Real-world Situations Research Articles

Transformasi Digital dan Profesionalisme dalam Akuntansi untuk Generasi Muda melalui Pelatihan Aplikasi EFAKTUR dan Web EFAKTUR untuk Pelaporan PPN MASA BADAN guna Meningkatkan Kemampuan Praktis bagi Pelajar SMKN 25 DKI Jakarta

This activity aims to overcome the high unemployment rate among vocational school graduates in Indonesia, with a focus on SMKN 25 DKI Jakarta. One of the main factors causing unemployment is the mismatch of graduate skills with current industry needs. To answer this challenge, this research uses a contextual method or approach that links learning to real world situations. The team from the Faculty of Economics and Business, Mercubuana University held training on the application of modern tax technology, namely EFAKTUR and Web EFAKTUR, to students of SMKN 25. Activities were carried out in several stages, including preparation and planning at Mercubuana University, online coordination, direct training at school, and evaluation training results. The results show an increase in students' capabilities in understanding and using electronic tax reporting technology, with participant satisfaction levels reaching 95% based on the questionnaire. These findings suggest that technology-based training not only improves students' technical competence, but also contributes to increasing their competitiveness in the job market, as well as helping schools achieve better rankings in national assessments. This training has succeeded in equipping students with relevant and up-to-date skills, reducing the gap between vocational education and the needs of the world of work.

3D Hand Motion Generation for VR Interactions Using a Haptic Data Glove

Recently, VR-based training applications have become popular and promising, as they can simulate real-world situations in a safe, repeatable, and cost-effective way. For immersive simulations, various input devices have been designed and proposed to increase the effectiveness of training. In this study, we developed a novel device that generates 3D hand motion data and provides haptic force feedback for VR interactions. The proposed device can track 3D hand positions using a combination of the global position estimation of ultrasonic sensors and the hand pose estimation of inertial sensors in real time. For haptic feedback, shape–memory alloy (SMA) actuators were designed to provide kinesthetic forces and an efficient power control without an overheat problem. Our device improves upon the shortcomings of existing commercial devices in tracking and haptic capabilities such that it can track global 3D positions and estimate hand poses in a VR space without using an external suit or tracker. For better flexibility in handling and feeling physical objects compared to exoskeleton-based devices, we introduced an SMA-based actuator to control haptic forces. Overall, our device was designed and implemented as a lighter and less bulky glove which provides comparable accuracy and performance in generating 3D hand motion data for a VR training application (i.e., the use of a fire extinguisher), as demonstrated in the experimental results.

Does Exposure to Election Fraud Research Undermine Confidence in Elections?

Abstract Can academic research on election fraud undermine public confidence in elections? As election results become instantaneously accessible all over the world, academic research can increasingly influence perceptions of election integrity. To examine how electoral fraud information from academic research can affect voters’ beliefs about the legitimacy of election results, we conducted a survey experiment following the 2020 general election in South Korea. Using a nationally representative sample of Korean voters and exploiting a unique real-world situation featuring foreign academics considered relatively impartial, we found that exposure to hypothetical election fraud claims undermined confidence in electoral outcomes, measured via both self-reports and behavioral measures. Moreover, the claims’ impact was conditional on voters’ partisan attachments—strongest among supporters of the losing party—which is in line with the logic of motivated reasoning. Our work contributes to the literature on election integrity, using a relatively new yet stable democracy as its test case.

Research on sales strategies for target customers of electric vehicles based on gradient boosting ensemble trees

This paper focuses on the automotive industry, especially the new energy vehicle sector, which is vital for the economy and needs careful market sales decisions. It analyzes data from 1,964 potential customers, including their satisfaction and personal information. The study uses machine learning to create a math model and give sales advice. The data is first cleaned without changing its original structure, and then normalized using the Z-Score method. For any outlier values in the data, the mean of similar items is used to fill in gaps. Specifically, for 500 missing entries about the number of children (B7), the paper groups customers by age, calculates the frequency of children in each group, and uses these frequencies to fill in missing values.Using the cleaned data, the XGBoost algorithm ranks the factors influencing customer decisions. For Brand 1, the top factors are economy (a3), work experience (B10), and safety performance (a4). For Brand 2, comfort (a2), disposable income (B15), and mortgage expenses (B16) are key. For Brand 3, car loan expenses (B17), battery technology (a1), and again mortgage expenses (B16) are most important.The paper also addresses imbalanced data by using an XGBoost binary classification model. This model predicts the likelihood of buying an electric vehicle using the Sigmoid function on tree node weights. It evaluates the model using Precision, Recall, and Area Under the Receiver Operating Characteristic Curve (AUC), which works well even with imbalanced data. The Precision and Recall rates for all three brands are above 90%, with AUC over 85% for Brands 1 and 2, and over 75% for Brand 3. This shows that the model is effective and can be used in real-world situations.

Goal orientations and typology of women entrepreneurs: an empirical study in Gampaha district

Despite making up more than half of the population in Sri Lanka, women's economic engagement is very low. Encouragement of their entrepreneurial endeavours would raise both their level of living and the economy. Knowing their goals, typologies, determinants of the typologies is useful in real-world situations given the growing significance of women entrepreneurs. The majority of studies on entrepreneurs in Sri Lanka, however, focus on men with little attention paid to women. This study examines goal orientations, typologies and determinants of the typologies of women entrepreneurs in Sri Lanka. Goal orientations were examined by using push and pull factors. Typologies were recognized based on Goffee and Scase model. Factors affecting on typologies were examined using multinomial logit model. The sample is 150 registered women entrepreneurs in Gampaha district. A simple random sampling method was adopt for the study. Primary data were collected using an interviewer administered questionnaire. It was done by direct interviews and telephone interviews. Result of the survey shows that insufficient family income is the main push factor for venturing into business. From pull factors, its hobby. There weren’t any radical women entrepreneurs in the sample. New typology was identified and named as “Conventional – Innovative”. Majority of the women entrepreneurs are conventional followed by domestic. Least number of respondents belong to new typology. Education is significant with the Innovative typology and Conventional – Innovative typology. Age of the current business is significant with the domestic typology and Innovative typology. Marital status, Number of children, previous employment status is significant with none of the typologies. By conducting a similar study in other districts, we can find out whether there are radical women entrepreneurs in Sri Lanka or not. These findings may have immense value for promoting women's entrepreneurship in impoverished nations like Sri Lanka because there is no any similar research in Sri Lanka.

Update in venous thromboembolism in cancer: Lessons from multi-centre registries

Cancer-associated thrombosis is a common problem in cancer patients and one of the leading causes of death in this population. Randomised clinical trials have shown that both low-molecular-weight heparins and direct oral anticoagulants are the treatments of choice for cancer-associated thrombosis. Despite this, small sample sizes, poor representation of some patient subgroups and lack of information about real-world clinical situations are some of the limitations of randomised trials. To overcome these problems, registries have been established to collect real-world data from patients with cancer-associated thrombosis, offering new evidence and information to supplement the findings of randomised clinical trials. However, few registries have focused exclusively on cancer patients, and some have excluded various subgroups of patients. Additionally, data collection and processing are another major challenge in analysing registry results, where the emergence of artificial intelligence will play a fundamental role.

Communicative Language Teaching (CLT) in Improving Speaking Skills of Tertiary Level EFL Students of Bangladesh

In the English as a Foreign Language (EFL) context, students must be given a proper foundation of communication skills necessary in various dynamic real-world situations. This study investigates learner perception of the effectiveness of Communicative Language Teaching (CLT) in improving the speaking skills of tertiary-level EFL students in Bangladesh. A mixed-methods approach was used to collect and analyze data from 30 students, of different private universities of Bangladesh. The data was gathered by analyzing the results from the survey. The results of the study showed there was a statistically significant improvement in the speaking skills of EFL students after the CLT intervention. The students demonstrated an increase in fluency and complexity in their speaking performance. Furthermore, findings revealed that the students had positive perceptions of CLT and found it effective in enhancing their speaking skills. The findings of this study provide valuable insights into the benefits of using CLT as a teaching method in EFL environments. This suggests the importance of incorporating communicative and interactive activities in language classrooms to promote language learning and student engagement.

Combining EEG and eye-tracking in virtual reality: Obtaining fixation-onset event-related potentials and event-related spectral perturbations.

Extensive research conducted in controlled laboratory settings has prompted an inquiry into how results can be generalized to real-world situations influenced by the subjects' actions. Virtual reality lends itself ideally to investigating complex situations but requires accurate classification of eye movements, especially when combining it with time-sensitive data such as EEG. We recorded eye-tracking data in virtual reality and classified it into gazes and saccades using a velocity-based classification algorithm, and we cut the continuous data into smaller segments to deal with varying noise levels, as introduced in the REMoDNav algorithm. Furthermore, we corrected for participants' translational movement in virtual reality. Various measures, including visual inspection, event durations, and the velocity and dispersion distributions before and after gaze onset, indicate that we can accurately classify the continuous, free-exploration data. Combining the classified eye-tracking with the EEG data, we generated fixation-onset event-related potentials (ERPs) and event-related spectral perturbations (ERSPs), providing further evidence for the quality of the eye-movement classification and timing of the onset of events. Finally, investigating the correlation between single trials and the average ERP and ERSP identified that fixation-onset ERSPs are less time sensitive, require fewer repetitions of the same behavior, and are potentially better suited to study EEG signatures in naturalistic settings. We modified, designed, and tested an algorithm that allows the combination of EEG and eye-tracking data recorded in virtual reality.

PARP inhibitor-related acute renal failure: a real-world study based on the FDA adverse event reporting system database

ABSTRACT Background Current clinical trial data on PARP inhibitors (PARPis)-related acute renal failure (ARF) are not entirely representative of real-world situations. Therefore, in this study, the US Food and Drug Administration Adverse Event Reporting System (FAERS) was used to evaluate PARPis-related ARF. Research design and methods Data were obtained from 1 January 2015, to 30 September 2023. ARF event reports were analyzed based on four algorithms. The time-to-onset (TTO) and clinical outcomes of PARPis-associated ARF were assessed. Results The total included cases were 2726. Significant signals were observed for olaparib, niraparib, and rucaparib (reporting odds ratio (ROR): 1.62, 95% confidence interval (CI): 1.49–1.78, 1.25, 95% CI: 1.19–1.32 and 1.59, 95% CI: 1.47–1.72 respectively). The median TTO of ARF onset was 57, 36, and 85 days for olaparib, niraparib, and rucaparib, respectively. The proportion of deaths with olaparib (9.88%) was significantly higher than for niraparib (2.52%) and rucaparib (2.94%) (p < 0.005). The proportion of life-threatening adverse events associated with niraparib (4.89%) was significantly higher than for rucaparib (0.98%) (p < 0.005). Conclusions ARF and PARPi were related, with the exception of talazoparib. More emphasis should be given to PARPis-related ARF due to the high proportion of serious AEs and delayed adverse reactions.

Airborne transient electromagnetic imaging method based on wavelet neural network

The airborne transient electromagnetic method is a crucial technique for near-surface exploration in rugged terrains. In this study, we introduce a multi-input, single-output double-hidden wavelet neural network design for airborne transient electromagnetic pseudo-resistivity imaging. We construct uniform half-space, stratified stratum, and three-dimensional geoelectric models. By evaluating various performance indicators of neural networks that employ different wavelet basis functions as activation functions, we identify the most suitable wavelet basis functions. The quasi-resistivity is computed using both the wavelet neural network and the backpropagation neural network and then juxtaposed with traditional apparent resistivity. Our findings indicate that the wavelet neural network's quasi-resistivity aligns more closely with the resistivity of the real model. It is also more responsive to low resistivity anomalies than the conventional apparent resistivity translation algorithm. The wavelet approach moderates the undershoot or overshoot occurrences during abrupt stratum changes, offering a more accurate representation of subterranean electrical properties. When compared to the backpropagation neural network, the wavelet neural network provides a superior fit for the model, rendering a smoother quasi-resistivity depth curve. Therefore, it stands out as an improved method for pseudo-resistivity imaging. By processing survey data via the trained wavelet neural network, we find that the all-time apparent resistivity and quasi-resistivity align well with real-world situations. The wavelet neural network prominently showcases the low-resistance calculation results, offering a broader resistivity range. This clarity enhances anomaly detection, confirming the wavelet neural network's applicability and practicality for airborne transient electromagnetic imaging.

A Systematic Review of the Efficacy of Full-Scale Simulation Exercises in Enhancing Hospital Disaster Preparedness

Introduction: A full-scale simulation exercise is a comprehensive drill designed to replicate a real-world emergency scenario, thereby identifying the strengths and weaknesses in current practices. The overarching goal is to enhance healthcare system resilience through improved protocols, as highlighted in this systematic review tailored for researchers. The study aims specifically to assess the impact of full-scale simulations on enhancing hospital disaster plans. Methodology: Following PRISMA guidelines, this systematic review investigates the impact of full-scale simulation exercises on hospital disaster preparedness. The focus was on hospital staff involved in disaster and emergency preparedness training. The primary intervention was the execution of full-scale simulation exercises, and our research included various study designs, including randomised controlled trials and observational study designs. A comprehensive electronic database search was conducted, spanning PubMed, Scopus, Web of Science, and the Cochrane Central Register of Controlled Trials, from inception until October 9, 2023. The risk of bias was assessed using NIH tools. Results: The literature search yielded 2398 results, with 28 publications finally included in the systematic review. Summarising a broad range of disaster preparedness simulation exercises with a specific focus on full-scale simulation (FSS), the studies consistently demonstrated a positive impact on participants' skills, as well as identifying safety issues in hospital settings. Moreover, they revealed that simulations effectively addressed crucial areas for improvement in disaster response, including communication breakdowns, equipment deficiencies, and flaws in emergency plans. The studies utilised a multidimensional approach to evaluation metrics, encompassing non-technical skills, communication, teamwork, decision-making, and operational readiness. The exercises varied in duration from 30 minutes to multi-day simulations, covering a diverse range of disaster scenarios, such as mass casualties, viral epidemics, large aviation accidents, and terrorist attacks. Conclusion: Full-scale simulation exercises are a preparatory learning tool to test facility and staff readiness for complex emergencies. This systematic review focused on the different exercise scenarios used to address critical aspects of disaster response such as communication breakdowns, equipment deficiencies, and flaws in emergency plans. The scenarios and their duration were varied, and involved a multidimensional approach to evaluation. Such exercises enhance critical thinking and problem-solving skills, increase familiarity with potential emergencies, and build confidence in making judgments in real-world situations. In our opinion, there is a need for further programs that align simulation exercises with community resources for better preparedness in the face of public health disasters.

SoK: Wildest Dreams: Reproducible Research in Privacy-preserving Neural Network Training

Machine Learning (ML), addresses a multitude of complex issues in multiple disciplines, including social sciences, finance, and medical research. ML models require substantial computing power and are only as powerful as the data utilized. Due to the high computational cost of ML methods, data scientists frequently use Machine Learning-as-a-Service (MLaaS) to outsource computation to external servers. However, when working with private information, like financial data or health records, outsourcing the computation might result in privacy issues. Recent advances in Privacy-Preserving Techniques (PPTs) have enabled ML training and inference over protected data through the use of Privacy-Preserving Machine Learning (PPML). However, these techniques are still at a preliminary stage and their application in real-world situations is demanding. In order to comprehend the discrepancy between theoretical research suggestions and actual applications, this work examines the past and present of PPML, focusing on Homomorphic Encryption (HE) and Secure Multi-party Computation (SMPC) applied to ML. This work primarily focuses on the ML model's training phase, where maintaining user data privacy is of utmost importance. We provide a solid theoretical background that eases the understanding of current approaches and their limitations. We also provide some preliminaries of SMPC, HE, and ML. In addition, we present a systemization of knowledge of the most recent PPML frameworks for model training and provide a comprehensive comparison in terms of the unique properties and performances on standard benchmarks. Also, we reproduce the results for some of the surveyed papers and examine at what level existing works in the field provide support for open science. We believe our work serves as a valuable contribution by raising awareness about the current gap between theoretical advancements and real-world applications in PPML, specifically regarding open-source availability, reproducibility, and usability.

Progression in low-intensity ultrasound-induced tumor radiosensitization.

Radiotherapy (RT) is a widely utilized tumor treatment approach, while a significant obstacle in this treatment modality is the radioresistance exhibited by tumor cells. To enhance the effectiveness of RT, scientists have explored radiosensitization approaches, including the use of radiosensitizers and physical stimuli. Nevertheless, several approaches have exhibited disappointing results including adverse effects and limited efficacy. A safer and more effective method of radiosensitization involves low-intensity ultrasound (LIUS), which selectively targets tumor tissue and enhances the efficacy of radiation therapy. This review summarized the tumor radioresistance reasons and explored LIUS potential radiosensitization mechanisms. Moreover, it covered diverse LIUS application strategies in radiosensitization, including the use of LIUS alone, ultrasound-targeted intravascular microbubble destruction, ultrasound-mediated targeted radiosensitizers delivery, and sonodynamic therapy. Lastly, the review presented the limitations and prospects of employing LIUS-RT combined therapy in clinical settings, emphasizing the need to connect research findings with practical applications. LIUS employs cost-effective equipment to foster tumor radiosensitization, curtail radiation exposure, and elevate the quality of life for patients. This efficacy is attributed to LIUS's ability to utilize thermal, cavitation, and mechanical effects to overcome tumor cell resistance to RT. Multiple experimental analyses have underscored the effectiveness of LIUS in inducing tumor radiosensitization using diverse strategies. While initial studies have shown promising results, conducting more comprehensive clinical trials is crucial to confirm its safety and effectiveness in real-world situations.

An Intermittent Fasting Intervention for Black Adults Can Be Feasibly Implemented in Black Churches: A Cluster Randomized Controlled Pilot Study

BackgroundIntensive lifestyle interventions, including modest reductions in daily caloric intake (ie, continuous calorie energy reduction [CER]), are recommended by US national professional health organizations (eg, American Heart Association). However, they are less effective in Black communities. A burgeoning literature has reported the promise of intermittent fasting (IF) as an alternative strategy for weight loss. However, IF studies have been conducted with White participants predominately and provided participant resources not readily available in real-world situations. ObjectiveWeight-loss and weight-related outcomes of a scalable (ie, able to be widely disseminated and implemented) IF intervention developed with and for Black adults were compared with a CER intervention for the purpose of determining IF’s feasibility (ie, initial effectiveness, adherence, and acceptance) in a Black community. DesignA cluster randomized controlled pilot study was conducted. Participants/settingA total of 42 Black adults with a body mass index (calculated as kg / m2) ≥25 were recruited from 5 Black churches (3 IF and 2 CER) in Western New York State from September 2021 to May 2022. Participants were free of medical conditions that might have contraindicated participation in a weight-reduction program and other factors that might affect weight loss. InterventionsCommunity health workers delivered the 6-month, 16-session, faith-based IF and CER interventions. Main outcome measuresThe primary outcome was feasibility, consisting of initial effectiveness on body weight (ie, percent body weight lost from baseline to 6-month follow-up), adherence, and acceptability. Statistical analyses performedDescriptive statistics and linear mixed models accounting for within-church clustering were used. A baseline covariate corresponding to the outcome variable was included in the model. Intent-to-treat analysis was used. ResultsThere was statistically significant weight loss within both arms (IF: –3.5 kg; 95% CI –6 to –0.9 kg, CER: –2.9 kg; 95% CI –5.1 to –0.8 kg) from baseline to 6-month follow-up. Compared with CER, IF led to significantly lower daily energy intake (414.2 kcal; 95% CI 55.2 to 773.2 kcal) and fat intake (16.1 g; 95% CI 2.4 to 29.8 g). IF may result in lower fruit and vegetable intake (–103.2 g; 95% CI –200.9 to –5.5 g) and fiber intake –5.4 g; 95% CI –8.7 to –2 g) compared with CER. Participants in the IF arm completed a mean (SE) of 3.8 (1.4) more self-monitoring booklets compared with those in the CER arm (P = .02). Participants reported high levels of satisfaction with the program. ConclusionsAn IF intervention developed with and for Black adults can be feasibly implemented in Black churches. Larger studies need to be conducted to ascertain the extent IF can serve as a viable weight-loss alternative to CER interventions in Black communities.

Understanding the influence of context on real-world walking energetics.

Speeds that minimize energetic cost during steady-state walking have been observed during lab-based investigations of walking biomechanics and energetics. However, in real-world scenarios, humans walk in a variety of contexts that can elicit different walking strategies, and may not always prioritize minimizing energetic cost. To investigate whether individuals tend to select energetically optimal speeds in real-world situations and how contextual factors influence gait, we conducted a study combining data from lab and real-world experiments. Walking kinematics and context were measured during daily life over a week (N=17) using wearable sensors and a mobile phone. To determine context, we utilized self-reported activity logs, GPS data and follow-up exit interviews. Additionally, we estimated energetic cost using respirometry over a range of gait speeds in the lab. Gross and net cost of transport were calculated for each participant, and were used to identify energetically optimal walking speed ranges for each participant. The proportion of real-world steady-state stride speeds within these ranges (gross and net) were identified for all data and for each context. We found that energetically optimal speeds predicted by gross cost of transport were more predictive of walking speeds used during daily life than speeds that would minimize net cost of transport. On average, 82.2% of all steady-state stride speeds were energetically optimal for gross cost of transport for all contexts and participants, while only 45.6% were energetically optimal for net cost of transport. These results suggest that while energetic cost is a factor considered by humans when selecting gait speed in daily life, it is not the sole determining factor. Context contributes to the observed variability in movement parameters both within and between individuals.

Protecting Prostate Cancer Classification From Rectal Artifacts via Targeted Adversarial Training.

Magnetic resonance imaging (MRI)-based deep neural networks (DNN) have been widely developed to perform prostate cancer (PCa) classification. However, in real-world clinical situations, prostate MRIs can be easily impacted by rectal artifacts, which have been found to lead to incorrect PCa classification. Existing DNN-based methods typically do not consider the interference of rectal artifacts on PCa classification, and do not design specific strategy to address this problem. In this study, we proposed a novel Targeted adversarial training with Proprietary Adversarial Samples (TPAS) strategy to defend the PCa classification model against the influence of rectal artifacts. Specifically, based on clinical prior knowledge, we generated proprietary adversarial samples with rectal artifact-pattern adversarial noise, which can severely mislead PCa classification models optimized by the ordinary training strategy. We then jointly exploited the generated proprietary adversarial samples and original samples to train the models. To demonstrate the effectiveness of our strategy, we conducted analytical experiments on multiple PCa classification models. Compared with ordinary training strategy, TPAS can effectively improve the single- and multi-parametric PCa classification at patient, slice and lesion level, and bring substantial gains to recent advanced models. In conclusion, TPAS strategy can be identified as a valuable way to mitigate the influence of rectal artifacts on deep learning models for PCa classification.

Real-World Situation of Eradication Regimens and Risk Factors for Helicobacter pylori Treatment in China: A Retrospective Single-Center Study.

The success rate of Helicobacter pylori (H. pylori) eradication in China is declining. The aim of this study was to evaluate eradication outcomes in clinical practice and identifies factors contributing to treatment failure. A retrospective review was conducted on patients treated for H. pylori infection with 14-day bismuth-containing quadruple therapy at a Beijing medical center from January 2020 to December 2023. We analyzed demographic and clinical data, eradication rates across regimens, and performed multivariate analysis to pinpoint predictors of failure. Out of 3340 participants, 2273 (68.1%) achieved eradication. Amoxicillin-based combinations (69.2%) outperformed other antibiotic regimens (58.9%, p < 0.001), with amoxicillin plus doxycycline reaching a 71.4% success rate. Esomeprazole-based regimens were more effective (73.6%) than other PPI regimens (65.2%, p = 0.001), notably, a rabeprazole, amoxicillin, doxycycline, and bismuth combination had an 80.0% success rate. Age, gender, and smoking and drinking were significant eradication failure predictors. In real-world settings, 14-day amoxicillin and esomeprazole-based quadruple regimens have been demonstrated to be more effective than other regimens. Age, gender, and lifestyle habits are identified as independent risk factors for eradication failure. This study was registered in the Chinese Clinical Trial Registry on 08/01/2024 (clinical trial registration number: ChiCTR2400079647).

Image semantic segmentation of indoor scenes: A survey

This survey provides a comprehensive evaluation of various deep learning-based segmentation architectures. It covers a wide range of models, from traditional ones like FCN and PSPNet to more modern approaches like SegFormer and FAN. In addition to assessing the methods in terms of segmentation accuracy, we propose to also evaluate the methods in terms of temporal consistency and corruption vulnerability. Most of the existing surveys on semantic segmentation focus on outdoor datasets. In contrast, this survey focuses on indoor scenarios to enhance the applicability of segmentation methods in this specific domain. Furthermore, our evaluation consists of a performance analysis of the methods in prevalent real-world segmentation scenarios that pose particular challenges. These complex situations involve scenes impacted by diverse forms of noise, blur corruptions, camera movements, optical aberrations, among other factors. By jointly exploring the segmentation accuracy, temporal consistency, and corruption vulnerability in challenging real-world situations, our survey offers insights that go beyond existing surveys, facilitating the understanding and development of better image segmentation methods for indoor scenes.

A Retinal Vessel Segmentation Method Based on the Sharpness-Aware Minimization Model.

Retinal vessel segmentation is crucial for diagnosing and monitoring various eye diseases such as diabetic retinopathy, glaucoma, and hypertension. In this study, we examine how sharpness-aware minimization (SAM) can improve RF-UNet's generalization performance. RF-UNet is a novel model for retinal vessel segmentation. We focused our experiments on the digital retinal images for vessel extraction (DRIVE) dataset, which is a benchmark for retinal vessel segmentation, and our test results show that adding SAM to the training procedure leads to notable improvements. Compared to the non-SAM model (training loss of 0.45709 and validation loss of 0.40266), the SAM-trained RF-UNet model achieved a significant reduction in both training loss (0.094225) and validation loss (0.08053). Furthermore, compared to the non-SAM model (training accuracy of 0.90169 and validation accuracy of 0.93999), the SAM-trained model demonstrated higher training accuracy (0.96225) and validation accuracy (0.96821). Additionally, the model performed better in terms of sensitivity, specificity, AUC, and F1 score, indicating improved generalization to unseen data. Our results corroborate the notion that SAM facilitates the learning of flatter minima, thereby improving generalization, and are consistent with other research highlighting the advantages of advanced optimization methods. With wider implications for other medical imaging tasks, these results imply that SAM can successfully reduce overfitting and enhance the robustness of retinal vessel segmentation models. Prospective research avenues encompass verifying the model on vaster and more diverse datasets and investigating its practical implementation in real-world clinical situations.

Co-evolutionary dynamics and heterogeneity in corporate social responsibility: A case study on multinational corporation subsidiaries

This study aims to understand the heterogeneity of corporate social responsibility (CSR) approaches across subsidiaries of the same multinational corporations (MNCs) and determine the co-evolutionary dynamics that enhance the global cohesiveness of MNCs' CSR strategies and performance. The study draws on co-evolutionary theory and adopts an explanatory research design based on embedded multiple case study methodology. Semi-structured interviews with top management and employees responsible for CSR or related areas were conducted, and secondary data collection was done for triangulation and validation. The findings suggest that MNCs experience heterogeneity in their CSR, particularly in the implementation approaches, philanthropic and environmental CSR dimensions, and that MNCs with significant heterogeneity at the strategic level have less cohesive strategic CSR practices. The study also found that MNCs that encourage and facilitate active collaboration between subsidiaries have more cohesive and strategic CSR, and each MNC's CSR is driven by different co-evolutionary dynamics. The study contributes to the expansion of co-evolutionary theory to the study of CSR and offers a dynamic CSR co-evolution model explaining MNC CSR heterogeneity in real-world situations, along with potential levers to achieve more globally cohesive strategic CSR.