bluuebunny/crossref_metadata_2025 huggingface dataset:
Mental Health Monitoring in the Digital Age
Authors: Kansal Mrignainy , Singh Pancham , Srivastava Prashant , Singhal Radhika , Deep Nishant , Singh Arpit
Published: March 2024

Social media has become a significant factor in the development of mental diseases, with the potential to significantly impact people's lives. This study explores the use of computational approaches and deep learning models to identify linguistic indicators suggestive of mental diseases such as depression, anorexia, and self-harm. The study also highlights the complex relationship between emotions and the underlying causes of mental diseases, emphasizing the need for understanding emotional triggers. The research demonstrates the effectiveness of machine learning models in detecting anxiety and depression on websites like Twitter, Facebook, and Reddit, particularly during the COVID-19 pandemic. The study highlights the potential of data mining techniques for automating the diagnosis of Social Network Mental Disorders among social media users, aiming to improve lives and address the rising incidence of mental illnesses in society.
Development of a Stretchable and Removable Electrical Interconnection Solution for Ultra-Thin Electronic Components
Authors: Despax-Ferreres Auriane , Tiquet Pascal , Souriau Jean-Charles , Jousseaume Vincent , De Girolamo Julia
Published: March 2024

Currently, the solutions for interconnecting electronic components with their active face facing on substrates are based on metallic soldering. The mechanical contacts are therefore

rigid. In order to enhance the reliability of the bonding, underfill

is usually used to redistribute the thermo-mechanical stress created by the Coefficient of Thermal Expansion (CTE) mismatch between the silicon chip and substrate. Underfill is

done with epoxy resins containing silica fillers (SiO2). As a result, the removal of components is no longer possible.

Moreover, this solution is not suitable for devices integrating ultra-thin silicon components (<100 μm) hybridized on flexible substrates that may be subject to deformation. This is the case, for example, of medical “patches” worn on the person and continuously solicited. Indeed, the rigid contact points are likely to break. To address this issue, we are developing a thin anisotropic conductive and stretchable adhesive film inspired by

the adhesion of the gecko. Thanks to the microstructuration of its toes involving about 1 million setae, the gecko can develop a large contact surface and thus a large force of attraction by the multiplication of van der Waals interactions. In this work, this “dry adhesion” based on the principle of “contact splitting”,

was implemented in order to improve the adhesion of a flexible

interconnection. For this purpose, the surface of a

polydimethylsiloxane (PDMS) film was structured with

micrometric mushroom-shaped patterns known to be the most efficient form of contact. To this end, silicon molds with varying mushroom geometries were used to shape the PDMS (with

different cap and pillar diameters) and the adhesion force

microstructured films were assessed (shear and pull-off

experiments). To make these films locally conductive through the thickness, a conductive composite was prepared and locally

deposited in the mold. One approach we investigated, was using a screen-printing mask. This approach has been implemented

and characterized using electrical tests (I-V measurements) in order to select the most suitable films to make a flexible

interconnection.
A Dual-Band Dual-Polarized 2x2 Antenna Array with Beamforming for 5G AiP and mmWave Applications
Authors: Hsieh Sheng-Chi , Huang Hong-Sheng , Hsiao Wen-Chun , Ho Cheng-Yu , Wang Chen-Chao
Published: March 2024

In this study, we present a dual-band (28/39GHz)

2 by 2 antenna array design on a 10(4+2+4) multi-layer organic substrate with broad bandwidth and higher isolation onto a compact AiP module. In addition, the H-type slot antenna structure can improve interference and isolation between 28 and 39GHz bands. The measured result shows the isolation can larger than 15dB between low and high band. For antenna measurement, the spherical of probing chamber is utilized to validate 28/39GHz antenna pattern and performance with a

package level passive testing. Our AiP measured result shows the return loss is better than 10 dB in 23.5-30.5 GHz range, with ~7 GHz bandwidth and provides a high-gain per element (above

~6 dBi) radiation pattern for 28GHz applications. For 39GHz band, the antenna has 7 GHz bandwidth and provides a 5 dBi

gain between 38-45 GHz. Comparison of S-parameter between simulated and measured results, there is a good correlation to

obtain the quality of manufacturing. Furthermore, a 28GHz beamforming array is designed and demonstrated. The

beamfoming array consists of a four-channel transceiver with 2 x 2 antenna array. The beamformer IC is implemented by

TSMC 90-nm CMOS technology and the flip-chip bonded on BT substrate. For beamforming test, an integrated socket and load board are designed for support the mmWave module and then

connection between DUT and test system. The load board

substrate includes the IF, LO, and DC distribution network for respectively. There are 4-channel transmit beamforming array

front-end module for testing. Finally, the 3D beam steering of 2 by 2 antenna array is measured with multi-states at 28 GHz, with a maximum realized gain of 11.8 dBi achieved in the main

beam (θ=0°). It shows that beamforming can be generated at a

specific beam direction by controlling the phase of the signal in

each antenna element.
Deposition of Fine-Pitch Indium Bumps on Single Die
Authors: Schneider Andreas , Ghorbanian Navid , Burt David , Hollingham James , Booker Paul , Brooks Toby G. , Lipp John D. , French Marcus J.
Published: March 2024

Photolithographic lift-off at wafer level is a wellestablished

method for depositing indium bumps onto pixels of

electronic components such as sensors and application-specific

integrated circuits (ASIC). These indium bumps form interconnects between pixels of the components during flip-chip bonding to make devices such as radiation detectors. Such components are not always available on wafer-scale, and single dies need to be processed in small-scale production, R&D, or for Cd(Zn)Te detector material. Photolithography on single dies is

ineffective due to the edge-bead of photoresist that compromises

perfect indium deposition onto pixels near the periphery of the

die. To utilize the maximum surface area of a die, a rigid mask with apertures (shadow mask) is an adequate substitute for the photoresist. Different mask designs and production techniques for fine-pitch indium bump arrays are investigated by testing electroformed stencils or micromachined silicon (Si) membranes as shadow masks. For the indium deposition, the

sensor and ASIC dies are clamped into custom-made jigs that include shadow masks. Alternatively, dies can be adhered to shadow masks with soluble adhesive. The apertures of a mask are aligned to the pixel array of the detector component. Bump arrays with 250μm- and 100μm-pitch were demonstrated with electroformed stencils. For smaller pitch (55μm) and bump diameter of ~20μm, specialized masks from Si membranes are tested. Components with successfully deposited indium bump arrays were subsequently flip-chip bonded at room temperature

without reflowing the indium. A bond yield of at least 99.9%

pixels for such flip-chip bonded radiation detector was shown using the read-out signal from exposure with a flat field X-ray radiation of an Am-241 sealed source.
A results-based monitoring and evaluation system for the Namibian Child Support Grant programme
Authors: Nshimyimana Brigitte , Rabie Babette
Published: March 2024

Background: Robust results-based monitoring and evaluation (ME) systems allow government to track the progress and impact of development programmes. This article focused on the current ME system as adopted for the Child Support Grant (CSG) programme of the Ministry of Gender Equality, Poverty Eradication and Social Welfare (MGEPESW) in Namibia.Objectives: The article examined whether the adopted ME processes and arrangements for the MGEPESW sufficiently track the outcomes for beneficiaries of the CSG programme to enable strategic decisions by the MGEPESW.Method: A desktop review of available literature on ME systems was complemented by a review of strategic reports and programme documentation of the MGEPESW that sets out the current ME arrangements. Further interviews with senior, middle and programme managers responsible for the implementation of the CSG programme assessed the suitability and gaps of these arrangements as well as the proposed recommendations to strengthen the existing ME arrangements.Results: The research found several gaps in current practice that hinders the MGEPESW and decision-makers in assessing the outcomes for child beneficiaries.Conclusion: Recommendations were offered to improve the current ME system to better track the outcomes of the CSG programme. This included a refined theory of change, revised indicators and strengthened institutional arrangements.Contribution: The improved results-based ME system for the MGEPESW offers a comparative base for the implementation of results-based ME systems for child-focused cash transfer programmes in similar contexts.
Suitability of risk assessment tools used during the portfolio recommendation process
Authors: Baloyi Solani , Lotter Melany
Published: March 2024

Orientation: Investors are overwhelmed with Collective Investment Schemes (CIS) portfolio options.Research purpose: The study set out to assess the emerging themes considered essential and prevalent in establishing a risk profile, establishing a framework for risk assessment tools, and evaluating existing risk assessment tools used in the industry.Motivation for the study: In 2020, newly amended legislation confirmed the importance of a risk assessment. This legislation made it mandatory and compulsory for financial planners to conduct a risk profile analysis concerning investment products in South Africa. Consequently, CIS have implemented various risk assessment tools to establish the investor risk profile.Research approach/design and method: Documentary research methodology was employed, where secondary data were analysed qualitatively. Twelve risk assessment tools were collected, and the established framework was used to evaluate existing risk assessment tools.Main findings: A sound theoretical framework for risk assessment tools, which is comprehensive in nature and include demographic factors, risk categorisation, and regulatory factors, was established. The results indicate that current tools used in the industry are inconsistent and do not address all the factors required to establish an investor risk profile.Practical/managerial implications: The study is significant in that it guides the financial services industry into the elements of assessment in establishing an investor risk profile.Contribution/value-add: A risk profile analysis is compulsory for financial planning. Financial planners and policymakers can consider the established framework to implement a standardised, comprehensive, holistic, risk assessment tool.
Case of Hypertrophic Cardiomyopathy with Clinical Progression to Restrictive Physiology
Authors: Nisarga M. J. , Hegde Anupama V. , Krishnamurthy Vishwanath , Rahul H.
Published: March 2024

Hypertrophic cardiomyopathy (HCM) is defined as an unexplained left ventricular (LV) hypertrophy ≥15 mm (13 mm if family history present) occurring in the absence of identifiable factor that may account for LV wall thickening, including pressure overload and infiltrative or storage disorder. Restrictive physiology is a pattern of ventricular filling with increased myocardial stiffness leading to elevation of ventricular pressure, causing diastolic dysfunction. A 60-year-old man was brought to the hospital with progressive worsening breathlessness for 2 days and bilateral lower limb swelling. The patient was a known case of HCM, ischemic heart disease (IHD) with single-vessel disease – ostial D1 70% stenosis, atrial fibrillation (AF), status post radiofrequency ablation, moderate LV systolic dysfunction (ejection fraction [EF] – 30%), with recurrent admission due to heart failure. The patient presented with hypotension and bilateral lower limb swelling, and systemic examination showed bilateral crepitations. The patient was diagnosed with acute decompensated heart failure, heart failure with reduced EF, HCM, old IHD, and AF with controlled ventricular rate. Serial two-dimensional echocardiography showed the progression of HCM to restrictive physiology. The patient deteriorated and was planned for a heart transplant with bridging LV assist device therapy. The transition is not usually reported, and literature related to this is scarce; hence, this case is being reported.
Management of Ekakushta (childhood psoriasis) through Ayurveda multimodal approach: A case report
Authors: Prajapati Vishal Nandlal , Jangir Jyoti Kumari , Rani Simmi
Published: March 2024

Kushta Roga is a broad term used in Ayurveda to refer to various skin ailments. Ekakushta is a type of Kshudra Kushta caused by the Vata and Kapha Dosha. Its symptoms are quite similar to those of psoriasis, including Aswedanam (no sweating/dryness), Mahavastum (affecting a large region), and Matsyashakalopamam (scaling like fish skin). Corticosteroids are usually prescribed topically to treat psoriasis, although this only offers temporary relief. Ayurveda has effective treatment of Ekakushta using a variety of interventions; nevertheless, because of the disease’s complicated etiology, a multimodal approach is necessary. A 15-year-old girl with a moderate build has been complaining for the last 4–5 years about reddish-patchy skin rashes that cause pain, scaling, and itching all over her body. She also reported recurrent relapses. The symptoms pointed to the involvement of the Vata and Kapha Dosha, suggesting Ekakushta (psoriasis). Due to the disease’s tendency toward remission and recurrence, we undertook purgative therapy (Virechana Karma) for the purification of vitiated Dosha. The patient was administered Shamana medicines for Dosha pacification and a topical application following purifying therapy. A complete remission of symptoms was noted after Ayurvedic therapy, as documented by pre- and post-therapy photos of lesions. Furthermore, there were notable declines in the Psoriasis Area and Severity Index score (32.6 to 0), Adolescent Psoriasis Quality of life Psychosocial Impact (42 to 8 at the end of treatment), and Adolescent Psoriasis Quality of life Physical Symptoms and Treatment (17 to 3) after 6 months of treatment with no documented adverse drug reactions during treatment. Ekakushta (psoriasis) is extremely difficult to treat; nonetheless, a comprehensive pathogenesis assessment, appropriate purification, and pacifying therapy work well.
Early-Stage Disease Prediction from Various Symptoms Using Machine Learning Models
Authors: Ajmera Devansh , Pandey Trilok Nath , Singh Shrishti , Pal Sourasish , Vyas Shrey , Nayak Chinmaya Kumar
Published: March 2024

Development and exploration of several Data analytics techniques in various real-time applications (e.g., Industry, Healthcare Neuroscience) in various domains have led to exploitation of it to extract paramount features from datasets. Following the introduction of new computer technology, the health sector had a significant transformation that compelled it to produce more medical data, which gave rise to a number of new disciplines of study. Quite a few initiatives are made to deal with the medical data and how its usage can be helpful to humans. This inspired academics and other institutions to use techniques like data analytics, its types, machine learning and different algorithms, to extract practical information and aid in decision-making. The healthcare data can be used to develop a health prediction system that can improve a person's health. Based on the dataset provided, making accurate predictions in early disease prediction benefits the human community.
Effective Facial Expression Recognition System Using Machine Learning
Authors: Hebri Dheeraj , Nuthakki Ramesh , Digal Ashok Kumar , Venkatesan K G S , Chawla Sonam , Raghavendra Reddy C
Published: March 2024

The co Facial expression recognition (FER) is a topic that has seen a lot of study in computer vision and machine learning. In recent years, deep learning techniques have shown remarkable progress on FER tasks. With this abstract, A Novel Is Advised By Us FER method that combines combined use of k-nearest neighbours and long short-term memory algorithms better efficiency and accurate facial expression recognition. The proposed system features two primary steps—feature extraction and classification—to get results. When extracting features, we extract features from the facial images using the Local Binary Patterns (LBP) algorithm. LBP is a simple yet powerful feature extraction technique that captures texture information from the image. In the classification stage, we use the KNN and LSTM algorithms for facial expression recognition. KNN is a simple and effective classification algorithm that finds the k closest to the given value neighbours to the test training-set-sample and assigning it to the class that is most frequent among its neighbours. However, KNN has limitations in handling temporal information. To address this limitation, we propose to use LSTM, which is a subclass of RNNs capable of capturing temporal relationships in time series data. The LSTM network takes as input the LBP features of a sequence of facial images and processes them through a series of LSTM cells to estimate the ultimate coding of the phrase. We examine the planned and system on two publicly available records: the CK+ and the Oulu-CASIA datasets. According on the experimental findings, the proposed system achieves performance at the cutting edge on both datasets. The proposed system performs better than other state-of-the-art methods, including those that use deep learning systems, quantitatively, in terms of F1-score and precision.In conclusion, the proposed FER system that combines KNN and LSTM algorithms achieves high accuracy and an F1 score in recognising facial expressions from sequences of images. This system can be used in many contexts, including human-computer interaction, emotion detection, and behaviour analysis.
Efficient Usage of Energy Infrastructure in Smart City Using Machine Learning
Authors: Rajaan Rajesh , Baishya Bhaskar Kamal , Rao Tulasi Vigneswara , Pattanaik Balachandra , Tripathi Mano Ashish , R Anitha
Published: March 2024

The concept of smart cities revolves around utilizing modern technologies to manage and optimize city operations, including energy infrastructure. One of the biggest problems that smart cities have to deal with is ensuring the efficient usage of energy infrastructure to reduce energy consumption, cost, and environmental impact. Machine learning is a powerful tool that can be utilized to optimize energy usage in smart cities. This paper proposes a framework for efficient usage of energy machine learning for city infrastructure in smart cities. The proposed framework includes three main components: data collection, machine learning model development, and energy infrastructure optimization. The data collection component involves collecting energy consumption data from various sources, such as smart meters, sensors, and other IoT devices. The collected data is then pre-processed and cleaned to remove any inconsistencies or errors. The machine learning model development component involves developing machine learning models to predict energy consumption and optimize energy usage. The models can be developed using various techniques such as regression, classification, clustering, and deep learning. These models can predict energy consumption patterns based on historical data, weather conditions, time of day, and other factors. The energy infrastructure optimization component involves utilizing the machine learning models to optimize energy usage. The optimization process involves adjusting energy supply and demand to reduce energy consumption and cost. The optimization process can be automated, and SVM based machine learning models can continuously enhance their precision over time by studying the data. The proposed framework has several benefits, including reducing energy consumption, cost, and environmental impact. It can also improve the reliability and stability of energy infrastructure, reduce the risk of blackouts, and improve the overall quality of life in highly developed urban areas. Last but not least, the projected framework for efficient usage of energy machine learning for city infrastructure in smart cities is a promising solution to optimize energy usage and reduce energy consumption and cost. The framework can be implemented in various smart city applications, including buildings, transportation, and industrial processes.
Enhancing Audio Accessory Selection through Multi-Criteria Decision Making using Fuzzy Logic and Machine Learning
Authors: Parida Sagar Mousam , Pande Sagar Dhanraj , Challa Nagendra Panini , Panigrahi Bhawani Sankar
Published: March 2024

This research paper aims to investigate the significance of electrical products, specifically earbuds and headphones, in the digital world. The processes of decision-making and purchasing of audio accessories are often characterized by a significant investment of time and effort, as well as a complex interplay of competing priorities. In addition, various methodologies are employed for the selection and procurement of audio equipment through the utilization of machine learning algorithms. This study aimed to gather responses from a diverse group of participants regarding their preferences for the latest functionalities and essential components in their gadgets. The data was collected through a questionnaire that provided multiple options about the specifications of the audio accessories for the participants to choose from. The study employed seven distinct input factors to elicit responses from participants. These factors included brand, type, design, fit, price, noise cancellation, and folding design. The quantification of each input parameter was executed through the utilization of a scaling function in the Fuzzy Logic Interface, which assigned the labels “Yes” or “No” to each parameter. In this study, the Mamdani approach, which is a widely used fuzzy reasoning tool, was employed to develop a fuzzy logic controller (FLC) consisting of seven input and one output processes. In this study, standard fuzzy algorithms were employed to enhance the accuracy of the process of selecting an audio accessory in accordance with the user's specific requirements on the basis of Fuzzy threshold where “Yes” signifies about the availability of such audio accessory and “No” refers to the non-availability and readjustment of the input parameters.
Improving Student Grade Prediction Using Hybrid Stacking Machine Learning Model
Authors: Reddy Seloni , Pande Sagar Dhanraj
Published: March 2024

With increasing technical procedures, academic institutions are adapting to a data-driven decision-making approach of which grade prediction is an integral part. The purpose of this study is to propose a hybrid model based on a stacking approach and compare its accuracy with those of the individual base models. The model hybridizes K-nearest neighbours, Random forests, XGBoost and multi-layer perceptron networks to improve the accuracy of grade prediction by enabling a combination of strengths of different algorithms for the creation of a more robust and accurate model. The proposed model achieved an average overall accuracy of around 90.9% for 10 epochs, which is significantly higher than that achieved by any of the individual algorithms of the stack. The results demonstrate the improvement of prediction results but using a stacking approach. This study has significant implications for academic institutions which can help them make informed grade predictions for the improvement of student outcomes.
Combination of Dabrafenib and Trametinib in Patients with Metastatic BRAFV600E-Mutated Thyroid Cancer
Authors: Jeon Youngkyung , Park Sehhoon , Lee Se-Hoon , Kim Tae Hyuk , Kim Sun Wook , Ahn Myung-Ju , Jung Hyun Ae , Chung Jae Hoon
Published: March 2024

Purpose BRAF mutations are detected in 30%-80% of papillary thyroid cancer (PTC) cases. DaBRAFenib and trametinib showed promising antitumor activity in patients with BRAFV600E-mutated metastatic melanoma and non–small cell lung cancer. This study aimed to evaluate the efficacy and safety of daBRAFenib and trametinib in patients with metastatic BRAFV600E-mutated thyroid cancer.Materials and Methods This was a retrospective study to evaluate the efficacy of daBRAFenib and trametinib in patients with metastatic BRAFV600E-mutated PTC. The patients received daBRAFenib 150 mg twice daily and trametinib 2 mg once daily at the Samsung Medical Center. This study evaluated the progression-free survival (PFS), objective response rate (ORR), disease control rate (DCR) overall survival (OS), and safety of daBRAFenib and trametinib.Results Between December 2019 and January 2022, 27 PTC patients including eight patients with poorly differentiated or anaplastic transformation, received daBRAFenib and trametinib. The median age was 73.0 years, and the median follow-up period was 19.8 months. The majority (81.5%) had undergone thyroidectomy, while 8 patients had received prior systemic treatments. ORR was 73.1%, with 19 partial responses, and DCR was 92.3%. Median PFS was 21.7 months, and median OS was 21.7 months. Treatment-related adverse events included generalized weakness (29.6%), fever (25.9%), and gastrointestinal problems (22.2%). Dose reduction due to adverse events was required in 81.5% of the patients.Conclusion DaBRAFenib and trametinib demonstrated a high ORR with promising PFS; however, most patients with BRAFV600E-mutated metastatic PTC required a dose reduction.
Perturbing isoradial triangulations
Authors: David François , Scott Jeanne
Published: March 2024

We consider an infinite planar Delaunay graph

\mathbf{G}_{\epsilon}

which is obtained by locally deforming the coordinate embedding of a general isoradial graph

\mathbf{G}_{\mathrm{cr}}

, with respect to a real deformation parameter

\epsilon

. Using Kenyon’s exact and asymptotic results for the critical Green’s function on an isoradial graph, we calculate the leading asymptotics of the first- and second-order terms in the perturbative expansion of the log-determinant of the Laplace–Beltrami operator

\Delta(\epsilon)

, the David–Eynard Kähler operator

\mathcal{D}(\epsilon)

, and the conformal Laplacian 

{\underline{\boldsymbol{\Delta}}}(\epsilon)

on the deformed Delaunay graph

\mathbf{G}_{\epsilon}

. We show that the scaling limits of the second-order
bi-local
term for both the Laplace–Beltrami and David–Eynard Kähler operators exist and coincide, with a shared value independent of the choice of the initial isoradial graph

\mathbf{G}_{\mathrm{cr}}

. Our results allow us to define a discrete analog of the stress-energy tensor for each of the three operators. Furthermore, we can identify a central charge (

c=-2

) in the case of both the Laplace–Beltrami and David–Eynard Kähler operators. While the scaling limit is consistent with the stress-energy tensor and the value of the central charge for the Gaussian free field (GFF), the discrete central charge value of 

c=-2

for the David–Eynard Kähler operator is, however, at odds with the value of 

c=-26

expected by Polyakov’s theory of 2D quantum gravity; moreover, there are problems with convergence of the scaling limit of the discrete stress-energy tensor for the David–Eynard Kähler operator. The second-order bi-local term for the conformal Laplacian involves anomalous terms corresponding to the creation of discrete
curvature dipoles
in the deformed Delaunay graph

\mathbf{G}_{\epsilon}

; we examine the difficulties in defining a convergent scaling limit in this case. Connections with some discrete statistical models at criticality are explored.
On rank one and Weyl–von Neumann theorem for multiplicative perturbations of unitary operators
Authors: Bazao Vanderléa R. , de Oliveira César R. , Diaz Pablo A.
Published: March 2024

For multiplicative perturbations of unitary operators, it is presented a version of Weyl–von Neumann theorem and a sufficient conditions for generic (in the intensity parameter) singular continuous spectrum under unitary rank one perturbations.
Does Minimizing Seabed Contact Alleviate the Impacts of Bottom Trawling? An Experimental Study on Bycatch from North-western Bay of Bengal
Authors: Shubhadeep Ghosh , Behera Pralaya Ranjan , Loveson Edward , Muktha Menon , Divipala Indira , Felix Jasmin , Manas H M , Phalguni Pattnaik , Madhumita Das , Gourisankar Rao , Satishkumar M , Dineshbabu Aayathan Puthiya Purayil , Gopalakrishnan A
Published: March 2024

In light of the ecological concerns associated with bottom trawling in tropical multispecies fisheries, an attempt was undertaken to implement operational modifications in experimental trawling practices aimed at reducing bottom contact. This endeavour was pursued to investigate the resulting effects on bycatch biomass and diversity along the north-western Bay of Bengal. The average total catch rate and bycatch rate in 66 hauls from January 2017 to December 2019 were 35.46 kg/h and 9.70 kg/h respectively. Bycatch biomass was mainly contributed by Teleostei (77.90%), of which the dominant species was Equuilites lineolatus. Temporal variations in bycatch composition were recorded, and average seasonal dissimilarity ranged between 35.03% and 59.61%. Juvenile percentages varied among six commercial species from 1.42 to 28.0, and their occurrences were related to their peak spawning seasons. The trophic index of bycatch calculated was 3.65. Species diversity and richness in bycatch were higher during post-monsoon and summer seasons. Biomass and abundance plots indicated the bycatch fauna to be relatively unstressed during most seasons. Trawling marginally above the bottom had improved ecological outcomes; as evident from the decreased proportion of bycatch to total trawl catch, reduced growth overfishing from lower juvenile proportions and within optima for various diversity indicators.
Functional Ingredients from Seafood Processing Wastes: Protein Hydrolysate and Biocalcium
Authors: Patil Umesh , Nilsuwan Krisana , Benjakul Soottawat
Published: March 2024

The fish processing industry generally generates large amounts of byproducts, including skin, bone, scale, or meat residues, etc. These byproducts contain a wide range of nutritional components such as protein, lipid, mineral, etc. These leftovers can be converted to functional ingredients or nutraceuticals. To produce high value-added functional ingredients, the remaining proteins in fish processing leftovers are hydrolyzed, in which hydrolysate containing different peptides and amino acids can be generated. Frame or backbone as well as scale can be converted to fish biocalcium, which serves as a potential source of calcium and minerals. Nevertheless, potential processing or technology must be adopted to obtain desired products with target bioactivitities. This review covers production, characteristics, and bioavailability of fish protein hydrolysate (FPH) and fish biocalcium (BC). This review also focuses on the recent applications of the FPH and BC in drink and foods. Overall, the review points out the better exploitation of fish processing leftovers and simultaneously lower pollution caused by the improper discard or disposal of those wastes to environment. Also, consumers have more choices to consume functional ingredients or nutraceutical from marine resources.
Regularization Effects in Deep Learning Architecture
Authors: Liman Muhammad Dahiru , Osanga Salamatu Ibrahim , Alu Esther Samuel , Zakariya Sa'adu
Published: March 2024

This research examines the impact of three widely utilized regularization approaches -- data augmentation, weight decay, and dropout --on mitigating overfitting, as well as various amalgamations of these methods. Employing a Convolutional Neural Network (CNN), the study assesses the performance of these strategies using two distinct datasets: a flower dataset and the CIFAR-10 dataset. The findings reveal that dropout outperforms weight decay and augmentation on both datasets. Additionally, a hybrid of dropout and augmentation surpasses other method combinations in effectiveness. Significantly, integrating weight decay with dropout and augmentation yields the best performance among all tested method blends. Analyses were conducted in relation to dataset size and convergence time (measured in epochs). Dropout consistently showed superior performance across all dataset sizes, while the combination of dropout and augmentation was the most effective across all sizes, and the triad of weight decay, dropout, and augmentation excelled over other combinations. The epoch-based analysis indicated that the effectiveness of certain techniques scaled with dataset size, with varying results.
IoT System for Real-Time Water Monitoring
Authors: G. Pise Kaustubh , H. More Sampat , Patil Gandharv , Chavan Aditi , Misal Divya
Published: March 2024

Water is one of the vital parameters in any living organism's life. Nature consists of five elements of which water is the most valued element. As it is very important, the composition of water or water bodies consists of parameters such as pH, temperature, TDS, turbidity etc. which have their specified values for specified types of reservoirs. To use the water bodies effectively, one should have adequate knowledge of water parameters. So, to depict these parameters there were lots of methodologies from the past few years. Every time technical evolution hits the traditional systems also, that made changes from traditional measuring techniques to new innovative and time-saving methods with the help of engaging technologies like IoT. The swift advancement of IoT technology offers a fresh method for acquiring, transmitting, and processing real-time data. Utilizing this technology, individuals can remotely access continuous updates on water quality from distant locations.
Beyond Role Conflict and Ambiguity: A Global Measure of Role Stress among South African School Teachers
Authors: Padmanabhanunni Anita , Pretorius Tyrone Brian
Published: March 2024

Background. Role stress is linked to reduced work performance, diminished organizational commitment, increased intention to leave a job, and negative physical and mental health effects. Given the significant implications of role stress, researchers have sought to understand and quantify the concept. The Role Conflict and Ambiguity (RCA) scales are widely utilized in job stress research as the predominant measurement tools. They were originally conceptualized as consisting of two independent dimensions: role conflict and role ambiguity.
Objective. This study advances the validation research of the RCA scales by exploring its dimensionality through Mokken Scale Analysis (MSA) and Classical Test Theory (CTT).
Method. South African school teachers responded to the RCA scales, Maslach Burnout Inventory, and Teaching Satisfaction Scale. Confirmatory Factor Analysis (CFA) and MSA were employed for data analysis.
Results. The research determined that a second-order model provided the optimal fit, indicating that role ambiguity and role conflict are subordinate dimensions within the overarching construct of role stress.
Conclusion. The findings from the CFA and supplementary bifactor indices reinforce the view that the instrument comprises 13 items, which assess a general dimension of role stress along with two sub-dimensions: role conflict and role ambiguity. Such specificity may lead to more effective strategies to mitigate role-related stress, thereby enhancing overall employee well-being, job satisfaction, and organizational productivity.
Differences of Harmonic Numbers and the abc-Conjecture
Authors: Da Silva Natalia , Raianu Serban , Salgado Hector
Published: March 2024

Our main source of inspiration was a talk by Hendrik Lenstra on harmonic numbers, which are numbers whose only prime factors are two or three. Gersonides proved 675 years ago that one can be written as a difference of harmonic numbers in only four ways: 2-1, 3-2, 4-3, and 9-8. We investigate which numbers other than one can or cannot be written as a difference of harmonic numbers and we look at their connection to the abc-conjecture. We find that there are only eleven numbers less than 100 that cannot be written as a difference of harmonic numbers (we call these ndh-numbers). The smallest ndh-number is 41, which is also Euler's largest lucky number and is a very interesting number. We then show there are infinitely many ndh-numbers, some of which are the primes congruent to 41 modulo 48. For each Fermat or Mersenne prime we either prove that it is an ndh-number or find all ways it can be written as a difference of harmonic numbers. Finally, as suggested by Lenstra in his talk, we interpret Gersonides's theorem as "The abc-conjecture is true on the set of harmonic numbers" and we expand the set on which the abc-conjecture is true by adding to the set of harmonic numbers the following sets (one at a time): a finite set of ndh-numbers, the infinite set of primes of the form 48k+41, the set of Fermat primes, and the set of Mersenne primes.
Minimal Generating Sets of the Monoid of Partial Order-Preserving Injections
Authors: Annin Scott , Lopez Saul
Published: March 2024

Monoids arise in such fields as computer science, physics, and numerous branches of mathematics including abstract algebra, cryptography and operator theory. In this research project we seek to determine minimal generating sets for the monoid of partial order-preserving injections of an n-element set, POI(n). A generating set for a monoid is a collection of elements S such that every element of the monoid can be expressed as a product of elements from S. Generating sets are of fundamental importance across math and science, and mathematicians have great interest in studying generating sets of a variety of algebraic structures. By a minimal generating set, we refer to a generating set for which no proper subset is a generating set. In this paper, we provide necessary and sufficient conditions for a set to be a minimal generating set for POI(n), and we show that there are exactly (n-1)! minimal generating sets for POI(n).
An IFS for the Stretched Level-n Sierpinski Gasket
Authors: Arauza Rivera Andrea , Lankford Adam , McClinton Matt , Torres Sandra
Published: March 2024

Broadly, fractals are sets that exhibit a repeating pattern at multiple scales. One important fractal set is the Sierpinski Gasket (SG) which is made up of nested equilateral triangles. A variation of the classic Sierpinski gasket is to create n-levels with the equilateral triangles. Another variation is to stretch the points of intersection for the triangles in SG into line segments of length 0 <  α  < 1/3. When one combines these variations, one arrives at the stretched level-n Sierpinski gaskets (SSGn) which are the focus of this work. We give an introduction to iterated function systems (IFS) and determine an IFS which generates SSG3. We then describe how one can acquire the IFS for SSGn in general, and conclude with a theorem which determines the Hausdorff  dimension for SSGn.
Sums of Two Generalized Tetrahedral Numbers
Authors: Lycan Ron , Ponomarenko Vadim
Published: March 2024

Expressing whole numbers as sums of figurate numbers, including tetrahedral numbers, is a longstanding problem in number theory. Pollock's tetrahedral number conjecture states that every positive integer can be expressed as the sum of at most five tetrahedral numbers. Here we explore a generalization of this conjecture to negative indices. We provide a method for computing sums of two generalized tetrahedral numbers up to a given bound, and explore which families of perfect powers can be expressed as sums of two generalized tetrahedral numbers.
Clumsy Packing of Polyominoes in Finite Space
Authors: Miller Emma , O'Connor Mitchel , Shank Nathan
Published: March 2024

Clumsy packing is considered an inefficient packing, meaning we find the minimum number of objects we can pack into a space so that we can not pack any more object. Thus, we are effectively spacing the objects as far apart as possible so that we cannot fit another object. In this paper, we consider the clumsy packing of polyominoes in a finite space, which must consider boundary conditions. We examine rectangle, L, T, and plus polyominoes of various sizes.
Analisis Tingkat Pemahaman Wasit dan Juri Pencak Silat Kabupaten Ponorogo Pada Peraturan Terbaru
Authors: Anik Tri Wahyuningtyas  , Achmad Rizanul Wahyudi  , Andri Suyoko  , Wijono Wijono , Waristra Tyo Nirwansyah
Published: March 2024

Dalam beberapa tahun terakhir ini terdapat pergantian peraturan pertandingan pencak silat diantaranya yaitu peraturan Persilat tahun 2021 dan peraturan IPSI terbaru tahun 2023. Penelitian ini bertujuan untuk mendeskripsikan tingkat pemahaman wasit dan juri Kabupaten Ponorogo terhadap peraturan pertandingan pencak silat terbaru kategori tanding. Penelitian ini mengunakan pendekatan deskriptif kuantitatif melalui penggunaan metode survei serta rancangan penelitiannya yaitu non-eksperimen. Sasaran penelitian ini ialah wasit dan juri Kabupaten Ponorogo dengan jumlah sampel 15 responden. Peneliti menggunakan kuesioner dengan skala Guttmen sebagai alat pengumpulan data dan uji validasi angket menggunakan Korelasi Pearson pada program SPSS serta uji reliabilitas menggunakan teknologi SPSS Cornbach Alpha dengan batasan angka 0.6 hingga diatas 0.8. Teknis analisis data menggunakan perangkat lunak ilmu sosial (SPSS). Hasil penelitian menunjukan peraturan pertandingan pencak silat terbaru kategori tanding sebesar 71,4%. Capaian pemahaman wasit dan juri Kabupaten Ponorogo dengan persentase 71,4% berada dalam kategori tinggi. Secara keseluruhan wasit dan juri di kabupaten Ponorogo memiliki pemahaman yang baik terhadap peraturan dan protokol dalam penyelenggaraan pertandingan pencak silat.
Exploring the Relationship between Environmental Cognition, Pro-Environmental Behavior, and Mental Health among Adolescents: A Narrative Review
Authors: Jacob Leema , Reddy K Jayasankara
Published: March 2024

Introduction: Adolescents’ cognition, behavior, and attitudes are essential for environmental conservation; hence, the physical environment is inevitable in their mental health. The environment can influence adolescents’ sense of support, freedom, and the stimulation they seek in life. Environmental factors such as pollution, weather, lifestyle changes, and working conditions significantly influence adolescents' mental health. Knowing and learning about environmental cognition and its influence on adolescents is vital. Objective: To understand the relationship between environmental cognition, pro-environmental behavior, and mental health among adolescents with the help of a narrative approach. Methods: The present article ‘employed a narrative review.’ Research articles were taken from four databases, PubMed, Research Gate, Web of Science, and Science Direct, using the PRISMA procedure from 2000 to 2023. The data was collected from various databases and narrowed down for the results. Results: This study proves a strong relationship between environmental cognition, pro-environmental behavior, and adolescents' mental health. Twenty-six selected research studies out of 45 initial studies state that solid environmental cognition may lead to enhanced pro-environmental behavior, which is crucial to adolescents’ mental health and well-being. Furthermore, pro-environmental cognition wholly mediated the relationship between environmental behavior and mental well-being. Conclusion: Implications of this study concentrate on developing focused interventions, creating urban planning policies, and developing adolescent-friendly community designs, which are solid pillars for promoting mental health. Adolescents must be motivated to protect the environment to become pioneers of pro-environmental behavior.
Effect of sleep on blood pressure among adults of urban slums: A Cross sectional study
Authors: Verma Saurabh , Kumar Arun , Gautam Neelam , Mittal Chhaya
Published: March 2024

Introduction: Hypertension, the “Silent, Invisible killer” - is a modern day’s universal epidemic. The adult populations in slum area are more vulnerable for hypertension as they are so busy in the fulfilment of even day to day basic needs and care, family commitments and economic stress. Aim and objectives: To estimate the prevalence of hypertension among the adults (25-44 years) and to study the effect of sleep on blood pressure levels. Methodology: The present cross-sectional study was carried among adults aged between 25 to 44 years residing in the slums of Meerut District. 30 Cluster Sampling technique was used. The slum was selected as a cluster unit. During the visit by house-to-house survey, members of the family were listed and eligible person were interviewed and examined. Data collected on a structured and pretested questionnaire. Results: Prevalence of hypertension among those who had sleep duration of >8 hours per day was 44.8% followed by 20.1% and 17.5% among those who had 7-8 hours and less than 7 hours of sleep time respectively. It was found that 24.2% monophasic sleep pattern follower were hypertensives while the prevalence was 19.4% in biphasic pattern. Conclusion: Majority (55.4%) had a sleep duration between 7-8 hours, followed by 33.9% and 10.7% with <7 hours and >8 hours of sleep time respectively. Majority (61.3%) of the study population were having monophasic sleep pattern, while 36.3% were biphasic and 2.4% were multiphasic.
A cross sectional study to assess anemia & its determinants among pregnant women in a rural area of Maharashtra
Authors: Puri Dipali C , Rasal Milind M , Giri Purushottam A
Published: March 2024

Background: In India, anaemia prevalence is more than 40% so it is a severe public health problem. National Family Health Survey V indicates that prevalence of anaemia in pregnant women in Maharashtra’s rural areas is 46.5%. As the pregnant women have high prevalence of anaemia, it highlights the need to conduct research to find out the determinants and its preventive measures. Aim: To determine prevalence of anaemia and to assess its determinants amongst pregnant women in a rural area Methods: Three eighty-four pregnant women in a rural area of Thane district were selected for a community based descriptive cross-sectional study. Association between anaemia and variables in the study was established using Pearson‘s Chi- square test. For studying association between anaemia as dependent variable and various determinants as independent variables, binary logistic regression analysis was used. Results: Prevalence of anaemia was found to be 62.5%. 29.2% participants were mildly anaemic, 33.0% were moderately anaemic, 0.3% were severely anaemic and 37.5% were non-anaemic. Occurrence of anaemia was significantly associated with age, religion, gravida status, trimester, gestational age at first ANC visit, no consumption of IFA tablets, no consumption of dark green leafy vegetables and dietary habits. Conclusions: High prevalence of anaemia amongst pregnant women reflects poor utilization of health care services, lack of awareness about adequate dietary patterns and poor nutritional status. Anaemia is still a severe public health issue amongst pregnant women of rural areas.