Abstract: Bioeconomy policy requires timely, economy-wide evidence; however, two persistent measurement constraints remain: official input–output (IO) tables are published with substantial time lags, novel start-up and novel prospective or hybrid bio-based activities are rarely identified as separate sectors in national accounts. This paper develops an applied methodology that addresses both limitations by combining IO nowcasting with a reduced-dimensional sector-embedding procedure. Using Ireland’s national IO system and an existing bioeconomy IO framework as the accounting backbone, we update the 2015 table to 2022 through calibration to macroeconomic control totals, providing a timely structural baseline. We then introduce a transparent method for constructing new bioeconomy sectors based on dominant input shares, import intensity, and output allocation, while preserving national accounting identities. The approach is demonstrated for aquaculture systems, anaerobic digestion scenarios, and plant-based protein value chains. Demand-driven Leontief multipliers reveal substantial heterogeneity in domestic propagation effects across activities and development stages. The framework offers a resource-efficient and replicable tool for evaluating bioeconomy strategies under real-world data constraints.

Abstract: Effective management of reservoir water for irrigation is crucial in arid regions prone to drought and water shortages. However, evaporation losses from reservoirs remain poorly understood. Direct measurements typically quantify evaporation only at the measurement site rather than across the entire reservoir. This study introduces the Turbulent Exchange Approach for Reservoir Evaporation Estimation (TEAREE). The TEAREE is a simple model that integrates a bulk aerodynamic formulation with Landsat 8–9 satellite water-surface temperature data and meteorological observations to estimate spatially distributed daily reservoir evaporation. The TEAREE model was first evaluated at Elephant Butte and Caballo reservoirs in New Mexico, USA, and subsequently applied across multiple reservoirs with diverse climatic conditions to demonstrate its applicability for estimating open-water evaporation. Daily evaporation was obtained by upscaling satellite overpass-time evaporation estimates using the daily-to-instantaneous vapor pressure deficit ratio (k_e) and wind speed. The model performed strongly across 12 lakes (R² = 0.91–0.99; RMSE = 0.27–0.85 mm/day) compared with the bulk aerodynamic (B_AER) method. Comparison with eddy covariance (EC) evaporation also showed good agreement. Monte Carlo analysis indicated moderate uncertainty associated with k_e variability, supporting the operational use of a constant k_e = 0.95 for daily upscaling.

Abstract: Neural decoding has demonstrated that population activity contains behaviorally relevant information, yet predictive accuracy alone does not constitute mechanistic explanation. Decoding models establish statistical mappings between neural responses and task variables but leave the underlying computational processes underdetermined. We argue that neural computation is more appropriately framed within a dynamical state-space perspective, in which population activity reflects the evolution of latent states governed by structured transition operators. Across empirical and theoretical work, neural trajectories increasingly appear as low-dimensional, nonlinear flows shaped by recurrent circuit structure and contextual inputs. This shift reframes the central scientific objective: not merely extracting representations, but learning the evolution operator that governs state transitions. However, even accurate reconstruction of latent dynamics does not guarantee mechanistic validity. Observational data typically constrain only an equivalence class of admissible operators, rendering the inferred dynamics structurally non-identifiable. We therefore propose that causal neural dynamics must be defined through perturbation and experimental design. By introducing directional constraints on state transitions, targeted interventions collapse equivalence classes and enable identification of operators that remain valid under manipulation. In this framework, evolution operators are treated as falsifiable hypotheses whose mechanistic status depends on predictive stability under perturbation. This perspective recasts neural modeling as the search for perturbation-validated dynamical laws governing population activity, moving the field from decoding-based description toward causal dynamical explanation.

Abstract: Background: Alzheimer’s disease (AD) is a progressive neurodegenerative disorder and the leading cause of dementia worldwide. Its hallmarks are extracellular amyloid-beta (Aβ) plaques and intracellular hyperphosphorylated tau forming neurofibrillary tangles, leading to synaptic dysfunction and neuronal loss. Despite extensive research, the mechanisms driving these proteinopathies and the contribution of genetic, molecular, and environmental factors remain unclear. Objective: This review summarizes the molecular mechanisms underlying AD and the factors influencing its onset and progression. Methods: A narrative review of peer-reviewed studies from PubMed, Scopus, and Web of Science was conducted. Relevant articles on neuropathology, molecular pathways, genetic susceptibility, oxidative stress, mitochondrial dysfunction, neuroinflammation, and metabolic and lifestyle risk factors were analyzed. Results: AD is marked by Aβ accumulation and tau pathology, causing synaptic and neuronal loss. Key mechanisms include abnormal amyloid precursor protein processing, tau hyperphosphorylation, oxidative stress, mitochondrial dysfunction, neuroinflammation, and calcium dysregulation. Genetic variants (APP, PSEN1, PSEN2, APOE ε4) increase risk, while aging, cardiovascular disease, diabetes, and lifestyle factors further influence disease onset and progression. Conclusion: AD arises from complex interactions among molecular and environmental factors. Understanding these pathways is essential for developing preventive strategies and effective therapies, with personalized approaches offering future promise.

Abstract: Water colour is an important optical proxy for trophic status and water quality, but its integration into regulatory assessment frameworks is still limited. This study assesses the potential of the Forel–Ule Index (FUI) derived from Sentinel-2 as a proxy indicator to support the assessment of the ecological status of reservoirs under the European Union’s Water Framework Directive (WFD). Seventeen reservoirs located in semi-arid Mediterranean climate agricultural basins in southern Portugal (Sorraia, Sado, and Guadiana) were analysed, combining 4,316 FUI observations (2017–2024) with in situ water quality data and official WGD ecological status classifications. FUI values covered virtually the entire scale (1–21), with most observations between 12 and 18 and with marked spatial and seasonal contrasts, particularly between more transparent reservoirs and persistently turbid ones, probably eutrophicated reservoirs. Principal component analysis showed that the first component (PC1, 39.5% of variance) represents a trophic gradient dominated by turbidity, chemical oxygen demand and chlorophyll-a, and is positively, albeit moderately, correlated with FUI (Spearman’s ρ = 0.439, p < 0.001), while the second component, dominated by nitrogen, showed no significant association. The ordinal logistic regression relating the FUI to the ecological status classes of the WFD captured the expected quality gradient, with FUI values between 10–13 reliably identifying the status ‘Good’ (probability > 0.70), but with greater uncertainty for the intermediate range (14–16) and a tendency to underestimate “Poor/Bad” conditions when the FUI > 16. Overall, the FUI proves to be a low-cost, high-frequency screening and early warning tool that is effective in detecting good conditions and state transitions. However, it should be complemented by physical-chemical and biological metrics when a fine distinction between WFD classes is required.

Abstract: The mutually reinforcing synergy between the development of the new energy industry and comprehensive land remediation is crucial for integrating ecologically fragile areas into the national "dual carbon" goals and supporting regional high-quality development. Based on an analysis of the challenges and opportunities facing the new energy industry in ecologically fragile areas, as well as the mechanisms for mutual promotion between new energy industry development and land remediation, this paper explores pathways for comprehensive land remediation coordinated with new energy development. Drawing on local practices, it further distills five typical models. The results show that: 1) The development of the new energy industry in ecologically fragile areas faces multiple challenges, including a fragile ecological environment, inadequate infrastructure, a mismatch between resource supply and demand, and land use conflicts. Against the backdrop of the energy transition, breakthroughs in key technologies, and the guidance of territorial spatial planning, the value of wind and solar resources in these areas is becoming increasingly prominent, offering broad prospects for the new energy industry. 2) The development of the new energy industry and comprehensive land remediation in ecologically fragile areas are mutually reinforcing. Factors such as resource endowment, ecological (environmental) constraints, new quality productive forces, and investment and financing mechanisms interact and integrate, leading to differentiated pathways for synergy. 3) Based on the focus of new energy industry development and the primary objectives of remediation, five remediation models are identified: ecological restoration-led land reclamation model, resource development-led land consolidation model, industry collaboration-led land consolidation model, technology innovation-led land consolidation model and integrated development model. Each model has distinct priorities and applicable scenarios. This study will provide a reference for new energy development and sustainable development in ecologically fragile areas, including desertified and Gobi desert areas, coal mining subsidence areas, and areas rich in wind, solar, and hydro energy resources.

Abstract: To address the core challenge in integrative medicine—the semantic incommensurability of heterogeneous medical data arising from divergent cognitive paradigms across medical systems—this paper proposes a multi-track cognition framework for global integrative medicine. Adopting a decoupled design of "flexibly customizable and extensible cognitive tracks with a fixed unified core architecture", this framework constructs exclusive cognitive tracks preserving the native logic for each medical system, takes the homeostatic representation network of multiple dimensions of human eight physiological systems as the general quantitative mediation benchmark, and establishes the system-level mapping relationship constrained by three core rules: cluster correspondence, network emergence, and context dependence, to realize the standardized transformation and system-level fusion of multi-source heterogeneous medical data. Empirical verification shows that the semantic alignment accuracy of this framework reaches 91.27%, the model goodness of fit ≥0.85, and the accuracy is improved by 32.14% compared with the traditional single-point linear mapping method. The determination results have a strong consistency with clinical expert judgments, which can provide a feasible and general technical support for basic research of integrative medicine, whole-cycle management of chronic diseases, and individualized health intervention.

Abstract: Background: Small-cell lung cancer (SCLC) represents an aggressive malignancy associated with a poor prognosis, underscoring the critical demand for enhanced monitoring methodologies. Circulating tumor DNA (ctDNA) constitutes a promising non-invasive biomarker; however, reports employing highly sensitive, tumor-informed assays in SCLC remain scarce. This investigation aimed to assess the clinical utility of a personalized ctDNA monitoring strategy for predicting therapeutic outcomes and resistance in SCLC patients. Methods: This prospective, observational study enrolled patients diagnosed with unresectable SCLC. Whole exome sequencing was conducted on baseline tumor specimens to design customized 16-plex multiplex PCR panels. Serial blood samples were obtained at baseline, at six-week intervals during treatment, and upon disease progression. Detection of ctDNA-based minimal residual disease (MRD) was performed using a tumor-informed assay (Huajianwei® bespoke MRD) with ultra-deep sequencing. Results: Among seven evaluable patients, the baseline ctDNA-MRD positivity rate was 100%. A significant positive correlation was observed between baseline ctDNA levels and radiographic tumor burden (r = 0.821, P = 0.034). Longitudinal analysis indicated that patients exhibiting an early decline in MRD levels (n=5) demonstrated a trend toward superior progression-free survival (PFS) compared to those with an MRD increase (n=2) (P = 0.0665, hazard ratio (HR) = 0.24 (95% CI: 0.02 - 3.19)). Notably, elevation in MRD preceded radiographic progression by as much as 135 days in certain instances. Conclusions: Tumor-informed ctDNA-MRD monitoring effectively mirrors tumor burden and offers early prediction of treatment response and clinical outcomes in SCLC. ctDNA kinetics may serve as a crucial prognostic indicator, presenting the potential to inform personalized management approaches and facilitate earlier therapeutic interventions compared to conventional imaging techniques.

Abstract:

This study investigates the influence of selected technical and technological parameters on cutting forces and power consumption during the milling of medium-density fibreboards. The main objective was to experimentally measure orthogonal cutting force components (F_x, F_y, F_z) and electrical power consumption under varying spindle speeds (14 000, 16 000 and 18 000 rpm), feed speed (6, 8 and 10 m/min), and milling strategies (conventional and climb), and to evaluate the suitability of the obtained data for predictive modelling. Cutting forces were measured using a Kistler 9257B piezoelectric dynamometer, and power consumption was recorded by a three-phase power quality analyser. Statistical analysis confirmed significant effects of machining parameters on force components, total cutting force, and power consumption. Spindle speed showed the strongest influence on total cutting force and power consumption, while milling strategy predominantly affected F_x and F_y components. Power consumption increased with increasing spindle speed. Based on the measured data, several machine learning models were developed to predict the total cutting force. After model comparison using RMSE, R², training time, and model size, a Fine Tree model was identified as the most suitable, achieving high prediction accuracy without signs of overfitting. The results confirm that experimentally obtained force and energy data are suitable for reliable predictive modelling in CNC milling of MDF.

Abstract: We show that the Standard Model of particle physics allows the recently observed 244 EeV ($= 244 \times 10^{18}$ eV) cosmic ray --- the Sun Goddess particle --- to be a proton with the active galaxy 2MASX J16574719+1832247, redshift z = 0.054 as its source. The Standard Model Theory is preferred over conventional theory by a Bayes Factor K = 490, which in Jefferys' Table is a `'decisive'' preference. Further, the Standard Model Theory has now been confirmed by direct observation of the Cosmic Background Radiation (CBR).

Abstract: Background: Caregiver interactions and resident interactions are important to resident satisfaction with long-term care (LTC). However, these are variously operationalized, and caregiver -resident interactions of "spending time" (activity and autonomy) and environmental quality are less well investigated modifiable factors to inform LTC resident support policies for health aging. Methods: This quantitative, cross-sectional study analyzed secondary survey data from 326 long-term care facility (LTCF) residents (aged ≥60) across Shanghai, Nanjing, and Changsha, China. Satisfaction was measured using the Chinese version of the Ohio Long-Term Care Resident Satisfaction Survey. Caregiver evaluations served as the primary predictor, with spending time and environment as parallel mediators. Analysis adjusted for age cohort, functional independence, and length of stay. Results: Personal and care service factors explained 26.1% of the variance in satisfaction. Caregiver qualities were positively associated with overall satisfaction (β = 0.30, p < 0.01). Spending time (effect = 0.14, 95% CI: -0.01 to 0.30) and environment quality (effect = 0.05, 95% CI: -0.03 to 0.15) showed small positive pathways between caregiver qualities and satisfaction, and the combined indirect effect of these domains was statistically significant (effect = 0.19, 95% CI: 0.04 to 0.36). It indicates partial mediation, although each specific indirect path was not significant when considered separately. The direct association between caregiver qualities and satisfaction remained significant after accounting for these mediators (effect = 0.36, 95% CI: 0.11 to 0.61). Conclusions: These findings clarify how caregiver interactions are important to resident satisfaction both directly and indirectly through spending time activity engagement and environmental perceptions. To promote longevity and healthy aging in LTCFs, providers should prioritize caregiver training that fosters resident autonomy, supports daily activity, and maintains age-responsive care environments.

Abstract: Few-shot remote sensing scene classification (FSRSSC) entails identifying images scene classes from limited labeled samples, facing the challenges of labeled data scarcity, as well as the intricacy and variety of remote sensing images with high intraclass variance and interclass similarity. To address these challenges, we propose a novel framework named as MMFF-Net in this article, which consists of four key components: diffusion augmentation (DA), multiscale feature fusion (MSFF), dual attention fusion module (DAFM), and information interaction mutual attention (IIMA). The DA is utilized to augment support set samples with high-quality. In addition, the MSFF focuses on obtaining the local spatial details, and the DAFM is utilized to fuse the local feature and the global feature. What is more, the IIMA module is employed to interact between the query set and support set information. What is more, we use word2vec to obtain the semantic features for reducing the disparity between them and the visual features with LSE Loss. The comparative experimental results with multiple models on three benchmark remote sensing scene (RSS) datasets validate the effectiveness of the proposed MMFF-Net, showcasing the superiority and feasibility of our approach in most FSRSSC cases.

Abstract: The biosphere is undergoing an unprecedented transformation driven by global warming, habitat loss, and resource depletion, threatening biodiversity through widespread species extinctions and population declines. Although conservation and restoration remain essential, the risk of irreversible tipping points demands new strategies. Synthetic biology offers one such approach: engineering existing ecosystems by modifying functional traits of resident communities to enhance resilience and prevent abrupt shifts. Despite and because of public concern, advances in biosafety and control have been achieved, mainly on a cellular scale. However, after decades of bioremediation efforts, a central question emerges: not only can interventions be perfectly controlled, but also whether they can persist and sustain ecological function. Meeting this challenge requires a paradigm shift in design philosophy, from classical to emergent engineering, embracing adaptation, feedback, and multiscale complexity as the foundation of ecosystem design.

Abstract: This paper employs simultaneous equation modeling to analyze the political economy of public health in Jammu and Kashmir, examining the interrelationships between fiscal allocation, infrastructure development, and health outcomes. Using data from the Union Budget 2026-27, J&K Budget documents, NFHS-5, and administrative health statistics, we specify and estimate a three-equation system addressing: (1) the paradoxical relationship between fiscal dependency and superior health outcomes, (2) the curative-preventive imbalance in resource allocation, and (3) the emerging epidemiological transition. The results reveal that fiscal dependency enables health investment (elasticity 0.42, p<0.01) while simultaneously reducing local accountability (direct effect -0.18, p<0.05), with net positive effect on outcomes. The curative share of expenditure negatively affects population health (coefficient -0.23, p<0.01) and exacerbates male anaemia (coefficient -0.31, p<0.01), confirming gender bias in nutrition programming. Non-communicable disease burden responds significantly to urbanization (0.34, p<0.01), aging (0.41, p<0.01), and curative expenditure share (0.19, p<0.05). Policy simulations indicate that reallocating 5% of curative spending to prevention would reduce IMR by 1.2 points and male anaemia by 3.1 percentage points. The findings support targeted interventions including workplace iron supplementation, population-wide screening, and nutrition-specific budget lines.

Abstract: Since September 2021, numerous seismic events with spectral peak below 1 Hz occurred on the island of Vulcano, Italy, 131 years after its last eruption. The local monitoring network recorded microseismicity mostly in the form of months-long swarms, concurrent with anomalous values of other geophysical and geochemical parameters. By applying a machine learning technique (Self-Organizing Maps, SOM), we obtained an inventory of ~6600 seismic signals, identifying distinct families of events. These families were located below La Fossa Crater (where the last eruption of the volcano happened) from the surface to a depth of 2.2 km b.s.l. Based on the seismic signature and source location of these events, we hypothesize unsealed/sealed processes through a network of shallow fractures favored by fluid pressure. After the return to background values of geochemical and geophysical parameters in 2023, a resumption of microseismicity occurred between May and June 2024. A test application of the SOM to the new data confirmed the non-destructive source of the new recorded signals, which shared families, location, and depths as our previous inventory. This test showcased that SOM can be an effective tool to support monitoring and warning of future unrest at Vulcano.

Abstract: This paper proposes a neurophilosophical conceptual model of human consciousness structured as two functional brain states: the personal mode and the meta-reflective mode. The personal mode is defined as a motivationally and socially embedded configuration of neural processes oriented toward adaptation, identity maintenance, and ego-relevant concerns. The meta-reflective mode is characterized as a functional state in which cognition turns upon itself, enabling abstraction, self-objectification, and existential evaluation.The model does not posit a metaphysical dualism nor strictly separable neural systems. Rather, both modes may recruit overlapping brain regions, including prefrontal structures, while differing in dominant functional orientation and hierarchical organization. The distinction is therefore not anatomical but configurational.It is argued that tensions between these modes may account for different categories of psychological crises: identity-based crises primarily emerging within the personal mode, and existential crises arising from intensified meta-reflective activation. The framework further suggests that the development of civilization reflects the structural coexistence of adaptive engagement and reflective distancing. While empirical validation remains limited, the model aims to provide a structured bridge between phenomenological analysis and contemporary neurocognitive theory.

Abstract: The severe acquired respiratory coronavirus–2 (SARS–CoV-2) infection has initiated both acute and chronic COVID–19 disease between 2020 and 2023, currently evolving with other homologous prior coronavirus strains of the Nidoviridae order, which encompasses other prevalent alpha/ beta coronaviruses, but also the Middle East Respiratory Syndrome (MERS-CoV) and SARS-CoV-1, with recent SARS–CoV–2 variants, increasing demands for effective immunogens and therapeutic approaches that will reduce global disease burden and further infection from SARS–CoV-2 affected individuals that may experience post acute sequelae (PASC) or “Long COVID”. Following a worldwide programme of prophylactic vaccination, there is still a dilemma in the efforts to find prophylactic and early therapeutic approaches that would treat novel SARS-CoV-2 variants and prevent future epidemics or pandemics within host human and animal populations, where zoonotic or cross species transfer naturally occurs. Concerns about viral immune escape intersect at a specific point; a gained evolutionary ability of several viruses to co–infect and compete against previous scientific advances since 1796 that remain undetected or asymptomatic during the early stages of infection progressing to symptomatic and severe disease via the double methylation of the 5' end of eukaryotic DNA or RNA-based viral genomes, the 7-MeGpppA2’-O-Me cap, and its double methylation capping process is performed by the activated viral 2’ - O - Methyltransferase (MTase) enzyme, a complex of two viral non-structural proteins (NSPs) joined together through an activation process (NSP10/16) and by N7-Methyltransferase (N7-MTase/NSP14), respectively. Moreover, it was discovered that polymorphic viruses translate NSP1, which prevents the activation of various Pattern Recognition Receptors (PRRs), and consequently, detection of Pathogen-Associated Molecular Patterns (PAMPs) and Damage-Associated Molecular Patterns (DAMPs) alike. NSP1 also silences important interferon-encoding genes (INGs) and interferon-stimulated genes (ISGs), is signalled in a paracrine manner to neighbouring cells, and that induces the apoptosis of host cells, inducing an effect of “trace erase” effect and making the viral infection as immunologically “invisible” as possible during the initial, key stages of viral replication and distribution, all such mechanisms occurring independently of the viruses in cause. Another important viral NSP is NSP14, as it plays two functional roles that are independent of each other; to produce new viral genetic material for the purpose of maintaining the validity of the viral genome as well, and not just transfer a methyl group to the 5’ end of the viral genome. Other viral NSPs share a role with NSP1, 10, 14 and 16 in directly suppressing the activation of PRRs and ISGs, and all such viral proteins help the virus in its process of self-camouflaging against first- and second-line immunity, thereby often severely impacting the quality of the produced adaptive immune responses. The outcome of all such phenomena is the sharp decrease in the host Type I and Type III interferons' (IFNs) rate of synthesis by the host cells, that would usually occur and affect homeostatic cellular pathways, resulting in further viral replication and induced apoptosis. Nonetheless, effects of microbial immune evasion during the development of other viral or carcinogenic pathologies are not widely known. In short, polymorphic viruses developed a proportionate evolutionary response against developed adaptive immune responses, by currently relying on gaps mostly situated in the natural immune system in their process of molecular self-camouflaging. Scientists developed numerous approaches of early treatment that generally showed good success rates and fewer risks of adverse events, and the still early present stages of COVID-19 research should also be taken into consideration whilst filtering for the most appropriate solutions. For example, the administration of recombinant human interferons I and III into the nasal mucosa cellular layer, as key mediators of anti–viral activity, can simulate intracellular infection and stimulate cellular activity in a timely manner, training the innate and adaptive immune system cells to develop and appropriately stimulate an adequate immune response through B and T cells. Another example could involve the treatment of natural and adaptive lymphocytes with a low dose of IFNs I and possibly III, prior to their insertion into the host lymphatic system, possibly alongside additional recruitment of plasmacytoid dendritic cells (pDCs) as further interferon “factories”, all with the purpose of early infection management. It might be that focusing on directly offering the immune system the information about the genetics and protein structure of the pathogen, rather than training its first-line mechanisms to develop faster, excessively increases its specificity, making it reach a level that brings the virus the opportunity to evolve and escape previously-developed host immune mechanisms. With regards to efforts to delay the onset of malignant diseases, approaches of chrono-biological oncotherapies that include a combination of Type I and Type III Interferon-based “immune re-awakening” and low-dose SSRI or SNRI approaches, could display meaningful extents of efficacy, at least in effective delays in the onset of malignant diseases. Such overall approaches could also be considerably effective in efforts to delay and/or even prevent a number of acquired immunodeficiencies (i.e. HIV-1-induced AIDS) and diverse forms of malignant cancer, potentially helping to notably decrease the overall burden of disease worldwide in the long run. It is until the scientific community realises this potentially crucial aspect that large proportions of the world population will probably continue to face serious epidemics and pandemics of respiratory diseases over the coming several decades, evidenced with dengue fever and more recently, monkeypox and possibly avian flu. Of note, it has been indicated that IFN I and / or III display significant immunising, early therapeutic and clinical disease onset-attenuating effects for many other microbial evoked diseases, as well as for a number of oncological diseases. Microbial agents could undergo loss-of-function research upon genes responsible for inducing clinical illness whilst keeping genes responsible for microbial reproduction and transmission at least generally as functional, CRISPR-Cas9 genome editing to have genes encoding proteins suppressive of the host interferon system eliminated prior to human genes encoding Pattern Recognition Receptor activator or agonist proteins, such as outer membrane proteins of Neisseria meningitidis, as well as Type I, Type III and possibly even Type IV Interferons and various ISGs inserted into the microbial genome. Importantly, the present study is theoretical and conceptual in nature and does not advocate for any practical steps or deployment into any real-world context. Such an approach is imagined as a potential prophylactic and early therapeutic method based upon the model of editing genes of harmless bacteria to transform such them into “producers” and “distributors” of human insulin, and could turn several microbial agents into clinically harmless, transmissible “factories” for various key elements of the host interferon system, potentially placing such microbes into a reverse evolutionary path that would be deemed as “natural de-selection”, visibly reducing the average burden of disease and metabolic stresses, which in turn could gradually increase average human and animal lifespans worldwide.

Abstract: The objective of this study was to produce, characterize, and optimize modified potato starch derived from locally sourced potatoes, and to evaluate the physicochemical properties of native, cross-linked, acetylated, and dual cross-linked–acetylated potato starches as disintegrants for tablet formulation. Starch modification was performed through cross-linking and acetylation using sodium hexametaphosphate (SHMP) and acetic anhydride (AA) as modifying agents, respectively. Native and modified potato starches were characterized using Fourier transform infrared spectroscopy (FTIR), differential scanning calorimetry (DSC), rapid visco analysis (RVA), and X-ray diffraction (XRD). The key modification parameters investigated included reaction temperature, reaction time, pH, concentration of the modifying agent (AA), and concentration of the NaOH catalyst. Based on preliminary experiments, reaction temperature (40, 60, and 80 °C), modifying agent concentration (10, 20, and 30%), and reaction time (40, 55, and 70 min) were selected as the primary variables. Process optimization for dual crosslinked-acetylated potato starch was carried out using response surface methodology based on a Box-Behnken experimental design, with acetyl content as the response variable. The optimized modification conditions were a reaction temperature of 40.22 °C, a reaction time of 69.85 min, and an acetic anhydride concentration of 21.92% (w/w). Under these optimized conditions, an acetyl content of 1.32 ± 0.077% was obtained. Tablets formulated using the dual crosslinked-acetylated potato starch as a disintegrant exhibited a disintegration time of 29.2 ± 0.29 min, a disintegration efficiency ratio of 500 ± 0.99 N min⁻¹, a crushing strength of 92.35 ± 0.86 N, and friability of 0.63 ± 0.08% (w/w). The modified starch was employed as a disintegrant in tablet formulations containing 10% paracetamol as the active pharmaceutical ingredient, magnesium stearate (10%) as a lubricant, and suitable fillers.

Abstract: Accurate monitoring and forecasting of vegetation health is essential for natural resource management, food security planning, and climate adaptation in water-stressed semi-arid environments. This study presents a comprehensive deep learning framework for forecasting the Enhanced Vegetation Index (EVI) across the four governorates of the Kurdistan Region of Iraq (KRI) --- Erbil, Duhok, Sulaymaniyah, and Halabja --- using a nine-year monthly record (January 2016 -- December 2024) derived from Sentinel-2 Level-2A Surface Reflectance imagery accessed via Google Earth Engine (GEE). Nine deep learning architectures spanning recurrent, hybrid convolutional-recurrent, and attention-based categories were trained and evaluated on a multivariate feature set comprising EVI, precipitation, air temperature, and cyclic month encoding. The Bidirectional Long Short-Term Memory (BiLSTM) model achieved the highest mean R² of 0.945 across all four governorates, with outstanding performance in Sulaymaniyah (R² = 0.977) and Halabja (R² = 0.964). Hybrid CNN-recurrent architectures, particularly CNN-BiLSTM-GRU, also demonstrated strong performance with the highest mean tolerance accuracy (0.985), confirming the complementarity of local convolutional feature extraction and temporal sequence modeling; however, BiLSTM remains the top-ranked model by R². By contrast, the standalone Transformer model performed poorly (mean R² = 0.132) due to the absence of positional encoding in the shallow single-block architecture. Predictive uncertainty was quantified using Monte Carlo Dropout inference, revealing well-calibrated epistemic uncertainty that peaks during the spring vegetation growing season. Autoregressive five-year EVI forecasts (2026--2030) and an exploratory ten-year projection (2026--2035) were generated by the BiLSTM model; forecasts commence in January 2026 as TerraClimate climate forcing data for 2025 were not yet publicly available at the time of analysis. Projected mean annual EVI values range from 0.145 to 0.194 across governorates, consistent with the historical climatological baseline. The 2022 regional drought anomaly is clearly captured in the historical record, confirming the sensitivity of the EVI signal to precipitation deficits. These results establish deep learning-based EVI forecasting as a viable and scalable tool for operational vegetation health monitoring in the KRI and comparable semi-arid dryland systems.

Abstract: The city of Baghdad is witnessing a continuous increase in traffic and urbanization, which has led to frequent traffic jams and deterioration of the urban environment and quality of life due to pollution and the waste of time and energy. Hence, it has become necessary to adopt integrated planning concepts that regulate land uses, promote transport efficiency, and support sustainable urban development. This research aims to investigate the concept of transport-oriented development (TOD) and explore its applicability in the city of Baghdad, focusing on identifying obstacles and challenges that may face the implementation of this concept in the local context, whether related to transport infrastructure, urban planning, or community participation, to provide an analytical framework that can be relied upon in the development of effective strategies to promote sustainable transport and integrated urban development. The BRT bus rapid transit system is an essential part of the Comprehensive Development Plan for Baghdad 2030, aiming to improve mass transit and reduce congestion on major streets such as Palestine Street by providing fast, efficient transportation that connects the city's neighborhoods and encourages walking and the use of sustainable transport. The project supports sustainable urban development by integrating the principles of TOD, increasing residential and commercial density around the stations, and adopting an integrative methodology that analyzes the relationships among transport, land uses, and urban density to provide a scientific framework to support planning and future decision-making.