Abstract: Background: Low back pain (LBP) is a leading global cause of disability with major personal and socioeconomic impact. Limitations of purely biomedical treatment have encouraged a shift toward holistic, biopsychosocial, and evidence-based management. This systematic review examined the effects of holistic, non-surgical interventions on pain, disability, muscle strength, walking balance, and quality of life in adults with LBP. Methods: A comprehensive search of PubMed, MEDLINE, CINAHL, EMBASE, Cochrane Library, Google Scholar, and HMIC was conducted. Randomized controlled trials (RCTs) involving adults (≥18 years) receiving holistic interventions including structured exercise, patient education, psychologically informed therapy, manual therapy adjuncts, or multidisciplinary rehabilitation were included. Two reviewers independently screened studies, extracted data, and assessed quality using the PEDro scale. Of 5,326 identified records, 43 RCTs met eligibility criteria. Data were synthesized narratively and through meta-analysis following PRISMA guidelines. Results: Forty-three moderate- and high-quality RCTs involving 4,144 participants were included. Holistic interventions consistently reduced pain intensity and functional disability, with meaningful improvements across intervention types. Exercise-based therapies enhanced muscle strength, endurance, and movement function, while balance outcomes improved but varied across studies. Mind–body and psychologically informed interventions (e.g., CBT, mindfulness, yoga) showed strong positive effects on psychosocial outcomes and quality of life. Multimodal programs combining exercise, education, and psychological components produced the most comprehensive and sustained improvements, outperforming single-modality interventions. Thirty-eight RCTs (n = 1,701) contributed to the meta-analysis. Exercise-based interventions significantly reduced pain (MD = –2.45; 95% CI: –3.28 to –1.62). Technology-assisted interventions were also effective (MD = –2.24; 95% CI: –2.52 to –1.97). Manual and complementary therapies produced the largest effect (MD = –2.53; 95% CI: –4.23 to –0.82). Mind–body and psychological interventions showed no statistically significant pooled effect (MD = –0.44; 95% CI: –1.56 to 0.69). Conclusion: Holistic, evidence-based, non-surgical interventions are safe and effective for improving pain, function, and quality of life in adults with LBP. Findings reinforce current international guidelines advocating biopsychosocial, patient-centred care. Clinical practice should emphasize individualized, supervised exercise integrated with psychological strategies and education. Protocol Registration: The review was registered with the International Prospective Register of Systematic Reviews (PROSPERO) under the registration number CRD420251166635.

Abstract: In emerging economies characterized by a predominance of the banking sector, the trans-mission of monetary policy to bank credit remains a central and ongoing topic of debate. Although the interest rate channel is the primary tool of central banks, numerous studies reveal persistent inertia in short-term bank credit, casting doubt on the effectiveness of monetary transmission. This study examines the transmission of monetary policy to bank credit for non-financial businesses in Morocco, adopting a dynamic, long-term approach. The empirical analysis is based on monthly data covering 2006–2023 and uses an ARDL–ECM model that distinguishes short-term dynamics from long-term adjustment mecha-nisms and incorporates structural breaks. The results indicate that variations in the policy rate do not have a significant effect on short-term bank credit, which confirms the weaken-ing of the traditional rate channel. However, this inertia is accompanied by a strong long-term equilibrium relationship between credit, monetary policy, and risk conditions. The results highlight a gradual monetary transmission, strongly influenced by credit risk and bank balance-sheet arbitrage. The apparent inefficiency of the short-term rate channel thus reflects a transmission modulated by prudential and structural constraints, rather than a breakdown of the monetary transmission mechanism.

Abstract: Microplastics (MPs) have become a widespread environmental contaminant, raising concern due to their persistence, capacity to transport pollutants, and potential risks to ecosystems and human health. Their increasing global production, prolonged degra-dation, and ubiquity in aquatic environments underscore the need for improved strategies for monitoring and mitigation. This review examines the definition, sources, environmental transport mechanisms, associated risks, and current detection methods for MPs in natural and engineered water systems. The methods discussed encompass a broad range of analytical and sensing technologies used to identify, characterize, and quantify MPs across diverse environmental matrices. The review highlights that no single technique is sufficient for comprehensive MP analysis; instead, the combination of multiple methods enhances sensitivity, specificity, and reliability. Current findings indicate widespread MP contamination, including within the human body, emphasizing significant ecological and health concerns. Progress in automated sample preparation, standardized protocols, and advanced sensing platforms is key to improving detection efficiency and comparability across different studies. Overall, the evidence presented supports the need for strengthened monitoring, continued technological innovation, and coordinated mitigation policies. Reducing MP pollution will require interdisciplinary collaboration, regulatory action, and increased public awareness to protect environ-mental integrity and human health.

Abstract: Background: Triple-negative breast cancer (TNBC) is an aggressive subtype associated with a high metastatic rate and low survival worldwide. Bacterial cyclodipeptides (CDPs) exhibit anticancer properties by targeting multiple signaling pathways. Methods: The effect of CDPs on the metastasis of the TNBC MDA-MB-231 cell line was evaluated both in vitro and in advanced-stage tumors in immunosuppressed female mice. Results: CDPs more effectively reduced the migratory and invasive abilities of the MDA-MB-231 cell line than methotrexate (MTX). The anti-metastatic effect correlated with inhibition of the Akt/mTOR/S6K pathway, evidenced by decreased expression of markers such as Gab1, Vimentin, and FOXO1. Mice with MDA-MB-231 xenografts treated with CDPs, alone or in combination with MTX, showed near-complete suppression of primary tumor growth and metastatic foci in tumor and organ tissues. Key proteins involved in signaling pathways associated with tumor progression and metastasis such as p-Akt, p-Gab1, and FOXO1 were markedly reduced expression in tumor tissues following xenografted mice CDPs treated. Notably, genes involved in EMT, invasiveness, and metastasis—including PTEN, SNAIL, CXCL1, BRCA1, GADD45A, and PD-L1—were dysregulated in the livers of animals with TNBC, but CDPs treatment restored them to healthy levels more effectively than MTX. Conclusions: The anti-metastatic effects of CDPs in the MDA-MB-231 line involve inhibiting phosphorylation of components of the Akt/mTOR/S6K pathway and reducing metastasis markers and proliferation regulators, as demonstrated in cultures of the MDA-MB-231 line and in tumor and liver tissues from the TNBC xenograft mouse model. The anti-metastatic activity of CDPs was more effective than MTX alone; however, combined treatment produced a synergistic effect, increasing efficacy. These findings offer new insights into the mechanism of action of CDPs and their potential as candidates for further preclinical development.

Abstract: Here we discuss three veterinary alphaherpesviruses—pseudorabies virus, equid alphaherpesvirus 1, and bovine alphaherpesvirus 1—that were instrumental in uncovering the true extent of transcriptome complexity through long-read RNA sequencing, which earlier short-read approaches could not resolve. We focus on three major transcriptomic features whose discovery and characterization relied heavily on these viral models: (i) widespread transcriptional overlaps that complicate read assignment and may drive transcriptional interference; (ii) diverse transcript isoforms arising from alternative 5′ and 3′ transcript termini, as well as splicing; and (iii) non-coding RNAs clustered near replication origins that illuminate replication–transcription interactions on a shared nuclear template. Long-read viromics in these veterinary systems has additionally served as a stringent benchmark for transcript callers and annotation pipelines, because the extreme density of overlaps and co-terminal transcript families exposes errors that often go unnoticed in typical mammalian transcriptomes. This has made veterinary herpesvirus datasets disproportionately influential in shaping best practices for full-length isoform calling, transcript end mapping, and artifact-robust cDNA library handling. We also discuss animal gammaherpesviruses as proxies for human gammaherpesviruses, allowing experimental investigation of viral programs difficult to study in human infection. Finally, we describe pseudorabies virus applications as a retrograde transneuronal tracer.

Abstract: The impending threat of cryptographically relevant quantum computers (CRQCs) necessitates a comprehensive migration to post-quantum cryptography (PQC) across all computing domains. While commercial Cryptographic Asset Discovery and Inventory (CADI) tooling has emerged to support enterprise IT environments, embedded systems, which dominate defense platforms, tactical communications, and critical infrastructure, remain inadequately addressed. This paper presents a comprehensive framework for embedded systems-specific CADI, establishing a six-class taxonomy based on cryptographic characteristics and discovery feasibility. We show through feasibility analysis that fundamental constraints of embedded systems, including severe resource limitations, mission/operational continuity requirements (often including availability and safety imperatives), certification requirements, and hardware-bound cryptography, render IT-centric CADI approaches largely ineffective. Documentation-based discovery through vendor Cryptographic Bills of Materials (CBOMs) should typically serve as the primary methodology, with automated scanning relegated to supplemental verification. We analyze technical barriers to detection, including static linking, stripped binaries, cryptographic hardware offload, and proprietary implementations. The framework addresses lightweight cryptography considerations for constrained devices that are unable to accommodate standard PQC algorithm sizes, and examines lifecycle and certification constraints, including those related to DO-178C, IEC 62443, and Common Criteria. We establish planning-assumption discovery accuracy expectations (Table 6) ranging from 55–99% by embedded system class, and propose detection methodologies calibrated to each class. The paper concludes with integration pathways for Department of Defense Risk Management Framework processes and PQC migration planning.

Abstract: Intermediate Syndrome (IMS) is a delayed neuromuscular complication of organophosphate poisoning, typically occurring 24–96 hours post-exposure and often leading to respiratory failure. The author reports a 39-year-old male who ingested 40% chlorpyrifos and developed IMS on day 4 post-ingestion, presenting with proximal muscle weakness, neck flexor involvement, and respiratory compromise. Laboratory tests revealed markedly decreased serum cholinesterase, and electromyography confirmed postsynaptic receptor desensitization with early proximal involvement. Pralidoxime was initially administered but discontinued after 48 hours due to early receptor desensitization. The patient received supportive care, including mechanical ventilation for eight days, physiotherapy, and structured follow-up. He regained independent walking by day 10 and was discharged on day 11. By integrating clinical, laboratory, electrophysiological, and follow-up data, this case highlights IMS progression and recovery, emphasizing the critical role of supportive care in resource-limited settings and providing valuable insights into its management.

Abstract: Background/Objective: Endohepatology has recently emerged as a field combining advanced endoscopy and hepatology. Endoscopic ultrasound (EUS) plays a key role in the management of patients with chronic liver disease. The main objective of this paper is to provide critical review on the recent advances in EUS-based liver diagnostics and therapeutics and how such advances have been central in establishing the field of Endohepatology. Methods: We searched the PubMed database for articles published since 1995 focused on the use of EUS in the field of Endohepatology. Results: EUS-guided liver biopsy (EUS-LB) now offers diagnostic yield and therapeutic options comparable to those of the percutaneous and/or transjugular approaches. In addition, EUS-guided fine needle biopsy (EUS-FNB) design and suction techniques have further optimized tissue sampling. Further-more, EUS-guided portal pressure gradient (EUS-PPG) measurement is an alter-native to transjugular method. EUS-based elastography enables real-time quantification of liver stiffness and fibrosis and evaluation of space-occupying lesions. Moreover, EUS-guided interventions can play important roles in the management of patients with portal hypertension-related bleeding. Finally, emerging applications include EUS-guided radiofrequency ablation (EUS-RFA), portal venous sampling, and intrahepatic shunt creation, which may further expand minimally invasive treatment options. Conclusions: State-of-the-art innovations expanded the role of EUS not only in diagnostics, but also in the therapeutic role of EUS, and provided a new paradigm for the care of patients with liver disease.

Abstract: Short-form video platforms such as TikTok host large volumes of user-generated, often ephemeral, content related to irregular migration, where relevant cues are distributed across visual scenes, on-screen text, and multilingual captions. Automatically identifying migration-related videos is challenging due to this multimodal complexity and the scarcity of labeled data in sensitive domains. This paper presents an interpretable few-shot multimodal classification framework designed for deployment under data-scarce conditions. We extract features from platform metadata, automated video analysis (Google Cloud Video Intelligence), and Optical Character Recognition (OCR) text, and compare text-only, OCR-only, and vision-only baselines against a multimodal fusion approach using Logistic Regression, Random Forest, and XGBoost. In this pilot study, multimodal fusion consistently improves class separation over single-modality models, achieving an F1-score of 0.92 for the migration-related class under stratified cross-validation. Given the limited sample size, these results are interpreted as evidence of feature separability rather than definitive generalization. Feature importance and SHAP analyses identify OCR-derived keywords, maritime cues, and regional indicators as the most influential predictors. To assess robustness under data scarcity, we apply SMOTE to synthetically expand the training set to 500 samples and evaluate performance on a small held-out set of real videos, observing stable results that further support feature-level robustness. Finally, we demonstrate scalability by constructing a weakly labeled corpus of 600 videos using the identified multimodal cues, highlighting the suitability of the proposed feature set for weakly supervised monitoring at scale. Overall, this work serves as a methodological blueprint for building interpretable multimodal monitoring pipelines in sensitive, low-resource settings.

Abstract: To address the critical challenges of hazardous material (HAZMAT) incidents in dense urban areas, this study develops a hybrid framework for spatial emergency response optimization tailored for Intelligent Transport Systems (ITS). Our approach integrates the Fuzzy Analytic Hierarchy Process (FAHP) with a rigorous technical benchmarking of multiple navigation APIs to improve routing decisions under volatile Bangkok traf-fic. By employing a normalized cost function (scale 0–1), we evaluated the perfor-mance of localized (Longdo) versus global (Google and OpenStreetMap) platforms across day and night scenarios. Experimental results, yielding normalized costs be-tween 0.464 and 0.748, identified Bon Kai as the optimal response node, whereas Chan Road showed the lowest efficiency. Interestingly, OpenStreetMap provided the highest temporal consistency for emergency logistics. These findings offer a practical deci-sion-support tool for authorities, proving that integrated API assessment is essential for building resilient and responsive urban mobility infrastructures.

Abstract: Symmetry is a key principle in physics that links basic invariances to the structure of matter and the evolution of the universe. In this review, we use symmetry as a unifying thread connecting nuclear structure, nuclear reactions, and dense matter, and we highlight how symmetry-based arguments connect laboratory observables to astrophysical constraints. We present the essential concepts in a form accessible to a broad physics audience.

Abstract: To enhance Customer Lifetime Value (CLV) within the lifecycle management of automated equipment, this study addresses the lack of foresight in traditional cost analysis by establishing an AI-based lifecycle cost modelling and CLV decision-making methodology grounded in multi-stage forecasting. Utilising operational and maintenance data, costs across distinct lifecycle phases are projected, with these forecasts integrated into a CLV optimisation model for empirical analysis.Results indicate this approach reduces cost prediction errors by approximately 30% and increases average equipment CLV by around 18%. The study concludes that decision mechanisms grounded in predictive costs effectively support value enhancement and refined management of automated equipment.

Abstract: Since approximately 2005, major processor manufacturers have shifted their architectural focus from instruction-level parallelism (ILP) toward multicore and manycore parallelism to achieve higher performance.Rather than relying on deeper pipelines and speculative execution, performance gains have increasingly been realized through thread-level parallelism (TLP).Consequently, the responsibility for efficiently utilizing processor resources has transitioned from hardware mechanisms to software implementations. This technical note examines design strategies for achieving deterministic, high-throughput packet processing on manycore architectures using the Data Plane Development Kit (DPDK).It presents a simplified Packet Gateway (PGW) pipeline implementation, analyzing cache-coherence effects, NUMA-local memory allocation, and multicore scheduling patterns critical to maintaining per-packet processing budgets under nanosecond-level constraints.

Abstract: Gas exchange between soil or water surfaces and the atmosphere is one of the main sources of greenhouse gas production and absorption. Faced with global climate change and increasing atmospheric concentrations of these gases, significant scientific efforts are being made to monitor this exchange using various techniques, including closed chambers. Although relatively simple, this technique requires careful attention to several key points. Furthermore, any installation using commercial chambers is relatively expensive. Indeed, given the specific variability of gas exchange, a single chamber cannot assess all the gas exchange in the soil of a given plot. Several chambers are therefore necessary, which increases the overall cost of the installation. In our laboratory, we have built different types of chambers: portable "nomad" ultra-low-cost chambers for punctual, large-area measurement campaigns and "automatic" cost-effective chambers for long-term installations. In this article, we aim to share our experience by describing our achievements and providing a link to the complete documentation, which includes 3D and 2D plans, Gerber files for manufacturing printed circuit boards, and a parts list.

Abstract: Background/Objectives: Franz Tappeiner (1816–1902) is often celebrated as a pioneer of alpine medicine and the founder of Tappeiner Promenade in Meran (South Tyrol, Italy). However, his legacy extends far beyond the scenic infrastructure. His multidisciplinary practice anticipated the principles of contemporary rehabilitation, preventive medicine, and climate-sensitive public health. Methods: This historical scientific analysis reinterprets Tappeiner’s writings, institutional engagements, and civic projects through the lens of modern public health frameworks. Drawing on primary materials (e.g., published articles, autobiographical fragments, and commemorative texts) and recent evidence from rehabilitation and environmental health research, these contributions were contextualized. Results: Tappeiner’s early focus on infectious disease prevention (e.g., cholera and tuberculosis) transitioned into a strategic emphasis on recovery and behavioral therapy through environmental design. The walking therapy model of Max Joseph Oertel, locally realized in the Tappeiner Promenade, prefigured modern concepts such as structured green rehabilitation, walkability, and urban-health citizenship. He contributed substantial personal funds to the path’s construction, embedding therapeutic gradients, curating vegetation, and promoting inclusive design to support convalescence. Contemporary research supports the intuition that green, low- to moderate-intensity walking improves cardiometabolic health, psychological well-being, and functional capacity. Moreover, his integrative ethos, merging clinical medicine, civic ethics, and spatial intervention, parallels contemporary eco-social models of public health. Conclusion: Franz Tappeiner’s career exemplifies a still-relevant model of physician leadership that is empirically grounded, socially accountable, and ecologically attuned. His work invites reflection on how medical professionals can shape not only individual care but also urban environments and collective health futures.

Abstract: Sexual dimorphism in morphological traits is widespread across animals and can result from differing life-history strategies, sex-specific competitive pressures, and ecological interactions, which may be influenced by habitat structure and complexity. For epifaunal organisms, e.g., amphipods that inhabit structurally diverse benthic habitats, the structure of the habitat plays a key role in mediating access to food, mate encounters, and refuge provision. Here, we explored patterns of variation in body size and gnathopod 2 ratio (gnathopod 2 length/body length) among amphipod species in different marine habitats. We focus on two amphipod species, Ampithoe ramondi and Caprella acanthifera, across four benthic habitat types: rhodolith beds, macroalgae- dominated reefs, seagrass meadows, and black coral forests. A. ramondi was present in all habitats except black coral forests, and males were significantly larger than females only in macroalgae-dominated reefs. Males also exhibited higher gnathopod ratios than females, increasing from macroalgae-dominated reefs to seagrass meadows and rhodolith beds. C. acanthifera was found in macroalgae-dominated reeds and black coral forests, where males were larger than females on average, but no significant habitat alterations were detected. Neither A. ramondi nor C. acanthifera was found in all four habitats. These results suggest that patterns of sexual dimorphism across coastal habitats are species-specific, with sexual selection operating more subtly in some taxa (e.g., C. acanthifera) than others, likely shaped by both habitat-specific ecological pressures and differences in life-history strategies. Expanding such analyses to more taxa and with balanced sampling across habitats and environmental gradients will offer deeper insight into how natural and sexual selection interact and inform how these dynamics may shift under changing climate regimes.

Abstract: We propose a centralized Activity Retrieval and Detection Intelligence (ActivityRDI) solution framework, demonstrate its application performance in Network Threat Detection in detail, and show its generalization in other domains. Network threat detection is challenging due to the complex nature of attack activities and the limited historically revealed threat data to learn from. To help enhance the existing methods (e.g., analytics, machine learning, and artificial intelligence) to detect the network threats, we propose a multi-agent AI solution for agile threat detection. In this solution, a Knowledge Graph is used to analyze changes in user activity patterns and calculate the risk of unknown threats. Then, an Imbalanced Learning Model is used to prune and weigh the Knowledge Graph, and also calculate the risk of known threats. Finally, a Large Language Model (LLM) is used to retrieve and interpret the risk of user activities from the Knowledge Graph and the Imbalanced Learning Model. The preliminary results show that the solution improves the threat capture rate by 3\%-4\% and adds natural language interpretations of the risk predictions based on user activities. Furthermore, a demo application has been built to show how the proposed solution framework can be deployed and used.

Abstract: We prove the irrationality of a family of normalized odd zeta values of the form \( \dfrac{\zeta(2n+1)}{\pi^{2n+1}},\,n\in\mathbb{N},\,n\geq 3. \) Our approach is based on constructing explicit integer linear forms in the quantities \( I_n=4(4^n-1)\left[\dfrac{\zeta(2n)\zeta(2n+2)}{\zeta(2n+1)^2}-1\right]-1 \), and applying a refinement of Dirichlet's approximation theorem. The construction of the \( I_n \) is probabilistic in origin. We prove that the sequence of denominators produced by successive rational approximations yields infinitely many nontrivial integer relations of the type \( \Lambda_m^{(q)}=A_m^{(q)} I_n-B_m^{(q)}, \) with \( |\Lambda_m^{(q)}| \) (\( q \) being a parameter) decaying towards zero as \( m \) approaches infinity. This permits us to invoke a general irrationality criterion and thereby deduce that \( I_n \) is irrational for each \( n\geq 3 \). Consequently, each corresponding normalized odd zeta value is irrational. Our method combines ideas from probability theory, analytic combinatorics and Diophantine approximation, and complements earlier work of Apéry, Beukers, Rivoal, and Zudilin.

Abstract: Model-based reinforcement learning (MBRL) offers improved sample efficiency but faces instability from model errors and compounding uncertainties. We present Contraction Dynamics Model (CDM), a framework that learns state-dependent Riemannian contraction metrics jointly with system dynamics and control policies to ensure stability during training and deployment. The method uses a softplus-Cholesky decomposition for positive definite metric parameterization and optimizes via virtual displacements to minimize trajectory divergence energy. An adaptive stability regularizer incorporates the learned metric into policy objectives, guiding exploration toward contracting state space regions. Theoretically, we establish exponential trajectory convergence in expectation, derive robustness bounds against model errors, and characterize sample complexity. Empirically, on continuous control benchmarks (Pendulum, CartPole, HalfCheetah), contraction-guided learning enhances stability, sample efficiency (38.9% step reduction), and resilience to model errors (78% performance retention vs 52% for baselines at 10% noise) compared to MBRL baselines (PETS, MBPO) and safe RL methods. Ablation studies confirm design choices, showing learned metrics yield 10-40% performance gains with 20% computational overhead. This work demonstrates that learning contraction metrics enables practical, scalable embedding of nonlinear stability guarantees in deep reinforcement learning.

Abstract: Background: Chronic kidney disease (CKD) continues to place immense strain on health systems globally, with nurses at the centre of care delivery physically, emotion-ally, and relationally. In dialysis units, nurses form long-term therapeutic relationships with patients who depend on life-sustaining treatment several times a week. Objective: This study explored the lived experiences of professional nurses caring for patients with CKD in a dialysis unit, using Watson’s Theory of Human Caring as a guiding frame-work. Methods: A qualitative, exploratory, descriptive design was employed. Data were collected through in-depth face-to-face interviews with twelve professional nurses and analysed using thematic analysis. Trustworthiness was ensured through credibility, dependability, confirmability, transferability, and authenticity. Ethical approval and informed consent were obtained. Results: Three themes emerged: (1) emotional and professional experiences, (2) systemic resource constraints, and (3) recommendations for practice improvement. These findings highlight the tension between caring ideals and systemic limitations. Conclusions: Interpreted through Watson’s Theory of Human Caring, the findings demonstrate that organizational support, emotional wellbeing, and adequate resource allocation are essential to sustaining caring-healing environments and advancing equitable, high-quality CKD care in alignment with Sustainable Devel-opment Goal 3. The study concludes that dialysis nursing is profoundly meaningful yet emotionally demanding. Strengthened emotional support, improved leadership visibility, consistent resource allocation, and enhanced nephrology nursing education are critical to sustaining compassionate care. The findings offer important insights for policy, workforce development, and quality improvement efforts in CKD care.