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Fisheries are among the human activities that are most strongly affected by ongoing climate-related changes in the presence and abundance of fish species across the globe. The ecological and social repercussions of such changes for recreational fisheries are however still poorly understood. Here, we compare selected ecological and social dimensions of both recreational angling and spearfishing targeting the bluefish (Pomatomus saltatrix) in Italy. The bluefish has undergone a northward expansion in the region over the last 20–30 years, during which it reached new areas and increased in abundance. Using digital videos and their associated data published by both recreational anglers and spearfishers on YouTube we characterized ecological and social dimensions using a culturomics approach. Specifically, we focused on harvesting patterns, social engagement and sentiments related to the bluefish. Our study revealed four major results: (1) similar harvesting patterns (i.e., declared mass and seasonal upload patterns) related to videos by both recreational anglers and spearfishers; (2) higher social engagement (i.e., number of views and likes) for videos by recreational anglers than spearfishers; (3) differences in themes of discussion, with anglers being mainly interested in fishing strategy and gears and spearfishers being more interested in fishing actions shown on the videos; (4) positive and negative sentiments of both recreational anglers and spearfishers towards the invasiveness and aggressiveness of the species. The latter represents an interesting trade-off associated with recreational fishing of the bluefish: it is perceived as an invasive species, but it is also a valued target fish because its voracity contributes to the quality of the recreational fishing experience. Our study showcases the value of exploring social media and associated data to better understand the ecological and human dimensions of marine recreational fisheries in relation to distributional range shifts of species associated with climate change.

The paper presents research on a specific approach to the issue of computed tomography with an incomplete data set. The case of incomplete information is quite common, for example when examining objects of large size or difficult to access. Algorithms devoted to this type of problems can be used to detect anomalies in coal seams that pose a threat to the life of miners. The most dangerous example of such an anomaly may be a compressed gas tank, which expands rapidly during exploitation, at the same time ejecting rock fragments, which are a real threat to the working crew. The approach presented in the paper is an improvement of the previous idea, in which the detected objects were represented by sequences of points. These points represent rectangles, which were characterized by sequences of their parameters. This time, instead of sequences in the representation, there are sets of objects, which allow for the elimination of duplicates. As a result, the reconstruction is faster. The algorithm presented in the paper solves the inverse problem of finding the minimum of the objective function. Heuristic algorithms are suitable for solving this type of tasks. The following heuristic algorithms are described, tested and compared: Aquila Optimizer (AQ), Firefly Algorithm (FA), Whale Optimization Algorithm (WOA), Butterfly Optimization Algorithm (BOA) and Dynamic Butterfly Optimization Algorithm (DBOA). The research showed that the best algorithm for this type of problem turned out to be DBOA.

(1) Introduction: Laryngeal cancer is one of the most common types of cancer affecting the upper aerodigestive tract. Despite ensuring good oncological outcome in many locoregionally advanced cases, total laryngectomy is associated with relevant physical and psychological sequelae. Treatment through tracheo-esophageal speech, if promising, can lead to very variable outcomes. Not all laryngectomee patients with vocal prosthesis benefit from the same level of rehabilitation mainly due to the development of prosthetic or fistula related problems. The relating sequelae in some cases are even more decisive in the patient quality of life, having a higher impact than communicational or verbal skills. (2) Material and Methods: A retrospective study was conducted on 63 patients initially enrolled with a history of total laryngectomy and voice rehabilitation, treated at the University Hospital of Catania from 1 January 2010 to 31 December 2018. Quality of life (QoL) evaluation through validated self-administrated questionnaires was performed. (3) Results: The Voice-Related Quality of Life questionnaire revealed significantly better outcomes in both socio-emotional and functional domains of the tracheoesophageal patient group compared to the esophageal group (p = 0.01; p = 0.01, respectively), whereas in the Voice Handicap Index assessment, statistically significant scores were not achieved (p = 0.33). (4) Discussion: The significant differences reported through the V-RQOL and Voice Handicap Index scales in the presence of fistula related problems and device lifetime reduction when compared to the oesophageal speech group have demonstrated, as supported by the literature, a crucial role in the rehabilitative prognosis. (5) Conclusions: The criteria of low resistance to airflow, optimal tracheoesophageal retention, prolonged device life, simple patient maintenance, and comfortable outpatient surgery are the reference standard for obtaining good QoL results, especially over time. Furthermore, the correct phenotyping of the patient based on the main outcomes achieved at clinical follow-up guarantees the primary objective of the identification of a better quality of life.

Climate change affects the shift range distribution of species, especially among mobile species, and this phenomenon can alter ecosystems and impacts human activities. Fishing is an anthropic activity that undergoes the effect not only of the introduction and increase of non-native species but also of native thermophilic ones. Some of these species can become a commercially exploitable resource. However, this information is often obscured by the negative effects these species can cause to the environment. We investigated how the thermophilic species Caranx rhonchus, neglected in Italy, could become a relevant resource. We studied the nutritional profile and the presence of heavy metal contamination and compared these traits with those of a similar common Mediterranean species, namely Trachurus trachurus. The proximate composition was determined following the AOAC procedure, while the fatty acid profile was determined by GC/MS, and the mineral component was obtained by mass spectrometer (ICP-MS). Caranx rhonchus is a nutritionally good species, although it is little consumed and exploited. Increasing the market supply with new commercially exploitable emerging species would benefit local communities and the environment. Therefore, it is necessary to investigate how a shift of the range caused by climate change can provide benefits within the human dimension.

The presented study discusses the possible disturbing effects of the electromagnetic field of antennas used in mobile phones or WiFi technologies on the pacemaker in the patient’s body. This study aims to obtain information on how the thickness of skin layers (such as the thickness of the hypodermis) can affect the activity of a pacemaker exposed to a high-frequency electromagnetic field. This study describes the computational mathematical analysis and modeling of the heart pacemaker inserted under the skin exposed to various electromagnetic field sources, such as a PIFA antenna and a tuned dipole antenna. The finite integration technique (FIT) for a pacemaker model was implemented within the commercially available CST Microwave simulation software studio. Likewise, the equations that describe the mathematical relationship between the subcutaneous layer thickness and electric field according to different exposures of a tuned dipole and a PIFA antenna are used and applied for training a neural network. The main output of this study is the creation of a mathematical model and a multilayer feedforward neural network, which can show the dependence of the thickness of the hypodermis on the size of the electromagnetic field, from the simulated data from CST Studio.

Understanding the relationship between environmental variables and gas concentrations from livestock production is essential for evaluating the impact of pollutants on animal housing and surrounding areas. This study investigates the use of ANNs to predict NH3 concentrations in a Mediterranean dairy barn under seasonal conditions—namely, hot, cold, and transitional weather. A Multi-Layer Perceptron (MLP) structure was employed, trained using Levenberg–Marquardt and Bayesian Regularization algorithms. The input dataset included ten variables related to internal and external environmental conditions, NH3 concentrations, and time of day. The models were evaluated using R2, R, MAE, MSE, and RMSE as performance metrics. Results showed strong predictive capabilities, with R2 values ranging from 0.75 to 0.96 and RMSE values between 0.47 and 0.80 due to the number of input data (different days) and environmental conditions. These findings highlight the potential of ANNs as effective tools for real-time pollutant prediction, supporting Precision Livestock Farming (PLF) strategies.

The seasonal timing of recurring biological processes is essential for organisms living in temperate regions. While ample knowledge of these processes exists for terrestrial environments, seasonal timing in the marine environment is relatively understudied. Here, we characterized the annual rhythm of habitat use in six fish species belonging to the Sparidae family, highlighting the main environmental variables that correlate to such rhythms. The study was conducted at a coastal artificial reef through a cabled observatory system, which allowed gathering underwater time-lapse images every 30 minutes consecutively over 3 years. Rhythms of fish counts had a significant annual periodicity in four out of the six studied species. Species-specific temporal patterns were found, demonstrating a clear annual temporal niche partitioning within the studied family. Temperature was the most important environmental variable correlated with fish counts in the proximity of the artificial reef, while daily photoperiod and salinity were not important. In a scenario of human-induced rapid environmental change, tracking phenological shifts may provide key indications about the effects of climate change at both species and ecosystem level. Our study reinforces the efficacy of underwater cabled video-observatories as a reliable tool for long-term monitoring of phenological events.

In recent years several numerical methods have been proposed to identify the five-parameter model of photovoltaic panels from manufacturer datasheets also by introducing simplification or approximation techniques. In this paper we present a fast and accurate procedure for obtaining the parameters of the five-parameter model by starting from its reduced form. The procedure allows characterizing, in few seconds, thousands of photovoltaic panels present on the standard databases. It introduces and takes advantage of further important mathematical considerations without any model simplifications or data approximations. In particular the five parameters are divided in two groups, independent and dependent parameters, in order to reduce the dimensions of the search space. The partitioning of the parameters provides a strong advantage in terms of convergence, computational costs, and execution time of the present approach. Validations on thousands of photovoltaic panels are presented that show how it is possible to make easy and efficient the extraction process of the five parameters, without taking care of choosing a specific solver algorithm but simply by using any deterministic optimization/minimization technique.

In this paper, chemiresistor sensor based on conductive polymer (regio-regular poly(3- hexyltiophene) (rr-P3HT) and zinc oxide (ZnO) nanomaterial blend has been investigated and tested including the significant measurements of response characteristic, humidity, and temperature under exposure of nitrogen oxides. For this purpose, an automated experimental setup has been provided in order to interface the gas chamber containing chemiresistors gas sensors and temperature sensor mounted on base chip with the system of pipes and valves, nitrogen and synthetic air gas cylinders, and Mass Flow controller. As well 3D FE model chemiresistor sensor device based on ZnO and graft comb copolymer contained within of a gas chamber has been developed. The rapid change of resistances values for chemiresistor sensor based on ZnO and graft comb copolymer has been observed at the time of exposure to the gas in accordance with the experimental data obtained by chemiresistor devices sensor manufactured in the laboratories of Silesian University of Technology, Gliwice, Poland.

In this paper, we present a novel 3D finite element model for analyzing the influence of chemical degradation on organic solar cells (OSC) exposed to electromagnetic radiation in the visible light range. In our model, the degradation defects due to oxidation are represented by oxygen spherical subdomains located within the bulk. Electromagnetic simulations have been carried out for an increasing number of defects and for wavelengths lying within the visible light range. The simulation results have been compared with experimental data showing excellent agreement. This model allows us to evaluate the decrease in electrical power generated by OSCs in relation with the number of defects due to the degradation mechanism.