Maharishi International University

The accelerating frequency and scale of infectious disease outbreaks underscore the urgent need for more reliable forecasting methods to strengthen global health resilience. Traditional epidemiological models, while valuable, often rely on limited and siloed datasets that fail to capture the multifactorial dynamics of disease transmission. Recent advances in Artificial Intelligence (AI) offer opportunities to overcome these limitations by integrating multimodal health datasets encompassing clinical records, genomic information, behavioral data, environmental exposures, and mobility patterns. This integration expands the analytical depth of epidemiological modeling, enabling more accurate detection of early outbreak signals and enhancing the capacity to predict transmission pathways across diverse populations. AI-driven approaches, including machine learning, deep learning, and hybrid ensemble techniques, provide the computational power to identify complex, non-linear relationships in large-scale health data. By combining multimodal inputs with epidemiological modeling, these systems can account for heterogeneous risk factors such as social determinants of health, co-morbidities, and population density. Beyond forecasting, such integrated frameworks support decision-making in real time, informing public health interventions such as vaccination campaigns, resource allocation, and quarantine strategies. However, challenges remain, particularly in ensuring data interoperability, managing privacy concerns, and addressing algorithmic bias that could reinforce existing inequities in healthcare delivery. This paper proposes a structured framework for integrating multimodal health datasets with AI-driven epidemiological modeling, emphasizing accuracy, equity, and ethical governance. By narrowing the scope from broad health system complexity to targeted AI-enabled modeling, the discussion illustrates how this convergence can revolutionize infectious disease surveillance and ultimately improve global preparedness for future pandemics.

Carbon taxation is a key climate policy tool that influences capital markets. While traditional models assume proportionate asset repricing after regulatory cost changes, investor reactions may also be shaped by behavioural biases and attention dynamics. This paper proposes the Behavioural Environmental Tax Response Model (BETRM), which views carbon taxation as a behavioural shock affecting markets through loss framing, salience amplification, and ESG signalling. These mechanisms may lead to short-term abnormal returns, sectoral capital shifts, and increased volatility. The model also highlights moderating factors such as financial literacy, regulatory credibility, and investor sophistication, providing a framework for future empirical research on investor behaviour under climate policies.

In this randomized controlled trial with women living in poverty in Uganda (n = 199) we explored the impact of Transcendental Meditation® (TM®) on perceived stress—the primary outcome measure, and self-efficacy, anger, anxiety, fatigue, sleep, and resilience as secondary measures. After baseline testing participants were randomly assigned to either practice of TM or to a waitlist control group. Participants learned TM over five consecutive days and then practiced at home for 20 ­minutes twice a day. Post testing took place after three months. A significant reduction was found in perceived stress, anger, and fatigue, and a significant improvement in self-efficacy and sleep quality. In a follow-up questionnaire at eight months, participants reported improved physical and mental health, increased ability to handle domestic violence, and improved relationships. Results indicate the TM program has a positive impact on psychological distress and coping and could add value to programs for women in Uganda.

Ransomware poses a growing threat to healthcare systems, compromising patient safety, operational continuity, and financial stability. Although machine learning techniques have been widely used for intrusion detection, most approaches do not support real-time, cost-sensitive response planning. In this paper, we propose a hybrid framework that integrates deep learning with quantum optimization to both predict the severity of ransomware infection and recommend optimal recovery strategies. The system employs a multilayer perceptron (MLP) trained on structured ransomware incident data to forecast infection rates, followed by a quantum post-processing module using the Quantum Approximate Optimization Algorithm (QAOA) to minimize operational and financial costs through discrete response decisions. We evaluated the framework on a realistic healthcare ransomware dataset consisting of 5,000 simulated attack scenarios. Our approach achieves a root mean squared error (RMSE) of 0.073 in the prediction of infection rates and demonstrates up to 25% cost savings over classical heuristics in recovery decisions. Extensive experiments confirm the generalizability of the model to unseen attack types, the scalability across data volumes, and the stability of the decision in risk contexts. The proposed method represents a step toward intelligent real-time ransomware mitigation systems for high-risk environments such as healthcare.

Real-time traffic data from the system was analysed by the M/G/1 queuing model in an IoT-linked traffic monitoring framework. Because of a growing number of vehicles in India and other densely populated countries, the usual traffic control methods are not enough to tackle congestion. The research is aimed at studying the traffic movement along a 500-meter highway in the city with the help of special equipment. In two months, data were gathered with the help of sensors and cameras connected to a cloud platform. Because the M/G/1 model supports unpredictable service times, it was used to analyse how traffic moves into and out of the system. Certain factors like traffic intensity, waiting time, and queue length were identified to analyse the congestion pattern, mainly during busy hours. Experts looked at how the queuing system worked and examined how long it took cars to wait by using performance indicators <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\lambda, \mu, \sigma$</tex>, and waiting time in queue (Wq). The graph proved that the traffic in the right lane was a lot denser than that in the left, which suggests a need for adjusting lane setup. It was also discovered that longer wait times during busy hours showed that adaptive traffic control worked. The results prove that queuing theory helps with anticipating traffic congestion and finding solutions to reduce it using modern traffic systems. The suggested IoT + M/G/1 queuing model-based traffic monitoring technique achieves an overall accuracy of 96.5% in actual-time traffic analysis of patterns.

Phosphodiesterase-4 (PDE4) enzymes are well-validated therapeutic targets implicated in inflammatory, neurological and oncological disorders. Among the PDE4 isoforms, PDE4B and PDE4D are of particular interest; however, their high structural similarity poses a major challenge for the development of isoform-selective inhibitors. To develop a ligand-based framework for predicting PDE4B and PDE4D selectivity in triazine analogues through systematic structure–activity relationship analysis and in silico pharmacokinetic profiling. An integrated in silico drug design approach was employed to evaluate thirty-four triazine-based ligands against PDE4B (PDB ID: 1XMU) and PDE4D using the GOLD 5.2 suite with the CHEMPLP fitness function. Docking analyses focused on binding scores, hydrogen-bond networks, interaction energies and hydrophobic fitting points to identify stable binding conformations. Docking protocol reliability was confirmed by redocking the co-crystallised ligand, yielding RMSD values below 2.0 Å. Binding modes were further examined using three-dimensional and two-dimensional interaction mapping. The most promising ligands were additionally evaluated for absorption, distribution, metabolism, excretion and toxicity (ADMET) properties using SwissADME and pkCSM. For PDE4B, docking scores ranged from 139.84 to 158.84, with ligands Aj11, Aj16, Aj24, Aj25 and Aj31 emerging as top candidates. Aj11 and Aj31 showed enhanced stability through multiple interactions with HIS278 and ASP348, while Aj25 exhibited the strongest hydrogen bonding with HIS234 (2.2 Å) and the most compact hydrophobic accommodation. In the case of PDE4D, the highest docking scores were observed for Aj5 (121.59), Aj32 (113.22), Aj31 (112.11), Aj11 (106.13) and Aj8 (105.05). Aj32 formed dual interactions with ASP318 and THR271, whereas Aj31 displayed the strongest hydrogen bonds with ASP318 (2.321–2.521 Å) and favourable hydrophobic fitting. Overall, Aj25 (PDE4B-selective) and Aj31 (PDE4D/dual inhibitor) emerged as the most promising candidates based on high docking scores, optimal hydrogen-bond geometry, compact hydrophobic accommodation, and favourable predicted ADMET profiles. These findings highlight triazine derivatives as promising scaffolds for further optimisation toward selective PDE4 inhibition.

The global economy is undergoing a rapid transformation driven by digital technologies that are reshaping international trade. Digitalization—defined as the integration of digital technologies into business and government processes—has emerged as a critical factor influencing export performance, competitiveness, supply chain efficiency, and market reach. This paper critically examines the role of digitalization in export trade by synthesizing empirical research from multiple regions and sectors, including manufacturing, services, and small and medium enterprises (SMEs). It assesses both quantitative outcomes (e.g., export volume growth, cost reductions) and qualitative implications (e.g., transformation of trade structures, enhanced service exports). Empirical studies indicate that digitalization reduces trade costs, enhances connectivity, and broadens access to global value chains, thereby positively affecting export participation and diversification. However, benefits depend on digital readiness, infrastructure quality, and firm-level capabilities. Digitalization also enables new forms of trade, such as e-commerce and digitally deliverable services, which are driving global export growth and contributing significantly to services trade. Despite advantages, digitalization presents challenges: skill gaps, cybersecurity risks, regulatory heterogeneity, and unequal access to digital infrastructure can limit its potential. Policy actions that strengthen digital infrastructure, promote digital adoption among exporters—particularly SMEs—and ensure regulatory harmonization can amplify the positive impact of digitalization on export trade. The paper concludes with recommendations for stakeholders to leverage digital technologies strategically to enhance export competitiveness and sustainable growth.

ABSTRACT The following two novel species of the genus Pheretima are described from Maria Aurora National Park, Aurora Province, Philippines: Pheretima auroraensis sp. n. and Pheretima balerensis sp. n. We also describe three putative species-level taxa, but lack sufficient data to assign formal names. All belong to the sangirensis group with one pair of spermathecal pores in 7/8. Pheretima auroraensis sp. n. has relatively few setae per segment, 26 at VII, 36 at XX; three setae between male pores and pigmented setal rings. The copulatory bursal opening is surrounded by six wedge-shaped pads. Pheretima balerensis sp. n. has relatively abundant setae numbering 45–50 at VII and 61–67 at XX but only 2–6 between the male pores, no ventral setal gap, and striped pigmentation. Two more putative species were found but are not being given formal names. Pheretima sp. 1 has spermathecae in VII (L) and VIII (R), copulatory bursae with ring of wedge-shaped pads; dorsal pigmentation stripes, and setal numbers low for its body size. The spermathecal locations may be an aberration. Pheretima sp. 2 has spermathecae in VIII, copulatory bursae with ring of wedge-shaped pads and an intestinal origin XVII. Pheretima sp. 3. has relatively anterior dorsal pores (9/10), copulatory bursae with an internal glandular mass and pad on roof, ring of five wedge pads surrounding the bursal opening, and very few setae between the male pores. Descriptions of the new species are provided, including illustrations of the ventral view and representative spermathecae. http://www.zoobank.org/urn:lsid:zoobank.org:pub:05527581-E65F-4191-B779-CEFE339405E1