Selection Process Research Articles

Managers’ Productivity and Recruitment in the Public Sector

Governments face many constraints in attracting talented managers to the public sector, which often lacks high-powered incentives. In this paper, we study how a civil service reform in Chile changed the effectiveness of a vital group of public sector managers: school principals. First, we estimate principal effectiveness by using an extension of the canonical teacher value-added model. Then we evaluate the effect of the reform on principal effectiveness using a difference-in-differences approach. We find that public schools appointed more effective managers and improved their students’ outcomes after increasing the competitiveness and transparency of their selection process. (JEL D73, H83, I21, J24, J45, O15)

Multi-trait selection for mean performance and stability in purple-fleshed sweet potato

Few purple-fleshed sweet potato (PFSP) cultivars are available for Brazilian growers, urging for the development of new and adapted cultivars. PFSP breeding programs should include multiple traits during the selection process to increase the chances of developing an adequate genotype, specially yield-related and quality-related traits. The objective was to select promising PFSP genotypes based on yield and tuberous-roots-related traits exploring genotype x environment interaction (GEI). Four experiments were carried out, in which 23 pre-selected PFSP genotypes were evaluated based on yield and quality traits, after screening among 2500 experimental genotypes. The first experiment adopted an augmented block design, with ‘Luiza’ interspersed as a control. From this first experiment, the 19 experimental genotypes with the best performance were selected for the following three experiments, which were conducted in a randomized block experimental design, with three replications each, including ‘Luiza’ as control. The best genotypes were selected in the first experiment using the multi-trait genotype-ideotype distance index (MGIDI). For the following experiments, the performance of the genotypes was assessed using deviance analysis, genotypic stability through weighted average of absolute scores (WAASB) index, and the multi-trait stability index (MTSI). In the first experiment, three factors were retained, being associated to commercial production of tuberous roots (‘K-104′, ‘K-25′ and ‘U1–29′), total production of tuberous roots (‘K-110′, ‘C-42′, ‘F-16′, and ‘U1–29′), and quality of tuberous roots (‘F-22′), respectively. Desired gains were observed for all traits. For the experiments II, III, and IV five factors were retained, being four related to quality of tuberous roots and one to yield components. The GEI was significant for yield-related traits and desired gains were observed for most traits. Due to high commercial tuberous root yield and low WAASB values, the genotypes ‘U1–29′, ‘K-98′, ‘F-22′, ‘K-57′, ‘C-36′, ‘U1–15′, ‘K-25′, and ‘U2–12′ were highly productive and stable. The genotypes selected by the MTSI index were ‘U1–15′, ‘U2–10′, ‘U1–29′, ‘K-98′, and ‘K-78′. The multi-trait selection enabled the identification of promising genotypes highlighting their strengths and weaknesses.

Understanding the features and effectiveness of randomized controlled trials in reducing COVID-19 misinformation: a systematic review.

This systematic review aimed to assess the features and effectiveness of individual-level randomized controlled trials targeting COVID-19 misinformation. The selection process included rigorous criteria, resulting in the inclusion of 24 individual studies from 21 papers. The majority of studies were conducted in high-income countries, with the accuracy/credibility of information as the primary outcome. Debunking and boosting interventions were the most common interventions while nudging and content labeling interventions were examined in a few studies. This study highlights that further research is needed to enhance the effectiveness of boosting strategies and to explore the impact of combined interventions. Addressing bias concerns and standardizing intervention assessment measures will contribute to the development of evidence-based approaches in this critical area.

Effect of Plant Identity in Wheat Mixtures on English Grain Aphid (Sitobion avenae) Control

ABSTRACTField experiments have demonstrated that wheat mixtures differ in their ability to regulate aphid populations. To further investigate the effectiveness of wheat mixtures (Triticum aestivum and Triticum turgidum) in controlling aphids, we conducted both laboratory and greenhouse experiments. Specifically, we assessed the associational resistance of two wheat mixtures (Florence‐Aurora with Forment, Florence‐Aurora with Montcada), and their respective monocultures, in different stages of the aphid host selection process. We analysed aphid acceptance rate, population growth, and load under different wheat treatments. Additionally, we characterised wheat aboveground biomass and nitrogen content as important functional traits for aphid resistant. Aphid acceptance decreased in plants of cv. Forment when exposed to volatiles from undamaged Florence‐Aurora plants, whereas the other tested combinations tested had no effect. Aphids performed differently in the two mixtures: Florence‐Aurora mixed with Forment significantly reduced aphid population growth and load compared to the monocultures, whereas the combination of Florence‐Aurora with Montcada wheat had no effect on aphid performance. The plant–plant interactions also modified the analysed traits. Nitrogen content of Florence‐Aurora wheat plants was reduced when mixed with Forment wheat, which may explain the lower aphid load observed in plants of cv. Florence‐Aurora when mixed with plants of cv. Forment. However, mixing wheats with similar aboveground biomass resulted in an increase in the average biomass of plants of both cultivars which could have led to a higher aphid population. The data supports the idea of right neighbour, as the benefits of wheat mixtures for aphid control were determined by the identity of the combined plants (or species). Finally, our results suggest that associating wheats with different traits may promote facilitative interactions, which in turn enhances associational resistance, whereas the combination of wheats with similar traits may result in competitive interactions that may hinder aphid control benefits.

Chemistry, Analysis, and Biological Aspects of Daprodustat, A New Hypoxia Inducible Factor Prolyl Hydroxylase Inhibitor: A Comprehensive Review.

The National Health and Nutrition Examination Survey (NHANES) carried out a survey between 2007-10 and found that as compared to the general population, the prevalence of anemia in chronic kidney disease (CKD) patients was twice high. Daprodustat is an investigational novel drug for the treatment of renal anemia. The objective of this study is to provide a comprehensive review of chemistry, synthesis, pharmacology, pharmacokinetic, and bioanalytical methods for the analysis of Daprodustat. To improve understanding, a review was carried out by creating a database of relevant prior research from electronic sources such as ScienceDirect and PubMed. The methodology is shown in the flowchart of the literature selection process. The drug was approved in 2020 for therapeutic purposes in Japan. It is a novel drug approved for the treatment of anemia in chronic kidney disease for oral administration. It is intended for adults who have undergone dialysis for a minimum of four months and are experiencing anemia as a result of chronic kidney disease. This review examines therapeutic, pharmacological, and analytical aspects related to the novel drug Daprodustat.

Postoperative conditions of rehabilitative interest in lung transplantation: a systematic review.

Lung transplantation is an elective treatment option for end-stage respiratory diseases in which all medical therapy options have been exhausted. The current study aimed to identify updated information on the postoperative conditions that may impair rehabilitation after lung transplantation and to provide specific considerations of their clinical relevance during the recovery process. The present study is a systematic review conducted by searching three primary databases: the United States National Library of Medicine PubMed system, Scopus, and the Cochrane Library. The databases were searched for articles published from database inception until May 2024; at the end of the selection process, 27 documents were included in the final analysis. The retrieved material identified 19 conditions of rehabilitative interest that potentially affect the postoperative course: graft dysfunction, dysphagia, postsurgical pain, cognitive impairment, chronic lung allograft dysfunction-bronchiolitis obliterans syndrome, phrenic nerve injury, delayed extracorporeal membrane oxygenation weaning, airway clearance, refractory hypoxemia, mediastinitis, reduced oxidative capacity, sternal dehiscence, coronavirus disease 2019 (COVID-19), gastroparesis, ossification of the elbow, Takotsubo cardiomyopathy, airway dehiscence, recurrent pleural effusion, and scapular prolapse. Although some patients are not amenable to rehabilitation techniques, others can significantly improve with rehabilitation.

Optimizing Supplier Selection using Fuzzy MCDM

This study explores the supplier selection problem within technology manufacturing by employing the Fuzzy Analytic Hierarchy Process (FAHP). Data was collected from a technology manufacturing company, and expert evaluations were sought from professionals with over 10 years of experience in the industry. The criteria considered for supplier selection included profitability of the supplier (C1), relationship closeness (C2), technological capability (C3), conformance quality (C4), and conflict (C5). The FAHP results indicated that relationship closeness (C2) ranked highest among the criteria, highlighting its significance in the supplier selection process, while conflict (C5) received the lowest ranking. These findings emphasize the importance of nurturing strong relationships with suppliers and vendors to enhance overall supply chain performance. The proposed FAHP model effectively addresses the complexities and uncertainties inherent in supplier evaluation, offering a robust decision-making tool tailored to the specific needs of technology manufacturing companies

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.

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.

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.

Radar-based Contactless Heart Beat Detection with a Modified Pan–Tompkins Algorithm

Abstract Background: Using radar for non-contact measuring human vital signs has garnered significant attention due to its undeniable benefits. However, achieving reasonably good accuracy in contactless measurement senarios is still a technical challenge. &amp;#xD;Materials and methods: The proposed method includes two stages. The first stage involves the process of datasegmentation and signal channel selection. In the next phase, the raw radar signal from the chosen channel is subjected to modified Pan-Tompkins.&amp;#xD;Results: The experimental findings from twelve individuals demonstrated a strong agreement between the contactless radar and contact electrocardiography (ECG) devices for heart rate measurement, with correlation coefficient of 98.74 percentage; and the 95% limits of agreement obtained by radar and those obtained by ECG were 2.4 beats per minute.&amp;#xD;Conclusion: The results showed high agreement between heart rate calculated by radar signals and heart rate by electrocardiograph. This research paves the way for future applications using non-contact sensors to support and potentially replace contact sensors in healthcare.

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.