Abstract: Building law allows the use of a building that is non-compliant with fire safety regulations, provided that enhanced fire exit strategies are implemented to mitigate the negative impact of this non-compliance on fire safety. This article demonstrates the potential of using a probabilistic fire risk analysis method—multisimulation—to increase the efficiency of selecting fire exit strategies. Multisimulation is a quantitative risk analysis method that utilizes, among other things, computer models of fire development and evacuation, as well as modern mathematics and computer science. The main aim of multisimulation is to perform multiple computer simulations (hence the name) for as many fire scenarios as possible in a given building. This article demonstrates the potential of using this method in a practical approach to ensuring fire safety. For this purpose, an existing auditorium building was analyzed, in which numerous non-compliances with applicable regulations were identified. The analysis included 1000 fire and evacuation simulations in a theater auditorium equipped with two emergency exits and 1000 fire and evacuation simulations in a theater auditorium equipped with three emergency exits. In the simulations of both scenarios, the duration of a performance conducted with a full audience and people performing on stage was modelled. The results clearly demonstrated a significant improvement in safety when three emergency exits were available. In terms of both the required safe egress time (RSET) and risk analyses, when three emergency exits were available (instead of the required two), the possibility of having only one functioning exit, which may occur due to a human error, was eliminated. Therefore, it was undoubtedly confirmed that the use of a third emergency exit is justified as an optimal fire exit strategy or a future legislative requirement.

Abstract: Lake Erhai is an important plateau freshwater lake in China. It serves not only as a crucial drinking water source for the local region but also as the core area of the Cangshan Erhai National Nature Reserve. Consequently, Lake Erhai plays an extremely significant role in the local economy, society, and ecology. However, since the 1970s, the lake has experienced a series of problems, including declining water levels and water pollution. In recent years, the water quality of Lake Erhai has continued to deteriorate, showing a eutrophic trend. To identify the primary driving forces behind these water quality changes, this study employed stepwise regression analysis. Climate conditions, socio-economic development within the basin, and implementation of environmental protection measure (IEPM) were considered as influencing factors for a comprehensive and systematic analysis of Lake Erhai's water quality. The results indicate that air temperature primarily affects total phosphorus (TP) concentration and exhibits a positive correlation. Rainfall predominantly influences TP and total nitrogen (TN) concentrations, also showing positive correlations. Wind speed affects chemical oxygen demand (CODMn), TP, and TN concentrations, exhibiting negative correlations with each. Socio-economic development mainly affects CODMn concentration. Based on these findings, this paper proposes recommendations focusing on formulating more effective non-point source pollution control measures and strengthening water quality monitoring in Lake Erhai during summer. This study systematically analyzed the anthropogenic and natural factors affecting Lake Erhai's water quality, identified the dominant influencing factors, and provides technical support for the subsequent enhancement of Lake Erhai protection measures.

Abstract: This study proposes a unified physical framework integrating conservation-based spatial foundations with discrete spatial quantum mechanics. By leveraging spatial quantum's localized splitting, adjacent capture, and density gradient effects, we develop a coherent explanation for the microscopic origins of gravity, cosmic expansion, dark matter, dark energy, and vacuum energy divergence. The theoretical mechanism posits that the total spatial volume remains strictly conserved, with space composed of indivisible fundamental units called spatial quantum. To maintain energy, momentum, and angular momentum conservation, bound matter continuously undergoes virtual particle processes—quantum information exchanges that require spatial quantum as the minimal physical degree of freedom, leading to their gradual increase over time. Gravity emerges as a geometric dynamics effect driven by spatial quantum density gradients, while cosmic expansion manifests as the continuous fragmentation of this conservation-based foundation into quantum units, observable through the light-cone causality structure. This model serves as a microscopic extension and refinement of general relativity, effectively addressing black hole singularities and Big Bang singularities. Without introducing dark matter particles, dark energy scalar fields, or additional gravitational corrections, it provides a self-consistent explanation for observed phenomena including galactic rotation curves, gravitational lensing, bullet clusters, and super-diffuse galaxies, while mitigating vacuum energy density divergence-induced "vacuum catastrophe" issues. The theory satisfies Lorentz covariance and local causality, featuring a relatively closed underlying structure with minimal assumptions, offering a potential pathway toward constructing a complete, singularity-free unified description of gravity and cosmology.

Abstract: The integration of building information modeling (BIM) and geographic information systems (GIS) is an important area of research aimed at improving interoperability between these domains. These domains often use different concepts for semantics such that non-interoperable vocabularies; schemes; metamodels for semantics; and, in general, non-interoperable IT architectures are used to publish semantic concepts. This study investigates the use of BIM data dictionaries for semantic classification of vector-based geospatial data in GIS, aiming to enable the use of common dictionaries and concepts to describe objects in both domains. The study addresses a particular problem: the fact that the domains use different metaconcepts to describe conceptual information and have different classification methods. The research focuses on identifying significant standards, comparing their metamodels to find similarities and explore the practical use of BIM data dictionaries for the semantic enrichment of GIS features. As a proof of concept, three approaches for the classification of features are developed and validated through implementation in the QGIS software. The results demonstrate that BIM data dictionaries can be used to semantically enrich geospatial data in GIS, with the buildingSMART Data Dictionary (bSDD) serving as a practical example. The conclusions drawn from the study are that although there are limitations and challenges, the integration of BIM data dictionaries into GIS is possible and beneficial for improving interoperability, particularly when cross-domain concepts are employed.

Abstract: Tumor hypoxia is a defining hallmark of solid cancers that profoundly influences tumor progression, genomic instability, and therapeutic response. Beyond its classical roles in angiogenesis and metabolic reprogramming, hypoxia has emerged as a central determinant of the tumor immune microenvironment (TME), promoting immune exclusion and resistance to immunotherapy. Our work has uncovered tumor cell–intrinsic mechanisms by which hypoxia drives immune escape. We identified hypoxia-induced autophagy as a key adaptive response that enables tumor cells to resist natural killer (NK) and cytotoxic T lymphocyte (CTL)–mediated killing. Under hypoxic stress, autophagy selectively degrades NK-derived granzyme B, neutralizing effector cytotoxicity, while genetic or pharmacologic inhibition of autophagy restores immune-mediated killing and enhances tumor regression in vivo. Furthermore, we demonstrated that Vps34 inhibition, a central regulator of autophagy and vesicular trafficking, converts poorly infiltrated “cold” tumors into inflamed “hot” tumors enriched in NK, CD4+, and CD8+ effector T cells, thereby potentiating the efficacy of PD‑1/PD‑L1 checkpoint blockade across multiple tumor models. Recently, we identified the atypical chemokine receptor ACKR2 as a hypoxia-inducible, HIF-1α–dependent checkpoint that restricts chemokine availability and limits immune infiltration. Targeting ACKR2 alleviates immune exclusion and synergizes with PD-1 blockades to induce tumor regression in otherwise refractory tumors. Collectively, these studies establish a coherent model in which hypoxia and its downstream stress-response pathways act as master regulators of tumor immune evasion. By rewiring autophagy and chemokine signaling, hypoxia shapes the immune landscape of solid tumors and defines responses to immunotherapy. Targeting these pathways represents a compelling strategy to overcome immune resistance and expand the clinical benefit of checkpoint inhibitor therapies.

Abstract: Counterfactual explanations are increasingly vital for understanding and trusting machine learning models. This study presents, Desirability Rating based Counterfactual (DeRaC), a generalized framework for generating valid counterfactual explanations applicable to multi-dimensional classification problems, including single and multi-output classification with binary and multi-label outputs. By expanding the definition of counterfactual validity through a novel “desirability rating,” the approach addresses limitations in existing methods for complex output spaces. This work details a novel framework, introducing concepts like partially valid counterfactuals and a quantitative measure of output desirability, which can be used with objective functions to find counterfactuals that also satisfy the various existing properties such as similarity, proximity, validity, actionability, etc. Experiments demonstrate the feasibility of systematically generating counterfactuals using existing optimization techniques, achieving varying degrees of validity and similarity. The research emphasizes the context-dependent nature of counterfactuals and lays the foundation for more transparent and trustworthy machine learning systems.

Abstract: As a core ground-based coronal observation facility in the low-latitude and high-altitude regions of China, the Lijiang Coronagraph takes advantage of the natural endowments of the Lijiang Astronomical Observation Station, such as an altitude of 3200 meters and low atmospheric turbulence. It has gone through a complete development process from introduction through China-Japanese cooperation to independent innovation and iteration. This paper systematically summarizes the core technological innovation achievements of this facility, including the upgrade of the automatic operating system, the integration of the dual-band observation system, the stray light suppression technology based on the image difference method before and after cleaning, as well as the high-precision image calibration and registration technology. These innovations have significantly improved observation efficiency and data quality, laying a solid foundation for high-quality observations. At the scientific research level, the observation data reveal that 1.1 solar radius is a highly correlated region between coronal green line brightness and magnetic field intensity. It also confirms a strong correlation between the coronal green line and the SDO/AIA 211 Å extreme ultraviolet band (correlation coefficient: 0.89 - 0.99), which can support the research on early warning of Coronal Mass Ejections (CMEs). These achievements provide key data support for the verification of coronal heating mechanisms and the exploration of the origin of the slow solar wind. The technical experience accumulated by the Lijiang Coronagraph has not only laid a solid foundation for the research and development of China's next-generation large-aperture coronagraphs, but also promoted China's leapfrog development from being a follower to a parallel runner in the international field of low coronal observation, making it an important part of the global coronal observation network.

Abstract:

Brown spot on the leaf sheath is an emerging disease of sweet corn (Zea mays L.) in Sinaloa, Mexico, with an unknown etiology. This study aimed to identify the causal agent of the disease and assess its pathogenicity on commercial sweet corn hybrids. Bacterial strains were isolated from symptomatic leaf sheaths collected from commercial fields. Identification was performed through biochemical profiling (API 50CHB/E), pathogenicity tests on alternative hosts (potato, onion, celery), and molecular analysis (16S rRNA gene sequencing and phylogenetic reconstruction). Pathogenicity and virulence were confirmed by inoculating four sweet corn hybrids in a greenhouse. The strains were Gram-negative rods, identified as Burkholderia gladioli based on biochemical profiles and molecular data (99.8% 16S rRNA similarity; phylogenetic clustering within the B. gladioli clade). In greenhouse trials, the strains induced brown spot lesions on the leaf sheaths of all tested hybrids, replicating field symptoms fulfilling Koch’s postulates. This is the first report of B. gladioli as the causal agent of brown spot on the leaf sheath of sweet corn in Mexico. The pathogen’s broad host range highlights its potential as an emerging threat to horticultural crops in the region.

Abstract: Background. NOTCH receptors play a pivotal role in carcinogenesis. Upon ligand binding, a cascade of proteolytic cleavages mediated by ADAM proteases and the γ‑secretase complex activates the receptor, ultimately releasing the NOTCH intracellular domain (NICD). NICD translocates to the nucleus, where it regulates gene expression. This review mainly aims to evaluate γ‑secretase inhibitors (GSIs) as anticancer agents in preclinical and clinical settings, with a focus on their ability to block tumor progression, target cancer stem cells, and overcome resistance to standard therapies. Methods. A systematic search was conducted in the ISI Web of Science, PubMed, and Scopus databases, following PRISMA guidelines. The review included preclinical in vitro and in vivo studies, as well as clinical trials, investigating GSIs, either as monotherapy or in combination with other treatments, in TNBC, metastatic melanoma, PDAC, gastric cancer, and NSCLC. Exclusion criteria included duplicates, non‑English articles, studies published before 2010, studies on non‑cancer conditions, research unrelated to NOTCH signaling, and studies outside the selected cancer types. 69 articles were included and categorized into the five types of cancer analyzed (20 on NSCLC, 22 on TNBC, 11 on metastatic melanoma, 7 on GC, and 9 on PDAC). Of these, 60 studies correspond to preclinical research in the types of cancer and 9 studies correspond to clinical trials in the types of cancer except for GC (Figure 7). Two independent authors screened and extracted relevant data, with disagreements resolved by the corresponding author. Findings were synthesized qualitatively across cancer types under study. Results. This review summarizes therapeutic advances involving GSIs in cancers driven by oncogenic NOTCH signaling, based on the 69 articles included. Preclinical studies show that GSIs synergize with chemotherapy and radiotherapy, particularly in NSCLC, melanoma, and TNBC, and block EMT, overcome therapeutic resistance, and improve prognosis. Commonly used GSIs include DAPT and RO4929097, which enhance the efficacy of agents such as gemcitabine (PDAC), paclitaxel, osimertinib, erlotinib, and crizotinib (NSCLC), and 5‑FU (gastric cancer, TNBC). Promising strategies include combining GSIs with SAHA, ATRA, CB‑103, and other NOTCH signaling targeting molecules, either alone or with chemo‑ and radiotherapy. Clinical trials with GSIs, however, remain limited. RO4929097 is the most extensively tested GSI in clinical settings. PDAC trials combining GSIs with gemcitabine showed no benefit; melanoma trials yielded modest outcomes; and TNBC trials demonstrated partial responses to GSIs but overall low efficacy and significant adverse events. Discussion and Conclusion. Despite encouraging preclinical evidence, clinical trials with GSIs have underperformed, largely due to tumor heterogeneity, dosing limitations, and the non‑selective nature of γ‑secretase inhibition. Other NOTCH inhibitors such as DLL4 antibodies also resulted in partial responses and secondary effects. Future strategies should prioritize receptor‑specific NOTCH inhibitors, patient stratification based on NOTCH pathway activation, and optimized combination regimens. Emerging approaches include integrating immunotherapy with advanced technologies such as CRISPR, CAR‑T cells, and bispecific antibodies, as well as targeted delivery systems to enhance efficacy and reduce toxicity. Additional research directions include addressing the tumor microenvironment and EMT‑driven resistance, elucidating mechanisms of immune evasion, and inhibiting tumor angiogenesis. Finally, leveraging artificial intelligence and big‑data‑driven personalized medicine, including sex‑specific considerations, will be essential for improving patient outcomes.

Abstract: Background/Objectives: Biventricular volume (BVV) can be measured from non-contrast-enhanced CT images in patients undergoing coronary artery calcium (CAC) scoring. BVV correlates with left ventricular mass and may predict mortality risk in type 2 diabetes mellitus patients. This study examines the relationships among body size, age, and BVV using the Standard Human Index (SHI), which combines height and bodyprint (BP = k*height – body surface area, h-BSA; k=1 for females, 1.1 for males). We hypothesize that this novel indexing method enhances the discrimination of increased BVV in over-weight and obese patients and assesses the relevance of age in interpreting BVV changes. Methods: We analyzed CT data from 2,466 patients (1,606 women, 860 men; mean age 64 ± 11 years) referred for CAC scoring. Fatless BVV was measured semi-automatically, and we compared raw BVV values and BVV normalized for height, body surface area (BSA), and SHI across sex, age, and body mass index (BMI) categories. Results: BVV was signifi-cantly higher in males (414±97 ml) than females (297±66 ml) (p< 0.001). BVV decreased non-linearly with age, stabilizing in older patients. Normal-weight males had higher BVV than females (p< 0.001). Normalization for height, BSA, and SHI indicated that BSA did not effectively distinguish BVV changes in overweight and obese patients. Conclusions: The proposed index effectively diagnosed BVV increases in overweight individuals, while BSA indexing may be misleading. Age dependence of BVV challenges the validity of standards based on younger populations for detecting ventricular enlargement in older adults.

Abstract: Plants exhibit complex internal dynamics in response to environmental conditions, yet whether these dynamics reflect structured affective regimes remains unclear. This study investigates whether internal plant signals encode information about affective states defined relationally by sustained environmental conditions. Valence and arousal were operationalised using temperature, humidity, and residualised light. Using only internal plant measurements—including bioelectrical activity and volatile gas emissions—we evaluated whether machine learning models could decode affective structure without access to environmental variables. Binary classification revealed that valence was reliably decoded over longer temporal windows, whereas arousal required shorter windows, suggesting distinct underlying timescales. Direct multiclass quadrant classification proved unstable, but an Echo State Network capturing temporal dependencies achieved improved performance. These results indicate that plant internal dynamics carry a learnable, temporally extended signature of environmentally defined affective regimes, supporting an interpretation of plant affect as embodied environmental engagement.

Abstract: Strategic decision-making (SDM) has traditionally been viewed as a human activity based on judgment, experience, and negotiation among senior managers. These decisions are limited by attention constraints, incomplete information, and bounded rationality. Today, many firms embed artificial intelligence (AI) and algorithmic decision-making systems into strategic processes. In some cases, algorithms do more than support managers. They filter options, rank priorities, and strongly shape final decisions. This article asks when SDM remains meaningfully human and when it becomes effectively algorithmic in algorithmically mediated enterprises. The study uses a theory-building integrative review of 62 contributions from strategy, information systems, behavioural research, and governance. It compares human and algorithmic decision-making across five dimensions: interpretive authority, search structure, time orientation, accountability, and scalability. Based on this analysis, it develops a framework of human–AI decision structures. The framework identifies three main forms: human-dominant, sequential hybrid (AI-to-human or human-to-AI), and aggregated human–AI governance structures. Each form affects not only decision accuracy but also power, learning, agency, and accountability. The key challenge is not to defend purely human strategy. It is to design governance systems where decision rights, oversight, and contestability remain strong when algorithms act as active decision participants.

Abstract:

Uropathogenic Escherichia coli (UPEC) is a leading cause of urinary tract infections (UTIs) in companion animals. This study characterized 42 UPEC isolates recovered from dogs and cats at the University of Florida, College of Veterinary Medicine Diagnostic Laboratories between 2023 and 2024, focusing on antimicrobial resistance (AMR), virulence gene profiles, biofilm-forming ability, and phylogroup distribution of the isolates. Antimicrobial susceptibility testing (AST) showed that 40.48% of the isolates were resistant to at least one of the tested antibiotics, and 9.52% exhibited multidrug resistance (MDR). Phylogroup B2 was predominant (69.05%), and 61.90% of isolates demonstrated strong biofilm formation in artificial human urine. Virulence gene analysis revealed the presence of genes mediating adhesion (fim, pap, sfa), iron acquisition (fyuA, iro), biofilm formation (csg, bcs, pga, ycg/ymg), motility (fli, mot, flh), and stress response (oxyR, soxR/S, kat). Multiple plasmids carrying AMR and virulence determinants were also identified. The co-occurrence of the traits underscores the potential for persistent and recurrent infections, which can complicate therapeutic outcomes and facilitate horizontal gene transfer (HGT). The detection of antimicrobial-resistant, highly virulent UPEC strains possessing human UPEC traits in companion animals suggests the risk of zoonotic and reverse-zoonotic transmission, particularly in households with close pet–owner interactions. These findings emphasize the importance of judicious antimicrobial use, routine molecular surveillance, and integrated One Health strategies to mitigate the veterinary and public health threats associated with UPEC infections in companion animals.

Abstract: We show that Quantum Mechanical Hilbert space can be paradoxical under some group action and explore its physical consequences. (1) Is there a more natural way of resolving the paradox of Wigner’s friend without invoking the Heisenberg’s cut?; (2) We notice the qualitative similarities between the paradox and paradoxical sets and use it as a motivation to rigorously prove that the Hilbert space H of the harmonic oscillator is paradoxical under the group action induced by SO(2, 1); (3) This paradoxical nature of the Hilbert space H provides the natural resolution for the paradox by using the Axiom of Choice instead of the Heisenberg’s cut; (4) Finally, we show that due to the very same paradoxical nature of H, certain class of quantum gravities naturally emerge from Quantum Mechanics that mediates a self-decoherence of the system.

Abstract: The valorization of agricultural by-products as functional feed additives represents a promising strategy for sustainable aquaculture. This study evaluated the effects of dietary fermented longan peel (FLP), produced through enzymatic hydrolysis and Lactiplanti-bacillus plantarum fermentation, on growth performance, digestive physiology, gut morphology, innate immunity, and gene expression in Nile tilapia (Oreochromis niloticus) cultured under a biofloc system. Five experimental diets were formulated with graded FLP levels (0, 5, 10, 20, and 40 g kg-1) and fed to fish for eight weeks. Growth indices, including final weight, weight gain, and specific growth rate, improved significantly in fish receiving 20 g kg-1 FLP, following a strong quadratic response pattern. In vitro di-gestibility assays showed enhanced carbohydrate and protein digestibility, coinciding with increased intestinal amylase and protease activities. Histological analysis indicated that moderate FLP inclusion (10–20 g kg-1) promoted villus height, crypt depth, and epithelial organization. Innate immune parameters, including lysozyme, peroxidase, and alternative complement activity, were markedly elevated in serum and mucus, partic-ularly at 20–40 g kg-1 after eight weeks. Gene expression profiling revealed significant up-regulation of growth-related (IGF-1, GH, NPY-α, Galanin), immune-related (TLR-7, TNF-α, NFκB), and antioxidant-related (hsp70, Keap-1, nrf-2, GST-α) genes in fish fed higher FLP levels, with responses plateauing beyond 20 g kg-1. Overall, FLP supple-mentation at 20 g kg-1 optimally enhanced growth, digestive efficiency, intestinal health, and innate immune status. These findings demonstrate the potential of fermented longan peel as a cost-effective, bioactive, and sustainable functional feed ingredient for tilapia and other warm-water aquaculture species.

Abstract: (1) Background: Flavored craft beer is favored for its diverse and distinctive aroma compounds; however, traditional fermentation processes are often plagued by poor yeast flocculation, which leads to substantial beer losses and compromised production efficiency. Yeast immobilization technology has emerged as a promising strategy to improve fermentation performance, shorten the primary fermentation period, and mitigate beer loss. (2) Methods: In this study, a natural material–based carrier was developed for the immobilization of yeast, and its application in mango craft beer fermentation was systematically investigated. The optimal fermentation conditions were screened, and the physicochemical properties, nutritional composition, and volatile flavor profiles of the resulting mango craft beer were comprehensively evaluated. (3) Results: The results showed that the maximum mass gain of yeast after immobilization on the natural carrier reached 13.3%. Compared with free yeast, the immobilized yeast exhibited 1.58-fold higher average extract consumption rate and 1.39-fold higher alcohol production rate, while the primary fermentation period was shortened by approximately 33%. Under the optimized fermentation conditions, the mango craft beer achieved a sensory score of 81 points, with a β-carotene retention rate of 91.25%. Furthermore, the mango craft beer exhibited a more diverse profile of volatile flavor compounds and enhanced nutritional composition compared with the control. (4) Conclusions: Overall, the natural immobilization carrier developed in this study effectively enhanced yeast fermentation efficiency, shortened the primary fermentation cycle. These findings demonstrate its significant potential for cost reduction and efficiency enhancement in the production of flavored craft beer, providing a practical technical support for the industrial application of natural carrier-based yeast immobilization technology.

Abstract: Diabetic kidney disease (DKD) is one of the most serious complications of diabetes and the leading cause of end-stage renal disease worldwide. Recently, renin-angiotensin system inhibitors, non-steroidal mineralocorticoid receptor antagonists, sodium-glucose cotransporter 2 inhibitors, and glucagon-like peptide-1 receptor agonists have been proposed as the four pillars for treating DKD. To understand the molecular mechanisms by which these drugs improve DKD, we described the histological and molecular changes due to diabetes. Based on our understanding of the molecular changes in DKD, we present evidence on the efficacy of these drugs in improving DKD and discuss why such drugs improve the prognosis of DKD. In addition to diabetes and hypertension, insulin resistance, dyslipidemia and hyperuricemia are risk factors for DKD. Metformin, fibrates, and febuxostat have been reported to improve DKD, however, caution is required when administering these drugs to patients with renal impairment due to concerns about the onset of lactic acidosis, rhabdomyolysis, and deterioration of renal function, respectively. Imeglimin, pemafibrate, and dotinurad have similar chemical structures or effects to metformin, fibrates, and febuxostat, respectively, but are safer in patients with renal impairment. Furthermore, they have specific mechanisms to improve DKD and may offer new options for its treatment.

Abstract: This research examines the relationship between energy generation, electricity produc-11 tion, energy consumption, and economic welfare across countries classified by income 12 level: high-income, upper-middle-income, lower-middle-income, and low-income. It uses 13 annual panel data from 2000 to 2023 sourced from the World Bank’s World Development 14 Indicators (WDI). Based on the World Bank’s income classifications and data availability 15 during the study period, 152 countries were selected, including 49 high-income, 35 upper-16 middle-income, 43 lower-middle-income, and 26 low-income economies. Pedroni cointe-17 gration tests indicate a long-term equilibrium relationship among energy generation, elec-18 tricity production, energy consumption, and economic welfare across all income groups, 19 with Kao cointegration tests confirming these results as robustness checks. The study uti-20 lizes panel dynamic differenced and system Generalized Method of Moments (GMM) to 21 estimate the model. Results reveal a significant positive long-term relationship among the 22 main energy and welfare variables across all income categories. However, when broken 23 down by income class, high-income and upper-middle-income countries show positive 24 associations between energy metrics and economic welfare. In contrast, lower-middle-in-25 come and low-income nations exhibit negative associations. The study recommends poli-26 cies focused on improving living standards and overall economic welfare, especially 27 through providing consistent, affordable, and clean energy.

Abstract: Matrix mechanics describes the spectral emissions of hydrogen gas, but quantum mechanics concerns the emission and absorption of radiation by a single atom. To obtain the spectral emissions of a single hydrogen atom we introduce thermal energy in infinitesimal increments registering the spectral lines one by one until a complete spectrum is obtained. We describe the gradual increase of the internal quantum mechanical energy in a non-conservative system by integrating between two specific endpoints, the atomic orbitals, and two specific times, the period τ, which are the conditions necessary for Hamilton’s principle. Wave mechanics is shown to be limited to conservative systems. A complete quantum mechanics is formulated using energy-time conjugate variables as the time integral of a Lagrangian independently of Hamiltonian formulations that use position-momentum conjugate variables.

Abstract: Childhood obesity is a globally pervasive condition driven not only by lifestyle and environmental factors but also by complex molecular mechanisms. These mechanisms involve genetic predisposition, epigenetic regulation, hormonal signaling pathways, adipose tissue biology, immuno-inflammatory responses, and interactions with environmental endocrine disruptors. This review synthesizes current evidence from scientific articles, systematic reviews, and mechanistic studies to elucidate how molecular processes shape the development, onset, and progression of obesity in children. Understanding these pathways is critical for developing precise prevention strategies and targeted interventions.