Exhaustive Search Research Articles

Meta‐heuristic optimization techniques for scheduling in multiple‐input multiple‐output multiple‐access channel system

SummaryIn a highly dense multiple‐input multiple‐output (MIMO) communication system, proper resource allocation is exceedingly challenging. The simultaneous selection of antennas at the base‐station (BS) and scheduling of users in an uplink multiple access channel (MAC) system add up to the signal processing load in the MIMO wireless system. The method chosen for combining the user scheduling and antenna selection must have a very low computational complexity. However, the exhaustive search algorithm (ESA), which achieves the best sum‐rate capacity in a MIMO‐MAC system, has a very high level of complexity. In this paper, we use a metaheuristic‐based optimization algorithms to optimize the sum‐rate capacity in a MAC system with significantly less computational complexity. First, we use the beluga whale optimization (BWO) metaheuristic method to solve the optimization problem. The BWO is a swarm‐based algorithm that draws inspiration from beluga whale behavior. An improved BWO (iBWO) is also presented and is utilized for the same optimization challenge. The proposed iBWO scheme improves the search capability of the algorithm and thereby ensures better exploration and exploitation capabilities of the beluga whales. We compare the performance of these algorithms with a standard differential evolution (DE) algorithm. The simulation results demonstrate that the proposed iBWO is a very competitive meta‐heuristic algorithm for solving optimization problems. The proposed iBWO algorithm achieves near optimal throughput in a MIMO‐MAC system. Also, the computational complexity related to the suggested optimization technique in a MIMO‐MAC system is found to be very less compared with the complexity related to ESA.

Folliculitis Decalvans: A Review of Current Treatment Modalities

Background: Folliculitis decalvans (FD) is a rare subtype of neutrophilic cicatricial alopecia, firstly described by Quinquad in 1888, as inflammatory scalp disease usually affecting young adults. The exact etiopathology of the disease is not fully understood, however the presence of Staphylococcus aureus, dysfunction of the host’s immune response, genetic factors, previous trauma of the scalp, as well as Epidermal Growth Factor Receptor inhibitors (EGFRi) use seem to play a role. Clinically, FD presents with scarring alopecic patches with follicular pustules, crusts and tufted hair. Objectives: As the full etiology of the disease is unknown and most data in the literature is based on retrospective date, therapeutic management is not precisely established. The objective of this review is to describe therapeutic options, as well as highlighting potential new therapeutic modalities reported in the literature. Limitations: Because FD is a rare disease, the main limitation is lack of randomized control trials, evaluating therapeutic modalities. Methods: An exhaustive search of references related to FD published in PubMed between 2017 and 2024 was undertaken, using the search term: "folliculitis decalvans". Articles with large patient cohorts and reviews were included, as well as case reports and case series, that reported novel treatment approaches for FD.Conclusions: The management of FD poses a challenge. Systemic antibiotics, particularly rifampicin and clindamycin, are considered the first-line agents and are commonly used in combination with local therapies. Systemic isotretinoin yields sustained remission in some cases, while biological agents exhibit promise in managing severe FD. Novel therapeutic modalities, incorporating, among others, botulinum toxin A injections, Platelet-Rich-Plasma (PRP), or surgical procedures, necessitate randomized double-blind trials to evaluate their safety and efficacy.

Prognostic value of EIF5A2 in solid tumors: A meta-analysis and bioinformatics analysis.

In cancer biology, the aberrant overexpression of eukaryotic translation initiation factor 5A2 (EIF5A2) has been correlative with an ominous prognosis, thereby underscoring its pivotal role in fostering metastatic progression. Consequently, EIF5A2 has garnered significant attention as a compelling prognostic biomarker for various malignancies. Our research endeavors were thus aimed at elucidating the utility and significance of EIF5A2 as a robust indicator of cancer outcome prediction. An exhaustive search of the PubMed, EMBASE, and Web of Science databases found relevant studies. The link between EIF5A2 and survival prognosis was examined using hazard ratios and 95% confidence intervals. Subsequently, The Cancer Genome Atlas (TCGA) and the Gene Expression Profiling Interactive Analysis (GEPIA) databases were employed to validate EIF5A2 expression across various cancer types. Through pooled analysis, we found that increased EIF5A2 expression was significantly associated with decreased overall survival (OS) and disease-free survival/progression-free survival/relapse-free survival (DFS/PFS/RFS). Moreover, TCGA analysis revealed that EIF5A2 was significantly upregulated in 27 types of cancer, with overexpression being linked to shorter OS in three, worse DFS in two, and worse PFS in six types of cancer. GEPIA showed that patients with EIF5A2 overexpression had reduced OS and DFS. In solid tumors, EIF5A2 emerges as a reliable prognostic marker. Our meta-analysis comprehensively analyzed the prognostic value of EIF5A2 in solid tumors and assessed its efficacy as a predictive marker.

In Silico Design and Synthesis of Antifungal Peptides Guided by Quantitative Antifungal Activity.

Antifungal peptides (AFPs) are emerging as promising candidates for advanced antifungal therapies because of their broad-spectrum efficacy and reduced resistance development. In silico design of AFPs, however, remains challenging, due to the lack of an efficient and well-validated quantitative assessment of antifungal activity. This study introduced an AFP design approach that leverages an innovative quantitative metric, named the antifungal index (AFI), through a three-step process, i.e., segmentation, single-point mutation, and global multipoint optimization. An exhaustive search of 100 putative AFP sequences indicated that random modifications without guidance only have a 5.97-20.24% chance of enhancing antifungal activity. Analysis of the search results revealed that (1) N-terminus truncation is more effective in enhancing antifungal activity than the modifications at the C-terminus or both ends, (2) introducing the amino acids within the 10-60% sequence region that enhance aromaticity and hydrophobicity are more effective in increasing antifungal efficacy, and (3) incorporating alanine, cysteine, and phenylalanine during multiple point mutations has a synergistic effect on enhancing antifungal activity. Subsequently, 28 designed peptides were synthesized and tested against four typical fungal strains. The success rate for developing promising AFPs, with a minimal inhibitory concentration of ≤5.00 μM, was an impressive 82.14%. The predictive and design tool is accessible at https://antifungipept.chemoinfolab.com.

Towards efficient control synthesis for nonlinear wave energy conversion systems: impedance-matching meets the spectral-domain

AbstractExisting studies within the literature that focus on designing parametric energy-maximizing controllers for Wave Energy Converter (WEC) systems predominantly rely on the impedance-matching (IM) principle, originally developed for linear time-invariant systems. Alternatively, iterative optimization routines are commonly employed for nonlinear WECs. However, these approaches often face a trade-off between effectiveness in maximizing energy extraction and computational efficiency. To address this limitation, this study proposes a computationally efficient controller tuning method for analogous synthesis in the case of nonlinear WECs. The proposed approach combines a statistical linearization technique known as spectral-domain modeling with the IM principle, to synthesize a Proportional–Integrative (PI) controller for a nonlinear WEC. Furthermore, a comparison is performed with two other synthesis methods: one based on a standard (i.e. linear) frequency-domain representation of the WEC that incorporates the IM principle, and the other employing a gradient-free optimization routine applied to the nonlinear time-domain model of the WEC for PI parameter tuning through exhaustive numerical search. A discussion on the effectiveness of each tuning method in maximizing energy absorption is provided, including an appraisal of their associated computational time requirements. Numerical analyses demonstrate that the proposed method, which integrates spectral-domain modeling and IM, can achieve (almost) optimal PI controller design for a nonlinear WEC. Furthermore, this study addresses the inaccuracies inherent in the frequency-domain approach and significantly reduces the computational time compared to the exhaustive search procedure. The findings of this research represent a significant advancement towards the development of simple, effective, and efficient IM-based techniques for synthesis of controllers in nonlinear WEC systems

Community Engagement in Vaccination Promotion: Systematic Review and Meta-Analysis.

Community engagement plays a vital role in global immunization strategies, offering the potential to overcome vaccination hesitancy and enhance vaccination confidence. Although there is significant backing for community engagement in health promotion, the evidence supporting its effectiveness in vaccination promotion is fragmented and of uncertain quality. This review aims to systematically examine the effectiveness of different contents and extent of community engagement for promoting vaccination rates. This study was performed in accordance with the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. A comprehensive and exhaustive literature search was performed in 4 English databases (PubMed, Embase, Web of Science, and Cochrane Library) and 2 Chinese databases (CNKI and Wan Fang) to identify all possible articles. Original research articles applying an experimental study design that investigated the effectiveness of community engagement in vaccination promotion were eligible for inclusion. Two reviewers independently performed the literature search, study selection, quality assessment, and data extraction. Discrepancies were resolved through discussion, with the arbitration of a third reviewer where necessary. A total of 20 articles out of 11,404 records from 2006 to 2021 were retrieved. The studies used various designs: 12 applied single-group pre-post study designs, 5 were cluster randomized controlled trials (RCTs), and 3 were non-RCTs. These studies targeted multiple vaccines, with 8 focusing on children's immunization, 8 on human papillomavirus vaccine, 3 on hepatitis B virus vaccine, and 1 on COVID-19 vaccine. The meta-analysis revealed significant increases in vaccination rates both in pre-post comparison (rate difference [RD] 0.34, 95% CI 0.21-0.47, I2=99.9%, P<.001) and between-group comparison (RD 0.18, 95% CI 0.07-0.29, I2=98.4%, P<.001). The meta-analysis revealed that participant recruitment had the largest effect size (RD 0.51, 95% CI 0.36-0.67, I2=99.9%, P<.001), followed by intervention development (RD 0.36, 95% CI 0.23-0.50, I2=100.0%, P<.001), intervention implementation (RD 0.35, 95% CI 0.22-0.47, I2=99.8%, P<.001), and data collection (RD 0.34, 95% CI 0.19-0.50, I2=99.8%, P<.001). The meta-analysis indicated that high community engagement extent yielded the largest effect size (RD 0.49, 95% CI 0.17-0.82, I2=100.0%, P<.001), followed by moderate community engagement extent (RD 0.45, 95% CI 0.33-0.58, I2=99.6%, P<.001) and low community engagement extent (RD 0.15, 95% CI 0.05-0.25, I2=99.2%, P<.001). The meta-analysis revealed that "health service support" demonstrated the largest effect sizes (RD 0.45, 95% CI 0.25-0.65, I2=99.9%, P<.001), followed by "health education and discussion" (RD 0.39, 95% CI 0.20-0.58, I2=99.7%, P<.001), "follow-up and reminder" (RD 0.33, 95% CI 0.23-0.42, I2=99.3%, P<.001), and "social marketing campaigns and community mobilization" (RD 0.24, 95% CI 0.06-0.41, I2=99.9%, P<.001). The results of this meta-analysis supported the effectiveness of community engagement in vaccination promotion with variations in terms of engagement contents and extent. Community engagement required a "fit-for-purpose" approach rather than a "one-size-fits-all" approach to maximize the effectiveness of vaccine promotion. PROSPERO CRD42022339081; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=339081.

Linear Codes for Hyperdimensional Computing.

Hyperdimensional computing (HDC) is an emerging computational paradigm for representing compositional information as high-dimensional vectors and has a promising potential in applications ranging from machine learning to neuromorphic computing. One of the long-standing challenges in HDC is factoring a compositional representation to its constituent factors, also known as the recovery problem. In this article, we take a novel approach to solve the recovery problem and propose the use of random linear codes. These codes are subspaces over the Boolean field and are a well-studied topic in information theory with various applications in digital communication. We begin by showing that hyperdimensional encoding using random linear codes retains favorable properties of the prevalent (ordinary) random codes; hence, HD representations using the two methods have comparable information storage capabilities. We proceed to show that random linear codes offer a rich subcode structure that can be used to form key-value stores, which encapsulate the most used cases of HDC. Most important, we show that under the framework we develop, random linear codes admit simple recovery algorithms to factor (either bundled or bound) compositional representations. The former relies on constructing certain linear equationsystems over the Boolean field, the solution to which reduces the search space dramatically and strictly outperforms exhaustive search in many cases. The latter employs the subspace structure of these codes to achieve provably correct factorization. Both methods are strictly faster than the state-of-the-art resonator networks, often by an order of magnitude. We implemented our techniques in Python using a benchmark software library and demonstrated promising experimental results.

Constrained optimal grouping of cloud application components

Cloud applications are built from a set of components often deployed as containers, which can be deployed individually on separate Virtual Machines (VMs) or grouped on a smaller set of VMs. Additionally, the application owner may have inhibition constraints regarding the co-location of components. Finding the best way to deploy an application means finding the best groups of components and the best VMs, and it is not trivial because of the complexity coming from the number of possible options. The problem can be mapped onto may known combinatorial problems as binpacking and knapsack formulations. However, these approaches often assume homogeneus resources and fail to incorporate the inhibition constraints. The main contribution of this paper are firstly a novel formulation of the grouping problem as constrained Coalition Structure Generation (CSG) problem, including the specification of the value function which fulfills the criteria of a Characteristic Function Game (CFG). The CSG problem aims to determine stable and disjoint groups of players collaborating to optimize the joint outcome of the game, and a CFG is a common representation of a CSG, where each group is assigned a value and where the value of the game is the sum of the groups’ contributions. Secondly, the Integer-Partition (IP) CSG algorithm has been modified and extended to handle constraints. The proposed approach is evaluated with the extended IP algorithm, and a novel exhaustive search algorithm establishing the optimum grouping for comparison. The evaluation shows that our approach with the modified algorithm evaluates on average significantly less combinations than the CSG state-of-the-art algorithm. The proposed approach is promising for optimized constrained Cloud application management as the modified IP algorithm can optimally solve constrained grouping problems of attainable sizes.

How Can Data Science Contribute to Understanding the Khipu Code?

Abstract In “How Can Spin, Ply, and Knot Direction Contribute to Understanding the Quipu Code?” (2005), mathematician Marcia Ascher referenced new data on 59 Andean khipus to assess the significance of their variable twists and knots. However, this aggregative, comparative impulse arose late in Ascher's khipu research; the mathematical relations she had identified among 200+ previously cataloged khipus were specified only at the level of individual specimens. This article pursues a new scale of analysis, generalizing the “Ascher relations” to recognize meaningful patterns in a 650-khipu corpus, the largest yet subjected to computational study. We find that Ascher formulae characterize at least 74% of khipus, which exhibit meaningful arrangements of internal sums. Top cords are shown to register a minority of sum relationships and are newly identified as markers of low-level, “working” khipus. We reunite two fragments of a broken khipu using arithmetic properties discovered between the strings. Finally, this analysis suggests a new khipu convention—the use of white pendant cords as boundary markers for clusters of sum cords. In their synthesis, exhaustive search, confirmatory study, mathematical rejoining, and hypothesis generation emerge as distinct contributions to khipu description, typology, and decipherment.

Prevalence of stress urinary incontinence and urge urinary incontinence in multiples sclerosis patients: a systematic review and meta-analysis.

Urinary incontinence (UI), encompassing stress urinary incontinence (SUI) and urge urinary incontinence (UUI), is a prevalent and debilitating condition in patients with multiple sclerosis (MS), profoundly impacting their quality of life. This systematic review and meta-analysis aimed to elucidate the worldwide prevalence rates of SUI and UUI among MS patients. This study was conducted by examining observational studies published between 2000 and 2023. An exhaustive literature search was conducted across databases such as PubMed, MEDLINE, Web of Science, Scopus, ProQuest, and Google Scholar. The Meta-prop method facilitated pooled prevalence estimation of UUI and SUI, while Egger tests assessed publication bias. In total, 27 studies with 15,052 participants were included in the meta-analysis. The findings revealed a high random effect pooled prevalence of UUI at 41.02% (95% Confidence Interval [CI]: 30.57-51.89; I2 = 99%, p < 0.001) and SUI at 25.67% (95% CI: 19.30-32.58%; I2 = 94.9%, P < 0.001). Additionally, the pooled prevalence of mixed urinary incontinence (MUI) was reported at 18.81% (95% CI: 7.55-33.48; I2:95.44%, p < 0.001). The high heterogeneity observed suggests variable prevalence across populations and highlights the intricate nature of UI in MS. These findings underscore the critical need for dedicated supportive, therapeutic, and rehabilitative interventions to manage this common complaint in MS patients effectively.

Drug Discovery Efforts to Identify Novel Treatments for Neglected Tropical Diseases - Cysteine Protease Inhibitors.

Neglected tropical diseases are a severe burden for mankind, affecting an increasing number of people around the globe. Many of those diseases are caused by protozoan parasites in which cysteine proteases plays a key role in the parasite's pathogenesis. In this review article, we summarize the drug discovery efforts of the research community from 2017 - 2022 with a special focus on activities such as the optimization of small molecule cysteine protease inhibitors in terms of selectivity profiles or drug-like properties as well as in vivo studies. The cysteine proteases evaluated by this methodology include Cathepsin B1 from Schistosoma mansoni, papain, cruzain, falcipain, and rhodesain. Exhaustive literature searches were performed using the keywords "Cysteine Proteases" and "Neglected Tropical Diseases" including the years 2017 - 2022. Overall, approximately 3'000 scientific papers were retrieved, which were filtered using specific keywords enabling the focus on drug discovery efforts. Potent and selective cysteine protease inhibitors to treat neglected tropical diseases were identified, which progressed to pharmacokinetic and in vivo efficacy studies. As far as the authors are aware of, none of those inhibitors reached the stage of active clinical development. Either the inhibitor's potency or pharmacokinetic properties or safety profile or a combination thereof prevented further development of the compounds. More efforts with particular emphasis on optimizing pharmacokinetic and safety properties are needed, potentially by collaborations of academic and industrial research groups with complementary expertise. Furthermore, new warheads reacting with the catalytic cysteine should be exploited to advance the research field in order to make a meaningful impact on society.

Towards Machine-FAIR: Representing software and datasets to facilitate reuse and scientific discovery by machines

ObjectiveTo use software, datasets, and data formats in the domain of Infectious Disease Epidemiology as a test collection to evaluate a novel M1 use case, which we introduce in this paper. M1 is a machine that upon receipt of a new digital object of research exhaustively finds all valid compositions of it with existing objects. MethodWe implemented a data-format-matching-only M1 using exhaustive search, which we refer to as M1DFM. We then ran M1DFM on the test collection and used error analysis to identify needed semantic constraints. ResultsPrecision of M1DFM search was 61.7%. Error analysis identified needed semantic constraints and needed changes in handling of data services. Most semantic constraints were simple, but one data format was sufficiently complex to be practically impossible to represent semantic constraints over, from which we conclude limitatively that software developers will have to meet the machines halfway by engineering software whose inputs are sufficiently simple that their semantic constraints can be represented, akin to the simple APIs of services. We summarize these insights as M1-FAIR guiding principles for composability and suggest a roadmap for progressively capable devices in the service of reuse and accelerated scientific discovery. ConclusionAlgorithmic search of digital repositories for valid workflow compositions has potential to accelerate scientific discovery but requires a scalable solution to the problem of knowledge acquisition about semantic constraints on software inputs. Additionally, practical limitations on the logical complexity of semantic constraints must be respected, which has implications for the design of software.

Image sequence sorting algorithm for commercial tasks.

The sorting of sequences of images is crucial for augmenting user engagement in various virtual commercial platforms, particularly within the real estate sector. A coherent sequence of images respecting room type categorization significantly enhances the intuitiveness and seamless navigation of potential customers through listings. This study methodically formalizes the challenge of image sequence sorting and expands its applicability by framing it as an ordering problem. The complexity lies in devising a universally applicable solution due to computational demands and impracticality of exhaustive searches for optimal sequencing. To tackle this, our proposed algorithm employs a shortest path methodology grounded in semantic similarity between images. Tailored specifically for the real estate sector, it evaluates diverse similarity metrics to efficiently arrange images. Additionally, we introduce a genetic algorithm to optimize the selection of semantic features considered by the algorithm, further enhancing its effectiveness. Empirical evidence from our dataset demonstrates the efficacy of the proposed methodology. It successfully organizes images in an optimal sequence across 85% of the listings, showcasing its effectiveness in enhancing user experience in virtual commercial platforms, particularly in real estate. This study presents a novel approach to sorting sequences of images in virtual commercial platforms, particularly beneficial for the real estate sector. The proposed algorithm effectively enhances user engagement by providing more intuitive and visually coherent image arrangements.

Knowledge mapping of neonatal electroencephalogram: A bibliometric analysis (2004-2022).

Electroencephalography (EEG), a widely used noninvasive neurophysiological diagnostic tool, has experienced substantial advancements from 2004 to 2022, particularly in neonatal applications. Utilizing a bibliometric methodology, this study delineates the knowledge structure and identifies emergent trends within neonatal EEG research. An exhaustive literature search was conducted on the Web of Science Core Collection (WoSCC) database to identify publications related to neonatal EEG from 2004 to 2022. Analytical tools such as VOSviewer, CiteSpace, and the R package "bibliometrix" were employed to facilitate this investigation. The search yielded 2501 articles originating from 79 countries, with the United States and England being the predominant contributors. A yearly upward trend in publications concerning neonatal EEG was observed. Notable research institutions leading this field include the University of Helsinki, University College London, and University College Cork. Clinical Neurophysiology is identified as the foremost journal in this realm, with Pediatrics as the most frequently co-cited journal. The collective body of work from 9977 authors highlights Sampsa Vanhatalo as the most prolific contributor, while Mark Steven Scher is recognized as the most frequently co-cited author. Key terms such as "seizures," "epilepsy," "hypoxic-ischemic encephalopathy," "amplitude-integrated EEG," and "brain injury" represent the focal research themes. This bibliometric analysis offers the first comprehensive review, encapsulating research trends and progress in neonatal EEG. It reveals current research frontiers and crucial directions, providing an essential resource for researchers engaged in neonatal neuroscience.

Heterogeneous Internet of Things Big Data Analysis System Based on Mobile Edge Computing

The big data heterogeneous Internet of Things (IoT) requires mobile edge computing (MEC) to process some data, and the data analysis system of MEC often has the problem of excessive terminal energy consumption (ECS) or long delay. So this study designed an energy-saving optimization algorithm for the task offloading processing module in the big data heterogeneous IoT analysis system, and designed and conducted simulation experiments to verify the application performance of the algorithm. The experimental results show that the #04 scheme of the designed algorithm has the lowest terminal ECS under the same conditions. Choosing the #04 scheme to build the algorithm, comparative analysis shows that when the edge server (ES) computing rate is 10 cycles/s, the weighted sum values of terminal ECS for EOPU, MPCO, exhaustive search, and local computing methods are 23.6 J, 23.9 J, 28.5 J and 84.5 J, respectively. Moreover, the algorithm possesses a significantly higher percentage of remaining time under different conditions of total SMD devices and total subchannels compared to other methods. This indicates that the designed algorithm can markedly enhance the processing performance of the task offloading model of the big data heterogeneous IoT data analysis system, and can also effectively reduce terminal ECS and system latency. The research results can provide reference for improving the processing ability of heterogeneous IoT big data analysis systems. The contribution of this study to the academic field lies in providing a model that can effectively reduce the operational ECS and time consumption of heterogeneous IoT big data analysis systems containing mobile animal networking devices. Moreover, from an industrial perspective, the results of this study contribute to improving the efficiency of information exchange and processing in the field of IoT computing, thereby promoting the promotion of IoT technology.

Wind turbine fault detection and identification using a two-tier machine learning framework

A proactive approach is essential to optimize wind turbine maintenance and minimize downtime. By utilizing advanced data analysis techniques on the existing Supervisory Control and Data Acquisition (SCADA) system data, valuable insights can be gained into wind turbine performance without incurring high costs. This allows for early fault detection and predictive maintenance, ensuring that unscheduled or reactive maintenance is minimized and revenue loss is mitigated. In this study, data from a wind turbine SCADA system in the southeast of Ireland were collected, preprocessed, and analyzed using statistical and visualization techniques to uncover hidden patterns related to five fault types within the system. The paper introduces a conditional function designed to test two given scenarios. The first scenario employs a two-tier approach involving fault detection followed by fault identification. Initially, faulty samples are detected in the first tier and then passed to the second tier, which is trained to diagnose the specific fault type for each sample. In contrast, the second scenario involves a simpler solution referred to as naive, which treats fault types and normal cases together in the same dataset and trains a model to distinguish between normal samples and those related to specific fault types. Machine learning models, particularly robust classifiers, were tested in both scenarios. Thirteen classifiers were included, ranging from tree-based to traditional classifiers, neural networks, and ensemble learners. Additionally, an averaging feature importance technique was employed to select the most impactful features on the model decisions as a starting point. A comparison of the results reveals that the proposed two-tier approach is more accurate and less time-consuming, achieving 95% accuracy in separating faulty from normal samples and approximately 91% in diagnosing each fault type. Furthermore, ensemble learners, particularly bagging and stacking, demonstrated superior fault detection and identification performance. The performance of the classifiers was validated using t-SNE and explainable AI techniques, confirming that the impactful features align with the findings and that the proposed two-tier solution outperforms the naive solution. These results strongly indicate that the proposed solution is accurate, independent, and less complex compared to existing solutions.

Low-Complexity User Scheduling Framework for mmWave Hybrid Beam-forming OFDM System under Non-Ideal CSI

Introduction: User scheduling in millimeter-wave (mmWave) multi-user hybrid beam-forming Orthogonal Frequency Division Multiplexing (OFDM) systems involves the joint optimization of resource block group (RBG) allocation, beam pairing, and user selection. However, choosing the optimal scheduled User Equipment (UE), allocated RBGs, and communicating beams in practical mmWave hybrid beam-forming systems with non-ideal Channel State Information (CSI) remains challenging. Method: In this paper, we propose a low-complexity user scheduling framework. On one hand, two user classification methods are proposed under non-ideal CSI, assisted by beam search and RBG allocation respectively. On the other hand, in order to ensure both the throughput and fairness performance, a novel user selection scheme, called weighted user selection based on feedback threshold (WUSFT) scheme is proposed, and approximate closed-form expression of fairness index for both full feedback and feedback based on threshold are derived. Our proposed methods consist of three steps. First, RBGs are allocated based on the maximum received signal power (MRSP) of each user on each RBG, utilizing quasi-omnidirectional beams. In the second step, the communicating beams are further searched to achieve the MRSP with the allocated RBG. Result: Finally, the allocated RBG, or the determined beam information obtained through the beam search, is used to represent the correlation of user channels and classify the UEs into groups. Only UEs whose reported factor is not less than the feedback threshold will report received information. This simplifies the operation of user scheduling. Moreover, simulation results demonstrate that our proposed schemes can achieve more than 92.3% of the sum rate performance of exhaustive search methods while maintaining relatively low complexity. Conclusion: In addition, the user feedback overhead (FO) reduces obviously, especially with large UE number.

Optimizing with Attractor: A Tutorial

This tutorial presents a novel search system—the Attractor-Based Search System (ABSS)—that can solve the Traveling Salesman Problem very efficiently with optimality guarantee. From the perspective of dynamical systems, a heuristic local search algorithm for an NP-complete combinatorial problem is a discrete dynamical system. In a local search system, an attractor drives the search trajectories into the vicinity of a globally optimal point in the solution space, and the convergence of local search trajectories makes the search system become a global and deterministic system. The attractor contains a small set of the most promising solutions to the problem. The attractor can reduce the problem size exponentially, and thus make the exhaustive search feasible. Therefore, this new search paradigm is called optimizing with attractor. The ABSS consists of two search phases: local search phase and exhaustive search phase. The local search process is used to quickly construct the attractor in the solution space, and the exhaustive search process is used to completely search the attractor to identify the optimal solution. Therefore, the exact optimal solution can be found quickly by combining local search and exhaustive search. This tutorial introduces the concept of an attractor in a local search system, and describes the process of optimizing with the attractor, using the Traveling Salesman Problem as the study platform.

Contaminação fúngica da cannabis medicinal: Avaliação bibliométrica, tendências e perspectivas futuras

Fungal contamination is an important problem in cannabis and medical cannabis production, as fungi can produce toxic compounds or reduce the quality and safety of the product. The aim of this study was to perform a bibliometric evaluation related to fungal contamination in cannabis, analyzing current trends in this field and providing future perspectives for research. For this purpose, an exhaustive search was carried out in the scientific literature in electronic databases such as Scopus and Web of Science (WOS), covering the period from 2000 to 2023. The results obtained were processed through bibliometrics and network analysis, using tools such as RStudio and Bibliometrix software. The results revealed that the main themes associated with fungal contamination in cannabis are focused on: diseases related to fungal contamination of the plant, use of fungi as an alternative biological control in cannabis cultivation, metabolites associated with cannabis and their potential in fungal control, as well as fungal diversity in cannabis cultivation and postharvest and the risk to consumer health. Finally, future research areas are proposed to address the challenge of fungal contamination in medicinal cannabis production, covering topics such as pest control, contamination, genetic resistance, active compounds, analysis methods, and the impact of fungi on cannabis.

Waste plastic oil as an alternative fuel: A review

Today, with the high population density of the world, the energy demand is increasing continuously. Global dependency on fossil fuels is very strong and there is a compelling need to reduce our energy consumption in order to offset greenhouse gas emissions. Due to regularly increasing prices of fossil fuels alternative fuels are needed to fulfill the requirements of developing countries like India. Plastics in today's world have become crucial. They are excessively used in industry, as well as in households and other fields due to their lightweight, durability, and design flexibility. Plastic demand is growing day by day, which now poses a huge environmental threat. The current study summarizes the use of WPO (waste plastic oil) in the diesel engine and also concludes the combustion, performance, and emission parameters. After an exhaustive literature search, some interesting results have been found. The study reveals that when using WPO as an alternative source in a diesel engine, the combustion, performance, and emissions are similar to those using conventional diesel fuel. An enhanced BTE (brake thermal efficiency) and reduced emissions of unburned hydrocarbons (UBHC) and carbon monoxide (CO) are reported.