Miracle-dz/UniFAR-FTUs huggingface dataset:
Toward a more comprehensive theory of zooplankton diel vertical migration: Integrating ultraviolet radiation and water transparency into the biotic paradigm

The current prevailing theory of diel vertical migration (DVM) of zooplankton is focused largely on two biotic drivers: food and predation. Yet recent evidence suggests that abiotic drivers such as damaging ultraviolet (UV) radiation and temperature are also important. Here we integrate current knowledge on the effects of abiotic factors on DVM with the current biologically based paradigm to develop a more comprehensive framework for understanding DVM in zooplankton. We focus on “normal” (down during the day, up at night) DVM of holoplanktonic, primarily herbivorous zooplankton. This new transparency‐regulator hypothesis differentiates between structural drivers, such as temperature and food, that vary little over a 24‐h period and dynamic drivers, such as damaging UV radiation and visual predation, that show strong variation over a 24‐h period. This hypothesis emphasizes the central role of water transparency in regulating these major drivers of DVM. In less transparent systems, temperature and food are often optimal in the surface waters, visual predators are abundant, and UV radiation levels are low. In contrast, in more transparent systems, vertical thermal gradients tend to be more gradual, food quality and quantity are higher in deeper waters, and visual predator abundance is often lower and damaging UV radiation higher in the surface waters. This transparency‐regulator hypothesis provides a more versatile theoretical framework to explain variation in DVM across waters of differing transparency. This hypothesis also enables clearer predictions of how the wide range of ongoing transparency‐altering local, regional, and global environmental changes can be expected to influence DVM patterns in both inland and oceanic waters of the world.
El estudio econométrico de la concentración espacial de la industria: Ejemplo de aplicación en Madrid, Toledo y Guadalajara

The interest for the spatial matters has accelerated thc renewal of conventional econometrie teehniques, consolidating the role of Spatial Econometries, in relation with a «New Economie Geography», which is focused on spatial inieractions like neighbour cffects (imitation, spillovers, externalities, contagion.). Initial experiencewith global and local methods of spatial auto-correlation analysis are applied at this time to new industrial births occurred between 1981and 1995 in Madrid, Toledo, and Guadalajara(Spain) The results confirm tbe capacity of proposed methods iii order <oidentify regional clusters of high industria1 birth-rates, dilfusion processand endogenousgrowtb. Keyn-ords:Spatialt-conometrics. New lndustry. Madrid Toledo. Guadalajara.
Piecewise Reconstruction of 3D Euler Spirals From Planar Polygonal Curves

In this article, we propose a method for reconstructing approximate piecewise 3D Euler spirals from planar polygonal curves. The method computes the 3D coordinates of approximate Euler spiral such that its orthogonal projection onto the 2D plane is the closest possible to the input curve. To achieve this, a dataset is created, comprising Euler spiral segments and their orthogonal projections. Given an input curve, it is sampled and split into segments. Each segment is matched with the closest Euler spiral segments from the dataset, forming a pool of candidates. The optimal set of connected Euler spiral segments is then selected to reconstruct the approximate piecewise 3D Euler spiral. The selection prioritizes smoothness continuity at connecting points while minimizing the distance between the orthogonal projection and the input curve. We evaluate our method against synthetic 3D Euler spirals by applying our reconstruction to the orthogonal projection of the synthetic Euler spirals.
Refining the genetic risk of breast cancer with rare haplotypes and pattern mining

A closed-pattern mining algorithm helps identify rare, noncontiguous haplotypes underlying breast cancer risk in British women in the UK Biobank. Hundreds of common variants have been found to confer small but significant differences in breast cancer risk, supporting the widely accepted polygenic model of inherited predisposition. Using a novel closed-pattern mining algorithm, we provide evidence that rare haplotypes may refine the association of breast cancer risk with common germline alleles. Our method, called Chromosome Overlap, consists in iteratively pairing chromosomes from affected individuals and looking for noncontiguous patterns of shared alleles. We applied Chromosome Overlap to haplotypes of genotyped SNPs from female breast cancer cases from the UK Biobank at four loci containing common breast cancer-risk SNPs. We found two rare (frequency <0.1%) haplotypes bearing a GWAS hit at 11q13 (hazard ratio = 4.21 and 16.7) which replicated in an independent, European ancestry population at P < 0.05, and another at 22q12 (frequency <0.2%, hazard ratio = 2.58) which expanded the risk pool to noncarriers of a GWAS hit. These results suggest that rare haplotypes (or mutations) may underlie the “synthetic association” of breast cancer risk with at least some common variants.
Health Benefits of Montmorency Tart Cherry Juice Supplementation in Adults with Mild to Moderate Ulcerative Colitis: A Protocol for a Placebo Randomized Controlled Trial

Ulcerative colitis, characterized by its relapsing and remissive nature, negatively affects perception, body image, and overall quality of life. The associated financial burden underscores the need for alternative treatment approaches with fewer side effects, alongside pharmaceutical interventions. Montmorency tart cherries, rich in anthocyanins, have emerged as a potential natural anti-inflammatory agent for ulcerative colitis. This manuscript outlines the study protocol for a randomized placebo-controlled trial investigating the effects of Montmorency tart cherry in individuals with ulcerative colitis. The trial aims to recruit 40 participants with mild to moderate disease activity randomly assign them to either a Montmorency tart cherry or placebo group. The intervention will span 6 weeks, with baseline and 6-week assessments. The primary outcome measure is the Inflammatory Bowel Disease Quality of Life Questionnaire. Secondary outcomes include other health-related questionnaires and biological indices. Statistical analysis will adhere to an intention-to-treat approach using linear mixed effect models. Ethical approval has been obtained from the University of Hertfordshire (cLMS/SF/UH/05240), and the trial has been registered as a clinical trial (NCT05486507). The trial findings will be disseminated through a peer-reviewed publication in a scientific journal.
Classifying Tweets with Keras and Tensorﬂow using RNN (Bi-LSTM)

: Due to the altering user behavior, the volume of data published by users on numerous social networking sites, including Inﬆagram, Twitter, Snapchat, etc., has signiﬁcantly increased. As a result, an enormous volume of textual, audio, and video poﬆs—reﬂecting millions of users' ideas and opinions—are produced daily. Even if some poﬆs are brief and don't matter, they provide insightful personal viewpoints on various subjects. This article employs Twitter as the analytic platform, where poﬆs are known as tweets, focusing on identifying the underlying emotions behind such poﬆs. This ﬆudy inveﬆigates techniques for preparing and extracting Twitter data using Python-TensorFlow. Additionally, this ﬆudy incorporates Recurrent Neural Network with Bi-Long Short-Term Memory (RNN (Bi-LSTM)) into the code, improving the model's performance. This research aims to eﬀectively extract sentiment from tweets by training and evaluating the data againﬆ a classiﬁer.
Exploring the Potentials of Augmented Reality Poster for Civic Education in Indonesia

Traditional postershave long been utilized in civic education; however, theyhavestruggledtocapturetheattentionofdigitalnatives.Thisstudyintroduces aninnovativesolutionbyproposinganAugmentedReality(AR)Posterapplication toaugmenttraditionalposters.Theresearchconductedanexperimentalstudywith university-levelstudentswhohadnopriorexperiencewithAugmentedReality technology.Thecontrolgroupreliedontraditionalpostersviewedwiththenaked eye,whiletheexperimentalgrouphadaccesstoadditionalvideocontentdisplayed abovethepostersthroughtheARPosterapplication.Dataanalysisrevealedthat theexperimentalgroupdemonstratedsuperiorlearningoutcomescomparedtothe controlgroup.Additionally,theexperimentalgroupexhibitedapositiveattitude towardstheARPosterapplication,perceivingitasuser-friendlyandbeneﬁcial fortheirlearning.Asaresult,thisstudyunderscoresthevalueofintegratingvideo augmentationintotraditionalpostersandrecommendstheinclusionofARPoster developmentinthecurriculumoftheciviceducationdepartment.
An Identiﬁcation Method for High Voltage Power Grid Insulator Based on Mobilenet-SSD Network

The identiﬁcation of power equipment using visible image and deep learning methods has become widespread in the power industry. However, current deep learning algorithms often face issues related to large model parameters and high hardware requirements, making it difﬁcult to integrate them into mobile devices. To overcome these challenges, a novel approach has been proposed to identify insulators on overhead transmission lines using UAVs that carry lightweight models. This method utilizes an enhanced lightweight MobileNet-SSD target detection network, enabling accurate classiﬁcation and location of power equipment. The results demonstrate that this approach can quickly and precisely label power equipment in complex backgrounds. Additionally, it has small model parameters, high efﬁciency, strong robustness, and an mAP of 82.47%, making it ideal for enhancing patrol accuracy and real-time monitoring of mobile equipment towards the transmission lines.
Compact Dual-Frequency 2-D Van Atta Array Using the Reconfigurable AMC Antenna

The complexity of the Van Atta array increases sharply with the expansion of scanning dimensions and working frequencies, making it difficult to achieve a planar dual-frequency 2-D Van Atta array. This study proposes a planar, compact, dual-frequency 2-D Van Atta array with 25 antennas and 13 microstrip lines. A novel reconfigurable artificial magnetic conductor (AMC) structure and a 2-D dual-frequency interleaving array are designed to reduce the complexity of the Van Atta array. The reconfigurable AMC is used to reduce the thickness of the planar monopole antenna at 11.6 GHz and is reused as a patch antenna at 5.8 GHz. The total size of the Van Atta array is 2.98 × 2.98 × 0. 068λh3. According to the measured results, the Van Atta array operates at 5.75 and 11.4 GHz. The −4 dB beamwidths of the monostatic radar cross section (RCS) at 5.75 GHz are 87° (H plane) and 145° (E plane), and the −4.5 dB beamwidths of the monostatic RCS at 11.4 GHz are 75° (H plane) and 96° (E plane).
Sliding Mode Control for High-Speed Train via Event-Triggered Strategy Under Energy-Limited Deception Attacks

The event-triggered sliding mode control (ETSMC) for high-speed train (HST) is addressed under the energy-limited deception attacks (DA), where the multibody, transmission delay and network bandwidth of HST are considered in unity. Firstly, the error equation of HST with transmission delays is built with the time-varying running resistance. Then, the sliding mode surface (SMS) function is constructed, and the event-triggered mechanism is established to balance the transmission frequency and HST stability. Next, under the energy-limited DA, sufficient conditions are deduced and converted into linear matrix inequalities (LMIs). An ETSMC scheme is established to guarantee the reachability condition of SMS within a finite time. Finally, an example from Japan Shinkansen HST is conducted to verify the effectiveness of the proposed ETSMC.
Real-Time Ultrasonography and the Evaluation of Static Images Yield Different Results in the Assessment of EU-TIRADS Categories

The studies on the effectiveness of various TIRADS in the diagnostics of thyroid nodules differ in the method of ultrasound image assessment: real time (rtUS) vs. static ultrasonography (stUS). The aim of the study was to evaluate the impact of those two methods on the categorization of nodules in EU-TIRADS. Three experienced raters assessed 842 nodules in routine rtUS and reassessed with the use of sUS. Reproducibility of the assessment of malignancy risk features and categorization of nodules with EU-TIRADS was estimated with Krippendorff’s alpha coefficient (Kα). The reproducibility of EU-TIRADS categories on sUS in relation to rtUS was in range 70.9–76.5% for all raters (Kα: 0.60–0.68) with the highest reproducibility for category 3 (80.0–86.5%) and the lowest for category 5 (48.7–77.8%). There was a total disagreement of the identification of microcalcifications on sUS in relation to rtUS, a strongly variable reproducibility of marked hypoechogenicity (12.5–84.6%, Kα: 0.14–0.48) and a tendency toward more frequent identification of the non-oval shape on sUS. The percentage of agreement for each pair of raters in assigning the EU-TIRADS category on sUS was in the range 71.6–72.3% (Kα: 0.60–0.62). The method of sonographic image evaluation influences the nodule’s feature assessment and, eventually, the categorization within EU-TIRADS.
Velocity range-based reward shaping technique for effective map-less navigation with LiDAR sensor and deep reinforcement learning

In recent years, sensor components similar to human sensory functions have been rapidly developed in the hardware field, enabling the acquisition of information at a level beyond that of humans, and in the software field, artificial intelligence technology has been utilized to enable cognitive abilities and decision-making such as prediction, analysis, and judgment. These changes are being utilized in various industries and fields. In particular, new hardware and software technologies are being rapidly applied to robotics products, showing a level of performance and completeness that was previously unimaginable. In this paper, we researched the topic of establishing an optimal path plan for autonomous driving using LiDAR sensors and deep reinforcement learning in a workplace without map and grid coordinates for mobile robots, which are widely used in logistics and manufacturing sites. For this purpose, we reviewed the hardware configuration of mobile robots capable of autonomous driving, checked the characteristics of the main core sensors, and investigated the core technologies of autonomous driving. In addition, we reviewed the appropriate deep reinforcement learning algorithm to realize the autonomous driving of mobile robots, defined a deep neural network for autonomous driving data conversion, and defined a reward function for path planning. The contents investigated in this paper were built into a simulation environment to verify the autonomous path planning through experiment, and an additional reward technique “Velocity Range-based Evaluation Method” was proposed for further improvement of performance indicators required in the real field, and the effectiveness was verified. The simulation environment and detailed results of experiments are described in this paper, and it is expected as guidance and reference research for applying these technologies in the field.
Non-dinosaurian lower vertebrates across the Cretaceous-Tertiary boundary in northeastern Montana

This study presents current data on vertebrate survival and extinction across the Cretaceous-Tertiary boundary in Montana. Nearly all the common taxa of reptiles (except dinosaurs), amphibians, and fish survived the end of the Cretaceous Period; extinctions were concentrated among rare groups and those found in near-shore habitats. The author concludes that ocean regression and climatic deterioration may explain these selective extinction patterns better than catastrophic hypotheses.
CMOS Clock-Gated Synchronous Up/Down Counter With High-Speed Local Clock Generation and Compact Toggle Flip-Flop

In this paper, a high-speed low-power CMOS synchronous up/down counter with a novel compact toggle flip-flop is proposed to achieve energy- and area-efficient speed enhancement. It adopts a high-speed local clock generation based on a single Manchester carry chain to improve counting speed. The counter embeds clock gating in the local clock generation to eliminate redundant power consumption. A compact toggle flip-flop is incorporated for device count and power reduction. Both the up- and down-counting capabilities are supported. A 16-bit proposed counter was fabricated in a 28-nm CMOS process. Performance evaluation results indicate that the proposed counters can provide up to 55% speed improvement as compared to conventional designs. It also indicates that up to 28% performance gain is obtained in terms of power-delay product compared to conventional clock-gated CMOS counters.
Prognostic value of diabetes and metformin use in a real-life population of head and neck cancer patients

Introduction Head and neck carcinoma (HNC) is a disease with a poor prognosis despite currently available treatments. The management of patients with this tumor is often complicated by several comorbidities. Among these, diabetes is the second most frequent and its influence on the prognosis is not known. Methods In this work, we collected data on progression free survival (PFS) and overall survival (OS) of one hundred twenty-three patients with HNC who received biweekly cetuximab maintenance treatment after first-line chemotherapy. We then compared the survival of nondiabetic patients versus diabetics’ one. Results Surprisingly, both PFS (4 vs. 5 months, HR 2.297, p < 0.0001) and OS (7 vs. 10 months, HR 3.138, p < 0.0001) were in favor of diabetic patients, even after excluding other clinical confounding factors. In addition, we also studied survivals in patients taking metformin, a widely used oral antidiabetic drug that has demonstrated antitumor efficacy in some cancers. Indeed, diabetic patients taking metformin had better PFS and OS than those not taking it, 7 vs. 5 months (HR 0.56, p = 0.0187) and 11 vs. 8.5 months (HR 0.53, p = 0.017), respectively. Discussion In conclusion, real-world outcomes of biweekly cetuximab maintenance remain comparable to clinical trials. The prognostic role of diabetes and metformin was confirmed to be significant in our series, but further prospective studies are needed for a definitive evaluation.
Optimized Unsymmetrical Per-Phase Droop for Soft Line Switching of Reconfigurable Unbalanced Inverter-Based Islanded Microgrid

The legacy of power distribution systems is evolving towards more flexible and dynamically reconfigurable microgrids, which substantially increases line-switching actions. This may introduce undesirable transients, poor reliability, deteriorated power quality, and significant wear and tear in switching devices. Frequent line switching is significant in unbalanced inverter-based islanded microgrids (UIBIM). This article proposes an optimized unsymmetrical per-phase droop-controlled approach to mitigate the influence of line switching during UIBIM reconfiguration and partitioning. To determine the unsymmetrical per-phase Optimal Transitional Droop Settings (OTDS), a new mathematical formulation is modeled to minimize power flow at the switching location(s). Given the unbalanced nature of the system, the proposed unsymmetrical droop will be optimized for each phase independently. The activation start and end instants of the OTDS are selected to reduce the UIBIM dynamics due to the transition between the states due to switching. The superiority and effectiveness of applying the proposed unsymmetrical OTDS are assessed via Matlab/Simulink, utilizing case studies implemented on a 6-bus and an IEEE 34-bus unbalanced systems. The simulation results reveal that the proposed approach can independently minimize the current flow in each phase during the switching process by nearly 98%. Furthermore, the transient voltage during the switching process is significantly reduced.
Recent Advances in Electrospun Nanofiber-Based Strategies for Diabetic Wound Healing Application

Diabetic ulcers are the second largest complication caused by diabetes mellitus. A great number of factors, including hyperchromic inflammation, susceptible microbial infection, inferior vascularization, the large accumulation of free radicals, and other poor healing-promoting microenvironments hold back the healing process of chronic diabetic ulcer in clinics. With the increasing clinical cases of diabetic ulcers worldwide, the design and development of advanced wound dressings are urgently required to accelerate the treatment of skin wounds caused by diabetic complications. Electrospinning technology has been recognized as a simple, versatile, and cost-reasonable strategy to fabricate dressing materials composed of nanofibers, which possess excellent extracellular matrix (ECM)-mimicking morphology, structure, and biological functions. The electrospinning-based nanofibrous dressings have been widely demonstrated to promote the adhesion, migration, and proliferation of dermal fibroblasts, and further accelerate the wound healing process compared with some other dressing types like traditional cotton gauze and medical sponges, etc. Moreover, the electrospun nanofibers are commonly harvested in the structure of nonwoven-like mats, which possess small pore sizes but high porosity, resulting in great microbial barrier performance as well as excellent moisture and air permeable properties. They also serve as good carriers to load various bioactive agents and/or even living cells, which further impart the electrospinning-based dressings with predetermined biological functions and even multiple functions to significantly improve the healing outcomes of different chronic skin wounds while dramatically shortening the treatment procedure. All these outstanding characteristics have made electrospun nanofibrous dressings one of the most promising dressing candidates for the treatment of chronic diabetic ulcers. This review starts with a brief introduction to diabetic ulcer and the electrospinning process, and then provides a detailed introduction to recent advances in electrospinning-based strategies for the treatment of diabetic wounds. Importantly, the synergetic application of combining electrospinning with bioactive ingredients and/or cell therapy was highlighted. The review also discussed the advantages of hydrogel dressings by using electrospun nanofibers. At the end of the review, the challenge and prospects of electrospinning-based strategies for the treatment of diabetic wounds are discussed in depth.
Secure Cloud Storage Based On Ring Learning With Errors Problem

: With the rapid development of cloud storage and quantum computing, ensuring the integrity of outsourced data for data owners has become a serious concern. To address this problem, existing protocols for auditing cloud storage are usually based on post-quantum cryptographies to check data integrity. Nevertheless, these protocols employ heavy cryptographic operations to construct the data tags, making their efficiency low and extensibility poor. In this paper, we take a new perspective and explore the possibility of designing Secure Cloud Storage (SCS) protocols based on the Ring Learning with Errors (RLWE) problem. Instead of matrix variables, our protocol only utilizes vector variables to generate data block tags so that it has a much lower computational complexity. We then give strict security proof of cheating resistance against the malicious cloud and privacy guarantee against the curious third-party auditor. We also extend the proposed protocol to support data dynamics and batch auditing for more application scenarios. As a further contribution, we summarize a systematic framework for designing lattice-based SCS protocols. Finally, exhaustive performance analysis and comparison are provided to verify that the proposed protocol outperforms the existing lattice-based SCS protocols in terms of both operational efficiency and functional extensibility.