Global Scale Research Articles

Effect of psychological nursing interventions on effectiveness and quality of life in schizophrenia patients receiving modified electroconvulsive therapy

BACKGROUND Schizophrenia is a common and severe mental disorder characterized by severe thought disturbances, hallucinations, delusions, and emotional instability. For some patients, conventional treatment methods may not effectively alleviate symptoms, necessitating the use of alternative therapeutic approaches. Modified electroconvulsive therapy (MECT) is an effective treatment modality for schizophrenia, inducing anti-depressive and antipsychotic effects through the stimulation of brain electrical activity. AIM To explore the impact of psychological nursing intervention (PNI) before and after MECT on the efficacy and quality of life of patients with schizophrenia. METHODS Eighty patients with schizophrenia who received MECT treatment from 2021 to 2023 were randomly divided into two groups: The intervention group (n = 40) and the control group (n = 40). The intervention group received PNI before and after MECT, while the control group received routine nursing care. The efficacy of MECT was evaluated by the Positive and Negative Syndrome Scale (PANSS) and the Clinical Global Impression Scale (CGI) before and after the treatment. The quality of life was assessed by the Short Form 36 Health Survey (SF-36) after the treatment. RESUITS The intervention group had significantly lower scores of PANSS and CGI than the control group after the treatment (P < 0.05). The intervention group also had significantly higher scores of SF-36 than the control group in all domains except physical functioning (P < 0.05). CONCLUSION PNI before and after MECT can improve the efficacy and quality of life of patients with schizophrenia. It is suggested that nurses should provide individualized and comprehensive psychological care for patients undergoing MECT to enhance their recovery and well-being.

Prefecture-level health risk assessment for hot extremes in China

Evaluating prefecture-level health risks for hot extremes is the core of localized adaptation measures to mitigate negative heat-related impacts. Here, by applying the heat hazard-human exposure-social vulnerability framework, we assessed the prefecture-level health risk for summertime hot extremes in China, with multisource data from 329 prefecture units. Regions suffering very high and high levels of health risk were observed in several main basins and plains, including the Turpan Basin, the North China Plain, the middle-lower Yangtze River Plain, the Pearl River Delta, the Chengdu Plain, and the Wei River Valley. Health risk structures were identified, and 72 prefecture units (22%) were found in the risk zone of Hh-He-Hv (high heat hazard, high human exposure, and high social vulnerability), of which 44% of sites were distributed in the North China Plain and must be prioritized for adaptation. The risk components of human exposure were the main drivers of health risk and its spatial heterogeneity. For the southeastern region, social vulnerability plays a more significant role than heat hazard. Four types of social vulnerability were summarized and will be useful for targeted adaptation plans. The assessment process will be insightful and can be applied in other countries or on a global scale.

Calculating daily dose in the Observational Medical Outcomes Partnership Common Data Model.

We aimed to develop a standardized method to calculate daily dose (i.e., the amount of drug a patient was exposed to per day) of any drug on a global scale using only drug information of typical observational data in the Observational Medical Outcomes Partnership Common Data Model (OMOP CDM) and a single reference table from Observational Health Data Sciences And Informatics (OHDSI). The OMOP DRUG_STRENGTH reference table contains information on the strength or concentration of drugs, whereas the OMOP DRUG_EXPOSURE table contains information on patients' drug prescriptions or dispensations/claims. Based on DRUG_EXPOSURE data from the primary care databases Clinical Practice Research Datalink GOLD (United Kingdom) and Integrated Primary Care Information (IPCI, The Netherlands) and healthcare claims from PharMetrics® Plus for Academics (USA), we developed four formulas to calculate daily dose given different DRUG_STRENGTH reference table information. We tested the dose formulas by comparing the calculated median daily dose to the World Health Organization (WHO) Defined Daily Dose (DDD) for six different ingredients in those three databases and additional four international databases representing a variety of healthcare settings: MAITT (Estonia, healthcare claims and discharge summaries), IQVIA Disease Analyzer Germany (outpatient data), IQVIA Longitudinal Patient Database Belgium (outpatient data), and IMASIS Parc Salut (Spain, hospital data). Finally, in each database, we assessed the proportion of drug records for which daily dose calculations were possible using the suggested formulas. Applying the dose formulas, we obtained median daily doses that generally matched the WHO DDD definitions. Our dose formulas were applicable to >85% of drug records in all but one of the assessed databases. We have established and implemented a standardized daily dose calculation in OMOP CDM providing reliable and reproducible results.

Automated classification of brain tumor-based magnetic resonance imaging using deep learning approach

The treatment of brain tumors poses significant challenges and contributes to a significant number of deaths on a global scale. The process of identifying brain tumors in medical practice involves the visual analysis of photographs by healthcare experts, who manually delineate the tumor locations. However, this approach is characterized by its time-consuming nature and susceptibility to errors. In recent years, scholars have put forth automated approaches to early detection of brain tumors. However, these techniques face challenges attributed to their limited precision and significant false-positive rates. There is a need for an effective methodology to identify and classify tumors, which involves extracting reliable features and achieving precise disease classification. This work presents a novel model architecture that is derived from the EfficientNetB3. The suggested framework has been trained and assessed on a dataset consisting of 7,023 magnetic resonance images. The findings of this study indicate that the fused feature vector exhibits superior performance compared to the individual vectors. Furthermore, the technique that was provided showed superior performance compared to the currently available systems and attained a 100% accuracy rate. As a result, it is viable to employ this technique within a clinical environment for the purpose of categorizing brain tumors based on magnetic resonance images scans.

Effects of alternate wetting and drying irrigation on yield, water-saving, and emission reduction in rice fields: A global meta-analysis

With limited natural resources and uncertain climate, increasing food production must mitigate the environmental impacts. Rice, a water-intensive grain crop, contributes significantly to greenhouse gas (GHG) emissions in agricultural systems. Alternate Wetting and Drying (AWD) Irrigation stands out as the prevailing water-saving irrigation technique in rice fields worldwide. Nonetheless, the effects of AWD on yield variation, water conservation, and emissions reduction as well as the synergies and trade-offs between the effects remain unclear. Here a global meta-analysis of 437 literature sources covering 93% of global rice production was conducted to quantify changes in yield, water utilization, and GHG emissions following AWD implementation. Results show that, on a global scale, AWD reduces irrigation water usage by 33.88% and enhances water use efficiency, irrigation water use efficiency, and water productivity by 20.27%, 47.58%, and 29.63%, respectively. However, there is a minor decrease in yield by 1.56%. Notably, AWD leads to a noteworthy 47.47% reduction in methane emissions, counterbalanced by a 52.20% rise in N2O emissions, ultimately resulting in a decrease in global warming potential (GWP) and greenhouse gas intensity (GHGI) by 39.38% and 38.06%, respectively. The AWD threshold is the most significant predictive factor, adopting a suitable threshold can greatly improve the accuracy of AWD implementation. Soil properties also play a pivotal role, with pH levels > 7 or soil organic carbon (SOC) < 12 g/kg correlating to more pronounced yield losses. Implementing reasonable field management measures, such as suitable rice varieties and straw incorporation can effectively increase rice productivity. Approximately 60.36% of the data falls within the "win-win" zone, demonstrating simultaneous in yield increases, water conservation, and emissions reduction. AWD is more suitable for acidic soils with high SOC (>12 g/kg) and a light texture. This study introduces novel insights for analyzing the synergies and trade-offs within the food–water–climate nexus.

Spatio-temporal features based deep learning model for depression detection using two electrodes

Deep learning has made significant contributions to the medical field and has shown great potential in various applications. Its ability to process vast amounts of data and extraction of patterns has enabled breakthroughs in medical research, diagnosis and treatment. The application of deep learning plays a vital role in depression detection. Depression is a neurological disorder characterized by persistent feelings of sadness, hopelessness and a lack of interest. The prevalence of depression is a significant factor contributing to the rise in suicide cases on a global scale. The electroencephalogram (EEG) is a non-invasive technique used to detect depression. It records brain activity using multiple electrodes. The number of EEG electrodes used for measurement directly affects the instrumentation and measurement complexity of the experiment. The present manuscript proposes a deep learning model for depression detection, focusing on two electrodes named FP1 and FP2. The purpose of employing two electrodes is to enhance the system’s portability while reducing data acquisition time and system cost. EEG is spatio-temporal data and possesses inherent spatial and temporal features. The present manuscript proposes a methodology for extracting temporal and spatial features. The temporal feature extraction module extracts temporal features in the time domain and the spatial module extracts spatial features in the spatial domain. This manuscript presents a study on the applicability of two electrodes for depression detection. This research can enhance accessibility, user-friendliness and easier data collection and analysis. The proposed deep learning model is evaluated on two benchmark datasets. It achieves 93.41% classification accuracy, 92.54% precision, 93.23% recall, 93.06% F1 score and 97.80% area under the curve (AUC) for Hospital University Sains Malaysia dataset and for Multi-modal Open Dataset for Mental-disorder Analysis dataset it achieves 79.40% accuracy, 81.18% precision, 67.73% recall, 73.80% F1 score and 85.66% AUC.

Compound and consecutive drought-flood events at a global scale

Flooding during or after droughts poses significant challenges to disaster risk management. However, interactions between droughts and floods are often overlooked as studies typically analyse these events in isolation. Here we explore historical occurrences of compound and consecutive drought-flood events and drought effects on flood severity and timing by analysing global datasets of hydrometeorological and biophysical variables for 8255 catchments worldwide. These data show that 24% of floods globally (anomalies above the 85th percentile) are preceded by, or happen during, drought conditions. Flood events occurring during drought conditions are typically of lower magnitude, especially in arid regions, while floods following drought events have a severity distribution comparable to single flood events. For most drought-flood events, flood timing appears relatively unaffected by drought conditions, but almost a quarter of the drought-flood events had flood timings occurring two to three months later than expected. These shifts in flood timing suggest droughts potentially affect flood-generating processes. As both drought and flood occurrences are projected to increase in a warming climate, interactions between them may become more common and need to be accounted for in flood risk assessment and management.

What to do With Cross-Border Environmental Pollution: Legislative Aspects

Abstract – Environmental torts and possibility of trans-border dispute settlement thereof has gained significant prominence in the recent years. Cases such as Urgenda foundation v the State of the Netherlends, Milieudefensie et al. v. Royal Dutch Shell plc., ClientEarth v. Enea, Neubauer et al. v. Germany demonstrate that environmental tort cases can be brought by stakeholders globally rather than relying on involvement of a relevant national responsible authority. EU climate policy of decreasing GHG emissions and the ambitious target of reaching climate neutrality by 2050 has resulted in a rapid development of environmental law. Thus, environmental regulation is becoming increasingly complex and possible breaches may cause serious legal consequences affecting everyone not just selected individuals or states. These developments could possibly lead to a necessity of a forum or rules where any natural person or an NGO could bring a claim against a state or multinational corporation which engages in illegal activities causing harm the environment on a global scale. Using litigation as a tool to empower society to stand against environmental wrongdoers may serve as a deterring factor to maintain order when it comes to environmental regulation. While such reasoning may seem farfetched and extreme, nevertheless it still raises a question of current regulation on determination of jurisdiction and the applicable law and whether it would be possible to benefit from developing these rules. By engaging in a systematic literature analysis identifying the relevant terms, the research shall explore the basic principles of private international law in relation to assigning jurisdiction and determining applicable law in claims concerning environmental torts. The research shall develop a methodology with the aim of covering and identifying a wider range of problems related to the determination of jurisdiction and application of law in cross-border environmental tort claims. Some of the main issues concern the determination of defendants in cases involving multinational corporations, multi-claimant cases, and the compensation of indirect damages. An exploration of material aspects in the developments of the current EU regulation and guidelines shall be analysed in conjunction with the aforementioned notion. The result of the study not only will reflect the specifics of the application of the Brussels Ibis Regulation and Rome II regulation in cross-border environmental torts, but also how the EU environmental regulation could potentially promote corporate responsibility and environmental protection. Thus, developing research on interaction between private international law and environmental law. Keywords - environmental tort, SEG emissions, jurisdiction, applicable law, cross-border pollution

Global, regional, and national burden of trachea, bronchus, and lung cancer among women of reproductive age, 1990–2019

BackgroundTrachea, bronchus, and lung (TBL) cancer has demonstrated a discernible feminization and a tendency towards younger onset in recent decades. Therefore, our objective is to examine the most recent patterns in the worldwide prevalence of TBL among women of reproductive age on a global, regional, and national scale. MethodsTo assess the prevalence trends of TBL in women of reproductive age, we calculated the estimated annual percentage change (EAPC), age-standardized incidence rate (ASIR), age-standardized death rate (ASDR), and disability-adjusted life years (DALYs) for 204 countries and territories from 1990 to 2019. These calculations were based on the Global Burden of Disease (GBD) 2019 database. ResultsFrom 1990 to 2019, there was a global increase in the absolute number of incidence cases, deaths, and DALYs of TBL in women of reproductive age. However, the ASIR, ASDR, and age-standardized DALY rates were decreasing over this period, with EAPC of -0.77 (95 % confidence interval [CI]: -1.03 to -0.51), -1.08 (95 % CI: -1.34 to -0.82), and -1.10 (95 % CI: -1.36 to -0.84), respectively. This trend was observed even in regions with higher Socio-Demographic Index (SDI). East Asia consistently had the highest ASIR, ASDR, and age-standardized DALY rate, but there was a decreasing trend. Conversely, Eastern Sub-Saharan Africa displayed an increasing burden pattern. When examining countries individually, Monaco, Greenland, and Palau had the highest ASIR. Moreover, in most countries, the ASIR for TBL increased with age, particularly among women aged 35–49 years. ConclusionsDespite a global decline in ASIR, ASDR, and age-standardized DALY rates for TBL in women of reproductive age over the past three decades, there is still a troubling increase observed in low- and low-middle SDI regions. It is crucial to implement effective preventive and curative measures in these regions in order to address this concerning trend.

Use of Metallic Mercury in Artisanal Gold Mining by Amalgamation: A Review of Temporal and Spatial Trends and Environmental Pollution

The introduction of mercury (Hg) into the environment by anthropogenic activities has resulted in negative implications for ecosystem functions and human health. Unlike the legacy of huge environmental pollution left by historic gold rushes in several developed countries, gold-rich nations in the developing world are currently witnessing what could qualify as a “new gold rush”, conducted primarily by small-scale mining operators and characterized by the use of metallic Hg (Hg0) in the amalgamation process to extract gold from crude ores. Once introduced into the environment, Hg0 can undergo biogeochemical transformations to produce Hg species such as methyl-Hg, with well-established adverse impacts on living organisms. This review summarizes published data on both historical and recent trends of the use of Hg0 in artisanal gold mining (AGM) on a global scale and emphasizes the impacts of AGM on the environment. To achieve this, we used citations from research conducted in North and South America, Europe, Asia, Africa, Australia, and New Zealand, obtained from several search engines and databases. Our findings show that, in addition to the well-known environmental and human health adverse effects of gold mining with Hg0, gold extraction by the Hg amalgamation technique is boosting the economy in parts of Africa, South America, and Asia. Unfortunately, this appealing aspect of AGM may not be easily halted, pending the creation of alternative employment. Therefore, there is a clear need for the development of safe and affordable gold extraction and purification technologies. Ultimately, the growth of this specific economic sector should be regulated to help protect both the environment and human health. Information compiled in this review should help to (i) improve the mapping of AGM-impacted soil and aquatic systems on a global scale and (ii) stimulate discussions and research on how to take down current barriers to the development and implementation of safe AGM methods.

Valuation of different organic and inorganic fertilizer combinations on the growth and yield of cabbage

abbage (Brassica oleracea var. capitata) is the most important winter leafy vegetable in the world and belongs to the Cruciferae family. An experiment was conducted at the Horticultural farm of Gauradaha Agriculture Campus, Tribhuwan University, Jhapa, Nepal from 26th December 2022 to 2nd April 2023 to evaluate the different organic and inorganic fertilizer combinations on the growth and yield of cabbage (Brassica oleracea var. capitata L. cv. Green coronet). The experiment was conducted as a randomized block design with 7 treatments replicated 3 times and the treatments are T1: 25-ton FYM/ha, T2: 25-ton PM/ha, T3: 25-ton FYM/ha + NPK (RDF), T4: 25-ton PM/ha + NPK (RDF), T5: 20-ton FYM/ha + 10-ton PM/ha + NPK (RDF), T6: 10-ton FYM/ha + 20-ton PM/ha + NPK (RDF) and T7: control. Significant variation was found among the treatments. The result showed treatment had a significant role in different parameters. T3- The highest number of leaves, T5- the highest (leaf area, weight with roots, net head yield, horizontal head diameter), T4-highest (plant height, gross yield), T6- highest (stem girth, vertical head diameter) was obtained. On the result basis T5 (20-ton FYM/ha + 10-ton PM/ha + NPK (RDF) can be adopted as sustainable crop production. This study not only provides valuable insights into optimizing cabbage production but also underscores the importance of tailored fertilizer combinations for sustainable agriculture. Furthermore, the findings contribute to the broader discourse on enhancing crop yield while minimizing environmental impact. By elucidating the efficacy of specific fertilizer blends, this research paves the way for more efficient and eco-friendly farming practices, thereby promoting food security and environmental sustainability on a global scale.

Plant beta-turnover rather than nestedness shapes overall taxonomic and phylogenetic beta-diversity triggered by favorable spatial–environmental conditions in large-scale Chinese grasslands

IntroductionAlthough it is widely acknowledged that biodiversity maintains plant community assembly processes, exploring the patterns and drivers of beta-diversity (β-diversity; species variation among local plant communities) has received much less attention compared to alpha-diversity (α-diversity; species variation within a local plant community). Here, we aim to examine the patterns and spatial–environmental drivers of taxonomic and phylogenetic β-diversity, and their components such as species turnover and nestedness, in large-scale Leymus chinensis grassland communities.MethodsWe collected plant community data from 166 sites across widely distributed L. chinensis communities in northern China, and then calculated the taxonomic and phylogenetic β-diversity indices (overall, turnover and nestedness) using a pairwise dissimilarity approach. To assess the effects and to explain the variation in the patterns of β-diversity, we collected data on geospatial, climate and soil conditions. We applied descriptive statistics, Mental correlations, and multiple linear regression models to assess the patterns and spatial–environmental drivers of β-diversity.ResultsThe β-turnover, as compared to β-nestedness, exhibited a predominant influence, constituting 92.6% of the taxonomic β-diversity and 80.4% of the phylogenetic β-diversity. Most of the spatial–environmental variables were significantly positively correlated with the overall taxonomic and phylogenetic β-diversity and β-turnover, but not with β-nestedness. Climatic factors such as MAP and MAT were the strongest predictors of both taxonomic and phylogenetic β-diversity and β-turnover. The variance partitioning analysis showed that the combined effects of spatial and environmental factors accounted for 19% and 16% of the variation in the taxonomic and phylogenetic β-diversity (overall), 17% and 12% of the variation in the β-turnover, and 7% and 1% of the variation in the β-nestedness, respectively, which were higher than independent effects of either spatial or environmental factors.DiscussionAt larger spatial scales, the turnover component of β-diversity may be associated with the species complementarity effect, but dominant or functionally important species can vary among communities due to the species selection effect. By incorporating β-diversity into grassland management strategies, we can enhance the provision of vital ecosystem services that bolster human welfare, serving as a resilient barrier against the adverse effects of climate change at regional and global scales.

Inter-Comparison of Multiple Gridded Precipitation Datasets over Different Climates at Global Scale

Comprehensive evaluations of global precipitation datasets are imperative for gaining insights into their performance and potential applications. However, the existing evaluations of global precipitation datasets are often constrained by limitations regarding the datasets, specific regions, and hydrological models used for hydrologic predictions. The accuracy and hydrological utility of eight precipitation datasets (including two gauged-based, five reanalysis and one merged precipitation datasets) were evaluated on a daily timescale from 1982 to 2015 in this study by using 2404 rain gauges, 2508 catchments, and four lumped hydrological models under varying climatic conditions worldwide. Specifically, the characteristics of different datasets were first analyzed. The accuracy of precipitation datasets at the site and regional scale was then evaluated with daily observations from 2404 gauges and two high-resolution gridded gauge-interpolated regional datasets. The effectiveness of precipitation datasets in runoff simulation was then assessed by using 2058 catchments around the world in combination with four conceptual hydrological models. The results show that: (1) all precipitation datasets demonstrate proficiency in capturing the interannual variability of the annual mean precipitation, but with magnitudes deviating by up to 200 mm/year among the datasets; (2) the precipitation datasets directly incorporating daily gauge observations outperform the uncorrected precipitation datasets. The Climate Precipitation Center dataset (CPC), Global Precipitation Climatology Center dataset (GPCC) and multi-source weighted-ensemble precipitation V2 (MSWEP V2) can be considered the best option for most climate regions regarding the accuracy of precipitation datasets; (3) the performance of hydrological models driven by different datasets is climate dependent and is notably worse in arid regions (with median Kling–Gupta efficiency (KGE) ranging from 0.39 to 0.65) than in other regions. The MSWEP V2 posted a stable performance with the highest KGE and Nash–Sutcliffe Efficiency (NSE) values in most climate regions using various hydrological models.

An Earthen Sill as a Measure to Mitigate Salt Intrusion in Estuaries

AbstractAt a global scale, deltas are vital economic hubs, in part due to the combination of their access to inland regions via river systems with their proximity to sea. However, with the sea in close vicinity also comes the threat of freshwater contamination by saline seawater, especially during droughts. This study explores the potential of a mitigation measure to estuarine salt intrusion, namely the construction of a (temporary) earthen sill—a measure implemented in the Lower Mississippi River near New Orleans (LA, USA). This study suggests design guidelines on how a sill can be used to mitigate estuarine salt intrusion: the design should focus on the longitudinal placement and the height of the sill, and the mitigating efficiency of the sill reduces with increasing tidal range. Overall, a (temporary) sill has great potential to reduce salt intrusion in salt wedge estuaries if there is sufficient water depth available.

Carbon Pricing Impacts on Four Pollutants: A Cross-Country Analysis

Research on climate change mitigation has increasingly considered carbon pricing, with these efforts concentrating on reductions in carbon dioxide (CO2) emissions. Our comprehensive cross-country analysis extends this focus by quantitatively evaluating the effects of carbon pricing on four major pollutants: CO2, nitrous oxide (N2O), methane (CH4), and particulate matter (PM). We use regressions and introduce entropy balancing to this research area. Analyzing data from 132 countries from 1992 to 2019, we find that carbon pricing is associated with an average annual reduction in CO2 emissions by 3 percentage points. A one-unit increase in a coverage-weighted carbon price is associated with reductions in N2O emissions by approximately 0.1 percentage points. A shorter panel for 2010–2017 shows a larger impact of 0.3 percentage points for PM. These findings underline the efficacy of carbon pricing not just in curtailing CO2 but in significantly mitigating other harmful pollutants on a global scale. Reductions in pollutants beyond CO2 provide further motivation for policymakers to pursue carbon pricing.

Effects of pain education on disability, pain, quality of life, and self-efficacy in chronic low back pain: A randomized controlled trial.

Low back pain stands as a prevalent contributor to pain-related disability on a global scale. In addressing chronic low back pain (CLBP), there is a growing emphasis on incorporating psychological strategies into the management process. Among these, pain education interventions strive to reshape pain beliefs and mitigate the perceived threat of pain. This randomized controlled trial sought to assess the effects of pain education on various aspects, including pain levels, disability, quality of life, self-efficacy, and prognostic characteristics in individuals grappling with CLBP. The clinical trial, retrospectively registered with the Clinical Trials Registry of India (CTRI/2021/08/035963), employed a two-arm parallel randomized design. Ninety-two participants with CLBP were randomly assigned to either the standard physiotherapy care with a pain education program or the control group. Both groups underwent a 6-week intervention. Assessment of pain intensity (using NPRS), disability (using RMDQ), self-efficacy (using the general self-efficacy scale), and well-being (using WHO 5I) occurred both before and after the 6-week study intervention. Post-intervention score comparisons between the groups revealed that the pain education intervention led to a significant reduction in disability compared to the usual standard care at 6 weeks (mean difference 8.2, p < 0.001, effect size Cohen d = 0.75), a decrease in pain intensity (mean difference 3.5, p < 0.001, effect size Cohen d = 0.82), and an improvement in the well-being index (mean difference 13.7, p < 0.001, effect size Cohen d = 0.58). The findings suggest that integrating a pain education program enhances the therapeutic benefits of standard physiotherapy care for individuals dealing with chronic LBP. In conclusion, the clinical benefits of pain education become apparent when delivered in conjunction with standard care physiotherapy during the management of chronic low back pain.

Lignocellulosic Residues from Fruit Trees: Availability, Characterization, and Energetic Potential Valorization

Reducing the carbon footprint of energy production is one of the most pressing challenges facing humanity today. Lignocellulosic biomass residues from fruit production industries show promise as a viable energy source. This paper presents a study of the Italian context concerning the utilization of orchard lignocellulosic residues for energy production as electricity or bioethanol. The potential of various orchard residues was assessed through chemical and physical analyses, and an equivalent electrical energy of about 6441.62 GWh or an amount of 0.48 Mt/y of bioethanol was obtained based on the average annual dry residue mass availability of about 3.04 Mt/y. These data represent 9.30% of the national electrical energy production from renewable sources, as well as 6.21% of the Italian demand for gasoline in 2022. Electricity generation from these residues has shown its potential as a reliable and sustainable baseload power source, as well as a source of renewable transportation fuel. The studied process could be a valuable reference to expand these concepts on a global scale to achieve a greener and more sustainable energy future.

Examining the Performance of the Sexual Minority Adolescent Stress Inventory with Transgender and Gender-Diverse Youth and Sexual Minority Emerging Adults: A Methodological Study.

Purpose: We examined the psychometric properties and criterion validity of the Sexual Minority Adolescent Stress Inventory (SMASI) among 730 sexual minority (SM) and transgender and gender-diverse (TGD) youth aged 14 to 24 years who participated in a human immunodeficiency virus study. Methods: We tested the factor structure of the global scale and subscales and measurement invariance across age, gender identity, sex assigned at birth, sexual identity, ethnoracial identity, and city. For criterion validity, we regressed mental health and substance use measures on the global scale. Results: The global scale had excellent fit (comparative fit index = 0.95) and high reliability (omega = 0.89). Subscale model fit was adequate. We confirmed invariance by gender identity and age and established criterion validity. Conclusion: The SMASI exhibits strong psychometric properties among SM emerging adults and TGD youth. Modifications could enhance the SMASI to better capture both sexual and gender minority stress among ethnoracial minority youth.

THE RIGHT TO A HEALTY ENVIRONMENT

The exercise of human rights is adversely affected by environmental degradation. Nevertheless, linking human rights to the right to a healthy environment is just one aspect of this right. The other aspect is environmental degradation, which currently does not negatively affect human rights. However, these environmental degradations may adversely affect human rights in the long term. This broader right covers ecological and ecosystem degradation, development, and protection in this context. A safe, clean, healthy, and sustainable environment was recognized as a fundamental human right for the first time ever on a global scale with the adoption of the UN General Assembly's resolution. There are only a few conventions that directly contain this right. However, it does not follow that other accords do not also safeguard this righ.This study aims to define the concept and ascertain to what extent the right to a healthy environment is protected by international law.

Investigating the Application of Regression Discontinuity in the Field of International Health Economics

As one of the most significant quasi-experimental research methods, with over eight decades of development, Regression Discontinuity Design (RDD) has garnered extensive attention within the field of international health economics. It has provided robust methodological support for causal inference in research conducted within this domain. This paper provides a comprehensive review and synthesis of the foundational concepts, origins, and evolutionary trajectory of the RDD. It systematically examines the relevant studies in the domain of international health economics, discusses the strengths and limitations of the RDD, and anticipates its future applications in the same field. The aim is to support the utilization of RDD in international health economic research. On a theoretical level, it has effectively enhanced the flexibility and applicability of research designs within the domain of international health economics, augmenting the economic efficiency and external validity of studies, and offering a versatile tool for related research on a global scale. On a practical level, RD accommodates the diverse policy needs of programs across various countries and regions. It provides a more robust and reliable empirical foundation for the formulation of pertinent health policies, aiming to improve the efficiency of health resource allocation and promote equity in health outcomes.