Miracle-dz/UniFAR-FTUs huggingface dataset:
Running cognitive evaluations on large language models: The do's and the don'ts

In this paper, I describe methodological considerations for studies that aim to evaluate the cognitive capacities of large language models (LLMs) using language-based behavioral assessments. Drawing on three case studies from the literature (a commonsense knowledge benchmark, a theory of mind evaluation, and a test of syntactic agreement), I describe common pitfalls that might arise when applying a cognitive test to an LLM. I then list 10 do's and don'ts that should help design high-quality cognitive evaluations for AI systems. I conclude by discussing four areas where the do's and don'ts are currently under active discussion -- prompt sensitivity, cultural and linguistic diversity, using LLMs as research assistants, and running evaluations on open vs. closed LLMs. Overall, the goal of the paper is to contribute to the broader discussion of best practices in the rapidly growing field of AI Psychology.
Coastal Pollution from Septic Tank Drainfields

Wet weather and dry weather sampling is utilized to monitor densities of coliform bacteria in waters draining residential areas of coastal North Carolina. The bacterial levels are compared to different densities of unsewered residences in each watershed and the limitations of the developed soils for assimilating septic tank effluent. An analysis of the data implicates septic tank drainfields installed in unsuitable soils as a major source of contamination of these shellfish waters. In order to reduce the threat to public health and the multimillion dollar economic loss to the fishing industry, several options are presented for rehabilitating concentrations of failing septic tank systems and for modifying overdesigned drainage systems that carry the contamination directly to shellfish waters. In addition, several common sense management practices that minimize the delivery of bacterial contamination to estuarine waters are suggested for use in siting future coastal residential development.
The Review of Typical Halogenated Flame Retardants, HFRs Pollution in Indoor Air and Dust

Halogenated Flame Retardants (HFRs) used to reduce the polymer flammability are a kind of widespread typical pollutants in the indoor environment medium. They have a severe negative impact on human health. Polybrominated Biphenyl Ethers (PBDEs), Polychorinated Biphenyls (PCBs) and Dechlorane Plus (DP) are three kinds of the important Halogenated Flame Retardants. The present paper reviews the source, sampling, quantification, the pollution characteristics, pollution levels and human exposure of these HFRs in indoor environment.
Solving the Feature Diversity Problem Based on Multi-Model Scheme

: Generally, the performance of deep learning models is related to the captured features of training samples. When the training samples belong to different domains, the diverse features may increase the difficulty of training high performance models. In this paper, we built a new framework that generates multiple models on the organized samples to increase the accuracy of classification. Firstly, our framework selects some existing models and trains each of them on organized training sets to get multiple trained models. Secondly, we select some of them based on a validation set. Finally, we use some fusion method on the outputs of the selected models to get more accurate results. The experimental results show that our framework achieved higher accuracy than the existing methods. Our framework can be an option for the deep learning system to increase the classification accuracy.
Synthesis of Reduced Graphene Oxide/Hexagonal Boron Nitride Nanocomposite as Efficient Hydrogen-Storage Medium for Fuel Cell Applications

In the present research, reduced graphene oxide (RGO) and hexagonal boron nitride (h-BN) nanoparticles-decorated RGO sheets for a solid-state hydrogen storage medium were synthesized and characterized. The nanocomposite of RGO/h-BN was prepared using the ultrasonic-assisted liquid-phase exfoliation approach and the modified Hummer's method was used for the synthesis of graphene oxide (GO). Using micro-Raman spectroscopy, XRD, SEM, CHNS elemental analysis, and TGA, the produced RGO and RGO/h-BN nanocomposites were analyzed. XRD and micro-Raman validated the RGO and the RGO decorated with h-BN nanoparticles. SEM analysis authorized the h-BN nanoparticles that were decorated on the surface of the RGO sheet. Using a hydrogenation system akin to Sievert's, the hydrogen adsorption behavior of the RGO and the RGO/h-BN nanocomposite was investigated. With a maximal hydrogen absorption of 2.1 wt% at 100 °C, RGO/h-BN nanocomposite performed better than the bare RGO. In the temperature ranges of 109° to 140° and 115° to 149°, the RGO and RGO/h-BN nanocomposites released 100% of the stored hydrogen. The corresponding binding energy of RGO and RGO/h-BN nanocomposites were 0.31 and 0.32 eV, which is adequate for fuel cell applications. The RGO/h-BN nanocomposite is therefore anticipated to have a promising future in hydrogen storage situations of fuel cell applications.
Fast Classiﬁcation of Handwritten Digits Using 2D-DCT Based Sparse PCA

—We propose to address the handwritten digits recognition (HWDR) problem by using a two-dimensional (2-D) discrete cosine transform (DCT) based sparse principal component analysis (PCA) algorithm for fast classiﬁcation. The gain of processing speed is achieved by utilizing the ability of 2-D DCT for energy compaction and signal decorrelation. The proposed algorithm was applied to the mixed national institute for standards and technology (MNIST) database of handwritten digits to demonstrate that when incorporated into the conventional PCA, the 2-D DCT helped reduce the dimension of the input data by 75%. As a result of the dimensionality reduction, the proposed algorithm is 35.7% faster for HWDR than the conventional PCA without sacriﬁcing recognition accuracy.
Hyperspectral 3D Reconstruction for Non-destructive Plant Nutrient Analysis

—Resource use efﬁciency in agriculture is becoming increasingly important as the world population grows and climate change impacts food production. Accordingly, precision agriculture adoption is growing, but requires more accurate plant growth modeling and monitoring to be most effective. The plant phenotyping community has devised many techniques for non-destructively estimating plant properties such as mass, stress state, and nutrient concentration using computer vision and specialized sensors. In particular, computer vision techniques have been used to analyze plant structure while hyperspectral data has been used to analyze plant nutrient concentrations at a ﬁeld-or plant-level. We seek to combine these techniques to measure the nutrient distribution within a plant over time to better model how nutrient ﬂows dictate plant growth. In this preliminary work, we collect a dataset of 6 sets of ∼ 50 images towards constructing hyperspectral 3D reconstructions of plants.
Effective Bayesian Heteroscedastic Regression with Deep Neural Networks

Flexibly quantifying both irreducible aleatoric and model-dependent epistemic uncertainties plays an important role for complex regression problems. While deep neural networks in principle can provide this flexibility and learn heteroscedastic aleatoric uncertainties through non-linear functions, recent works highlight that maximizing the log likelihood objective parameterized by mean and variance can lead to compromised mean fits since the gradient are scaled by the predictive variance, and propose adjustments in line with this premise. We instead propose to use the natural parametrization of the Gaussian, which has been shown to be more stable for heteroscedastic regression based on non-linear feature maps and Gaussian processes. Further, we emphasize the significance of principled regularization of the network parameters and prediction. We therefore propose an efficient Laplace approximation for heteroscedastic neural networks that allows automatic regularization through empirical Bayes and provides epistemic uncertainties, both of which improve generalization. We showcase on a range of regression problems— including a new heteroscedastic image regression benchmark—that our methods are scalable, improve over previous approaches for heteroscedastic regression, and provide epistemic uncertainty without requiring hyperparameter tuning.
A successful therapeutic management of Demodex canis infestation in dog: A case report

A ten month old local male dog was presented with a history of inappetence, severe itching alopecia, erythema, dullness, papular and pustular lesions on face, fore limbs, hind limbs, ventral abdomen, inner thigh, loin region and tail. On the basic of history, clinical findings, hematological parameters and skin scraping confirm the case of generalized demodicosis. The dog was treated with acaricide, Anthelmintic, Keratolytic agent, Antibiotic, Antihistaminic and immunostimulant. Treatment was continued until complete recovery of the animal. Two consecutive negative skin scrapings after treatment were considered as recovery from Demodex canis .
CASE SERIES Paediatric choroid plexus carcinoma: a retrospective case series from Karachi

The objective of this study is to report clinical, radiological, and histopathological characteristics of three paediatric patients diagnosed as Choroid plexus carcinoma seen at our hospital, between 2015 and 2020. Three patients were diagnosed with choroid plexus carcinomas between 2015 and 2018. The mean age at diagnosis was 1.3 years (range 8 months to 1.5 years). All the three patients had subtotal resection and received adjuvant chemotherapy. One patient also received adjuvant radiotherapy. Despite these treatment measures, residual disease was noted in all three patients and two patients were subsequently treated on palliative care grounds. The average duration of follow-up after the first surgery for all three patients was approximately 33 months. Attaining satisfactory outcome in patients with CPC is challenging. Our case series reflects the difficulty in achieving gross total resection and ensuring that the disease does not recur.
BASIC AND TRANSLATIONAL — PANCREAS Activating Immune Recognition in Pancreatic Ductal Adenocarcinoma via Autophagy Inhibition, MEK Blockade, and CD40 Agonism

BACKGROUND AND AIMS: Patients with pancreatic ductal adenocarcinoma (PDA) have not yet bene ﬁ tted from the revolution in cancer immunotherapy due in large part to a dominantly immunosuppressive tumor microenvironment. MEK inhibition combined with autophagy inhibition leads to transient tumor responses in some patients with PDA. We examined the functional effects of combined MEK and autophagy inhibition on the PDA immune microenvironment and the synergy of combined inhibition of MEK and autophagy with CD40 agonism (aCD40) against PDA using immunocompetent model systems. METHODS: We implanted immunologically “ cold ” murine PDA cells orthotopically in wide type C57BL/6J mice. We administered combinations of inhibitors of MEK1/2, inhibitors of autophagy, and aCD40 and measured anticancer ef ﬁ cacy and immune sequelae using mass cytometry and multiplexed immuno ﬂ uorescence imaging analysis to characterize the tumor microenvironment. We also used human and mouse PDA cell lines and human macrophages in vitro to perform functional assays to elucidate the cellular effects induced by the treatments. RESULTS: We ﬁ nd that coinhibition of MEK (using cobimetinib) and autophagy (using me ﬂ oquine), but not either treatment alone, activates the STING/type I interferon pathway in tumor cells that in turn activates paracrine tumor associated macrophages toward an immunogenic M1-like phenotype. This switch is further augmented by aCD40. Triple therapy (cobimetinib þ me ﬂ oquine þ aCD40) achieved cytotoxic T-cell activation in an immunologically “ cold ” mouse PDA model, leading to enhanced antitumor immunity. CONCLUSIONS: MEK and autophagy coinhibition coupled with aCD40 invokes immune repolarization and is an attractive therapeutic approach for PDA immunotherapy development.
Artisanal and farmer bread making practices differently shape fungal species community composition in French sourdoughs

36 Preserving microbial diversity in food systems is one of the many challenges to be met to 37 achieve food security and quality. Although industrialization led to the selection and spread of specific 38 fermenting microbial strains, there are still ongoing artisanal processes that may allow the conservation 39 of a wider species and genetic diversity. We examined whether the diversity of artisanal practices could 40 lead to an increased level in fungal species diversity for bread making. We used an interdisciplinary 41 participatory research approach including bakers, psycho-sociologists and microbiologists to analyze 42 French bread making practices and describe fungal communities in naturally fermented sourdough of 43 27 bakers and 12 farmer bakers. Bread making practices were classified in two groups: the farmer-like 44 practices group and the artisanal-like practices group. The well-known bakery yeast, Saccharomyces 45 cerevisiae, was dominant (i.e. with a relative abundance over 50%) in only 24% of sourdoughs while 46 other yeast species belonging to the Kazachstania genus were dominant in 54% of sourdoughs. Bread 47 making practices were found to drive the distribution of fungal species across sourdoughs. The most 48 striking bread making practice effect was the occurrence of Kazachstania humilis in sourdoughs made 49 with artisanal-like practices and the occurrence of Kazachstania bulderi in sourdoughs made with 50 farmer-like practices. Phenotypic divergences between sourdough and non-sourdough strains were 51 found for K. humilis but not for K. bulderi . Overall, our results showed that preserving bread making 52 practice diversity allows the preservation of a higher species and phenotypic diversity in microbial 53 communities. 54 55
Measuring Sex Differences and Similarities

This chapter offers a concise, systematic introduction to quanti ﬁ cation in sex differences research. The chapter reviews the main methods used to measure sex differences and similarities, including standardized distances (Cohen ’ s d and Mahalanobis ’ D ), indices of overlap, variance ratios, and tail ratios. Some less common approaches (e.g., relative distribution methods, taxometrics) are also reviewed and discussed. The chapter examines the strengths and limitations of each method, considers various statistical and methodological factors that may either in ﬂ ate or de ﬂ ate the size of sex differences, and discusses the available options to minimize their in ﬂ uence. Other topics addressed include the effective visualization of sex differences/similarities, and the rationale for treating sex as a binary variable despite the complexities of sex-related identity and behavior.
Nearshore sea temperature variability off Rottnest Island (Western Australia) derived from satellite data

While satellite‐derived sea‐surface temperatures (SSTs) have been widely used in open ocean monitoring, they have rarely been applied to the nearshore region. In this study, full‐resolution Advanced Very High Resolution Radiometer (AVHRR) satellite data were used to derive a sea temperature climatology for three nearshore sites at Rottnest Island (Western Australia) for the 7‐year period 1995 to 2001. Because of the proximity to the land, image geolocation and careful pixel selection were crucial. The mean annual cycle shows the influence of both air–sea heat flux in the shallow waters and the seasonal strengthening of the tropical Leeuwin Current. Self‐recording temperature loggers were installed on near‐surface buoys for a few months in 2001 to assess how reliably the satellite temperatures could be used in such a dynamic nearshore environment. About 80% of the individual satellite temperatures were within ±0.5°C of the logger measurements (at the same time of day) and over 95% were within ±1°C. Occasionally, however, the satellite temperatures differed from the in situ measurements by more than 2°C and possible reasons for this are discussed. The monthly mean satellite temperatures were generally within 0.3°C of the monthly logger averages, which may be taken as a practical reliability limit for the climatology.
Industrial By-Products as Potentially Economic and Promising Adsorbents for Removing Dyes from Effluents: A Review Study

Exposure to pigments from waste effluents, which is visible through some amount of dyes in polluted water, has been referred to as a serious environmental threat and can have ruinous effects on both humans and the ecosystem. Lately, the development of technologies that can diminish contaminants to an acceptable level has received increased attention. Of all the developed techniques for eliminating contaminants from wastewater, biosorption is one of the most popular ones. Adsorption is an effective method to treat chromatic components in effluents and protect the ecosystem. Diverse categories of economic and productive adsorbents that are compatible with the ecosystem are utilized to remove dyes. Amongst the economic adsorbents, the utilization of industrial wastes is common due to their low cost, optimal yield, facile availability, excellent recyclability, and high adsorption capacity. Based on previous studies, FA, iron-containing waste, and blast furnace dust are the industrial by-products that have been reported to have a favorable percentage of removal efficiency (%) and the adsorption capacity to remove dyes under optimal conditions, which confirms their potential and unique characteristics for dye removal. This study aimed to examine various types of dyes and their detrimental effects as well as the process of biosorption. Additionally, the utilization of industrial by-products as promising adsorbents to eliminate dyes from wastewater and their potential implications for the industry are thoroughly discussed.
Global assessment of the state-of-the-science of endocrine disruptors

Considering the complexity of the matter, it appears appropriate to base possible categorisation on considerations in a decision matrix, which take into account severity, reversibility, potency and consistency of an adverse effect. Based on this decision matrix it should be possible to allocate substances falling under the WHO/IPCS definition to categorise as endocrine disruptors (EDs) or even dispense such substances from categorisation.
Nano ﬂ uidic osmotic power generators – advanced nanoporous membranes and nanochannels for blue energy harvesting

The increase of energy demand added to the concern for environmental pollution linked to energy generation based on the combustion of fossil fuels has motivated the study and development of new sustainable ways for energy harvesting. Among the di ﬀ erent alternatives, the opportunity to generate energy by exploiting the osmotic pressure di ﬀ erence between water sources of di ﬀ erent salinities has attracted considerable attention. It is well-known that this objective can be accomplished by employing ion-selective dense membranes. However, so far, the current state of this technology has shown limited performance which hinders its real application. In this context, advanced nanostructured membranes (nanoporous membranes) with high ion ﬂ ux and selectivity enabling the enhancement of the output power are perceived as a promising strategy to overcome the existing barriers in this technology. While the utilization of nanoporous membranes for osmotic power generation is a relatively new ﬁ eld and therefore, its application for large-scale production is still uncertain, there have been major developments at the laboratory scale in recent years that demonstrate its huge potential. In this review, we introduce a comprehensive analysis of the main fundamental concepts behind osmotic energy generation and how the utilization of nanoporous membranes with tailored ion transport can be a key to the development of high-e ﬃ ciency blue energy harvesting systems. Also, the document discusses experimental issues related to the di ﬀ erent ways to fabricate this new generation of membranes and the di ﬀ erent experimental set-ups for the energy-conversion measurements. We highlight the importance of optimizing the experimental variables through the detailed analysis of the in ﬂ uence on the energy capability of geometrical features related to the nanoporous membranes, surface charge density, concentration gradient, temperature, building block integration, and others. Finally, we summarize some representative studies in up-scaled membranes and discuss the main challenges and perspectives of this emerging ﬁ eld.
PHYTOCHEMICAL SCREENING AND BIOLOGICAL EFFICACY OF EXTRACTS FROM CADABA FRUTICOSA L. AGAINST HUMAN PATHOGENIC BACTERIA

Medicinal plants are proving as an important source of potentially therapeutic drugs which are curing all kinds of infectious diseases throughout the world since the tradition of mankind, are still broadly used and have considerable importance in international trade. The present study deals with the phytochemical screening and biological activities of methanol extract of Cadaba fruticosa L. (Capparidaceae family). Varying amount of alkaloids, phenols, flavonoids and phytosterols were detected. Methanol extracts were showed excellent antimicrobial and antibiofilm activities. Based on the results this can take it further research to identify the active principles of above activities.
A Microﬂuidic Device With Optically-Controlled Electrodes for On-Demand Electrical Impedance Measurement of Targeted Single Cells

—Electrical impedance measurement of a live cell is important for monitoring the cell’s status. Label-free and non-invasive techniques for measuring the impedance of live cells have attracted much attention. Existing techniques are capable of measuring the impedance of entire cell populations and/or the instantaneous impedance of single cells, but an approach to track and monitor the electrical properties of single cells during their growth process has not yet been reported. This paper presents a microﬂuidic device integrated with optically-controlled electrodes (MOCE) for electrical impedance measurement of multiple individual cells over a time period. An equivalent circuit model to quantify the seal resistance, membrane capacitance, cytoplasmic resistance of single cells is proposed. In experiments, the adherence process of C2C12 myoblast cells was characterized by measuring individual cells’ impedance data. During cell growth, the seal resistance R seal increased gradually, while the membrane capacitance stayed at approximately 10 − 9 F and the cytoplasmic resistance stayed at approximately 10 9 (cid:2) . The results demonstrate the feasibility and effectiveness of the MOCE-based method for on-demand single-cell electrical impedance measurement.