Miracle-dz/UniFAR-FTUs huggingface dataset:
Diversity and Typology of Land-Use Explain the Occurrence of Alien Plants in a Protected Area

Plant life history and functional characteristics play an important role in determining the invasive potential of plant species and have implications for management approaches. We studied the distribution of 24 alien plant taxa in a protected area in relation to different land-uses by applying ordination analyses and generalized linear models. Taxa richness is best explained by the presence of built-up areas, followed by residential areas, marshlands, and agricultural lands with semi-natural formations. The diversity of land-use within the grid cell proved to be an important explanatory factor, being the only significant variable explaining the richness of wood perennials and vines. The richness of annual herbs and seed-dispersed taxa is explained by a similar set of variables, with the exception of residential areas. The richness of invasive species is explained only by agricultural land and the diversity of land-use. The richness of taxa with predominant vegetative dispersal is best explained by built-up, marshland, and seminatural areas along with land-use diversity. When we consider only the presence of plant groups within grid cells, the results are similar. The results of similar studies may provide an important tool for defining sustainable practices and overall conservation management in protected areas.
Testing Proportionality in Duration Models with Respect to Continuous Covariates

Several omnibus tests of the proportional hazards assumption have been proposed in the literature. In the two-sample case, tests have also been developed against non-parametrically specified ordered alternatives. This paper considers a natural extension of such monotone ordering to the case of continuous covariates, and develops tests for the proportional hazards assumption against such ordered alternatives. Small sample properties of the tests are explored. The use of the test statistics, and use of histogram sieve estimators in the case where proportionality does not hold, are illustrated with application to data on strike durations.
Bonding Strategies for Thermoplastics Applicable for Bioanalysis and Diagnostics

Microfluidics is a multidisciplinary science that includes physics, chemistry, engineering, and biotechnology. Such microscale systems are receiving growing interest in applications such as analysis, diagnostics, and biomedical research. Thermoplastic polymers have emerged as one of the most attractive materials for microfluidic device fabrication owing to advantages such as being optically transparent, biocompatible, cost-effective, and mass producible. However, thermoplastic bonding is a key challenge for sealing microfluidic devices. Given the wide range of bonding methods, the appropriate bonding approach should be carefully selected depending on the thermoplastic material and functional requirements. In this review, we aim to provide a comprehensive overview of thermoplastic fabricating and bonding approaches, presenting their advantages and disadvantages, to assist in finding suitable microfluidic device bonding methods. In addition, we highlight current applications of thermoplastic microfluidics to analyses and diagnostics and introduce future perspectives on thermoplastic bonding strategies.
The Determinants of ESG for Community LOHASism Sustainable Development Strategy

Owing to the increasing trends of the LOHAS lifestyle (LOHASism) adopted by a majority of Taiwanese communities, most community development associations have begun to explore the most effective LOHASism sustainable development strategies for advancing a high-quality lifestyle for these extensive community residents. Hence, this research cross-employed the three core principles in LOHASism, three analytical dimensions (environment, society and governance) of Environment Society and Governance (ESG) and 17 evaluated sustainable indexes of the Sustainable Development Goals (SDGs) in order to effectively induce the determinants of ESG for a community LOHASism sustainable development strategy. Significantly, the three most valuable conclusions and contributive findings are: (1) the main research question has been comprehensively solved through interdisciplinary analyses of the consolidation among the three principles (Dogood, Feelgood, Lookgood) of LOHASism, three dimensions of ESG and 17 evaluated sustainable indexes of the SDGs; (2) the highest scale of standardized comparative weights was located in the Positively Promoting Community Development Sustainability (PPCDS) of 0.7194, which means LOHASism, ESG and SDGs did positively promote community sustainable development in order and empirically achieved the brief research goal; and (3) industry, innovation and infrastructure (III), reduced inequality and responsible consumption and production in the governance of the EGS into LOHASism were the highest three weighted scales in the Positively Promoting Community Development Sustainability (PPCDS). This means that most community’s residents expect the advancement of innovation and infrastructure (III), the improvement of reduced inequality and the increment of responsible consumption and production to be the top three critical sustainable development strategies in their community development governance in order to advance the most residents to be good-looking and have body health.
See-through obscurants via compressive sensing in degraded visual environment

This paper proposes a new approach for seeing through obscurants in a severe degraded visual environment (DVE). The proposed method allows extraction of hidden information from the raw sensor data via computational imaging technologies. We show that although to the human eye a sensor captures a zero-visibility view of an object through dense obscurants, it is possible to recover the hidden visual information of the object and display its visual cue to the aircraft pilot to aid in landing and maneuvering. The proposed approach uses a compressive sensing algorithm incorporating an over-complete dictionary composed of pose transformation of the targeted object. Information on the recovered image is used in a feedback loop to further remove perception noise and maintain tracking of the object from a moving aircraft. The proposed algorithm is sensor agnostic and can be applied to images taken from Infrared, LIDAR, or RF imagery sensors. We quantify the upper bound of the DVE noise in which the proposed method is effective via simulation results.
Synthesis and characterization of Fe+2, Cu+2 and Ni+2 complexes with thiosemicarbazide-di-ortho-bromobenzylacetone Schiff base and evaluation of their cytotoxicity against breast cancer cells

In this study, Schiff base is synthesized through the reactance of di-o-bromobenzylacetone with thiosemicarbazide by acid-catalyzed condensation reaction (Aldol condensation). And using the novel Schiff base to prepare three metal complexes by reacting divalent transition metals (nickel, copper, iron) with the synthesized Schiff base. The prepared compounds were characterized by mass spectrometer, infrared spectrophotometer (FTIR), visible-ultraviolet rays (UV-Vis) and both 1H and 13C nuclear magnetic resonance spectrometer (NMR). The molar conductivity and magnetic susceptibility of the synthesized metal complexes were also measured. The cytotoxicity of the synthesized metal complexes against human breast cancer cell line MDA-MB231 was also measured in-vetro. All of the metal complexes were non-electrolyte and the predicted shape of these metal complexes is tetrahedral geometry. Results obtained from in-vetro cytotoxicity against breast cancer cell line type MDA-MB231 type showed that copper complex had best cytotoxicity with IC50 equal to 18. 5µM .
SEAL-RF: Secure Adiabatic Logic for Wirelessly Powered IoT Devices

Wireless power harvesting has become popular for various Internet-of-Things (IoT)-based devices, such as wireless sensors, radio frequency identification (RFID)-based systems, and implantable devices for healthcare. Since these ubiquitous computing devices are likely to handle confidential information, ensuring their security is critical. In this work, a secure adiabatic logic family, referred to as SEAL-RF, is developed, specifically for RF-powered IoT devices. SEAL-RF is highly energy efficient and resistant against power-based side-channel attacks. Specifically, at the gate-level, the normalized energy deviation (NED) of SEAL-RF is up to $180\times $ lower than conventional (unprotected) adiabatic logic, while consuming up to 39% less average energy per transition. Furthermore, the NED of SEAL-RF is up to $5.5\times $ lower than an existing secure adiabatic logic, while consuming up to 32% less average energy per transition. A lightweight encryption core based on SIMON algorithm is also designed with the proposed SEAL-RF and conventional adiabatic logic. A correlation power analysis (CPA)-based side-channel attack is mounted on both designs. The secret key for the unprotected SIMON core is retrieved with less than 800 traces whereas the secret key for the SEAL-RF-based SIMON core cannot be retrieved with more than 40K traces. Furthermore, the average power and energy per encryption of the SEAL-RF-based SIMON core is 15.6% lower than the conventional adiabatic logic-based SIMON.
Dimethyl Fumarate (DMF) Alleviated Post-Operative (PO) Pain through the N-Methyl-d-Aspartate (NMDA) Receptors

The management of post-operative (PO) pain has generally been shown to be inadequate; therefore, acquiring a novel understanding of PO pain mechanisms would increase the therapeutic options available. There is accumulating evidence to implicate N-methyl-d-aspartate (NMDA) receptors in the induction and maintenance of central sensitization during pain states by reinforcing glutamate sensory transmission. It is known that DMF protects from oxidative glutamate toxicity. Therefore, NMDA receptor antagonists have been implicated in peri-operative pain management. Recent advances demonstrated that dimethyl fumarate (DMF), a non-opioid and orally bioavailable drug, is able to resolve neuroinflammation through mechanisms that drive nociceptive hypersensitivity. Therefore, in this study, we evaluated the role of DMF on pain and neuroinflammation in a mouse model of PO pain. An incision of the hind paw was performed, and DMF at two different doses (30 and 100 mg/kg) was administered by oral gavage for five consecutive days. Mechanical allodynia, thermal hyperalgesia and locomotor dysfunction were evaluated daily for five days after surgery. Mice were sacrificed at day 7 following PO pain induction, and hind paw and lumbar spinal cord samples were collected for histological and molecular studies. DMF administration significantly reduced hyperalgesia and allodynia, alleviating motor disfunction. Treatment with DMF significantly reduced histological damage, counteracted mast cell activation and reduced the nuclear factor kappa-light-chain-enhancer of the activated B cell (NF-κB) inflammatory pathway, in addition to downregulating tumor necrosis factor-α (TNF-α), Interleukin-1β (Il-1β) and Il-4 expression. Interestingly, DMF treatment lowered the activation of NMDA receptor subtypes (NR2B and NR1) and the NMDA-receptor-interacting PDZ proteins, including PSD93 and PSD95. Furthermore, DMF interfered with calcium ion release, modulating nociception. Thus, DMF administration modulated PO pain, managing NMDA signaling pathways. The results suggest that DMF positively modulated persistent nociception related to PO pain, through predominantly NMDA-receptor-operated calcium channels.
Electrochemiluminescence Systems for the Detection of Biomarkers: Strategical and Technological Advances

Electrochemiluminescence (ECL)-based sensing systems rely on light emissions from luminophores, which are generated by high-energy electron transfer reactions between electrogenerated species on an electrode. ECL systems have been widely used in the detection and monitoring of diverse, disease-related biomarkers due to their high selectivity and fast response times, as well as their spatial and temporal control of luminance, high controllability, and a wide detection range. This review focuses on the recent strategic and technological advances in ECL-based biomarker detection systems. We introduce several sensing systems for medical applications that are classified according to the reactions that drive ECL signal emissions. We also provide recent examples of sensing strategies and technologies based on factors that enhance sensitivity and multiplexing abilities as well as simplify sensing procedures. This review also discusses the potential strategies and technologies for the development of ECL systems with an enhanced detection ability.
Generalized Linear Models for Loss Calculation in General Insurance

In most cases, loss in non-life insurance is calculated based on claim severity and frequency and an assumption of independence. However, in some cases, claim severity depends upon the claim frequency. This paper presents the derivation of aggregate loss calculation by modeling claim severity and frequency as the assumption of independence is eliminated. The authors modeled average claim severity using claim frequency as the covariate to induce the dependence among them. For that purpose, we use the generalized linear model. After doing parameters estimation, we will obtain the calculated loss.
CL$_s$ Method at Gaussian Limit to Present Searches

We describe a new method based on the CLs approach [1] to present results in searches of new physics under the condition that the relevant nuisance parameter space is continuous. Essentially, this method uses a Gaussian approximation for the distribution of a log-likelihood ratio test statistic used in a two-hypotheses testing problem, when the sample size is large. It leads to a simple way to form exclusion sets for the parameters of interest. This Gaussian CLs method can be seen as an alternative to the traditional method of setting confidence intervals based on p-values. And one advantage of the Gaussian CLs method is that, it requires a simple procedure that is applicable in many situations where the p-value method requires computationally-intensive Monte Carlo instead of a popular thresholding rule based on a Chi-square distribution. We illustrate this method in a problem of searching for a sterile neutrino. Specifically, we derive exclusion sets in the two-dimensional parameter space of (sin 2θ, |∆m|) based on the Gaussian approximation, and demonstrate that it provides adequately accurate results. Finally, we discuss the advantages and disadvantages of our method in comparison to the traditional methods of setting confidence intervals.
Analysis of College Students’ Ideological and Political Dynamics and Communication Path Based on Reinforcement Learning

Contemporary college students are the main force of future national construction. Their ideological political dynamics are related to the development of the party and the country. Some students have some problems with study concepts and study habits. For a long time, the ideological political education of university students has not been paid attention to, resulting in the inability to accurately analyze the ideological political dynamics of university students. Grasping the ideological political dynamics of university students in the new era is the top priority of current educational work and an important guarantee for the development of ideological political education in universities. With the development of the times, communication channels are also constantly updated. The focus of this article is to analyze the ideological political dynamics and communication channels of university students. To some extent, traditional analysis methods cannot satisfy current research. This paper constructs an analysis model of university students’ ideological political dynamics and communication paths based on reinforcement learning. The Markov decision process and Monte Carlo method are used to analyze the ideological political dynamics and communication paths of college students. The results show the following: (1) the highest accuracy of reinforcement learning is 99.7%, and the lowest is 96.2%; the highest accuracy is 99.7%, and the lowest is 97.4%; the highest recall is 99.6%, and the lowest is 97.6%. (2) The average accuracy rate of reinforcement learning is 98.16%, the average accuracy rate is 98.75%, and the average recall rate is 98.65%. (3) In the ideological political dynamics of college students, the score of value orientation is 6.975, the score of learning status is 8.025, the score of consumption concept is 7.7, and the score of employment is 7.45. (4) In the communication path analysis, there are 12 people in interpersonal communication, 15 people in organizational communication, 21 people in mass communication, 28 people in network communication, and 24 people in Internet communication.
Pengenalan Huruf Hijaiah (ARENYA) Menggunakan Augmented Reality

Pengenalan huruf hijaiah yang dilakukan dengan media cetak biasanya menggunakan media berupa buku iqra yang terdapat huruf hijaiah. Augmented Reality (AR) adalah sebuah teknologi yang mengombinasikan benda dua dimensi ataupun tiga dimensi ke dalam lingkungan dunia nyata, kemudian membuat obyek maya tersebut seolah terdapat di dunia nyata. Tujuan dari penelitian ini untuk memberikan sebuah pengenalan huruf hijaiah dan membaca makhorijul huruf. Media pengenalan ini dibuat dengan metode marked based tracking. Metode ini memanfaatkan fungsi sebuah marker sebagai media yang menampilkan objek tiga dimensi. Dalam pengembangan aplikasi menggunakan metode Waterfall sebagai panduan terhadap tahap perancangan sistem. Pengujian fungsionalitas dilakukan untuk menguji pada masing-masing kebutuhan fungsionalitas dan pengujian usability menggunakan mdetode System Usability Scale (SUS). Hasil dari pengujian usability dengan metode SUS kepada responden aplikasi ARENYA memperoleh hasil rata-rata skor yaitu 77,2 yang dapat disimpulkan bahwa aplikasi dapat diterima oleh pengguna dengan tingkat kategori ‘Good’.
Development and validation of enhanced fuzzy logic controller and boost converter topologies for a single phase grid system

Introduction. Solar photovoltaic system is one of the most essential and demanding renewable energy source in the current days, due to the benefits of high efficiency, reduced cost, no pollution, and environment friendly characteristics. Here, the maximum power point tracking controller has been implemented for obtaining an extreme power from the photovoltaic array. For this purpose, there are different controller and converter strategies have been deployed in the conventional works. It includes perturb and observation, incremental conductance, fuzzy logic systems, and hill climbing, and these techniques intend to extract the high amount of power from the solar systems under different climatic conditions. Still, it limits with the issues like increased design complexity, high cost consumption, high harmonics, and increased time consumption. The goal of this work is to deploy an improved controlling and converter topologies to regulate the output voltage and power fed to the single phase grid systems. The novelty of the work aims to develop an enhanced fuzzy logic controller based maximum power point tracking mechanism with the boost DC-DC converter topology for a single phase grid tied photovoltaic systems. Practical value. Also, the higher order harmonics suppression and unbalanced current elimination are handled by the use of LCL filtering technique, which efficiently reduces the harmonics in the output of inverter voltage and current. Moreover, it helps to obtain the reduced total harmonics distortion value with improved accuracy and efficiency. Results. There are different performance indicators have been evaluated for validating the proposed enhanced fuzzy logic controller–maximum power point tracking controlling technique. Moreover, the obtained results are compared with some of the conventional controlling algorithms for proving the betterment of the proposed work.
Supporting parental caregivers of seriously ill children: findings from a feasibility and acceptability study of a hope intervention

ABSTRACT Parents of seriously ill children face complex experiences for which support is valuable. We evaluated the Keeping Hope Possible Toolkit with 16 parental caregivers to assess feasibility and acceptability using a quasi-experimental design. Participants used the Toolkit for two to four weeks, and completed quantitative measures of hope, self-efficacy, uncertainty, and distress pre- and post-intervention and a qualitative interview. The measures were appropriate, despite no significant differences across timepoints. Qualitatively, the Toolkit supported hope, positive experiences, and navigation of emotions, confirming acceptability and feasibility following minor refinement. Findings will inform future research and subsequent implementation within family-centered pediatric healthcare.
Investigation of Effects of Near‐Surface Complexities on Measurement of Mantle Discontinuity Using SS and Its Precursors

Seismic observations of the mantle discontinuities at depths of 410 and 660 km provide crucial information to constrain the thermal and compositional states and further shape our understanding of mantle dynamics. The topography and impedance contrast of the mantle discontinuities, at both global and regional scales, have been imaged through differential travel times and amplitude ratios between SS and its precursors. However, a relatively simple reference phase (SS) is often assumed, which may overlook potential bias caused by near‐surface complexities. To assess these effects, we developed an algorithm to simulate the waveforms of SS and its precursors efficiently based on the propagation matrix method. We found that the crustal thickening increases the differential travel times but does not change the amplitude ratios much, while the sedimentary layer significantly affects both the differential travel times and amplitude ratios. The global anomalies of differential travel times and amplitude ratios are mapped incorporating earth models from CRUST1.0 and PREM. The time shift can reach ∼9 s, which will bring a depth bias of mantle discontinuity of more than 20 km, while the amplitude ratio anomaly is up to ∼40%, which will make the impedance contrast of the mantle discontinuity overestimated. The near‐surface complexities cannot be ignored in the investigation of mantle discontinuities with SS precursors, especially when the bounce points located in the regions with sediment.
An Econometric Analysis of a Complementarity between profit-wage and capital-labour: Skill-Biased Technological Change in Indian Services Sector

This paper econometrically tests a complementary between the profit-wage and capital-labour ratios in the Indian services sector for the period of 1999-2017. The higher surplus-value is examined in terms of higher labour productivity, lower employment and lower wages. Two specific policy implications are advocated to revamp the Indian services sector for its sustainable future: (i) there is a need to increase public investment in the Indian services sector to expand employment opportunities and wages, to address the issues of deficiency of aggregate demand; (ii) there is also a need to reverse the complementary between profit-wage and capital-labour ratio to a complementary between wage-profit and labour-capital ratios, as that can lead to stable and sustainable demand and supply in Indian economy.