Abstract:

Background/Objectives: The stability of pharmaceutical compounds is a critical quality attribute; it is an essential step in the drug development process. Significant focus is required to understand the variation of quality pharmaceutical compounds under prevailing environmental storage conditions. Simultaneously, many issues arise in understanding updated regulations, knowledge of data sciences, and appreciation of common practices, presenting a challenge for defining a retest period and in predicting a prolongation of the shelf life of drug products. The purpose of this paper is to conduct a statistical study to assess stability and to forecast a prolongation of drugs shelf-life. Methods: A case study is suggested to identify the most appropriate statistical test for assessing stability. The results of physical and chemical tests are considered to detect changes and variability during different conditions (accelerate, intermediate and real). Results: In the stability study, minimal variability in the content of the substance of interest was obtained using the predictive interval approach over a period of 31 months, and an interval of ±1,2%. Conclusion: The example of the statistical study is given to provide different perspectives on statistical approaches for market approval.

Abstract:

Cocoa production in West Africa—dominated by Côte d’Ivoire, Ghana, Nigeria, Cameroon, and Togo—faces interconnected agronomic, environmental, and socio-economic challenges that limit productivity and threaten smallholder livelihoods. Integrating Regenerative Agriculture (RA), Unmanned Aerial Systems (UAS), and Artificial Intelligence (AI) present a transformative framework for achieving sustainable and climate-resilient cocoa farming. This review synthesizes evidence from 2000 to 2024 and establishes a tri-axial model that unites ecological regeneration, spatial diagnostics, and predictive intelligence. Regenerative practices such as composting, mulching, cover cropping, and agroforestry rebuild soil organic matter, enhance biodiversity, and strengthen ecosystem services. UAS-based multispectral, thermal, and LiDAR sensing provide high-resolution insights into canopy vigor, nutrient stress, and microclimatic variability across heterogeneous cocoa landscapes. When coupled with AI-driven analytics for crop classification, disease detection, yield forecasting, and decision support, these tools collectively enhance soil organic carbon by 15–25%, stabilize yields by 12–28%, and reduce fertilizer and water inputs by 10–20%. The integrated RA–UAS–AI framework also facilitates carbon-credit quantification, ecosystem-service valuation, and inclusive participation through cooperative drone networks. Overall, this convergence defines a precision-regenerative model tailored to West African cocoa systems, aligning productivity gains with ecological restoration, resilience, and regional sustainability.

Abstract: The binary Goldbach conjecture states that every even integer greater than 2 is the sum of two primes. We analyze a variant of this conjecture, positing that every even integer 2N ≥ 8 is the sum of two distinct primes P and Q. We establish a novel equivalence between this statement and a geometric construction: the conjecture holds if and only if for every N ≥ 4, there exists an integer M ∈ [1, N − 3] such that the L-shaped region N2 − M2 (between nested squares) has a semiprime area P · Q, where P = N − M and Q = N + M. We define the set DN of all such valid M values for a given N. The conjecture is equivalent to there existing an M ∈ DN with N − M prime. We conduct a computational analysis for N ≤ 214 and define a gap function G(N) = log2(2N) − ((N − 3) − |DN|). Our experimental results show that the minimum of G(N) is positive and increasing across intervals [2m, 2m+1]. This empirically-derived result, G(N) > 0, provides strong computational evidence that |DN| > (N − 3) − log2(2N). Under this computationally-supported bound, the pigeonhole principle on the cardinality of DN and the number of primes P < N (corresponding to squares SP) implies |DN| ≥ 1 for all N ≥ 4, yielding a conditional proof of the conjecture. While an analytical proof of this bound remains an open problem, our work establishes a novel geometric framework and demonstrates its viability through extensive computation.

Abstract: Erasure is not forgetting; it is memory that has lost its path of return. This paper proposes a five-mode typology of structural erasure through Silencing, Reclassification, Compression, Substitution, and Tactical Forgetting as mechanisms by which memory systems fail. Historical erasure is not an incidental lapse in collective memory but a structured process shaped by social, institutional, and cultural gatekeeping. To examine how significant ideas and figures become omitted, the study applies this typology to five distinct cases from different periods and regions. Sophie Germain faced early denial of formal recognition that stifled her mathematical achievements and later reduced them to a token identity. Rosalind Franklin produced critical X-ray data on DNA, yet her work was overshadowed by a narrative that reassigned her contributions to Watson, Crick, and others while limiting her legacy to a single discovery. In Peru, María Elena Moyano was acknowledged only after her assassination; her socialist feminist activism was recast into a depoliticized image of martyrdom. Nwanyeruwa led the 1929 Aba Women’s War in colonial Nigeria, yet her leadership was obscured by British reclassification that renamed a coordinated uprising as a mere riot. Paul Robeson, once a global icon of radical internationalism and civil rights, was reclassified and his wide-ranging legacy compressed into a narrow, sanitized story that mirrored gendered cases where complex contributions were reduced to one attribute. By placing these cases together, the paper shows that modes of erasure overlap, change through time, and reinforce entrenched patriarchal, racial, and ideological hierarchies. Recognizing historical erasure as a systematic, multistage process rather than a series of isolated oversights identifies the points where collective memory becomes distorted or blocked. In doing so, the five-mode typology provides a conceptual framework linking media studies, archival research, and historiography. It offers scholars a concrete tool for corrective action through archival reform, analytical transparency, and historical repair.

Abstract: The Riemann Hypothesis, one of the most celebrated open problems in mathematics, addresses the location of the non-trivial zeros of the Riemann zeta function and their profound connection to the distribution of prime numbers. Since Riemann’s original formulation in 1859, countless approaches have attempted to establish its truth, often by examining the asymptotic behavior of arithmetic functions such as Chebyshev’s function θ(x). In this work, we introduce a new criterion that links the hypothesis to the comparative growth of θ(x) and primorial numbers. By analyzing this relationship, we demonstrate that the Riemann Hypothesis follows from intrinsic properties of θ(x) when measured against the structure of primorials. This perspective highlights a striking equivalence between the distribution of primes and the analytic behavior of ζ(s), reinforcing the deep interplay between multiplicative number theory and analytic inequalities. Beyond its implications for the hypothesis itself, the result offers a fresh framework for understanding how prime distribution governs the analytic landscape of the zeta function, thereby providing new insight into one of mathematics’ most enduring mysteries.

Abstract: COVID-19 has been associated with neurological and psychiatric manifestations, both at disease onset and post-infectious sequelae, most commonly anxiety, depression, and sleep disturbances. Previous pandemics suggest potential for long-term neuropsychiatric consequences. We conducted a prospective study in patients hospitalized with non-critical COVID-19, evaluating symptoms using validated psychometric instruments at discharge, after 3–6 and 12 months post-infection. Additionally, a four-year follow-up was performed through telephone interviews, to document newly diagnosed psychiatric disorders and mortality. At baseline, 22% of patients reported anxiety, 8% depression, and 16% poor sleep. Most symptoms improved within the first year, particularly during the first 3–6 months. At four-year follow-up, mortality reached 5%, while clinician-diagnosed psychiatric disorders increased to 6% for anxiety, 11% for depression, and 3% for mixed disorders. Anxiety and poor sleep—but not depression—were associated with the severity of the acute episode. Overall, post-COVID-19 anxiety, depression, and sleep disturbances were more prevalent than in the general population, though rates were lower than reported in other studies. Most symptoms resolved within the first year. However, new-onset cases of depression and other psychiatric disorders emerged during long-term follow-up, suggesting distinct trajectories of post-COVID psychiatric morbidity.

Abstract: Drought is a major constraint to Cannabis sativa productivity and cannabinoid yield, yet the physiological mechanisms underlying genotypic variation in drought responses remain poorly understood. We hypothesized that (i) more vigorous genotypes would sustain higher photosynthetic rates, (ii) drought would constrain photosynthesis through both diffusional and non-diffusional limitations, and (iii) water deficit would alter cannabinoid production in a genotype-dependent manner. To test these hypoth-eses, two contrasting genotypes (one tetrahydrocannabinol- (THC) dominant and an-other cannabidiol- (CBD) dominant) were grown under greenhouse conditions, with water deficit imposed at early flowering. Leaf water relations remained stable across genotypes and treatments. Although CBD plants accumulated more biomass, they did not exhibit higher photosynthetic rates under well-watered conditions. Under drought, THC plants relied primarily on stomatal regulation, whereas CBD plants showed addi-tional mesophyll and biochemical impairments, resulting in stronger declines in pho-tosynthesis. Despite contrasting photoprotective adjustments, both genotypes con-verged to similar oxidative damage, suggesting that photoprotection was not decisive for their physiological divergence. At the agronomic level, THC plants maintained a higher harvest index under drought, greater baseline cannabinoid concentrations, and inflorescence biomass with higher energetic value. In CBD plants, drought-induced reductions in cannabinoid content and harvest index largely reflected greater photo-synthetic impairment and less efficient carbon use. Overall, resilience of C. sativa to drought imposed at early flowering appears to depend less on hydraulic stability and more on sustaining photosynthetic performance, secondary metabolism, and efficient biomass partitioning. These traits represent key targets for breeding genotypes better adapted to cultivation under increasingly variable water availability.

Abstract: Preliminary evidence suggests air pollution, particularly fine particulate matter 2.5 (PM2.5), poses a significant threat to maternal health and women of reproductive age. While emerging evidence suggests a link between air pollution and maternal anemia, the specific effect of PM2.5 exposure on hemoglobin levels among reproductive-aged women (15-49 years) remains insufficiently studied. Maternal hemoglobin decline is a known risk factor for adverse pregnancy outcomes with potentially long-term consequences. Understanding the impact of PM2.5 exposure is crucial in regions like Sub-Saharan Africa, where both anemia rates and air pollution levels are significantly elevated. This population-based study investigates the association between ambient PM2.5 concentrations and maternal hemoglobin levels across 43 Sub-Saharan African countries from 2000-2019. Using generalized linear regression models adjusted statistically significant negative association between PM2.5 exposure and hemoglobin levels were observed in Central Africa, while no significant associations were found in Eastern, Western, or Southern Africa. These results suggest that PM2.5 may be an environmental determinant of maternal anemia, with effects that vary by geography. Further research is needed in understudied regions to validate and expand on these findings.

Abstract: Background and Clinical Significance: Visual agnosia and speech production deficits are well-described sequelae of neurosurgical interventions, but their selective dissociation remains rare. This report presents an unusual combination of postoperative deficits following awake resection of a left frontal low-grade glioma; Case Presentation: We present the case of a right-handed female with left hemisphere language dominance who had a left frontal low-grade glioma. Preoperatively, she exhibited anomia and dysexecutive syndrome, including difficulty completing everyday goal-directed tasks such as sending emails and paying for parking. Following awake tumor resection, she developed two rare, dissociated deficits: (1) speech restricted to infinitive verb forms and (2) selective visual agnosia for static images, with preserved recognition of dynamic stimuli. Conclusions: This unique clinical constellation highlights the vulnerability of left frontal language and ventral visual processing networks during surgery and supports the dual-stream model of vision and language production; we propose the term “astatopsia” to describe this peculiar clinical condition which was never described in such manner in literature at the best of our knowledge.

Abstract: Chronic allograft dysfunction (CLAD) is the main cause of graft loss after lung transplantation (LTR). Within the immunological factors involved in CLAD development, the antibody-mediated rejection (ABMR) has the most impact. However, ABMR diagnosis is difficult due to the limited sensitivity of histopathological, immunhistochemical, and immunological criteria currently used. Growing evidence is demonstrating the impact of molecular mismatch in ABMR; here, we ought to assess the potential role of molecular mismatch in CLAD development. A total of 457 LTR were recruited for the study, with HLA type from donors and recipients to assess molecular mismatch, and with a minimum follow-up of 180 days. The combination of molecular mismatch in class-II (HLA-EMMA and HLA-Matchmaker algorithms) with EMMA DR score >12 and antibody verified eplet mismatch in DRB1345 (AbV DRB1345) > 3 predicts CLAD development independently of ex-smoker, prolonged period of hospitalization (>33 days), acute cellular rejection (ACR), and ABMR. The HR of the prediction model for molecular mismatch in class-II was 1.52 (1.01-2.56, p=0.045). This observation could point to a potential role of poor molecular mismatch in class-II to fill the gap of underdiagnosis of ABMR, previous to CLAD development. Prospective studies should be addressed to confirm the utility of molecular mismatch in the identification of patients at risk of CLAD development.

Abstract: Given Hilbert space operators A,B and X, let △_A,B and δ_A,B denote, respectively, the elementary operators △_A,B(X) = I − AXB and the generalised derivation δ_A,B(X) = AX − XB. This paper considers the structure of operators D^m _d1,d2 (I) = 0 and Dm d1,d2 compact, where m is a positive integer, D =△ or δ, d1 =△_A∗,B∗ or δA_∗,B∗ and d2 = △_A,B or δ_A,B. This is a continuation of the work done by C. Gu for the case △^m _δA∗,B∗, _δA,B (I) = 0, and the author with I.H. Kim for the cases △^m _{δA∗,B∗,δA,B} (I) = 0 or △^m _{δA∗,B∗,δA,B} is compact, and δ^m _{△A∗,B∗,△A,B} (I) = 0 or δ^m _{△A∗,B∗,δA,B} is compact. Operators D^m _d1,d2 (I) = 0 are examples of operators with finite spectrum, indeed the operators A,B have at most a two point spectrum, and if D^m _d1,d2 is compact, then (the non-nilpotent operators) A, B are algebraic. D^m _d1,d2 (I) = 0 implies Dⁿ _d1,d2 (I) = 0 for integers n ≥ m: the reverse implication, however, fails. It is proved that D^m _d1,d2 (I) = 0 implies Dd1,d2 (I) = 0 if and only if of A and B (are normal, hence) satisfy a Putnam-Fuglede commutativity property.

Abstract: Objective. To evaluate the prevalence, risk factors, aetiology, and management of traumatic dental injuries (TDIs) among children aged 1–18 years attending the De-partment of Pediatric Dentistry, Damascus University, Syria, during 2023–2024, and to illustrate representative clinical cases with documented outcomes. Methods. This ret-rospective cross-sectional study reviewed 2,716 patient records (2023–2024) and identi-fied 301 children with TDIs. Demographic, clinical, and behavioural variables were ex-tracted and analysed using χ², t tests, ANOVA, and binary logistic regression (IBM SPSS v26). Results. The overall TDI prevalence was 11.08%. Males were over twice as likely as females to experience TDIs (OR = 2.30; 95% CI = 1.76–3.01; p < 0.001). Older age acted as a protective factor (OR = 0.56; 95% CI = 0.43–0.74; p < 0.001). Falls were the most common cause (63.7%), and injuries most often occurred at home (48.9%). The maxillary central incisors were most frequently affected (68.5% of cases). Children with special healthcare needs had significantly more traumatised teeth (mean = 2.61 ± 1.13) than healthy chil-dren (1.66 ± 0.92; p < 0.001). Nearly half of the patients (45.3%) presented > one month after injury, and asymptomatic apical periodontitis and reversible pulpitis were the most frequent diagnoses. Representative case presentations demonstrated multidisci-plinary management using restorative, endodontic, and orthodontic approaches with favourable follow-up outcomes. Conclusions. TDIs affected about one in nine children in this Syrian cohort. Male gender, younger age, and previous trauma were key risk factors. The predominance of delayed presentation underscores the need for community education, early referral systems, and targeted preventive programs within school and home environments.

Abstract:

Foggy weather poses substantial challenges for unmanned aerial vehicle (UAV) object detection by severely degrading image contrast, obscuring object structures, and impairing small target recognition, often leading to significant performance deterioration in existing detection models. To address these issues, this work presents an enhanced YOLO11-based framework, called hazy aware-YOLO (HA-YOLO), which is specifically designed for robust UAV object detection in foggy weather. HA-YOLO incorporates wavelet convolution into its structure to suppress haze-induced noise and strengthen multi-scale feature fusion without introducing additional computational overhead. In addition, a novel context-enhanced hybrid self-attention (CEHSA) module is developed, which sequentially combines channel attention aggregation (CAA) and multi-head self-attention (MHSA) to simultaneously capture local contextual cues and mitigate global noise interference. Experimental results demonstrate that the proposed HA-YOLO and its variants achieve higher detection and precision with robustness compared to the baseline YOLO11, while maintaining model efficacy. In particular, in comparison with several state-of-the-art detectors, HA-YOLO exhibits a better balance between detection accuracy and complexity, offering a practical solution for real-time UAV perception tasks in adverse weather conditions.

Abstract: This review examines the progress made in the field of polymer nanocomposites for additive manufacturing. This study focuses on developing sustainable filaments from nanokaolin and recycled high-density polyethylene (HDPE) waste. Adding nanokaolin as a filler to recycled HDPE matrices created filaments with significantly enhanced mechanical and thermal properties. They achieve up to 35% higher tensile strength, 25% greater thermal stability, and 40% reduction in material costs compared to traditional biobased and virgin-polymer filaments Using the Taguchi method, a well-known optimization technique, we systematically adjusted the extrusion parameters of the filaments. This method is part of a broader strategy known as the Design of Experiments (DOE) framework. This helps to identify the best production settings. This review investigates the links between processing conditions, microstructure, and material properties, supported by advanced characterization and modeling methods. In addition to economic factors, we also detail the environmental benefits of using recycled HDPE and nanokaolin, such as reduced carbon footprint and plastic waste, compared to standard filaments. This highlights the sustainability of this method. This study establishes a scientific basis for circular material flow in additive manufacturing. This promotes the adoption of high-performance, cost-effective, and environmentally friendly 3D printing solutions.

Abstract: This research answers the knowledge gap regarding the explanation of the quantum jump of the electron. This scientific paper aims to complete Einstein’s research regarding general relativity and attempt to link general relativity to quantum laws.

Abstract: This paper proposes a comprehensive analytical framework designed to synergize business intelligence, big data technologies, and project management processes into a unified platform. Emphasizing the transformational role of advanced data analytics, it investigates how seamless integration of diverse IT tools can optimize project scheduling, risk mitigation, resource utilization, and stakeholder collaboration. The framework addresses complexities arising from multidimensional project data and highlights architectural principles for supporting dynamic decision-making within project portfolios. Key technological enablers such as cloud-based BI services, NoSQL databases, and workflow automation are discussed to demonstrate how datadriven insights can elevate project performance and strategic alignment. This conceptual design is positioned to empower business analysts and project managers with actionable intelligence, fostering organizational agility in managing multifaceted project landscapes.

Abstract: The design of selective kinase inhibitors remains a formidable challenge due to the high structural conservation of the ATP-binding site across the kinome, and the topological complexity of pharmacophores required for potent inhibition. While modern generative AI has enabled rapid exploration of chemical space, many advanced models operate as black boxes, obscuring the chemical rationale behind design choices and limiting interpretability for medicinal chemists. Here, we present a modular, chemistry-first generative framework for de novo design of SRC kinase inhibitors, integrating ChemVAE-based latent space modeling, a chemically interpretable Kinase Inhibition Likelihood scoring function, Bayesian optimization, and cluster-guided local neighborhood sampling. Our generative pipeline employs a hybrid AI framework that integrates deep variational autoencoding, interpretable machine learning–based scoring, and probabilistic optimization to enable targeted exploration of kinase inhibitor chemical space. Our analysis reveals three pivotal findings. We demonstrate that kinase inhibitors—spanning ten families—spontaneously organize into a coherent, low-dimensional manifold in latent space, with SRC acting as a structural “hub” that enables rational scaffold transformation. Our local neighborhood sampling-based approach successfully converts inhibitors from other kinase families (notably LCK) into novel SRC-like chemotypes, with LCK-derived molecules accounting for ~40% of high-similarity outputs. However, both generative strategies reveal a critical limitation: SMILES-based representations systematically fail to recover multi-ring aromatic systems—a hallmark of clinical kinase inhibitors—despite aromatic ring count being a top feature in Kinase Inhibition Likelihood scoring function. This “representation gap” underscores that no amount of scoring refinement can compensate for a generative engine that cannot access topologically complex regions. By diagnosing these constraints within a transparent, interpretable pipeline, our work provides a foundational benchmark for current AI and a blueprint for hybrid systems that blend algorithmic innovation with medicinal chemistry principles.

Abstract: Physiological and psychological resilience has important implications for health, disease and treatment. Resilience is shown to boost treatment compliance as well as response and thereby reduce mortality. We consider the possibility that individuals having lower resilience are more likely to discontinue treatment in response to side effects of a drug. In randomized control trials (RCT) if a considerable proportion of individuals discontinue from the treatment group because of side effects, the average resilience in the remaining treatment group would be greater. As a result, the frequency or severity of adverse outcomes in the treatment group will be smaller than the control even when the drug has no effect. This bias is more likely to be serious for drugs with more frequent and/or serious side effects, but following intention to treat (ITT) protocols with some additional precautions can help in avoiding it. We suggest testable predictions of the resilience selection bias hypothesis along with ways to quantify and correct for the bias in RCTs. Attempts to detect, measure and correct for the resilience selection bias should be considered necessary for realistic evaluation of drug action in a clinical trial. Retrospective studies are more sensitive to RS bias than RCTs and need to be interpreted carefully.

Abstract: Background: Nutraceuticals are increasingly used in clinical practice for their anti-inflammatory, antiproliferative, and antioxidant properties. This study aimed to evaluate the safety and efficacy of a fixed nutraceutical combination containing chondroitin sulfate, α-lipoic acid, astaxanthin, lycopene, escin, and omega-3 fatty acids [eicosapentaenoic acid (EPA) and docosahexaenoic acid (DHA)] in improving pain and quality of life in patients with knee osteoarthritis (OA). Methods: This observational study included patients with knee OA referred to the ambulatory pain clinic at Dulbecco University Hospital, Catanzaro, Italy. Participants received one tablet daily for three months. Quality of life was assessed using the 36-Item Short Form Health Survey (SF-36), and adverse drug reactions (ADRs) were evaluated using the Naranjo scale. Results: Fifty patients (20 men and 30 women; mean age, 63.6 ± 11.4 years; range, 26–88 years; mean body mass index, 26.9 ± 3.7 kg/m²) were enrolled. A statistically significant improvement in pain symptoms was observed over time (p < 0.01). No ADRs were reported during the study period. Conclusions: The fixed nutraceutical combination improved pain and quality of life in patients with knee osteoarthritis and demonstrated an excellent safety profile.

Abstract:

Superhydrophobic micropillar surfaces, inspired by the lotus leaf, have been extensively studied over the past two decades for their self-cleaning, anti-friction, anti-icing, and anti-corrosion properties. In this study, we introduce a simple and effective method for introducing porosity into polydimethylsiloxane (PDMS) micropillar arrays using salt templating. We then evaluate the wetting behaviour of these surfaces before and after infusion with perfluoropolyether (PFPE) oil. Apparent contact angle and sliding angle were measured relative to a non-porous control surface. Across five porous variants, the contact angle decreased by approximately 5° (from 157° to 152° on average), while the sliding angle increased by about 3.5° (from 16.5° to 20° on average). Following PFPE infusion, the porous arrays exhibited reduced sliding angles while maintaining superhydrophobicity. These results indicate that introducing porosity slightly reduces water repellency and droplet mobility, whereas PFPE infusion restores mobility while preserving high water repellency. The change in wettability following PFPE infusion highlights the potential of these surfaces to function as robust, self-cleaning materials.