Miracle-dz/UniFAR-FTUs huggingface dataset:
IOT Based Smart Farming Application

Smart agriculture is one of the Internet of Things' most important uses. Water, fertilizer, and crop yield waste are all reduced via smart agriculture. The manual detection of specifications like temperature, moisture, and humidity in the existing agricultural system drives up labor costs, and continuous monitoring is not possible. The irrigation procedure is carried out automatically in this study utilizing various sensors, which reduces manual work. It is suggested to utilize a sensor-based monitoring system for crop fields. It would entail gathering information on the soil moisture, humidity, and temperature. Automation of irrigation is possible by keeping an eye on all these variables. Unquestionably, smart farming is a key facilitator in providing more food with less resources for a growing global population. While this is essential to feeding the world's expanding population responsibly, smart farming also offers producers and communities throughout the world additional advantages. Farmers may raise yields and improve environmental management by using these strategies. By monitoring the field, IoT-based smart agriculture enhances the overall farming system. The Internet of Things in agriculture helps farmers save time and lessen the usage of resources like water thanks to sensors and connections. electricity, internet-connected temperature monitoring.
Legal Safeguards for a Supportive Workplace Environment: The role of Pakistan Anti-Harassment Laws in Supporting Workplace Environment and Preventing Workplace Harassment

: This study determined how workplace harassment can influence supportive workplace harassment, directly and indirectly, using Anti- harassment laws as a mediating variable. Workplace harassment with multiple dimensions (Physical harassment, sexual harassment, verbal conduct, and non-verbal conduct) was used in this study. We used a questionnaire survey approach to evaluate the data. A total of 53 items were used in the questionnaire with a five-point Likert scale. The data were collected from small- and medium-sized enterprises (SMEs) based in Pakistan. The authors distributed 600 questionnaires among targeted employees (senior managers, middle-level managers, and supportive staff) and received 417 filled questionnaires, among which six were incomplete. So, the completed sample size of the research was 411, and the overall response rate was 69%. To estimate the proposed relationships in the research model, we used partial least-squares structural equation modeling (PLS-SEM 3.2). The outcomes of this study indicate that for direct and indirect relationships, workplace harassment negatively influences a supportive workplace environment. Additionally, the study shows a negative relationship between WH and AHL. Moreover, the outcomes of this study also show that anti-harassment laws positively impacts and helps to create a supportive workplace environment. The study concludes with a discussion, limitations, and future research directions.
The Language of Mechanical Support in Children: Is It ‘Sticking’, ‘Hanging’, or Simply ‘On’?

Research has found that children begin to differentiate in the terms they use to encode support. In English, BE on, the Basic Locative Construction (Levinson & Wilkins, 2006; “put on” in dynamic events) tends to encode support-from-below (e.g., cup on table), whereas lexical support verbs (e.g., hang, stick, tape, glue, etc.) tend to encode mechanical support. We see this differentiation in semantic space by six years of age (Johannes et al., 2016). Although this differentiation occurs, we can still use BE on to encode all types of support, just as we can use a variety of lexical verbs (e.g., the picture is on/put on the wall, the picture is hanging on the wall, the picture is stuck to the wall, the picture is taped to the wall), In this study we ask several questions: 1) do four-year-olds use more lexical verbs to describe dynamic events compared to previous studies (Johannes et al., 2016; Landau et al., 2017) that use static stimuli, 2) does visibility of the support mechanism play a role in the lexical support verbs used and 3) do participants show a bias to encode specific aspects of the spatial configuration over another by using one class of verbs more, specifically do they encode the resulting spatial configuration (use Verbs of Putting in a Spatial configuration more; “hang”) or the manner of attachment (use Verbs of Attaching more; “stick, tape, clip”). The results suggest that four-year-olds use lexical verbs more than previous studies, suggesting that dynamic events elicit more lexical verbs compared to static stimuli. Further, we found that visibility does impact participants’ verbs use, and that participants have a linguistic bias when describing mechanical support events. Our results have implications for children’s knowledge of support mechanisms and how children reason about physical support events.
NBER WORKING PAPER SERIES EVICTION AND POVERTY IN AMERICAN CITIES

More than two million U.S. households have an eviction case filed against them each year. Policymakers at the federal, state, and local levels are increasingly pursuing policies to reduce the number of evictions, citing harm to tenants and high public expenditures related to homelessness. We study the consequences of eviction for tenants, using newly linked administrative data from Cook County (which includes Chicago) and New York City. We document that prior to housing court, tenants experience declines in earnings and employment and increases in financial distress and hospital visits. These pre-trends are more pronounced for tenants who are evicted, which poses a challenge for disentangling correlation and causation. To address this problem, we use an instrumental variables approach based on cases randomly assigned to judges of varying leniency. We find that an eviction order increases homelessness, and reduces earnings, durable consumption, and access to credit. Effects on housing and labor market outcomes are driven by impacts for female and Black tenants. Davin K. Reed Federal Reserve Bank of Philadelphia 10 N Independence Mall Philadelphia, PA 19106 davin.reed@phil.frb. org Daniel I. Tannenbaum Department of Economics University of Nebraska – Lincoln HLH 525 B P.O. Box 880489 Lincoln, NE 68588 dtannenbaum@unl.edu Winnie van Dijk Department of Economics Harvard University Littauer Center 1805 Cambridge Street Cambridge, MA 02138 and NBER winnie_vandijk@fas.harvard. edu Robert Collinson University of Notre Dame Department of Economics Wilson Sheehan Lab for Economic Opportunities 3027 Jenkins Nanovic Hall Notre Dame, INDI 46556 rcollin8@nd.edu John Eric Humphries Department of Economics Yale University 37 Hillhouse Ave New Haven, CT 06511 and NBER johneric.humphries@yale.edu Nicholas S. Mader Chapin Hall at the University of Chicago 313 E 60th St Chicago, IL 60637 nmader@chapinhall.org A data appendix is available at http://www.nber. org/data-appendix/w30382
Machine learning for tt̄H mechanism Higgs boson detection from CERN ATLAS data

One aspect of studying subatomic particles by observing proton-proton collision is being able to identify those collisions where the particles of interest occur, since thousands of collisions are happening in an accelerator such as the Large Hadron Collider (LHC) at any given time. Machine learning methods have shown the potential to improve the performance of the detection while using either handengineered features or low-level measurements from the detector as input features. One such particle, which has been studied by multiple research groups, is the Higgs boson. The aim of this thesis is to test and compare several machine learning algorithms and compare the usage of hand-engineered features with the usage of direct measurements of the detector on the task of detecting Higgs boson events, namely the tt̄H process. Gradient boosting, multi-layered perceptron (MLP) and TabNet algorithms were tested and the results show superior performance of gradient boosting algorithms. Hand-engineered features show superior performance as opposed to direct measurements from the detector. Combination of all types of features show the best performance. We also show that classifiers training with only the most important features can achieve results with only a small performance decrease, while on the other hand providing benefits in terms of training time and model simplicity. In addition, it is shown that for an increased amount of training data, the performance of the classifiers is expected to improve.
Pricing barrier and lookback options using finite difference numerical methods

This research work focuses on the estimation of barrier and lookback option prices using finite difference numerical methods. Here, we aim at approximating the fair prices of the zero rebate up-and-out and down-and-out knock out barrier options, as well as the fixed strike lookback options. Simulation and finite difference techniques will be used to approximate these prices. The Monte-Carlo simulation, the antithetic MonteCarlo simulations and the Crank-Nicolson approach will be specifically employed on the barrier options. Other finite difference methods like the implicit and the explicit method will be discussed but the Crank-Nicolson method will be employed in the numerical valuations owing to its accuracy in comparison to others. Next, the fixed strike lookback option prices will be estimated using the Monte-Carlo and the antithetic Monte-Carlo simulation methods. An extended version of the Black-Scholes model will be used in the valuation of their exact prices owing to their exotic nature. The Monte-Carlo and the antithetic Monte-Carlo methods are next employed to simulate the values of these option prices. The resulting prices will be compared to the exact fair prices and this will be followed by some error analysis. From the findings, the antithetic method gave the best option price estimate in comparison to the ordinary Monte-Carlo method when the simulation approach was used. It will also be observed that the Monte-Carlo simulation had a slow rate of convergence as a result of higher variances of the estimate from the true solution. Hence, such inefficiency was curbed by the introduction of antithetic Monte-Carlo simulation which had smaller variances of the estimate, and this in turn gave a better estimate. Furthermore, it will also be observed that the Crank-Nicolson method converged faster with increase in the discretisation steps of the underlying asset and the time.
MIL in Post-Pandemic Southeast Asia: Approaches to Measuring Effectiveness in the Academic Literature

This article reviews recent scholarship on the effectiveness of media information literacy (MIL), advanced as a tool to fight an “infodemic” in Southeast Asia. The latter refers to the overload of false information that has consumed Southeast Asia during the COVID-19 pandemic, ravaging the world since January 2020 and is reaching an endemic stage by mid-2022. Asia Centre has suggested that of all of the remedies available to combat this infodemic, media literacy may be the most effective for the long term, in combination with other approaches including accurate information sharing by authorities, fact-checking, take-down action by technology/social media companies and quality journalism. This article probes scholarship on the effectiveness of MIL to distil models of MIL and an assessment of its utility in light of extant studies. The review is based on desk research using publicly available and specialized databases to identify academic articles on MIL.
Dental students' self-efficacy and intention to use clear communication techniques that support individuals with low health literacy

Objectives. Clear and effective communication is critical to delivering quality dental care to all patients, especially those who have low health literacy. There is limited information about U.S. dental students exposure to communication techniques shown to improve patient understanding. Our primary objective was to assess rising fourth-year dental students knowledge, understanding, skills, self-efficacy, and behavioral intention to use communication techniques such as speaking slowly, using simple language and practicing the teach-back method. We also inquired about whether their dental curricula provided education about and evaluated them on the communication techniques. Methods: This 2018 national cross-sectional study used a 34-item online survey to assess fourth-year dental students behavioral capability, self-efficacy and behavioral intention related to 17 communication techniques. The survey link was sent to 6,061 students; 242 finished it. Statistical analyses included descriptive statistics; ANOVA to examine associations between demographic variables and behavioral intention; and logistic regression to analyze associations between the predictor variables behavioral capability and self-efficacy and the dependent variable behavioral intention. The level of significance was set at p<0.05 for all analyses. Results: Over 90 percent of students reported having the knowledge and skills to speak slowly, use simple language and teach-back, but they had lower self-efficacy and intention to use these techniques. They also were less knowledgeable, less confident and had lower intention to use other techniques. Students who reported higher self-efficacy were 9.2 times as likely to report higher behavioral intention to use the techniques than those who reported lower self-efficacy, 95% CI (4.10, 16.96), p<. 01. Conclusions: Our results indicate some dental students need additional education and training to increase their knowledge, skills, self-efficacy and behavioral intention related to plain language communication skills. It is possible that communication concepts are introduced in dental school but are not mastered at a level sufficient to be used in practice. Key Words. dentist-patient communication, provider plain language skills, clinical communication, dental students communication skills education, health literacy
Any bias in climate forcing directly influences the model projection of carbon cycle dynamics

Teckentrup et al. quantify the impacts of different bias correction (including univariate correction, multivariate correction, model averages, and random forest method) methods on improving the outputs of carbon stock changes from a dynamic global vegetation model (LPJGUESS). This draft was well-written, but I still have some comments on the algorithms used in this analysis, and I think the novelty is insufficient for a paper in ESD.
X-ray Photon Temporal Diagnostics for the European XFEL

The European XFEL facility (XFEL.EU), which will be commissioned starting 2016 shows exceptional features with its high delivery rate of light pulses and extremely high average brilliance. Characterizing the temporal properties of the FEL pulses on a shot-to-shot basis is important and challenging. Here we report about the concept and recent progress concerning temporal diagnostics for XFEL.EU. Spectral encoding and THzstreaking are the techniques which will be implemented to deliver arrival time and pulse duration monitoring while coping with the high repetition rate and high brilliance of XFEL.EU.
ARAŞTIRMA MAKALESİ/ORIGINAL RESEARCH

GİRİŞ ve AMAÇ: Egzersize dayalı kardiyak rehabilitasyonun (CR), koroner arter hastalığı (KAH) olan hastalarda kardiyovasküler (KV) morbi mortaliteyi azaltabileceği gösterilmiştir. Artan arteriyel sertliğin KAH'lı hastala rda morbi-mortalitenin bağımsız bir belirleyicisi olduğu gösterilmiştir. Bu çalışmada, stabil KAH'lı hastalarda, kardiyo-ayak bileği vasküler indeksi (CAVI) ile CR'nin arter sertliği üzerindeki etkisini araştırmayı amaçladık. YÖNTEM ve GEREÇLER: Doktorları tarafından Faz III KR programına sevk edilen ayaktan 202 ardışık stabil KAH olan hasta ve KR programını kabul etmeyen 96 stabil KAH olan hasta çalışmaya dahil edildi. CAVI, VaSera VS-1000 (Fukuda Denshi Co. Ltd, Tokyo) tarafından hesaplandı. <0,05 p değeri istatistiksel olarak anlamlı kabul edildi. BULGULAR: Çalışma popülasyonu (298 hasta), önce-sonra (Delta) CAVI değerleri arasındaki farka göre 3 eşit persentile göre gruplandırıldı. Grup 1: 1-33 persentil arasında [Delta CAVI ≥ (-0,5)]; Grup 2: yüzde 34-66 arasında [Delta CAVI <(-0,5) ≥ (-1,3)]; Grup 3: 67-100 persentil arasında [Delta CAVI < (-1.3)]. Çok değişkenli analizde, CAVI-pre (p: <0,001) ve CR'nin (p: <0,001) Delta CAVI'nin bağımsız belirleyicileri olduğu gösterildi. TARTIŞMA ve SONUÇ: Bu çalışma, egzersize dayalı CR'nin KAH'lı hastalarda arteriyel sertliği iyileştirdiğini gösterdi.
Multi-modes switching problem, backward stochastic differential equations and partial differential equations

There are three main results in this thesis. The first is existence and uniqueness of the solution in viscosity sense for a system of nonlinear m variational integral-partial differential equations with interconnected obstacles. From the probabilistic point of view, this system is related to optimal stochastic switching problem when the noise is driven by a Levy process. As a by-product we obtain that the value function of the switching problem is continuous and unique solution of its associated Hamilton-Jacobi-Bellman system of equations. Next, we study a general class of min-max and max-min nonlinear second-order integral-partial variational inequalities with interconnected bilateralobstacles, related to a multiple modes zero-sum switching game with jumps. Using Perron’s method and by the help of systems of penalized unilateral reflected backward SDEs with jumps, we construct a continuous with polynomial growth viscosity solution, and a comparison result yields the uniqueness of the solution. At last, we deal with the solutions of systems of PDEs with bilateral inter-connected obstacles of min-max and max-min types in the Brownian framework. These systems arise naturally in stochastic switching zero-sum game problems. We show that when the switching costs of one side are smooth, the solutions of the min-max and max-min systems coincide. Furthermore, this solution is identified as the value function of the zero-sum switching game.
Daily consumption of Ambon banana (Musa paradisiaca var. sapientum) improve cardiometabolic indicators and reduce plasma MDA levels in adult male with central obesity

This study aimed to determine the effect of Ambon banana fruit consumption on plasma malondialdehyde (MDA) levels and other cardiometabolic indicators in adult men with central obesity. A total of 30 volunteers were involved. The volunteers were examined for their plasma MDA levels and cardiometabolic indicators before and after treatment. The results showed that consuming Ambon banana fruit at breakfast for 28 days significantly reduced body weight, BMI, waist circumference, and systolic and diastolic blood pressure (p<0.05). Consuming Ambon banana at breakfast for 28 days improved cardiometabolic indicators and reduced plasma MDA levels in men with central obesity.
Properties of elementary particles, dark matter, and dark energy

This paper suggests new elementary particles, a specification for dark matter, and modeling regarding dark-energy phenomena. Thereby, this paper explains data that other modeling seems not to explain. Suggestions include some methods for interrelating properties of objects, some catalogs of properties, a method for cataloging elementary particles, a catalog of all known and some method-predicted elementary particles, neutrino masses, quantitative explanations for observed ratios of non-ordinary-matter effects to ordinary-matter effects, qualitative explanations for gaps between data and popular modeling regarding the rate of expansion of the universe, and insight regarding galaxy formation and evolution. Key assumptions include that nature includes six isomers of most elementary particles and that stuff that has bases in five isomers underlies dark-matter effects. Key new modeling uses integer-arithmetic equations; stems from, augments, and does not disturb successful popular modeling; and helps explain aspects and data regarding general physics, elementary-particle physics, astrophysics, and cosmology.
C ACHE GNN: E NHANCING G RAPH N EURAL N ET - WORKS WITH G LOBAL I NFORMATION C ACHING

Graph neural networks (GNNs) have achieved impressive results on various graph learning tasks. Most GNNs merely leverage information from a limited range of local neighbors, which is difficult to effectively capture global information in the graph. However, utilising global information enables GNNs to capture long-range dependencies and learn more informative node representations. To this end, we propose CacheGNN, an approach that leverages information from global similar nodes to enhance GNNs. Our CacheGNN uses a cache to store node representations and utilises those cached embeddings to efficiently find global similar nodes. To quickly and efficiently making predictions at test time, our CacheGNN retrieves global similar nodes from a set of representative nodes, which is selected from a sparse node selection distribution with Dirichlet prior. We conduct node classification experiments on seven real-world datasets under inductive and transductive settings. Experimental results verify the effectiveness of our CacheGNN.
ApoER2-Dab1 disruption as the origin of pTau-related neurodegeneration in sporadic Alzheimer’s disease

BACKGROUND Sporadic Alzheimer’s disease (sAD) is not a global brain disease. Specific regions, layers and neurons degenerate early while others remain untouched even in advanced disease. The prevailing model used to explain this selective neurodegeneration—prion-like Tau spread—has key limitations and is not easily integrated with other defining sAD features. Instead, we propose that in humans Tau hyperphosphorylation occurs locally via disruption in ApoER2-Dab1 signaling and thus the presence of ApoER2 in neuronal membranes confers vulnerability to degeneration. Further, we propose that disruption of the Reelin/ApoE/ApoJ-ApoER2-Dab1-P85α-LIMK1-Tau-PSD95 (RAAAD-P-LTP) pathway induces deficits in memory and cognition by impeding neuronal lipoprotein internalization and destabilizing actin, microtubules, and synapses. This new model is based in part on our recent finding that ApoER2-Dab1 disruption is evident in entorhinal-hippocampal terminal zones in sAD. Here, we hypothesized that neurons that degenerate in the earliest stages of sAD (1) strongly express ApoER2 and (2) show evidence of ApoER2-Dab1 disruption through co-accumulation of multiple RAAAD-P-LTP components. METHODS We applied in situ hybridization and immunohistochemistry to characterize ApoER2 expression and accumulation of RAAAD-P-LTP components in five regions that are prone to early pTau pathology in 64 rapidly autopsied cases spanning the clinicopathological spectrum of sAD. RESULTS We found that: (1) selectively vulnerable neuron populations strongly express ApoER2; (2) numerous RAAAD-P-LTP pathway components accumulate in neuritic plaques and abnormal neurons; and (3) RAAAD-P-LTP components were higher in MCI and sAD cases and correlated with histological progression and cognitive deficits. Multiplex-IHC revealed that Dab1, pP85αTyr607, pLIMK1Thr508, pTau and pPSD95Thr19 accumulated together within dystrophic dendrites and soma of ApoER2-expressing neurons in the vicinity of ApoE/ApoJ-enriched extracellular plaques. These observations provide evidence for molecular derangements that can be traced back to ApoER2-Dab1 disruption, in each of the sampled regions, layers, and neuron populations that are prone to early pTau pathology. CONCLUSION Findings support the RAAAD-P-LTP hypothesis, a unifying model that implicates dendritic ApoER2-Dab1 disruption as the major driver of both pTau accumulation and neurodegeneration in sAD. This model provides a new conceptual framework to explain why specific neurons degenerate and identifies RAAAD-P-LTP pathway components as potential mechanism-based biomarkers and therapeutic targets for sAD.