Search Results (26,073)

Finding the best configuration of a neural network’s hyper-parameters may take too long to be feasible using an exhaustive search, especially when the cardinality of the search space has a big combinatorial number of possible solutions with various hyper-parameters. This problem is aggravated when we also need to optimize the parameters of the neural network, such as the weight of the hidden neurons and biases. Extreme learning machines (ELMs) are part of the random weights neural network family, in which parameters are randomly initialized, and the solution, unlike gradient-descent-based algorithms, can be found analytically. This ability is especially useful for metaheuristic analysis due to its reduced training times allowing a faster optimization process, but the problem of finding the best hyper-parameter configuration is still remaining. In this paper, we propose a modification of the artificial bee colony (ABC) metaheuristic to act as parameterizers for a regularized ELM, incorporating three methods: an adaptive mechanism for ABC to balance exploration (global search) and exploitation (local search), an adaptation of the opposition-based learning technique called opposition local-based learning (OLBL) to strengthen exploitation, and a record of access to the search space called forbidden redundant indexes (FRI) that allow us to avoid redundant calculations and track the explored percentage of the search space. We set ten parameterizations applying different combinations of the proposed methods, limiting them to explore up to approximately 10% of the search space, with results over 98% compared to the maximum performance obtained in the exhaustive search in binary and multiclass datasets. The results demonstrate a promising use of these parameterizations to optimize the hyper-parameters of the R-ELM in datasets with different characteristics in cases where computational efficiency is required, with the possibility of extending its use to other problems with similar characteristics with minor modifications, such as the parameterization of support vector machines, digital image filters, and other neural networks, among others. Full article

Access to raw materials has always been one of the main drivers of economic growth. In Italy, where the relationship between exports and imports has always been negative, during the fascist period, several new opportunities and limits were introduced and many efforts were made to promote the exploitation of Italian resources to support the energy transition, focusing on energy autonomy. But were these efforts sufficient to ensure the achievement of the objectives, or did the internal demand for coal always make trade and technological exchanges with foreign countries necessary, despite what fascist propaganda showed through its communication strategy during the so-called Ventennio? This research, which is part of a significant debate regarding the role of mines in the economic, social, and cultural development in Italy during fascism, was based on the analysis of several series of unpublished data regarding energy consumption, imports, and production in Italy and the international technological debate about Sardinia Island, where the most productive Italian coal mines were opened. Based on a comparison of results, this research aimed at refuting the hypothesis that during the energy transition from wood to coal, Italy was closed both to the international technical, scientific, and methodological debates regarding the mining sector and energy imports. Full article

This research paper focuses on the application of a new method for the simultaneous reconfiguration and the optimum placing of Soft Open Points (SOPs) in Radial Distribution Systems (RDS). The proposed Lévy Flight-based Improved Equilibrium Optimizer (LF-IEO) algorithm enhances the standard Equilibrium Optimizer (EO) by integrating several techniques to improve exploration and exploitation capabilities. SOPs are highly developed power electronics devices that can enhance distribution utility networks in terms of reliability and effectiveness. However, identifying their optimum place along with network reconfiguration is a challenging task that requires advanced computation techniques. The performance of the proposed LF-IEO algorithm has been first verified on several benchmark functions. Subsequently, it is implemented on a IEEE 33-Bus, 69-Bus, 118-Bus, and Algerian 116-Bus distribution network to solve the problem of simultaneous network reconfiguration and optimal SOP placement. For the Algerian 116-bus system case study, the algorithm achieved a significant 14.89% reduction in power losses, improved the minimum voltage, and generated substantial net annual savings of 74,426.40 $/year. To prove its superiority in terms of solution quality and robustness, the proposed LF-IEO approach was compared with several newly developed algorithms from the literature. Full article

Background: Oncogene-driven NSCLC is usually treated with targeted therapies using tyrosine kinase inhibitors (TKIs) to inhibit oncogene downstream signaling pathways, affecting tumor survival and proliferation. EGFR- and KRAS-mutant NSCLCs are the most represented subtypes, and they are treated in clinical practice with oncogene-targeting drugs in the first and second line, respectively. Unfortunately, the development of oncogene-independent resistant clones limits TKI efficacy. Here, we used non-oncogene addiction (NOA) as an innovative therapeutic strategy to target other essential proteins that support changes in tumor phenotype. Specifically, we tested, for the first time, a combination of inhibitors, namely ATR, involved in DNA damage response, and pyruvate dehydrogenase kinases (PDKs), involved in energy metabolism. Methods: Sensitive PC9 and the corresponding EGFR-TKI-resistant PC9/OR, EGFR-mutant H1975, and KRAS-mutant A549 NSCLC cells, were treated with TKIs (osimertinib and selumetinib, respectively). In parallel, cells were exposed to two combination regimens: one using the TKI with an ATR inhibitor and the other one combining the two selected NOA inhibitors (ATR inhibitor, M4344; and PDK inhibitor, DCA). Results: The effect of these two combined approaches, compared to TKI alone, produced similar results in terms of cell proliferation, cell death, and migration. Thus, depending on tumor biology, selecting between the proposed therapeutic strategies will be different, to maximize tumor response. Conclusions: The major translational relevance of this study is to exploit new targets for the development of innovative and improved therapeutic strategies with NOA drugs, over combinations including target genes within the oncogene pathway, to overcome resistance to TKI therapies in patients with NSCLC who are oncogene-addicted. Full article

Our study investigates the trophic ecology of the anchoveta (Engraulis ringens). The anchoveta plays a key role in the Greater Humboldt Ecosystem and is extensively exploited by countries from the south-eastern Pacific Ocean. For a comprehensive study of trophic ecology, we employed a combined approach that included stomach content analysis, stable isotope analysis, and scaled mass index of body condition. Our results showed that the multivariate composition of the diet varies significantly between life stage and fishing zones in Chile. Copepods and euphausiids emerged as the dominant prey found in the stomachs across all fisheries zones. Stable isotope analysis revealed significant differences among different zones. The scaled mass index values were higher in the northern zone compared to the southern zones, for both juveniles and adults. This research carries significant implications for fisheries management and conservation efforts, such as the development of targeted management strategies that address variations in the trophic structure of anchoveta across different life stages and fishing zones. Full article

Medical thermography is a non-invasive technique that allows the measurement of the temperature of the human body surface, exploiting the heat emitted by the body through the skin in the form of infrared electromagnetic radiation. Recently, smartphone-based thermography (ST) has drawn considerable attention. This scoping review (SR) aims to describe its current applications and reliability based on currently available research findings, also taking into account the medico-legal implications linked to its use. A search of the sources was conducted on multiple databases (PubMed, Scopus, Cochrane, Lilacs, Google Scholar). Based on a set of eligibility criteria, all articles deemed useful were included in the SR. Collected data, processed with descriptive statistics, are then discussed. From the initial 241 results, after duplicate removal and full-text reading based on inclusion/exclusion criteria, 20 articles were classified according to the main characteristics and indications and outcomes are highlighted based on clinical evidence. The most frequently documented fields of ST are wound care management and vascular surgery. Other disciplines are less explored (dentistry, ophthalmology, otorhinolaryngology, orthopedics, etc.). Practicality, operational simplicity and affordability of mobile thermographic devices are the chief strengths of this technology. Comparative studies with traditional thermal imaging methods are poor in terms of the number of patients analyzed but this technology showed high sensitivity and accuracy in the large number of patients enrolled in observational studies, encouraging the development of further operational protocols in all medical specialties. Gaining a deeper understanding of such techniques will also help settle the medico-legal issues which may arise from the clinical implementation of ST, thus appraising its reliability and safety from that perspective as well. Full article

Here, we present a novel method for the separation of overlapping emission spectral lines corresponding to nonlinear processes, which differ by effective nonlinearity with respect to the pump field power. The method exploits the factorization of wavelength and pumping power dependencies of the components processes contributions to the total luminescence. The advantage of our method is an ability to be self-testing and robust with respect to noise and experimental imperfections. We successfully demonstrate functionality of the method in the experiment with up-conversion luminescence of the fluorophosphate glass doped with rare-earth ions Yb${}^{3+}$ and Tm${}^{3+}$ pumped by 975 nm CW diode laser. Full article

As an important data source, the Building Information Model (BIM) plays an important role in modern building safety management. Numerous studies have closely examined automatic compliance inspections for building safety and the safety management of dangerous projects. However, the value of the BIM has not been fully exploited in evidence-based practices of building safety. To address this limitation, this paper proposes an ontology-based Evidence/Risk-Based Safety (ERBS) management framework for divisional and subdivisional works with high risk, which includes: (1) BIM data extraction based on dynamo; (2) creation of an ontology based on building information and the ERBS management process model; (3) converting BIM data and evidence into ontology individuals; and (4) integrating the ontology through semantic web technology and using the Semantic Web Rule Language (SWRL) to conduct rule-based reasoning on the ontology. A case study shows that the framework is effective for the ERBS management of divisional and subdivisional works with high risk. The framework proposed in this study provides effective safety management methods for high-risk projects that can be applied in wider engineering practice in the future. Full article

The Lake Faro brackish basin (Sicily, Italy) was established as a Global Geosite as a key locality of tectonic coastal lakes, but little research has been devoted to this rare geological and ecological framework. To fill this gap, the main stratigraphical, sedimentological, ecological, morpho-bathymetric, and structural features were reported, linking geodiversity with biodiversity. In Lake Faro, a shallow platform develops alongside a deep funnel-shaped basin, reaching a maximum depth of 29 m. A NNW-SSE trending steep cliff, representing the abrupt transition from the platform to the basin, was interpreted as a dextral transtensional fault (Lake Faro Fault), presumably active since the middle-late(?) Pleistocene. The switches of the steep cliff NW-wards, acquiring an E-W trend, was interpreted as being due to the occurrence of the normal Mortelle Fault, cut by the Faro Lake Fault. Bottom terrigenous deposits consisted of coarse- to fine-grained quartzo-lithic rich sediments deriving from high-grade metamorphic and igneous rocks, whereas bioclasts mainly derived from clam farming actives for several centuries up until today. The Quaternary shallow platform, from top to base, includes the following: (i) soft cover composed of coarse terrigenous and prevalent bioclastic deposits; (ii) hard conglomerates cemented by carbonates; and (iii) siliciclastic coarse deposits of the Messina Fm. In the deep basin, siliciclastic silty loams with minor amounts of bioclastic deposits prevailed in the soft cover. Substrate heterogeneity coupled with brackish-marine gradients are the main factors responsible for an articulate patchiness of different lagoon habitats and related benthic associations, which, according to the Barcelona Convention classification, can be summarized as follows: (i) MB1.541 (marine angiosperms or other halophytes), (ii) MB1.542 (Fucales), (iii) MB5.543 (photophilic algae, except Fucales), (iv) MB5.544 (Facies with Polychaeta), and (v) MB5.545 (Facies with Bivalvia). Typical marine associations, such as rhodolite beds, also occur. Finally, the lake, which has been exploited since the prehistoric age because of its high biodiversity and productivity, maintains some evidence of millennial relationships with the resident human cultures, attracted there by the favorable geomorphological and ecological peculiarities. Full article

High-flying insects that exploit tropospheric winds can disperse over far greater distances in a single generation than species restricted to below-canopy flight. However, the ecological consequences of such long-range dispersal remain poorly understood. For example, high-altitude dispersal may facilitate more rapid range shifts in these species and reduce their sensitivity to habitat fragmentation, in contrast to low-flying insects that rely more on terrestrial patch networks. Previous studies have primarily used surface-level variables with limited spatial coverage to explore dispersal timing and movement. In this study, we introduce a novel application of niche modelling to insect aeroecology by examining the relationship between a comprehensive set of atmospheric conditions and high-flying insect activity in the troposphere, as detected by weather surveillance radars (WSRs). We reveal correlations between large-scale dispersal events and atmospheric conditions, identifying key variables that influence dispersal behaviour. By incorporating high-altitude atmospheric conditions into niche models, we achieve significantly higher predictive accuracy compared with models based solely on surface-level conditions. Key predictive factors include the proportion of arable land, altitude, temperature, and relative humidity. Full article

Diversity is crucial for metaheuristic algorithms. It prevents early convergence, balances exploration and exploitation, and helps to avoid local optima. Traditional metaheuristic algorithms tend to rely on a single strategy for generating new solutions, often resulting in a lack of diversity. In contrast, employing multiple strategies encourages a variety of search behaviors and a diverse pool of potential solutions, thereby improving the exploration of the search space. Evolutionary Game Theory (EGT) modifies agents’ strategies through competition, promoting successful strategies and eliminating weaker ones. Structured populations, as opposed to unstructured ones, preserve diverse strategies through localized competition, meaning that an individual’s strategy is influenced by only a subset or group of the population and not all elements. This paper presents a novel metaheuristic method based on EGT applied to structured populations. Initially, individuals are positioned near optimal regions using the Metropolis–Hastings algorithm. Subsequently, each individual is endowed with a unique search strategy. Considering a certain number of clusters, the complete population is segmented. Within these clusters, the method enhances search efficiency and solution quality by adapting all strategies through an intra-cluster competition. To assess the effectiveness of the proposed method, it has been compared against several well-known metaheuristic algorithms across a suite of 30 test functions. The results indicated that the new methodology outperformed the established techniques, delivering higher-quality solutions and faster convergence rates. Full article

Background/Objectives: Horseradish (Armoracia rusticana L.) roots—largely used in traditional medicine for their multiple therapeutic effects—are a rich source of health-promoting phytochemicals. However, their efficacy can be compromised by low chemical stability and poor bioavailability. Incorporation into phospholipid vesicles is often proposed to tackle this problem. Methods: In this study, a hydroalcoholic extract was produced from horseradish roots. The extract was characterized by UPLC-MS and HPLC-PDA and formulated in conventional liposomes and Penetration Enhancer-containing Vesicles (PEVs) for skin application. Results: The obtained nanovesicles were small in size (<100 nm), negatively charged, uni/bilamellar, and with high values of entrapment efficiency (>85%) for the flavonoids identified in the extract. Both the free and the nanoformulated extract showed optimal biocompatibility, measured as the absence of hemolysis of erythrocytes and absence of cytotoxicity in skin cell lines. Furthermore, the nanoformulations displayed antioxidant activity in vitro. Conclusions: The proposed nananoformulations could be exploited to counteract oxidative stress involved in the pathogenesis and progression of numerous skin disorders. Full article

Since the ban of neonicotinoid insecticides in the European Union, sugar beet production is threatened by outbreaks of virus yellows (VY) disease, caused by several aphid-transmitted viruses, including the polerovirus beet mild yellowing virus (BMYV). As the symptoms induced may vary depending on multiple infections and other stresses, there is an urgent need for fast screening tests to evaluate resistance/tolerance traits in sugar beet accessions. To address this issue, we exploited the virus-induced gene silencing (VIGS) system, by introducing a fragment of a Beta vulgaris gene involved in chlorophyll synthesis in the BMYV genome. This recombinant virus was able to generate early clear vein chlorosis symptoms in infected sugar beets, allowing easy and rapid visual discernment of infected plants across five sugar beet lines. The recombinant virus displayed similar infectivity as the wild-type, and the insert remained stable within the viral progeny. We demonstrated that the percentage of VIGS-symptomatic plants was representative of the infection rate of each evaluated line, and depending on the susceptibility of the line to BMYV infection, VIGS symptoms may last over months. Our work provides a polerovirus-based VIGS system adapted to sugar beet crop allowing visual and rapid large-scale screens for resistance or functional genomic studies. Full article

Rapid industrial development has led to increased crude oil extraction and oily wastewater discharge. Achieving oil–water separation and marine oil adsorption in a cost-effective, efficient, and environmentally friendly manner remains a global challenge. In this work, natural wood was chemically treated to prepare a degradable and environmentally friendly wood sponge structure. In situ polymerization and spraying methods were used to produce an environmentally friendly oil–water separation sponge with superhydrophobic and superoleophilic properties (Fe₃O₄@P-P@WS). Fe₃O₄@P-P@WS had excellent superhydrophobicity (WCA = 154.2°) and self-cleaning properties. Additionally, Fe₃O₄@P-P@WS could convert solar and electrical energy into thermal energy, reaching a surface temperature of 74 °C under sunlight irradiation with an intensity of 1.0 kW m⁻². When a voltage of 9 V was applied, the surface temperature reached 120.5 °C. Moreover, under the suction of a vacuum pump or the action of gravity, the continuous separation of highly fluid oil substances was achieved. The designed Fe₃O₄@P-P@WS offers advantages such as easily obtained raw materials, energy efficiency, simple preparation, and the ability to solve secondary pollution issues, providing a new technology for cleaning organic matter in industrial wastewater discharge and for round-the-clock cleaning of high-viscosity crude oil leaked during offshore oil exploitation. Full article

UAV-assisted communication facilitates efficient data collection from IoT nodes by exploiting UAVs’ flexible deployment and wide coverage capabilities. In this paper, we consider a scenario in which UAVs equipped with high-precision sensors collect sensing data from ground terminals (GTs) in real-time over a wide geographic area and transmit the collected data to a ground base station (BS). Our research aims to jointly optimize the trajectory scheduling and the allocation of collection time slots for multiple UAVs, to maximize the system’s data collection rates and fairness while minimizing energy consumption within the task deadline. Due to UAVs’ limited sensing distance and battery energy, ensuring timely data processing in target areas presents a challenge. To address this issue, we propose a novel constraint optimization-based deep reinforcement learning–Lagrangian UAV real-time data collection management (CDRLL—RDCM) framework utilizing centralized training and distributed execution. In this framework, a CNN–GRU network units extract spatial and temporal features of the environmental information. We then introduce the PPO–Lagrangian algorithm to iteratively update the policy network and Lagrange multipliers at different time scales, enabling the learning of more effective collaborative policies for real-time UAV decision-making. Extensive simulations show that our proposed framework significantly improves the efficiency of multi-UAV collaboration and substantially reduces data staleness. Full article