Search Results (49)

This study re-examines the ongoing debate over corporate disclosure frequency amid renewed calls to replace quarterly with semiannual reporting in U.S. markets. While traditional theories hold that frequent disclosure enhances informational efficiency by reducing asymmetry, emerging evidence highlights trade-offs involving managerial myopia, earnings management, and heightened short-term volatility. Using data from 2007 to 2024, the study compares Dow Jones Industrial Average firms, which report quarterly, with STOXX 50 firms, which report semiannually, to assess how disclosure cadence affects market reactions to earnings news The methodology involves identifying volatility regimes using Self-Exciting Threshold Autoregressive (SETAR) models, estimating dynamic betas with the GARCH(1,1) model, and analyzing shock transmission through vector autoregressions with cumulative impulse response functions (CIRFs). The results show that quarterly reporters exhibit larger immediate price reactions but faster normalization, implying that more frequent reporting accelerates information assimilation while amplifying contemporaneous volatility. Sectoral heterogeneity is pronounced: cyclical industries display higher beta volatility and steeper, but shorter-lived responses, whereas defensive stocks exhibit smoother convergence. These findings suggest that disclosure frequency influences both the intensity and duration of information shocks, providing insights for regulators who aim to balance transparency, market efficiency, and reporting costs across varying volatility and sectoral environments. Full article

As China advances its green transition, digital finance broadens firms’ access to external financing; however, whether improved access enhances financial allocation efficiency or drives green technology innovation remains unclear. This study addresses this through a dual-layer framework examining financing mechanisms and governance conditions. Using panel data from 2165 Chinese A-share firms (2011–2022) with two-way fixed-effects models, the analysis yields three key findings: First, digital finance significantly enhances green technology innovation. Second, financial mismatch partially mediates this relationship. Third, governance moderates these effects. Equity incentives exhibit threshold effects, where positive impacts emerge only above certain levels. Cash incentives, however, vary by period: they are positive in 2011–2018 and negative in 2019–2022. These results underscore that financial reform must accompany governance improvements, especially equity incentives. The Chinese experience also provides insights for emerging economies navigating digital finance expansion and green transition. Full article

Reliable rainfall prediction is essential for effective climate adaptation yet remains challenging due to complex atmospheric interactions that vary across regions. This study investigates next-day rainfall predictability in tropical and temperate climates using daily atmospheric data—including pressure, temperature, dew point, relative humidity, wind speed, and wind direction—collected from topographically similar sites in Alor Setar (tropical) and Vercelli, Williams, and Ashburton (temperate) between 2012 and 2015. Logistic regression and random forest models were used to predict rainfall occurrence as a binary outcome. Key variables were identified using Wald’s statistics and p-values in the logistic regression models, while the random forest models relied on mean decrease accuracy for ranking variable importance. The results reveal that rainfall in temperate climates is significantly more predictable than in tropical regions, with the Williams model demonstrating the highest accuracy. Atmospheric pressure consistently emerged as the dominant predictor in temperate regions but was not significant in the tropical model, reflecting the greater atmospheric variability and complexity in tropical rainfall mechanisms. Crucially, the study highlights that as global warming continues to alter temperate climate patterns—bringing increased variability and more convective rainfall—these regions may experience the same predictive uncertainties currently observed in tropical climates. These findings underscore the urgency of developing robust, climate-specific rainfall prediction models that account for changing atmospheric dynamics, with critical implications for weather forecasting, disaster preparedness, and climate resilience planning. Full article

Water quality is a crucial determinant in decision-making processes aimed at optimizing resource allocation across various industries. Pollutant impurities that hinder the sufficient supply of water have a deleterious impact on the quality and are damaging to living species, especially aquatic life. Various chemical and biological treatments are used to reduce water pollution levels. A technology involving a mixture of anaerobic and aerobic beneficial microbes is becoming popular for its eco-friendly characteristics. Effective Microorganism (EM) technology utilizes naturally existing microorganisms that can purify and restore the environment. The study investigated the application of Effective Microorganism-Activated Solution (EMAS), TeMo Decomposer (TeMo), and Lactic Acid Bacteria (LAB) to enhance water quality. Additionally, microbial testing will be carried out to identify bacteria present in each EM. EM-based rehabilitation of polluted and degraded water bodies significantly contributes to the restoration of aquatic habitats and ecosystems. This study aimed to assess the water quality at Tasik Alumni, Kampus Pauh, Perlis, Malaysia. Four sampling points in Tasik Alumni were chosen to reflect the water quality status of the lake. The sampling was conducted once at four points locations in Tasik Alumni. Seven water quality measures, including pH, dissolved oxygen (DO), biochemical oxygen demand (BOD), chemical oxygen demand (COD), ammoniacal nitrogen (NH₃-N), total suspended solid (TSS), and turbidity, were analysed ex-situ and categorised according to Water Quality Index (WQI) and National Water Quality Standard (NWQS) classifications. The Tasik Alumni was categorised as mildly contaminated. The results clearly showed the efficiency of this technique in restoring and conserving water quality in a degraded or polluted lake. Full article

Background/Objectives: Colorectal cancer (CRC) arises from the epithelial lining of the colon or rectum, often following a progression from benign adenomatous polyps to malignant carcinoma. Screening modalities such as colonoscopy, faecal immunochemical tests (FIT), and FIT-DNA are critical for early detection and prevention, but non-invasive methods lack sensitivity to polyps and early CRC. Chromosome conformations (CCs) are potent epigenetic regulators of gene expression. We have previously developed an epigenetic assay, EpiSwitch^®®, that employs an algorithmic-based CCs analysis. Using EpiSwitch^®® technology, we have shown the presence of cancer-specific CCs in peripheral blood mononuclear cells (PBMCs) and primary tumours of patients with melanoma and prostate cancer. EpiSwitch^®®-based commercial tests are now available to diagnose prostate cancer with 94% accuracy (PSE test) and response to immune checkpoint inhibitors across 14 cancers with 85% accuracy (CiRT test). Methods/Results/Conclusions: Using blood samples collected from n = 171 patients with CRC, n = 44 patients with colorectal polyps and n = 110 patients with a ‘clear’ colonoscopy we performed whole Genome DNA screening for CCs correlating to CRC diagnosis. Our findings suggest the presence of two eight-marker CC signatures (EpiSwitch^®® NST) in whole blood that allow diagnosis of CRC and precancerous polyps, respectively. Independent validation cohort testing demonstrated high accuracy in identifying colorectal polyps and early versus late stages of CRC with an exceptionally high sensitivity of 79–90% and a high positive prediction value of 60–84%. Linking the top diagnostic CCs to nearby genes, we have built pathways maps that likely underline processes contributing to the pathology of polyp and CRC progression, including TGFβ, cMYC, Rho GTPase, ROS, TNFa/NFκB, and APC. Full article

This study investigates the occurrence and persistence of abnormal stock returns surrounding corporate earnings announcements, particularly emphasizing how varying frequencies of financial reporting influence market behavior. Specifically, this research examines the effects of the timing and frequency of disclosures on market reactions and stock price volatility during critical earnings announcement periods. By analyzing firms within the Dow Jones Industrial Average (DJIA) from 2014 to 2024, this study evaluates the interplay between financial reporting schedules and market responses to stock prices. Furthermore, it considers the impact of peer firms’ reporting practices on the assimilation of firm-specific information into stock prices. Using econometric models, including Vector Auto Regression (VAR), Impulse Response Functions (IRFs), and Self-Exciting Threshold Autoregressive (SETAR) models, causal relationships between reporting frequency, stock price volatility, and abnormal return patterns across different volatility regimes are identified. The findings highlight that quarterly reporting practices intensify market responses and contribute to significant variations in stock price behavior in high-volatility periods. These insights provide a deeper understanding of the role of financial disclosure practices and forward-looking guidance in shaping market efficiency. This study contributes to ongoing discussions about balancing the transparency benefits of frequent reporting with its potential to amplify market volatility and sector-specific risks, offering valuable implications for policymakers, investors, and corporate managers. Full article

Air pollution in Jakarta, one of the most polluted cities globally, has reached critical levels, with PM_2.5 concentrations exceeding the WHO guidelines and posing significant health risks. Accurate forecasting of PM_2.5 is crucial for effective air quality management and public health interventions. PM_2.5 exhibits significant nonlinear fluctuations; thus, this study employed two machine learning approaches: self-exciting threshold autoregressive tree (SETAR-Tree) and long short-term memory (LSTM). The SETAR-Tree model integrates regime-switching capabilities with decision tree principles to capture nonlinear patterns, while LSTM models long-term dependencies in time-series data. The results showed that: (1) SETAR-Tree outperformed LSTM, achieving lower RMSE (0.1691 in-sample, 0.2159 out-sample) and MAPE (2.83% in-sample, 2.98% out-sample) compared to LSTM’s RMSE (0.2038 in-sample, 0.2399 out-sample) and MAPE (3.48% in-sample, 4.05% out-sample); (2) SETAR-Tree demonstrated better responsiveness to sudden regime changes, capturing complex pollution patterns influenced by meteorological and anthropogenic factors; (3) PM_2.5 in Jakarta often exceeds the WHO limits, highlighting this study’s importance in supporting strategic planning and providing an early warning system to reduce outdoor activity during extreme pollution. Full article

The purpose of this paper is to show how the self-exciting threshold autoregressive (SETAR) model might be a suitable econometric framework for characterizing the dynamics of the US public debt/GDP ratio after the Bretton Woods collapse. Our preferred SETAR specifications are capable of capturing the main stylized facts of the US public debt/GDP ratio between 1974 and 2024. In addition, the estimated SETAR models are consistent with theoretical frameworks that look to explain the behavior of the US public debt/GDP ratio before and after the Global Financial Crisis (GFC). Finally, under the assumption of public debt/GDP ratio stationarity, for which we find only limited and inconclusive evidence, this paper provides some arguments for why previous studies, which use the exponential smooth threshold autoregressive (ESTAR) models, logistic smooth threshold autoregressive (LSTAR) models or SETAR-type models for the first differences of the US public debt/GDP ratio, are potentially misspecified on both econometric and economic grounds. Full article

We examine the relationship between gold prices and the U.S. dollar exchange rate, arguing that their interactions are state-dependent and asymmetric under different market conditions. State dependency hinges on different short-term interest rate zones. To prove this point, we determine three distinct levels or zones of the effective federal funds rate using SETAR(2,p) tests. Subsequently, we perform conditional least square estimations of log changes in gold prices as a function of log changes in the nominal broad U.S. dollar exchange rate index for each of the obtained zones. Their relationship is consistently inverse, suggesting that gold and the U.S. dollar are risk-hedging substitutes for normal market periods. This also implies that gold is a safe-haven asset against the U.S. dollar exchange rate risk against a broad range of currencies. The substitution is weaker in the low-interest rate zone, more robust in the intermediate zone, and very pronounced in the high zone. We also perform a Markov switching test on the double-log function of gold prices and the exchange rate. The tests show a pronounced inverse relationship, i.e., substitution between assets, at normal market conditions. The relationship becomes significantly positive during episodes of financial distress, indicating complementarity between gold and U.S. dollar assets. Full article

This study focused on the development of a teacher self-efficacy measurement addressing the contextualization of information and communication technologies (ICTs) with education for sustainability (EfS) using principal component analysis. Furthermore, this study, with the participation of 1815 teachers, examined the predictive value of some hypothesized predictors of the ICTeEfS self-efficacy construct such as gender, school setting, years of teaching, knowledge of education for sustainability, knowledge of ICTs, and experience in using ICTs to support the integration of education for sustainability in teaching and school curricula using multiple regression analysis. The research results revealed that gender did not explain any statistically significant variance of teachers’ ICTeEfS self-efficacy; contrary to this, teachers possessing a high level of knowledge on issues about sustainability and ICT competence explained most of the extracted variance. However, a gap remains in utilizing these skills pedagogically. This study also discusses the varying levels of self-efficacy among teachers based on their workplace location, finding that urban teachers demonstrate higher self-efficacy compared to their rural counterparts. This could be attributed to the disparities in resources and support systems, thereby affecting their capacity to employ ICT in EfS effectively. It was also found that novice teachers exhibited higher predictive power to ICTeEfS self-efficacy, possibly due to their recent exposure to ICT training. This study assumes that a profound understanding of EfS, coupled with ICT tools, bolsters the creation of contextualized curricula and enriches the teaching and learning experience towards sustainability. Full article

This study aims to investigate the role of Information and Communication Technologies-enabled Education for Sustainability (ICTeEfS), critical reflection, and transformative teaching and learning beliefs in predicting students’ attitudes about seeking sustainability justice. A total of 1497 students from seven universities in Indonesia (374), Malaysia (426), and Vietnam (697) trialed four new scales measuring (a) knowledge of merging ICT with education for sustainability, (b) critical reflective practice, (c) sustainability justice attitudes, and (d) transformative teaching and learning beliefs. The findings show that the four scales are reliable and could be used in other research on education for sustainability. Differences were observed for gender, year of study, subject of study, ICT skills, and knowledge of education for sustainability. Regression analysis highlighted that sustainability justice is a multidimensional concept composed of several constructs with a specific reference to critical reflection, transformative teaching and learning beliefs. The implications for education, practice and further research are discussed. Full article

Background: Routine human papillomavirus (HPV) vaccine uptake continues to be suboptimal since its recommendation in 2006 for girls and in 2011 for boys. This paper aims to review published quality improvement (QI) methodologies on interventions to improve HPV vaccine uptake among adolescents. Methods: Science Direct and Scopus databases were searched for QI initiatives evaluating the effect of multimodal interventions to improve HPV vaccination rates (initiation and/or completion of series) among adolescents. Studies that included an outcome of interest among adolescents aged 10 to 18 years old were included. Two investigators worked independently to screen for potential articles and a designated investigator extracted data on study characteristics and evaluated the outcomes. Results: A preliminary search yielded a total of 523 articles and 13 were included in the final analysis. Common strategies were provider-specific (i.e., webinar, telementoring, train-the-trainer approach) and patient- and/or parent-specific interventions (i.e., reminder emails, phone calls and text messages, social events), with an emphasis on education and knowledge empowerment. System-level interventions such as policy changes and revised protocols were less commonly prescribed despite being associated with a more significant weight on the overall outcome. Conclusions: Creative, sustainable, and economical multilevel interventions that focus not only on provider training and public education but also incorporate local policies and system enhancements can substantially improve HPV vaccination coverage among adolescents. Full article

The detection of pain in persons with advanced dementia is challenging due to their inability to verbally articulate the pain they are experiencing. Pain Assessment in Advanced Dementia (PAINAD) is an observer-rated pain assessment tool developed based on non-verbal expressions of pain for persons with severe dementia. This study aimed to perform construct validation of PAINAD for pain assessment in persons with severe dementia in Malaysia. This was a prospective cross-sectional study conducted from 27 April 2022 to 28 October 2022 in eight public hospitals in Malaysia. The PAINAD scale was the index test, and the Discomfort Scale—Dementia of the Alzheimer Type (DS-DAT) and Nurse-Reported Pain Scale (NRPS) were the reference tests for construct and concurrent validity assessment. Pain assessment for the study subjects was performed by two raters concurrently at rest and during activity. The PAINAD score was determined by the first rater, whereas the DS-DAT and NRPS were assessed by the second rater, and they were blinded to each other’s findings to prevent bias. PAINAD showed good positive correlations ranging from 0.325 to 0.715 with DS-DAT and NRPS at rest and during activity, with a p-value of <0.05. It also demonstrated statistically significant differences when comparing pain scores at rest and during activity, pre- and post-intervention. In conclusion, the PAINAD scale is a reliable observer-rated pain assessment tool for persons with severe dementia in Malaysia. It is also sensitive to changes in the pain level during activity and at rest, pre- and post-intervention. Full article

Cyberattacks in the modern world are sophisticated and can be undetected in a dispersed setting. In a distributed setting, DoS and DDoS attacks cause resource unavailability. This has motivated the scientific community to suggest effective approaches in distributed contexts as a means of mitigating such attacks. Syn Flood is the most common sort of DDoS assault, up from 76% to 81% in Q2, according to Kaspersky’s Q3 report. Direct and indirect approaches are also available for launching DDoS attacks. While in a DDoS attack, controlled traffic is transmitted indirectly through zombies to reflectors to compromise the target host, in a direct attack, controlled traffic is sent directly to zombies in order to assault the victim host. Reflectors are uncompromised systems that only send replies in response to a request. To mitigate such assaults, traffic shaping and pushback methods are utilised. The SYN Flood Attack Detection and Mitigation Technique (SFaDMT) is an adaptive heuristic-based method we employ to identify DDoS SYN flood assaults. This study suggested an effective strategy to identify and resist the SYN assault. A decision support mechanism served as the foundation for the suggested (SFaDMT) approach. The suggested model was simulated, analysed, and compared to the most recent method using the OMNET simulator. The outcome demonstrates how the suggested fix improved detection. Full article

(1) Background: Achieving successful tuberculosis (TB) treatment outcomes among diabetic patients is a real challenge as TB complicates control of diabetes. This study aimed to evaluate the effectiveness of an integrated dual management educational module, MID-DOTS, which uses the mmotivational interviewing (MI) technique implemented within directly observed treatment as part of a short course (DOTS) program in TB patients with diabetes (TB/DM). A randomized controlled trial was conducted in the northeastern state of Malaysia. (2) Methods: One hundred and twenty-four TB patients with diabetes received educational intervention using a MID-DOT module that used the MI technique, which was repetitively applied by TB nurses throughout a 6-month DOTS program while another 122 patients were given standard health education. Study outcomes include the proportion of patients with successful TB treatment, and changes in HbA1c and diabetic self-care scores at 6 months. (3) Results: The successful TB treatment outcome was 88% in the intervention group versus 72% in the control group (RR = 1.24; 95%CI 1.16, 1.58). A significant reduction of HbA1c (mean difference 0.82%; 95%CI 0.66, 0.98) and significantly higher diabetes self-care score (mean difference 8.49; 95%CI 7.38, 9.59) were also shown in the intervention group. (4) Conclusions: A dual TB/DM educational strategy which integrates the MI technique applied repetitively within the DOTS program is effective in increasing successful TB treatment as well as improving diabetic outcomes. Full article