Miracle-dz/UniFAR-FTUs huggingface dataset:
When and how to adjust statistical forecasts in supply chains? Insight from causal machine learning

ABSTRACT Empirical studies have discovered that most statistical forecasts in supply chains are subjected to judgemental adjustments during a forecast review. Although such a practice requires significant management effort and frequently reduces forecast accuracy, many organisations prefer this approach as part of their Sales & Operations Planning process. This study aims to identify the optimal policy to achieve significant accuracy improvement from forecast review. We focus on a practical situation where managers periodically review forecasts from the statistical software and compare them with judgemental forecasts from the sales and marketing functions. Managers must decide whether to disregard the judgement and continue with the existing forecast or revise the statistical forecast based on the judgement. To determine the best course of action, we conducted a numerical experiment using data from five supply-chain companies wit more than 12,000-point forecasts. The experiment considered three alternative actions: “do-nothing”, “follow the judgement”, and “simple-average”. Using a causal machine learning method, namely a policy tree, we develop a set of decision rules that maximise the expected accuracy gains given the variation in forecasting features. The result proposes a simple yet effective policy to recommend suitable actions based on two identified key features: “judgment direction” and the “accuracy of statistical forecasts”. The policy was tested against real-world data and achieved remarkable accuracy with roughly a 3–11 percentage points improvement over the baseline. Our findings offer valuable insights for managers to customise their forecast review policy based on their unique environment.
Finite-time sensor fault diagnosis observer design for attitude control systems of satellite based on sampled-data descriptor systems

In this paper, a finite-time robust sensor fault diagnosis observer with non-singular structure is proposed for Attitude Control Systems of satellite based on Lipschitz non-linear sampled-data descriptor systems with state time-varying delay. Firstly, an Attitude Control Systems model based on sampled-data descriptor system with time-varying delay is proposed and transformed into a discrete-time non-singular one. Then, a sensor fault diagnosis observer is designed upon on the state estimation error and the measurement residual. The finite-time stability and robustness of the augmented error dynamic model is analyzed, the H infinity performance index in finite-time is satisfied. As the confining matrices of the observer parameters do not meet the Linear Matrix Inequality, a cone complementary linearization iteration algorithm is proposed to solve this problem. Residual evaluation function and the threshold are introduced for judging if the faults occur. At the end, simulation examples are given to illustrate the effectiveness of this method.
Modeling The Impact Of Climate Change On Seed Cotton (gossypium Hirsutum L.) Yield In Punjab, (pakistan)

Climate change and its impact on crop productivity is a burning issue of the day according to predictions of international panel on climate change (IPCC) and Pakistan Meteorological Department (PMD) these changes are taking place very rapidly on global level and affecting crop productivity.Their predictions are based on long term past climate data analysis with the help of general circulation models (GCMS). The aim of present study was to calibrate and validate CSM-Crop-Grow Cotton Model in DSSAT V 4.0. 2. to simulate development, growth, and yield of cotton (Gossypium hirsutum L) under increased CO2 concentration, elevated temperatures and varying rainfall scenarios.For these purpose field trials at three locations were conducted with two sowing dates, four cotton cultivars and four nitrogen levels with split-split plot design having three replications during year 2005 and 2006.The data on phenology, growth and yield were recorded and used for model calibration during 2005 (Genetic co-efficient of four cotton cultivars were determined). The model was then validated with independent set of data collected during year 2006 with same set of coefficient, long term past historic climate data of three locations (1974-2006), soil and crop management data used as input data for model and environmental modification sub menu of seasonal tool selected various climate change scenarios viz current i-e no change in environment.Elevated temperatures of + 0.9 O C and 1.8 O C, increased concentrations of CO2 from 360 ppm to 550 ppm with no change in rainfall and increased / decreased 3% , 6% levels of rainfall expected in 2025 & 2050.The results indicated that model successfully simulated 80-90 % crop phenology and growth and 90-98 % yield accurately at various sites and can be used as reliable agronomic tool for strategy management in future.The elevated CO2 levels would effect cotton crop growth and yield positively on all locations. Climate change analysis indicated strong influence of temperature on cotton production in Punjab, Pakistan.The yield will be substantially decreased with increasing temperature 1.8 O C and the effect was more severe at locations like Sahiwal.Increasing or decreasing 6% rainfall would affect cotton growth and yield negatively and drastic yield reductions were observed from the current. Selection of suitable cultivar, changing sowing time and nitrogen application are helpful in mitigation of adverse effects of changed climate of 2025-2050.Dominance analysis of different planting dates and cultivars at various locations suggested that 60 days early sowing of cotton crop from current at Faisalabad and Sahiwal with cultivar SLH-284, N applied @ 200 kg ha-1 is the best choice under changed climate scenarios (2050) whereas cultivar CIM -506 with nitrogen rate of 200 kg ha-1 out yielded all other cultivars when sown 15 days late from current at Multan location under changed climate scenario of 2050.
The effect of aridification on dry and wet habitats of Illancs microregion, SW Great Hungarian Plain, Hungary.

By the beginning of the 21 century, investigations aiming landscape history and landscape changes became especially important in the environmental research. The decreasing number of natural areas called the attention of the world to the rapid changes in the landscape caused by human activities and climate change. Certain places are facing with increased problems caused by the sensitiveness of landscapes. In Hungary, in the Danube–Tisza Interfluve a continuous groundwater-table sinking process was observed, as a result of anthropogenic effects of the last century and the consequences of climate change. On the highest part of this mesoregion (e. g. Illancs microregion), the decrease of the groundwater-level has reached 7 metres compared with the 1970s which affected significantly the nature and the local inhabitants as well. This study aimed to investigate the effect of this aridification process on dry and wet habitats of Illancs microregion. It reveals the ongoing processes in the landscape, referring to the previously affecting factors. In case of dry habitats, fragmentation and invasive species play important role as threatening factors. Wet habitats are significantly decreasing, shift of the vegetation zones can be observed. The stands are degraded, their steppification is dominant and can be observed only in the deepest parts of the depressions.
Overview of Aerosol and Air Pollution in South Eastern Asia Countries

This paper consists of several topics on aerosol and air pollution in South Eastern Asia countries, including exposure and health effects of aerosol in Malaysia, characteristics/sources of particulate matter (PM) in Surabaya, Indonesia, size fraction of polycyclic aromatic hydrocarbons (PAHs) in Chiang Mai, Thailand, and removal of PMs using sodium hydroxyl and electrostatic precipitator (ESP) in Vietnam. Findings in Malaysia indicated that exposure to PM was associated with respiratory symptoms such as phlegm, coughing, wheezing and chest tightness among children in urban areas. Characterization of PM 2.5 and PM 2.5-10 samples collected in an industrial area in Surabaya, Indonesia showed that the highest levels of individual elements in PM 2.5 were S, Na, Si and K, and in PM 2.5-10 were Si, Ca, Cl, Na, and Mg. The main potential sources of PM 2.5 were diesel vehicle emission, a mixture of Cu industry and biomass combustion, metal industries using Ni, and construction, with contributions of 33%, 24.1%, 11.4%, and 7.9%, respectively. Meanwhile, main sources of PM 2.5-10 were soil dust and port industry, construction, road dust, and sea salt, with contributions of 32%, 28.8%, 14%, and 10%, respectively. In Chiang Mai, the highest PM mass and PAHs concentrations were found in the finest particle sizes (0.65 µ m – 0.43 µ m) in periods of intensive open burning (IOB) and low open burning (LOB), in both urban and rural areas, and the PAHs concentration (5.10 ng m – 3 ) in the fine fraction accounted for 45% to 47% and 32% to 37% during IOB and LOB periods, respectively. The study of particle removal from a charcoal kiln in Vietnam using a water and sodium hydroxyl solution sprayed in a top-down direction with fine droplets showed a removal efficiency of total dust of about 47.5% on average, while an ESP removed PM with high collection efficiency and low-pressure drop.
Multidrug-Resistant Salmonella Species and Their Mobile Genetic Elements from Poultry Farm Environments in Malaysia

The prevalence and persistent outbreaks of multidrug-resistant (MDR) Salmonella in low-income countries have received growing attention among the public and scientific community. Notably, the excessive use of antibiotics in chicken feed for the purpose of treatment or as prophylaxis in the poultry industry have led to a rising rate of antimicrobial resistance. Therefore, this study aimed to determine the presence of antimicrobial-resistant Salmonella species and its mobile genetic elements from soil and effluent samples of 33 randomly selected poultry farms in Selangor, Malaysia. Salmonella species were isolated on selective media (CHROMagar™ Salmonella). VITEK® 2 system was used to identify the isolates and their antimicrobial susceptibility. Subsequently, eight isolates were subjected to the whole genome sequencing (WGS). Based on the results, Salmonella spp. was detected in 38.1% (24/63) of samples, with the highest resistance to ampicillin (62.5%), followed by ampicillin/sulbactam (50.0%) and ciprofloxacin (45.8%). Meanwhile, the identified serovars were Salmonella enterica subspecies enterica serovar Weltevreden (S. Weltevreden), S. Jedburgh, and S. Brancaster. The most prevalent resistance genes detected include qnrS1, blaTEM-176, dfrA14, and tet(A). The IncX1 plasmid, with encoded resistance genes, was also detected in four isolates. Furthermore, mutations in the quinolone resistant-determining regions (QRDR) were discovered, specifically in the gyrA, gyrB, and parC genes. In short, surveillance such as continuous monitoring of antimicrobial resistance and emerging trends in resistance patterns through farm environmental samples could provide information to formulate public health interventions for effective infection prevention and disease control.
Assessing the effects of a mixed Eimeria spp. challenge on performance, intestinal integrity, and the gut microbiome of broiler chickens

A mixed Eimeria spp. challenge model was designed to assess the effects of challenge on broiler chicken performance, intestinal integrity, and the gut microbiome for future use to evaluate alternative strategies for controlling coccidiosis in broiler chickens. The experimental design involved broiler chickens divided into two groups: a control group (uninfected) and a positive control group, infected with Eimeria acervulina (EA), Eimeria maxima (EM), and Eimeria tenella (ET). At day-of-hatch, 240 off-sex male broiler chicks were randomized and allocated to one of two treatment groups. The treatment groups included: (1) Non-challenged (NC, n = 5 replicate pens); and (2) challenged control (PC, n = 7 replicate pens) with 20 chickens/pen. Pen weights were recorded at d0, d16, d31, d42, and d52 to determine average body weight (BW) and (BWG). Feed intake was measured at d16, d31, d42, and d52 to calculate feed conversion ratio (FCR). Four diet phases included a starter d0–16, grower d16–31, finisher d31–42, and withdrawal d42–52 diet. At d18, chickens were orally challenged with 200 EA, 3,000 EM, and 500 ET sporulated oocysts/chicken. At d24 (6-day post-challenge) and d37 (19-day post-challenge), intestinal lesion scores were recorded. Additionally, at d24, FITC-d was used as a biomarker to evaluate intestinal permeability and ileal tissue sections were collected for histopathology and gene expression of tight junction proteins. Ileal and cecal contents were also collected to assess the impact of challenge on the microbiome. BWG and FCR from d16–31 was significantly (p < 0.05) reduced in PC compared to NC. At d24, intestinal lesion scores were markedly higher in the PC compared to the NC. Intestinal permeability was significantly increased in the PC group based on serum FITC-d levels. Cadherin 1 (CDH1), calprotectin (CALPR), and connexin 45 (Cx45) expression was also upregulated in the ileum of the PC group at d24 (6-day post-challenge) while villin 1 (VIL1) was downregulated in the ileum of the PC group. Additionally, Clostridium perfringens (ASV1) was enriched in the cecal content of the PC group. This model could be used to assess the effect of alternative coccidiosis control methods during the post-challenge with EA, EM, and ET.
Comparison of bio ﬁ lm formation and motility processes in arsenic-resistant Thiomonas spp. strains revealed divergent response to arsenite

the presence of toxic compounds. This study focused on the process of bio ﬁ lm formation in three Thiomonas strains (CB1, CB2 and CB3) isolated from the same AMD. The results obtained here show that these bacteria are all capable of forming bio ﬁ lms, but the architecture and the kinetics of formation of these bio ﬁ lms differ depending on whether arsenite is present in the environment and from one strain to another. Indeed, two strains favoured bio ﬁ lm formation, whereas one favoured motility in the presence of arsenite. To identify the underlying mechanisms, the patterns of expression of some genes possibly involved in the process of bio ﬁ lm formation were investigated in Thiomonas sp. CB2 in the presence and absence of arsenite, using a transcriptomic approach (RNA-seq). The ﬁ ndings obtained here shed interesting light on how the formation of bio ﬁ lms, and the motility processes contribute to the adaptation of Thiomonas strains to extreme environments.
The Impact of Silence on Speech Anti-Spoofing

The current speech anti-spoofing countermeasures (CMs) show excellent performance on specific datasets. However, removing the silence of test speech through Voice Activity Detection (VAD) can severely degrade performance. In this article, the impact of silence on speech anti-spoofing is analyzed. First, the reasons for the impact are explored, including the proportion of silence duration and the content of silence. The proportion of silence duration in spoof speech generated by text-to-speech (TTS) algorithms is lower than that in bonafide speech. And the content of silence generated by different waveform generators varies compared to bonafide speech. Then the impact of silence on model prediction is explored. Even after retraining, the spoof speech generated by neural network based end-to-end TTS algorithms suffers a significant rise in error rates when the silence is removed. To demonstrate the reasons for the impact of silence on CMs, the attention distribution of a CM is visualized through class activation mapping (CAM). Furthermore, the implementation and analysis of the experiments masking silence or non-silence demonstrates the significance of the proportion of silence duration for detecting TTS and the importance of silence content for detecting voice conversion (VC). Based on the experimental results, improving the robustness of CMs against unknown spoofing attacks by masking silence is also proposed. Finally, the attacks on anti-spoofing CMs through concatenating silence, and the mitigation of VAD and silence attack through low-pass filtering are introduced.
G protein–coupled estrogen receptor: a promising therapeutic target for aldosterone-induced hypertension

Aldosterone is one of the most essential hormones synthesized by the adrenal gland because it regulates water and electrolyte balance. G protein–coupled estrogen receptor (GPER) is a newly discovered aldosterone receptor, which is proposed to mediate the non-genomic pathways of aldosterone while the hormone simultaneously interacts with mineralocorticoid receptor. In contrast to its cardio-protective role in postmenopausal women via its interaction with estrogen, GPER seems to trigger vasoconstriction effects and can further induce water and sodium retention in the presence of aldosterone, indicating two entirely different binding sites and effects for estrogen and aldosterone. Accumulating evidence also points to a role of aldosterone in mediating hypertension and its risk factors via the interaction with GPER. Therefore, with this review, we aimed to summarize the research on these interactions to help (1) elucidate the role of GPER activated by aldosterone in the blood vessels, heart, and kidney; (2) compare the non-genomic actions between aldosterone and estrogen mediated by GPER; and (3) address the potential of GPER as a new promising therapeutic target for aldosterone-induced hypertension.
Morpho-physiological and biochemical characterization of African spider plant (Gynandropsis gynandra (L.) Briq.) genotypes under drought and non-drought conditions

The African spider plant (Gynandropsis gynandra (L.) Briq. ) is a nutrient-dense, climate-resilient indigenous vegetable with a C4 carbon fixation pathway. Understanding African spider plant drought tolerance mechanisms is essential for improving its performance in water-stressed areas. The objective of this study was to evaluate the stress tolerance potential of African spider plant accessions based on thirteen morphological, physiological, and biochemical traits under three different water treatment regimes. Eighteen accessions were evaluated over two growing seasons in the greenhouse using a split-split plot design with four replications and three water treatment-regimes namely optimum (100% field capacity), intermediate drought (50% field capacity) and, severe drought (30% field capacity). The results revealed that water regime had a significant effect (P< 0.01) on the accessions for the traits studied. A significant reduction across most of the studied traits was observed under drought conditions. However, proline content in all the accessions significantly rose under drought conditions. The principal component analysis revealed a considerable difference in the performance of the 18 African spider plant accessions under optimum and drought stress conditions. Several morphological and physiological parameters, including days to 50% flowering (r = 0.80), leaf length (r = 0.72), net photosynthesis (r = 0.76) and number of leaves per plant (r = 0.79), were positively associated with leaf yield under drought conditions. Cluster analysis categorized the 18 accessions and 13 measured parameters into 4 clusters, with cluster-1 exhibiting greater drought tolerance for most of the studied traits, and cluster-4 having the most drought-sensitive accessions. Among the accessions tested, accessions L3 and L5 demonstrated excellent drought tolerance and yield performance under both conditions. As a result, these accessions were selected as candidates for African spider plant drought tolerance breeding programs. These findings will serve as the foundation for future studies and will aid in improving food and nutrition security in the face of drought.
MMML: Multimanifold Metric Learning for Few-Shot Remote-Sensing Image Scene Classification

Deep neural networks driven by large amounts of annotated samples have been successfully applied in remote-sensing scene classification (RSSC). On most common public datasets, the accuracies of scene recognition tasks have been close to saturation. A learning paradigm that specifically tackles this issue has emerged, that is, few-shot learning (FSL). FSL methods for natural image recognition are developing rapidly. Recently, these methods have been widely used in RSSC. Compared with natural images, the problems of intraclass differences and interclass similarities in RSSC are more serious, which hinders the further development of FSL methods for RSSC. To address this issue, we propose a multimanifold metric learning (MMML) framework for RSSC with FSL. Specifically, we use a lightweight convolutional neural network (CNN) as a feature extraction block. To further enhance the representation ability of features, we embed the feature map into two heterogeneous and complementary Riemannian manifold geometric structures, that is, the Grassmannian manifold and symmetric positive-definite (SPD) manifold. Then, to facilitate the measurement, we introduce the Riemann kernel function to embed two heterogeneous manifold structures into the high-dimensional Hilbert space for fusion. Finally, we design a learnable distance metric scheme that can be optimized according to the divergence of interclass pairs and intraclass pairs, thereby reducing the impact of large intraclass differences and high interclass similarity on scene recognition. We verify the effectiveness of MMML on three commonly used datasets. The results show that the accuracy of our proposed method is from 1.21% to 3.12% higher than the state-of-the-art methods.
On the Performance of Cooperative NOMA Downlink: A RIS-Aided D2D Perspective

For future wireless communication networks to be highly efficient with both spectrum and energy, reconfigurable intelligent surface (RIS) has emerged as a practical technology. In this paper, we investigate an RIS-aided cooperative non-orthogonal multiple access (C-NOMA) system serving two power-domain users, where a near user as a decode-and-forward (DF) half-duplex (HD) relay performs device-to-device (D2D) communications for a far user. RIS is configured in time division multiple access (TDMA), which is on the one hand used to support the communication between BS and near user in the direct transmission phase, and on the other hand, for enhancing D2D communications between the two users. In an effort of evaluating the network performance, we derive closed-form expressions of achievable rates, outage probabilities (OPs), and the corresponding asymptotic OPs. The numerical results demonstrate the achievable performance improvements of the proposed cooperative RIS-NOMA system in comparison to that of the orthogonal multiple access (OMA) counterpart. In addition, the proposed cooperative RIS-NOMA system achieves various levels of diversity gains by incorporating different numbers of RIS elements.