Miracle-dz/UniFAR-FTUs huggingface dataset:
Landslide susceptibility mapping using GIS and Remote Sensing: A case study of the Rangamati District, Bangladesh

The landslide is a natural phenomenon and one of the most commonplace disasters in the Rangamati Hill tract area which appeals for better forecasting and specify the landslide susceptible zonation. This research work examines the application of GIS and Remote Sensing techniques based on different parameters such as altitude, slope angle, slope aspect, rainfall, land-use land-cover (LULC), geology and stream distance by heuristic model to identify the landslide susceptible zones for the study area. Among the parameters, rainfall, steep slope, geology and LULC are the dominant factor that triggering the landslide. Clayey or silty soils of the study area during heavy and prolong rainfall behave a flow of debris due to water pressure within the soil, resulting landslides. Steep slope has greater influences for weather zones of the rock-masses for susceptible landslides. Result and field observation indicate that the population density and LULC has a vital effect on landslide within the study area. However, landslide susceptible zones were created based on the susceptibility map of the study area which shows that about 19.43% of the area are at low susceptible zone, 56.55% of the area are at medium susceptible zone, 19.19% of the area are in the high susceptible zone and 4.81% of the area is at the very high susceptible zone.
Composition, enzyme and antioxidant activities of pineapple

ABSTRACT Three pineapple cultivars smooth cayenne, Tainung 17, and Tainung 19 were processed into flesh, peel, and stem. Nine samples were analyzed for their proximate compositions and bromelain activity existed in their water extracts. Pineapple fleshes were rich in reducing sugars and soluble fiber whereas peel and stem were rich in crude fiber. Stem showed higher bromelain activity (102–247.3 U/mg). All 18 samples including water and ethanol extracts were quite effective in the scavenging ABTS+- and DPPH-radical activities and reducing power as evidenced by their low EC50 (0.98–15 mg/mL). Water extracts of stem were more effective than their ethanol extracts. The scavenging ABTS+-radical ability was highly related with total phenols and flavonoids. Overall, peel and stem of pineapple showed high amounts of reducing sugars and soluble fiber, and higher bromelain activity and exhibit effective antioxidant activities. These wastes of pineapple would be utilized to develop as a sweet supplement or functional product.
Immunohistochemical Detection of Enteropathogens Associated with Calf Diarrhea

The objective of the study was to determine the infectious agents responsible for causing diarrhea in neonatal calves through immunohistochemistry. One hundred intestinal samples of dead calves with a history of diarrhea were collected from farms in and around Ludhiana and the postmortem hall of the Department of Veterinary Pathology, GADVASU, Ludhiana. All the samples were subjected to routine histopathology. Immunohistochemistry was employed to detect the presence of etiological agents in all of these samples. The primary gross lesions observed in the intestine were congestion, catarrhal enteritis, and fibrin on the mucosal surface. Common histopathologic observations were necrotic enteritis along with massive infiltration of mononuclear cells. 54 samples were found to be positive for Salmonella spp. , 50 for Clostridium perfringens, 8 for Cryptosporidium spp. , 4 for E. coli (K99), 4 for rotavirus. Eimeria spp. was also detected in 5 samples through immunohistochemistry. A total of 38 samples showed the involvement of multiple agents. The study concludes that calf diarrhea can be caused by multiple infectious etiological agents, either single or multiple, and that Clostridium perfringens and Salmonella spp were the main etiological agents responsible for causing diarrhea. Immunohistochemistry was found to be an effective tool for detecting the various etiological agents of calf diarrhea.
Template-Based Adaptive Training Acceleration Framework for Deep Learning Algorithms

: Field-programmable gate array (FPGA) is usually used to accelerate the training phase of deep learning algorithms, but it usually requires a long development cycle and rich hardware design expertise for satisfied execution performance. In order to deal with this challenge, an adaptive acceleration framework for deep learning algorithm is proposed in this paper. We investigate the application scale, parallel scheduling strategy, resource usage and the scalability of functionality. With the CPU-FPGA heterogeneous acceleration template based technology, an adaptive model compiler is proposed to customize the accelerator based on the algorithm’s complexity and hardware resources available. The proposed hardware and software co-design framework can effectively adapt to different FPGA hardware resources and support the fast evolution of deep learning algorithms. Taking the graph neural network as an example, it can obtain 7~41x performance improvements compared to the general purpose CPU platform.
Virtual Interactive Reality Telepresence Robot

A Telepresence Robot allows a user to have a limited virtual access to a physical environment. The proposed system aims to use the concept of telepresence robots incorporating Virtual Reality to create a VR surveillance robot for the military. The novelty of our robot is that no conventional controllers are used for the motion control rather the user's movement in the physical space itself is mapped to the robot. The robot design is based on an Arduino Mega coupled with motors for driving the wheels. The VR design is based on a Raspberry Pi to which a camera module is connected. The live footage captured by the camera is then streamed wirelessly to an android smartphone running the VR app. The smartphone is placed in a VR headset worn by the user. The main advantage of the proposed system is that the user will not have to depend on an additional controller for controlling the robot.
Dense Robotic Packing of Irregular and Novel 3D Objects

Recent progress in the field of robotic manipulation has generated interest in fully automatic object packing in warehouses. This article presents a formulation of the robotic packing problem that ensures stability of the object pile during packing and the feasibility of the robot motion while maximizing packing density. A constructive packing algorithm is proposed to address this problem by searching over the space of item positions and orientations. Moreover, a new heightmap minimization heuristic is shown to outperform existing heuristics in the literature in the presence of nonconvex objects. Two strategies for improving the robustness of executed packing plans are also proposed: 1) conservative planning ensures plan feasibility under uncertainty in model parameters; and 2) closed-loop packing uses vision sensors to measure placement errors and replans to correct for them. The proposed planner and error mitigation strategies are evaluated in simulation and on a state-of-the-art physical packing testbed. Experiments demonstrate that the proposed planner generates high-quality packing plans, and the error mitigation strategies improve success rates beyond an open-loop baseline from 83% to 100% on five-item orders.
High Resolution Information Reserved Anchor-Free Detection Algorithm

: Recently, object detection has gradually become a critical domain in the visual research community, while it’s also challenging to locate objects of different scales accurately for detectors. For the application of detecting objects in images, a high resolution anchor free (HOAR) detection strategy is proposed to meet the challenge of variable object scales. HOAR first inputs the image into a parallel high-resolution network with multiple paths (which is corresponding to different scales). Then the output feature map is extracted from each path as the deep feature representation of the image at each scale. Next, the dense feature pyramid (DenseFPN) is adopted to fuse the information of these feature maps to obtain the re-combined feature maps of multiple scales. Finally, the anchor-free detection sub-network is used to determine the object’s category and rectangle box position of each point on these feature maps. To verify the effectiveness of HOAR, comparative experiments are carried out on COCO dataset. First, the results on ablation study show
Meta Distribution and Secrecy of Partial Non-Orthogonal Multiple Access (NOMA) in Poisson Networks

This work studies the meta distribution in a partial-NOMA network to obtain fine-grained information about the network performance. As the meta distribution is approximated using the beta distribution via moment matching of the first two moments, reduced integral expressions are derived for the first two moments of the meta distribution. Accurate approximate moments are also proposed to further simplify the calculation. Security is an issue in partial-NOMA because the strong user may decode the weak user’s message in the process of decoding its own message using flexible successive interference cancellation (FSIC). Therefore, a measure of secrecy is defined in this context and the secrecy probability is derived for the case of: 1) a malicious strong user that prioritizes eavesdropping, 2) an innocent strong user that decodes the weak user’s message only when it is required to do so. The obtained results highlight the superiority of partial-NOMA over traditional NOMA in terms of secrecy. They also show that receive filtering and FSIC have a significant positive impact on the secrecy of partial- OMA. Furthermore, partial-NOMA with a small overlap of the resource-block can secure the network from the additional deterioration a malicious eavesdropper may cause.
FORECASTING THE DYNAMICS OF BITCOIN BASED ON CRYPTOCURRENCY PRICE FORMATION FACTORS

Cryptocurrencies have become one of the most relevant topics in recent financial and economic issues. Today, the main features of crypto-currency are decentralization, ability to be considered as means of payment, high liquidity, absence of an issuer as any central authority, irreversibility of payments and transparency. The growth of cryptocurrencies among other world currencies allows them to be a highly profitable asset to invest in and stimulates the interest of new miners. However, there are some risks arising. Firstly, the risk related to governmental restriction and regulating laws about usage of cryptocurrency. Secondly, there is a risk of fraud and no opportunity to accuse those responsible due to the anonymity of users. And last but not least, high volatility of cryptomarket.
Exponential attractors for nonclassical diffusion equations with arbitrary polynomial growth nonlinearity

In this paper, the dynamical behavior of the nonclassical diffusion equation is investigated. First, using the asymptotic regularity of the solution, we prove that the semigroup $ \{S(t)\}_{t\geq 0} $ corresponding to this equation satisfies the global exponentially $ \kappa- $dissipative. And then we estimate the upper bound of fractal dimension for the global attractors $ \mathscr{A} $ for this equation and $ \mathscr{A}\subset H^1_0(\Omega)\cap H^2(\Omega) $. Finally, we confirm the existence of exponential attractors $ \mathscr{M} $ by validated differentiability of the semigroup $ \{S(t)\}_{t\geq 0} $. It is worth mentioning that the nonlinearity $ f $ satisfies the polynomial growth of arbitrary order.
Valor de la imagenología (TC y PET-TC) en el diagnóstico de la carcinomatosis peritoneal. Estudio preliminar

Imagenology contributes to the accurate diagnosis of peritoneal compromise in the assessment of peritoneal carcinomatosis, although its effectiveness is still limited. CT is the most frequently used study and PET/CT is graduallly more requested in the assessment of different neoplasms. This study aims to assess performance of CT and PET/CT for diagnosis of peritoneal carcinomatosis in digestive and gynecological tumours.Method: retrospective, observational study of 11 cases operated on peritoneal carcinomatosis after a CT and/or PET/CT had been performed. The results of both image studies were analysed separately and altogether and they were subsequently compared to intraoperative and pathology studies.Results: CT matched findings in 8 out of 10 cases (sensitivity 78%, specificity 100%, positive predictive value 100%, negative predictive value 33%), whereas PET/TC matched in 9 out of 11 patients (sensitivity 100%, PPV 81%). When considered together, sensitivity was 100% y el PPV de 82%. Conclusions: the results of the study confirm imagenology studies overestimate peritoneal carcinomatosis based on the characteristics of nodules and its uptake pattern. In CT/PET, SUV> 6 could increase the probability of diagnosing carcinomatosis. This small series of cases enables our suggesting CT and 18F-FDG PET-TC to be requested and analysed together to increase its efficiency, although surgical exploration continues to be the most effective method to diagnose and assess peritoneal carcinomatosis.
Endmember Estimation From Hyperspectral Images Using Geometric Distances

Endmember estimation consists of two tasks, that is, determining the number of pure spectral constituents (endmembers) and extracting their spectral signatures. We present a new geometric distance-based method for endmember estimation from hyperspectral images (HSIs), which does not need to know the number of endmembers in advance. Our strategy optimizes the widely used maximum distance analysis (MDA) method from two viewpoints. First, the traditional MDA method performs endmember estimation by computing the maximum distances between any pixel and one specific pixel, line, plane, or affine hull (AH) composed by the endmembers that have been formerly extracted. Instead, our new strategy only requires computing the maximum distance between any pixel and one specific AH. This operation provides a simpler way than MDA to estimate endmembers. Second, our strategy exploits a new distance computation between any pixel and an AH and just needs the normal vector (compared to the traditional MDA method, which uses the normal vector and offset). The new distance computation in our method is much more efficient than that in the traditional MDA method.
Challenges and Problems Faced by Pakistani Virtual University ’ s Students in Web-Based Learning

The study concerned with the identified the problems and challenged Web Based Learning faced by master and graduation level students of Virtual University in Pakistan. The main purpose of this study was to know the problems faced by male and female students from different background and difficulties of web browsing among the web based learners of different semesters. The participants of the study were One hungered (100) students enrolled in graduation and master level in Virtual University Lahore, Pakistan. A Questionnaire consist of thirty eight (38) items was developed and validated through experts to collect the data and the reliability of the questionnaire was calculated by Cronbach Alpha i.e. 0.946 a high internal consistency of the items. The findings of the study shows that there is no significant difference of facing problems in web based learning between Male and Female. There is no significant difference of facing problems in web based learning between graduation and master students. The significant difference exists between Internet Explorer and Netscape, Internet Explorer and Opera. The mean differences between Internet Explorer and Opera are 20.431(*) and 24.859(*) respectively which are significant at p<0.05. The other three browsers do not differ from each other significantly. The mean differences of quality = 3.7887 and design = 3.7683 is bigger than mean of access = 3.5089.
The social media revolution and shifts in the climate change discourse

The paper analyses the role of social media in shifting the climate change discourse in the North Atlantic region. Changes in the media environment have removed traditional gatekeepers of information dissemination and empowered new kinds of actors to reach large audiences. Yet, the techniques and the particular messaging through which these audiences can be reached has had to change as well. Messages spread widely on social media if they get shared, liked, retweeted frequently. They need to provoke a reaction in their audience, that leads the audience to actively respond to the messages, be it only with a mouse click. Within the climate change field two new kinds of actors have the potential to seize upon this new opportunity structure: climate sceptics and pro-climate activist social movements. Through a qualitative social media analysis, this paper compares the specific messaging strategies these two communities have deployed. It finds that the climate strike movement, notably led by Greta Thunberg, could effectively seize the opportunities social media provide to reach large audiences. By contrast, climate sceptics have been significantly less successful. Counter-intuitively, the paper finds that digitization can not only empower tech-savvy individuals, but also specific, comparatively low tech, and hitherto marginalized individuals. Notably, young women, if they can draw on their vulnerability, aesthetics, and emotional messaging, can acquire high attention scores when advocating for political change.
Influence of morphological structure of pork on thermophysical characteristics in the process of freezing-defrost

75 Література: 1. Бендерська О. В. Удосконалення технології томатних соусів із додаванням пасти із насіння томатів : автореф. дис. канд. техн. наук : спец. 05.18. 13 «Технологія консервованих і охолоджених харчових продуктів». НУХТ. Київ, 2019. 23 с. 2. Benderska О. , Bessarab А. , Shutyuk V. Research of fatty acidcomposition of tomato seeds. Technology audit and production reserves. Vol. 4, No 3(42). 2018. 3. Marcus, J.B. Culinary nutrition: the science and practice of halthy cooking. New York: Academic Press.1. 2013.
From preference to landscape sustainability: a bibliometric review of landscape preference research from 1968 to 2019

ABSTRACT Landscape preference (LP) studies highlight one of the most important issues of human-environment relationship, and their related publications have increased rapidly over the last two decades. However, there is no systematic review with a holistic understanding of this field. here we applied a bibliometric approach to examine the evolution of LP research and identify its status and future prospects. We obtained 7,637 LP research publications from Web of Science core collection from 1968 to 2019 and analyzed the characteristics of publication outputs as well as performances in various countries and institutions. Besides, content keywords analysis was conducted to discover the drivers, focus, motivation, and trends of LP research. We found that 1) publications, subject categories, and active journals increased rapidly since the 2000s ; 2) the USA, England, and Australia are the leading countries in LP research, while China is starting to have some influence; 3) LP research is most closely linked to ecological and environmental studies, being developed by objective drivers, i.e., interactions with landscape change, and subjective motivations ; and 4) LP research is advancing the landscape sustainability science, integrating natural and social science together through ecosystem services. By comprehensively reviewing the evolution and prospect of LP research, we provide insights for further research in this field.
Autonomous Control System for an Electric ATV

This paper lays the foundations of an autonomous control system for an electric ATV type vehicle. The conversion of a classic motor vehicle into an electric one is shortly presented and the main advantages deriving from such a motorization especially in terms of the main impact on the environment in the sense that the noxious substances resulting from a classic thermal motorization are eliminated and the use of the vehicle can be extended for industrial halls too. This electric vehicle has, in addition to the classic proximity sensors, a high-resolution LIDAR type scanning system that allows it to map an enclosure so that the processed data can then be used in the autonomous driving algorithm. The LIDAR type scanning system is also correlated with a location system based on a LoRa communication system to allow a predictive location of the route followed by the autonomous vehicle. It is desired to use the whole complex, especially in some industrial halls and in the current context it is suitable to be used as a disinfection vehicle in order to prevent the personnel health issues in the context of SARS-COV2 epidemics.
Deep Reinforcement Learning for Hybrid Energy Storage Systems: Balancing Lead and Hydrogen Storage

We address the control of a hybrid energy storage system composed of a lead battery and hydrogen storage. Powered by photovoltaic panels, it feeds a partially islanded building. We aim to minimize building carbon emissions over a long-term period while ensuring that 35% of the building consumption is powered using energy produced on site. To achieve this long-term goal, we propose to learn a control policy as a function of the building and of the storage state using a Deep Reinforcement Learning approach. We reformulate the problem to reduce the action space dimension to one. This highly improves the proposed approach performance. Given the reformulation, we propose a new algorithm, DDPGαrep, using a Deep Deterministic Policy Gradient (DDPG) to learn the policy. Once learned, the storage control is performed using this policy. Simulations show that the higher the hydrogen storage efficiency, the more effective the learning.
A graphene oxide modified cellulose nanocrystal/PNIPAAm IPN hydrogel for the adsorption of Congo red and methylene blue

A simple strategy is developed to fabricate a graphene oxide modified cellulose nanocrystal/PNIPAAm IPN (GO-CNC/PNIPAAm IPN) hydrogel. It is a high-efficiency and low-cost adsorbent for the removal the anionic dye CR and cationic dye MB.
Warning weakens retrieval-enhanced suggestibility only when it is given shortly after misinformation: The critical importance of timing.

Recalling details from an experienced event can sometimes exacerbate eyewitnesses' susceptibility to subsequent misinformation. This finding, known as retrieval-enhanced suggestibility (RES), can be eliminated when participants are warned about possible inaccuracies in the misinformation source (Thomas et al., 2010). In three experiments, we investigated whether this warning benefit persists across delays. When the warning was issued shortly after participants were exposed to misinformation, it inoculated participant witnesses against RES, regardless of whether the final memory test occurred immediately or 48 hr after the warning. However, the warning lost its effectiveness when it was delivered 48 hr after participants were exposed to misinformation. These results applied to both recognition memory and the confidence-accuracy relationship. We considered these data from the perspective of temporal distinctiveness, and we argue that a warning serves a similar function to a forget cue in the directed forgetting paradigm. (PsycInfo Database Record (c) 2022 APA, all rights reserved).