Scale Problems Research Articles

Social innovation for inclusion and sustainable development: a case study on BRAC institute of skills development (BISD)

PurposeThe paper aims to understand the stages of the social innovation process and how it can be used for sustainable development.Design/methodology/approachThis single case study used multiple sources, especially data from extensive field visits and selective in-depth interviews. Direct observation, web content analysis, journals, annual reports and news articles were also used.FindingsThe findings show that BRAC institute of skills development (BISD) adopted a unique formula for the social innovation process: problem identification, new idea, design prototype, pilot, perfect and scale up. This study also finds that BISD has a significant socio-economic impact in Bangladesh. The impacts of BISD are associated with several United Nations (UN) Sustainable Development Goals (SDGs), including SDG 1 on poverty, SDG 4 on inclusive learning, SDG 5 on gender equality and women empowerment, SDG 8 on decent work and economic growth and SDG 16 on social inclusion.Practical implicationsThe discussions of this study ultimately pave a clear roadmap for policymakers, practitioners and academics to improve mechanisms for sustainable development through social innovations in emerging countries.Originality/valueThis paper provides a practical application of the social inclusive innovation process theory by which vocational training institutes can scale their sustainable impact. More knowledge is needed on how organisations can implement social innovation projects in emerging countries. This paper provides exploratory evidence to fill this gap. It demands a promising area of interest to investigate further research on the compatibility of social innovation in skills development programmes to gear up the status of an underprivileged community.

Comparison of HTN Planning and OR-based Approaches for Solving Problems in Logistics Domains

Logistics is a day-to-day real-life situation leading to a very large domain. Among the various kinds of logistics problems we encounter are those pertaining to transportation, product inventories and location. Traditionally, Operations Research (OR) techniques are used most often and are popular candidates for solving logistics problems. With the development of Hierarchical Task Network (HTN) planning algorithms, there has been a recent trend to use HTN planners to address planning problems in general. It is also envisaged that HTN planners could solve logistics problems; yet, no proof or evidence could be found as to whether this approach is more efficient or suitable to handle logistics problems. In this paper, a comparison is done to ascertain whether HTN planning could be more efficient than OR-based approaches in solving logistics problems. Results revealed that HTN planning outperforms the traditional OR-based approach, especially in solving moderate-sized and more difficult harder problems. Furthermore, HTN planning is capable of solving very large scale problems, whereas OR based approach was unable to handle them within the same computational resource limitations.Keywords: Logistics, Operations research, Hierarchical task network, Transportation simplex method, HyperTensioN

Perceived Strengths and Difficulties in Children and Adolescents With Chronic Physical Health Conditions

Abstract: Theoretical Background: The Strengths and Difficulties Questionnaire (SDQ) is often used for screening of psychological problems in children with chronic physical health conditions (CPHC). Objective: The present meta-analysis tested (a) whether the SDQ scores of young people with CPHC show elevated psychological symptoms and lower prosocial behavior compared with healthy peers or test norms, (b) which conditions are associated with the strongest increase in symptoms, and (c) whether results vary by sample and study characteristics. Method: Studies that compared SDQ scores between young people with CPHC and healthy peers or test norms or that provided sufficient information for a comparison with established normative data or sociodemographically equivalent healthy groups from that country, and if studies were published or made available online before July, 2023, were included in the meta-analysis. Study quality was evaluated with the Mixed Methods Appraisal Tool. A systematic search in the electronic databases Medline, PsycINFO, PSYNDEX, Web of Science, and the SDQ database resulted in 605 studies of 217,440 children and adolescents with CPHC that were included in random-effects meta-analyses. Results: There were, on average, small-to-moderate elevations in total difficulties (parent/self-report: g = .52/.31) and the four problem scales as well as slight reductions in parent reports on prosocial behavior ( g = -.17). Elevations in parent- and self-reports on emotional symptoms ( g = .48/.31) and peer problems ( g = .43/.26) were significantly stronger than elevations in conduct problems ( g = .30/.10) and hyperactivity–inattention ( g = .33/.13). In addition, children with cerebral palsy (parent/self-report of total problems: g = .82/.66), chronic fatigue syndrome ( g = .82/.66), and epilepsy ( g = .95/.40) showed above-average elevations in psychological symptoms. Stronger effect sizes were found, among others, in older children and in the case of a later age at onset. Direct comparisons of the results of published and unpublished studies on self-rated SDQ found some evidence for a possible publication bias, while results of published and unpublished studies did not vary when parent ratings were used. With regard to limitations, only few studies were available on teacher ratings and from non-Western countries. Illness-specific analyses were impossible for rarely assessed conditions, such as cystic fibrosis and spina bifida. Discussion and Conclusion: It is concluded that children with epilepsy, cerebral palsy, chronic fatigue syndrome, and other conditions affecting the brain should be, in particular, screened for psychological problems. Symptom overlap of some CPHC with the Emotional Symptoms scale has to be considered when interpreting the data.

Design of a news recommendation model based on collaborative filtering and LSTM

This paper aims to design a news recommendation model based on user preferences to address the issues in recommendation systems under large datasets. Initially, four datasetsclick_history, news, news_embedding, and user_predictwere integrated into a single table, followed by data cleaning and feature engineering. Due to the large volume of data, this paper proposes necessary data filtering for the training and testing sets, utilizing temporal data to construct user feature vectors and news feature vectors. One challenge is how to effectively integrate user preferences and news features into the model to avoid overfitting or underfitting. In the model design and building phase, different methods were attempted to merge the information of users and news. Ultimately, the user preference features were processed using a fully connected layer, and the news embedding vectors were handled using an LSTM model. These two data parts were then combined into another fully connected layer, using ReLU as the activation function and CELoss as the loss function. Subsequently, the model's hyperparameters were adjusted and evaluated, achieving favorable model performance. The prediction accuracy for recommending news to users in user_predict was calculated as an evaluation criterion. Finally, this paper proposes directions for generalization and optimization in three aspects: data processing, model design, and experimental design. This includes data processing methods, potential improvements or mechanisms that could be incorporated into the model, and hyperparameter tuning. The paper primarily proposes data filtering to solve the problem of excessive data scale, which may aid in addressing recommendation system issues under large-scale datasets.

Synthesis and Performance Evaluation of Modified Polyaspartic-Acid-Based Scale Inhibitor

This paper focuses on the selection and application of scale inhibitor by studying the problem of pipeline scaling in geothermal well development. Adding scale inhibitor can effectively reduce the treatment cost and achieve a good scale resistance effect, but the commonly used polyaspartic acid scale inhibitor has problems such as poor scale inhibition effect and large use limitations. Therefore, a new modified polyaspartic acid scale inhibitor (His-Tyr-SA-PASP) was prepared using polysuccinimide (PSI) as the raw material and histidine (His), tyrosine (Tyr), and sulfonic acid (SA) as the modification reagent. When the dosage of His-Tyr-SA-PASP was 8 mg/L, the scale inhibition rate of CaCO3 was 94.40%. In addition, the scale inhibition effect of His-Tyr-SA-PASP on CaCO3 was better than that of PASP. At the same time, under the condition of a static experiment at 75 °C, according to the ion concentration of water samples in different scale zones, this paper also identified the ratio of four composite scale inhibitors. When the dosage of compound scale inhibitor was 100 mg/L, Sodium of Polyaspartic Acid–Diethylene Triamine Penta (Methylene Phosphonic Acid)–2-Phosphonobutane-1,2,4-Tricarboxylic Acid–Amino Trimethylene Phosphonic Acid–Copolymer of Maleic and Acrylic Acid = (10:10:5:1:9), (15:10:5:2.5:2.5), (12.5:5:10:1:6.5), and (15:5:10:4:1) and the scale inhibition rate was more than 95%. Under the condition of a dynamic experiment, the optimized composite scale inhibitor still showed a scale inhibition rate of more than 90%. It provides a useful reference for the practical application of water treatment in geothermal wells and has the prospect of industrial application.

PM2D: A parallel GPU-based code for the kinetic simulation of laser plasma instabilities at large scales

Laser plasma instabilities (LPIs) have significant influences on the laser energy deposition efficiency and therefore are important processes in inertial confined fusion (ICF). Numerical simulations play important roles in revealing the complex physics of LPIs. Since LPIs are typically a three wave coupling process, the precise simulations of LPIs with kinetic effects require to resolve the laser period (around one femtosecond) and laser wavelength (less than one micron). In the typical ICF experiments, however, LPIs are involved in a spatial scale of several millimeters and a temporal scale of several nanoseconds. Therefore, the precise kinetic simulations of LPIs in such scales require huge computational resources and are hard to be carried out by present kinetic codes like particle-in-cell (PIC) codes. In this paper, a full wave fluid model of LPIs is constructed and numerically solved by the particle-mesh method, where the plasma is described by macro particles that can move across the mesh grids freely. Based upon this model, a two-dimensional (2D) GPU code named PM2D is developed. The PM2D code can simulate the kinetic effects of LPIs self-consistently as normal PIC codes. Moreover, as the physical model adopted in the PM2D code is specifically constructed for LPIs, the required macro particles per grid in the simulations can be largely reduced and thus overall simulation cost is considerably reduced comparing with PIC codes. More importantly, the numerical noise in the PM2D code is much lower, which makes it more robust than PIC codes in the simulation of LPIs for the long-time scale above 10 picoseconds. After the distributed computing is realized, our PM2D code is able to run on GPU clusters with a total mesh grids up to several billions, which meets the requirements for the simulations of LPIs at ICF experimental scale with reasonable cost. Program summaryProgram Title: PM2DCPC Library link to program files:https://doi.org/10.17632/xscj6vnkkw.1Licensing provisions: GNU General Public License v3.0.Programming language: C++, CUDA.Nature of problem: Although the large scale simulations of laser plasma instabilities (LPIs) is of great significance for the inertial confinement fusion (ICF), there is still no suitable code to simulate these problems. PM2D code based on a GPU platform provides an effective method to simulate these large scale problems in ICF.Solution method: A fluid model for LPIs is established firstly, which contains wave equations that describe the laser propagating process, electron and ion fluid equations that describe the plasma motions, and a Poisson's equation that describes the electrostatic field induced by charge separation. The wave equation is solved on a rectangular region using absorption boundary conditions on all of four boundaries. The absorption boundary condition on the left boundary is further extended to allow the incidence of driven lasers and absorption of scattering lasers simultaneously. The fluid equations in the physical model are solved by the particle-mesh method, in which the macro particles are driven to move by fluid forces. Since macro particles can move freely within the fixed fluid grids, the PM2D code can capture the kinetic effects self-consistently. The Poisson's equation for the electrostatic field is solved by a Fourier decomposition method in the y direction, which helps to decrease the simulation cost greatly. The PM2D code is developed on a GPU platform base on CUDA toolkit, which largely increase the computational speed.

From a national elite to the global elite: Possibilities and problems in scaling up.

This research note highlights emerging findings that speak to the challenges of joining the transnational elite, particularly for those coming from the Global South. For a longitudinal study of wealth inheritors becoming more transnational via their educational paths, we spoke with 16 young people who were all in their early 20s and primarily from economic elite families in the Global South. Some participants had clear ambitions, while others were less sure about their future, wondering where they should move and what they should do when they got there. Their various narratives reveal that underlying the possibilities and problems of where to locate themselves was our participants' access to different constellations of economic, social and cultural capital, as well as their race, citizenship and 'home' country's geopolitical situation. Their parents' ambitions that they become part of a global elite remained in most cases largely unfulfilled-despite a significant economic investment in their secondary and university educations. Only a small minority of our participants aspired to and/or were able to secure such transnational futures.

Strategic flood impact mitigation in developing countries’ urban road networks: Application to Hanoi

Due to climate change, the frequency and scale of flood events worldwide are increasing dramatically. Flood impacts are especially acute in developing countries, where they often revert years of progress in sustainable development and poverty reduction. This paper introduces an optimization-based decision support tool for selecting cost-efficient flood mitigation investments in developing countries’ urban areas. The core of the tool is a scenario-based, multi-period, bi-objective Mixed Integer Linear Programming model which minimizes infrastructure damage and traffic congestion in urban road networks. The tool was developed in collaboration with Vietnamese stakeholders (e.g., local communities and government authorities), and integrates data and inputs from other disciplines, including social science, transport economics, climatology and hydrology. A metaheuristic, combining a Greedy Randomized Adaptive Search Procedure with a Variable Neighborhood Descent algorithm, is developed to solve large scale problem instances. An extensive computational campaign on randomly generated instances demonstrates the efficiency of the metaheuristic in solving realistic problems with hundreds of interdependent flood mitigation interventions. Finally, the applicability of the interdisciplinary approach is demonstrated on a real case study to generate a 20-year plan of mitigation investments for the urban area of Hanoi. Policy implications and impacts of the study are also discussed.

On the benefits of a multiscale domain decomposition method to model-order reduction for frictional contact problems

In this paper, the efficiency of a multiscale strategy based on a domain decomposition method (DDM) for model-order reduction of time-dependent frictional contact problems is presented. The proposed strategy relies on the LArge Time INcrement (LATIN) nonlinear solver combined with model reduction based on the Proper Generalized Decomposition (PGD). The LATIN presents a robust treatment of contact conditions, sharing similarities with augmented Lagrangian approaches, and naturally leads to a mixed DDM. In addition, the global space–time formulation of the method allows PGD-based model reduction to be used during computations, creating and enriching on-the-fly reduced bases per substructure to better track sliding fronts and propagative phenomena. The introduction of a multiscale strategy in the LATIN framework is consistent with the physics of contact problems, in which phenomena with different wavelengths interact: local solutions at contact interfaces presents high gradient effects with a short wavelength compared to the characteristic length of the structure. By taking advantage of this, the coarse scale problem of the strategy enables to capture efficiently the behavior of the problem at the structural level, focusing then on capturing the local contact variations at the contact interfaces. The most important features of the approach are shown comprehensively on a simple one-dimensional frictional contact problem. Then, its robustness and effectiveness are tested on a two-dimensional multibody frictional contact problem with more complex loadings. Guidelines are also given for choosing the parameters of the strategy, in particular those driving the construction of the reduced basis.

Blockchain-based crowdsourcing for human intelligence tasks with dual fairness

Human intelligence tasks (HITs), such as labeling images for machine learning, are widely utilized for crowdsourcing human knowledge. Centralized crowdsourcing platforms face challenges of a single point of failure and a lack of service transparency. Existing blockchain-based crowdsourcing approaches overlook the low scalability problem of permissionless blockchains or inconveniently rely on existing ground-truth data as the root of trust in evaluating the quality of workers' answers. We propose a blockchain-based crowdsourcing scheme for ensuring dual fairness (i.e., preventing false reporting and free riding) and improving on-chain efficiency concerning on-chain storage and smart contract computation. The proposed scheme does not rely on trusted authorities but rather depends on a public blockchain to guarantee dual fairness. An efficient and publicly verifiable truth discovery scheme is designed based on majority voting and cryptographic accumulators. This truth discovery scheme aims at inferring ground truth from workers' answers. The ground truth is further utilized to estimate the quality of workers' answers. Additionally, a novel blockchain-based protocol is designed to further reduce on-chain costs while ensuring truthfulness. The scheme has O(n) complexity for both on-chain storage and smart contract computation, regardless of the number of questions, where n denotes the number of workers. Formal security analysis is provided, and extensive experiments are conducted to evaluate its effectiveness and performance.

How to improve data quality to empower business decision-making process and business strategy agility in the AI age

Machine learning (ML) and predictive analytics (PA) can provide invaluable assistance for forecasting trends and informing decision-making using data. Considering the business strategy agility as a quick response to market dynamics, this article presents solutions to enhance data quality and management, model interpretation ability, and availability using Explainable AI, with cloud computing and distributed systems that can address scaling problems. When companies utilize these technologies wisely, they can gain an edge, achieve sustainable growth, and make informed decisions. We show how ML and PA can enhance decision-making and business strategy. We also remind that there are antecedents to data quality management: Data culture and Leadership, preparing the company to benefit from the information business strategy agility.

Innovative stimuli-responsive membrane MSF brine rejection dilution by tertiary treated sewage effluent

In this study, treated wastewater and Multi-Stage Flash (MSF) brine were integrated into the Forward Osmosis (FO) system using pressure stimuli-responsive Nanofiltration (PSRNF) membranes to dilute magnesium, calcium, and sulfate MSF plant brine reject. The deposition of magnesium sulfate and calcium sulfate in the heat exchanger is one of the main issues affecting the performance and efficiency of MSF thermal desalination plants. Reducing the concentration of the divalent ions can minimize scale formation and deposition to a level that allows the MSF plant to operate at high top brine temperature (TBT) and without scale problems. The PSRNF membranes were chosen in the FO process because of their high water permeability, rejection of divalent and monovalent ions, small structure parameter (S), and inexpensiveness compared to commercial FO membranes. Three PSRNF membranes were tested in the FO process with the feed solution facing the active membrane layer to avoid active layer delamination. Although the PSRNF membrane exhibited negligible water flux at 0 bar, it increased when a 2–4 bar was applied to the feed solution. The wastewater temperature was set at 25 °C while 40 °C was the brine operational temperature to mimic the field situation. A maximum average water flux of 39.5 L/m2h was recorded at 4 bar feed pressure when the PSRNF membrane was used for the brine dilution, achieving up to 42% divalent ions dilution at 0.02 kWh/m3 specific power consumption. The average water flux in the PRSNF membrane was 35% higher than that in the commercial TFC FO membrane. Notably, the PSRNF membrane is ten times cheaper than commercial FO membranes. Notably, the PSRNF membrane is ten times cheaper than commercial FO membranes, achieving substantial cost reductions and pioneering advancements in FO purification technology.

An Exploration of the Relationship Between Social Work Students’ Awareness of Environmental Ethics and Their Attitudes and Behaviors Toward Environmental Problems

In a changing global environment, sustaining social work as a profession necessitates comprehensive education and heightened awareness of ecocentric perspectives and environmental ethics. Based on this information, the study aims to explore the relationship between social work students’ environmental ethics awareness and their attitudes and behaviors regarding environmental issues. The study was conducted utilizing the general and relational survey model, a quantitative research methodology; the data collection was performed with an interview form consisting of the Personal Information Form, Environmental Ethics Awareness Scale, Attitude Toward Environmental Problems Scale, and Behavior Toward Environmental Problems Scale. The study comprised 461 students selected from 10 different universities offering undergraduate social work education in Türkiye, employing the purposive sampling technique. The data collected from the participants was analyzed using SPSS software. The study findings reveal that the environmental ethics awareness and attitudes of social work students toward environmental problems positively impact their behaviors regarding environmental problems. In this context, heightened environmental ethics awareness and positive attitudes toward environmental problems among students correlate with increased engagement in actions addressing environmental issues. These findings emphasize the need for future studies to assess the effectiveness of environmental education curricula and conduct evidence-based quasi-experimental group interventions.

Accurate and gate-efficient quantum Ansätze for electronic states without adaptive optimization

The ability of quantum computers to overcome the exponential memory scaling of many-body problems is expected to transform quantum chemistry. Quantum algorithms require accurate representations of electronic states on a quantum device, but current approximations struggle to combine chemical accuracy and gate efficiency while preserving physical symmetries, and rely on measurement-intensive adaptive methods that tailor the wave function to each molecule. In this contribution, we present a symmetry-preserving and gate-efficient that provides chemically accurate molecular energies with a well-defined circuit structure. Our approach exploits local qubit connectivity, orbital optimization, and connections with generalized valence bond theory to maximize the accuracy that is obtained with shallow quantum circuits. Numerical simulations for molecules with weak and strong electron correlation, including benzene, water, and the singlet-triplet gap in tetramethyleneethane, demonstrate that chemically accurate energies are achieved with as much as 84% fewer two-qubit gates compared to state-of-the-art adaptive techniques. Published by the American Physical Society 2024

Scaling Type-Based Points-to Analysis with Saturation

Designing a whole-program static analysis requires trade-offs between precision and scalability. While a context-insensitive points-to analysis is often considered a good compromise, it still has non-linear complexity that leads to scalability problems when analyzing large applications. On the other hand, rapid type analysis scales well but lacks precision. We use saturation in a context-insensitive type-based points-to analysis to make it as scalable as a rapid type analysis, while preserving most of the precision of the points-to analysis. With saturation, the points-to analysis only propagates small points-to sets for variables. If a variable can have more values than a certain threshold, the variable and all its usages are considered saturated and no longer analyzed. Our implementation in the points-to analysis of GraalVM Native Image, a closed-world approach to build standalone binaries for Java applications, shows that saturation allows GraalVM Native Image to analyze large Java applications with hundreds of thousands of methods in less than two ‍minutes.

Study on the current situation and influencing factors of corn import trade in China – based on the trade gravity model

Abstract With the rapid development of China’s economy and society, the income level of Chinese residents is increasing day by day, and the dietary consumption structure of domestic residents is constantly optimized and upgraded. The consumption demand for livestock products such as meat, eggs and milk, and corn starch industrial products is increasing day by day. Therefore, as the main raw material of feed industry and deep processing owners, the consumption scale of China corn market is constantly expanding. However, the corresponding domestic corn production scale has not expanded, and the domestic corn production presents an obvious problem of insufficient supply scale, which leads to a sharp and rapid increase in the number of corn imports in China, which seriously impacts the domestic corn market and affects China’s healthy development of corn industry. In recent years, the continuous spread of the COVID-19 epidemic has seriously affected the global grain production and trade. The fierce military conflict between Russia and Ukraine, the two major grain exporting countries, has once again brought a serious adverse impact on the supply and price of the global grain market, resulting in an increase in the import cost of corn in China and a change in the import pattern of corn in China. In this regard, this study analyzes the current situation of corn production and import trade in China, and based on the relevant trade data between China and major corn importing countries from 2013 to 2022, establishes a trade gravity model to quantitatively evaluate the main factors affecting China’s corn import trade, and analyzes the trade potential of sample countries. It was found that in 2022, China’s import and export trade volume and trade value were 20.62 million tons and 7.11 billion US dollars, respectively, compared with 2013, trade volume and trade volume increased by 6.3 times. Statistics on the potential types of various trading countries showed that the proportion of potential countries was 14.2%, and Brazil had the greatest potential in China’s corn import trade. The proportion of potential expanding countries is about 71.4%, and Laos is a mature country with less opportunities for further development. In order to promote the benign development of the domestic corn industry and the development of China’s corn import trade, this paper puts forward a series of countermeasure suggestions based on the above research.

Impact of Covid-19 on Psychosocial Well-Being of School-Going Children: A Cross-Sectional Study.

The mandated closure of schools due to Covid-19 is likely to have a negative impact on school-going children. This study aimed to assess the psychosocial well-being of school children during the pandemic in eastern India. This cross-sectional study was conducted in the outpatient pediatric department of tertiary care teaching hospitals. Children between the ages of 4 and 14 were enrolled. The main outcome measures included the Emotional Symptoms Scale, Conduct Problem Scale, Hyperactivity Scale, Peer Problem Scale, and Prosocial Scale from the Strength and Difficulties Questionnaire (SDQ), as well as the Children's Hope Scale. Out of 169 children aged 4-14, 104 (61.5%) were male, 140 (82.8%) were from urban areas, 66 (39.1%) had a family member who was a healthcare worker or frontline worker, and 12 (7.1%) had experienced the death of a family member due to Covid-19. Anxiety-related and depressive symptoms were observed in 81 (47.9%) and 70 (41.4%) children, respectively. Psychosocial difficulties with a 'clinically significant problem likely' were observed in 26 (15.4%) children, more common in males (16.35%, P=0.035) and older children (12-14 years). Children from families with healthcare/frontline workers, Covid-affected families, loss of job in the earning member, and uninvolved parenting style were associated with more psychosocial difficulties. The mean (SD) hope score was 22.46 ± 6.42 in children above eight years. The psychosocial well-being of school-going children is adversely affected during Covid-19, particularly in families with frontline workers, loss of job, and death of family members due to Covid-19. The poor hope score in children aged 8 years and above indicates an adverse impact on their ability to achieve future goals.

SwissGenVar: A Platform for Clinical-Grade Interpretation of Genetic Variants to Foster Personalized Healthcare in Switzerland

Large-scale next-generation sequencing (NGS) germline testing is technically feasible today, but variant interpretation represents a major bottleneck in analysis workflows. This includes extensive variant prioritization, annotation, and time-consuming evidence curation. The scale of the interpretation problem is massive, and variants of uncertain significance (VUSs) are a challenge to personalized medicine. This challenge is further compounded by the complexity and heterogeneity of the standards used to describe genetic variants and the associated phenotypes when searching for relevant information to support clinical decision making. To address this, all five Swiss academic institutions for Medical Genetics joined forces with the Swiss Institute of Bioinformatics (SIB) to create SwissGenVar as a user-friendly nationwide repository and sharing platform for genetic variant data generated during routine diagnostic procedures and research sequencing projects. Its aim is to provide a protected environment for expert evidence sharing about individual variants to harmonize and upscale their significance interpretation at the clinical grade according to international standards. To corroborate the clinical assessment, the variant-related data will be combined with consented high-quality clinical information. Broader visibility will be achieved by interfacing with international databases, thus supporting global initiatives in personalized healthcare.

성인의 아동기 정서적 학대 경험이 대인관계문제에 미치는 영향

Objectives The purpose of this study was to confirm the mediating effect of traumatized self-system, emotion dysregulation, and interpersonal problems. when emotional abuse experiences in childhood affect interpersonal problems in adults. Methods Data were collected and analyzed by using the childhood Emotional Abuse scale, traumatized self-system scale, emotion dysregulation scale, and interpersonal problems scale on 376 men and women aged 19 or older (77 men and 299 women). An analysis was conducted using SPSS 22.0 and PROCESS Macro v4.1 programs to verify the superior relationship and mediating effect of childhood emotional abuse, traumatized self-system, emotion dysregulation, and interpersonal problems. Results The main results of the study are summarized as follows. First, there was a significant positive correlation between childhood emotional abuse, traumatized self-system, emotion dysregulation, and interpersonal problems. Second, in the relationship between emotional abuse and interpersonal problems in childhood, self-system damage and emotion dysregulation showed complete mediating effects. Third, in the relationship between emotional abuse and interpersonal problems in childhood, there was a sequential dual mediating effect of traumatized self-system and emotion dysregulation. Conclusions The results of this study suggest that the greater the emotional abuse experience of adults in childhood, the higher the level of self-system damage, and the higher the level of self-system damage, the more difficulty in controlling emotions, causing interpersonal problems. Finally, the limitations of the study and the direction of the follow-up study were suggested.

Tikhonov regularization with conjugate gradient least squares method for large-scale discrete ill-posed problem in image restoration

Image restoration is a large-scale discrete ill-posed problem, which can be transformed into a Tikhonov regularization problem that can approximate the original image. Kronecker product approximation is introduced into the Tikhonov regularization problem to produce an alternative problem of solving the generalized Sylvester matrix equation, reducing the scale of the image restoration problem. This paper considers solving this alternative problem by applying the conjugate gradient least squares (CGLS) method which has been demonstrated to be efficient and concise. The convergence of the CGLS method is analyzed, and it is demonstrated that the CGLS method converges to the least squares solution within the finite number of iteration steps. The effectiveness and superiority of the CGLS method are verified by numerical tests.