Miracle-dz/UniFAR-FTUs huggingface dataset:
Rapid and Ultrasensitive Colorimetric Biosensors for Onsite Detection of Escherichia coli O157:H7 in Fluids

This study presents a breakthrough in the field of onsite bacterial detection, offering an innovative, rapid, and ultrasensitive colorimetric biosensor for the detection of Escherichia coli (E. coli) O157:H7, using chemically modified melamine foam (MF). Different from conventional platforms, such as 96-well plates and fiber-based membranes, the modified MF features a macroporous reticulated three-dimensional (3D) framework structure, allowing fast and free movement of large biomolecules and bacteria cells through the MF structure in every direction and ensuring good accessibility of entire active binding sites of the framework structure with the target bacteria, which significantly increased sensitive and volume-responsive detection of whole-cell bacteria. The biosensing platform requires less than 1.5 h to complete the quantitative detection with a sensitivity of 10 cfu/mL, discernible by the naked eye, and an enhanced sensitivity of 5 cfu/mL with the help of a smartphone. Following a short enrichment period of 1 h, the sensitivity was further amplified to 2 cfu/mL. The biosensor material is volume responsive, making the biosensing platform sensitivity increase as the volume of the sample increases, and is highly suitable for testing large-volume fluid samples. This novel material paves the way for the development of volume-flexible biosensing platforms for the record-fast, onsite, selective, and ultrasensitive detection of various pathogenic bacteria in real-world applications.
Therapeutic Management in Ulcerative Colitis

Abstract Ulcerative colitis is a chronic idiopathic inflammatory disorder of the colon, characterized by a diffuse continuous superficial inflammation that always begins within the rectum and affects the proximal colon to a varying extent. One typical feature of the disease is that it does not involve the small intestine. A small percentage of patientsmay present a superficial inflammation of the terminal ileum. Clinically the disease is associated with rectal bleeding and urgency, tenesmus, abdominal pain and diarrhea. In the assessment of the disease severity, various factors are used, including the frequency of bowel movements, rectal bleeding, laboratory tests, endoscopic extension and appearance and patient’s quality of life. The treatment of ulcerative colitis is adapted to the disease severity and to the individual patient, no treatment being universally effective. Treatment decisions should be based on disease activity (mild, moderate, severe) and distribution (proctitis, left-sided, extensive colitis) If mild to moderate inflammation is limited to the rectum and for left-sided colitis, the mainstay on the treatment are 5-aminosalicylates, which are highly effective as a topical treatment (suppositories, enemas, foams). Corticosteroids are indicated in patients with more severe disease, and in cases where mesalamine has failed in inducing remission. For extensive mild to moderate ulcerative colitis, therapy recommendation consists of a combined therapy using oral and topical 5-ASA, which proved to be superior to single use of either. Use of systemic corticosteroids should be considered earlier for patients with extensive colitis and/or treatment failure. For patients with severe ulcerative colitis, hospitalization is generally required. In these cases, steroid therapy is still the gold standard. If there is no improvement in the first 72 hours after the initiation of treatment or symptoms are worsening, second-line conventional therapy must be attempted or proctocolectomymay be considered. In steroid-refractory patients, as well as in patients with severe ulcerative colitis, early introduction of immunosuppressive drugs may be effective during severe episodes, and for the prevention of complications, such as toxic megacolon. Without doubt, the introduction of anti-TNF antibodies have improved the therapeutic options available for patients with ulcerative colitis. But, there still is a substantial amount of patients not responding to anti-TNF antibodies or experiencing a secondary loss of response. Fortunately, for these cases, new classes of drugs have been developed and have been evaluated in early clinical trials. To illustrate the heterogeneity of cases of ulcerative colitis, we present three particular situations.
Effective use of Resources for Cost Minimization on Big Data in Distributed Data Centers

The speed at which business moves today, combined with the sheer volume of data created in digitized world, requires new approaches to deriving value from data. Big data is high volume, high velocity, and includes a high variety of sources of information. If you living in 100% of data world 90% of data has been generated over past 2 years because accessing the information has become gradually increasing in number, hence there will be a huge amount of data gets generated. Now a days all the internet providers has their data centers and those data centers has number of servers,eg,Google has 13 data centers across 8 contries and facebook has 6 data centers. According to gartner predicts 71% of worldwide data center hardware spending will come from the big data processing, which surpass $126 billon. Hence it is vital importance to study cost minimization problem for big data processing in distributed data centers. As a result, four factors, i.e., how to place chunks to the server, how to distribute these chunks to the servers, task assignment and data movement, deeply influences operational expenditure of data centers. Hence we propose a twodimensional Markov chain model to describe a task completion time, joint optimization of that four factors. Hence we propose an matrix approach for data placing in the data centers. we model the problem as a mixed-integer non-linear programming (MINLP) and propose an efficient solution to linearize it. Keywords-Big Data; Chunks placing; Matrix Approach; Task Assignment; Data Movement; ;Cost Minimization ;Distributed Data Cente ;
A dynamical systems approach to studying midlatitude weather extremes

Extreme weather occurrences carry enormous social and economic costs and routinely garner widespread scientific and media coverage. The ability to predict these events is therefore a topic of crucial importance. Here we propose a novel predictability pathway for extreme events, by building upon recent advances in dynamical systems theory. We show that simple dynamical systems metrics can be used to identify sets of large‐scale atmospheric flow patterns with similar spatial structure and temporal evolution on time scales of several days to a week. In regions where these patterns favor extreme weather, they afford a particularly good predictability of the extremes. We specifically test this technique on the atmospheric circulation in the North Atlantic region, where it provides predictability of large‐scale wintertime surface temperature extremes in Europe up to 1 week in advance.
Edu-ConvoKit: An Open-Source Library for Education Conversation Data

We introduce Edu-ConvoKit, an open-source library designed to handle pre-processing, annotation and analysis of conversation data in education. Resources for analyzing education conversation data are scarce, making the research challenging to perform and therefore hard to access. We address these challenges with Edu-ConvoKit. Edu-ConvoKit is open-source (https://github.com/stanfordnlp/edu-convokit ), pip-installable (https://pypi.org/project/edu-convokit/ ), with comprehensive documentation (https://edu-convokit.readthedocs.io/en/latest/ ). Our demo video is available at: https://youtu.be/zdcI839vAko? si=h9qlnl76ucSuXb8- . We include additional resources, such as Colab applications of Edu-ConvoKit to three diverse education datasets and a repository of Edu-ConvoKit related papers, that can be found in our GitHub repository.
Nature of the diffuse emission sources in the H i supershell in the galaxy IC 1613

We present a study of the nearby low-metallicity dwarf galaxy IC 1613, focusing on the search for massive stars and related feedback processes, as well as for faint supernova remnants (SNR) in late stages of evolution. We obtained the deepest images of IC 1613 in the narrow-band Hα, He ii and [S ii] emission lines and new long-slit spectroscopy observations using several facilities (6-m BTA, 2.5m SAI MSU, and 150RTT telescopes), in combination with the multi-wavelength archival data from MUSE/VLT, VLA, XMM-Newton, and Swift/XRT. Our deep narrow-band photometry identifies several faint shells in the galaxy, and we further investigate their physical characteristics with the new long-slit spectroscopy observations and the archival multi-wavelength data. Based on energy balance calculations and assumptions about their possible nature, we propose that one of the shells is a possible remnant of a supernova explosion. We study five out of eight Wolf-Rayet (WR) star candidates previously published for this galaxy using the He ii emission line mapping, MUSE/VLT archival spectra, and new long-slit spectra. Our analysis discards the considered WR candidates and finds no new ones. We found P Cyg profiles in Hα line in two stars, which we classify as Luminous Blue Variable (LBV) star candidates. Overall, the galaxy IC 1613 may have a lower rate of WR star formation than previously suggested.
Smooth real-time motion planning based on a cascade dual-quaternion screw-geometry MPC

This paper investigates the tracking problem of a smooth coordinate-invariant trajectory using dual quaternion algebra. The proposed architecture consists of a cascade structure in which the outer-loop MPC performs real-time smoothing of the manipulator's end-effector twist while an inner-loop kinematic controller ensures tracking of the instantaneous desired end-effector pose. Experiments on a $7$-DoF Franka Emika Panda robotic manipulator validate the proposed method demonstrating its application to constraint the robot twists, accelerations and jerks within prescribed bounds.
Hierarchical Tree-structured Knowledge Graph For Academic Insight Survey

Research surveys have always posed a challenge for beginner researchers who lack of research training. These researchers struggle to understand the directions within their research topic, and the discovery of new research findings within a short time. One way to provide intuitive assistance to beginner researchers is by offering relevant knowledge graphs(KG) and recommending related academic papers. However, existing navigation knowledge graphs primarily rely on keywords in the research field and often fail to present the logical hierarchy among multiple related papers clearly. Moreover, most recommendation systems for academic papers simply rely on high text similarity, which can leave researchers confused as to why a particular article is being recommended. They may lack of grasp important information about the insight connection between"Issue resolved"and"Issue finding"that they hope to obtain. To address these issues, this study aims to support research insight surveys for beginner researchers by establishing a hierarchical tree-structured knowledge graph that reflects the inheritance insight of research topics and the relevance insight among the academic papers.
Offline Deep Model Predictive Control (MPC) for Visual Navigation

In this paper, we propose a new visual navigation method based on a single RGB perspective camera. Using the Visual Teach&Repeat (VT&R) methodology, the robot acquires a visual trajectory consisting of multiple subgoal images in the teaching step. In the repeat step, we propose two network architectures, namely ViewNet and VelocityNet. The combination of the two networks allows the robot to follow the visual trajectory. ViewNet is trained to generate a future image based on the current view and the velocity command. The generated future image is combined with the subgoal image for training VelocityNet. We develop an offline Model Predictive Control (MPC) policy within VelocityNet with the dual goals of (1) reducing the difference between current and subgoal images and (2) ensuring smooth trajectories by mitigating velocity discontinuities. Offline training conserves computational resources, making it a more suitable option for scenarios with limited computational capabilities, such as embedded systems. We validate our experiments in a simulation environment, demonstrating that our model can effectively minimize the metric error between real and played trajectories.
ProtAgents: Protein discovery via large language model multi-agent collaborations combining physics and machine learning

Designing de novo proteins beyond those found in nature holds significant promise for advancements in both scientific and engineering applications. Current methodologies for protein design often rely on AI-based models, such as surrogate models that address end-to-end problems by linking protein structure to material properties or vice versa. However, these models frequently focus on specific material objectives or structural properties, limiting their flexibility when incorporating out-of-domain knowledge into the design process or comprehensive data analysis is required. In this study, we introduce ProtAgents, a platform for de novo protein design based on Large Language Models (LLMs), where multiple AI agents with distinct capabilities collaboratively address complex tasks within a dynamic environment. The versatility in agent development allows for expertise in diverse domains, including knowledge retrieval, protein structure analysis, physics-based simulations, and results analysis. The dynamic collaboration between agents, empowered by LLMs, provides a versatile approach to tackling protein design and analysis problems, as demonstrated through diverse examples in this study. The problems of interest encompass designing new proteins, analyzing protein structures and obtaining new first-principles data -- natural vibrational frequencies -- via physics simulations. The concerted effort of the system allows for powerful automated and synergistic design of de novo proteins with targeted mechanical properties. The flexibility in designing the agents, on one hand, and their capacity in autonomous collaboration through the dynamic LLM-based multi-agent environment on the other hand, unleashes great potentials of LLMs in addressing multi-objective materials problems and opens up new avenues for autonomous materials discovery and design.
Wasserstein Gradient Flows for Moreau Envelopes of f-Divergences in Reproducing Kernel Hilbert Spaces

Most commonly used $f$-divergences of measures, e.g., the Kullback-Leibler divergence, are subject to limitations regarding the support of the involved measures. A remedy consists of regularizing the $f$-divergence by a squared maximum mean discrepancy (MMD) associated with a characteristic kernel $K$. In this paper, we use the so-called kernel mean embedding to show that the corresponding regularization can be rewritten as the Moreau envelope of some function in the reproducing kernel Hilbert space associated with $K$. Then, we exploit well-known results on Moreau envelopes in Hilbert spaces to prove properties of the MMD-regularized $f$-divergences and, in particular, their gradients. Subsequently, we use our findings to analyze Wasserstein gradient flows of MMD-regularized $f$-divergences. Finally, we consider Wasserstein gradient flows starting from empirical measures. We provide proof-of-the-concept numerical examples for $f$-divergences with both infinite and finite recession constant.
On multicolor Tur\'an numbers

We address a problem which is a generalization of Tur\'an-type problems recently introduced by Imolay, Karl, Nagy and V\'ali. Let $F$ be a fixed graph and let $G$ be the union of $k$ edge-disjoint copies of $F$, namely $G = \mathbin{\dot{\cup}}_{i=1}^{k} F_i$, where each $F_i$ is isomorphic to a fixed graph $F$ and $E(F_i)\cap E(F_j)=\emptyset$ for all $i \neq j$. We call a subgraph $H\subseteq G$ multicolored if $H$ and $F_i$ share at most one edge for all $i$. Define $\text{ex}_F(H,n)$ to be the maximum value $k$ such that there exists $G = \mathbin{\dot{\cup}}_{i=1}^{k} F_i$ on $n$ vertices without a multicolored copy of $H$. We show that $\text{ex}_{C_5}(C_3,n) \le n^2/25 + 3n/25+o(n)$ and that all extremal graphs are close to a blow-up of the 5-cycle. This bound is tight up to the linear error term.
Enhancing User Interaction in ChatGPT: Characterizing and Consolidating Multiple Prompts for Issue Resolution

Prompt design plays a crucial role in shaping the efficacy of ChatGPT, influencing the model's ability to extract contextually accurate responses. Thus, optimal prompt construction is essential for maximizing the utility and performance of ChatGPT. However, sub-optimal prompt design may necessitate iterative refinement, as imprecise or ambiguous instructions can lead to undesired responses from ChatGPT. Existing studies explore several prompt patterns and strategies to improve the relevance of responses generated by ChatGPT. However, the exploration of constraints that necessitate the submission of multiple prompts is still an unmet attempt. In this study, our contributions are twofold. First, we attempt to uncover gaps in prompt design that demand multiple iterations. In particular, we manually analyze 686 prompts that were submitted to resolve issues related to Java and Python programming languages and identify eleven prompt design gaps (e.g., missing specifications). Such gap exploration can enhance the efficacy of single prompts in ChatGPT. Second, we attempt to reproduce the ChatGPT response by consolidating multiple prompts into a single one. We can completely consolidate prompts with four gaps (e.g., missing context) and partially consolidate prompts with three gaps (e.g., additional functionality). Such an effort provides concrete evidence to users to design more optimal prompts mitigating these gaps. Our study findings and evidence can - (a) save users time, (b) reduce costs, and (c) increase user satisfaction.
The Impact of Non‐Equilibrium Plasma Distributions on Solar Wind Measurements by Vigil's Plasma Analyser

In order to protect society from space weather impacts, we must monitor space weather and obtain early warnings for extreme events if possible. For this purpose, the European Space Agency is currently preparing to launch the Vigil mission toward the end of this decade as a space‐weather monitor at the fifth Lagrange point of the Sun–Earth system. Vigil will carry, amongst other instruments, the Plasma Analyser (PLA) to provide quasi‐continuous measurements of solar wind ions. We model the performance of the PLA instrument, considering typical solar wind plasma conditions, to compare the expected observations of PLA with the assumed input conditions of the solar wind. We evaluate the instrument performance under realistic, non‐equilibrium plasma conditions, accounting for temperature anisotropies, proton beams, and the contributions from α‐particles. We examine the accuracy of the instrument's performance over a range of input solar wind moments. We identify sources of potential errors due to non‐equilibrium plasma conditions and link these to instrument characteristics such as its angular and energy resolution and its field of view. We demonstrate the limitations of the instrument and potential improvements such as applying ground‐based fitting techniques to obtain more accurate measurements of the solar wind even under non‐equilibrium plasma conditions. The use of ground processing of plasma moments instead of on‐board processing is crucial for the extraction of reliable measurements.
Do random initial degrees suppress concentration in preferential attachment graphs?

We consider the open problem concerning the possible lack of concentration of the degree distribution in preferential attachment graphs with random initial degree, when its distribution is characterized by extremely heavy tails of power-law type. We show that the addition of such a large number of edges causes a significant upset of the degree distribution, leading to its non-concentration. Furthermore, we show that the smallest value of the exponent for which the degree distribution exhibits concentration is 2.
Evolving Mobile Cloud Gaming with 5G Standalone Network Telemetry

Mobile cloud gaming places the simultaneous demands of high capacity and low latency on the wireless network, demands that Private and Metropolitan-Area Standalone 5G networks are poised to meet. However, lacking introspection into the 5G Radio Access Network (RAN), cloud gaming servers are ill-poised to cope with the vagaries of the wireless last hop to a mobile client, while 5G network operators run mostly closed networks, limiting their potential for co-design with the wider internet and user applications. This paper presents Telesa, a passive, incrementally-deployable, and independently-deployable Standalone 5G network telemetry system that streams fine-grained RAN capacity, latency, and retransmission information to application servers to enable better millisecond scale, application-level decisions on offered load and bit rate adaptation than end-to-end latency measurements or end-to-end packet losses currently permit. We design, implement, and evaluate a Telesa telemetry-enhanced game streaming platform, demonstrating exact congestion-control that can better adapt game video bitrate while simultaneously controlling end-to-end latency, thus maximizing game quality of experience. Our experimental evaluation on a production 5G Standalone network demonstrates a 178-249% Quality of Experience improvement versus two state-of-the-art cloud gaming applications.
Continuous K-biframes in Hilbert spaces and their tensor products

A generalization of continuous biframe in a Hilbert space is introduced and a few examples are discussed. Some characterizations and algebraic properties of this biframe are given. Here we also construct various types of continuous K-biframes with the help of a bounded linear operator. Relationship between continuous K-biframe and quotient operator is established. Finally, we define continuous K-biframe for the tensor products of Hilbert spaces and give an example.
RIS-NOMA integrated low-complexity transceiver architecture: Sum rate and energy efficiency perspective

This paper aims to explore reconfigurable intelligent surface (RIS) integration in a millimeter wave (mmWave) communication system with low-complexity transceiver architecture under imperfect CSI assumption. Towards this, we propose a RIS-aided system with a fully analog (FA) architecture at the base station. However, to overcome the disadvantage of single-user transmission due to the single RF-chain, we employ NOMA. For such a system, we formulate sum rate (SR) and energy efficiency (EE) maximization problems to obtain the joint transmit beamformer, RIS phase shift matrix, and power allocation solutions under minimum rate constraint. We first tackle the fractional objectives of both problems by reformulating the SR and EE maximization problems into equivalent quadratic forms using the quadratic transform. On the other hand, we employ successive convex approximation and the semi-definite relaxation technique to handle the non-convex minimum rate and unit modulus constraint of the RIS phase shifts, respectively. Next, we propose an alternating optimization-based algorithm that iterates over the transmit beamformer, power allocation, and RIS phase shift subproblems. Further, we also show that the quadratic reformulation is equivalent to the WMSE-based reformulation for the case of SR maximization problem. Our numerical results show that the proposed RIS-NOMA integrated FA architecture system outperforms the optimally configured fully digital architecture in terms of SR at low SNR and EE for a wide range of SNR while still maintaining low hardware complexity and cost. Finally, we present the numerical performance analysis of the RIS-NOMA integrated low-complexity system for various system configuration parameters.