Search Results (215)

The proliferation of wireless sensor networks in remote and inaccessible areas demands efficient data collection approaches that minimize energy consumption while ensuring comprehensive coverage. Traditional data retrieval methods face significant challenges when sensors are sparsely distributed across extensive areas, particularly in scenarios where direct sensor access is impractical due to terrain constraints or operational limitations. This research addresses these challenges through a novel hybrid optimization framework that combines integer linear programming (ILP) with multiple traveling salesperson problem (mTSP) algorithms for drone-based data collection in wireless sensor networks (WSNs). The methodology employs a two-phase approach, where ILP optimally determines strategic access point locations for sensor clustering based on communication capabilities, followed by mTSP optimization to generate efficient inter-AP flight trajectories rather than individual sensor visits. Comprehensive simulations across diverse network configurations and drone quantities demonstrate consistent performance improvements, with travel distance reductions reaching 32% compared to conventional mTSP implementations. Comparative evaluation against established clustering algorithms including Voronoi, DBSCAN, Constrained K-Means, Graph-Based clustering, and Greedy Circle Packing confirms that ILP consistently achieves optimal access point allocation while maintaining superior routing efficiency. Additionally, a novel quality assessment metric quantifies sensor grouping effectiveness, revealing that ILP-based clustering advantages become increasingly pronounced with higher sensor densities, providing substantial operational benefits for large-scale wireless sensor network deployments. Full article

Background/Objectives: Bioactive peptides derived from animal venoms, toxins, and secretions demonstrate considerable pharmacological potential for use in the management of diabetes mellitus—a highly prevalent metabolic disorder of substantial global health significance. This integrative review systematically evaluated the current evidence regarding the pharmacological mechanisms underlying the antidiabetic properties of these bioactive peptides. Methods: This study was guided by the research question “What are the mechanisms of action of peptides derived from animal venoms in modulating parameters associated with diabetes?” developed using the PECo framework. A comprehensive literature search was executed across Scopus, PubMed, and Web of Science, focusing on studies from the last five years. Out of 190 identified articles, 17 satisfied the inclusion criteria. Results: Twenty-eight distinct peptides were characterized, exhibiting structural diversity with 7–115 amino acid residues and molecular weights of 900–13,000 Da. These compounds were sourced from venomous taxa including sea anemones, marine snails, spiders, centipedes, scorpions, and snakes. Their antidiabetic mechanisms encompassed glucagon-like peptide-1 (GLP-1) receptor agonism, insulin receptor activation, potassium channel inhibition, glucose transporter type 4 (GLUT4) upregulation, and α-amylase inhibition. Sequence analyses revealed substantial homology among peptides with analogous mechanisms—notably Con-Ins and ILP-Ap04, plus SpTx1 and SsTx-4—suggesting that structural determinants underlie their functional characteristics. Toxicological evaluations of nine peptides demonstrated low-toxicity profiles despite originating from toxic venom, crucial for therapeutic development. Conclusions: These peptides exhibited exceptional pharmacological potency with effective doses in nanogram-to-nanomole per kilogram ranges. Collectively, our findings underscore the therapeutic potential of venom-derived peptides as innovative candidates for use in diabetes management. Full article

The invertebrate neuropeptide F (NPF) signaling plays versatile roles in diverse biological activities and processes. Still, whether and how it mediates feeding and digestion in Pomacea canaliculate remain gaps in our knowledge. Herein, we first identified and characterized PcNPFR via bioinformatics analysis in P. canaliculate, which is a polyphagous herbivore with a voracious appetite that causes devastating damages to ecosystem functioning and services in colonized ranges. Double stranded RNA (dsRNA)-based RNA interference (RNAi) and exogenous rescue were utilized to decipher and substantiate underlying mechanisms whereby NPFR executed its modulatory functions. Multiple sequence alignment and phylogeny indicated that PcNPFR harbored typical seven transmembrane domains (7 TMD) and belonged to rhodopsin-like GPCRs, with amino acid sequence sharing 27.61–63.75% homology to orthologues. Spatio-temporal expression profiles revealed the lowest abundance of PcNPFR occurred in pleopod tissues and the egg stage, while it peaked in male snails and testes. Quantitative real-time PCR (qRT-PCR) analysis showed that 4 µg dsNPFR and 10⁻⁶ M trNPF (NPFR agonist) were optimal doses to exert silencing and rescue effects, accordingly with sampling time at 3 days post treatments. Moreover, the dsNPFR injection (4 µg) at 1/3/5/7 day/s delivered silencing efficiency of 32.20–74.01%. After 3 days upon dsNPFR knockdown (4 µg), mRNA levels of ILP7/InR/Akt/PI3Kc/PI3K_R were significantly downregulated compared to dsGFP controls, except FOXO substantially upregulated at both transcript and translation levels. In addition, the activities of alpha-amylase, protease and lipase were significantly suppressed, accompanied by decreased leaf area consumption, attenuated feeding behavior and diminished feeding rate. Moreover, expression trends were opposite and proxies were partially or fully restored to baseline levels post exogenous compensation of trNPF, suggesting phenotypes specifically attributable to PcNPFR RNAi but not off-target effects. PcNPFR is implicated in both feeding and digestion by modulating the ISP pathway and digestive enzyme activities. It may serve as a promising molecular target for RNAi-based antifeedants to manage P. canaliculate invasion. Full article

To address the severe challenges of task allocation for UAV swarms in uncertain complex environments, this paper introduces the concept of equivalent load, constructs the load capability matrix of a single UAV and the task required load matrix of the task area, and designs a new task resilience capability indicator accordingly to conduct research on a resilience-based optimization framework. Aiming at this multi-objective optimization problem, the “Problem Decomposability Theorem” is proposed, which theoretically proves the feasibility of decomposing the UAV swarm problem into “lower-level Integer Linear Programming (ILP) cost optimization” and “upper-level Particle Swarm Optimization (PSO) resilience optimization”. Based on this, a Particle Swarm Optimization–Integer Linear Programming (PSO-ILP) two-layer nested optimization algorithm is designed. Simulation experiments covering three task areas, five payload types and multiple UAV types are carried out, and the results show that the proposed method has outstanding performance in multi-objective optimization, especially in terms of algorithm convergence and the comprehensive efficiency of swarm load cost and task resilience. In particular, when the interruption probability is in the range of 0.2 to 0.6, it can not only maintain high task resilience but also achieve cost minimization, with a significant improvement in resilience performance. These results not only enrich the theoretical research on UAV swarm resilience but also provide a universal solution for UAV swarm task optimization in multiple fields. Full article

Shiga toxin-producing Escherichia coli (STEC) are genetically diverse foodborne pathogens of major global public health concerns. Serogroup-level identification is critical for effective surveillance and outbreak control; however, it is often challenged by STEC’s genome plasticity and frequent recombination. In this study, we employed a standardized pangenomic pipeline integrating Roary ILP Bacterial Core Annotation Pipeline (RIBAP) and Panaroo to analyze 160 complete, high-quality STEC genomes representing eight major serogroups at a 95% sequence identity threshold. Candidate serogroup-specific markers were identified using gene presence/absence profiles from RIBAP and Panaroo. Our analysis revealed several high-confidence markers, including metabolic genes (dgcE, fcl_2, dmsA, hisC) and surface polysaccharide-related genes (capD, rfbX, wzzB). Comparative pangenomic evaluation showed that RIBAP predicted a larger pangenome size than Panaroo. Additionally, some genomes from the O104:H1, O145:H28, and O45:H2 serotypes clustered outside their expected clades, indicating sporadic serotype misplacements in phylogenetic reconstructions. Functional annotation suggested that most candidate markers are involved in critical processes such as glucose metabolism, lipopolysaccharide biosynthesis, and cell surface assembly. Notably, approximately 22.9% of the identified proteins were annotated as hypothetical. Overall, this study highlights the utility of pangenomic analysis for potential identification of clinically relevant STEC serogroups markers and phylogenetic interpretation. We also note that pangenome analysis could guide the development of more accurate diagnostic and surveillance tools. Full article

The melon fly Zeugodacus cucurbitae (Coquillett) is a globally invasive pest responsible for substantial economic losses in the fruit and vegetable industries. Insulin-like peptides (ILPs) are evolutionarily conserved neuropeptides that play a crucial role in insect reproduction. In this study, six ZcILPs from the melon fly, designated as ZcILP1–6, were cloned. Phylogenetic analysis demonstrated a strong orthologous link with Dipteran ILPs. Spatiotemporal expression profiling revealed that ZcILP1 and ZcILP3 exhibit preferential enrichment in the adult female fat body, with their expression specifically and significantly upregulated in 5-day-old individuals. Their expression decreased 12, 24, and 48 h post-starvation and increased upon re-feeding. Silencing ZcILP1 and ZcILP3 resulted in reduced ovarian size by 51.42% and 69.17%, respectively. Furthermore, silencing ZcILP1 or ZcILP3 significantly decreased the transcriptional levels of genes downstream of the insulin signaling pathway (ISP), notably the target of rapamycin (ZcTOR) and Forkhead box O (ZcFOXO). Concurrently, the expression of Vitellogenin (ZcVg), a gene associated with reproduction, was significantly downregulated. These findings indicate that ZcILP1 and ZcILP3 regulate ZcVgs expression and ovarian development through ISP, suggesting them as potential targets for green control of Z. cucurbitae. Full article

We consider mixed Hamming packings, addressing the maximal cardinality of codes with a minimum codeword Hamming distance. We do not rely on any algebraic structure of the alphabets. We extend known-integer linear programming models of the problem to be efficiently tractable using standard ILP solvers. This is achieved by adopting the concept of contact graphs from classical continuous sphere packing problems to the present discrete context, resulting in a reduction technique for the models which enables their efficient solution as well as their decomposition to smaller subproblems. Based on our calculations, we provide a systematic summary of all lower and upper bounds for packings in the smallest Hamming spaces. The known results are reproduced, with some bounds found to be sharp, and the upper bounds improved in some cases. Full article

Efficient and accurate panel assignment is critical in expert and peer review processes. Traditional methods—based on manual preferences or Heuristic rules—often introduce bias, inconsistency, and scalability challenges. We present an automated framework that combines transformer-based document similarity modeling with optimization-based reviewer assignment. Using the all-mpnet-base-v2 from model (version 3.4.1), our system computes semantic similarity between proposal texts and reviewer documents, including CVs and Google Scholar profiles, without requiring manual input from reviewers. These similarity scores are then converted into rankings and integrated into an Integer Linear Programming (ILP) formulation that accounts for workload balance, conflicts of interest, and role-specific reviewer assignments (lead, scribe, reviewer). The method was tested across 40 researchers in two distinct disciplines (Chemical Engineering and Philosophy), each with 10 proposal documents. Results showed high self-similarity scores (0.65–0.89), strong differentiation between unrelated fields (−0.21 to 0.08), and comparable performance between reviewer document types. The optimization consistently prioritized top matches while maintaining feasibility under assignment constraints. By eliminating the need for subjective preferences and leveraging deep semantic analysis, our framework offers a scalable, fair, and efficient alternative to manual or Heuristic assignment processes. This approach can support large-scale review workflows while enhancing transparency and alignment with reviewer expertise. Full article

Interval Linear Programming (ILP) presents several compelling challenges when applied to real-world problems that cannot be easily captured by traditional robust uncertainty models. In this paper, we propose a novel solution method that employs a comparison index for interval ordering based on the generalized Hukuhara difference. This approach proves to be highly effective in comparing solutions within ILP frameworks. Additionally, we discuss the robustness of the proposed methodology and its implications for decision-making under uncertainty. Full article

Parasitoid venom significantly influences host physiology and development. Our previous research identified high levels of insulin-binding protein IMP-L2 in the venom of Scleroderma guani. IMP-L2 may inhibit the insulin/insulin-like growth factor signaling (IIS) cascade by competitively binding insulin-like peptides (ILPs) with insulin receptor (InR). However, how to regulate IIS transduction is unclear. We speculate that venom-derived IMP-L2 may bind ILPs to inhibit IIS transduction. Consequently, we investigated the regulation of the IIS/TOR pathway by venom-derived IMP-L2. An expression analysis of IIS/TOR pathway genes across various developmental stages of Tenebrio molitor demonstrated that this pathway governs the entire developmental process. By examining gene expression before and after parasitism, we determined that S. guani predominantly inhibits TOR pathway signaling in T. molitor post-parasitism. Bioinformatics and expression analyses revealed that IMP-L2 is critically involved in Hymenoptera insects, exhibiting high expression in the venom apparatus, and is upregulated in response to S. guani parasitism factors. Additionally, recombinant IMP-L2 was produced via eukaryotic expression. Finally, the recombinant IMP-L2 was found to inhibit the TOR and IIS/TOR signaling pathways at early (6 h) and late (24 h) stages post-injection. Knockdown of IMP-L2 in S. guani parasitized T. molitor pupae, resulting in accelerated death of T. molitor. During parasitism, S. guani may suppress host growth and development by modulating the IIS/TOR signaling pathway through venom-derived IMP-L2, potentially affecting host lifespan. Full article

Superparamagnetic iron oxide nanoparticles (SPIONs) have gained significant attention for Magnetic Fluid Hyperthermia (MFH)-based cancer therapy. However, achieving high heating efficiency under a biologically safe Alternating Magnetic Field (AMF) remains a challenge. This study investigates the synthesis and optimization of SPIONs encapsulated in TPGS-stabilized PLGA nanoparticles (TPS-NPs) using a modified single emulsion solvent evaporation (M-SESE) method. The aim was to achieve efficient magnetic heating under biologically safe AMF conditions while maintaining biocompatibility and colloidal stability, making these magnetic nanoplatforms suitable for MFH-based cancer treatment. TPS-NPs were characterized using various techniques, including Dynamic Light Scattering (DLS), Atomic Force Microscopy (AFM), Transmission Electron Microscopy (TEM), and Superconducting Quantum Interference Device (SQUID) magnetometry, to evaluate their hydrodynamic size (Dh), zeta potential (ζ), encapsulation efficiency, and superparamagnetic properties. Calorimetric MFH studies demonstrated superior heating efficiency, with Specific Absorption Rate (SAR) and Intrinsic Loss Power (ILP) values optimized at an AMF of 4.1 GAm⁻¹s⁻¹, remaining within Hergt’s biological safety limit (~5 GAm⁻¹s⁻¹). These findings suggest that SPION-encapsulated TPS-NPs exhibit enhanced heat induction, making them promising candidates for MFH-based cancer therapy. The study highlights their potential as multifunctional nanoplatforms for magnetic hyperthermia therapy, paving the way for clinical translation in oncology for advanced cancer treatment. Full article

The initial saccade of experienced readers tends to land halfway between the beginning and the middle of words, at a position originally referred to as the preferred viewing location (PVL). This study investigated whether a simple physical manipulation—namely, increasing the saliency (brightness or color) of the letter located at the PVL—can positively influence saccadic targeting strategies and optimize reading performance. An eye-movement experiment was conducted with 25 adults and 24 s graders performing a lexical decision task. Results showed that this manipulation had no effect on initial landing positions in proficient readers, who already landed most frequently at the PVL, suggesting that PVL saliency is irrelevant once automatized saccade targeting routines are established. In contrast, the manipulation shifted the peak of the landing site distribution toward the PVL for a cluster of readers with immature saccadic strategies (with low reading-level scores and ILPs close to the beginning of words), but only in the brightness condition, and had a more compelling effect in a cluster with oculomotor instability (with flattened and diffuse landing position curves along with oculomotor and visuo-attentional deficits). These findings suggest that guiding the eyes toward the PVL may offer a novel way to improve reading efficiency, particularly for individuals with oculomotor and visuo-attentional difficulties. Full article

Radio mean labeling of a connected graph G is an assignment of distinct positive integers to the vertices of G satisfying a mathematical constraint called radio mean condition. The maximum label assigned to any vertex of G is called the $span$ of the radio mean labeling. The minimum $span$ of all feasible radio mean labelings of G is the radio mean number of G, denoted by $rmn\left(G\right)$. In our previous study, we proved that if G has order n, then $rmn\left(G\right)\in [n,rmn\left(P_n\right)]$ where $P_n$ is a path of order n. All graphs of diameters 1, 2 and 3 have the radio mean number equal to order n. However, they are not the only graphs on n vertices with radio mean number n. Graphs isomorphic to path $P_n$ are the graphs having the maximum diameter among the set of all graphs of order n and they possess the maximum feasible radio mean number. In this paper, we show that, for any integer in the range of achievable radio mean numbers, there always exists a graph of order n with the given integer as its radio mean number. This is approached by introducing a special type of tree whose construction is detailed in the article. The task of assigning radio mean labels to a graph can be considered as an optimization problem. This paper critiques the limitations of existing Integer Linear Programming (ILP) models for assigning radio mean labeling to graphs and proposes a new ILP model. The existing ILP model does not guarantee that the vertex labels are distinct, positive and satisfy the radio mean condition, prompting the need for an improved approach. We propose a new ILP model which involves $n^2$ constraints is the input graph’s order is n. We obtain a radio mean labeling of cycle of order 10 using the new ILP. In our previous study, we showed that, for any graph G, we can extend the radio mean labelings of its diametral paths to the vertex set of G and obtain radio mean labelings of G. This insight forms the basis for an algorithm presented in this paper to obtain radio mean labels for a given graph G with n vertices and diameter d. The correctness and complexity of this algorithm are analyzed in detail. Radio mean labelings have been proposed for cryptographic key generation in previous works, and the algorithm presented in this paper is general enough to support similar applications across various graph structures. Full article

This study presents a novel, two-stage algorithm that minimizes the number of machines and operators required to produce multiple product types repeatedly in cyclic scheduling. Our algorithm treats the problem of minimum machines as a bin packing problem (BPP), and the problem of determining the number of operators required is also modeled as the BPP, but with constraints. The BPP is NP-hard, but with suitable heuristic algorithms, the proposed model allocates multiple product types to machines and multiple machines to operators without overlapping setup times (machine interference). The production schedule on each machine is represented as a circle (donut). By using lower bounds, it is possible to assess whether the number of machines required by our model is optimal; if not, the optimality gap can be quantified. The algorithm has been validated using real-world data from an industrial facility producing 17 types of products. The results of our algorithm led to significant cost savings and improved scheduling performance. The outcomes demonstrate the effectiveness of the proposed algorithm in optimizing resource utilization by reducing the number of machines and operators required. Although this study focuses on a manufacturing system, the model can also be applied to other contexts. Full article

Some knowledge bases (KBs) extracted from Wikipedia articles can achieve very high average precision values (over 95% in DBpedia). However, subtle mistakes including inconsistencies, outliers, and erroneous relations are usually ignored in the construction of KBs by extraction rules. Automatic detection and correction of these subtle errors is important for improving the quality of KBs. In this paper, an inductive logic programming with exceptional information (EILP) is proposed to automatically detect errors in large knowledge bases (KBs). EILP leverages the exceptional information problems that are ignored in conventional rule-learning algorithms such as inductive logic programming (ILP). Furthermore, an inductive logical correction method with exceptional features (EILC) is proposed to automatically correct these mistakes by learning a set of correction rules with exceptional features, in which respective metrics are provided to validate the revised triples. The experimental results demonstrate the effectiveness of EILP and EILC in detecting and repairing large knowledge bases, respectively. Full article