Miracle-dz/UniFAR-FTUs huggingface dataset:
On the time decay for the MGT-type porosity problems

In this work we study three different dissipation mechanisms arising in the so-called Moore-Gibson-Thompson porosity. The three cases correspond to the MGT-porous hyperviscosity (fourth-order term), the MGT-porous viscosity (second-order term) and the MGT-porous weak viscosity (zeroth-order term). For all the cases, we prove that there exists a unique solution to the problem and we analyze the resulting point spectrum. We also show that there is an exponential energy decay for the first case, meanwhile for the second and third case only a polynomial decay is found. Finally, we present some one-dimensional numerical simulations to illustrate the behaviour of the discrete energy for each case.
Rural Transportation Infrastructure in Low- and Middle-Income Countries: A Review of Impacts, Implications, and Interventions

The rural transport infrastructure sector is a critical force for sustainable development that is interwoven with many other sectors. Rural transportation is an underlying driver of many of the Sustainable Development Goals (SDGs) and a crucial contributor to many socioeconomic benefits for rural people around the world. This review paper expands upon, enhances, and cross-references the perspectives outlined in previous rural infrastructure-focused review papers. Firstly, this work gives a thorough look into the progress of the rural transportation sector in recent years by focusing on the thematic relationships between infrastructure and other components of sustainable development, namely, economics and agriculture, policy and governance, health, gender, education, and climate change and the environment. Secondly, several strategies, approaches, and tools employed by governments and practitioners within the rural transport sector are analyzed and discussed for their contributions to the wellbeing of rural dwellers in low- and middle-income countries (LMICs). These include rural roads, bridges, maintenance, and non-infrastructural approaches that include concepts such as advanced technological innovations, intermediate modes of transport (IMTs), and transport services. This paper concludes that enhancement, improvement, and extension of rural transportation infrastructure brings significant benefits to rural dwellers. However, this paper also calls for additional integration of the sector and increased usage of systems approaches that view rural transport as an active part of many other sectors and a key leverage point within rural development as a whole. Further, this paper notes areas for future research and investigation, including increased investigation of the relationship between rural transportation infrastructure and education, improved data collection and management in support of improved policymaking, improved prioritization of interventions and institutionalization of maintenance, and expansion of pro-poor transportation strategies and interventions.
Effect of Post-Harvest Handling and Ripening Methods on Quality and Shelf-Life of Banana

Banana (Musa spp.) is a highly perishable fruit that requires special handling. In Eritrea, post-harvest handling practices are characterized to be poor and as a result, poor fruit quality and high post-harvest loss are common. Thus, the purpose of this study was to evaluate the effect of different post-harvest handling and ripening methods on quality and shelf-life of banana. A Complete Randomized Design (CRD) was used for evaluating a combination of two levels of post-harvest handling and four ripening methods in a factorial combination conducted in the laboratory of Horticulture at Hamelmalo Agricultural College. Peel colour change, physiological weight loss, pulp to peel ratio, total soluble solids (TSS), pulp pH, ripening period and shelf life were parameters studied. The results showed fruits treated with ripened tomato showed improved characteristics in all parameters compared to those treated with smoke from kerosene burning or mixed with moringa leaf. Similarly, fruits brought directly from the farm were better than those collected from the ripening room after passing the conventional post-harvest handling. Based on the results of this study, it can be concluded that ripening of banana with the help of tomato improves fruit quality and shelf life. Thus, it can be a safe and better alternative to smoking from kerosene burning. While moringa leaf has no potential as an alternative.
Reliability of Frail and Barthel Tests for Detecting Frailty in Palliative Oncological Patients in a Home Hospitalization Unit: A Comparative Study

Cancer is a condition that can increase the risk of frailty. In addition, palliative oncological patients in home hospitalization can find their activities of daily living affected. The main objective was to measure the degree of frailty in the oncological population in home hospitalization comparing Barthel and Frail-VIG Indexes. This is a descriptive cross-sectional study. A sample of oncological patients in home hospitalization (n = 50) that included 27 men and 23 women were recruited, and disability due to frailty was measured using the VIG frailty index and the Barthel scale for Activities of Daily Living (ADLs). Spearman’s correlation coefficients were categorized as weak (rs ≤ 0.40), moderate (0.41 ≤ rs ≥ 0.69) or robust (0.70 ≤ rs ≥ 1.00), with a strong indirect correlation between the domains using the toilet, transferring and wandering on the Frail-VIG scale with an r (s) value −0.810 (p < 0.001), −0.831 (p < 0.001) and −0.805 (p < 0.001), respectively, and a moderate indirect correlation for the domains of eating −0.718 (p < 0.001), dressing −0.770 (p < 0.001) and urination −0.704 (<0.001). The Frail-VIG index above 0.35 points, that is, from moderate to severe, does not affect ADLs except in the nutritional dimension in a palliative oncological population in home hospitalization. The preliminary outcomes obtained should be considered to determine the impact of nutritional status with regard to ADLs in palliative oncological patients in a home hospitalization unit.
Hubs, hopes and high stakes for a relatively disadvantaged low tech place

The transition to a digitally inclusive and knowledge-based rural society can be challenging. Digital hubs are often proposed as a way of overcoming digital exclusion in rural and small-town contexts, yet studies into how to set up such a hub in these challenging contexts are scarce. While hubs are usually associated with an urban environment, this case study deals with the development of a rural digital hub over several years in a small town in East Groningen (NL). In the context of an Interreg project, observations during project meetings, in-depth interviews and document analysis were conducted to closely monitor the hub’s development process. Initially, the hub initiators aimed at stimulating business activities and innovation linked to digital technologies. Thereby, an originally urban digital hub concept was copied into a rural context without a rural translation. Along the way, they were forced to adapt and scale down the scope of the project while at the same time, a broader target group had to be formulated. Moreover, the municipality lacked an overarching digital strategy, which compromised demand aggregation and supply synchronisation – two essential ingredients for rural digital hubs.
Joint Optimization of Trajectory and Resource Allocation in Secure UAV Relaying Communications for Internet of Things

As unmanned aerial vehicle (UAV) communication has been widely used in all walks of life, its secrecy issue has also received more and more attention. This article studies the physical-layer security of UAV relaying communication system in multiterminal Internet of Things (IoT) scenarios. Specifically, while receiving the information from the ground base station, the UAV safely forwards the information to one of a group of IoT terminals in the presence of an eavesdropper. Under the constraints of information causality and UAV mobility, our goal is to maximize the minimum average secrecy rate among all IoT terminals. Based on the nonconvex problem, this article proposes a high-efficiency algorithm for joint optimization of UAV trajectory and resource allocation. The simulation results show that the proposed algorithm not only effectively improves information secrecy of IoT terminals, but also enhances the fairness of communication between the IoT terminals.
Examining Association of Personality Characteristics and Neuropsychiatric Symptoms in Post-COVID Syndrome

Background: We aimed to evaluate personality traits in patients with post-COVID syndrome, as well as the association with neuropsychiatric symptoms present in this disorder. Methods: The Big Five Structure Inventory was administered to 93 consecutive patients with a diagnosis of post-COVID syndrome as defined by the WHO and to demographically matched controls. We also performed a comprehensive evaluation of depression, anxiety, fatigue, sleep quality, cognitive function, and olfactory function. Results: Patients with post-COVID syndrome scored lower for emotional stability, equanimity, positive mood, and self-control. Extraversion, emotional stability, and openness correlated negatively with anxiety and depression levels. Conscientiousness correlated negatively with anxiety. No statistically significant correlations were observed between personality traits and cognitive function, sleep quality, olfactory function, or fatigue. Personality scores explained 36.3% and 41% of the variance in scores on the anxiety and depression scales, respectively. Two personality profiles with lower levels of emotional stability were associated with depression and anxiety. Conclusions: Our study shows higher levels of neuroticism in patients with post-COVID syndrome. Personality traits were predictive of the presence of depression and anxiety, but not cognitive function, sleep quality, or fatigue, in the context of post-COVID syndrome. These findings may have implications for the detection of patients at risk of depression and anxiety in post-COVID syndrome, and for the development of preventive and therapeutic interventions.
Intraoperative vancomycin powder to reduce surgical site infections after posterior spine surgery: a systematic review and meta-analysis

The purpose of the study was to evaluate the effect of local application of vancomycin powder (VP) to prevent surgical site infections (SSIs) after posterior spine surgery. A comprehensive search of Web of Science, EMBASE, Pubmed, Ovid, and Cochrane Library databases for articles published was performed to collect comparative studies of intrawound vancomycin in posterior spine surgery before March 2021. Two reviewers independently screened eligible articles based on the inclusion and exclusion criteria, assessed the study quality, and extracted the data. Revman 5.4 software was used for data analysis. A total of 22 articles encompassing 11 555 surgical patients were finally identified for meta-analysis. According to the information provided by the included literature, the combined odds ratio showed that topical use of VP was effective for reducing the incidence of SSIs (P< 0.00001) after posterior spine surgery without affecting its efficacy in the treatment of deep infections (P< 0.00001). However, there is no statistical significance in superficial infections. In a subgroup analysis, VP at a dose of 1, 2, and 0.5–2 g reduced the incidence of spinal SSIs. The result of another subgroup analysis suggested that local application of VP could significantly reduce the risk of SSIs, whether it was administered after posterior cervical surgery or thoracolumbar surgery. Moreover, the percentage of SSIs due to gram-positive germs (P< 0.00001) and MRSA (P< 0.0001) could reduce after intraoperative VP was used, but did not significantly reduce to gram-negative germs. The local application of VP appears to protect against SSIs, gram-positive germs, and MRSA (methicillin-resistant Staphylococcus aureus) infections after the posterior spinal operation.
Gravitational Search Algorithm for Engineering: A Review

This paper presents a review on gravitational search algorithm (GSA). Nowadays, GSA has been used in various engineering studies such as production cost, production time, power consumption and emission. The GSA also mainly focuses to solve the problem related to optimization, modeling, scheduling and clustering. This paper also highlights the current researches using improved GSA.
Adaptive stochastic gradient descent for large-scale learning problems

As an effective strategy to enhance stochastic optimization, determining an appropriate step size sequence when performing these algorithms has been warmly encouraged in solving large-scale learning problems. This paper equips stochastic optimization algorithms with an adaptive step size strategy by using the diagonal Barzilai-Borwein (DBB) step size. Specifically, this work proposes a novel stochastic variance reduced algorithm by incorporating DBB into the variance-reduced algorithm, termed mS2GD-DBB. mS2GD-DBB uses a diagonal matrix to approximate the curvature information and accesses to a better curvature information than the conventional BarzilaiBorwein (BB) technique, where the latter works with the scalar approximation of the curvature information. We prove that the proposed algorithm attains a linear convergence rate for strongly convex cases. Moreover, we show that the complexity of the proposed algorithm matches the best known complexity of stochastic optimization algorithms. Numerical experiments suggest that the proposed algorithm shows much promise.
An Efficient and Secure Identity-Based Signature System for Underwater Green Transport System

The smart ocean has aroused the interest of government, business, and academia because of the wealth of marine resources. It has been suggested to use underwater Internet of Things (IoT) frameworks to collect a variety of data from smart seas that can aid in the underwater green transport system, ecological sustainability, military intelligence gathering, and a variety of other operations. Because of the limited resources accessible to IoT devices regarding communication overhead, processing expenses, and battery capacity, security and privacy concerns in underwater green transport systems have lately been a critical source of worry. In this context, We presented a unique identity-based authentication mechanism for underwater green transport systems. Our suggested solution uses lightweight authentication mechanisms that prove secure communication between different elements of the green transport system.
Beyond the encounter: Predicting multi‐predator risk to elk (Cervus canadensis) in summer using predator scats

Abstract There is growing evidence that prey perceive the risk of predation and alter their behavior in response, resulting in changes in spatial distribution and potential fitness consequences. Previous approaches to mapping predation risk across a landscape quantify predator space use to estimate potential predator‐prey encounters, yet this approach does not account for successful predator attack resulting in prey mortality. An exception is a prey kill site that reflects an encounter resulting in mortality, but obtaining information on kill sites is expensive and requires time to accumulate adequate sample sizes. We illustrate an alternative approach using predator scat locations and their contents to quantify spatial predation risk for elk (Cervus canadensis) from multiple predators in the Rocky Mountains of Alberta, Canada. We surveyed over 1300 km to detect scats of bears (Ursus arctos/U. americanus), cougars (Puma concolor), coyotes (Canis latrans), and wolves (C. lupus). To derive spatial predation risk, we combined predictions of scat‐based resource selection functions (RSFs) weighted by predator abundance with predictions that a predator‐specific scat in a location contained elk. We evaluated the scat‐based predictions of predation risk by correlating them to predictions based on elk kill sites. We also compared scat‐based predation risk on summer ranges of elk following three migratory tactics for consistency with telemetry‐based metrics of predation risk and cause‐specific mortality of elk. We found a strong correlation between the scat‐based approach presented here and predation risk predicted by kill sites and (r = .98, p < .001). Elk migrating east of the Ya Ha Tinda winter range were exposed to the highest predation risk from cougars, resident elk summering on the Ya Ha Tinda winter range were exposed to the highest predation risk from wolves and coyotes, and elk migrating west to summer in Banff National Park were exposed to highest risk of encountering bears, but it was less likely to find elk in bear scats than in other areas. These patterns were consistent with previous estimates of spatial risk based on telemetry of collared predators and recent cause‐specific mortality patterns in elk. A scat‐based approach can provide a cost‐efficient alternative to kill sites of quantifying broad‐scale, spatial patterns in risk of predation for prey particularly in multiple predator species systems.
Nonlinear control algorithm for induction motors

The nonlinear control algorithms have been becoming one of the most important fields in the research of AC drives. In order to improve the robustness of control system, a new method for the induction motor drives is proposed by using nonlinear auto disturbance rejection controller (ADRC). This controller is mainly composed of an “extended state observer” (ESO), an input reference signal tracking differentiator (TD) and a nonlinear state error feedback (NLSEF) control law . By making of the “extended state observer” that can be used to track successfully the “extended state” of the system, it is possible to realize the “state feedback” and “model, external disturbance compensation”. Because the design of ADRC is independent of the controlled system model, this controller can manifest its adaptability and robustness not only in some operation area but also in the whole working area. To demonstrate the effectiveness of proposed scheme, the ADRC is applied to the speed control of a 2.2kW induction motor. The proposed control system has been successfully tested by means of simulations. It is shown that ADRC is robust against modeling uncertainty and measurement noise, and the dynamic performances are improved as well.
Band structure of love wave in a one-dimensional piezoelectric layered phononic crystal with imperfect interface

Abstract The propagation of Love wave in a phononic crystal consisting of superlattice lithium niobate substrate carrying a piezoelectric waveguide layer is analyzed. The interface between the waveguide layer and the substrate is imperfectly bonded, which is described by means of a shear spring model. Numerical results show that the bandgap shifts to a lower frequency range and the bandwidth decreases as the interface imperfection increases. Moreover, the transmission coefficient calculations using finite element simulation revealed that a perfect interface results in more effective bandgap attenuation.
A neuro-mechanical model for the switching of stepping direction and transitions between walking gaits in the stick insect

In this study, a mathematical model for the locomotion of the stick insect is developed. This model takes physiological conditions into account and it is capable of mimicking biological relevant features. The model is predicated on the crucial role, that sensory feedback plays in the coordination of limbs during walking. Central Pattern Generators (CPGs), which produce the rhythm of locomotion, are affected by sensory influences between the segments. The activities of the CPGs are transferred by the motoneurons to the muscles. Starting with existing neuron models and neuronal network models, a neuro-mechanical model is developed that includes the coupling of segments inside of a leg as well as the coupling of multiple legs. Firstly, mechanical models concerning the motion of the three isolated main joints are derived. These mechanical models are fused with the neuronal one. Thus, they represent neuro-mechanical models for the single joints that are coupled via sensory feedback. By means of the introduction of a switching mechanism the model is able to produce forward, backward and sideward stepping of a middle leg. Through the junction of two stepping middle legs to the body of the modeled stick insect, curve walking sequences with different curvatures can be produced. By extending the model to the front and the hind leg, the structure of intersegmental connection between the legs during the tripod and tetrapod gait can be generated. The change of stepping direction can be brought about by changing one single central command. If the middle leg is stepping backwards, the curvature during turning is smaller than in the case of sideward stepping. Weakly inhibitory intersegmental connections show the most accommodating leg coordination during both the tetrapod and the tripod gait.
Artificial Intelligence and Cardiovascular Genetics

Polygenic diseases, which are genetic disorders caused by the combined action of multiple genes, pose unique and significant challenges for the diagnosis and management of affected patients. A major goal of cardiovascular medicine has been to understand how genetic variation leads to the clinical heterogeneity seen in polygenic cardiovascular diseases (CVDs). Recent advances and emerging technologies in artificial intelligence (AI), coupled with the ever-increasing availability of next generation sequencing (NGS) technologies, now provide researchers with unprecedented possibilities for dynamic and complex biological genomic analyses. Combining these technologies may lead to a deeper understanding of heterogeneous polygenic CVDs, better prognostic guidance, and, ultimately, greater personalized medicine. Advances will likely be achieved through increasingly frequent and robust genomic characterization of patients, as well the integration of genomic data with other clinical data, such as cardiac imaging, coronary angiography, and clinical biomarkers. This review discusses the current opportunities and limitations of genomics; provides a brief overview of AI; and identifies the current applications, limitations, and future directions of AI in genomics.
Estimation of Pipe Wall Thinning Using a Convolutional Neural Network for Regression

Abstract A pipe wall thinning diagnosis method based on vibration characteristics is proposed. Elbow specimens with artificial pipe wall thinning were fabricated and combined in a loop. By running a pump in the loop, vibration was induced by flow, and the vibrational signals were measured with accelerometers. The effect of pipe wall thinning on the vibrational signals was investigated by analyzing the spectral data of the acceleration signals. The analyzed vibration characteristics were difficult to observe because the change in characteristics was small. A convolutional neural network (CNN) specialized for data recognition was applied to recognize the small change in vibrational signal resulting from the pipe wall thinning. A regression model based on CNN was chosen to learn the tendency of change in the vibrational signals with varying thinning. The data types advantageous for training the regression model were identified. An early stopping technique using the validation data set was adopted to regularize the regression model. The trained regression model was able to predict pipe thinning.