Abstract: Background/Objectives: Repetitive magnetic stimulation (rMS) and static magnetic stimulation (sMS) are currently used as adjunctive therapies for certain neurological conditions. Despite substantial advances in cancer treatment, unfavorable prognoses and outcomes persist, especially for aggressive neoplasms, including glioblastoma and acute myeloid leukemia. In this context, the application of magnetic fields has demonstrated significant anti-tumoral benefits in both in vitro and animal studies, indicating its potential as an effective non-invasive therapeutic strategy; nevertheless, the precise mechanisms of action remain unclear. This scoping review was intended to identify published research investigating the effects of sMS and rMS in in vitro and in vivo models to evaluate their impacts on morphological and molecular parameters. Methods: Four databases (PubMed, Embase, Web of Science, and Scopus) were assessed; the search strategy was limited to the past twenty-five years of data publication. Studies employing rMS or sMS as a primary therapy for conditions apart from neoplasms, and those not addressing these interventions as an adjuvant therapy were excluded. Results: Nine articles using rMS were included: three in vitro, two employing animal models, and the remaining four including both cellular and animal-based analyses. Seventeen studies using sMS were identified: thirteen in vitro and four in vivo. Conclusions: This review indicates that sMS and rMS are employed as adjuvant therapies for increasing the efficacy of conventional drugs like chemotherapy. Their efficacy relies on specific factors: type of cancer, location, cell type, metabolism, and exposure parameters, including intensity, frequency, and duration.

Abstract: Background: Multimorbidity, the presence of two or more chronic health conditions in an individual, presents a significant challenge for healthcare systems worldwide. Physical activity (PA) is an important intervention for the management of chronic health conditions and prevention of disease complications. However, individuals with multimorbidity face unique barriers to PA participation. Artificial intelligence (AI) has emerged as a promising tool to enhance digital health interventions, offering tailored PA promotion. This review synthesised the current evidence on trials using AI-integrated digital intervention tools (including machine learning, natural language processing and predictive analysis) designed to support PA among individuals with multimorbidity.Methods: A rapid review was conducted following PRISMA guidelines. A comprehensive search was performed across six electronic databases (MEDLINE, EMBASE, CINAHL, OVID, Cochrane Library, PsycINFO, Scopus) covering studies from January 2015 to May 2025. Eligible studies were randomised controlled trials (RCTs) involving adults (≥18 years) with multimorbidity using AI-informed digital health interventions to promote PA. Two reviewers independently screened the articles and extracted the data. Owing to the heterogeneity of the included studies, meta-analysis was not possible, and the results were narratively synthesised.Results: Our initial search identified 276 studies. After removing duplicates and screening titles, abstracts, and full texts, 4 studies met the inclusion criteria. All included studies were RCTs that used AI-integrated digital interventions to promote PA in adults with multimorbidity. AI technology interventions included personalised mobile applications (n=2), decision-support systems (n=1), and socially assistive robotics (n=1). The study populations ranged from generically described multimorbid individuals to those with specific cardiometabolic and respiratory combinations of multimorbidity. PA outcomes were assessed through both self-report questionnaires and objective fitness measures. Attrition was common, particularly in longer-duration studies. While some improvements in PA have been reported, overall evidence remains limited and heterogeneous.Conclusions: The limited number of RCTs suggests emerging but inconclusive evidence on the effectiveness of AI-integrated digital health interventions to support PA in multimorbid individuals. Interventions may offer benefits, but heterogeneity in study design, population, and outcomes limits generalisability. Further research using consistent data collection and outcome measures, as well as longer-term follow-up, is needed.

Abstract: Background: Vulvar diseases remain underreported and possibly under-recognised in Nigeria due to limited awareness, primarily, poor health-seeking behaviour, and absence of structured screening programmes. Vulvar self-examination (VSE) has been proposed as a low-cost method for early detection of vulvar pathology. Objective: To assess the knowledge, attitudes and practices surrounding vulvar self-examination and determine vulvar disease prevalence in a community-based Nigerian cohort. Methods: This cross-sectional observational study was conducted in September 2025 across three centres (two urban and one rural). Women attending a community cervical screening programme were recruited through convenience sampling. Participants completed a survey assessing knowledge, attitudes and practices related to VSE. Clinicians performed vulvar examinations, and detailed findings were recorded. Descriptive and inferential statistics were used. Results: A total of 183 women participated, with only 2.2% of women demonstrating some knowledge of structured VSE. Over 95% admitted they had benefited from the VSE education. The prevalence of vulvar disease was 15.8%, with all conditions being benign. Increasing age, urban residence and longer duration of menopause were significantly associated with higher odds of vulvar disease, though not statistically significant. Conclusion: Knowledge and practice of vulvar structured self-examination are poor among Nigerian women and represent a significant unmet need. Structured education on VSE may facilitate earlier detection of vulvar disease and improve outcomes.

Abstract: The rapid evolution toward sixth-generation (6G) wireless networks introduces Integrated Sensing and Communication (ISAC) as a key enabler for intelligent and resource-efficient systems. Traditional resource allocation schemes for ISAC primarily focus on maximizing spectral efficiency, sensing accuracy, or energy efficiency. However, as networks increasingly support semantics-driven applications, the fidelity of transmitted information becomes equally critical. In this paper, we propose a semantic-aware resource allocation mechanism for 6G ISAC systems that leverages the Deep Deterministic Policy Gradient (DDPG) reinforcement learning algorithm. Unlike conventional approaches, our method explicitly incorporates semantic constraints into the optimization process, prioritizing semantic fidelity while jointly enhancing sensing accuracy and energy efficiency. Simulation results, benchmarked against 3GPP’s emerging 6G standards, demonstrate that the proposed mechanism achieves notable performance improvements across all three dimensions, highlighting its potential to support the next generation of intelligent, context-aware communication systems.

Abstract: As remarked by Boltzmann, the Second Law of Thermodynamics is notable for the fact that it is readily proved using elementary statistical arguments, but becomes harder and harder to verify the more precise the microscopic description of a system. In this article, we investigate one particular realization of the 2nd Law, namely Joule heating in a wire under electrical bias. We analyze the production of entropy in an exactly solvable model of a quantum wire wherein the conserved flow of entropy under unitary quantum evolution is taken into account using an exact formula for the entropy current of a system of independent quantum particles. In this exact microscopic description of the quantum dynamics, the entropy production due to Joule heating does not arise automatically. Instead, we show that the expected entropy production is realized in the limit of a large number of local measurements by a series of floating thermoelectric probes along the length of the wire, which inject entropy into the system as a result of the information obtained via their continuous measurements of the system. The decoherence resulting from inelastic processes introduced by the local measurements is essential to the phenomenon of entropy production due to Joule heating, and would be expected to arise due to inelastic scattering in real systems of interacting particles.

Abstract: The Michelson–Morley experiment yielded a null result, indicating equal light travel times in the longitudinal and transverse arms of an interferometer, traditionally interpreted as evidence against a light-propagating medium. This paper re-examines this conclusion by postulating that space itself possesses elastic properties and constitutes the fundamental medium. Beginning with this premise and modeling matter as standing waves within this space-medium, we first demonstrate that the complete mathematical framework of Special Relativity—including Lorentz transformations, time dilation, and mass-energy equivalence—emerges naturally from the Doppler deformation of these wave patterns under motion. We then extend this wave-mechanical approach to gravity, showing that the Newtonian potential and inverse-square law can be interpreted as the gradient of a spatial deformation field, with gravitational interaction energy arising from the overlap of these deformations. We show that Special and General Relativity emerge as effective geometric descriptions of an underlying elastic dynamics of space, in which relativistic effects correspond to physical deformations of wave-based matter. This framework preserves all empirical predictions of relativity while providing a unified mechanical interpretation of inertia, gravitation, Equivalence Principle, and spacetime curvature.

Abstract: Papua New Guinea is one of the least studied regions in the Southwest Pacific, and large areas of the country, such as the Fly Plat-form, remain poorly understood due to limited exposure and access constraints. This study presents the first documentation of basaltic volcanism on the Fly Platform, based on new field discoveries at Mea-hill and Yemsigi, two areas located approximately 25 km apart. Inte-grated field observations, petrography, mineral chemistry, and whole-rock geochemistry show that both basalt suites were derived from a similar magma source but record contrasting emplacement histories. Meahill basalts, which include welded tuffs and highly ve-sicular basalt units, reflect rapid magma ascent, vigorous degassing, and locally explosive activity. In contrast, the massive, less vesicular porphyritic basalts at Yemsigi preserve a quieter emplacement history, but with more extensive post-magmatic alteration. Geochemical sig-natures from least altered rocks of both suites support an intraplate origin with similarities to Pliocene-Pleistocene lava fields of Northeast Queensland. The origin of the intra-plate basaltic magmatism is enig-matic, but both young volcanic provinces correlate spatially with a lower mantle anomaly that may represent residual slab material and a seated-seated magma source. These findings provide further insight into the tectono-magmatic evolution of the Fly Platform region and highlight the need for continued geological investigation in this underexplored district.

Abstract: Large Language Model (LLM)-based agents have fundamentally reshaped artificial intelligence by integrating external tools and planning capabilities. While memory mechanisms have emerged as the architectural cornerstone of these systems, current research remains fragmented, oscillating between operating system engineering and cognitive science. This theoretical divide prevents a unified view of technological synthesis and a coherent evolutionary perspective. To bridge this gap, this survey proposes a novel evolutionary framework for LLM agent memory mechanisms, formalizing the development process into three stages: Storage (trajectory preservation), Reflection (trajectory refinement), and Experience (trajectory abstraction). We first formally define these three stages before analyzing the three core drivers of this evolution: the necessity for long-range consistency, the challenges in dynamic environments, and the ultimate goal of continual learning. Furthermore, we specifically explore two transformative mechanisms in the frontier Experience stage: proactive exploration and cross-trajectory abstraction. By synthesizing these disparate views, this work offers robust design principles and a clear roadmap for the development of next-generation LLM agents.

Abstract: Contemporary infrastructure management confronts unprecedented challenges arising from ageing systems, resource constraints, and escalating demands for efficiency and sustainability. This review examines the transformative potential of integrating smart sensor networks with predictive analytics and machine learning (ML) to address these challenges through data-driven, proactive management approaches. Smart sensors enable continuous, real-time monitoring of critical infrastructure parameters, including structural integrity, environmental conditions, and operational performance, thereby facilitating early detection of anomalies and potential failures. When combined with predictive analytics and ML algorithms—ranging from regression models and decision trees to neural networks and support vector machines—these sensor data streams enable infrastructure managers to transition from reactive maintenance strategies to predictive and preventive paradigms. This paper synthesises evidence from diverse applications across smart cities, structural health monitoring, and energy utilities, demonstrating substantial improvements in operational efficiency, cost reduction, and asset longevity. Case studies illustrate how predictive models optimise traffic flow, enhance grid reliability, detect pipeline leaks, and forecast structural deterioration. Whilst acknowledging persistent challenges related to data quality, system scalability, model interpretability, and cybersecurity, this review highlights the considerable promise of sensor fusion techniques, edge computing, and autonomous systems in advancing infrastructure management practices. The findings underscore that interdisciplinary collaboration and continued technological innovation are essential to realising fully intelligent, adaptive infrastructure networks capable of meeting the complex demands of urbanisation and sustainability in the twenty-first century.

Abstract: Tyrosine kinase inhibitors (TKIs) added to chemotherapy have improved outcomes ofadult patients with Philadelphia-positive B-cell acute lymphoblastic leukemia (Ph+ B-ALL). These improvements initially led to a larger proportion of patients realizing allogeneic stem cell transplantation (alloSCT), long considered essential for cure, but there has been a re-evaluation of alloSCT. At Princess Margaret Hospital (PM), adult patients with Ph+ B-ALL have been treated with a pediatric-inspired chemotherapy protocol with mostly imatinib. In the last two decades, we have witnessed many iterative changes in our approach. Here we examine the outcomes of all Ph+ B-ALL patients treated at our institution from 2001 to 2019. During this time, there were two major protocol changes – omission of asparaginase in 2009, and discontinuation of routine referral for first complete remission (CR1) alloSCT from the early 2010s. Median follow-up was 41.13 months (range, 0.46-228.79). 141 patients (91.56%) achieved CR1. Patient outcomes improved iteratively, with best results seen in the final (2016-2019) cohort: no asparaginase, no routine alloSCT referral in CR1; 4-year OS and RFS were 87.0% and 69.3%, respectively. The long-term OS in this patient group retained statistical significance in the multivariable analysis (p=0.0176) when BCR::ABL1 molecular residual disease (MRD) were considered.

Abstract: Generative Artificial Intelligence (AI) is transforming quality management (QM) and auditing by expanding automation, supporting data-driven decisions, and enabling more personalized stakeholder interaction. However, its adoption also raises concerns related to system robustness, operational resilience, and regulatory compliance, including potential deviations from Critical-to-Quality (CTQ) requirements, gaps in traceability, and misalignment with established quality standards. This paper proposes a structured conceptual framework for proactive, generative AI-enabled QM and auditing, organized into three functional domains: supplier performance, in-process control, and post-market feedback. The framework shows how generative AI can: 1) strengthen supplier oversight via automated documentation and early risk identification; 2) improve in-process control through real-time anomaly detection and Statistical Process Control (SPC)–based triage; and 3) enhance post-market surveillance using predictive analytics for warranty clustering and prioritized Corrective and Preventive Action (CAPA) preparation. To ensure compliance and auditability, the framework incorporates policy-based constraints, human-in-the-loop checkpoints, and end-to-end digital traceability. Verification was performed through a proof-of-concept case study spanning discrete manufacturing and process-based production environments, comparing a conventional quality workflow with a generative AI-augmented alternative. Expert assessment indicated that the generative AI-assisted workflow achieved better performance on key criteria, including documentation completeness, defect detection, process stability, governance and time efficiency. The obtained results suggest that the proposed framework can support a shift from reactive quality control towards predictive and preventive improvement while preserving alignment with quality standards and organizational quality objectives.

Abstract: Background: Major depressive disorder (MDD) is a highly heritable psychiatric condition with complex polygenic architecture. Competing hypotheses emphasize glutamatergic/synaptic plasticity deficits or neurodevelopmental synaptic pruning dysregulation, but integrated testing across large-scale genetic data remains limited.Methods: We re-analyzed the latest Psychiatric Genomics Consortium MDD GWAS (approximately 358,000 cases and 1.28 million controls, European ancestry) using gene-based and competitive gene-set testing (MAGMA), partitioned heritability (LDSC), transcriptome-wide association studies (TWAS with GTEx v8 brain models), and two-sample Mendelian randomization (MR) with cognitive reserve proxies (e.g., educational attainment).Results: MAGMA and LDSC revealed robust enrichment in synaptic pruning-related gene sets (Bonferroni-corrected p < 0.001; up to 1.30-fold LD-adjusted heritability enrichment, p < 10-91), surpassing glutamatergic/plasticity sets (moderate MAGMA enrichment, p = 0.014; no LDSC signal). TWAS showed modest glutamatergic enrichment (1.10-fold mean |Z|, p = 0.007) with heterogeneous directions, while pruning sets were null in TWAS despite strong polygenic signals. MR demonstrated causal protective effects of genetically proxied cognitive reserve on MDD risk (e.g., educational attainment OR = 0.72, 95% CI [0.66–0.79], p = 7.53 × 10-14).Conclusions: These findings prioritize developmental synaptic pruning dysregulation as the primary polygenic substrate of MDD, with downstream impairments in neuroplasticity and cognitive reserve mediating vulnerability. We propose a "pruning-mediated plasticity deficit" framework, integrating neuroimmune and circuit-level mechanisms, with implications for novel therapeutics targeting pruning pathways or plasticity enhancers.

Abstract: This study explores how demystifying Earth Observation (EO) through co-creation path-16 ways and local language can enhance flood resilience and environmental governance in 17 African informal cities. Using case studies from Maiduguri and Hadejia, Nigeria, the re-18 search employed a transdisciplinary mixed-methods design combining rapid evidence as-19 sessment, surveys, participatory workshops (n = 50 stakeholders) integrating simplified 20 Sentinel-1/2 demonstrations, indigenous knowledge mapping, and pre-/post-engagement 21 surveys. Participants (non-experts) were trained to interpret satellite data in both Hausa 22 and English, linking distant teleconnections with local flood experiences. Findings re-23 vealed significant gains in EO literacy and improvements in interpretive confidence, gen-24 der-inclusive participation, and policy engagement. The use of local learning process en-25 abled participants to translate technical EO concepts into locally meaningful narratives, 26 fostering cognitive empowerment and practical application in flood preparedness and ad-27 vocacy. The study demonstrates that data democratization is not only a matter of open 28 access but also of open understanding. It advances a conceptual model linking Demysti-29 fication, Literacy, Empowerment, Co-Production and Resilience, positioning EO as a so-30 cial technology that bridges scientific and indigenous knowledge systems. The findings 31 contribute to debates on decolonizing environmental science and propose a participatory 32 framework for integrating EO into community-based adaptation, legal accountability, and 33 policy reform across Africa’s rapidly urbanizing landscapes.

Abstract: Drivers with color vision deficiency (CVD) often face difficulty recognizing traffic light colors at intersections, putting at risk their safety and independence while driving in city environments.  This study presents the development of an assistive prototype designed with Python and a PyQt5 graphical user interface. The system applies a YOLOv12 model, a Convolutional Neural Network-based object identification method that uses the OpenCV Python library that has been trained and evaluated on a comprehensive dataset consisting of various conditions, such as daytime and nighttime circumstances, clear and rainy weather, and traffic density, to recognize traffic light signals as red, yellow, and green.  The detection result of traffic light color from a car webcam is delivered to users with offline audio feedback available in Indonesian, Mandarin, and English.  During testing, we found a mean average precision of 0.74 across eight challenging scenarios and a maximum confidence of 0.95. The system aims to improve driving safety for individuals with color vision deficiency, offering an additional assistive device rather than replacing standard driving regulations.

Abstract: Triple-negative breast cancer (TNBC) is an aggressive malignancy that disproportionately affects young women. The integration of immune checkpoint inhibitors (ICIs) has significantly improved outcomes in both early-stage and metastatic TNBC, shifting attention toward long-term survivorship issues, particularly endocrine function and fertility. However, the reproductive safety profile of ICIs remains insufficiently characterized. This narrative review synthesizes current preclinical and clinical evidence on ICI-associated reproductive toxicity, focusing on both direct immune-mediated gonadal injury and indirect disruption of the hypothalamic–pituitary–gonadal axis. Experimental models consistently demonstrate immune cell infiltration of ovarian and testicular tissue, cytokine-driven inflammatory cascades, follicular atresia, impaired spermatogenesis, and altered steroidogenesis following PD-1/PD-L1 and CTLA-4 blockade. Emerging clinical data report cases of immune-related orchitis, azoospermia, testosterone deficiency, diminished ovarian reserve, and premature ovarian insufficiency. Secondary hypogonadism due to immune-mediated hypophysitis represents an additional and frequently underdiagnosed mechanism. We further discuss the oncofertility challenges faced by young patients with TNBC treated with chemoimmunotherapy, emphasizing the uncertainty of fertility risk stratification and the importance of early fertility counseling and individualized fertility preservation strategies. To illustrate the potential clinical impact, we present the case of a 34-year-old nulliparous woman who developed premature ovarian insufficiency two years after neoadjuvant chemoimmunotherapy including atezolizumab, despite ovarian suppression. In conclusion, while ICIs have transformed the therapeutic landscape of TNBC, their potential long-term impact on reproductive and endocrine health represents a clinically significant concern. A precautionary, multidisciplinary oncofertility approach and prospective clinical registries are essential to define the true incidence and mechanisms of ICI-associated reproductive toxicity.

Abstract: Creativity and technology have each become central to contemporary education, yet scholarship examining their intersection has developed across diverse disciplines, cre-ating a need for integrative perspectives. This review examines how digital technologies mediate creative possibility and practice in educational contexts, tracing the evolution from physical and analog tools through networked systems to contemporary generative technologies. Drawing on sociocultural theories of creativity and affordance theory, the review explores how each technological era has reshaped both creative practice and participation structures. The contemporary landscape encompasses networked platforms enabling participatory creativity, physical-digital tools supporting embodied making, and generative AI systems challenging traditional notions of creative authorship. Critical tensions emerge around defining and assessing creativity in digital contexts, addressing equity and access barriers, and navigating institutional pressures that simultaneously demand innovation and standardization. Implications point toward pedagogical ap-proaches emphasizing distributed creativity, teacher education grounded in crea-tive-technological experience, policy frameworks providing coherent guidance beyond rhetoric, and research attending to equity and practice-based knowledge. The co-evolution of creativity and technology continues, with education's challenge being to participate purposefully in shaping technologies and practices toward equitable and humanizing ends.

Abstract: Recombinogenic DNA damage can initiate chromosomal rearrangements that can alter gene expression or accelerate cancer progression in higher eukaryotes. Thus, there is a critical need to identify genes that suppress chromosomal rearrangements and environmental exposures that promote genetic instability. Cell cycle checkpoints modulate the cell cycle so that DNA repair occurs before the replication or segregation of damaged chromosomes. Saccharomyces cerevisiae (budding yeast) RAD9 was the first cell cycle checkpoint gene identified, which initiated intensive research studies into the mechanisms of checkpoint activation and the phenotypes of checkpoint mutants. The budding yeast Rad9 protein serves as both an adaptor and scaffold that facilitates downstream effector activation to orchestrate a DNA damage response at multiple stages of the cell cycle, which facilitate double-strand break (DSB) repair by sister chromatid recombination. However, the role of RAD9 in homologous recombination and in suppressing gross chromosomal rearrangements (GCRs) is not completely understood. In this review we discuss how RAD9 can promote genome instability resulting from aberrant DNA replication intermediates, while suppressing DSB-associated rearrangements. We also discuss possible mechanisms accounting for the synergistic increase in genomic instability in double mutants defective in both RAD9 and recombinational repair. We emphasize that while there is an overlap between checkpoint and recombinational repair pathways, RAD9 and checkpoint pathways can function independently to suppress chromosomal instability. These studies thus elucidate checkpoint mechanisms that control homologous recombination between repeated sequences.

Abstract: Deep learning systems processing temporal and sequential data are increasingly deployed in safety-critical applications including healthcare monitoring, autonomous navigation, and algorithmic trading. However, these systems exhibit severe vulnerabilities to adversarial attacks—carefully crafted perturbations that cause systematic misclassification while remaining imperceptible. This paper presents a comprehensive systematic survey of adversarial attacks on time series classification, human activity recognition (HAR), and reinforcement learning (RL) systems, reviewing 127 papers published between 2019 and 2025 following PRISMA guidelines with documented inter-rater reliability (kappa = 0.83).We establish a unified four-dimensional taxonomy distinguishing attack characteristics across target modalities (wearable IMU sensors, WiFi/radar sensing, skeleton-based recognition, medical/financial time series, and RL agents), perturbation strategies, temporal scope, and physical realizability levels. Our quantitative synthesis reveals severe baseline vulnerabilities—FGSM attacks degrade HAR accuracy from 95.1% to 3.4% under white-box conditions—while demonstrating that cross-sensor transferability varies dramatically from 0% to 80% depending on body placement and modality. Critically, we identify a substantial gap between digital attack success rates (85–98%) and physically validated attacks, with hardware-in-the-loop validation demonstrating 70–97% success only for WiFi and radar modalities, while wearable IMU physical attacks remain entirely unvalidated.We provide systematic analysis of defense mechanisms including adversarial training, detection-based approaches, certified defenses, and ensemble methods, proposing the Temporal AutoAttack (T-AutoAttack) framework for standardized adaptive attack evaluation. Our analysis reveals that current defenses exhibit 6–23% performance degradation under adaptive attacks, with certified methods showing the smallest gap but incurring 15–30% clean accuracy costs. We further identify emerging vulnerabilities in transformer-based HAR architectures and LLM-based time series forecasters that require urgent attention.The survey culminates in a prioritized research roadmap identifying eight critical gaps with specific datasets, evaluation pipelines, and implementation timelines. We provide actionable deployment recommendations for practitioners across wearable HAR, WiFi/radar sensing, RL systems, and emerging LLM-based temporal applications. This work offers the first unified treatment bridging time series and reinforcement learning adversarial research, establishing foundations for developing robust temporal AI systems suitable for real-world deployment in safety-critical domains.

Abstract: Background: Fibromyalgia is a chronic disease that predominantly affects women and lasts over several months, causing problems both to individuals and society. While several studies have demonstrated the potential of electroacupuncture (EA) to alleviate fibromyalgia pain in mice, further research is needed to investigate its underlying mechanisms. Programmed cell death ligand-1 (PD-L1)/PD-1 was first identified to be involved in cancer immunotherapy, but its application to pain management has not been yet investigated. Methods: This study aimed to explore the mechanism underlying action of PD-L1 on PD-1 pathway in a mouse model of fibromyalgia. Results: We established such a mouse model using intermittent cold stress (ICS) and confirmed mechanical (D4: 2.02 ± 0.13 g, n = 9) and thermal (D4: 4.28 ± 0.21 s, n = 9) hyperalgesia. We found that EA, intracerebral ventricle (ICV) PD-L1 injection, or transient receptor potential vanilloid 1 (Trpv1) knockout effectively counteracted hyperalgesia. We observed low PD-1 expression in the cerebellum of fibromyalgia mice but increased expression of TRPV1 and pain-related kinases. These phenomena could be further reversed by EA, ICV PD-L1 injection, and Trpv1 knockout. To confirm that these effects were caused by PD-L1 release, we added PD-L1 neutralizing antibodies to the EA and PD-L1 treatment. The analgesic effects and EA and PD-L1 mechanisms were inhibited. Conclusions: Our results elucidate the role of the PD-L1/PD-1 pathway in EA treatment of fibromyalgia and reveal its potential value for fibromyalgia.

Abstract: Taiwan faces significant water resource challenges driven by pronounced seasonal variability, regional hydrological contrasts, and growing anthropogenic pressures. To mitigate shortages and uneven distribution, this article emphasizes the urgent need for integrated water resource management that jointly considers surface water and groundwater. Building on principles of sustainability and resilience, we synthesize recent advances in hydrological modeling, sediment transport analysis, and infrastructure optimization—including reservoir desiltation, seawater desalination, rainwater harvesting, and assessments of land subsidence from groundwater extraction. Particular attention is given to spatial sediment dynamics across river reaches and their implications for enhancing storage capacity. We further evaluate the feasibility of single-unit seawater desalination facilities in Taiwan’s coastal zones, analyzing energy demand and unit water costs under varying scenarios. Design guidelines for rainwater harvesting systems are proposed to reflect the distinct hydrological characteristics of northern and southern Taiwan, while integrating ecological resilience and cultural narratives. By bridging technical rigor with socio-cultural perspectives, this article offers a holistic framework for sustainable water resource planning in Taiwan and comparable island contexts. Finally, we outline preliminary guidelines for incorporating artificial intelligence into future management strategies. This research proposes reasonable cost reflection, differentiated water pricing, recycling goals, and a social equity perspective. These measures all have positive indicator benefits for the implementation of carbon budget management, global energy conservation, carbon reduction, and zero-carbon emission goals, and the achievement of carbon reduction targets of 40% reduction by 2030 and 50% reduction by 2050 for Taiwan.