Miracle-dz/UniFAR-FTUs huggingface dataset:
Market-oriented activities and communal wine consumption events: does coopetition make a difference?

ABSTRACT Earlier work has indicated that communal wine consumption events (e.g. wine tourism) are driven through employing a market orientation, namely, the firm-wide implementation of the marketing concept. Although market-oriented activities are intended to create value for customers, many vineyards and wineries are small and lack the resources and capabilities that are needed to achieve these outcomes. Consequently, there could be merits in owner-managers employing a collaborative (rather than individualistic) business model to overcome their limited tangible and intangible assets. In practice, this could be undertaken via cooperating with their competitors (coopetition) to help them to host or participate in communal wine consumption events. Therefore, grounded in resource-based theory, this current investigation reviews the literature surrounding these issues (focusing on the wine industry) to develop a conceptual framework examining the relationship between market-oriented activities and communal wine consumption events under the moderating role of coopetition. This provides the wider alcohol-focused community of scholars with new evidence on how a market orientation can be enhanced by wine producers collaborating with rival businesses to create positive experiences for their chosen customer segments. This includes drawing upon ‘best practices’ from several wine-producing nations about how decision-makers can navigate these organisation-wide activities.
Benefits of multi-day supplementation with probiotic kefir in Rasmussen encephalitis: the first case report

ABSTRACT Introduction Rasmussen encephalitis (RE) is a rare inflammatory disease, characterized by unilateral hemispheric atrophy, focal intractable seizures, progressive hemiparesis, and neurological deficits. Case report The patient is a young man under pharmacotherapy for epilepsy, exhibiting classical abnormal movements, which are consider typical hallmarks of RE. During clinical care sessions, he presented many episodes of tonic-clonic seizures involving sudden loss of consciousness followed by a post-ictal phase with weakness and interaction difficulty. During the kefir supplementation, the patient presented only short-term absence seizures, quickly returning to activities. Additionally, he presented cognitive and language improvement, being more responsive to commands. The daily diary control of patient’s mother and caregiver at school reported an impressive reduction in number and severity of seizures, becoming less aggressive and more involved in school activities. The serum biochemical markers showed that kefir administration caused a significant decrease of pro-inflammatory and a simultaneous increase of anti-inflammatory cytokine levels. In parallel, after treatment, this probiotic reduced reactive oxygen species levels, increased NO bioavailability, revealing antiapoptotic and antigenotoxic effects. Regarding the microbiological analysis, kefir increased Lactobacillus and Bifidobacterium species. Conclusion To our knowledge, this is the first case reporting remarkable beneficial effects of the probiotic kefir in RE. This case report strongly suggests kefir supplementation as a potential and safe-effective adjuvant therapeutic strategy in the control and treatment of RE. GRAPHICAL ABSTRACT
Combating Reverberation in NTF-Based Speech Separation Using a Sub-Source Weighted Multichannel Wiener Filter and Linear Prediction

Sound source separation (SS) from the microphone signals capturing speech in reverberant conditions is a formidable task. This paper addresses the problem of joint separation and dere-verberation of speech using the multichannel Wiener ﬁlter (MWF) that is tailored to the sub-source modeling of each speech source with a full-rank mixing matrix. Speciﬁcally, the parameters of the proposed sub-source-weighted (SSW) spatial ﬁlter are estimated using the sub-source based expectation maximization (EM) algorithm with multiplicative updates (MU) and the localization prior distribution (LP) on the mixing matrix (SSEM-MU-LP). In addition, we strengthen dereverberation by incorporating a Generalized Weighted Prediction Error (GWPE) algorithm. The proposed method is evaluated using a large dataset of two-channel recordings of clean speech convolved with both real and synthesized impulse responses. The results of the experiments show the superior performance of the proposed method in reverberant conditions in comparison to us-ing the standard NTF-based separation with the vanilla MWF in terms of signal-to-distortion ratio (improvement of 3 − 5 . 6 dB) and other commonly used sound separation metrics.
Evidence of a South Asian Proto‐Monsoon During the Oligocene‐Miocene Transition

The geological history of the South Asian monsoon (SAM) before the Pleistocene is not well‐constrained, primarily due to a lack of available continuous sediment archives. Previous studies have noted an intensification of SAM precipitation and atmospheric circulation during the middle Miocene (∼14 Ma), but no records are available to test how the monsoon changed prior to this. In order to improve our understanding of monsoonal evolution, geochemical and sedimentological data were generated for the Oligocene‐early Miocene (30–20 Ma) from Indian National Gas Hydrate Expedition 01 Site NGHP‐01‐01A in the eastern Arabian Sea, at 2,674 m water depth. We find the initial glaciation phase (23.7–23.0 Ma) of the Oligocene‐Miocene transition (OMT) to be associated with an increase in water column ventilation and water mass mixing, suggesting an increase in winter monsoon type atmospheric circulation, possibly driven by a relative southward shift of the intertropical convergence zone. During the latter part of the OMT, or “deglaciation” phase (23.0–22.7 Ma), a long‐term decrease in Mn (suggestive of deoxygenation), increase in Ti/Ca and dissolution of the biogenic carbonate fraction suggest an intensification of a proto‐summer SAM system, characterized by the formation of an oxygen minimum zone in the eastern Arabian Sea and a relative increase of terrigenous material delivered by runoff to the site. With no evidence at this site for an active SAM prior to the OMT we suggest that changes in orbital parameters, as well as possibly changing Tethyan/Himalayan tectonics, caused this step change in the proto‐monsoon system at this intermediate‐depth site.
Incorporating Cross-Speaker Style Transfer for Multi-Language Text-to-Speech

Recently multilingual TTS systems using only monolingual datasets have obtained signiﬁcant improvement. However, the quality of cross-language speech synthesis is not comparable to the speaker’s own language and often comes with a heavy foreign accent. This paper proposed a multi-speaker multi-style multi-language speech synthesis system (M3), which improves the speech quality by introducing a ﬁne-grained style encoder and overcomes the non-authentic accent problem through cross-speaker style transfer. To avoid leaking timbre information into style encoder, we utilized a speaker conditional variational encoder and conducted adversarial speaker training using the gradient reversal layer. Then, we built a Mixture Density Network (MDN) for mapping text to extracted style vectors for each speaker. At the inference stage, cross-language style transfer could be achieved by assigning any speaker’s style type in the target language. Our system uses existing speaker style and gen-uinely avoids foreign accents. In the MOS-speech-naturalness, the proposed method generally achieves 4.0 and signiﬁcantly outperform the baseline system.
High speed Power efficient Vedic arithmetic modules on Zedboard‐Zynq‐7000 FPGA

Hardware implementation of dedicated arithmetic modules is inevitable for any signal processing system development and computational complexity of such modules could be significantly reduced with improved performance by utilizing Vedic algorithms. Five novel Vedic arithmetic modules for multiplication, square, cube, square root, and cube root are implemented on Zedboard Zynq‐7000 FPGA. A novel 4:2 compressor that uses only primitive gates in critical path is proposed to reduce partial products in the Urdhwa Tiryakhbhyam parallel multiplier achieving the best performance reported so far. Elimination of recursiveness in Antyayordashekepi‐Dwanda squarer results in reduced area while modified Dwanda squarer results in reduced delay. Anurupyena cubic module with 4:2 and 5:2 compressors is implemented from which the one with 4:2 compressor provides the least delay with more than 50% reduction with respect to that of reported cube modules. Modified Vargamula square root and modified cube root modules are also implemented incorporating pipelining, priority encoder, and padding of zero's to achieve better performance. In addition to the delay, in terms of area occupancy, power, and energy consumed (power‐delay‐product), better or comparable performance is also achieved by all the above‐mentioned modules implemented. The dedicated multiplier is used in a 64‐point FFT Implementation and results similar to existing structures are obtained.
Analysis and Experimental Verification of the Demagnetization Vulnerability in Various PM Synchronous Machine Configurations for an EV Application

Safety is a critical feature for all passenger vehicles, making fail–safe operation of the traction drive system highly important. Increasing demands for traction drives that can operate in challenging environments over wide constant power speed ranges expose permanent magnet (PM) machines to conditions that can cause irreversible demagnetization of rotor magnets. In this paper, a comprehensive analysis of the demagnetization vulnerability in PM machines for an electric vehicle (EV) application is presented. The first half of the paper presents rotor demagnetization characteristics of several different PM machines to investigate the impact of different design configurations on demagnetization and to identify promising machine geometries that have higher demagnetization resistance. Experimental verification results of rotor demagnetization in an interior PM (IPM) machine are presented in the latter half of the paper. The experimental tests were carried out on a specially designed locked-rotor test setup combined with closed-loop magnet temperature control. Experimental results confirm that both local and global demagnetization damage can be accurately predicted by time-stepped finite element (FE) analysis.
Impact of hybridized rider optimization with cuckoo search algorithm on optimal VANET routing

VANET routing is a serious concern due to some of the limitations in terms of routing costs. More research has already worked on VANET routing strategies for enhancing the performance of the network. However, optimal route selection is a tedious or complex issue in this scenario. To cope with this issue, this research work attempts to introduce a new VANET routing model that considers certain network quality measures such as congestion, travel, collision, and QoS awareness cost. A new cost model is designed based on the given constraints. Along with this, the fuzzification of the QoS factor is considered along with the total routing cost. This work determines the optimal route selection with minimized routing cost by employing a novel optimization algorithm called Rider Integrated Cuckoo Search (RI‐CS) that hybridizes the concept of Rider Optimization Algorithm (ROA) and Cuckoo Search (CS). Finally, the performance of the proposed RI‐CS is analyzed against the conventional models like Particle Swarm Optimization (PSO), Firefly algorithm (FA), Grey Wolf Optimizer (GWO), Whale Optimization Algorithm (WAO), ROA, CS, Mean Computing Jaya Algorithm (MC‐JA), and Grey Updated Butterfly Operator (GU‐BO) in terms of certain cost analysis.
Non-Parallel Any-to-Many Voice Conversion by Replacing Speaker Statistics

This paper proposes a non-parallel any-to-many voice conversion (VC) approach with a novel statistics replacement layer. Non-parallel VC is usually achieved by ﬁrstly disentangling linguistic and speaker representations, and then concatenating the linguistic content with the learned target speaker’s embedding at the conversion stage. While such a concatenation-based approach could introduce speaker-speciﬁc characteristics into the network, it is not very effective as it entirely relies on the network to learn to combine the linguistic content and the speaker characteristics. Inspired by X-vectors, where the statistics of hidden representation such as means and standard deviations are used for speaker differentiation, we propose a statistics replacement layer in VC systems to directly modify the hidden states to have the target speaker’s statistics. The speaker-speciﬁc statistics of hidden states are learned for each target speaker during training and are used as guidance for the statistics replacement layer during inference. Moreover, to better concentrate the speaker information into the statistics of hidden representation, a multitask training with X-vector based speaker classiﬁcation is also performed. Experimental results with Librispeech and VCTK datasets show that the proposed method can effectively improve the converted speech’s naturalness and similarity.
Non-alcoholic fatty liver disease in pregnancy

Non-alcoholic fatty liver disease (NAFLD) is now the commonest liver pathology in the UK; however, relatively little is known about its course in pregnancy or the effect it has on maternal or fetal outcomes. Described here is a 24-year-old woman in her first pregnancy who presented with non-specific symptoms and raised alanine aminotransferase with ultrasonography of her liver showing changes of steatosis and suspicious for cirrhosis, leading to a diagnosis of NAFLD. The case illustrates the need for the clinician to have awareness of this increasingly prevalent condition and for multidisciplinary management.
Learning Speech Structure to Improve Time-Frequency Masks

Time-frequency (TF) masks are widely used in speech enhancement (SE). However, accurately estimating TF masks from noisy speech remains a challenge to both statistical or neural network approaches. Statistical model-based mask estimation usually depends on a good parameter initialization, while NN-based mask estimation relies on setting proper and stable learning targets. To address these issues, we propose a novel approach to extracting TF speech structures from clean speech data, and partition a noisy speech spectrogram into mutually exclusive regions of core speech, core noise, and transition. Using such region targets derived from clean speech, we train bidirectional LSTM to learn region prediction from noisy speech, which is easier to do than mask prediction. The predicted regions can further be used in place of masks in beamforming, or integrated with statistical and NN based mask estimation to constrain mask values and model parameter updates. Our experimental results on ASR (CHiME-3) and SE (CHiME-3 and Lib-riSpeech) have demonstrated the effectiveness of our approach of learning speech region structure to improve TF masks.
Impact of graphitic carbon nitrides synthesized from different precursors on Schottky junction characteristics

Graphitic carbon nitride (g-CN) has gained wide interest in many areas, such as energy and the environmental remediation as a layered polymeric semiconductor that allows the formation of catalytically active Schottky junctions due to its proper electronic band structure. Interestingly, although it is known that the precursors used in the synthesis, can influence the properties of the g-CN, no detailed study on these effects on Schottky junctions could be found in the literature. In this research, the effects of g-CNs synthesized by thermal polycondensation of different precursors on the photocatalytic efficiency of Schottky junctions were investigated. For this purpose, urea, thiourea, melamine, and guanidine hydrochloride were used as different precursors, while the photocatalytic dehydrogenation of formic acid was used as a test reaction. The Schottky junctions were formed by decorating the as-prepared g-CNs with AgPd alloy nanoparticles (NP), which were synthesized by reduction of Ag and Pd salts with NaBH4. The structural, electronic and charge carrier dynamics of all prepared structures have been fully characterized by TEM, XRD, BET, XPS, UV-Vis DRS, PL, and PL life measurements. The results showed that the charge transfer dynamics of g-CNs surface defects are more effective in the photocatalytic performance of Schottky junctions than in structural features such as the size of the metal NPs or the surface area of the catalysts.
Simulation Modeling of a Photovoltaic-Green Roof System for Energy Cost Reduction of a Building: Texas Case Study

This study aims at introducing a modeling and simulation approach for a green roof system which can reduce energy cost of a building exposed to high temperatures throughout the summer season. First, to understand thermal impact of a green roof system on a building surface, a field-based study has been conducted in Commerce, Texas, U.S., where the average maximum temperature in summer is 104 °F (40 °C). Two types of analyses were conducted: (1) comparison of temperature between different plant type via Analysis of variance (ANOVA) and (2) polynomial regression analysis to develop thermal impact estimation model based on air temperature and presence of a green roof. In addition, an agent-based simulation (ABS) model was developed via AnyLogic® University 8.6. 0 simulation software, Chicago, IL, U.S., in order to accurately estimate energy cost and benefits of a building with a photovoltaic-green roof system. The proposed approach was applied to estimate energy reduction cost of the Keith D. McFarland Science Building at Texas A&M University, Commerce, Texas (33.2410° N, 95.9104° W). As a result, the proposed approach was able to save $740,325.44 in energy cost of a heating, ventilation, and air conditioning (HAVC) system in the subject building. The proposed approach will contribute to the implementation of a sustainable building and urban agriculture.
Analisis Sentimen Masyarakat Pada Twiter Terhadap Isu Covid-19 Menggunakan Metode Lexicon Based

Twitter merupakan salah satu media sosial yang memungkinkan penggunanya untuk menulis tentang berbagai topik dan membahas isu-isu yang sedang terjadi. Salah satu isu yang sedang menjadi trending topic saat ini adalah Covid-19. Banyak kalangan di masyarakat tersebar isu bahwa Covid-19 ini merupakan konspirasi dan tidak sedikit juga yang mempercayai bahwa Covid-19 ini nyata. Oleh karena itu perlu suatu analisis sentimen untuk mengetahui apakah Covid-19 ini nyata atau konspirasi berdasarkan opini masyarakat di Twitter, metode yang digunakan adalah Lexicon Based untuk mengelompokkan opini masyarakat ke dalam kategori sentimen positif, negatif atau netral. Hasil dari penelitian ini menunjukkan bahwa masyarakat pada twitter yang percaya bahwa Covid-19 adalah nyata masih cukup tinggi dibandingkan dengan masyarakat yang mempercayai isu konspirasi Covid-19. Hal tersebut dapat dilihat dari persentase sentimen kategori positif sebesar 58.08%, opini sentimen kategori negatif sebesar 37.61%, dan opini sentimen kategori netral sebesar 4.31%.
Beyond exposure therapy: formulation-based therapy treating a fear of urinary incontinence – a case study

Abstract Background: The current literature on the specific phobia of urinary incontinence is limited, with no specific empirically established model or treatment protocol. Aims: This article consists of a case study of formulation-driven cognitive behaviour therapy (CBT) for phobia of urinary incontinence. Method: Martin attended a total of 12 treatment sessions. The treatment included the development of an idiosyncratic formulation, and the use of well-established cognitive and behavioural treatment strategies from other anxiety disorders. Results: Both outcome measures and Martin’s subjective report indicate that the treatment was effective. Conclusion: This case study contributes to the current limited literature on this phobia, and emphasises the importance of formulation-driven CBT to map for idiosyncratic features and target cognitive and behavioural factors.
Dialectical behavior therapy-skills system for cognitively challenged individuals with self-harm: a Swedish pilot study

Background: Dialectical behavior therapy (DBT) is an evidence-based treatment for self-harm and emotion regulation difficulties. A modified version, DBT-Skills System (DBT-SS), has been developed in the USA for individuals with cognitive difficulties. The present study is a pilot study, testing the DBT-SS in a Swedish context. Methods: Six participants were treated with individual therapy and group skills training for 48 sessions each. A case series design was used to follow individual development over time. The primary outcome measure was reduction in challenging behaviors. Secondary outcomes were level of functioning in daily life, hospital admissions, and resilience and vulnerabilities in different risk domains. Data was analyzed using time-series diagrams. Effect sizes of changes were calculated using Cohen’s d. Results: Challenging behaviors decreased over time and participants' global level of functioning increased. There was a reduction in number of hospital admissions over time. As for resilience and vulnerabilities, participants’ overall level of risk in various areas remained unchanged or decreased after treatment. Conclusions: The results indicate that DBT-SS might be a promising treatment for cognitively challenged individuals with emotion regulation difficulties and challenging behaviors in a Swedish context. The study provides suggestions for a future randomized controlled trial. Supplemental data for this article is available online at here.
Concentrated matrix exponential distributions with real eigenvalues

Abstract Concentrated random variables are frequently used in representing deterministic delays in stochastic models. The squared coefficient of variation ($\mathrm {SCV}$) of the most concentrated phase-type distribution of order $N$ is $1/N$. To further reduce the $\mathrm {SCV}$, concentrated matrix exponential (CME) distributions with complex eigenvalues were investigated recently. It was obtained that the $\mathrm {SCV}$ of an order $N$ CME distribution can be less than $n^{-2.1}$ for odd $N=2n+1$ orders, and the matrix exponential distribution, which exhibits such a low $\mathrm {SCV}$ has complex eigenvalues. In this paper, we consider CME distributions with real eigenvalues (CME-R). We present efficient numerical methods for identifying a CME-R distribution with smallest SCV for a given order $n$. Our investigations show that the $\mathrm {SCV}$ of the most concentrated CME-R of order $N=2n+1$ is less than $n^{-1.85}$. We also discuss how CME-R can be used for numerical inverse Laplace transformation, which is beneficial when the Laplace transform function is impossible to evaluate at complex points.
Technology storylines: A narrative analysis of the rural education research

This piece offers a systematic review of rural (P-12) education technology literature. Drawing upon a social change frame (Ogburn, 1922), current rural education technology research within the subfield is collected, examined, and synthesized. Findings explicate that methodological diversity is a strength; however, some populations (e.g., middle school teachers) have thicker coverage than others (e.g., high school students). Additionally, many studies lean on rhetorical structures about what could and should be happening in rural schools, rarely delving into the how’s and whys associated with actual technology use in rural contexts. The piece concludes with a call for scholarship which assists in shifting power structures to support rural schools in their efforts to work with technology for the betterment of rural students and communities in place.