We introduce a kinematic framework for cosmology based on the Momentum-First principles, proposing that the observable universe is defined by a static spatial manifold populated by a vacuum state undergoing thermodynamic relaxation. Instead of metric expansion, we identify the core driver of cosmic evolution as Universal Mobility Decay—a monotonic decline in the vacuum's "Translation Yield," or kinematic capacity. We introduce the Mobility-Coupled Directional Momentum as the governing equation for cosmic kinematics, where the vacuum's ability to support momentum dipoles decays over time. This mechanism forces an adiabatic relaxation of longitudinal momentum, reproducing the cosmological redshift relation. We derive the "Sectoral Co-Scaling Laws," proving that fundamental gauge and gravitational couplings co-evolve with the vacuum to maintain the stability of local bound states (atoms and planetary orbits), effectively screening them from the cosmic decay. Furthermore, we demonstrate that the decay of the kinematic yield alters the density of states in frequency space, exactly compensating for the static volume to preserve the Planckian spectrum of the Cosmic Microwave Background. Finally, we show that this framework resolves the Horizon and Tolman Surface Brightness problems without inflation or expanding metrics.

The Momentum-First (M-First) framework, posited axiomatically, has shown promise in resolving several physical anomalies. This paper demonstrates that the central tenets of M-First are not axioms, but are derivable consequences of the Banks–Fischler–Shenker–Susskind (BFSS) matrix model of M-Theory in the large-$N$ limit. We first show that conservation of the 16 supercharges in scattering processes uniquely determines the M-First kinematic law of Absolute Directional Momentum Conservation. We then show that preserving the full supersymmetry of the D0-brane effective theory mandates that gravity enters as a quadratic modification to a particle's on-shell condition. This leads to a modified mass-shell relation, $\hat{M}_g^2 = \hat{M}^2 + \Phi_g$, where $\hat{M}$ is the free-particle core momentum operator, $\hat{M}_g$ is its gravitationally-influenced counterpart, and $\Phi_g$ represents the gravitational modifier. By grounding its principles in M-Theory, this work elevates the M-First framework from a phenomenological model to to a derivable and falsifiable theory.

Stroke recurrence and cancer continue to pose significant challenges to global healthcare systems, contributing to high rates of morbidity, mortality, and financial burden. The emergence of COVID-19 has further complicated stroke prevention efforts, with studies highlighting an elevated risk of stroke following infection, attributed to increased inflammation and coagulation abnormalities. These insights reinforce the necessity for advanced, evidencebased strategies to enhance stroke prevention and management, ensuring better patient outcomes and healthcare resilience. Effective management of blood pressure and lipid levels, particularly the reduction of low-density lipoprotein cholesterol (LDL-C), is widely recognized as a cornerstone of stroke prevention. While lifestyle interventions, including dietary modifications and weight management, are frequently recommended to lower both blood pressure and LDL-C, these approaches require time and may present challenges, such as the rebound effect of LDL-C following statin discontinuation. A crucial question remains: at what LDL-C level can statin therapy be safely discontinued while maintaining a post-discontinuation LDL-C level of approximately 1.8 mmol/L (70 mg/dL)? Current guidelines suggest that targeting an LDL-C level of approximately 1.8 mmol/L is optimal for individuals with heart disease or those at high risk, as this threshold is associated with a lower likelihood of stroke recurrence. Identifying strategies to maintain this level post-statin therapy is essential for long-term cardiovascular and cerebrovascular health. While statins remain highly effective in managing LDL-C levels, prolonged use has raised concerns about potential associations with an increased risk of cancer. To address these challenges, this study introduces the Peter Chew LDL Rebound Formula, a novel, personalized approach to optimizing LDL-C regulation post-statin therapy. The formula integrates predictive modeling to tailor LDL-C management following statin discontinuation, coupled with lifestyle interventions such as dietary modifications and weight management. This combined strategy aims to minimize long-term statin dependency while sustaining optimal LDL-C levels. A unique case study, based on the author's personal experience, documents recovery from a mild stroke in July 2024. Within two months, LDL-C levels dropped from 3.1 mmol/L to 0.7 mmol/L, with predictive modeling estimating a post-statin discontinuation LDL-C level of 1.8 mmol/L. Subsequent blood tests conducted six months after statin cessation confirmed this prediction, reinforcing the real-world efficacy of the proposed formula. This research explores the broader implications of the Peter Chew LDL Rebound Formula in stroke recurrence prevention, cancer risk mitigation associated with extended statin use, and reducing global healthcare costs. By leveraging predictive analytics and lifestyle adjustments, this approach offers a data-driven, individualized strategy for long-term cardiovascular health.

The Momentum-First (M-First) framework posits that gravity acts as a kinematic modifier, leading to environment-dependent predictions for fundamental couplings. In this Letter, we derive from first principles the M-First prediction for the time-variation of the fine-structure constant, α, within a dense baryonic medium. The theory predicts that the fundamental gravitational influence is strongly suppressed by a parameter-free screening factor S = (me/mN) 2 , which arises from the partitioning of kinematic inertia in a collective system. The resulting prediction for the Oklo natural reactor environment is in excellent agreement with the stringent observational null result, providing a powerful, non-trivial confirmation of the framework.

The Sonerila gigantea is a new acaulescent species found in Mallalli Falls, Kodagu District, Karnataka, India, part of the Western Ghats. This species has distinguishable characteristics like habit from the genus Sonerila worldwide. S. gigantea is compared with Sonerila wallichii Benn. & Sonerila grandis Ridl. The species shown differ in several characteristics like habit, leaf and inflorescence, described in a table form with taxonomic keys and even explained micro morphology characters like pollen and seed. This report deliberates on the new species Sonerila gigantea from the Western Ghats, India.

Rheumatoid arthritis (RA) is a systemic autoimmune and chronic arthropathic disease in which environmental, genetic, and immune factors play a crucial role. The familial history plus the wide range of genetic polymorphisms in affected individuals indicate a hereditary genetic predisposition. Based on the regulatory T cells’ (Treg) function in controlling immunological responses, it has been hypothesized that the genetic polymorphisms in Treg-associated genes might be linked to the loss of immune tolerance in Tregs. Therefore, revealing how missense variations in candidate genes affect disease progression will facilitate the understanding of disease pathophysiology and contribute significantly to the design of superior therapies for RA. In this study, we highlight the genetic polymorphisms that influence Tregs in their generation, function, signaling, and stability, due to the apparent effect of Tregs dysfunction on the RA pathogenesis and other autoimmune diseases. Knowing some general concepts, we now turn our attention specifically to describing the identified polymorphisms in many different ligand-receptor interactions and developmental pathways of Treg cells.

This paper examines the critical exponents for the existence of global solutions to the equation u t − ∆ H u = ∫ 0 t ( t − s ) − γ | u ( s ) | p − 1 u ( s ) ds , 0 ≤ γ < 1 , η ∈ H n , t > 0 , on the Heisenberg groups H n . There exists a critical exponent p c = max { 1 γ , n + 2 n + γ } ∈ ( 0 , + ∞ ] , such that for all 1 < p ≤ p c , no global solution exists regardless of the non-negative initial data, while for p > p c , a global positive solution exists if the initial data is sufficiently small. The results obtained are a natural extension of the results of Cazenave et al. [Nonlinear Analysis 68 (2008), 862-874], where similar studies were carried out in R n . Furthermore, several theorems are presented that provide lifespan estimates for local solutions under various initial data conditions. The proofs of the main results are based on test function methods and Banach fixed point principle.

Abstract Zero-day attacks pose a significant threat to computer systems and networks as they exploit weaknesses that have not been recognized by security professionals or software creators and for which there are no existing protective measures. This study introduced an innovative method for identifying zero-day attacks through a recurrent neural network model. To effectively mitigate these risks, not only is continuous monitoring essential, but also the implementation of machine learning. The model was trained on network traffic data and leveraged on the ability of RNNs to learn complex patterns and identify anomalies that may indicate the presence of a zero-day attack. To enhance the model’s ability to generalize and accurately identify novel attack vectors that traditional methods may overlook, we trained it on a dataset that includes both known and synthetically generated zero-day attack signatures. Our methodology involves preprocessing network traffic data to extract relevant features, which are then fed into an RNN architecture. The RNN model was trained on a labeled dataset containing both normal and attack traffic samples, allowing it to learn the fundamental patterns of benign and malicious network activity. The recurrent architecture of the model enables it to retain a memory of previous observations, making it well-suited for identifying deviations from the expected network behavior in real-time. By leveraging the temporal dimension of network traffic data, we enhanced the ability to identify novel threats and bolster the security of computer systems and networks. This approach held the promise for mitigating the ever-evolving landscape of cyber threats and ensuring the integrity and availability of critical digital infrastructure.   KEYWORDS machine learning, Recurrent Neural Networks, Zero-day Attack, Anomalies

Course selection for higher learning institutions is crucial yet challenging, often leading to a mismatch between what learners have chosen and their interests and abilities. This project aims to use historical data to create a predictive model to assist high school students in Kenya in selecting the right courses for high-level education at the university. This study assumes that students have different intelligence capabilities ([Howard Gardner Theory](https://www.verywellmind.com/gardners-theory-of-multiple-intelligences-2795161)) and have taken part in subject assessments before. Therefore, course selection will be affected by their intelligence level and subject assessment scores. After reviewing the existing systems, the researcher realized that: The systems have limited specializations/ study paths. The studies have represented the models as black boxes (there is no clear understanding to humans). Using a wide variety of specializations, explainable AI (XAI) techniques (LIME and SHAP) are employed for better transparency of the best-performing model, in this research study. It is noted that all the features have an impact, and that means learners should focus on improving their intelligence and performance in particular subjects. While modifying the input values, a shift in the model’s confidence in predicting a specific course is observed.

As an important component of landscape design, the impact of planted landscape space on human health has received considerable attention in modern urban landscape gardening research. However, most existing studies focus on the overall benefits of urban green space and lack quantitative analyses of planted landscape spatial features (e.g., spatial scale, slope, and plant community structure level) in relation to psychological recovery and physiological regulation. This study aimed to investigate the effects of different scale groups, slope groups, and plant composition groups on stress reduction and attention recovery. Thirteen spatial models of planted landscape space were constructed using virtual reality technology, covering five scale groups, five slope groups, and three plant composition groups. Fifty-nine subjects were randomly assigned to three experimental groups, and their stress, attention, and subjective preferences were assessed. The results showed that: the largest scale space (12,000 m 2) was the most effective for stress reduction; the smallest scale (400 m 2) was more beneficial for attention restoration; and the moderate scale (6,000 m 2) received the highest subjective preference. Gently sloped space (5%) demonstrated the best performance across stress reduction, attention restoration, and subjective preference. Mid-level planting was most significant for attention restoration and preference, while lower-level planting was more conducive to stress regulation. This study provides a quantitative basis for the spatial design of planted landscape space, elucidates the mechanisms through which different spatial features influence psychological and physiological health, and offers theoretical support for urban green space planning.

Within the same species of eusocial insects, individuals of different castes typically display widely different life-history traits: sterile workers live for a few months, while queens can live for decades. Aging theories emphasise the importance of metabolism and oxidative stress in explaining longevity, with mitochondrial bioenergetics standing at the crossroads of energy and reactive oxygen species production. The study of mitochondrial functioning is therefore of great relevance in determining the nature of the mechanisms that explain the contrasting longevities between insect social castes. We addressed this question in the eusocial black garden ant Lasius niger. Our findings reveal that caste differences in mitochondrial bioenergetics and the oxidative balance only partially align with oxidative stress theory predictions. Long-lived queens display lower metabolic rates and mitochondrial density, yet maintaining higher cellular energy availability, as reflected by a high adenylate energy charge (AEC). This may result from enhanced mitochondrial maintenance processes and specific recourse to the purine salvage pathway, promoting ATP availability while limiting oxidative cost. This perspective highlights the unique bioenergetic adaptations that may be behind the queens’ remarkable lifespan.

Rhizosphere engineering offers a promising strategy to improve crop productivity and soil health by optimizing plant-microbe interactions through targeted modulation of rhizosphere functioning. A key step in this approach is the effective recruitment and functional activation of inoculated plant growth-promoting rhizobacteria (PGPR), mainly driven by root exudate-mediated signaling. This study investigates the response of five phylogenetically diversified PGPR strains viz., Azotobacter chroococcum (Ac1), Azospirillum lipoferum (Az204), Pseudomonas chlororaphis (ZSB15), Bacillus altitudinis (FD48), and Pristia endophytica (NE14) to root exudates derived from three different rice cultivars (BPT5204, Co51, and Co55) at two developmental stages (45 and 75 days after sowing). Functional traits including growth, chemotaxis, biofilm formation, and cell wall degrading enzyme activity of rhizobacteria were assessed. The results revealed strain- and cultivar-specific modulation of these traits, with NE14 and FD48 showing significant upregulation of all the assessed traits in response to exudates from BPT5204 and Co51. Gas chromatography-mass spectrometry profiling of root exudates confirmed compositional differences between cultivars and developmental stages, highlighting key metabolite signals likely involved in regulating these bacterial functional traits. Principal component and correlation analyses identified cell wall-degrading enzymes and chemotaxis as major contributors to strain variability, underscoring their primary role in establishing rhizosphere competence. These findings strengthen the importance of functional trait-based screening for identifying PGPR strains with high adaptability to the rhizosphere environment. By demonstrating that root exudate-mediated modulation of PGPR traits can enhance bacterial colonization and functionality, this study offers a conceptual foundation and experimental framework for PGPR-mediated rhizosphere engineering.

Mud volcanoes are dynamic geological formations that emit mixtures of gases, fluids, and sediments from deep underground. These unique ecosystems, often characterized by extreme physicochemical conditions such as high salinity, temperature fluctuations, and low oxygen levels, harbor a rich diversity of specialized microbial communities. Among them, methanobacteria --- a group of strictly anaerobic archaea --- play a pivotal role in the carbon cycle through methanogenesis, the biological production of methane. Methanobacteria thrive in anoxic niches within mud volcanoes by utilizing hydrogen and carbon dioxide as substrates, a metabolic adaptation well-suited to the harsh and energy-limited environments of subsurface systems. The microbial diversity in mud volcanoes reflects a complex interplay between geochemical gradients and microbial evolution. Methanobacteria demonstrate remarkable physiological adaptations, such as unique membrane lipids, specialized enzymes, and efficient energy conservation strategies that allow them to survive and function in environments toxic to most life forms. These adaptations not only enable their persistence in extreme habitats but also highlight their evolutionary significance. Studying methanobacteria in mud volcanoes provides critical insights into microbial resilience, the origin of life in extreme environments, and the potential for life on other planets. Furthermore, understanding methanogenesis in such natural settings contributes to global climate research, as biogenic methane is a potent greenhouse gas. This review underscores the ecological and biogeochemical importance of methanobacteria, offering a valuable model for exploring microbial life under extreme conditions and its influence on Earth's methane budget.

The prevalence of technology use in the education system has increased the need for students to be constantly updated. The phone, as a portable tool, offers numerous advantages, but also increases the risk of addiction due to algorithmic developments, affecting the quality of life and stress level. This study aims to identify the level of stress and the need to disconnect from the phone during study time among students of the 'Luigj Gurakuqi' University. The findings in this study show that 40% of students use the phone for more than 5 hours a day. Out of 2645 Bachelor level students, 11.57% (n=306) participated in the study and the average stress score, according to the SSI (Student Stress Inventory), resulted in 119.83 points, which indicates a moderate level of stress, close to the border for severe stress (122-160). 65.35% of students reported that reducing phone use time can improve the quality of daily life, while 45.1% admitted the difficulty of disconnecting from the phone. This study can serve as a basis for other studies on the same topic and contribute to raising student awareness, as well as developing alternative ways to address the impact of phone use on mental health.

Admir DahatiHow can you be yourself, living in a world that tends to teach you how to live? Antiquity marks the beginnings of philosophy, with the expression hardened over the centuries: “Man, know thyself”, and to this day literature suggests the continuity of this tableau. The recognition of life as a sacred gift to be lived is the main stimulus that the human being must inject throughout the stages of development. However, the pressure of the “social air” has infected the influence of personal truthfulness with prophecies created by society itself for the benefit of itself. If we base ourselves on the historical context, it is suggested that the change of the self is a consequence of evolution, taking into account the social need to show strength and power for survival. The constant wars for territories, for royal chairs with red cloth, the sick ambition to have the reins of everything in hand has forced the transitions from originality to temperament induced by intense neurotic needs. In order not to rely on subjective orientations and conclusions, reasoning carried over from the early collective unconscious, we can rely on theories approved by scientific leaders at the time, discussing them according to our era: the era of social media.

Multidisciplinary Occlusal Rehabilitation of Severe Dental Wear: A Fully Digital-Guided Interdisciplinary Approach

This study examines the antitumor properties of HM in NSCLC and elucidates its molecular mechanisms. The experimental findings indicate that HM markedly suppresses the proliferation and migration of A549 and H1299 cells, induces cell cycle arrest, and promotes apoptosis in a dose-dependent manner. Subsequent investigations reveal that HM upregulates ferroptosis marker genes (PTGS2, ACSL4, LPCAT3) while downregulating antioxidant genes (GPX4, SLC7A11, NFE2L2), resulting in intracellular iron accumulation and lipid peroxidation, thereby triggering ferroptosis. Through bioinformatics analysis and molecular dynamics simulations, PDE4D was identified as a critical target of HM. Further validation confirmed that HM binds to PDE4D and inhibits the PI3K-AKT-Nrf2 signaling pathway, leading to the suppression of GPX4 and SLC7A11 expression. Rescue experiments demonstrated that PDE4D overexpression counteracted HM’s inhibitory effects on cell proliferation and migration, as well as its induction of apoptosis and ferroptosis. Subcutaneous xenograft models in nude mice corroborated that HM impedes tumor growth by targeting PDE4D, with no significant hepatorenal toxicity observed. This study elucidates the molecular mechanism by which HM induces ferroptosis via the PDE4D-PI3K-AKT-Nrf2 signaling axis, offering experimental evidence and identifying a potential therapeutic target for the treatment of NSCLC.

Fine-grained object recognition remains a challenging task due to the subtle visual differences between similar categories. This paper proposes a novel approach to address this challenge by enhancing both feature representation and object localization. Our method introduces a Multi-Classification Module (MCM) and a weakly supervised Multi-Segmentation Module (MSM). The MCM refines feature representations by training each sub-network within the backbone as an independent classifier. The MSM generates object masks from feature maps using a U-Net architecture, providing valuable localization information. These modules can be seamlessly integrated into various backbone networks. Extensive experiments on several benchmark datasets, including CUB, Stanford Cars, and FGVC-Aircraft, demonstrate the superior performance of our method. We also conducted experiments on surface defect datasets including Ball Screw and NEU-DET, to showcase the potential of our approach in machine vision applications.

The treatment of Ewing’s sarcoma in children remains difficult and unpredictable. Especially in the group with lung metastases (PM), the role of metastectomy has not been still clearly defined. The study aimed to define the best management strategies in children with PM. Clinical factors (age, localization, response to chemotherapy (CHT) of the primary tumor (PT), R0 and R1 primary resections, metastectomy, the use of radiotherapy (RT), autologous hematopoietic stem cell transplantation (aHSCT) were analyzed in the group of 137 patients (pts) with PM (85-isolated, 52-lungs and extrapulmonary (ExPM)) treated during the period 1998-2024. The mean f-up was 103.8 months (8.19 years). Descriptive statistics were used to summarize the characteristics of a data set. Survival curves were obtained by using the Kaplan-Meier method. Log-rank test was used to identify the prognostic significance of clinical factors. Pts with isolated PM (85 pts) had better OS than pts with PM and ExPM (p=0.000003). The good histological response of the PT (<10% viable tumor cells) to CHT was only the EFS-predictive factor in this group (p = 0.002). Pts with isolated PM and good histological response treated or not with metastectomy had better EFS than pts with poor response to CHT (p = 0.001). Pts treated with thoracotomy had significantly better OS (p = 0.001) than pts treated with RT of metastases. Good response to CHT of the PT had the same impact on OS (p = 0.001) in this group. A similar trend was observed in the groups with good response to CHT treated with thoracotomy and RT of metastases vs RT only (OS – p = 0.02). Pts with poor response to CHT and isolated PM treated with RT without thoracotomy had the worst prognosis (p = 0.02). In the group of 52 pts with PM and ExPM RT of PM and extrapulmonary metastases (p = 0.04) was EFS-predictive factors. Conclusions Metastectomy of isolated PM may improve the results of patients with good histological response to CHT of the PT. Patients with simultaneous pulmonary and ExPM achieve worse treatment results. The use of RT on PM and extrapulmonary metastatic lesions seems to improve outcomes.

Background The European Society for Blood and Marrow Transplantation (EBMT) recommends a busulfan target cumulative area under the curve (cAUC) of 78-101 mg·h/L for hematopoietic stem cell transplantation (HSCT). Currently, no population pharmacokinetic (PopPK)-optimized four-times-daily (Q6H) regimen reliably achieves this range. We developed a PopPK-guided dosing protocol to address this gap. Methods Clinical and demographic data from pediatric HSCT recipients receiving busulfan were retrospectively analyzed to develop a PopPK model. Bayesian estimation identified the optimal busulfan dose (0.50-1.25 mg/kg/dose) for target attainment in pediatric patients (age 0.5-18 years; weight 5-80 kg). Finally, the proposed dosing strategy was validated using an independent retrospective cohort. Results The PopPK model was developed using data from 65 pediatric patients. Age and body weight identified as significant covariates. Optimized dosing recommendations for the Q6H × 4 days busulfan regimen were established through Bayesian estimation. For validation, two cohorts were retrospectively analyzed: (1) weight-based dosing (n=19) and (2) model-informed dosing (n=15). The groups demonstrated comparable distributions in both age ( p=0.78) and weight ( p=0.63). Notably, the model-informed group achieved significantly higher cAUC values (mean difference, 18.0 mg·h/L; 95% confidence interval , 9.83-26.1 mg·h/L; p < .001), with 67% of these patients reaching the target exposure range. Conclusion We developed a Q6H × 4 days busulfan dosing regimen through PopPK modeling. For a representative 6-year-old patient with a body weight of 20 kg, the recommended dose is 1.25 mg/kg/dose. Therapeutic drug monitoring following the initial dose remains clinically essential.

Species Area Relationships (SARs) are one of the most well-established conservation biogeography patterns, and in rivers habitat area is mediated by discharge. Species richness and river discharge have a well-established positive relationship, but how discharge affects trophic diversity is less clear. Free-flowing tropical river ecosystems are hotspots of global biodiversity, but they are under increasing threat from water resource developments which alter river discharge regimes. Here we investigate relationships between river discharge metrics and the species richness of freshwater fish trophic guilds in tropical rivers of northern Australia, using data collated from 40 catchments. We analysed relationships between the species richness of freshwater fish trophic guilds and discharge metrics including mean annual discharge (Q), mean daily dry and wet season discharge and the coefficient of variation (CVQ) of Q. Invertivores and omnivores were the most species rich trophic guilds. Our results show that the species richness of trophic guilds in north Australian freshwater fishes was correlated with multiple components of wet-dry tropical river discharge regimes. The species richness of predators, invertivores and herbivore-detritivores increased with Q and wet season discharge, whereas omnivore and invertivore richness increased with dry season discharge. Increasing variability in discharge had a negative effect on the species richness of invertivores and omnivores suggesting adverse effects of low discharge periods. We found no statistical support for the hypothesis that the slope of SARs increases with trophic level as predicted by the Trophic Island Biogeography Theory. These findings suggest that decreases in wet and dry season discharge, or increases in flow variability due to water resource development or climate change, may result in the loss of trophic diversity from tropical rivers. Our results suggest that the conservation of both wet and dry season natural flow regimes in tropical rivers will be needed to protect freshwater fish trophic diversity.

Aim Floral and nesting resources are two major components of habitat specialization that drive patterns of bee distribution. However, nesting resources are largely understudied in comparison to floral resources. For nesting, sand preference is seen as a community-wide trait for bee species and numerous species are considered sand specialists. With little empirical evidence to date, we aimed to test how soil sand content and floral resource availability interact to affect patterns of bee distribution. We also designed the study to evaluate whether sand specialist bees predominantly occur in sandy habitats. Location Midwestern prairies in Minnesota, USA. Methods We set up a controlled field experiment across three classes of soil sand content (N = 28 plots) using a focal prairie plant species (Dalea purpurea) that attracts a wide range of bee species and can grow in different soil types. Results We identified four key results: 1) soil sand content, not floral resource availability, affected patterns of bee distribution; 2) contrary to expected, sandier sites did not host the highest diversity of bee species; 3) there was clear evidence of sand specialization for select bee species, but sand specialists were associated with both moderate and high amounts of sand rather than extremely sandy areas; and 4) the proportion of abundance of sand specialists increased as the soil sand content within discrete pockets increased, even in areas with average values of low sand content. Main Conclusion Our findings highlight that soil properties, specifically soil sand content, can be more important than floral resources in driving patterns of bee distribution, specifically sand specialists. Therefore, not factoring soil properties into bee conservation and restoration decisions may limit our effectiveness and ability to support habitat specialists and rarer bee communities. Sandy areas and places with high soil heterogeneity should be prioritized for conservation.

The k 2 -tree compact data structure has gained great popularity to represent binary relationships in main memory. It presents a good performance and a good trade-off between storage and execution time. However, datasets being too large, or limited resources, may prevent the dataset from fitting into RAM even in compressed form. This work presents an experimental evaluation of a k 2 -tree in external memory (disk), in terms data access (I/O operation or cache misses) and execution time for 4 types of common queries. We compare the k 2 -tree with other data structures, namely a Quadtree (specifically, a Linear QuadTree, LQT) and the classical adjacency matrix, all of them being in external memory. We used for the test both synthetical as well as large, real world, datasets. Several aspects, such as the size of the memory buffer and its replacement scheme, or specific parameters like the arity ( k value) of the k 2 -tree were considered in the experiments. In terms of storage needs, the k 2 -tree clearly outperforms the other alternatives, in extreme cases needing only 1% of the LQT space, or 0.01% of the adjacency matrix (and for some large datasets neither the LQT nor the adjacency matrix could be built). In terms of performance, except for cases with small datasets, where the adjacency matrix is the best option for some queries, the k 2 -tree is either competitive or outperforms the alternatives.

Quicksort’s elegant, 1960, bare bone version is extended to address the diversity of settings in which it is applied. We show that a best version for referenced types has worse performance than a best version for native types, and the other way around. Hence two versions developed around 1992 that can handle arrays with referenced as well as with native type elements are suboptimal. Different design choices are required. Using more than one pivot has been prototyped without follow up. We present several 3-pivot versions. We show also the results of parallel versions. The performance of these versions relies on novel designs: guaranteeing O(NlogN) worst case complexity with the Musser-defense minimizing recursive invocations or the number of comparisons, a novel technique to obtain high quality pivots that also supports linear complexity for constant array segments, using two gap array layouts in addition to single gap layouts, using an alternative to pairwise swapping for moving elements around, using more hybridization for members with specialized functionality, and using more layers than the traditional two.