Program Performance Research Articles

Analisis Kinerja BAZNAS Kota Bengkulu dengan Metode Indonesia Magnificence Zakat (IMZ)

This study aims to find out how the management performance, financial performance, and performance of the utilization program at BAZNAS Bengkulu City during 2021-2022 using the Indonesia Magnificence Zakat (IMZ) measurement method. Indonesia Magnificence Zakat is a non-profit organization empowerment and management consulting institution engaged in training, consulting and mentoring, as well as zakat research and advocacy, this performance measurement method is called Zakat Management Excellence Performance. The type of research used in this study is a qualitative evaluation method. The results of the study on three components in BAZNAS Bengkulu City, namely management performance, financial performance, and quality of utilization programs, show that the performance of BAZNAS Bengkulu City obtained a good score in each performance component based on IMZ measurement. In some performances, they show good value components, but there are also those that show low scores, from these results are averaged so that good scores are obtained.

Predicting Admission and Future Performance of Veterinary School Applicants: Evaluation of Scores of Self-Reported Animal Experience and Rural Versus Urban Background.

Admission to veterinary school is generally based on academic and nonacademic measures. Descriptions of animal or veterinary experience and rural versus urban background are often sought from applicants, but little is objectively known about their impact on admission success or future performance. We evaluated scores from written descriptions from 590 veterinary school applicants for the nature and extent of self-reported animal experience. For those admitted to the program, we compared animal experience and rural versus urban background to performance in discipline-based courses, professional skills courses, clinical rotations, and the North American Veterinary Licensing Exam (NAVLE). More than 98% of applicants reported animal experience, with small animal veterinary experience most reported. There was no difference in animal experience or background between successful and unsuccessful applicants, but rural and urban applicants reported different experiences. There was a small correlation between small animal experience and performance in clinical rotations (.21), a small negative correlation between rural background and NAVLE performance (-.23), but otherwise, no significant correlations between animal experience or background and future performance. These findings suggest that scores of self-reported animal experience do not provide predictive information on applicants, or, alternatively, that the nature and extent of animal experience, the methods used to score these experiences, and/or the measures assessed during veterinary school need to be explicitly defined to ensure that we are capturing the appropriate information. More investigation into the scoring and impact of animal experience and background on applicant performance in the DVM program and success in a veterinary career is warranted.

The Community Acute Respiratory Infection surveillance programme: an evaluation of a newly established surveillance programme in Scotland.

The Community Acute Respiratory Infection (CARI) surveillance programme, established by Public Health Scotland (PHS) in November 2021, aims to monitor respiratory infections in communities, replacing prior schemes to ensure early detection of outbreaks and inform public health interventions. Positioned as a cornerstone of PHS's national infectious respiratory diseases plan, CARI is pivotal for safeguarding public health. This study presents key findings from the 2022/23 CARI season and evaluates the programme's performance during this period. CARI uses a network of sentinel general practitioner (GP) practices across Scotland to monitor patients with acute respiratory infection symptoms, employing multiplex polymerase chain reaction testing for 10 common pathogens. Results are linked to enhanced surveillance data, providing insights into infection trends during the season. The evaluation comprised an online GP survey and a quantitative assessment of programme performance. In the 2022/23 season, 180 GP practices participated in CARI, testing 15,823 samples. Swab positivity peaked in December 2022, driven by a large spike in influenza A activity. The evaluation showed that CARI is highly useful, with positive feedback on simplicity, flexibility, and acceptability. Representativeness varied across health boards and age groups. Despite occasional laboratory processing delays, data quality remained good, with timely reporting and stable participation. CARI reflected patterns in infections observed in secondary care in Scotland and Europe, providing valuable insights into disease patterns and impact. It also provided timely intelligence to key decision-makers, enabling prompt public health response. Changes for the 2023/24 season aim to further optimize the programme.

Clustering of Study Program Using of Block-Based K-Medoids

The purpose of this research is to classify Study Programs based on eleven mixed data from InternalQuality Management System (QMS) indicators. This grouping can provide a clearer picture of howQMS affects the performance and quality of study programs. By understanding these clusters, universities can identify and design more effective strategies to improve the quality of education. The dataused comes from the National Accreditation Board for Higher Education (BAN-PT) and the websitedatabase, which consists of seven numerical variables: number of lecturers, percentage of doctors, percentage of professors and associate professors, student enumeration, percentage of graduates, programexperience, and availability of laboratories. Meanwhile, the categorical variable consists of four variables: National Accreditation Board of Higher Education (BAN-PT) research ranking, accreditation,international recognition, and level of community service. The clustering method used is the blockbased k-medoids (block-based KM), and multivariate analysis of variance (MANOVA). We applied theDeviation Ratio Index based on K-Medoids (DRIM) to determine the number of clusters. This researchresults that the optimal number of groups that must be formed is three. Based on MANOVA the resultsshowed that the group consisting of 12 study programs had better QMS outcomes than the other twogroups.

Performance of a Full-Coverage Cervical Cancer Screening Program Using on an Artificial Intelligence- and Cloud-Based Diagnostic System: Observational Study of an Ultralarge Population.

The World Health Organization has set a global strategy to eliminate cervical cancer, emphasizing the need for cervical cancer screening coverage to reach 70%. In response, China has developed an action plan to accelerate the elimination of cervical cancer, with Hubei province implementing China's first provincial full-coverage screening program using an artificial intelligence (AI) and cloud-based diagnostic system. This study aimed to evaluate the performance of AI technology in this full-coverage screening program. The evaluation indicators included accessibility, screening efficiency, diagnostic quality, and program cost. Characteristics of 1,704,461 individuals screened from July 2022 to January 2023 were used to analyze accessibility and AI screening efficiency. A random sample of 220 individuals was used for external diagnostic quality control. The costs of different participating screening institutions were assessed. Cervical cancer screening services were extended to all administrative districts, especially in rural areas. Rural women had the highest participation rate at 67.54% (1,147,839/1,699,591). Approximately 1.7 million individuals were screened, achieving a cumulative coverage of 13.45% in about 6 months. Full-coverage programs could be achieved by AI technology in approximately 1 year, which was 87.5 times more efficient than the manual reading of slides. The sample compliance rate was as high as 99.1%, and compliance rates for positive, negative, and pathology biopsy reviews exceeded 96%. The cost of this program was CN ¥49 (the average exchange rate in 2022 is as follows: US $1=CN ¥6.7261) per person, with the primary screening institution and the third-party testing institute receiving CN ¥19 and ¥27, respectively. AI-assisted diagnosis has proven to be accessible, efficient, reliable, and low cost, which could support the implementation of full-coverage screening programs, especially in areas with insufficient health resources. AI technology served as a crucial tool for rapidly and effectively increasing screening coverage, which would accelerate the achievement of the World Health Organization's goals of eliminating cervical cancer.

NLTSP: A cost model for tensor program tuning using nested loop trees

This paper introduces NLTSP, a deep learning-based cost model designed to optimize tensor program performance in deep learning compilers. NLTSP, short for Nested Loop Tree Structure Processing, facilitates tensor program tuning by extracting information directly from the nested loop tree structure of sampled programs. NLTSP extracts features upstream in the compilation flow and eliminates the need for complex feature engineering. By utilizing a unified format for CPU and GPU architectures and extracting simple high-level features, NLTSP significantly accelerates feature extraction speed while maintaining performance accuracy. We have integrated this technology into Ansor, a leading search framework in the TVM compiler, and conducted experiments. Compared with TenSet MLP, the state-of-the-art cost model utilizing Ansor features as inputs, NLTSP achieves feature extraction speeds on CPU and GPU that are, on average, 97.9 times and 41.4 times faster, respectively, and can reduce the average search time for CPU and GPU workloads by 2.50 times and 4.11 times, respectively. It is worth noting that NLTSP is not specifically designed for Ansor. Any auto-tuning framework capable of representing scheduled tensor programs as nested loop trees can potentially benefit from using NLTSP to achieve superior performance. The code is available at https://github.com/xhq0/NLTSP.

Trends of drug-resistant tuberculosis and risk factors to poor treatment-outcome: a database analysis in Littoral region-Cameroon, 2013-2022.

Tuberculosis(TB), currently has limited treatment options, and faces worldwide threat of drug-resistance(DR). In 2022, the DR-TB prevalence in Cameroon was 1.4% among new-cases and 8.3% among retreatment-cases. We analyzed the DR-TB database to descript the trends and DR-TB profile, treatment-outcome and associated risk-factors so-as-to propose measures to enhance program performance in Cameroon. We conducted a retrospective cohort study, analysed the DR-TB database of the Littoral region from 2013 to 2022. We appreciated the data-quality using zero-reporting, completeness, consistency, and validity indicators. We categorized DR-TB into Rifampicin-resistant-TB(RR-TB), multi-drug-resistant-TB(MDR-TB), pre-extensive-drug-resistant-TB(pre-XDR-TB), and XDR-TB and performed descriptive statistics. We assessed DR-TB treatment outcome targeting > 80% cure and/or completed treatment. Multiple logistic regression was used to determine risk factors related to poor treatment outcomes, and adjusted relative risk(RR) was considered significant at p < 0.05. Overall database quality was 93.7% with uniqueness 100%, data-completeness 82.5%, consistency 97% and validity 95.1%. A total of 567 DR-TB cases were reported, with median age of 34 (1-80) years, male-to-female sex ratio (3:2). Cases were classified as 19(3.4%) RR-TB, 536(94.6%) MDR-TB, 7(1.3%) pre-XDR-TB, and 4(0.7%) XDR-TB. Case-reporting increased from 2013, reaching their peak in 2018. The overall treatment refusal rate was 123(11.9%) and treatment outcomes of 270(60.8%) cured, 116(26.4%) completed, 32(7.2%) deaths, 19(4.3%) lost-to-follow-up, and 6(1.4%) failure were recorded. We identified 84 dead (CFR:14.8%) amongst whom 52(62%) refused treatment, 17(20%) occurred during the first month of therapy and 13(15.5%) HIV-TB co-infected. Male gender [p = 0.006, RR = 2.5 (95% CI: 1.3-4.7)], HIV positive status [p = 0.012, RR = 2.1 (95% CI: 1.2-3.7)], and previous DR-TB status [p = 0.02, RR = 3.9 (95% CI: 1.3-12.0)] were statistically associated to poor treatment outcomes. In the Littoral Region-Cameroon, cases of DR-TB increased from 2013, reaching their peak in 2018 befor dropping right up to 2022. RR-TB, MDR-TB, Pre-XDR-TB and XDR-TB represented 3.4%, 94.6%, 1.3% and 0.7% of all reported DR-TB cases. Overall, DR-TB treatment success rate was 87.2%. Male-gender, HIV-positive status, and previous DR-TB are associated with poor TB treatment outcomes. We recommend universal drug susceptibility testing to ensure early/maximum DR-TB case-detection and proper pre-treatment counselling to limit the high death rates and anti-TB treatment refusal rates which are setbacks from achieving end-TB strategies.

Core Components of Cardiac Rehabilitation Programs: 2024 Update: A Scientific Statement From the American Heart Association and the American Association of Cardiovascular and Pulmonary Rehabilitation

The science of cardiac rehabilitation and the secondary prevention of cardiovascular disease has progressed substantially since the most recent American Heart Association and American Association of Cardiovascular and Pulmonary Rehabilitation update on the core components of cardiac rehabilitation and secondary prevention programs was published in 2007. In addition, the advent of new care models, including virtual and remote delivery of cardiac rehabilitation services, has expanded the ways that cardiac rehabilitation programs can reach patients. In this scientific statement, we update the scientific basis of the core components of patient assessment, nutritional counseling, weight management and body composition, cardiovascular disease and risk factor management, psychosocial management, aerobic exercise training, strength training, and physical activity counseling. In addition, in recognition that high-quality cardiac rehabilitation programs regularly monitor their processes and outcomes and engage in an ongoing process of quality improvement, we introduce a new core component of program quality. High-quality program performance will be essential to improve widely documented low enrollment and adherence rates and reduce health disparities in cardiac rehabilitation access.

Effect of cereal rye cover crop termination timings on weed control and corn yield under a two-pass herbicide program

Cover crops, including cereal rye, are generally known to suppress weeds in various cropping systems; however, research on their effective use is lacking. This study investigated the effects of different cereal rye termination timings on weed control and corn performance in a two-pass herbicide program across 5 site-years (2021–2023) in Arkansas, USA. The herbicide program consisted of one application at cereal rye (Secale cereale) termination (cereal rye environment) or at corn (Zea mays) planting (conventional system) and another at the V4 corn stage. Palmer amaranth (Amaranthus palmeri) at V4 corn was better controlled in cereal rye environments regardless of its termination timing (&amp;gt;75%) than in the conventional corn system (&amp;lt;50%), and the control was generally excellent in both systems 4 weeks later, with subtly greater control in the cereal rye environments. In contrast, yellow nutsedge (Cyperus esculentus) control, in general, was slightly less in the cereal rye environments than in the conventional system. Barnyardgrass (Echinochloa crus–galli) and broadleaf signalgrass (Urochloa platyphylla) control was similar to that of Palmer amaranth, especially at the V4 corn stage, with clear benefits in its control with the later termination timings. Corn height was greater (by 8% to 10%) in cereal rye environments terminated at 1 or 2 weeks after corn planting compared to the conventional system or other cereal rye environments. Corn yield was similar between the conventional system and the cereal rye environments terminated before corn planting; however, the yield was 16% to 22% less with those terminated at or after corn planting. These findings indicate the constrained use of cereal rye in corn, with its termination time optimized for 2 weeks before corn planting. Further research could identify factors that mitigate the yield loss from delayed cereal rye termination and improve its use as a cover crop in corn.

Performance of cash transfer program on farmers’ livelihood: evidence from PM-KISAN scheme of India

IntroductionResearch into various aspects of vulnerability and poverty has played a key role in shaping cash transfer systems. Cash-based direct benefit transfers (DBT) are frequently discussed, as they help reduce corruption and limit intermediary involvement, which often impedes policy implementation in countries like India. Small and marginal farmers (SMFs), constituting 86% of India’s farming population, are essential for sustainable agricultural growth and ensuring food and nutrition security. The Pradhan Mantri Kisan Samman Nidhi (PM-KISAN) scheme provides Rs.6000 annually to the farmer families via DBT mechanism. Despite reports and information from various government sources, significant concerns remain regarding the performance of PM-KISAN scheme in improving living conditions of SMFs.MethodsFor having the highest number of SMFs, a research study was conducted in Bihar and Uttar Pradesh. Two cluster of villages from each of these two states were chosen by simple random sampling (SRS) method. An ex post facto research design was used; data was gathered from 240 SMFs, with 120 beneficiaries and 120 non-beneficiaries, using a stratified disproportionate simple random sampling technique. The study used 18 indicators related to livelihood assets—human capital, social capital, natural capital, physical capital, and financial capital—to generate a Livelihood Index (LI) through Principal Component Analysis (PCA).ResultsThe beneficiaries of the scheme had an average LI score of 0.396, while non-beneficiaries had a score of 0.366. Propensity Score Matching (PSM) was employed to compare the beneficiaries and non-beneficiaries in order to determine the impact of the scheme. In the Livelihood Index of beneficiaries, in comparison to non-beneficiaries, there was a significant improvement of 3.34 to 4.13 percentage points, according to the analysis, which utilized a variety of matching algorithms.DiscussionImplications from the study suggests that PM-KISAN should be integrated into a broader rural development strategy to maximize multiplier effects. By combining cash transfers with complementary initiatives like modern technology adoption and high-yield seeds, farmers can make productive investments that could enhance their livelihood status.

Influence of COVAW Capacity Building on Gender-Based Violence Program Performance in Narok County, Kenya

Purpose: The study explores the influence of employee capacity building on the performance of gender-based violence (GBV) programs in Narok County, Kenya. The specific objectives include examining the effect of leadership style, the impact of community empowerment on program effectiveness, the role of resource allocation, and the influence of organizational innovation on program outcomes. Methodology: Utilizing a descriptive survey design grounded in modernization theory and performance and human capital theory, the research targets full-time staff from ten GBV programs operated by COVAW. A sample of 92 respondents is collected through convenience and stratified sampling techniques, incorporating both community participants and program personnel. Data collection employs interview schedules and semi-structured questionnaires, with quantitative data analyzed using SPSS software and qualitative data thematically analyzed. Findings: Findings reveal a prevailing democratic leadership style that fosters collaboration; however, community empowerment faces challenges due to inadequate integration and mobilization. The study concludes that there is a significant interconnectedness among leadership, community engagement, resource management, and organizational adaptability in enhancing GBV program performance. Unique Contribution to Theory, Policy and Practice: Recommendations include adopting diverse leadership styles, strengthening community engagement, and optimizing resource management practices to ensure the sustainability and impact of GBV initiatives.

Effect of Stakeholders’ Management on Programme Performance: A Case of Crop Intensification Programme in Ngoma District, Rwanda (2018-2023)

Effect of Stakeholders’ Management on Programme Performance: A Case of Crop Intensification Programme in Ngoma District, Rwanda (2018-2023)

Determinants of Students Performance in Information and Communication Technology Program in DEPED Secondary Schools

This study investigated the relationship between the observed organizational attributes and determinants of students performance in the information and communications technology program along five factors: provision of equipment, training of teachers, administrative support, monitoring and Evaluation assessment, and leadership style of school heads and learner’s academic performance in terms of mean percentile scores involving 59 classroom teachers in five public secondary schools in DepEd Eastern Samar division during the school year 2023-2024. The data concerning the respondents’ perceived observation on the determinant factors of students’ performance in ICT program were collected via an expert validated researcher-modified survey instrument and analyzed using frequency count, percentage, mean, spearman rho correlation at 0.05 level of significance. Findings revealed that all of the organizational attributes and determinant factors were “moderately observed” with a mean of 3.3146 and the learner’s academic performance in ICT were “satisfactory” at an average of 78.98%. Test on correlation indicated that training of teachers (p=0.45) as a determinant of success is significantly related to the learner’s academic performance in ICT. All the other determinant factors have p-values greater than the level of significance i.e., provision of equipment (p=.864), administrative support (p=.134), M &amp; E Assessment (p=.191) and leadership style of school head (p=.160) and are not significantly related to the performance of learners in the DepEd secondary schools. Thus, the null hypothesis saying that there is no significant relationship between these determinant factors to the academic performance of learners in DepEd secondary schools is therefore true and accepted. Hence, the researcher recommends the conduct of extensive ICT training programs for teachers for them to teach better and to advance the digital literacy in Philippine schools.

The Change Detection of Mangrove Forests Using Deep Learning with Medium-Resolution Satellite Imagery: A Case Study of Wunbaik Mangrove Forest in Myanmar

This paper presents the development of a U-Net model using four basic optical bands and SRTM data to analyze changes in mangrove forests from 1990 to 2024, with an emphasis on the impact of restoration programs. The model, which employed supervised learning for binary classification by fusing multi-temporal Landsat 8 and Sentinel-2 imagery, achieved a superior accuracy of 99.73% for the 2020 image classification. It was applied to predict the long-term mangrove maps in Wunbaik Mangrove Forest (WMF) and to detect the changes at five-year intervals. The change detection results revealed significant changes in the mangrove forests, with 29.3% deforestation, 5.75% reforestation, and −224.52 ha/yr of annual rate of changes over 34 years. The large areas of mangrove forests have increased since 2010, primarily due to naturally recovered and artificially planted mangroves. Approximately 30% of the increased mangroves from 2015 to 2024 were attributed to mangrove plantations implemented by the government. This study contributes to developing a deep learning model with multi-temporal and multi-source imagery for long-term mangrove monitoring by providing accurate performance and valuable information for effective conservation strategies and restoration programs.

EFFECT OF IN-SERVICE TEACHER TRAINING PROGRAM ON TVET TRAINERS PERFORMANCE IN TECHNICAL SECONDARY SCHOOLS IN RWANDA,A CASE OF KICUKIRO DISTRICT

The general objective of this study was to examine the influence of an in-service teacher training program on the performance of TVET trainers in Kicukiro district. Specifically, this studys objectives were: to identify the level to which TVET trainer receive in-service teacher training program in Technical Secondary School Kicukiro District, to assess the level of performance of TVET trainers in Technical Secondary Schools in the Kicukiro District and to evaluate the relationship between in-service training program and TVT trainers performance in Technical Secondary School Kicukiro District. The combination learning theory and system theory was served as the researchers guide. The studys population was 207 people (197 teachers and 10 head teachers) from all of the Kicukiro Districts Technical Secondary Schools. The survey was designed in a descriptive manner. A sample size of 137 was chosen using Yamanes formula. Using random sampling and purposeful selection, the population was chosen as the sample size for the study. Every questionnaire was dispersed by the researcher. The format of the analysis was influenced by the particular qualitative and qualitative methodology employed as well as the format of the data. The data that was collected in this study was examined and displayed in the order of the research objectives utilizing frequency, percentages, and tables as descriptive statistics. Data was gathered via teacher questionnaires and head teacher interview guides. The researcher examined and interpret the results after going to the field. The study found that 84.5% of the respondents agreed that they participate in service teacher training program every time, 94% of the respondents agreed that In-service teacher training program is very necessary for all teachers, 52.5 of the respondents disagreed that In-service teacher training program is only for new teachers, 57.6% of the respondents Disagreed that In-service teacher training program is necessary for old teachers while 60.5% of the respondent disagreed that In-service teacher training program is not necessary at all. 83.8% of the respondents agreed that their participation in an in-service teacher training program has helped them improve personalities and instructional techniques in the classroom and at school, 89.7% of the respondents agreed that with the help of the in-service teacher training program, and they are able to acquire new methods of classroom management and instruction, while 94.1% of the respondents agreed that the in-service teacher training program motivates and makes teachers love their professions.The research suggests that the Ministry of Education, education planners, and stakeholders should enhance in-service training programs for TVET trainers in Kicukiro District, Rwanda, focusing on upgrading technical skills, improving instructional methodologies, establishing robust monitoring mechanisms, and investing in infrastructure.

Enhancing computational thinking in children through logical puzzles: A machine learning approach for early coding education

This recent growth in early education in coding evidences the rising need for the computing-thinking-skills development in children. Though most of the programming curricula are based upon syntax and other basic concepts of coding, computational thinking encompasses broader cognitive skills, such as problem-solving, abstraction, and logical reasoning. This research therefore intends to fill a gap in research and will investigate the ways pre-coding games employ logical puzzles as an enhancement in developing computational thinking skills in children aged 7-10 years and how these affect the acquisition of programming knowledge. In all, there will be two groups assigned: one will be exposed to logical-oriented puzzle training, Group A, and the other will be a control group, Group B, which receives only regular standard lessons in programming introduction. Pre- and post-measures of computational thinking and basic programming skills will be obtained from the participants. The machine learning algorithms, such as Random Forest for prediction in terms of improvement in subject performance in computational thinking and K-Means clustering to identify patterns in their development of skills, will be implemented in their pure or integrated forms to analyze data. Logistic Regression will be executed in order to model the odds of improved performance in programming given an intervention of exposure to puzzles. The research study will, therefore, apply these algorithms in comparing the performance of the two groups and reveal the aspects of computational thinking to which the greatest influence in a positive way through puzzle activities is exerted. It is expected that children exposed to logical puzzles may show significant improvements in both computational thinking and programming skills. These findings should be useful to educators in their effort to show how logical puzzles can be integrated into the curricula of early coding education to build foundational skills and thereby contribute to a refinement of best practices in STEM education.

Leveraging Partially Context-Sensitive Profiles for Enhanced AOT Compilation: A Review

In the realm of compiler optimization, just-in-time (JIT) compilation dynamically adjusts code execution based on runtime profiling, contrasting with the static approach of ahead-of-time (AOT) compilation. While JIT benefits from real-time profiling data, AOT lacks this advantage, necessitating innovative strategies to enhance performance without runtime feedback. This review article explores the integration of partially context-sensitive profiles into AOT compilation, offering insights into optimizing statically compiled programs through advanced profiling techniques. Also, it explores the utilization of partially context-sensitive profiles in ahead-of-time (AOT) compilation to enhance program performance. It delves into the challenges of AOT optimization without runtime profiling, contrasting it with the dynamic optimization capabilities of just-in-time (JIT) compilation. The proposed algorithm strategically leverages partial profiles to identify and optimize hot code segments, presenting a promising avenue for improving AOT compilation efficiency. Through empirical evaluation of diverse benchmarks, the article validates the technique's effectiveness, underscoring its significance in advancing compiler optimization strategies for statically compiled programs.

Efficient Implementation of Interior-Point Methods for Quantum Relative Entropy

Quantum relative entropy (QRE) programming is a recently popular and challenging class of convex optimization problems with significant applications in quantum computing and quantum information theory. We are interested in modern interior-point (IP) methods based on optimal self-concordant barriers for the QRE cone. A range of theoretical and numerical challenges associated with such barrier functions and the QRE cones have hindered the scalability of IP methods. To address these challenges, we propose a series of numerical and linear algebraic techniques and heuristics aimed at enhancing the efficiency of gradient and Hessian computations for the self-concordant barrier function, solving linear systems, and performing matrix-vector products. We also introduce and deliberate about some interesting concepts related to QRE such as symmetric quantum relative entropy. We design a two-phase method for performing facial reduction that can significantly improve the performance of QRE programming. Our new techniques have been implemented in the latest version (DDS 2.2) of the software package Domain-Driven Solver (DDS). In addition to handling QRE constraints, DDS accepts any combination of several other conic and nonconic convex constraints. Our comprehensive numerical experiments encompass several parts, including (1) a comparison of DDS 2.2 with Hypatia for the nearest correlation matrix problem, (2) using DDS 2.2 for combining QRE constraints with various other constraint types, and (3) calculating the key rate for quantum key distribution (QKD) channels and presenting results for several QKD protocols. History: Accepted by Giacomo Nannicini, Area Editor for Quantum Computing and Operations Research. Accepted for Special Issue. Funding: This work was supported by the National Science Foundation [Grant CMMI-2347120] and Discovery Grants from the Natural Sciences and Engineering Research Council of Canada. Supplemental Material: The software that supports the findings of this study is available within the paper and its Supplemental Information ( https://pubsonline.informs.org/doi/suppl/10.1287/ijoc.2024.0570 ) as well as from the IJOC GitHub software repository ( https://github.com/INFORMSJoC/2024.0570 ). The complete IJOC Software and Data Repository is available at https://informsjoc.github.io/ .

Investigation of SHGS Credit Linkage Program Performance: A Comparative Assessment

SHGs have become instrumental in promoting financial inclusion and socio-economic empowerment among marginalized communities. This research employs a comparative assessment framework to evaluate the effectiveness of various credit linkage initiatives across Mysore and Chamarajanagr, highlighting key performance dimensions such as accessibility, repayment rates, impact on income, and group sustainability.

Dynamic Capabilities and Technology Competence: A Study on Firm Performance in Groom Big Programs

Dynamic Capabilities and Technology Competence: A Study on Firm Performance in Groom Big Programs