Miracle-dz/UniFAR-FTUs huggingface dataset:
Optimizing energy efficiency in data center cooling towers through predictive maintenance and project management

Optimizing energy efficiency in data center cooling towers is crucial for reducing operational costs and environmental impact. This review explores the integration of predictive maintenance and project management to achieve this goal. By leveraging predictive maintenance techniques, data center operators can anticipate and address potential issues before they lead to costly downtime or inefficiencies. Project management plays a key role in coordinating these efforts, ensuring that maintenance activities are carried out efficiently and effectively. Predictive maintenance relies on data analytics and machine learning algorithms to monitor the condition of cooling towers in real-time. By analyzing data such as temperature, pressure, and flow rates, these algorithms can detect anomalies and predict potential failures. This proactive approach allows data center operators to schedule maintenance activities during planned downtime, minimizing disruptions to operations. Project management practices, such as Agile or Waterfall methodologies, are essential for coordinating predictive maintenance efforts. Project managers oversee the planning, execution, and monitoring of maintenance activities, ensuring that they are completed on time and within budget. They also facilitate communication between stakeholders, including maintenance teams, data analysts, and management, to ensure that everyone is aligned and working towards the same goals. By integrating predictive maintenance with project management, data center operators can achieve significant improvements in energy efficiency. By addressing maintenance issues proactively, operators can reduce energy consumption, extend the lifespan of cooling equipment, and minimize downtime. Additionally, project management practices ensure that these efforts are coordinated and effective, maximizing the benefits of predictive maintenance. In conclusion, optimizing energy efficiency in data center cooling towers requires a holistic approach that combines predictive maintenance and project management. By leveraging predictive maintenance techniques and project management practices, data center operators can achieve significant improvements in energy efficiency, reduce operational costs, and enhance environmental sustainability.
Single Pass Client-Preprocessing Private Information Retrieval

Recently, many works have considered Private Information Retrieval (PIR) with client-preprocessing: In this model a client and a server jointly run a preprocessing phase, after which client queries can run in time sublinear in the size of the database. In addition, such approaches store no additional bits per client at the server, allowing us to scale PIR to a large number of clients. In this work, we propose the first client-preprocessing PIR scheme with “single pass” client-preprocessing. In particular, our scheme is concretely optimal with respect to preprocessing, in the sense that it requires exactly one linear pass over the database. This is in stark contrast with existing works, whose preprocessing is proportional to λ · N , where λ is the security parameter (e.g., λ = 128). Our approach yields a pre-processing speedup of 45-100 × and a query speedup of up to 20 × when compared to previous state-of-the-art schemes (e.g., Checklist, USENIX 2021), making preprocessing PIR more attractive for a myriad of use cases that are “session-based”. In addition to fast preprocessing, our scheme features extremely fast updates (additions and edits)—in constant time. Previously, the best known approach for handling updates in client-preprocessing PIR had time complexity O ( log N ) , while also adding a log N factor to the bandwidth. We implement our update algorithm and show concrete speedups of about 20 × in update time when compared to the previous state-of-the-art updatable scheme (e.g., Checklist, USENIX 2021).
Successful Robot-Assisted Simple Prostatectomy Years After a Prostate Artery Embolization: A Case Report and Review of the Literature

Simple prostatectomy (SP) can be utilized for patients with large prostates with lower urinary tract symptoms. Prostate artery embolization (PAE) does not have robust clinical evidence to support its use in treating urinary symptoms; however, it is an effective treatment for refractory hematuria from a prostatic source. There have been limited papers regarding preoperative PAE prior to SP. However, there are no papers regarding the feasibility of delayed SP after PAE. We present, to our knowledge, the first paper demonstrating a successful robot-assisted SP years after a PAE in a patient with a 380g prostate with recurrent refractory gross hematuria.
The Use of Coffee Bean Oil to Repel Amblyomma americanum (Acari:Ixodidae)

: There is growing interest in using natural alternatives to repel ticks, particularly essential oils as opposed to synthetic chemicals. Previous studies have found volatile monoterpenes in many essential oils to be repellent or acaricidal, leading us to test the repellency of coffee bean oil to ticks. Amblyomma americanum (Acari: Ixodidae) were the subjects tested in two separate studies. One determined the repellency by testing the number of ticks repelled from five different dilutions of coffee bean oil, with dichloromethane as the solvent and control. The other study investigated the farthest distance at which the oil could repel ticks. The study shows that concentrations as low as one percent were effective at repelling A. americanum, however, twenty percent repelled the greatest number of ticks. We conclude that the twenty percent solution was repellent up to sixty centimeters. Coffee bean oil does show the ability to repel A. americanum . This work shows that coffee bean oil is another example of essential oils that show ability to repel ticks.
Design for Embedding the Value of Privacy in Personal Information Management Systems

Personal Information Management Systems (PIMS) aim to facilitate the sharing of personal information and protect privacy. Efforts to enhance privacy management, aligned with established privacy policies, have led to guidelines for integrating transparent notices and meaningful choices within these systems. Although discussions have revolved around the design of privacy-friendly systems that comply with legal requirements, there has been relatively limited philosophical discourse on incorporating the value of privacy into these systems. Exploring the connection between privacy and personal autonomy illuminates the instrumental value of privacy in enabling individuals to live their lives autonomously, highlighting the importance of intentionally embedding the value of privacy into these systems. To translate the value of privacy into concrete design requirements, this study constructs a values hierarchy consisting of values, norms, and design requirements. After analyzing the relationships between privacy and autonomy and identifying norms, the design requirements translated from the norms associated with the components of personal autonomy are specified at the lowest layer. These requirements include a design to prevent unauthorized access and dark patterns and to provide effective and efficient notices and choices. The findings contribute to expanding the requirements for designing the aspect of privacy as a legal requirement to incorporate the value of privacy into systems.
Distinct Thresholds for the Initiation and Cessation of Aeolian Saltation From Field Measurements

Wind‐blown sand and dust models depend sensitively on the threshold wind stress. However, laboratory and numerical experiments suggest the coexistence of distinct fluid and impact thresholds for the initiation and cessation of aeolian saltation, respectively. Because aeolian transport models typically use only a single threshold, existence of separate higher fluid and lower impact thresholds complicates the prediction of wind‐driven transport. Here we extend the statistical Time Frequency Equivalence Method to derive the first field‐based estimates of distinct fluid and impact thresholds from high‐frequency wind and saltation measurements at three field sites. Our measurements show that when saltation is mostly inactive, its instantaneous occurrence is governed primarily by wind exceedance of the fluid threshold. As saltation activity increases, so too does the relative importance of the impact threshold, until it dominates under near‐continuous transport conditions. Although both thresholds are thus important for high‐frequency saltation prediction, our results suggest that the time‐averaged saltation flux is primarily governed by the impact threshold.
Potential Intersections between lncRNA, Vascular Cognitive Impairment, and Immunization Strategies: Insights and Future Directions

Vascular cognitive impairment (VCI) encompasses a wide range of cognitive disorders stemming from cerebrovascular issues, such as strokes or small vessel disease. These conditions often pose challenges to traditional diagnostic approaches due to their multifactorial nature and varied clinical presentations. Recently, next-generation sequencing (NGS) technologies have provided detailed analyses of long non-coding RNAs (lncRNAs) in the molecular pathobiology of VCI. These new findings help with molecular-based diagnostics and treatments of VCI. Within this realm, the concept of immune modulation, especially through specific vaccinations, emerges as a promising therapeutic strategy in VCI mitigation. In this review, we comprehensively elucidate the function of lncRNAs in VCI, emphasizing the advanced understanding of VCI’s molecular underpinnings made possible through NGS technologies. Significant focus is placed on the immune system’s role in VCI, particularly the neuroinflammatory processes which are consequential to cerebrovascular abnormalities. We believe that lncRNAs participate in regulating these immunological pathways, potentially guiding the development of vaccines targeting VCI. In this context, we propose a novel perspective: using knowledge about lncRNA profiles and functions to guide vaccine development, we can potentially exploit the body’s immune response to mitigate or prevent VCI. This approach has the potential to revolutionize VCI management by introducing targeted immunization strategies informed by molecular signatures, a concept that remains largely unexplored in current research endeavors. In addition, we summarize current progress and propose future directions, advocating for robust, interdisciplinary studies to validate the potential intersections between lncRNA landscapes, VCI pathology, and immunology. This review aims to spur innovative research and promote the development of lncRNA-informed vaccine strategies as proactive interventions against the cognitive consequences of VCI.
Automatic Extraction of Coarse-Grained Data-Flow Threads from Imperative Programs

This article presents a general algorithm for transforming sequential imperative programs into parallel data-flow programs. The algorithm operates on a program dependence graph in static-single-assignment form, extracting task, pipeline, and data parallelism from arbitrary control flow, and coarsening its granularity using a generalized form of typed fusion. A prototype based on GNU Compiler Collection (GCC) is applied to the automatic parallelization of recursive C programs.
A Zero-Dimensional Gröbner Basis for Poseidon

In this paper we construct dedicated weight orders > so that a > -Gröbner bases of Poseidon can be found via linear transformations for the preimage as well as the CICO problem. In particular, with our Gröbner bases we can exactly compute the F q -vector space dimension of the quotient space for all possible Poseidon configurations. This in turn resolves previous attempts to assess the security of Poseidon against Gröbner basis attacks, since the vector space dimension quantifies the complexity of computing the variety of a zero-dimensional polynomial system.
The Effect of Stereocomplexation and Crystallinity on the Degradation of Polylactide Nanoparticles

Polymeric nanoparticles (PNPs) are frequently researched and used in drug delivery. The degradation of PNPs is highly dependent on various properties, such as polymer chemical structure, size, crystallinity, and melting temperature. Hence, a precise understanding of PNP degradation behavior is essential for optimizing the system. This study focused on enzymatic hydrolysis as a degradation mechanism by investigation of the degradation of PNP with various crystallinities. The aliphatic polyester polylactide ([C3H4O2]n, PLA) was used as two chiral forms, poly l-lactide (PlLA) and poly d-lactide (PdLA), and formed a unique crystalline stereocomplex (SC). PNPs were prepared via a nanoprecipitation method. In order to further control the crystallinity and melting temperatures of the SC, the polymer poly(3-ethylglycolide) [C6H8O4]n (PEtGly) was synthesized. Our investigation shows that the PNP degradation can be controlled by various chemical structures, crystallinity and stereocomplexation. The influence of proteinase K on PNP degradation was also discussed in this research. AFM did not reveal any changes within the first 24 h but indicated accelerated degradation after 7 days when higher EtGly content was present, implying that lower crystallinity renders the particles more susceptible to hydrolysis. QCM-D exhibited reduced enzyme adsorption and a slower degradation rate in SC-PNPs with lower EtGly contents and higher crystallinities. A more in-depth analysis of the degradation process unveiled that QCM-D detected rapid degradation from the outset, whereas AFM exhibited delayed changes of degradation. The knowledge gained in this work is useful for the design and creation of advanced PNPs with enhanced structures and properties.
Bulletin of Electrical Engineering and Informatics

Islam A. Monir, Mohamed W. Fakhr, Nashwa El-Bendary College of Computing and Information Technology, Arab Academy for Science and Technology and Maritime Transport (AASTMT), Smart Village, Egypt College of Engineering and Technology, Arab Academy for Science and Technology and Maritime Transport (AASTMT), Cairo, Egypt College of Computing and Information Technology, Arab Academy for Science and Technology and Maritime Transport (AASTMT), Aswan, Egypt
Nanosensors for animal infectious disease detection

Infectious diseases in farm animals triggered by pathogenic microorganisms affect the health and well-being of livestock and human populations. Pathogen detection is an important step for the successful diagnosis, treatment and control of infectious diseases in animals. Pathogens that persist in the poultry and livestock industries can be responsible for more than 70% of emerging infections. Thus, rapid diagnostic tools are extremely important. In recent years, nanotechnology has emerged as a great opportunity to tackle this challenge and to develop fast, accurate and economical diagnostics for the detection of pathogens. Various nanostructures, due to the presence of unique characteristics shown in nanomaterials, have already been applied in biodiagnostics to detect specific molecular targets, including pathogen detection. In this context, this review focuses on the application, role and challenges of nanosensors in detecting disease-causing pathogens in agriculture. Several nanostructures are investigated for their utility in providing innovative solutions for pathogen detection in farm animals. This comprehensive examination seeks to unravel the intricate nanosensors landscape, shedding some light on their role in advancing diagnostic capabilities within the agricultural domain. By elucidating the challenges inherent in their application, the review contributes to the ongoing discourse on harnessing nanotechnology for the detection and management of infectious diseases in livestock, ultimately paving the way for developments in veterinary diagnostics.
Challenges in Benchmarking Optimization Heuristics

This report documents the program and outcomes of the Dagstuhl Seminar 23251 “Challenges in Benchmarking Optimization Heuristics”. In the domain of optimization heuristics, a stable basis for fairly evaluating the performance of optimization algorithms and other solution approaches – commonly referred to as “benchmarking” – is fundamental to ensuring steady scientific progress. Although many pitfalls are well known in the community, the development of sound benchmarking protocols is slow, and the adoption of community-wide recognized and implementable standards requires lasting and joint efforts among research groups. This seminar brought together people from diverse backgrounds and fostered discussions among different optimization communities, focusing on how to cope with “horse racing papers”, landscape analysis techniques for understanding problem instances, and discussions about the overarching goals of benchmarking. Seminar
Journal of University Teaching & Learning Practice Journal of University Teaching & Learning Practice

Abstract Second career preservice teachers’ perceptions about the out-of-field teaching phenomenon might influence career decisions, such as retention and attrition. A target group of 133 second career graduating Master of Teaching students voluntarily participated in this mixed method study which offered findings through analysed open and Likert-scale questions and semi-structured interview data. The pilot offers new information about second career preservice teachers’ perceptions about their possible involvement in out-of-field teaching practices and the phenomenon’s implications for capabilities, skills knowledge, self-efficacy, support needs and professional identity. A critical reflection on quality teaching and teacher training programs, and preparation for the teaching profession form the foundation for further research in this field. The analyses further stimulate a deeper understanding of the future second career teaching workforce and perceptions of support resources.
Dynamic Load Balancing in Hierarchical Parallel Database Systems

We consider the execution of multi-join queries in a hierarchical parallel system, i.e., a shared-nothing system whose nodes are shared-memory multiprocessors. In this context, load balancing must be addressed at two levels, locally among the processors of each shared-memory node and globally among all nodes. In this paper, we propose a dynamic execution model that maximizes local load balancing within shared-memory nodes and minimizes the need for load sharing across nodes. This is obtained by allowing each processor to execute any operator that can be processed locally, thereby taking full advantage of interand intra-operator parallelism. We conducted a performance evaluation using an implementation on a 72-processor KSR1 computer. The experiments with many queries and large relations show very good speedup results, even with highly skewed data. We show that, in shared-memory, our execution model performs as well as a dedicated model and can scale up very well to deal with multiple nodes. Key-words: Databases
CS 237B: Principles of Robot Autonomy II Problem Set 3: Human-Robot Interaction Due March 12th 11:59PM

Starter code for this problem set has been made available online through github; to get started download the code by running git clone https://github.com/PrinciplesofRobotAutonomy/CS237B_HW3. git. You will submit your homework to Gradescope. Your submission will consist of (1) a single pdf with your answers for written questions (denoted by the symbol) and (2) a zip folder containing your code for the programming questions (denoted by the symbol). Also include the policies folder in your submission. Remember, your written part must be typeset (e.g., LATEXor Word).
Real Time Processing of GPS Data using DSP Techniques

The developments in applications of GPS over last 10 years have done at a higher rate than advancements in the realization of GPS constellation.Owing to the advantages of high accuracy, all weather conditions, no requirements of intervisibility between measuring points, GPS is playing more and more important role in high precision positioning missions in structure/construction health, land formation, and movement monitoring etc. In order to meet a particular purpose objective, a properly configured GPS measurement can meet most of the possible static and dynamic measurement needs in such applications for absolute positioning and relative displacement. GPS has enormous benefits to myriad military, civil, commercial and scientific users around the world. The use of GPS in various areas such as Navigation, Surveying, navigation/Landing Systems, Farming, Weather Forecasting, Fleet Management provides the stimulus for its future improvement. Thus the book is useful for error estimation in observed GPS data using signal processing techniques. The estimated data may be used for measurement of overhead conductor sag in power transmission lines and for vehicles tracking in real time.
Reconstructing Archaeological Sites

Reconstructing Archaeological Sites offers an important text that puts the focus on basic theoretical and practical aspects of depositional processes in an archaeological site. It contains an in-depth discussion on the role of stratigraphy that helps determine how deposits are organised in time and space. The authors — two experts in the field — include the information needed to help recognise depositional systems, processes and stratigraphic units that aid in the interpreting the stratigraphy and deposits of a site in the field.
Leveraging digital technology to bridge the global knowledge divide: The promise of MOOCs revisited-G20 Insights

Massive open online courses (MOOCs) can be critical in bridging the global knowledge divide. However, the promise of MOOCs has remained largely unfulfilled, mainly because of the global digital divide. The closure of schools following the COVID-19 pandemic has reignited interest in MOOCs, with many innovative solutions emerging worldwide. We offer a strong rationale and propose specific measures for national governments and the Group of Twenty (G20) to scale up the uptake of MOOCs— these include investment in infrastructure to adapt globally, and develop indigenous MOOCs in lowand middle-income countries; G20 funding for large-scale infrastructure initiatives in least developed countries; leveraging the deep penetration of mobile technology in developing countries; and expanded funding for UNESCO.
Attributing the U.S. Southwest's Recent Shift Into Drier Conditions

The U.S. Southwest experienced a strong hydroclimate trend from the 1980s to the 2010s, from cool and wet to warm and dry conditions. Attribution of this trend is challenging due to the influence of internal variability but desired by water managers eager to plan for robust signals of climate change in this water‐scarce region. Here we use an empirical method based on constructed circulation analogues to assess the contribution of atmospheric circulation variability to the recent observed hydroclimate trend. Consistent with other studies, we find the observed precipitation trend from 1983 to 2012 to be mainly due to internal atmospheric circulation variability that is driven in part by decadal‐scale tropical Pacific sea surface temperature changes. Removing this internal dynamical component brings the observed precipitation trend into closer agreement with the anthropogenically forced response in climate models, demonstrating progress toward an integrated perspective on climate change attribution.