Selection Process Research Articles

DISCRIMINATION AGAINST FEMALE GENDER IN THE PRIVATE RENTED SECTOR IN IBADAN, NIGERIA

The private rented sector in Nigeria has been bewildered by non-lease performance, and the tenant selection process has been the center of attraction for proffering solutions to this menace. The Data for this study were obtained from sixty-eight (68) Estate Surveying and Valuation firms in Ibadan and two hundred and seventy (270) female tenants in their management portfolio. The data gathered by the researcher were analyzed using descriptive statistics (frequency distribution table, weighted mean score, and factor analysis). The result of the study revealed that 29 (42.6%) of the sampled estate surveying and valuation firms were rarely disposed to giving accommodation to female prospective tenants, while 14 (20.6%) and 10 (14.7%) were occasionally and never disposed to providing accommodation to female tenants. Sampled female tenants opined that discrimination against female tenants by property agents, marital status, and low income were the major factors causing delays in getting accommodation in the Ibadan private rented sector. Landlord discriminatory attitudes, property agent consideration, and security factors were the significant factors influencing discrimination against the female gender in tenant selection in the study area, as opined by the respondent Estate Surveyors and Valuers. The study underscores the crucial role of government and non-governmental organizations' intervention in sensitization against gender discrimination in Nigeria, developing and implementing a clear policy against female gender discrimination in the private rented sector, and the economic empowerment of the female gender.

Enhancing IVF Outcomes with Artificial Intelligence: Current Advances and Future Possibilities

The integration of Artificial Intelligence (AI) into In Vitro Fertilization (IVF) practices has marked a revolutionary shift in reproductive medicine, offering enhanced precision, efficiency, and personalized treatment plans. The rapid advancement of Artificial Intelligence (AI) has led to significant innovations in the field of reproductive medicine, particularly in In Vitro Fertilization (IVF). Traditional IVF procedures, while effective, often face challenges such as variable success rates, high costs, and the emotional burden on patients due to multiple treatment cycles. AI offers a promising solution to these issues by enhancing accuracy, personalization, and efficiency throughout the IVF process. AI algorithms have shown remarkable capabilities in diagnosing infertility by analyzing complex datasets from hormone profiles, genetic testing, and medical imaging, enabling early identification of conditions like polycystic ovary syndrome (PCOS) and endometriosis. Moreover, one of the most promising applications of AI in IVF is embryo grading. However, AI systems have been developed to objectively evaluate embryos based on time-lapse imaging, morphology, and other parameters, improving the selection process. Additionally, AI has been instrumental in optimizing ovarian stimulation protocols by analyzing patient data to determine the appropriate medication dosage, minimizing the risk of ovarian hyperstimulation syndrome (OHSS). This review discusses the current state of AI integration in fertility treatments, successful case studies, and ongoing research to develop more sophisticated AI models. Overall, AI holds immense promise in making IVF more accessible, affordable, and successful for patients worldwide, ushering in a new era of precision medicine in reproductive health.

Non-Invasive Therapeutic Approaches for Mechanical Low Back Pain: An Integrative Systematic Review

Background: Mechanical low back pain (LBP) is one of the most prevalent musculoskeletal conditions, affecting a significant portion of the global population at some point in their lives. It is characterized by pain in the lumbar region without an obvious inflammatory or infectious origin, typically exacerbated by movement, posture, or mechanical load. LBP, whether acute, subacute, or chronic, is a leading cause of temporary and permanent disability, with significant economic repercussions due to lost workdays and healthcare costs. Recent clinical guidelines, such as those from the American College of Physicians (ACP) and the National Institute for Health and Care Excellence (NICE), emphasize the importance of non-invasive interventions for managing mechanical LBP, particularly in acute and chronic cases. In this context, this systematic review aims to consolidate current evidence on available therapeutic interventions, focusing on non-invasive treatments. Objective: To evaluate the available evidence on non-invasive therapeutic interventions for the treatment of mechanical low back pain. The review aims to synthesize international clinical guideline recommendations and identify the most effective practices for reducing pain and improving functional outcomes. Methods: This review was conducted in accordance with the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. Randomized controlled trials, systematic reviews, and clinical guidelines published up to 2023 addressing the management of mechanical low back pain in adults were included. Databases such as PubMed, Cochrane Library, and grey literature sources, including guidelines from medical societies, were searched. The study selection process followed strict eligibility criteria, including publications in English and Portuguese focused on non-invasive treatments for LBP. The methodological quality of the studies was assessed using the GRADE (Grading of Recommendations Assessment, Development, and Evaluation) tool. A total of 15 studies met the inclusion criteria, covering a range of non-invasive interventions such as physical therapy, exercise, acupuncture, manual therapy, and pharmacological approaches. Results: Evidence suggests that exercise-based interventions, particularly programs focused on muscle strengthening and stretching, are effective in reducing pain and improving functional outcomes in patients with mechanical LBP. Manual therapies and acupuncture also demonstrated benefits, albeit with less robust evidence. Reviewed guidelines recommend caution in the use of pharmacological interventions, such as nonsteroidal anti-inflammatory drugs (NSAIDs), particularly in chronic cases. Conclusion: This review highlights that non-invasive therapies, especially supervised exercise programs, represent the most effective and safe treatment for mechanical low back pain. The revised international guidelines reinforce the importance of avoiding early invasive interventions, except in cases of persistent or refractory pain. Appropriate management of mechanical LBP should be individualized, integrating evidence-based interventions to promote functional rehabilitation and minimize the impact of pain on patients' quality of life.

Evaluating the Role of Community Resource Person in the Establishment of the Startup Village Entrepreneurship Programme (SVEP) Ecosystem in West Bengal

The role of CRP-EPs (Community Recourse Persons for Enterprise Promotion) in Bolpur-Sriniketan and Simlapal blocks of West Bengal was analysed based on mixed quantitative and qualitative research methodologies. The selection process for CRP-EPs is an essential part of the success of the Startup Village Entrepreneurship Programme (SVEP). The performance of CRP-EPs is measured by their maximum support to entrepreneurs in receiving the CEF Loan, its repayment, and the and the business facilitation for market linkage and bank linkage under the SVEP-Programme. The support is not limited to promotion but focuses on identifying potential entrepreneurs from the beginning with training, market, financial linkages, etc. In both blocks, entrepreneurs are using the retailer market as a mode of marketing, followed by wholesalers and other modes of marketing for business. Facebook, followed by WhatsApp and Instagram, is the online mode of marketing for entrepreneurs, guided by both CRP-EPs and the BRC (Block Resource Centre) Committee.

Optimization of U-23 futsal player selection through a simple additive weighting approach

Futsal playing skills are the main support in successfully achieving the goals of the game. There are several underlying techniques that every player needs to master, including passing, dribbling, controlling, chipping and shooting. This study aims to optimize the selection process for the best futsal players using the Simple Additive Weighting (SAW) method. The SAW method was chosen because of its ability to combine various selection criteria into one composite score, which makes decision-making easier. This research involves analyzing the needs of a futsal group to determine the relevant criteria and weights in selecting a group of players, such as passing, control, dribbling, chipping, and shooting. These indicators provide a more comprehensive picture of the capabilities and potential of the player group. Potential futsal group data are collected and processed using the SAW method with a weighted sum of the performance ratings for each alternative from all attributes. The SAW method requires a matrix normalization process on a scale that can be compared with all existing alternative ratings to produce objective and transparent ratings for the player groups. Based on the results of calculations using the SAW method, the assessment results for the prospective group of futsal players were obtained based on the specified criteria and weights. The highest score was achieved by Banyumas 1 with a score of 0.85. The research results show that the application of the SAW method in the selection process for futsal players increases the efficiency and accuracy of selecting new players. By using this approach, Porprov can more easily identify the best candidates to meet their needs.

Future Site Suitability for Urban Waste Management in English Bazar and Old Malda Municipalities, West Bengal: A Geospatial and Machine Learning Approach

The rapid urbanization occurring globally has significantly intensified the challenges of waste management in densely populated metropolitan areas. A growing amount of waste has become a major concern for municipal authorities and local governments due to the limited availability of suitable land. Geospatial techniques, such as Geographic Information Systems (GISs) and remote sensing, combined with machine learning, play a crucial role in identifying suitable sites for urban waste management. These techniques assist planners in making well-informed decisions that strike a balance between environmental preservation and urban expansion by examining spatial data on land use, population density, and environmental concerns. Geospatial tools provide a data-driven basis for policy and urban planning, ensuring effective land use, reducing ecological hazards, and promoting sustainable urban growth for municipalities such as English Bazar and Old Malda. It can also pose serious threats to the environment, public health, and communities. Focusing on the English Bazar and Old Malda Municipalities in India, this paper examines the use of geospatial technologies to identify suitable sites for waste disposal. The research aims to address the complex processes of waste generation, collection, and disposal in urban environments. Using GIS and a Multi-Criteria Decision Analysis (MCDA) approach, the study employs the Analytic Hierarchy Process (AHP) alongside the Random Forest (RF) model and a machine learning (ML) technique to identify potential waste disposal sites within the English Bazar and Old Malda Municipalities in the Malda district. Eight key criteria were considered in the site selection process: land elevation; distances from surface water, roads, railways, and urban areas; groundwater depth; land use and land cover; and distance from sensitive and restricted areas. AHP analysis showed that 8%, 26%, and 27% of the sites were categorized as very highly suitable, moderately suitable, and unsuitable, respectively. Meanwhile, 38%, 17%, and 13% of the areas were classified as unsuitable, moderately suitable, and very highly suitable according to the RF model. The overall accuracy and Kappa coefficient indicated that the AHP method (overall capacity of 83.83% and Kappa coefficient of 0.7894) was slightly better than the RF model (overall capacity of 80.61% and Kappa coefficient of 0.7474) for site suitability analysis. This research underscores the broad relevance of geospatial technology in creating resilient and environmentally sustainable cities while offering valuable guidance on effectively allocating waste disposal sites. The findings provide crucial insights for urban planners and decision-makers, facilitating the identification of optimal locations for sustainable waste management in urban settings.

A Study on Implementation and Evalutation Methods in Recruitment and Selection Process

The study examines implementation and evaluation methods used in the recruitment and selection process, focusing on best practices that improve candidate fit and organizational effectiveness. Technological advancements, changing workforce demographics, and evolving employer branding strategies have dramatically changed the recruiting landscape. Using a mixing approach, this study evaluates the current practices of many companies by combining quantitative data and qualitative data.The structured interviews of the candidate's surveillance tools, predictive analysis, and the improvement of the selection process are emphasized by major conclusions. He also emphasizes the importance of the employer and the experience of candidates who attracts the best talent, and the evaluation methods such as feedback loops and productivity measurements are optimized tactical adoption and are the goals of the organization. This is key to alignment so that a global strategy focused on data use and choice will promote more diverse and inclusive workplaces, in addition to improving workforce levels. The study concludes with a recommendation that educational institutions systematically evaluate their hiring practices to implement best practices and adapt to an ever-changing job market.

The Accelerator State

China is creating an “accelerator state” through a multi-layered system to identify and fast track the growth of high-tech SMEs in strategic sectors. Certified high-tech SMEs enjoy unique advantages, most notably privileged access to public and private financing. An analysis of the “Little Giants” program—a central feature of the accelerator state—proves that selected firms are indeed benefiting from enhanced financing. The study also reveals flaws in its selection process. The implications for foreign actors are significant. The accelerator state aims to replace imports in key value chains, which poses a direct challenge to foreign firms. The blurred lines between state support and market forces in the scheme also make it more difficult for foreign governments to track distortionary practices and enforce fair competition.

Optimization of autonomous vehicle control system reliability on a commercial scale through LIF dombi methodologies.

The resilient framework of Linguistic Intuitionistic Fuzzy Sets (LIFSs) allows for the representation and management of uncertainties related to intuitionistic judgments and linguistic expressions. Recent advances in passive and active safety systems have reduced highway fatalities. Autonomous vehicles can improve safety, efficiency, and mobility by navigating traffic without a driver. One of the primary benefits associated with this technology is that it reduces the number of traffic collisions that result in millions of fatalities and numerous injuries. In this research work, we devise two novel aggregation operators: the linguistic intuitionistic fuzzy Dombi ordered weighted averaging (LIFDOWA) operator and the linguistic intuitionistic fuzzy Dombi ordered weighted geometric (LIFDOWG) operator, and explore their fundamental structural properties. We provide innovative score and accuracy functions for multiple attribute decision-making (MADM) problems within the framework of LIF knowledge. Moreover, we use these techniques to develop a specialized algorithm for MADM issues that addresses the complexities arising from ambiguous data during the selection process. We also demonstrate the effectiveness of our proposed methods by applying them to solve the MADM scenario of selecting an optimal approach to improve the credibility of autonomous vehicle control systems on a commercial scale. In addition, we also compare and evaluate the authenticity and practicability of the newly designed techniques in comparison to existing methodologies.

Relative Radiometric Normalization for the PlanetScope Nanosatellite Constellation Based on Sentinel-2 Images

Detecting and characterizing continuous changes on Earth’s surface has become critical for planning and development. Since 2016, Planet Labs has launched hundreds of nanosatellites, known as Doves. Despite the advantages of their high spatial and temporal resolution, these nanosatellites’ images still present inconsistencies in radiometric resolution, limiting their broader usability. To address this issue, a model for radiometric normalization of PlanetScope (PS) images was developed using Multispectral Instrument/Sentinel-2 (MSI/S2) sensor images as a reference. An extensive database was compiled, including images from all available versions of the PS sensor (e.g., PS2, PSB.SD, and PS2.SD) from 2017 to 2022, along with data from various weather stations. The sampling process was carried out for each band using two methods: Conditioned Latin Hypercube Sampling (cLHS) and statistical visualization. Five machine learning algorithms were then applied, incorporating both linear and nonlinear models based on rules and decision trees: Multiple Linear Regression (MLR), Model Averaged Neural Network (avNNet), Random Forest (RF), k-Nearest Neighbors (KKNN), and Support Vector Machine with Radial Basis Function (SVM-RBF). A rigorous covariate selection process was performed for model application, and the models’ performance was evaluated using the following statistical indices: Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), Lin’s Concordance Correlation Coefficient (CCC), and Coefficient of Determination (R2). Additionally, Kruskal–Wallis and Dunn tests were applied during model selection to identify the best-performing model. The results indicated that the RF model provided the best fit across all PS sensor bands, with more accurate results in the longer wavelength bands (Band 3 and Band 4). The models achieved RMSE reflectance values of approximately 0.02 and 0.03 in these bands, with R2 and CCC ranging from 0.77 to 0.90 and 0.87 to 0.94, respectively. In summary, this study makes a significant contribution to optimizing the use of PS sensor images for various applications by offering a detailed and robust approach to radiometric normalization. These findings have important implications for the efficient monitoring of surface changes on Earth, potentially enhancing the practical and scientific use of these datasets.

Individual and organizational interventions to reduce burnout in resident physicians: a systematic review and meta-analysis

BackgroundBurnout among resident physicians during training has been prevalent, prompting training centers to introduce interventions at the individual or organizational level. However, empirical evidence is crucial before implementing such programs in practice.MethodsA systematic review and meta-analysis was carried out to evaluate the effectiveness of individual and organizational interventions in reducing burnout among resident physicians. Searching was done across five databases—PubMed, Scopus, ScienceDirect, Embase, and Cochrane Library from 1 December 2023 to 26 August 2024. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement was used for our reporting of study selection process. Eligibility criteria were randomized or non-randomized designs, with prospective intervention, with a comparator group focused on individual or organizational interventions reducing burnout, in any language and publication date. The Maslach Burnout Inventory scores for emotional exhaustion (EE), depersonalization (DP), and personal accomplishment (PA) were the three outcome measures. Two investigators independently extracted the data. The risk of bias was evaluated using Cochrane risk-of-bias tool for randomized trials (RoB2) and non-randomized studies of interventions (ROBINS-I). Cohen’s d and heterogeneity was estimated using a random-effects DerSimonian-Laird model and visualized by forest plots. Sensitivity analyses were carried out by leave-one-out meta-analysis.ResultsWe identified 33 eligible studies (n = 2536), comprising 25 (75.8%) individual intervention studies and 8 (24.2%) organizational intervention studies. Cohen’s d for individual intervention versus control were as follows: EE -0.25 (95% CI -0.40 to -0.11, p < 0.01, I2 = 49.3%), and DP -0.17 (95% CI -0.32 to -0.03, p = 0.02, I2 = 50.0%). The organizational intervention showed no significant association with any domain. Sensitivity analyses were robust in all outcomes, with differences in intervention description and design identified as potential contributors to heterogeneity.ConclusionsVarious interventions, including individual coaching, meditation, and organization interventions, have been implemented to improve resident burnout. The effectiveness of intervention demonstrated none to small practical significance in improving burnout. Data inconsistency and high risk of bias across studies limited the validity of the pooled results. Further studies should focus on a combined approach.RegistrationThe study was registered on PROSPERO, under PROSPERO registration number CRD42022349698.

AUTOMATED GESTURE RECOGNITION USING APPLIED LINGUISTICS WITH DATA-DRIVEN DEEP LEARNING FOR ARABIC SPEECH TRANSLATION

Gesture recognition for Arabic speech translation includes developing advanced technologies that correctly translate body and hand movements corresponding to Arabic sign language (ArSL) into spoken Arabic. This leverages machine learning and computer vision techniques in complex systems simulation platforms to scrutinize the gestures utilized in ArSL, detecting mild differences in facial expressions, hand shapes, and movements. Sign Language Recognition (SLR) is paramount in assisting communication for the Deaf and Hard-of-Hearing communities. It includes using vision-based methods and Surface Electromyography (sEMG) signals. The sEMG signal is crucial for recognizing hand gestures and capturing muscular activities in sign language. Researchers have comprehensively shown the capability of EMG signals to approach specific details, mainly in classifying hand gestures. This progression is a stimulating feature in extracting the interpretation and recognition of sign languages and investigating the phonology of signed language. Leveraging machine learning algorithms and signal processing techniques in complex systems simulation platforms, researchers aim to extract relevant traits from the sEMG signals that correspond to different ArSL gestures. This study introduces an Enhanced Dwarf Mongoose Algorithm with a Deep Learning-Driven Arabic Sign Language Detection (EDMODL-ASLD) technique on sEMG data. In the initial phase, the presented EDMODL-ASLD model is subjected to data preprocessing to change the input sEMG data into an attuned format. In the next stage, feature extraction with fractal theories is used to gather relevant and nonredundant data from the EMG window to construct a feature vector. In this study, the absolute envelope (AE), energy (E), root-mean square (RMS), standard deviation (STD), and mean absolute value (MAV) are the five time-domain extracted features for the EMG window observation. Meanwhile, the dilated convolutional long short-term memory (ConvLSTM) technique is used to identify distinct sign languages. To improve the results of the dilated ConvLSTM model, the hyperparameter selection process is executed using the EDMO model. To illustrate the significance of the EDMODL-ASLD technique, a brief experimental validation is made on the Arabic SLR database. The experimental validation of the EDMODL-ASLD technique portrayed a superior accuracy value of 96.47% over recent DL approaches.

Site selection for biomass-solar hybrid renewable energy facilities: Spatial modelling based on fuzzy logic-geographic information systems

The aim of this study is to propose a fuzzy logic-geographic information systems (GIS) based spatial model within the context of hybrid renewable energy, rural areas, and spatial planning approach in the site selection process of renewable energy facilities. In this context, potential suitability index and site selection suitability index for biomass-solar hybrid renewable energy facilities are determined within the spatial model, through a case study conducted in Türkiye. The study involves a two-stage process: the first stage focuses on establishing the national-level potential, and the second stage involves site selection in areas with high potential. Suitable locations are determined based on the optimal suitability index values, which are derived from fuzzy logic operators including AND, OR, SUM, PRODUCT, and GAMMA, applied in the GIS. The study results indicate that by assigning different γ values to the fuzzy GAMMA operator, results for the fuzzy AND (γ = 0.45–0.60), fuzzy OR (γ = 1.00), fuzzy SUM (γ = 1.00), and fuzzy PRODUCT (γ = 0.00–0.15) operators can be obtained. Furthermore, as the γ value increases, the suitability index also rises, and the optimal site selection occurs at γ = 0.90. The performance of the fuzzy GAMMA operator in big data sets and two-stage processes demonstrates its applicability in site selection.

A multi-criteria decision-making model for sustainable selection of coastal protection structures

Although nature-based and hybrid (i.e., a combination of hard and nature-based) solutions provide well-documented benefits to both the environment and people living nearby, they cannot be implemented in all coastal zones. Thus, the selection process of coastal protection structures (CPSs) requires an in-depth and comprehensible assessment with an effective and robust decision-support framework. Existing frameworks in the literature, however, do not focus on the selection of nature-based solutions and environmental and social factors were not considered thoroughly in these frameworks. In this respect, this research aims to develop a decision-support model that guides decision-makers in selecting the most appropriate and sustainable CPSs. The framework was designed in a way that includes a variety of environmental and social factors along with economic and technical aspects. Then, fuzzy AHP and TOPSIS analyses were conducted to determine the relative importance of the selection factors and to select the most appropriate CPS. Factors such as sediment dynamics, geotechnical issues, construction cost, transportation and navigation effects, and technological know-how were among the top-rated factors. TOPSIS analysis results revealed that mangrove forests and artificial reefs were the top two most sustainable CPSs. The proposed framework can minimize the social and environmental impacts of CPSs and expedite the green transition in the coastal environment.

Silk Proteins: Designs from Nature with Multipurpose Utility and Infinite Future Possibilities.

This is a Perspective on nature as a story-teller, where inputs of evolution drove the remarkable protein designs found in silks. This selection process has resulted in silk materials with novel chemistry and properties to support organism survival in nature, yet with newfound utility in everything from comic books and automobiles to medicine. With growing global concerns related to environmental health, silks also serve as an invaluable instructional guide to the future of sustainable material designs.

Exploring the Barrier between Language and Culture from the Perspective of Postmodernist Aesthetics - Taking the Translation of One Hundred Years of Solitude by Huang Jinyan and the Translation by Fan Ye as an Example

Focusing on the translations of Marquez’s One Hundred Years of Solitude by Huang Jinyan and Fan Ye, this paper explores how language shapes and reflects cultural differences, thus influencing cross-cultural communication from a postmodernist aesthetic perspective. The opening chapter briefly describes the relevance of postmodern aesthetics to translation studies, with a special focus on the strategies of naturalization and alienation in magical realist translations and their role in conveying cultural connotations. Subsequently, a comparative analysis of the two translations reveals the differences in the translators’ strategies in dealing with the complex symbols and cultural elements of the original work: Fan’s translation tends to alienate, retaining the exotic and magical qualities and preserving the uniqueness of Latin American culture, but it may increase the barriers to comprehension for Chinese readers; Huang’s translation is inclined towards naturalization, which is closer to local reading habits and enhances the readability of the text, but it may diminish the cultural shock and depth of the original text. The core of the article emphasizes that translation is a dual process of cultural selection and re-creation. It requires the translator to strike a balance between preserving the original style and transmitting the cultural essence within the framework of postmodern aesthetics. The article further discusses the crucial role of this balance in enhancing cross-cultural communication and expanding horizons. It also proposes guidelines for future translation practice and research, advocating for a harmonious and symbiotic translation strategy that respects the original work while adapting to the target culture, particularly in the translation of cross-cultural literature.

Enhancing Portfolio Optimization: A Two-Stage Approach with Deep Learning and Portfolio Optimization

The portfolio selection problem has been a central focus in financial research. A complete portfolio selection process includes two stages: stock pre-selection and portfolio optimization. However, most existing studies focus on portfolio optimization, often overlooking stock pre-selection. To address this problem, this paper presents a novel two-stage approach that integrates deep learning with portfolio optimization. In the first stage, we develop a stock trend prediction model for stock pre-selection called the AGC-CNN model, which leverages a convolutional neural network (CNN), self-attention mechanism, Graph Convolutional Network (GCN), and k-reciprocal nearest neighbors (k-reciprocal NN). Specifically, we utilize a CNN to capture individual stock information and a GCN to capture relationships among stocks. Moreover, we incorporate the self-attention mechanism into the GCN to extract deeper data features and employ k-reciprocal NN to enhance the accuracy and robustness of the graph structure in the GCN. In the second stage, we employ the Global Minimum Variance (GMV) model for portfolio optimization, culminating in the AGC-CNN+GMV two-stage approach. We empirically validate the proposed two-stage approach using real-world data through numerical studies, achieving a roughly 35% increase in Cumulative Returns compared to portfolio optimization models without stock pre-selection, demonstrating its robust performance in the Average Return, Sharp Ratio, Turnover-adjusted Sharp Ratio, and Sortino Ratio.

Agronomic Evaluation and Chemical Characterization of Salvia lavandulifolia Vahl. over 3 Consecutive Years Cultivated Under Harsh Climatic Conditions in Southeast Spain.

The cultivation of Salvia lavandulifolia, Spanish sage, makes an important contribution to the economy of many rural areas in Southeastern Spain. This aromatic plant species is characterized by high intraspecific variability, which makes the selection process for the establishment of homogeneous crops difficult. Additionally, imminent climate change threatens to reduce its production, especially when cultivated in drylands. Therefore, to guarantee the continued production of this type of sage, it is essential to study its agronomic behavior and production quality. For this, clones from four ecotypes were cultivated for three years, assessing changes in their biomass production, essential oil yield and quality, and phenolic fraction, as well as the corresponding antioxidant activity. The results suggest that essential oil yield is genetically predetermined, greater biomass not being associated with higher quantities of essential oil. Weather conditions affected both essential oil and phenolic fraction secondary metabolism. Under very harsh conditions, Spanish sage produces higher concentrations of camphor and 1,-8-cineole along with luteolin-7-O-glucoside, and lithospermic, rosmarinic, and salvianolic A acids in its phenolic fraction. The synthesis of these components helps the species to withstand the hot and dry conditions typical of southeast Spain.

Childhood socioeconomic status and depression in adulthood: systematic review

Abstract Background Low socioeconomic status (SES) is generally associated with poorer mental health, particularly depression. We carried out a systematic review to address the question of whether childhood SES is associated with depression in adulthood and the potential pathways which may explain this association. Methods We followed a systematic search strategy in PubMed and Scopus with specific inclusion and exclusion criteria. After removing duplicates, studies were screened for eligibility and quality was assessed independently by two assessors using the Newcastle-Ottawa scale. Results After the selection process, 20 observational studies (7 cohort and 13 cross-sectional) that examine the association between SES during childhood and depression in adulthood were included in the systematic review. The most common measures of childhood SES were parental education, occupation or family income, estimated subjectively and/or objectively. In 17 of the studies an inverse relationship between childhood SES and depression was shown. In 17 studies, models were employed to demonstrate the possible pathways which explain the relationship between childhood SES and depressive symptoms. The most important mediators were adult SES, psychosocial factors, childhood trauma and physical health. Conclusions Our findings reveal the long-term association between childhood socioeconomic conditions and depression later in life. Suboptimal childhood SES seems to be an important risk factor for depression in adulthood. Improving childhood socioeconomic conditions can potentially have lasting effects well beyond childhood. Key messages • Protecting children against the effects of socioeconomic adversity could reduce depression in adult life. • Policy makers should direct resources towards alleviating childhood disadvantage for promoting health in adulthood.

The use of digital technologies to promote sexual health in young adults: a narrative review

Abstract Background New technologies have the potential to play an important role in promoting sexual health, especially among young people, who are the biggest users and those at greatest risk of contracting Sexually Transmitted Infections (STI). The risk behavior assumed by young people today highlights this vulnerability in several contexts, such as individual, social, economic, and family. In this sense, it becomes relevant to approach sex education in schools using digital technologies. Objectives To identify the evidence in the literature on the use of digital technologies to promote the sexual health of young adults. Methods Narrative literature review. Three independent reviewers extracted, analyzed and synthesized the articles and results. Results Through the selection process, four studies were included. The articles show that the use of digital technologies (mobile phones/applications) leads to increased knowledge, improved attitudes and sexual behavior among young adults. The interventions studied in the studies were based on Short Message Service, electronic mail interventions and the MyPEEPS mobile application, with the following main focuses: Increased knowledge, attitudes, and behaviors of young adults. Considering the results of the studies, all interventions proved to be effective as facilitators of sexual health promotion. Conclusions The use of digital technologies allowed an increase in the promotion of the sexual health of the participants, since there was an increase in the level of knowledge with the use of digital technologies such as SMS, emails and mobile applications when comparing the experimental and control groups. The use of these digital technologies also allowed for a change in attitudes and behaviors, such as a significant increase in serological testing for STIs and an increase in health care seeking. Key messages • The use of digital technology promotes the sexual health of young adults, so these strategies should be implemented by health professionals. • The dissemination of these digital strategies will have an impact on Public Health, specifically regarding the sexual health of young adults.