Abstract: Ibuprofen is among the most widely consumed non-steroidal anti-inflammatory drugs and is frequently detected in wastewater treatment effluents, is not efficiently removed by conventional biological processes. In this study, a physicochemical treatment train−acidification, homogeneous Fenton oxidation, neutralization, iron oxidation/precipitation, and solid-liquid separation−was simulated in Aspen Plus for ibuprofen degradation (150 mg/L; COD 385 mg/L; TOC 114 mg/L) at a design flow of 2.08 m³/h. The Fenton reactor (CSTR, ≈2.08 m³, 1 h residence time) operated at pH 3, following third-order power law kinetics with a rate constant of 1.1×10⁶ m⁶/(kmol²-s) and an activation energy of 56.5 kJ/mol. At the design point (Fe²⁺:H₂O₂≈1:2.4). The model predicted 85.99% ibuprofen removal alongside 46.8% COD and 39.6% TOC removal (mass-load basis), with H₂O₂ consumed almost completely; a higher-dose scenario (≈1:10) was evaluated for comparison. The effluent’s BOD5/COD ratio (≈0.50) reflects a residual dominated by benzoic acid, a partial-oxidation by-product indicating incomplete mineralization. Residual dissolved iron was oxidized with air and precipitated as ferric hydroxide before separation, and the simulated reagent operating cost was 0.62 USD/m³ of treated effluent. These findings demonstrate that Aspen Plus provides a reliable framework for simulating Fenton-based ibuprofen degradation together with downstream iron management and preliminary process economics.

Abstract: Complexity science has proposed many measures of complexity, structure, information, emergence and (self-)organisation, often in isolation and without systematic understanding of their relationships, redundancies or distinct properties. As new measures continue to appear, comparative assessment is increasingly needed. Here, we systematically compare classical and recent complexity measures, characterising their relationships and sensitivities in discriminating the qualitative behaviour of discrete and continuous dynamical systems. The primary analysis used an initial 11-system benchmark comprising four elementary cellular automata, Lorenz dynamics, Mandelbrot and Julia fractals, three Boolean-network topologies, and a yeast association network. Across the binarization threshold sweep, Block-level Algorithmic Information Dynamics (AID) (\( |\Delta\mathrm{BDM}| \)) preserved cross-system rankings with mean Spearman agreement 0.881 (95\% CI, 0.848--0.910), \( \mathrm{ICC}(3,1)=0.893 \), and Kendall's \( W=0.894 \). Independent validation comprised a five-regime ladder spanning trivial/ordered, periodic/ordered, critical, chaotic, and maximally random dynamics, together with a continuous logistic-map sweep. In the categorical analysis, 2-D cell and block AID ranked first and second, respectively, in the task-specific composite combining categorical discrimination, Lyapunov tracking, and assigned perturbation capability. Post-hoc, 1-D BDM and Epiplexity separated critical from chaotic dynamics. Epiplexity and expiplexity-derived coding statistics did not distinguish chaotic from maximally random endpoints, whereas AID additionally retained signed, localised, multiscale intervention effects. Sequential structural epiplexity \( S_T \) separated validation categories (\( \epsilon_H^2=0.626 \), 95\% interval 0.515--0.782), with a much lower point separation effect size than Cell and Block AID (BDM perturbation). We develop a proposed characterisation of Epiplexity as a functional over an AID information landscape, making explicit the sufficiency assumptions needed to identify it with the original bounded-MDL definition. Thus, CTM/BDM provide estimators of algorithmic information, AID provides the perturbational calculus, and explicitly constructed scalar observables project selected aspects of that landscape.

Abstract: Pigmentation and hair morphology are both among the earliest visible traits studied in the context of human heredity and genetics. Over the past 100+ years, both traits have been defined in various ways and their genetic underpinnings have been studied across major methodological approaches in the field to varying degrees of success. Here we review what can be learned from the divergent trajectories of genetic research on these two traits, particularly about how phenotypic definitions shape connections between genetic findings, biological mechanisms, and measured variation. We consider how a new era of functional genomics may integrate these bodies of knowledge, and to move forward our understanding of complex traits, for which pigmentation and hair morphology provide two illustrative models.

Abstract: Calcareous desert deposits present significant challenges for geotechnical site characterisation because of their heterogeneous composition, variable carbonate cementation, and complex mechanical behaviour. This study investigates the practical engineering value of electrical resistivity as a supplementary site-characterisation tool within an integrated geotechnical investigation framework at the Shagaya Renewable Energy Project, Kuwait. The investigation combined an extensive field database comprising 50 boreholes, 10 Cone Penetration Tests (CPTs), 19 Plate Load Tests (PLTs), laboratory testing, and six electrical resistivity survey locations. The results indicate that the site is predominantly underlain by medium-dense to very dense calcareous sandy deposits exhibiting substantial variability in density, fines content, carbonate cementation, penetration resistance, and foundation stiffness. Standard Penetration Test (SPT) N-values ranged from 10 to 84, CPT cone resistance values ranged from 15 to 27.5 MPa, and PLT-derived modulus of subgrade reaction (Ks) values ranged from 24.3 to 210.3 MN/m³, demonstrating considerable spatial variability in engineering behaviour. Electrical resistivity values ranged from 57.2 to 271.9 Ω·m and provided additional insight into subsurface heterogeneity. Higher resistivity values generally corresponded to denser, more strongly cemented deposits, whereas lower values were associated with finer-grained or less cemented materials. Unlike previous studies that focused on developing empirical resistivity-based correlations, this research evaluates electrical resistivity as a complementary investigation tool integrated with conventional geotechnical methods. Because direct spatial matching between geophysical and geotechnical observations was unavailable, the study does not seek to establish site-specific predictive relationships. Instead, resistivity measurements are interpreted alongside borehole, laboratory, SPT, CPT, and PLT data to improve understanding of subsurface variability and support engineering judgement. The proposed integrated geotechnical-geophysical framework enhances site characterisation, reduces uncertainty, and provides practical guidance for foundation investigations in heterogeneous calcareous deposits and large-scale renewable-energy developments in arid environments.

Abstract: Solution Combustion Synthesis (SCS) continues to emerge as a green synthesis method for the preparation of advanced inorganic nanomaterials with tailored physicochemical properties. This review discusses the basic concepts of fuel–oxidizer ratios in solution combustion synthesis and explores in detail the effects of different types of fuel-oxidizer mixtures, oxidizer nature, fuel type, experimental conditions, and targeted material properties on the optimization of combustible mixtures. Furthermore, highlights on the impact of varying fuel–oxidizer ratios on powder properties and their performance in different applications such as biosensing, catalysis, photocatalysis, and electrochemical energy storage were analyzed. Findings demonstrate that careful modulation of the combustible mixture enables structured tuning of nanomaterial properties for specific technological applications. However, despite the significant progress achieved in solution combustion synthesis, challenges still exist with controlling the combustion reaction, carbonaceous impurities, scalability, and morphology regulation. Hence, the review suggest that future perspectives should focus on the integration of advanced thermodynamic modelling, real-time in situ characterization techniques, and the development of novel fuels and oxidants to achieve improved combustion precision and reproducibility. These advancements are expected to enhance the scalability and industrial applicability of SCS while enabling the design of next-generation functional nanomaterials with superior performance and sustainability.

Abstract: Sickle Cell Disease is among the top 20 causes of mortality worldwide. Hydroxyurea is one of the most readily available and economical solutions approved for the treatment of Sickle Cell Disease patients: it induces the synthesis of foetal haemoglobin and decreases the inflammatory phenotype. Hydroxyurea shows high interindividual variability, re-quiring individualized dosing to balance therapeutic efficacy against escalating toxicity. In addition to non-genetic factors, hereditary variables can also be investigated to adopt a more tailored dosing regimen according to each patient's demographic, pharmacokinetic and pharmacogenetic profile. This study evaluated the possible influence of different genetic variants on hydroxyurea pharmacokinetics, by conducting a retrospective analysis on 79 treated patients, stratified by ethnicity, age and sex. SLC22A3*698 C>G variant was found to be the most significantly associated with an en-hanced function of the encoded transporter, improving the drug elimination process. ABCB1 1236 C>T, ABCB1 2677 G>A, ABCB1 3435 C>T, CYP2B6 516 G>T, CYP2C19*2 G>A, CYP2C9*3A A>C, CYP2D6*10 A>G, MAP3K5 13257 C>T, MAP3K5 6292 C>A and OCT1 480 C>G gene variants were instead found linked to a general loss-of-function phenotype, reflecting slower and less efficient drug metabolism. These findings demonstrate the importance of assessing hydroxyurea pharmacogenetics while taking into account different ethnic and demographic variables, in order to support a more personalized and effective therapeutic approach for sickle cell disease patients.

Abstract: systems-on-programmable-chip combine processors and reconfigurable logic, but task communication can remain coupled to the current placement of neighboring tasks. This paper presents an architecture-level communication contract for dynamic hardware/software task migration. Software and hardware adapters expose a common logical task namespace, while cooperating managers configure routes, manage explicit state buffers, and coordinate local mailboxes. The migration specification distinguishes quiescence, state preservation, route rebinding, partial-bitstream loading, and resumption, with explicit cache and buffer ownership responsibilities. An analytical evaluation examines interface-variant counts, transport-cycle budgets, mailbox preservation, and configuration-transfer sensitivity. Under an illustrative effective drain rate of 800 MB/s and a fixed overhead of 1 microsecond, preserving 1-64 KiB requires 2.28-82.92 microseconds in the model. These values exclude unmodeled quiescence and state-restoration costs and are not board measurements. The accompanying code reproduces the calculations and records their assumptions. The contribution is a reproducible architectural specification and cost decomposition, rather than a validated implementation or a claim of performance superiority. A prospective validation protocol identifies the measurements needed to establish correctness, resource use, and end-to-end migration downtime on a CPU/FPGA platform.

Abstract: Modern LLM agent harnesses augment the underlying model with reusable components such as external skills and procedural memories, making effective retrieval increasingly important as these repositories grow. However, existing retrieval pipelines often rank stored skills or memories by semantic similarity, which measures how similar a skill or memory appears to the current retrieval query but does not necessarily reflect how useful it is for completing the task. We introduce TaskMatch, a lightweight plug-and-play model that learns task-aware relevance from offline examples labeled as required, supportive, or irrelevant. Using the fixed query and skill and memory representations already available in the host retrieval pipeline, TaskMatch combines direct feature-wise matching with nonlinear interactions while leaving candidate generation and downstream agent execution unchanged. We instantiate the same architecture separately for two representative harness components—skill retrieval and procedural-memory retrieval—with fewer than 0.30M trainable parameters per model and no additional online LLM calls. Across both components, TaskMatch consistently improves retrieval ranking quality and downstream task execution. It raises SKILLDAG success on ALFWorld from 67.10% to 81.43%, improves MEMP Proceduralization success from 81.34% to 90.30%, and increases TravelPlanner hard-constraint satisfaction from 64.06 to 76.19. Our code is available at https://github.com/ldx825/learned-relevance-scorer-agent-retrieval.git

Abstract: Better verification can make a firm cut genuine sustainability investment. When verified performance exceeds the assessment based on nonverified information, verification raises the assessment at unchanged activities. If further assessment gains bring sufficiently smaller stakeholder rewards, additional investment can become less attractive. With aligned metric weights, the model identifies a verification threshold for implementing socially preferred positive investments in both genuine activities. Below it, pressure sufficient to induce those investments also rewards communication. In a benchmark with linear rewards and separate activity choices, governance can improve genuine outcomes while lowering assessments. An extension allows separate verification of carbon and non-carbon outcomes. Supporting results explain when pressure corrects managers’ underinvestment in projects generating operating benefits. Operating performance excludes stakeholder payments; these enter firm payoff and cancel as transfers in welfare. Improved broad sustainability assessments need not deliver substantial decarbonization.

Abstract: Electromagnetic vibration energy harvesters with magnetic levitation are of interest for powering autonomous low-power devices due to their high output power; however, their narrow operating region makes them sensitive to changes in the parameters of external vibration excitation. This study investigates the effects of excitation frequency and base acceleration amplitude on the harvester’s output characteristics and evaluates mean power retention under combined deviations in these parameters. Numerical simulations of steady-state regimes were performed. Within prescribed neighborhoods of operating points, the minimum mean power and local relative power variation were determined. For the configuration considered, increasing the deviation bounds from ±0.2 Hz and ±0.2 g to ±0.8 Hz and ±0.8 g reduces the highest retained fraction of the global maximum power in the investigated domain from 77.73% to 24.58%, while the local relative power variation at the corresponding operating points increases from 16.46% to 66.48%. These results highlight the risk of selecting a high-power operating point near a sharp power-drop boundary, where excitation deviations can leave the load underpowered. The proposed analysis addresses this risk by evaluating the minimum mean power across the prescribed deviation range and identifying operating regions that satisfy the load’s power requirement.

Abstract: Honeypots occupy a peculiar place in the security landscape: they are among the few mechanisms whose value is defined by their failure. A firewall that is never tested, an intrusion detection system that never alarms—these are successes. A honeypot that is never attacked is useless. This inversion of the usual security logic gives honeypots an unusually clean signal, but it has also kept their theoretical foundations fragmented. Game theorists model deception in isolation; information theorists treat intrusion detection generically; queuing theorists model traffic separately; and the recent wave of AI-driven honeypots—powered by Large Language Models and Reinforcement Learning—has proceeded largely without formal mathematical treatment.This paper attempts a synthesis. We develop a unified mathematical framework for AI-driven honeypot systems, building on six pillars: stochastic processes (the honeypot as a continuous time Markov chain), game theory (the honeypot as a Bayesian signaling device), detection theory (the honeypot as a near-ideal hypothesis test), information theory (the honeypot as a channel for intelligence), optimization (the honeypot as a constrained POMDP), and AI integration (LLM as observation kernel, RL as policy optimization).Along the way, we derive equilibrium conditions, detection thresholds, optimal deployment fractions, information-theoretic bounds, and LLM deception limits. We prove that the mixed strategy equilibrium is unique and Lyapunov stable, solve the POMDP for a reduced state space to obtain a threshold policy, and show that in the myopic case this threshold coincides exactly with the game-theoretic equilibrium under consistent payoff specifications. We generalize the Poisson detection assumption to a Weibull hazard model and derive a bound on the mutual information achievable under LLM-generated observations via the data processing inequality.We accompany the analytical development with numerical Monte Carlo experiments based on exact Gillespie simulation of the CTMC, replicator dynamics for the game-theoretic component, and parametric sampling for the detection and information-theoretic components. These experiments validate the closed-form predictions and expose the qualitative behavior of the system in transient and steady state. All quantitative claims are analytical; the simulations serve as numerical illustrations, not as empirical validation of real-world deployments.

Abstract: Large language models (LLMs) achieve strong reasoning performance through Chain-of-Thought (CoT), yet their autoregressive generation provides limited supervision over the correctness of intermediate steps. We introduce Step-wise Check Optimization for Process-Enhanced Reinforcement Learning (SCOPE-RL), a reinforcement learning framework that equips reasoning steps with executable mathematical checks. Each step is associated with a typed verification operator, such as symbolic equivalence, substitution, boundary evaluation, or range checking. Candidate checks are certified using reference solutions and controlled counterexamples to ensure both validity and discriminative ability. During reinforcement learning, these checks are executed directly on intermediate reasoning states to provide a process-level reward, which is combined with the final-answer reward under the standard GRPO framework. Experiments across multiple language models and mathematical reasoning benchmarks show that SCOPE-RL consistently improves accuracy over strong RLVR baselines while producing more verifiable and mathematically consistent reasoning trajectories.

Abstract: Geopolitical instability presents an inherent paradox for renewable energy economics, reinforcing the strategic imperative for renewable energy while acting as a cost multiplier for its deployment. This study examines how geopolitical escalation associated with the US-Israel-Iran conflict would affect renewable energy investment in Egypt, specifically utility-scale solar photovoltaic (PV), through physical supply side and financial risk transmission channels. Drawing on the theory of investment under uncertainty, a quantitative scenario-based techno-economic framework is developed using recent secondary data. Physical pressures arising from commodity, manufacturing, and logistics disruptions are measured through changes in capital expenditure (CAPEX), while changes in the weighted average cost of capital (WACC) measure financial risk. A reference solar PV system is evaluated alongside limited escalation, prolonged disruption, and severe escalation stress scenarios, followed by two-way sensitivity and robustness analyses. The reference case yields a levelized cost of electricity (LCOE) of 4.62 US cents/kWh under the baseline assumptions. Findings show that LCOE increases to 5.17 cents/kWh under limited escalation, 6.04 cents/kWh under prolonged disruption, and 7.06 cents/kWh under severe combined stress, with an increase of 52.9% relative to the reference case. Results further suggest that financing conditions exert a larger independent effect than the physical supply shock; a 20% increase in CAPEX raises LCOE by 18.4%, whereas increasing WACC by 4% raises it by 28.7%. This ranking remains stable across alternative reference specifications, while combined scenarios reveal an amplification mechanism when higher financing costs are applied to an enlarged capital requirement. Consistent with the theoretical proposition, the findings indicate that abundant renewable resources do not necessarily imply resilient investment conditions under geopolitical stress. Geopolitically exposed emerging economies like Egypt should therefore prioritize financial de-risking and supply chain resilience when formulating energy policy.

Abstract: Background: Abdominal aortic aneurysm (AAA) is characterised by irreversible dilatation of the abdominal aorta and a risk of rupture. Clinical assessment still relies mainly on diameter and growth rate and does not capture the immune, vascular and matrix heterogeneity of the aortic wall. Protein S-palmitoylation is catalysed by ZDHHC palmitoyltransferases and reversed by depalmitoylating enzymes. It regulates membrane localisation and trafficking of immune receptors, chemokine receptors, ion channels and vascular signalling proteins. However, whether palmitoylation-related machinery and substrates jointly define an AAA disease axis has not been systematically examined. This study aimed to identify a reproducible palmitoylation-related module in human AAA tissue, to screen axis-level cis-eQTL multiplicity-controlled significant genes and to independently define the strict genetic effect boundaries of that module at a cis-eQTL layer, to converge the module onto core targets using multi-omics, machine learning, single-cell analysis and computational docking, and to validate the hub genes at the tissue-expression level by real qPCR. Methods: We used GSE232911 (110 patients with AAA and 13 donors) as the discovery cohort and assembled an S-palmitoylation machinery-and-substrate axis gene set. A tissue-level module was frozen from the intersection of differential expression, the axis gene set and pre-specified AAA vascular and inflammatory programmes. We then performed axis-level cis-eQTL Mendelian randomisation (MR) screening with Benjamini-Hochberg FDR and Bonferroni double correction to select multiplicity-controlled significant genes, together with independent validation at a strict cis-eQTL layer with coloc.abf colocalisation; disease-internal consensus clustering; LASSO, random forest and SVM-RFE feature selection within the frozen module; a single-gene logistic model with two external directional checks; construction of the GSE166676 single-cell atlas; myeloid monocle3 trajectory and scTenifoldKnk in-silico perturbation; L1000CDS2, CTD, P2Rank and AutoDock Vina public-resource integration and computational docking; and real qPCR validation of CD80 and HTR2B. Results: The discovery cohort yielded 3,100 differentially expressed genes. The frozen three-way intersection produced a 15-gene module, including 12 up-regulated and 3 down-regulated genes. In the axis-level cis-eQTL screening layer, 161 of the 341 axis genes were estimable, and 42 reached nominal significance (IVW random-effects p<0.05); after Benjamini-Hochberg FDR and Bonferroni double correction, 9 genes simultaneously satisfied q<0.01 and p<0.05/161 and constitute the multiplicity-controlled significant genes: SORT1, BAALC, ZDHHC6, MICA, CD7, CANX, PI4K2A, GSDMD and ADORA2B (five risk-increasing and four risk-decreasing, all p<0.001). In the independent strict cis-eQTL layer (341 axis genes, 35 passing the r²<0.001 instrument gate, 15 estimable), ENTPD1 (OR 0.884, 95% CI 0.790-0.989, p=0.031) and NOD2 (OR 0.942, 95% CI 0.888-0.998, p=0.042) reached nominal significance but none passed BH-FDR<0.05 (minimum q=0.317); single-SNP and leave-one-out analyses quantified the instrument-contribution boundaries of the two signals. None of 106 coloc.abf tests showed strong colocalisation, and the outcome-region audit showed limited AAA GWAS signal in the relevant windows (Figure S1 ). AAA tissue resolved into K=3 disease-internal molecular groups (C1=40, C2=55 and C3=15). The three machine-learning methods uniquely converged on CD80 and HTR2B. The single-gene CD80 model had a training AUC of 0.9685 and remained directionally consistent in two external cohorts (AUC 0.7551 and 0.7449). The single-cell atlas retained 7,596 cells, and the myeloid perturbation converged on cytokine-cytokine receptor interaction (KEGG, k=9/50, FDR=0.00355). The druggability layer returned 38 unique perturbagens, 29 high-evidence CD80-related compounds and computational docking for both targets; the best mode-1 affinities were -7.46 kcal/mol for CD80 and -8.98 kcal/mol for HTR2B. Real qPCR showed higher CD80 (1.54-fold, p=0.0032) and HTR2B (1.66-fold, p=4.8x10^-5) expression in AAA tissue than in donor tissue. Conclusion: We delineate an S-palmitoylation-contextualised immune-vascular disease axis in human AAA and place CD80 and HTR2B at the intersection of multi-omics screening, computational perturbation, computational docking and real qPCR tissue validation. The genetic layer returned nine multiplicity-controlled significant genes at the axis-level screen and retained two nominal associations with their full correction boundaries at the independent strict layer. The single-cell, perturbation and docking layers provide mechanistic and druggability directions, and qPCR provides expression-level validation in human aortic tissue. Collectively, the evidence supports a testable palmitoylation-immune costimulation-vascular inflammation axis and establishes an executable framework for functional and cohort validation.

Abstract: Pilot samples arise naturally in two-stage statistical procedures, where preliminary observations are used to estimate an unknown optimal allocation before the remaining observations are assigned. The central question is the choice of pilot size. A small pilot gives poor estimation of the optimal allocation, while a large pilot commits too many observations before that allocation is known. Under allocation MSE and regular parametric estimation, these two effects lead to a sharp balance. The pilot size grows as the two-thirds power of the total sample size. The square-root pilot is first-order adequate, whereas the two-thirds rule gives the higher-order balance between estimation and early commitment. Four pilot-size regimes are considered to show the effect of choosing the pilot below, at, or above this order. The result is general and does not depend on a particular distribution. Exponential lifetime populations are used as an application for which the optimal allocation can be obtained explicitly. Reliability and medical examples illustrate implementation of the rule.

Abstract: Higher income may be associated with lower medical financial hardship, but this association may differ by race. Guided by Minorities’ Diminished Returns (MDRs), we examined two related outcomes among Black and White adults in California: medical bill payment difficulties and inability to afford basic necessities due to medical bills among adults reporting payment difficulties. We used annual California Health Interview Survey estimates for 2017–2024, comparing income below versus at or above 400% of the federal poverty level. We calculated relative reductions first and absolute contrasts second, using equally weighted annual mean prevalences. For payment difficulties, relative reductions were smaller among Black than White adults in 2017–2024 (47.7% versus 58.0%) and 2019–2024 (45.6% versus 58.3%). Full-period absolute reductions were also smaller (6.96 versus 8.29 percentage points). For inability to afford necessities, the 2019–2024 relative reduction was smaller among Black adults (38.2% versus 43.4%), while absolute reductions were similar (16.0 versus 15.6 points). The smaller relative reduction among Black adults was consistent with MDRs. The two outcomes have different denominators. Future research should formally test race-by-income interactions when respondent-level survey data and design information become available; these tests were not possible using the supplied online-analysis exports.

Abstract: Quantifying coastal spatial complexity across continuous display scales in digital mapping architectures is traditionally constrained by static raster resolution boundaries, quantization errors, and manual operator bias. Furthermore, dynamic web-mapping services deploy scale-dependent cartographic generalization, causing displayed geometries to reflect geospatial database complexity rather than static physical boundaries. To address these challenges, this study presents an automated, hybrid Web-GIS and MATLAB geospatial processing pipeline that integrates commercial web-mapping API vector-rendering engines with digital image processing to facilitate continuous multi-scale cartographic complexity (D_b) benchmarking and feature extraction. By maintaining a constant physical-scale measuring grid—calibrated via a dimensionless coordinate-ratio parameter—over programmatically scaled vector cartography, the pipeline leverages sub-pixel geometric precision to achieve seamless multi-scale coastal feature extraction. The framework incorporates connected-component labeling (CCL) noise filtration, Otsu thresholding, Canny edge detection, Gecko-engine tile rendering to eliminate WebKit-induced stitching grid artifacts, and latitudinal scale-distortion compensation for Web Mercator projections. Deployed across 16 global geospatial databases, the pipeline demonstrates robust spatial benchmarking capabilities: along Norway's glaciated coastline (D_b=1.4395), it effectively captures a scale-dependent, biphasic "fjord-to-fjard" morphometric transition, resolving spatial thresholds corresponding to meso-scale glacial valley morphometry. Global database benchmarking reveals clear structural distinctions in cartographic complexity, where active convergent boundaries exhibit elevated fractal complexity (e.g., Southeast Asia, D_b=1.4578) compared to transform-suppressed margins (e.g., US West Coast, D_b=1.0460). Additionally, a macro-scale cartographic low-pass filter (>0.8 km, Zoom 2–7) effectively attenuates high-frequency cartographic noise and anthropogenic structural inflation (e.g., 56.29% artificial coastlines in Taiwan). This open-source workflow provides a robust, reproducible Web-GIS framework for dynamic spatial database benchmarking, digital cartographic analysis, and automated coastal feature extraction.

Abstract: Reality appears saturated with recurrent but non-identical patterns. Distinguishable regularities give grounds to investigate constraints on possibilities and the dynamics through which configurations arise, persist, change and dissolve. The Teabag Test justifies selective reverse engineering: a purpose-relevant reconstruction can support enquiry or participation without an exhaustive causal history. Minimum relational legibility floors identify what must become sufficiently intelligible for that purpose and what would require further reconstruction. Viability Studies is proposed as a field of comparative enquiry into these relationships across domains, scales and boundaries. The Working Repository supplies a substantive initial instance: a candidate map and an architecture for accumulating situated relational accounts, their evidence, limits, comparisons and revisions. Specialist participation is constitutive of its development. Contributions need not report new discoveries; organising knowledge already dispersed across specialisms may expose relationships, consequential differences and further questions. Each instance remains partial, while maintaining and comparing its records can develop the resolution available for enquiry. Participants can extend the architecture or build independent instances from the public orientation; comparisons between builds can investigate what organising choices reveal or obscure. Sustained availability and stewardship exceed the current author's capacity. The Repository is subject to the constraints and dynamics it investigates, including the effects of its own categories and uses on participation. Increased legibility does not guarantee desirable emergence. The contribution is the connected orientation, substantive map and cumulative comparative architecture. Close antecedents inform its assessment; historical distinctiveness and additional utility remain open. No specialist pilot or implemented governance is reported.

Abstract: For \(q \ge 1\) and \(\gcd(a,q)=1\) we study the restricted weighted Goldbach sum \(R_{a,q}(N) = \sum_{p_1+p_2=N,\ p_i\equiv a(q)} (\log p_1)(\log p_2)\) for even \(N\), with candidate main term \(M_{a,q}(N) = 2C_2\varphi(q)^{-1}\Xi_0(N)N\), where \(C_2\) is the twin-prime constant and \(\Xi_0(N)\) is the odd part of the Hardy--Littlewood singular series. We first prove an elementary local-obstruction theorem: if \(q\) has an odd prime factor \(\ell\), then \(R_{a,q}(N) = O_q(\log N)\) for every \(N\equiv a\ (\ell)\), a set of positive density, so no asymptotic of size \(\asymp N/\varphi(q)\) can hold; the restriction to \(q=2^k\) in the rest of the paper is therefore forced by the truth of the statement. For \(q=2^k\) we evaluate the major arcs through an exact additive-shift decomposition of the progression and a factorisation of the resulting singular-series coefficients, which yields the main term \(M_{a,q}\) with an explicit coefficient, and, combined with the Siegel--Walfisz theorem and Vaughan's bound on the minor arcs, the mean-square estimate \(\sum_{N\le X}|R_{a,q}(N)-M_{a,q}(N)|^2 \ll_{A,q} X^3(\log X)^{-A}\) for every fixed \(A>0\). This gives an exceptional set \(\#\{N\le X \text{ even}: R_{a,q}(N)=0\} \ll_{A,q} X(\log X)^{-A}\). An elementary transfer principle then shows that the same bound controls \(R_{a,q}\) along any injective map into the even integers of at most linear growth. Consequently, for every residue class ("ray") \(c\) coprime to \(q\), almost all primes \(p_n\equiv c\ (q)\) have \(p_n+p_{n+1}\), \(p_n+p_{n+k}\) and \(p_n+h\) (\(h\) odd) representable with first summand in the class \(a\). For a modulus with an odd prime factor \(\ell\) we prove that \(N_n=p_n+p_{n+1}\equiv a\ (\ell)\) is a condition on the pair of rays of \(p_n\) and \(p_{n+1}\), that it holds infinitely often by a theorem of Shiu, and, for \(q=3\), we verify computationally up to \(6\times10^6\) that \(R_{1,3}(N_n)=0\) exactly when \(N_n\equiv1\ (3)\) and \(N_n-3\) is composite (only the implication from this condition to the vanishing of the sum is proved). We document by exact computation below \(10^8\) the digit-repulsion bias between consecutive primes, the repetition of rays according to the size of the gap, the doubling of the relative variance of \(R_{a,q}\) on the class \(N\equiv2a\ (q)\), and the out-of-sample predictability of the next ray; the ray-gap identity and the symmetry \(p_1\leftrightarrow p_2\) behind this doubling are proved. The same circle-method machinery gives the restricted ternary asymptotic \(W_{a,q}(n)=\Xi_3(n)n^2/(2\varphi(q))+O_{A,q}(n^2(\log n)^{-A})\) and positivity for all large odd \(n\). Finally, the amplitude \(\Xi_0(p+h)\) of the main term along shifted primes converges in distribution to an explicit random Euler product \(Y_h\), with all moments converging to absolutely convergent Euler products, an entire moment generating function with \(\log\mathbb E Y_h^s \le s\log\log s+O(s)\), a doubly exponentially thin upper tail that is sharp on a doubly logarithmic scale, and no atoms. Every theorem is unconditional; computations are labelled with their exact range.

Abstract: The goal of this study is to model the rate of change between spring and winter in groundwater quality for irrigation in coastal wells in the municipality of Guasave, Sinaloa, Mexico. In 58 well-water samples (29 sampled in spring and 29 in winter), the following parameters were measured: pH, electrical conductivity (E.C.), total dissolved solids, temperature, salinity, static water level, Na⁺, Ca²⁺, Mg²⁺, K⁺, Cl^–, SO₄^2–, CO₃^2–, and HCO^3–. The original sodium adsorption ratio (SARor), corrected (SARco), and adjusted (SARad) were calculated. For spring and winter and using Pearson and Spearman (Sr) coefficients, ordinary and repeated measures correlations were calculated. To identify differences between seasons, paired tests and Piper diagrams were used. The rate of quality change was modeled using multiple non-linear regressions. All models were rigorously validated. For the quality change rate, three predictive quadratic polynomial models were obtained. These three models presented the following significant values for Sr: 0.653 (E.C. vs. SARor), 0.575 (E.C. vs. SARco), and 0.470 (E.C. vs. SARad). Observed and predicted quality were numerically classified based on E.C. vs. SARor, SARco, and SARad. The information from these models can be applied to prevent soil contamination caused by irrigation with saline and sodic water, potentially boosting agricultural yields in ‘‘the agricultural heart of Mexico.’’