level. We disclosed that at least two main clusters of grape cultivars could be identified based on the TEs content. Multiple TEs families appeared either significantly enriched or depleted. In addition, in silico and cytological analyses provided evidence for a diverse chromosomal distribution of several satellite repeats between Aglianico, Falanghina, and other grapes. Overall, our data further improved our understanding of the intricate grape diversity held by two Italian traditional varieties, unveiling a pool of unique candidate genes never so far exploited in breeding for improved fruit quality.

This work proposes a new unsupervised method to evaluate the behavior of urban green areas in the presence of heatwave scenarios by analyzing three indices extracted from satellite data: the Normalized Difference Vegetation Index (NDVI), the Normalized Difference Moisture Index (NDMI), and Land Surface Temperature (LST). The aim of this research is to analyze the behavior of urban vegetation types during heatwaves through the analysis of these three indices. To evaluate how these indices characterize urban green areas during heatwaves, an unsupervised classification method of the three indices is proposed that uses the Elbow method to determine the optimal number of classes and the Jenks classification algorithm. Each class is assigned a Gaussian fuzzy set and the green urban areas are classified using zonal statistics operators. The membership degree of the corresponding fuzzy set is calculated to assess the reliability of the classification. Finally, for each type of greenery, the frequencies of types of green areas belonging to NDVI, NDMI, and LST classes are analyzed to evaluate their behavior during heatwaves. The framework was tested in an urban area consisting of the city of Naples (Italy). The results show that some types of greenery, such as deciduous forests and olive groves, are more efficient, in terms of health status and cooling effect, than other types of urban green areas during heatwaves; they are classified with NDVI and NDMI values of mainly High and Medium High, and maximum LST values of Medium Low. Conversely, uncultivated areas show critical behaviors during heatwaves; they are classified with maximum NDVI and NDMI values of Medium Low and maximum LST values of Medium High. The research results represent a support to urban planners and local municipalities in designing effective strategies and nature-based solutions to deal with heat waves in urban settlements.

Analyzing the uncertainty of outcomes based on estimates of the data’s membership degrees to fuzzy sets is essential for making decisions. These fuzzy sets are often designated by experts as strong fuzzy partitions of the data domain with trapezoidal fuzzy numbers. Some indices of the fuzzy set’s fuzziness provide an assessment of the degree of uncertainty of the results. It is feasible to bring the fuzzy sets’ fuzziness below a tolerable level by suitably redefining the strong fuzzy partition. Significant differences in the original fuzzy partition, however, result in disparities concerning the decision maker’s approximative reasoning and the interpretability of the results. In light of this, we provide in this study a technique applied to trapezoidal strong fuzzy partitions that, while not appreciably altering the original fuzzy partition, reduces the fuzziness of its fuzzy sets. The fuzziness of the fuzzy sets is assessed using the De Luca and Termini fuzzy entropy. An iterative process is then executed, with the aim of modifying the cores of the trapezoidal fuzzy partitions to decrease their fuzziness. This technique is tested on datasets containing average daily temperatures measured in various cities. The findings demonstrate that this approach strikes a great balance between the goal of lessening the fuzziness of the fuzzy sets and the goal of not appreciably altering the original fuzzy partition.

BackgroundInterest in the application of predictive risk models (PRMs) in health care to identify people most likely to experience disease and treatment-related complications is increasing. In cancer care, these techniques are focused primarily on the prediction of survival or life-threatening toxicities (eg, febrile neutropenia). Fewer studies focus on the use of PRMs for symptoms or supportive care needs. The application of PRMs to chemotherapy-related symptoms (CRS) would enable earlier identification and initiation of prompt, personalized, and tailored interventions. While some PRMs exist for CRS, few were translated into clinical practice, and human factors associated with their use were not reported. ObjectiveWe aim to explore patients’ and clinicians’ perspectives of the utility and real-world application of PRMs to improve the management of CRS. MethodsFocus groups (N=10) and interviews (N=5) were conducted with patients (N=28) and clinicians (N=26) across 5 European countries. Interactions were audio-recorded, transcribed verbatim, and analyzed thematically. ResultsBoth clinicians and patients recognized the value of having individualized risk predictions for CRS and appreciated how this type of information would facilitate the provision of tailored preventative treatments or supportive care interactions. However, cautious and skeptical attitudes toward the use of PRMs in clinical care were noted by both groups, particularly in relationship to the uncertainty regarding how the information would be generated. Visualization and presentation of PRM information in a usable and useful format for both patients and clinicians was identified as a challenge to their successful implementation in clinical care. ConclusionsFindings from this study provide information on clinicians’ and patients’ perspectives on the clinical use of PRMs for the management of CRS. These international perspectives are important because they provide insight into the risks and benefits of using PRMs to evaluate CRS. In addition, they highlight the need to find ways to more effectively present and use this information in clinical practice. Further research that explores the best ways to incorporate this type of information while maintaining the human side of care is warranted. Trial RegistrationClinicalTrials.gov NCT02356081; https://clinicaltrials.gov/study/NCT02356081

Shallow landslides, frequently triggered by extreme events such as heavy rainfall, snowmelt, or earthquakes, affect vast areas with remarkable density. In the immediate aftermath of such events, it becomes crucial to rapidly assess landslides distribution and pinpoint the most severely affected areas to prioritize damage assessments and guide field survey operations effectively. Once the emergency phase subsides, the attention can shift to enhancing the accuracy of landslide inventory. In this work, we introduce the two-phase methodology “PANDA”, the unsuPervised shAllow laNdslide rapiD mApping, for the low-cost mapping of the potential landslides, firstly in the emergency phase and then, with an improved version, in the post-emergency one. This approach utilizes variations in NDVI derived from Sentinel-2 satellite imagery and geomorphological filters. We applied PANDA to rainfall events in the northeastern Apennine range, Italy, occurred in May 2023, causing dramatic social and economic consequences for this mountain territory. Within just five days of obtaining Sentinel-2 post-event imagery, we produced a reliable, ready-to-use map covering a vast area (∼4000 km2). The map tested during emergency field mapping shows positive feedback. In the post-emergency phase, accuracy was enhanced using completely cloud-free imagery, a filter to identify false positives associated with land use changes, a higher resolution digital terrain model (DTM), and an iterative approach to optimize NDVI and slope thresholds. Potential landslide density related with rainfall, indicating that the most severely affected region attained a density of approximately 50 landslides/km2. Validation against an independent manual inventory based on high-resolution imagery demonstrated encouraging accuracy results from both inventories, with a noticeable increase in the F1 score for the post-emergency version.

The use of alternative renewable sources to totally or partially replace bitumen is one of the most current challenges in the road pavement sector. The growing energy cost and environmental concerns lead to the necessity to find alternative solutions, by supporting at the same time sustainability and circular economy principles. Within this framework, this paper presents the application of a powder lignin, a natural bio-polymer deriving from by-products of wood pulp and paper industry, to replace part of bitumen. The bituminous blend consisting in 70% of bitumen and 30% of powder lignin (by weight) was made in laboratory through the use of a high shear stirring mixer, and a reference plain bitumen characterized by a similar consistency (i.e., same penetration grade) was used as comparison. Then, an extensive investigation on chemical and rheological properties of the bio-binder is presented. Fourier transform infrared spectroscopy (FTIR), saturates, aromatics, resins and asphaltenes (SARA), bending beam rheometer (BBR), frequency sweep tests and multiple stress creep recovery tests (MSCR) with a dynamic shear rheometer (DSR) were performed. Moreover, unaged, short- and long-term aging conditions were considered. Results indicated that powder lignin dominates the rheological behavior of the bio-binder and, from chemical analysis, it seems that it partially acts as a filler and partially as a binder. This would result in improved performances at both low and high temperatures, leading to a wider temperature range of performance grade (PG). Moreover, despite a stiffening effect is recognized, lignin also offers an antioxidant potentiality, reducing the aging susceptibility of the investigated bio-binder.