Miracle-dz/UniFAR-FTUs huggingface dataset:
Indigenous knowledge of rural communities for combating climate change impacts in west central Ethiopia

The study examined how local knowledge of climate change plays a role in adjusting to changing climate and how these beliefs may influence future decision making about how to go about adjusting to climate change at a local level. The study was conducted in west central Ethiopia at the edge of the Blue Nile. The current indigenous knowledge practiced by the local community in adopting the changing environmental conditions was discussed. Rural communities have local knowledge in areas such as weather and seasonal forecasting (44%), drought forecasting (20.9%), crop pest & disease (47%), and weed (99.7%) control methods to adapt to some of the climate change impacts. Not all households have the same levels and types of indigenous knowledge. Therefore, awareness creation and experience sharing among community members are important in increasing the application of indigenous knowledge for climate change adaptation. Keywords: Climate change adaptation, Climate change impacts, Ethiopia, Indigenous knowledge
Food Safety and Climate Change

Mycotoxins are chemical compounds produced mainly by mounds of genera Aspergillus, Penicillium, and Fusarium on various grains and agricultural commodities at different stages in the field, before harvest, post-harvest, during processing, packaging, distribution, and storage. The production of mycotoxins depends on several environmental factors such as temperature and moisture. This chapter gives an overview about the major mycotoxins (e.g., aflatoxins, ochratoxin A, and Fusarium toxins), masked mycotoxins, and emerging mycotoxins. The toxicity of these mycotoxins and their negative economic impact was also discussed together with the effect of climate change on their production. A section on mycotoxins regulations by international agencies and organisms (WHO, FAO, EU, etc.) was discussed. Finally, the different strategies to reduce or eliminate the toxic effects of mycotoxins in contaminated foods and feeds by using chemical, physical, and biological/biotechnological methods or innovative approaches were explained.
Trade-offs among intensive forestry, ecosystem services and biodiversity in boreal forests

Pohjanmies, Tähti Trade-offs among intensive forestry, ecosystem services and biodiversity in boreal forests Jyväskylä: University of Jyväskylä, 2018, 53 p. (Jyväskylä Studies in Biological and Environmental Science ISSN 1456-9701; 342 ISBN 978-951-39-7341-4 (nid.) ISBN 978-951-39-7342-1 (PDF) Yhteenveto: Metsätalouden, ekosysteemipalveluiden ja luonnon monimuotoisuuden väliset ristiriidat boreaalisissa metsissä Diss. Finnish forests are used extensively for timber production but are also providers of other ecosystem services and harbor unique biodiversity. The ecosystem services approach has so far been used marginally in the context of Finnish forestry; however, due to the multiple values associated with Finnish forests and the impacts forestry operations have on forest ecosystems, it is clearly applicable in this context. In this thesis, I studied the occurrence and severity of trade-offs among ecosystem services and biodiversity conservation in Finnish forests. I used forest inventory data, forest growth simulations, and multi-objective optimization to reveal how the severity of the trade-offs varies among combinations of ecosystem services, across spatial scales, and across time, and how the trade-offs could be mitigated by forest management planning. Overall, the results showed that there are clear and challenging conflicts between intensive forestry and ecosystem services in Finland. Ecosystem services provided by forests were found to diminish when the forests were used intensively as a source of timber, whereas reducing or refraining from harvests maintained comparatively high levels of multiple nontimber services and biodiversity. Non-timber services and biodiversity were also shown to recover from intensive forestry the slower the longer intensive forestry was continued, suggesting that forestry’s negative impacts may be long-lasting. The use of optimization tools can help planners to identify management strategies that balance conflicting objectives as well as possible, especially if the analyses are conducted at large enough scales. However, the fact that there are trade-offs means that losses in some objectives are inevitable. It is left to forest managers and other stakeholders to consider which of these losses they are willing to accept.
Tornado climatology of China

A tornado climatology in China was derived based on a recently completed data set with details on 4763 tornadoes in the period 1948–2012. The tornadoes were rated on the Fujita scale, and design basis tornado wind speeds were estimated. Annual tornadoes were estimated 108 (±44) in China, among which 0–4 exceed F3 on the Fujita scale. Three strongest tornadoes in the data set were rated F4. Assessing from frequency distribution of ratings, tornadoes in China could be mostly of the non‐supercell type. In average of all the country, most tornadoes occur in summer (June–August, about 64%). The peak month is July (31%). Geographically, the majority of tornadoes is distributed in eastern China, with the most frequent tornado occurrence being in the coastal provinces. There are southern type and northern type of tornado occurrence in eastern China. The former peaks in spring, while the latter peaks in summer. Two belts of design basis tornado wind speeds higher than 70 m s−1 were identified (on probability of 10−7 per year). One extends approximately along the coastline of China from south to north, while the other crosses this belt from east to west, approximately upstream of the Yangtze River. The Yangtze River Delta and Pearl River Delta have design basis tornado wind speeds higher than 90 m s−1, with maxima of 105 and 95 m s−1, respectively. However, by transferring into the enhanced Fujita scale, the wind speed is much lower, with the corresponding maxima being 82 and 76 m s−1 respectively.
Dual-Channel Target Speaker Extraction Based on Conditional Variational Autoencoder and Directional Information

Target speaker extraction (TSE) has become an attractive research topic in recent years. However, TSE under the underdetermined conditions is still a challenge. In this paper, we deal with a dual-channel TSE problem under underdetermined conditions. Geometric source separation (GSS) is used to be a solution to the TSE problem, but the performance of conventional GSS methods is limited under underdetermined conditions because of the lack of a powerful source model. We propose a dual-channel TSE method with the combined capabilities of target selection based on geometric constraints, more powerful source modeling, and nonlinear postprocessing. A geometric constraint (GC) on the target direction of arrival (DOA) is applied to select the target, and two conditional variational autoencoders (CVAEs) are used to model a single speaker's speech and interference mixture speech. For postprocessing, an ideal ratio time–frequency (T–F) mask estimated from the separated interference mixture speech is used to extract the target speaker's speech. Moreover, to overcome the impact of DOA estimation errors, we improve the objective function so that the target DOA information can be modified. The experimental results demonstrate that the proposed method achieves 6.24 dB and 8.37 dB improvements compared with the baseline method in terms of signal-to-distortion ratio (SDR) and source-to-interference ratio (SIR), respectively, under medium reverberation for 470 ms. Furthermore, through the analysis of experimental results, we found that the improvement method is robust against DOA estimation errors.
Mortality Factors in Crush Syndrome

ABSTRACT BACKGROUND: Crush Syndrome is a major cause of morbidity and mortality following large-scale catastrophic earthquakes. Since there are no randomized controlled studies on Crush Syndrome, knowledge on this subject is limited to expert experience. The primary objective is to analyze the epidemiological and demographic characteristics, clinical outcomes, and mortality factors of earthquake victims after the Pazarcik and Elbistan earthquakes on February 6, 2023. METHODS: This cross-sectional and observational retrospective study evaluated 610 earthquake victims who presented to our center between February 6 and April 30, 2023. Among these patients, 128 with Crush Syndrome were included in the study. Patient information was gathered from hospital records during their stay and from national registries upon referral. The primary outcome was to identify risk factors for mortality. Demographic and laboratory data were analyzed by acute kidney injury (AKI) stages; mortality-affecting factors were identified through regression analysis. RESULTS: Of the 128 Crush Syndrome patients (100 adults, 28 children), 64 were female. The AKI rate was 32.8%. Among patients with AKI, the frequency of hemodialysis requirement was 69%, and the mortality rate was 14.2%. The overall mortality rate for patients with Crush Syndrome was 4.6%, compared to 3.9% (19/482) in earthquake victims without Crush Syndrome (p=0.705). Notably, low systolic blood pressure at admission was the only factor significantly affecting mortality in Crush Syndrome patients (Hazard Ratio [HR]: 1.088, p=0.021, 95% Confidence Interval [CI]). CONCLUSION: Our study highlights low systolic blood pressure upon admission as a significant risk factor for increased mortality in Crush Syndrome patients. This finding may contribute to the literature by emphasizing the importance of monitoring blood pressure under rubble and administering more aggressive fluid therapy to patients with low systolic blood pressure.
Out‐of‐Plane Seismic Reflections Beneath the Pacific and Their Geophysical Implications

We detect seismic P wave arrivals that reach the surface from a different horizontal direction than the theoretical back azimuth of the earthquake. Slowness, back azimuth, and traveltime of observed out‐of‐plane signals are measured with array methods in relation to the main phases that travel along the great circle path. This directivity information is used to back trace the wave through a 1‐D velocity model to its scattering or reflection location. The focus of this study lies on out‐of‐plane signals reflected once beneath the Pacific at a depth greater than 800 km. Data analysis is carried out for a broad frequency range (band‐pass filter with corner periods of 0.5–5 s up to 5–50 s) to enable the detection of different structures and heterogeneities. In addition to mapping seismic heterogeneities in the lower mantle, we also qualitatively analyze waveforms and polarities of these signals to understand the nature of the structure. The observed 21 reflections with a reflection depth between 800 and 2,200 km illuminate heterogeneities in the mid‐ and lower mantle. Back‐traced locations show shallowest depths around Hawaii and increase in depth to the north and southwest. Analysis of the polarities indicates low velocities for the imaged structure, and complexity of waveforms argues for a likely thermochemical origin. Additional 11 deep reflections/scatterers with depth larger than 2,200 km suggest internal heterogeneities or a presence of the D'' reflector.
Predicting soil organic carbon in a small farm system using in situ spectral measurements and the random forest regression

Soil organic carbon is considered as the most determining indicator of soil fertility. The purpose of this research was to predict the soil organic carbon in the Mokhotlong region, eastern of Lesotho using in situ spectral measurements and random forest regression. Soil reflectance spectra were acquired by a portable field spectrometer. The performance of random forest regression was assessed by comparing it with one of the most popular models in spectroscopy, partial least square regression. Laboratory spectroscopy measurements of the soil samples were analysed for assessing the accuracy of in situ spectroscopy based-models. The effect of the Savitzky−Golay first derivative in improving partial least square regression and random forest regression in both spectral data was also assessed. The results indicated that the random forest regression could accurately predict the soil organic carbon contents on an independent dataset using in situ spectroscopy data (RPD = 3.77, Rp = 0.88, RMSEP = 0.64%). The overall best predictive model was achieved with the derivative laboratory spectral data using random forest with the optimum number of key wavelengths (RPD = 3.77, Rp = 0.88, RMSEP = 0.64%). In contrast, partial least square regression was likely to overfit the calibration dataset. Important wavelengths to predict soil organic contents were localised around the visible range (400-700 nm). An implication of this research is that soil organic carbon can accurately be estimated using derivative in situ spectroscopy measurements and random forest regression with key wavelengths. Keys word: soil organic carbon, spectroscopy, regression models.
Analysis of the Pellet and Concentrate Chicken Animal Feed Production Process at PT. Japfa Comfeed Indonesia Gedangan Sidoarjo Unit

This article reviews the production process of pellet and concentrate animal feed using the work mapmethod, both production process flow maps and production operation process maps. This research aims to carry outan analysis of the production process flow and find out the causes of any obstacles in the production process at PTJapfa Comfeed Indonesia Tbk Sidoarjo Unit, so that we can get a clear picture of the early stage process of producingpellet and concentrate animal feed. This type of research includes case study research because this research takes acompany as a case study, namely PT. Japfa Comfeed Indonesia, Tbk Gedangan Unit. The data collection method usesa field method, namely a data collection method that is carried out by conducting direct observations of the production process in the field, both by observation, interviews and documentation. The results are an analysis of the pellet and concentrate type animal feed production process at PT. Japfa Comfeed Indonesia Gedangan Sidoarjo Unit.
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Résumé. Dans cet article, nous étudions l’accouplement de Cassels-Tate sur les jacobiennes des courbes de genre 2 possédant une isogénie dite de Riche-lot. Soit ϕ : J → b J une isogénie de Richelot entre les jacobiennes de deux courbes de genre 2. Nous donnons une formule explicite et un algorithme pra-tique pour calculer l’accouplement de Cassels-Tate sur Sel ˆ ϕ ( b J ) × Sel ˆ ϕ ( b J ) où b ϕ est l’isogénie duale de ϕ . Ces résultats sont obtenus sous l’hypothèse sim-plificatrice que tous les points de 2-torsion sur J sont définis sur K . Nous donnons un exemple explicite qui montre que nous pouvons transformer la descente par l’isogénie de Richelot en 2-descente en calculant l’accouplement de Cassels-Tate. Abstract. In this paper, we study the Cassels-Tate pairing on Jacobians of genus two curves admitting a special type of isogenies called Richelot isogenies. Let ϕ : J → b J be a Richelot isogeny between two Jacobians of genus two curves. We give an explicit formula as well as a practical algorithm to compute the Cassels-Tate pairing on Sel ˆ ϕ ( b J ) × Sel ˆ ϕ ( b J ) where b ϕ is the dual isogeny of ϕ . The formula and algorithm are under the simplifying assumption that all two-torsion points on J are defined over K . We also include a worked example demonstrating we can turn the descent by Richelot isogeny into a 2-descent via computing the Cassels-Tate pairing.
Assessing the Potential of Open Source Data for Expanding the Building Database: A case study in Huangpu District, Shanghai

Urban Building Energy Modelling necessitates extensive building information. This study examines the potential application of open data for urban building energy modeling, with a specific focus on the Huangpu District of Shanghai, China. This study initially gathered open data for the area and subsequently merged them using geographic information system information processing software. Finally, it was assessed how much data on building size could be provided by combining retrieved open data with current open data processing methods. The results showed that open data has great potential for expanding building data.
Climate change adaptation for peri-urban horticulture: a case study of the Adelaide Hills apple and pear industry

Despite a growing body of climate change adaptation research, translating findings into action remains a challenge. The need for early intervention looms as a significant problem for horticulture industries in Australia’s peri-urban regions where competing demands for resources generate land use conflicts. Tools and methods that help stakeholders and decision-makers understand climate risks and adaptation options at a more strategic level provide a mechanism to bridge the gap between research and action, enabling stronger, collectively planned responses. This paper describes and then revisits the outcomes of a research project developed to assist the Adelaide Hills apple and pear industry understand and respond to projected climate risks. A participatory research method was implemented using critical stakeholder discussion and GIS representations of climatic, natural resource, and land use data to inform an analysis of risks and planning needs for the apple and pear industry. By working closely with practitioners and decision-makers during the development of a spatial model of climatic risks to the industry, key parameter and data selection decisions and various assumptions, as well as the values and limits of the approach could be reflexively understood and critiqued by all participants. Several years after its completion, key stakeholders were invited to reflect on the project and its influence. While the original project generated useful information and provided insights into climatic risks to South Australian pome fruit production, it has had limited influence on industry planning, which highlights the challenges confronting adaptation in multiple use land-
Camera-based Context-aware Traffic Light Detection for Self-Driving Vehicles

In the rapidly evolving field of autonomous driving, accurately detecting traffic lights is a paramount challenge. This paper introduces a novel dataset and an accompanying model designed specifically for detecting traffic signals crucial to autonomous vehicles. Our approach focuses on lights directly relevant to the ego vehicle, incorporating 44,000 meticulously annotated images that represent diverse signal states. The development of this dataset involved an iterative process, guided by the initial model’s performance, resulting in significant enhancements in efficiency and accuracy. The model exhibits a robust capability to discern pertinent traffic signals in complex environments, marking a significant advancement in improving navigation and safety for autonomous vehicles.
Assessing and Mitigating Network Vulnerabilities in Philips Hue and Nest Protect Smart Home Devices

—The Internet of Things (IoT) has gained momentum across various sectors, particularly in the consumer market with the adoption of smart devices. IoT extends internet connectivity to physical devices, enabling control via smartphones, environmental sensing, and updates. However, smart home devices are susceptible to cyberattacks due to vulnerabilities, lack of monitoring, and built-in security. They can also participate in botnets, leading to large-scale attacks. Vulnerabilities in these devices may exist at the sensing, network, or application layers, impacting data confidentiality, integrity, and service availability. This research aims to identify network-layer vulnerabilities affecting the 'Availability' of Philips Hue and Nest Protect. By establishing a test environment, the baseline behavior of these devices is examined, followed by scans for open ports and services to detect network-based threats. Volumetric flood attacks are then conducted to assess susceptibility, and findings are shared to define the devices' default security posture. The research also addresses security issues related to home routers and aims to reduce the attack surface of smart home devices through isolation and network-level protection. This involves deploying a Firewall to isolate smart devices from non-IoT devices and prevent intrusions
Machine Learning-Based Prediction of Hashimoto Thyroiditis Development Risk

Introduction: Hashimoto Thyroiditis (HT) is a prevalent autoimmune disorder impacting thyroid function. Early detection allows for timely intervention and improved patient outcomes. Traditional diagnostic methods rely on clinical presentation and antibody testing, lacking a robust risk prediction tool. Objective: To develop a high-precision machine learning (ML) model for predicting the risk of HT development. Method: Data patients were acquired from PubMed. A binary classifier was constructed through data pre-processing, feature selection, and exploration of various ML models. Hyperparameter optimization and performance evaluation metrics (AUC-ROC, AUC-PR, sensitivity, specificity, precision, F1 score) were employed. Results: Out of a total of 9,173 individuals, 400 subjects within this cohort exhibited normal thyroid function, while 436 individuals were diagnosed with HT. The mean patient age was 45 years, and 90% were female. The best performing model achieved an AUC-ROC of 0.87 and AUC-PR of 0.85, indicating high predictive accuracy. Additionally, sensitivity, specificity, precision, and F1 score reached 85%, 90%, 80%, and 83% respectively, demonstrating the model's effectiveness in identifying individuals at risk of HT development. Hyperparameter tuning was optimized using a Random Search approach. Conclusion: This study demonstrates the feasibility of utilizing ML for accurate prediction of HT risk. The high performance metrics achieved highlight the potential for this approach to become a valuable clinical tool for early identification and risk stratification of patients susceptible to HT. Keywords: Hashimoto Thyroiditis, Machine Learning, Risk Prediction, Algorithms.
Accuracy of Apple Watch to Measure Cardiovascular Indices in Patients with Chronic Diseases: A Cross Sectional Study

Background The validity of the Apple Watch to measure the heart rate (HR) and oxygen saturation (Spo2) for patients with chronic diseases such as diabetes mellitus (DM), dyslipidemia, and hypertension is still unclear. Therefore, this study aims to investigate the accuracy of the Apple Watch in measuring the Spo2 and HR in patients with chronic diseases. Methods Forty-one patients with chronic diseases, including 20 with hypertension, 10 with diabetes, and 11 with dyslipidemia, completed a cross-sectional study. All participants used the Apple Watch against the Polar chest strap and the pulse oximeter at rest and during moderate intensity exercise sessions to measure HR and the SpO2 at rest for 5 minutes, during exercise for 16 minutes, and followed by 3 minutes of rest. The HR was measured during all previous periods, but evaluation of the Spo2 included 5 measures, done only before and after exercise, with a minute interval between each measure. Results Overall, a strong correlation exists between measuring the SpO2 using the Apple Watch against the pulse oximeter (Contec) at rest (r = 0.92, p < 0.001) and after exercise (r = 0.86, p < 0.001) in all patients. The HR had a very strong correlation between the Apple Watch and the Polar chest strap (r = 0.99, p < 0.001) in all patients. There was no significant difference (p = 0.76) between the twenty-seven white and fourteen brown-skinned patients. Conclusion The Apple Watch is valid to measure the HR and SpO2 in patients with chronic diseases. Clinical Trial Registration No NCT05271864.