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As soil fungi are major players in the carbon accumulation process, the two main fungal degraders in topsoil, Ascomycetes and Basidiomycetes, were investigated as indicators of land use effectiveness in increasing soil carbon accumulation and soil function. The study focused on the soil organic carbon content increase in a 20-year short rotation forest cycle with broadleaf woody plantations compared to a nearby arable cropping system. Total fungi, Ascomycota and Basidiomycota, were quantified in terms of DNA copy number, with specific probes using SYBR® Green I dye on the QuantStudio™ 3D digital PCR system (dPCR). Previously, next generation sequencing analysis using a general primer confirmed that Ascomycota and Basidiomycota were the most represented phyla and that fungal community composition significantly differed between treatments. A range of key soil enzyme activities for the C-cycle were also assessed. Total organic carbon content (TOC), microbial biomass in term of dsDNA and enzyme activities significantly increased in woody plantations compared to arable soil. The TOC increase differed significantly also between wood species, Salix and Robina gave the greatest increase (+30 and 20 % respectively), followed by Populus (+12), microbial biomass highly correlated with TOC showing the same trend. Total fungi, Ascomycota and Basidiomycota increased significantly in three woody plantations compared to the arable soil system. Ascomycota in the woody plantations increased two to four times (average 3144 n copies µl−1 of DNA) compared to the arable soil (1419 n copies µl−1); Basidiomycota were almost absent in arable soil (av. 94n copies µl−1) and increased five to six times in woody plantations (av. 490 n copies µl−1). Total fungi and ascomycetes correlated strongly with microbial biomass and TOC, whereas basidiomycetes did not. These findings showed that Ascomycota represent the largest portion of fungi in agricultural soils even after a 20-years short rotation forest cycle and they can be taken as indicators of carbon accumulation processes. Therefore, this study suggests that joining the Ascomycetes quantity in the topsoil to the Ascomycota:Basidiomycota ratio seems a good option when setting regional strategies for improving C accrual in farmland with the short-term afforestation.

Deadwood is one of the main terrestrial carbon (C) pools and its decomposition is fundamental in biogeochemical cycles. As fungi are the main wood degraders, a study was carried out on deadwood-colonising fungal community changes as a function of host plants and wood degradation. Deadwood fungal communities were investigated in adjacent woody plantations of hybrid poplar, willow and black locust established in the early 2000 s and abandoned for fifteen years. Lying deadwood was estimated in the three plantations and wood decay (WD) visually assessed using a 3-class scale. Fungal colonization of wood samples was assessed using culture-based methods and identified with morphological and molecular features. Wood colonising fungal communities differed in composition both between host plants and WD classes (P

In the last decade, the adoption of technological tools in manufacturing industry, such as the use of the Internet of Things (IoT) and Machine Learning (ML), has led to the advent of the industry 4.0 (I4.0). In this scenario, intelligent devices can generate large volumes of data about industrial machinery and equipment that can be used to make maintenance more efficient. Prognostics and Health Management (PHM) is an emerging maintenance strategy that uses systems’ Condition Monitoring through IoT sensors installed on machinery to diagnose their faults or estimate their Remaining Useful Life (RUL). This study aims to conduct a Systematic Literature Review (SLR) on the use of ML techniques in the field of PHM of industrial mechanical systems and equipment. 50 studies resulted eligible for the above-mentioned SLR. Diagnostics and prognostics approach and the ML algorithm types used in the 50 analyzed papers have been analyzed together with the Key Performance Indicators (KPIs) used for their validation. From the analyses, it was found that Shallow Learning and Deep Learning (DL) algorithms are the most applied ones, while KPIs are used differently according to the type of task classification or regression. Moreover, results highlighted that many authors still use artificial datasets to test their algorithms, instead of datasets based on real data retrieved by their components. For the last type of datasets, this paper also introduces a schematic framework to standardize the step-by-step diagnostics and prognostics process carried out by the authors.

Gastric and gastro-esophageal junction adenocarcinoma (GEA) remains a considerable major public health problem worldwide, being the fifth most common cancer with a fatality-to-case ratio that stands still at 70%. Angiogenesis, which is a well-established cancer hallmark, exerts a fundamental role in cancer initiation and progression and its targeting has been actively pursued as a promising therapeutic strategy in GEA. A wealth of clinical trials has been conducted, investigating anti-angiogenic agents including VEGF-directed monoclonal antibodies, small molecules tyrosine kinase inhibitors and VEGF-Trap agents both in the resectable and advanced setting, reporting controversial results. While phase III randomized trials testing the anti-VEGFR-2 antibody Ramucirumab and the selective VEGFR-2 tyrosine kinase inhibitor Apatinib demonstrated a significant survival benefit in later lines, the shift of angiogenesis inhibitors in the perioperative and first-line setting failed to improve patients’ outcome in GEAs. The molecular landscape of disease, together with novel combinatorial strategies and biomarker-selected approaches are under investigation as key elements to the success of angiogenesis blockade in GEA. In this article, we critically review the existing literature on the biological rationale and clinical development of antiangiogenic agents in GEA, discussing major achievements, limitations and future developments, aiming at fully realizing the potential of this therapeutic approach.

A growing interest in intelligent fault detection may sometimes lead to practical issues when existing malfunctions reveal analogous indications and the number of observations is limited. This article addresses the classification problem of two identical malfunctions, i.e., unbalancing and shaft bow in rotary machines, where only 56 observations were utilized for the training. The faulty systems are modeled in ABAQUS/CAE; a data set for each fault is created by simulation under various physical and operational conditions employing the uncertainty concept. The wavelet time scattering (WTS) technique extracts low-variance presentations from signals. With respect to the classification procedure of the faulted rotor systems, two models are examined with the extracted features from WTS as the input. Initially, a long short-term memory (LSTM) network is trained and tested, and then, the capability of a support vector machine (SVM) model is inquired. Ultimately, the classification models are trained and tested using the raw time series data and the extracted features to compare the effectiveness of the suggested methods, i.e., WTS. The employed approach for feature extraction demonstrated remarkable effectiveness in addressing a potential hurdle in identifying faults in rotating systems: the ability to differentiate between unbalanced and bowed rotors, irrespective of the classification model utilized.

Biomechanical overload is considered a significant occupational risk in manufacturing and a potential cause of musculoskeletal disorders. This research aims to introduce new methodologies for the quantitative risk evaluation of biomechanical risk by combining surface electromyography with a motion acquisition system based on inertial measurement units. Due to the lack of experimental data in the literature acquired in a real industrial environment during the working shift, an on-the-field study regarding an automotive assembly line workstation has been carried out in collaboration with Fiat Chrysler Automobiles Italy S.p.A. Data related to the trunk flexion forward and the erector spinae muscle activity have been acquired for several consecutive working cycles by considering three different workers. Data analyses indicated kinematic and muscular activity patterns consistent with those expected and that the proposed wearable technologies can be integrated and used simultaneously during work activities. Furthermore, the results demonstrated data repeatability, strengthening the feasibility and usefulness of the combined use of kinematic and electromyography technologies to assess biomechanical overload in production lines. This study could lay the bases for the future definition of a method for assessing biomechanical overload due to awkward postures.

In a response to the appeal for securing the rice production efficiency to achieve the United Nations Sustainable Development Goals, we adopted a geographic detector model to investigate the spatiotemporal evolution trajectory and driving forces of the rice production in the world’s largest rice-producing country, China. We have analyzed the spatiotemporal evolution features and aggregation patterns of county rice production efficiency based on panel data of 122 counties in Hunan Province, one of the main grain production provinces in China, from 2006 to 2018. Our findings indicate: (1) Hunan Province’s rice production in three counties (i.e., Taoyuan, Liling, and Anren) showed the highest efficiency; there were pronounced regional variances in rice productivity which results in a sharp and rapid shrink of the range of rice productivity, (2) financial investments in agriculture, forestry, and water resources, as well as per capita disposable income of farmers, were the main determinants of the spatiotemporal variation in rice production efficiency, (3) the spatiotemporal divergence of rice production efficiency at the province level was U-shapedly, influenced by the share of secondary industry in GDP; the southern Hunan region received the biggest contribution from farmers in terms of disposable income per person at the regional level. Overall, theoretically, this study offers fresh evidence for regional optimization of rice and other grain production from a novel integrative approach of the geospatial and the land resource preservation. Practically, it provides feasible guidance for the high-quality development of grain production in China, which may also help eradicate hunger and attain sustainable grain production all over the world.

Objectives: The ABC-06 and the NIFTY trials recently established the role of second-line chemotherapy (2L) in patients with advanced biliary tract cancer (BTC). Our real-world study aimed to explore 2L in BTC patients aged ≥ 70 years old and to compare their outcomes with younger subjects. Methods: Institutional registries across three academic medical centers were retrospectively reviewed. The Kaplan–Meier methods were used to estimate survival, and the log-rank test was used to make comparisons. Results: A total of 190 BTC patients treated with 2L were identified and included in the analysis. Among them, 52 (27.3%) were aged ≥ 70 years (range 70–87 years). No statistically significant differences in both median overall survival (mOS) and median progression-free survival (mPFS) were recorded between the elderly and younger patients. Absolute lymphocyte count < 1000/mmc (p < 0.001) and albumin level < 3 g/dL (p < 0.001) were independently associated with worse prognoses. Conclusions: The results of this real-world study suggest that for patients aged ≥ 70 years, 2L could be equally effective for younger patients with survival outcomes aligned to those from the ABC-06 and NIFTY trials. The delivery of 2L should be carefully evaluated and monitored in this patient subset.

Driven/driving shafts are the most important portion of rotating devices. Misdiagnosis or late diagnosis of these components could result in severe vibrations, defects in other parts (particularly bearings), and ultimately catastrophic failures. A shaft bow is a common problem in heavy rotating systems equipped with such attachments as blades, discs, etc. Many factors can cause the shaft bending; this malfunction can be temporary, such as the bow resulting from a rotor gravitational sag, or can be permanent, such as shrink fitting. Since bending effects are similar to those induced by the classic eccentricity of the mass from the geometric center, i.e., unbalancing, distinguishing the differences in dynamic behaviors, as well as the symptoms, can be a labor-intensive and specialized task. This article represents a review of almost all the investigations and studies that have been carried out on the diagnosing and balancing of bowed rotating systems. The articles are categorized into two major classes, diagnosing and balancing/correcting approaches to bowed rotors. The former is divided into three subclasses, i.e., time-domain, frequency-domain, and time–frequency-domain analyses; the latter is divided into three other sub-sections that concern influence coefficient, modal balancing, and optimization method in correcting. Since the number of investigations in the time domain is relatively high, this category is subdivided into two groups: manual and smart inspection. Finally, a summary is provided, as well as some new research prospects.

In this paper, the use of a phase-sensitive optical time-domain reflectometry (ϕ-OTDR) sensor for the detection of the Lamb waves excited by a piezoelectric transducer in an aluminum plate, is investigated. The system is shown to detect and resolve the Lamb wave in distinct regions of the plate, opening the possibility of realizing structural health monitoring (SHM) and damage detection using a single optical fiber attached to the structure. The system also reveals the variations in the Lamb wave resulting from a change in the load conditions of the plate. The same optical fiber used to detect the Lamb waves has also been employed to realize distributed strain measurements using a Brillouin scattering system. The method can be potentially used to replace conventional SHM sensors such as strain gauges and PZT transducers, with the advantage of offering several sensing points using a single fiber.

Genetic alterations in CRC have shown a negative predictive and prognostic role in specific target therapies. The onset of immunotherapy has also undergone remarkable therapeutic innovation, although limited to a small subgroup of patients, the MSI-H/dMMR, which represents only 5% of CRC. Research is moving forward to identify whether other biomarkers can predict response to ICIs, despite various limitations regarding expression and identification methods. For this purpose, TMB, LAG3, and PD-L1 expression have been retrospectively evaluated in several solid tumors establishing the rationale to design clinical trials with concurrent inhibition of LAG3 and PD-1 results in a significant advantage in PFS and OS in advanced melanoma patients. Based on these data, there are clinical trials ongoing in the CRC as well. This review aims to highlight what is already known about genetic mutations and genomic alterations in CRC, their inhibition with targeted therapies and immune checkpoints inhibitors, and new findings useful to future treatment strategies.

BACKGROUND AND AIMS:The present study aims to investigate the role of the prognostic nutritional index (PNI) on survival in patients with advanced hepatocellular carcinoma (HCC) treated with sorafenib. METHODS:This multicentric study included a training cohort of 194 HCC patients and three external validation cohorts of 129, 76 and 265 HCC patients treated with Sorafenib, respectively. The PNI was calculated as follows: 10 × serum albumin (g/dL) + 0.005 × total lymphocyte count (per mm3). Univariate and multivariate analyses were performed to investigate the association between the covariates and the overall survival (OS). RESULTS:A PNI cut-off value of 31.3 was established using the ROC analysis. In the training cohort, the median OS was 14.8 months (95% CI 12-76.3) and 6.8 months (95% CI 2.7-24.6) for patients with a high (>31.3) and low ( 70 years (p< 0.0038) were independent prognostic factors for OS. By performing the same multivariate analysis of the training cohort, the PNI 31.3 was found to be an independent prognostic factor for predicting OS in all the three validation cohorts. CONCLUSIONS:PNI represents a prognostic tool in advanced HCC treated with first-line Sorafenib. It is readily available and low-cost, and it could be implemented in clinical practice in patients with HCC.

In this paper, a new methodology supporting the design of landing gears is proposed. Generally, a preliminary step is performed with simplified FE model, usually one-dimensional, to achieve the reaction forces involving each component during all aforementioned aircraft operations. Though this approach gives a valid support to the designer, it is characterized by several problems, such as the related approximations. So, it is important, by a numerical point of view, to develop an isostatic FE model equivalent to the real one. In fact, if the landing gear is modelled as hyperstatic, the static equilibrium equations are insufficient for determining the internal forces and reactions on each sub-component; so, the modelled material properties and geometries assume an increasing importance, which gets the model too approximating. The proposed methodology consists of achieving the reaction forces by means of multibody simulations, by overcoming such problems, since each component is modelled as rigid. In this paper, also a FE model for the investigation of the structural response is proposed. Aimed to Certification by Analysis purposes, the developed multibody and the FE models have been assessed against an experimental landing gear drop test carried out by Magnaghi Aeronautica S.p.A., according to the EASA CS 25 regulations

Artificial Neural Networks (ANNs) have rapidly emerged as a promising tool to solve damage identification and localization problem, according to a Structural Health Monitoring approach. Finite Element (FE) Analysis can be extremely helpful, especially for reducing the laborious experimental campaign costs for the ANN development and training phases. The aim of the present work is to propose a guided wave-based ANN, developed through the use of the Finite Element Method, to determine the position of damages. The paper first addresses the development and assessment of the modeling technique. The FE model accuracy was proven through the comparison of the predicted results with experimental and analytical data. Then, the ANN was developed and trained on an aluminum plate and subsequently verified in a composite plate, as well as under different damage configurations. According to the results herein proposed, the ANN allowed to detect and localize damages with a high level of accuracy in all cases of study.

The aim of this work is to propose a numerical methodology based on the finite element (FE) method to investigate the dispersive behavior of guided waves transmitted, converted, and reflected by reinforced aluminum and composite structures, highlighting their differences. The dispersion curves of such modes can help designers in improving the damage detection sensitivity of Lamb wave based structural health monitoring (SHM) systems. A preliminary phase has been carried out to assess the reliability of the modelling technique. The accuracy of the results has been demonstrated for aluminum and composite flat panels by comparing them against experimental tests and semi-analytical data, respectively. Since the good agreement, the FE method has been used to analyze the phenomena of dispersion, scattering, and mode conversion in aluminum and composite panels characterized by a structural discontinuity, as a stiffener. The research activity allowed emphasizing modes conversion at the stiffener, offering new observations with respect to state of the art. Converted modes propagate with a slightly slower speed than the incident ones. Reflected waves, instead, have been found to travel with the same velocity of the incident ones. Moreover, waves reflected in the composite stiffened plate appeared different from those that occurred in the aluminum one for the aspects herein discussed.

A fatigue test on a full scale panel with complex loading condition and geometry configuration has been carried out using a triaxial test machine. The demonstrator is made up of two skins which are linked by a transversal butt-joint, parallel to the stringer direction. A fatigue load was applied in the direction normal to the longitudinal joint, while a constant load was applied in the longitudinal joint direction. The test panel was instrumented with strain gages and previously quasi-static tests were conducted to ensure a proper load transferring to the panel. In order to support the tests, geometric nonlinear shell finite element analyses were conducted to predict strain and stress distributions. The demonstrator broke up after about 177000 cycles. Subsequently, a finite element analysis (FEA) was carried out in order to correlate failure events; due to the biaxial nature of the fatigue loads, Sines criterion was used. The analysis was performed taking into account the different materials by which the panel is composed. The numerical results show a good correlation with experimental data, successfully predicting failure locations on the panel.

The aging issue in the work context is becoming a significant element of the future sustainability of service and industrial companies. It is well known that with increasing worker age the problem of maintaining the performance and the safety level when fatigue increases is a crucial point, and fatigue increases with the age. Due to social and political developments, especially in Western countries, the retirement age is increasing and companies operate with a higher workforce mean age. Therefore, the problem of recognizing and measuring fatigue has become a key aspect in the management of aging. Note that in the scientific engineering field, the problem of fatigue evaluation when a worker is performing his/her work activities is an important issue in the industrial and service world and especially in the context of the researchers that are investigating the human reliability assessment. As it is clear from the literature, the industrial operations management are suffering from some misleading concepts that only the medicine scientific context can clarify. Therefore, the aim of this paper is to define what are the open issues and the misleading concepts present in the classical fatigue evaluation methods, and second to define two experimental curves of fatigue that will help the decision makers to minimize the impact of fatigue on the workers, thus maximizing the sustainability of the working tasks assigned. This aim is achieved by examining the medical literature about the measurement of a particular kind of fatigue related to the circadian cycle, i.e., the cognitive one; after that, a survey about the possible technologies for measurements is performed. On the basis of technology selection, an experiment on real work activities is performed and some remarkable results about the fatigue in the workers observed and the technology use and its limitations are defined.

Saving water resources in agriculture is a topic of current research in Mediterranean environments, and rational soil management can allow such purposes. The Beerkan Estimation of Soil Transfer parameters (BEST) procedure was applied in five olive orchards of Salento peninsula (southern Italy) to estimate the soil physical and hydraulic properties under alternative soil management (i.e., no-tillage (NT) and minimum tillage (MT)), and to quantify the impact of soil management on soil water conservation. Results highlighted the soundness of BEST predictions since they provided consistent results in terms of soil functions or capacitive-based soil indicators when (i) the entire data set was grouped by homogeneous classes of texture, bulk density, and capillarity of the soil, (ii) the predictions were compared with the corresponding water retention measures independently obtained in lab, and (iii) some correlations of literature were checked. BEST was applied to establish a comparison at Neviano (NE) and Sternatia (ST) sites. The two neighboring NT soils compared at NE showed substantial discrepancies in soil texture (i.e., sandy loam (NE-SL) or clay (NE-C)). This marked difference in soil texture could determine a worsening of the relative field capacity at the NE-SL site (relative field capacity, RFC < 0.6), as compared to NE-C where RFC was optimal. The current soil management determined a similar effect (RFC < 0.6) at Sternatia (ST-MT vs. ST-NT), but the worsening in soil properties, due to soil tillage, must be considered substantially transient, as progressive improvement is expected with the restoration of the soil structure. The results of this work suggest that strategic MT can be a viable solution to manage the soil of Salento olive orchards.

The optimization of production processes has always been one of the cornerstones for manufacturing companies, aimed to increase their productivity, minimizing the related costs. In the Industry 4.0 era, some innovative technologies, perceived as far away until a few years ago, have become reachable by everyone. The massive introduction of these technologies directly in the factories allows interconnecting the resources (machines and humans) and the entire production chain to be kept under control, thanks to the collection and the analyses of real production data, supporting the decision making process. This article aims to propose a methodological framework that, thanks to the use of Industrial Internet of Things—IoT devices, in particular the wearable sensors, and simulation tools, supports the analyses of production line performance parameters, by considering both experimental and numerical data, allowing a continuous monitoring of the line balancing and performance at varying of the production demand. A case study, regarding a manual task of a real manufacturing production line, is presented to demonstrate the applicability and the effectiveness of the proposed procedure.

Colorectal cancer (CRC) is one of the leading causes of mortality and morbidity in the world. It is a heterogeneous disease, which can be classified into different subtypes, characterized by specific molecular and morphological alterations. In this context, BRAF mutations are found in about 10% of CRC patients and define a particular subtype, characterized by a dismal prognosis, with a median survival of less than 12 months. Chemotherapy plus bevacizumab is the current standard therapy in first-line treatment of BRAF-mutated metastatic CRC (mCRC), with triplet (FOLFOXIRI) plus bevacizumab as a valid option in patients with a good performance status. BRAF inhibitors are not so effective as compared to melanoma, because of various resistance mechanisms. However, the recently published results of the BEACON trial will establish a new standard of care in this setting. This review provides insights into the molecular underpinnings underlying the resistance to standard treatment of BRAF-mutated CRCs, with a focus on their molecular heterogeneity and on the research perspectives both from a translational and a clinical point of view.

This paper deals with the redesign of an aircraft passenger seat, placed at the first seat row, which was not compliant with Federal Aviation Regulations FAR 25.562 “Emergency landing dynamic conditions” regulation (due to a high value for the Head Injury Criterion (HIC)) and related guidelines. Starting from an accurate analysis of some results obtained via an experimental seat sled test, a numerical procedure was developed in order to improve the passenger safety with respect to head injury. Specifically, the proposed numerical procedure, using the advantages of a Finite Element (FE) model and a Design of Experiment (DoE) approach for simulation modeling, was aimed at identifying a new design solution to avoid the impact between the passenger’s head and the bulkhead. The redesign of the passenger seat was validated against an experimental test carried out at Geven S.p.A. Company by demonstrating, consequently, the compliance of the modified seat-belt system with the regulations.

A fatigue test on a full scale panel with complex loading condition and geometry configuration has been carried out using a triaxial test machine. The demonstrator is made up of two skins which are linked by a transversal butt-joint, parallel to the stringer direction. A fatigue load was applied in the direction normal to the longitudinal joint, while a constant load was applied in the longitudinal joint direction. The test panel was instrumented with strain gages and previously quasi-static tests were conducted to ensure a proper load transferring to the panel. In order to support the tests, geometric nonlinear shell finite element analyses were conducted to predict strain and stress distributions. The demonstrator broke up after about 177000 cycles. Subsequently, a finite element analysis (FEA) was carried out in order to correlate failure events; due to the biaxial nature of the fatigue loads, Sines criterion was used. The analysis was performed taking into account the different materials by which the panel is composed. The numerical results show a good correlation with experimental data, successfully predicting failure locations on the panel.

In this paper, a multibody calculation methodology has been applied to the vibration analysis of a 4-cylinder, 4-stroke, turbocharged diesel engine, with a simulation driven study of the angular speed variation of a crankshaft under consideration of different modeling assumptions. Moreover, time dependent simulation results, evaluated at the engine supports, are condensed to a vibration index and compared with experimental results, obtaining satisfactory outcomes. The modal analysis also considers the damping aspects and has been conducted using a multibody model created with the software AVL/EXCITE. The influence of crankshaft torsional frequencies on the rotational speed behavior has been evaluated in order to reduce the vibration phenomena. The focus of this work is related to industrial aspects since, for an existing and commercialized engine, a numerical and experimental complex study has been performed to enable design improvements aimed at reducing noise and vibrations. Existing procedures and algorithms are combined here to reach the abovementioned objectives in the most efficient way.

The increasing relevance of technology and its impact on our everyday life requires multi- and trans-disciplinary studies in order to investigate the ways in which the Information and Communication Technology (ICT) tools are affected by users’ features and by the external environment. In order to enrich existing contributions on this topic, the paper focuses its attention on the city as example of service systems, in order to investigate dimensions and dynamics that influence the city employees’ evaluation and satisfaction in the use of ICT platforms. By adopting the interpretative lens offered by Service Science and Systems Thinking, the domain of city as a service system is analysed and some hypotheses are formulated with reference to the relationships between environment, users, and ICT platform. The hypotheses are investigated through a questionnaire survey on a sample of 1032 municipalities in the Czech Republic and the results are tested using Structural Equation Modelling. The research shows that users’ features and external environment affect the evaluation and the satisfaction of city stakeholders about the use of ICT platforms by underlining the need for enlarging the study on informatics tools, in order to also include subjective variables. The findings are discussed from both theoretical and practical points of view.