This study examines the cinema-going habits and motivations of university students in Türkiye from a class-based perspective. This study addresses a gap in the literature by examining cinema-going motivations in Türkiye through a class-based perspective. Participants (N = 1183) were recruited using a convenience sampling method from 12 universities across the NUTS 1 regions of Türkiye. This quantitative study employed three measurement tools to assess participants' sociodemographic and socioeconomic characteristics, cinema-going habits, and motivations. The findings suggest that socialization was the most prominent factor associated with cinema-going motivation among participants. Cinema was not widely perceived by participants as a means of coping with loneliness, but rather as an activity associated with social interaction. The technical capabilities of cinema also emerged as an important factor associated with cinema attendance. While individuals reported different motivations, class-related differences were associated with variations in cinema-going behaviors within this sample. Participants with higher socioeconomic indicators tended to report higher levels of cinema attendance, whereas those with lower socioeconomic indicators reported lower levels of attendance. Given the convenience sampling design and the student-only composition of the sample, the findings should be interpreted with caution and should not be generalized beyond the study population.
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The catalytic isomerization of glucose to fructose is a pivotal step in the valorisation of lignocellulosic biomass. In this study, polyaluminium chloride (PAC), a low-cost, industrially available material characterised by polynuclear aluminium-oxo species, is investigated for the first time as an alternative catalyst for this transformation. The catalytic performance of PAC was systematically investigated by varying the temperature (70-130 °C), solvent (pure water, H2O:MeOH 1:1 or 4:1) and reaction time (30-120 min). A fructose yield of up to 55% with a selectivity of 85% was obtained by using PAC at 120 °C in H2O:MeOH 1:1 for 120 min, confirming its effectiveness in promoting glucose isomerization to fructose. Mechanistic insights and quantitative monitoring were achieved using benchtop NMR spectroscopy. 27Al-NMR of PAC in aqueous solution exhibits two main signals at 1.12 ppm (attributed to the hexacoordinated Al complex) and at 63.3 ppm (associated with a tetrahedral Al centre typical of the Al13 Keggin-type cluster). With the increase in temperature, as well as by changing the reaction media from pure aqueous to a mixed aquo-alcoholic system, new Al species were generated that are more reactive than the starting AlCl3∙6H2O and PAC. Overall, this work demonstrates that PAC represents a viable, scalable, and more sustainable alternative to conventional aluminium-based catalysts, offering a promising route toward more efficient biomass conversion processes.
A minimum unit price (MUP) of AUD$1.30 per standard drink was implemented in 2018 in the Northern Territory (NT), Australia, to reduce the availability of cheap alcohol. Despite evidence of its efficacy in decreasing alcohol consumption and harms, the MUP was repealed on 1 March 2025. This study aimed to determine the immediate impacts of the MUP repeal on the price of off-premises (i.e. takeaway) alcohol in Darwin, NT, assessing the proportion of products available <$1.30 per standard drink after the repeal in March 2025, identifying whether these products were pre-existing or new to market, and analysing any broader shifts in the price distribution across all alcoholic products that could be attributable to the repeal. Observational study of all alcoholic products available from three large alcohol retailer websites. Darwin, NT, between October 2024 and March 2025. Price per standard drink was estimated, and fixed-effects panel quantile regression was used to test for price differences. Within one month of the repeal, no beer or spirits, 5.1% of all wine and 2.5% of all ciders were available <$1.30 per standard drink, including 67.6% of the 37 wine products in vessels ≥1 L. Of the products identified at <$1.30 per standard drink, most had been available in previous months (i.e. not new or restocked). Quantile regression results showed a downward shift in price across the market for all product types, including beer and spirits, which were priced substantially above the $1.30 threshold at all percentiles. The largest reductions were observed for cheaper spirits, beer, cider and wine ≥1 L, ranging from $0.08 at the 20th percentile for spirits to $0.48 for wine ≥1 L at the 10th percentile. The minimum unit price repeal in Northern Territory, Australia, in March 2025 resulted in an immediate increase in the availability of cheap alcohol products and a downward shift in the overall price of alcohol in the off-premises market, particularly for lower-priced products.
Empirical conclusions depend not only on data but also on analytic decisions. Many-analyst studies have quantified this dependence: independent teams testing the same hypothesis on the same dataset regularly reach conflicting conclusions. But such studies require costly human coordination. We show that fully autonomous AI analysts built on large language models (LLMs) can, cheaply and at scale, produce the analytic dispersion observed in human many-analyst studies. In our framework, each AI analyst independently executes a complete analysis pipeline on a fixed dataset and hypothesis; a separate AI auditor screens every run for methodological validity. Across three datasets, AI analyst-produced analyses exhibit substantial dispersion in effect sizes, [Formula: see text]-values, and conclusions. This dispersion can be traced to identifiable analytic choices in preprocessing, model specification, and inference that vary systematically across LLM and persona conditions. Critically, the outcomes are steerable: reassigning the analyst persona or LLM shifts the distribution of results even among methodologically sound runs. These results highlight a central challenge for AI-automated empirical science: when defensible analyses are cheap to generate, evidence becomes abundant and vulnerable to selective reporting. The same capability also helps address it: treating analyst results as distributions makes analytic uncertainty visible, and deploying AI analysts against a published specification can reveal how much disagreement stems from underspecified design choices. Taken together, our results motivate a transparency norm: AI-generated analyses should be accompanied by multiverse-style reporting and full disclosure of the prompts used, on par with code and data.
In this study feather hydrolysate served as the sole nitrogen source for the cultivation of Spirulina (Limnospira platensis) replacing nitrate used in the standard medium. Although Spirulina is well known for its excellent nutritional content ideal for food and animal feed application and it allows a climate smart production system, its wide use remains limited because of high production cost. The growth medium used for cultivation where nitrate serves as the nitrogen source has significant contribution to the high production cost. Therefore, the use of cheaper growth substrates could help lower production cost and make Spirulina biomass accessible to most users. The feather hydrolysate used in this study was prepared through microbial solubilization which also resulted in the production of a protease that has properties suitable for different industrial applications. The whole feather hydrolysate and the enzyme-free feather hydrolysate obtained after recovery of the enzyme supported good growth of Spirulina equivalent to nitrate in the standard medium. Currently the poultry industry releases huge quantities of feather as waste posing serious risks of environmental pollution. The results of this study, therefore, indicate that feather could serve as a cheap substrate to produce an industrially important enzyme while the enzyme free hydrolysate serve as a substrate for the cultivation of Spirulina. This process, in addition to lowering production costs, could help to reduce environmental pollution. Therefore, integration of feather hydrolysis, enzyme production, and Spirulina cultivation could lead to a circular bioeconomy offering huge environmental and economic benefits.
Insulin resistance is a serious public health concern. The triglyceride-glucose (TyG) index is a simple, cheap, and reproducible surrogate of insulin resistance, and sodium-glucose cotransporter-2 (SGLT2) inhibitors have reshaped cardio-metabolic care beyond glycemic control. We followed PRISMA and registered the protocol in PROSPERO (CRD420251056341). We searched PubMed, Scopus, Web of Science, EMBASE, and Cochrane from inception to May 31st, 2026, including observational studies and clinical trials reporting baseline and follow-up TyG in adults. Two reviewers screened records, a third resolved disagreements, data were extracted with a standardized form, and quality was assessed with Cochrane RoB 2.0, the Newcastle-Ottawa Scale, and the JBI checklists. The primary outcome was within-group change in TyG pooled with random-effects (Hartung-Knapp); tests were two-sided with a significance threshold of 0.05. Twelve studies comprising thirteen study arms were included, with a total of 1845 participants. Follow-up ranged from 12 weeks to 5 years. Across studies, SGLT2 inhibitor therapy was associated with a significant reduction in TyG index (mean difference = -0.28, 95% confidence interval [-0.41; -0.14], I2 = 99.5%). Egger's test suggested possible small-study effects, whereas Begg's test and trim-and-fill analysis did not show clear evidence of publication bias; leave-one-out analyses showed that no single study materially influenced the pooled estimate. Despite heterogeneity in populations, drug choice, and follow-up duration, SGLT2 inhibitors were associated with a significant decrease in TyG, although small-study effects cannot be excluded.
We report a skeletal rearrangement of pyridines that enables functionalisation of the 2 and 3-positions, via Zincke ketone intermediates. The ring-opened pyridines undergo selective reaction with cheap and readily available acylating agents, setting up an alternative ring closure pathway to afford rearranged structures incorporating groups such as CF3, CF2H, and CO2Et at the pyridine C2 position. The process enables highly selective C2 over C4 trifluoromethylation of pyridines, a reaction that challenges conventional Minisci methods. Sequential functionalisation of the Zincke intermediate is further demonstrated, to access triply-substituted pyridine products from mono-substituted starting materials.
Out-of-Distribution (OoD) detection is vital for the reliability of deep neural networks, the key of which lies in effectively characterizing the disparities between OoD and In Distribution (InD) data. In this work, such disparities are exploited through a fresh perspective of non-linear feature sub spaces. That is, a discriminative non-linear subspace is learned from InD features to capture representative patterns of InD, while informative patterns of OoD features cannot be well captured in such a subspace due to their different distribution. Grounded on this perspective, we exploit the deviations of InD and OoD features in such a non-linear subspace for effective OoD detection. To be specific, we leverage the framework of Kernel Principal Component Analysis (KPCA) to attain the discriminative non linear subspace and deploy the reconstruction error on such subspace to distinguish InD and OoD data. Two challenges emerge: (i) the learning of an effective non-linear subspace, i.e., the selection of kernel function in KPCA, and (ii) the computation of the kernel matrix with large-scale InD data. For the former, we reveal two vital non-linear patterns that closely relate to the InD-OoD disparity, leading to the establishment of a Cosine Gaussian kernel for constructing the subspace. For the latter, we introduce two techniques to approximate the Cosine-Gaussian kernel with significantly cheap computations. In particular, our approximation is further tailored by incorporating the InD data confidence, which is demonstrated to promote the learning of discriminative subspaces for OoD data. Our study presents new insights into the non-linear feature subspace for OoD detection and contributes practical explorations on the associated kernel design and efficient computations, yielding a KPCA detection framework with distinctively improved efficacy and efficiency.
Abnormal broadcast activities are becoming more and more rampant, since it is easy and cheap to achieve a radio broadcasting station. These abnormal broadcasts disseminate illegal information, which can have serious negative impacts on society. However, detecting and tracing these abnormal broadcast contents can be challenging as they are often scattered and difficult to identify. In this article, we propose a semantic-aided analysis method for broadcast content (SAAM), which is based on Latent Dirichlet Allocation (LDA) to construct a model for detecting and analyzing abnormal broadcasts. The LDA model intelligently captures the latent semantic topics implied in the broadcasting content, enabling the establishment of criteria for identifying anomaly and facilitating to investigate. The analysis method further exploits the Apriori algorithm to explore the associations between the content of abnormal broadcast and relevant organizations based on semantic topics from LDA. This allows for detailed analysis of abnormal broadcasts as well as tracing relevant organizations involved in such activities. In the experiment, the abnormal broadcast data is collected to verify the accuracy of SAAM in detecting and tracing abnormal broadcast. The experiment results show that, the accuracy rate of SAAM in detecting abnormal broadcasts is more than 0.91 and the tracing-accuracy is more than 0.8.
The Euler buckling of rods is a long-studied mechanical instability, and it remains relevant to this day, as the constituent components in many biological and physical systems are linear polymers, such as microtubules or carbon nanotubes. At finite temperature, if a polymer is shorter than its persistence length, then the polymer is semiflexible, and its elasticity remains rodlike. But polymers can also stretch due to their finite extensibility, which can couple to energetically cheap bending deformations in nonlinear ways when a load is applied to the system. We show how the interplay between thermal fluctuations and nonlinear elasticity dramatically modifies the Euler buckling instability for compressed semiflexible polymers in a fixed strain ensemble. We identify a Ginzburg-like length scale beyond which thermally excited undulations lead to a softened Young's modulus, while the polymer nevertheless remains semiflexible. Both perturbative calculations and numerical Monte Carlo simulations suggest a qualitative change in several scaling properties of the buckling transition. The critical compressional strain for thermal buckling now increases with system size, in contrast to athermal buckling, where it decreases with system size. Renormalization group calculations confirm this picture, and also show that thermal buckling is controlled by a new fixed point with different critical exponents compared to classical Euler buckling.
Phenols are cheap, abundant bulk chemicals that serve as versatile starting materials for diverse organic transformations. Herein, we report a mild, visible-light-induced FeCl3-catalyzed protocol for converting aryl triflates to aryl chlorides using MgCl2 as an inexpensive and green chlorine source. The reaction proceeds without noble metals or additional photoredox catalysts, shows broad functional group tolerance, and delivers products in good to excellent yields. Notably, the method is air- and moisture-tolerant and readily scalable.
Hands-on experience is key for STEM learning, yet logistical and financial constraints are an obstacle for teaching and demonstrations with live organisms. Fruit flies are a classic genetic model organism with many published educational resources, yet anesthetizing fruit flies without specialized equipment remains a barrier to their use, particularly in low-resource settings. Here, I present a setup for fly anesthesia induction and maintenance using supplies that are cheap, portable, quick to assemble, and easily sourced. These methods have been tested in an undergraduate classroom laboratory setting but could also translate to demonstrations or activities in high school biology classrooms and science outreach events. These methods will facilitate the use of fruit flies in hands-on activities, even in settings that lack conventional setups for fly anesthesia.
A comprehensive study of three novel hydrophilic CHON-compliant ligands, two hydrophilized with longer diethylene glycol chains (DEG-BTrzPhen and DEG-PTD) and one with shorter hydroxyethyl chains (HE-PTD), encompassing synthesis, solubility, solvent extraction, coordination, spectral, crystallographic and computational studies is presented herein. Experimental results demonstrated that under weakly acidic conditions, these ligands exhibited selectivity for Am(III) vs. Eu(III), with maximum separation factors (SFEu/Am) reaching 74, 240, and 127 for DEG-BTrzPhen, DEG-PTD, and HE-PTD, respectively. Among these ligands, DEG-PTD, a derivative of the well-known PTD, exhibits exceptionally high aqueous solubility, offering a significant technological advantage. With its overall characteristics, being CHON-compliant, selective, cheap, and with high aqueous solubility even at very high concentrations, the DEG-PTD ligand is a viable option for actual industrial applications.
The dive response decreases heart rate, regulates blood flow distribution, conserves oxygen, and extends dive duration. In diving animals, dive heart rate can be modulated to meet increased demands of exercise during foraging. However, lunge-feeding rorquals represent an extreme example of exercise under breath-hold conditions: Though most of their dive time is spent gliding and filtering, lunges require high-power, acrobatic sprints to engulf massive volumes of prey-laden water. Our biologging data show that heart rate repeatedly increases with lunging but only gradually declines during filtering, dissimilar from the heart rate-activity coupling observed in other divers. We suggest that the unique nature of rorqual exercise likely requires glycolytic metabolic substrates, rather than aerobic substrates, during short, powerful lunges. During slow filtering, high heart rates may help partially renew these energy sources via oxygen-dependent pathways. By temporarily buffering oxygen demand from supply, the flexible dive response appears to optimize oxygen use in lunging rorquals and support aerobically "cheap" foraging. The data also show that dive cycle heart rate scope increases with rorqual size. We propose that cardiovascular plasticity during high and low power phases of foraging dives underpins rorquals' ability to achieve high foraging efficiencies and combine explosive predation with grazing-like efficiency in a single lineage.
Herein, we report a sustainable, one-pot synthetic strategy for the synthesis of aldazines, ketazines, and quinoxalines in air using a cheap, nontoxic, and air-stable Zn-catalyst (1) bearing a redox-active tridentate arylazo pincer, 2-(4-chlorophenyldiazenyl)-1,10-phenanthroline (L). Catalyst 1 exhibits promising catalytic activity for the synthesis of a broad spectrum of aldazines, ketazines, and quinoxalines in moderate to good isolated yields, using alcohols as inexpensive starting materials. Mechanistic investigations revealed an exclusively ligand-centered redox-driven catalytic dehydrogenation of alcohols, followed by subsequent coupling reactions, in which the Zn-center serves merely as a template.
Cardiovascular-kidney-metabolic (CKM) syndrome recognises the connection between metabolic conditions (particularly obesity, diabetes and metabolic dysfunction-associated fatty liver disease), chronic kidney disease and cardiovascular disease. There is a high and growing prevalence of CKM syndrome. While genetic and epigenetic factors predispose to CKM syndrome, the emergence of disease is heavily influenced by social determinants of health and individual behaviours. The pathophysiology of CKM syndrome is driven by insulin resistance, inflammation, oxidative stress and vascular dysfunction. Urinary albumin:creatinine ratio is a relatively cheap and accessible biomarker of CKM syndrome, which can be used to identify and monitor disease trajectory. Primary and secondary prevention is relevant across the life course. Healthy behaviours, including diet, physical activity, sleep and stress management are important. There are established and emerging drugs that are effective across a range of metabolic conditions and that confer reno- and cardio-protective effects. Remission may be achievable.
Occupational exposure to per- and polyfluoroalkyl substances (PFAS) is under scrutiny as a potential cancer risk for firefighters. Existing methods for PFAS biomonitoring involve solid-phase extraction (SPE), either conventional or online, followed by liquid chromatography-tandem mass spectrometry (LC-MS/MS). SPE requires a significant investment of time and technical skill for optimizing many variables to achieve maximum accuracy and precision. This increases analytical costs, making routine PFAS biomonitoring cost-prohibitive for a majority of fire service members. The motivation for this study was to develop an analytical method for measuring 30 PFAS in firefighters' serum (N = 84) using a QuEChERS (quick, easy, cheap, effective, rugged, and safe) protocol. For all target analytes, process efficiency (PE) and recovery efficiency (RE) were between 89 and 126% at 1 μg L-1 and between 85 and 133% at 25 μg L-1 (%RSD ≤ 13) (N = 5). At 1 and 25 μg L-1, matrix effects (ME) for all target PFAS ranged from 78 to 110% and 87 to 117%, respectively (%RSD ≤ 11). Precision criteria were met for all target analytes in both intra-day and inter-day precision assessments (%RSD ≤ 19). Limits of detection (LOD) and quantitation (LOQ) ranged from 0.006 to 0.3 μg L-1 and 0.02 to 0.9 μg L-1, respectively. This high-performance, high-throughput, and cost-effective alternative to the current gold standard for PFAS measurement in serum advances current firefighter biomonitoring capabilities by decreasing the time and cost of analysis, reducing constraints imposed by specialized laboratory equipment and expertise, and optimizing analytical batch processing and workflow.
Between 2022 to 2024, soil samples were taken from 101 sampling sites under agricultural use to assess residues of various compounds in a Germany-wide study. This sub-project investigated residues of five neonicotinoids: imidacloprid, thiamethoxam, clothianidin, acetamiprid and thiacloprid. A highly sensitive analytical method was developed using Quick, Easy, Cheap, Effective, Rugged, Safe (QuEChERS) sample preparation and ultra-high-performance liquid chromatography-tandem mass spectrometry detection, achieving limits of quantification of 10 ng/kg. The contamination was examined across different agricultural uses (arable land, vineyards, orchards, and grassland), geographical distribution, soil depths (0-5 cm and 5-20 cm) and temporal trends. Results revealed widespread contamination, with detection rates (>limit of detection) of 24% (acetamiprid), 24% (thiamethoxam), 35% (thiacloprid), 72% (clothianidin), and 89% (imidacloprid) over all sampling sites. In grassland without known application of these insecticides, imidacloprid and clothianidin were also regularly detected, indicating unintended substance entries, for example, by airborne deposition, manure or sewage sludge applications. Despite comprehensive bans on four of the substances, neonicotinoid residues remain widely present in agricultural soils. Even though the calculated risk for the individual neonicotinoids remains below regulatory acceptable concentrations, cumulative risks for soil organisms may result from the presence of multiple contaminants in soils. These findings provide an important basis for assessing the actual environmental risk in light of the total chemical burden. Additionally, the findings help describe the current levels of neonicotinoid residues in soil, providing important information which should be fed back to chemical regulation.
Aiming at the problems of glare interference, local overexposure and detail loss caused by artificial light sources such as vehicle lamps and street lamps in nighttime road scenes, as well as the challenges of existing glare suppression models with large parameters, high computational complexity and difficulty in deploying on edge devices, this paper proposes a lightweight glare suppression network (LGSNet) based on ghost depthwise separable convolution and Lightweight Parallel Attention. Based on the U-Net architecture, the network introduces ghost depthwise separable convolution blocks (GhostDSC) in the encoder and decoder, which generates ghost features through cheap linear transformations by exploiting feature map redundancy, significantly reducing model parameters and computational costs while maintaining feature representation ability. Meanwhile, a Lightweight Parallel Attention (LPA) module is designed in the decoder stage, which integrates channel attention and pixel attention in parallel, enhancing the network's attention to glare regions and edge details with extremely low parameter increment to improve detail recovery accuracy. In addition, a joint loss function consisting of background loss, glare loss and reconstruction loss is constructed to collaboratively optimize glare suppression and detail preservation. Experimental results on the public Flare7K++ dataset and the self-built nighttime road glare dataset NRGD show that the proposed method has only 7.45 M parameters, much lower than standard U-Net and Uformer. It achieves competitive results on full-reference metrics such as PSNR, SSIM, LPIPS and no-reference metrics such as NIQE, BRISQUE, PIQE, and can effectively suppress various types of glare interference and restore obscured scene details. It achieves a superior trade-off between model complexity and enhancement performance, significantly reducing the parameter count and computational overhead compared to heavy baselines, thereby offering a highly efficient solution for resource-aware glare suppression tasks.