While fermented finfish products dominate the global discourse on marine fermentation, the indigenous fermentation of marine invertebrates, including crustaceans, bivalves, cephalopods, and echinoderms, remains a highly complex and under-researched domain of food science. This review synthesizes the current literature to highlight the phylum-specific biotechnological processes governing these foods. By analyzing distinct coastal micro-ecologies, we contrast traditional halophilic bacterial dominance with modern and unconventional processing deviations, such as sugar-assisted low-salt fermentations, sequential strain fortification of fungi and bacteria, enzymatic pretreatments, and targeted microbial consortia. We explore advanced proteolytic pathways that yield specific umami markers, functional compounds like γ-aminobutyric acid (GABA), angiotensin-converting enzyme (ACE)-inhibitory peptides, and improve the textural gelation of myofibrillar proteins. Crucially, this review addresses modern safety paradigms, detailing how targeted fermentation windows mitigate biogenic amines and how seasonal temperature fluctuations impact the proliferation of pathogens like Aeromonas spp. in bivalves. We highlight breakthrough interventions in shellfish allergy management, demonstrating how specific microbial starters (e.g., Limosilactobacillus fermentum ) and synergistic physical and chemical hurdle technologies destroy immunoglobulin E (IgE)-binding epitopes on tropomyosin. Finally, we explore the future of marine fermentation, emphasizing its evolution from spontaneous preservation into precise bio-engineering. We provide a comprehensive matrix of industrial cross-applications, discuss the circular blue economy via the green biorefinery of shell waste and shrimp heads, and introduce the critical concept of microbial heritage transfer by applying indigenous fermentation methodologies to sustainable substrates to recreate the flavors of endangered species without ecological harm.

Environmental geophysics holds significant but underutilized potential for tackling Indonesia’s diverse environmental challenges by supporting investigations of groundwater contamination, water infiltration, and the accumulation of metals in soils and crops—issues crucial for agriculture, public health, and sustainable land management. Emerging technologies such as UAV-assisted imaging, airborne surveys, and oceanographic geophysical observations further demonstrate the flexibility of modern geophysics in studying coastal processes, wave behaviour, and coral reef conditions. However, its application—especially in urban Indonesia—remains limited due to physical obstructions, cultural noise, restricted workspace, regulatory hurdles, and safety concerns, while the absence of geophysical test sites (GTS) and a shortage of skilled practitioners constrain methodological advancement and training. Based on bibliometric analysis of Scopus-indexed publications, this study shows that integration between geophysics and environmental science is growing but still insufficient. Strengthening environmental geophysics in Indonesia requires developing dedicated training and calibration facilities, fostering interdisciplinary collaboration, and adopting technological innovations tailored to dense urban and complex geological settings so that geophysics can play a more effective role in environmental monitoring, resource sustainability, and resilience to ecological and urban challenges.

Background Global collaborations play an increasing role in knowledge production, yet geographical proximity remains an important facilitator. The social sciences and sciences exhibit diverse output, citation, and collaboration patterns. This spatial scientometric study investigates the interdisciplinary Smart City (SC) field to determine whether field-specific knowledge regimes shape distinct global collaboration patterns. Methods Using metadata from 21,930 Web of Science publications, we employed co-word analysis and Louvain community detection to disaggregate the SC field into technology-oriented and social-oriented meta-domains, focusing on inter-city collaborations across two periods (2010–2017 and 2018–2022). Using affiliations, we identified cities and categorized collaborations across five spatial scales: single-affiliation, intra-city, intranational inter-city, intra-macro-regional, and cross-macro-regional. Analysis variables included number of publications, mean Category Normalized Citation Impact (CNCI), and highly cited papers (Top 5% and Top 10%). Results While the centres of output were East Asian hubs, the Global North maintains the highest citation impact in SC domain. Collaborations shifted from localized, single-institution research toward global networks. Inter-city collaborations comprised the majority of highly cited papers, with significant cross-regional links. Single-affiliation research remained high in the social-oriented sub-field, though inter-city collaborations gained prominence, and intra-city collaborations stayed vital. In contrast, in the technology-oriented sub-field intra-city research declined, global links strengthened, and high-impact global collaborations were of great importance. Conclusions The two major Smart City sub-fields exhibit typical social science and science collaboration patterns. The technology-oriented sub-field follows a globalized “Big Science” model, while social-oriented research relies more on local context and cognitive proximity. Although geographic distance decay still appears, as intra-city collaboration gives the majority of research output share, there is a tendency of rising importance of global collaboration networks, especially in the case of ‘Top 5%’ highly cited papers.

Background Sepsis remains a leading cause of mortality in critical care. Despite extensive preclinical research on sepsis pathophysiology, the development of effective therapies has been largely unsuccessful. Key obstacles include limited construct validity of animal models, insufficient methodological rigor and the lack of collaborative frameworks akin to clinical trials. These issues plague not only sepsis research, but preclinical research in general. The National Preclinical Sepsis Platform (NPSP), an interdisciplinary network under Sepsis Canada, addresses these challenges in sepsis research through multilaboratory, randomized, controlled preclinical studies. NPSP-01 will establish baseline conditions for future investigations using an acute fecal-induced peritonitis model of sepsis. Methods This randomized, controlled study will evaluate the effect of standard sepsis therapy in a mouse model of sepsis across six centres. Interlaboratory variability and the interaction of biological sex on outcomes will also be examined. C57BL/6 mice of both sexes will be randomized into sham (healthy control) + treatment, sepsis, or sepsis + treatment groups. Sepsis will be induced via intraperitoneal injection of fecal slurry, while sham mice will receive vehicle control. Antibiotics and fluids will be administered to treatment groups at 4 hours post-induction, and mice with be humanely killed at 8 hours post-induction. The primary outcome is plasma interleukin-6 levels. Secondary outcomes include biological (blood gas and chemistry, white blood cell count, bacterial load), clinical (body weight, core temperature, sepsis score, mortality as measured by surrogate humane endpoints), and feasibility measures. Conclusions NPSP-01 will be the first multilaboratory study of sepsis and represents a shift in preclinical critical illness research, mirroring the rigor of clinical multicenter trials. By addressing procedural standardization, interlaboratory variability, and sex-based differences, this study aims to enhance the reliability and translational relevance of preclinical findings. The outcomes of NPSP-01 will establish foundational data for future investigations and provide a roadmap for rigorous collaborative preclinical studies to accelerate the evaluation of novel sepsis therapies. Registration PreclinicalTrials.eu PCTE0000552 Protocol Version 1.0, October 21, 2024

Background Africa hosts the world’s largest youth cohort, yet its labour markets remain characterised by structural imbalances, agricultural dependence, gender inequalities, and educational credential mismatches. This study provides a comprehensive, cross-national analysis of youth labour market participation across all 53 African Union member states in 2026, disaggregated by sex, age group, economic sector, and educational attainment. Methods A cross-sectional descriptive-correlational analysis was conducted using International Labour Organisation ( ILO ) harmonised modelled estimates for the 15–35 age cohort. Youth unemployment rates ( YUR ), labour force participation rates ( LFPR ), and notinemployment,educationortraining ( NEET ) rates were computed by country, sub-region, sex, age group, and education level. Pearson’s correlation coefficient tested the association between YUR and NEET rate at country level. Results Of 545.1 million African youth aged 15–35, 311.5 million (57.1%) were employed, 23.1 million (4.2%) unemployed, 81.7 million (15.0%) inactive, and 128.8 million (23.6%) in education. The continental YUR was 6.91%; North Africa recorded 11.09% versus 6.40% in sub-Saharan Africa. Country-level YUR ranged from 0.4% (Niger) to 37.4% (Eswatini). The NEET rate reached 19.23% (104.8 million youth). Agriculture dominated employment (45.9%). Female YUR (7.56%) exceeded male YUR (6.40%). Upper secondary and tertiary graduates accounted for 52.7% of unemployment. A strong positive correlation existed between YUR and NEET rate ( r = 0.720, p = < 0.001). Conclusions African youth labour markets exhibit pronounced heterogeneity, structural informality, and credential–employment mismatches. Targeted policies addressing structural transformation, skills alignment, gender equity, and social protection are urgently required.

Parental knowledge of newborn care is essential, as it can influence the newborn's health, growth, and development. It is challenging due to a lack of mothers' knowledge and skills, insufficient resources, and limited training facilities. The study evaluates the effectiveness of the prenatal training module (PTM) on maternal knowledge and skills on newborn care. A quasi-experimental research design was adopted. Forty women at or beyond 36weeks of gestation were recruited using a non-probability purposive sampling. The pre-test knowledge was assessed during the antenatal period for both the interventional and control groups. The interventional group received two sessions of prenatal training using a simulation, with a one-week interval. The post-test was conducted for both the Interventional and control groups on the third day of child birth. There was an evident variation between post-test knowledge score (Z=-5.345; P<0.05) and post-test skill score (Z=-5.144; P<0.05) among mothers on newborn care between groups. Post-training skill demonstration and knowledge were markedly better among women in the interventional group than in the control group. The PTM with the newborn simulation model has shown success in improving knowledge and skills among mothers.

Background This study aims to analyze the effect of CEO characteristics on sustainability reports and tax avoidance, as well as to examine the effect of sustainability reports as a mediating variable. The focus of this study is on companies with relatively homogeneous sustainability commitments in order to gain an understanding of the effect of CEO characteristics on company policy. Methods The study uses panel data from the sustainability reports and annual reports of 25 companies listed on the SRI KEHATI Index during the period 2018–2024, with a total of 172 observations. Sustainability reports are proxied using the Global Reporting Initiative (GRI) Index, CEO characteristics are measured through education, age, and tenure, while tax avoidance is measured using the effective tax rate. The analysis was conducted using panel data regression with Eviews 13, incorporating company control variables. Results The results show that CEO education has a significant effect on sustainability report disclosure, while CEO age and tenure do not have a significant effect. In addition, CEO age and tenure are proven to affect the level of corporate tax avoidance. However, sustainability reports do not act as a mediating variable in the relationship between CEO characteristics and tax avoidance. Conclusions These findings indicate that CEO characteristics have different influences on corporate sustainability and taxation policies. CEO education can influence corporate strategic decisions related to sustainability disclosure, while CEO age and tenure are more closely related to tax avoidance strategies. However, sustainability report disclosure has not been proven to be a mediating mechanism between CEO characteristics and tax avoidance practices.

Background Pancreatic cancer has a poor prognosis and is highly aggressive and deadly. Most pancreatic cancer diagnoses are adenocarcinomas, which account for more than 90 % of all cases. Developing effective therapeutic strategies requires an understanding of the molecular mechanisms associated with pancreatic cancer progression. Small nucleolar RNA 64 (SNORA64) was presented as a predictive marker for pancreatic cancer stages in our previous study. SNORA64 showed a gradual loss of its expression throughout the carcinogenesis process, and it inhibited metastasis by interfering with epithelial to mesenchymal transition (EMT). In this study, we investigated the role of SNORA64 on an intrinsic apoptotic pathway in pancreatic cancers by using human pancreatic cell line derived from adenocarcinoma PK-8 with SNORA64 knockdown and the scramble to compare with. Methods QPCR techniques used to measure the gene expression level of apoptosis related genes and cell viability assay using trypan blue exclusion are implanted in this study as investigational methods. Results Pk-8 SNORA64 knockdown shows a significant low expression of tumor suppressor P53 in contrast to the scramble control cell line. Pk-8 SNORA64 knockdown shows significantly high expression of anti-apoptotic genes B-cell leukemia/lymphoma-2 (BCl2) and B-cell lymphoma-extra-large (BCL-Xl) in contrast to the scramble control cell line. Conversely, the pro-apoptotic genes BH3 interacting domain death agonist (BID), BCL2 Associated X (BAX), and BCL2 homologous antagonist/killer (BAK) show significantly low expression compared to the scramble control. However, there is no change in the expression of BAD and BIM in Pk-8 with SNORA64 knockdown compared to the scrambled control cell line. Furthermore, the Pk-8 with SNORA64 knockdown shows a significant high proliferation rate and viability percentage compared to the scramble control cell line. Conclusion The downregulation of SNORA64 affects apoptosis pathways by manipulating pro- and anti-apoptotic gene regulators. The SNORA64 interactions with apoptotic inhibitor molecules and downregulation of pro-apoptotic molecules significantly sustain cellular viability. Therefore; SNORA64 may serve as a sensitizer to apoptosis-inducing therapies.

Background Due to the intermittent, nonlinear, and uncertain behavior of renewable energy sources (res) such as solar and wind, grid stability and reliability require very high forecasting and optimization skills as widely reported in the literature. Traditional optimization methods work very well in small or static systems but are suffer difficulty on large-scale, dynamic and stochastic renewable environment due to their NP-hard nature. Methods The framework introduces the concept of a Machine Learning-Assisted Hybrid Cuckoo Search (ML-HCS) that combines CS with a hybrid metaheuristic and integrates Long Short-Term Memory (LSTM) networks for forecasting based on both regression models of LSTMs and hybrid optimization algorithms. LSTM model produces predictive signals that help inform the search trajectory of CS, enabling better exploration–exploitation tradeoff of resource scheduling on uncertainty. Results Simulation experiments on benchmark renewable energy datasets showed that ML-HCS not only converges 12% faster than the best of the GA, PSO, and classical CS, but also achieves 7–10% better quality of solutions and 9% higher robustness. This model also adapted better in multi-objective optimization tasks: cost minimization, scheduling stability and prediction accuracy. Conclusions Finally, the ML-HCS framework provides a prediction-oriented, data-driven, scalable optimization methodology for renewable energy systems. Its use of machine learning and metaheuristic search provide for high forecasting accuracy and resiliency in operation, which will enable its future large scale smart grid and renewable energy management applications.

Background Healthcare systems face escalating cyber threats that compromise patient safety and institutional resilience. While artificial intelligence (AI) is increasingly deployed for intrusion detection and automated response, many systems remain reactive and insufficiently aligned with governance and regulatory standards. Methods This policy brief proposes a Governance Protocol for Ethical Agentic Artificial Intelligence in healthcare cybersecurity. The protocol articulates procedural and infrastructural principles for embedding ethical rule enforcement, explainability, regulatory alignment, and structured human oversight within autonomous AI decision loops. An operational illustration is provided through the Agentic Artificial Intelligence Framework (AAIF), developed using a Design Science Research methodology and evaluated in a simulated healthcare network environment using benchmark cybersecurity datasets. Results Evaluation across more than 280,000 healthcare-relevant network events demonstrated improvements in response time (34.8% reduction), recovery rate (41.6% increase), adaptability (27.5% improvement), and detection accuracy (96.8%). Governance metrics, including Ethical Compliance Rate (0.99), zero false escalations, and 35 ms decision latency, indicate that embedding governance constraints did not compromise technical performance. Conclusions Governance-embedded AI architectures can reconcile cybersecurity resilience with ethical accountability and regulatory coherence. Embedding governance as an architectural design condition rather than an external compliance layer offers a scalable pathway toward trustworthy digital health ecosystems, particularly in resource-constrained settings.