bluuebunny/crossref_metadata_2025 huggingface dataset:
Automated Design of Affine Maximizer Mechanisms in Dynamic Settings
Authors: Curry Michael , Thoma Vinzenz , Chakrabarti Darshan , McAleer Stephen , Kroer Christian , Sandholm Tuomas , He Niao , Seuken Sven
Published: March 2024

Dynamic mechanism design is a challenging extension to ordinary mechanism design in which the mechanism designer must make a sequence of decisions over time in the face of possibly untruthful reports of participating agents.
Optimizing dynamic mechanisms for welfare is relatively well understood. However, there has been less work on optimizing for other goals (e.g., revenue), and without restrictive assumptions on valuations, it is remarkably challenging to characterize good mechanisms. Instead, we turn to automated mechanism design to find mechanisms with good performance in specific problem instances.
We extend the class of affine maximizer mechanisms to MDPs where agents may untruthfully report their rewards. This extension results in a challenging bilevel optimization problem in which the upper problem involves choosing optimal mechanism parameters, and the lower problem involves solving the resulting MDP.
Our approach can find truthful dynamic mechanisms that achieve strong performance on goals other than welfare, and can be applied to essentially any problem setting---without restrictions on valuations---for which RL can learn optimal policies.
Information Design for Congestion Games with Unknown Demand
Authors: Griesbach Svenja M. , Hoefer Martin , Klimm Max , Koglin Tim
Published: March 2024

We study a novel approach to information design in the standard traffic model of network congestion games. It captures the natural condition that the demand is unknown to the users of the network. A principal (e.g., a mobility service) commits to a signaling strategy, observes the realized demand and sends a (public) signal to agents (i.e., users of the network). Based on the induced belief about the demand, the users then form an equilibrium. We consider the algorithmic goal of the principal: Compute a signaling scheme that minimizes the expected total cost of the induced equilibrium. We concentrate on single-commodity networks and affine cost functions, for which we obtain the following results.

First, we devise a fully polynomial-time approximation scheme (FPTAS) for the case that the demand can only take two values. It relies on several structural properties of the cost of the induced equilibrium as a function of the updated belief about the distribution of demands. We show that this function is piecewise linear for any number of demands, and monotonic for two demands.
Second, we give a complete characterization of the graph structures for which it is optimal to fully reveal the information about the realized demand. This signaling scheme turns out to be optimal for all cost functions and probability distributions over demands if and only if the graph is series-parallel. Third, we propose an algorithm that computes the optimal signaling scheme for any number of demands whose time complexity is polynomial in the number of supports that occur in a Wardrop equilibrium for some demand. Finally, we conduct a computational study that tests this algorithm on real-world instances.
Worst-Case VCG Redistribution Mechanism Design Based on the Lottery Ticket Hypothesis
Authors: Guo Mingyu
Published: March 2024

We study worst-case VCG redistribution mechanism design for the public project problem. The mechanism design task comes down to designing a payment function that maximizes the worst-case allocative efficiency ratio.

We use a multilayer perceptron (MLP) with ReLU activation to model the payment function and use mixed integer programming (MIP) to solve for the worst-case type profiles that maximally violate the mechanism design constraints. We collect these worst-case type profiles and use them as training samples to train toward better worst-case mechanisms.

In practice, we require a tiny neural network structure for the above approach to scale. The Lottery Ticket Hypothesis states that a large network is likely to contain a "winning ticket" -- a much smaller subnetwork that "won the initialization lottery", which makes its training particularly effective. Motivated by this hypothesis, we train a large network and prune it into a tiny subnetwork. We run MIP-based worst-case training on the drawn subnetwork and evaluate the resulting mechanism's worst-case performance. If the subnetwork does not achieve good worst-case performance, then we record the type profiles that cause the current draw to be bad. To draw again, we restore the large network to its initial weights and prune using recorded type profiles from earlier draws, therefore avoiding drawing the same ticket twice. We expect to eventually encounter a tiny subnetwork that leads to effective training for our worst-case mechanism design task. Lastly, a by-product of multiple ticket draws is an ensemble of mechanisms with different worst cases, which improves the worst-case performance further.

Using our approach, we find previously unknown optimal mechanisms for up to 5 agents. Our results confirm the tightness of existing theoretical upper bounds. For up to 20 agents, we derive significantly improved worst-case mechanisms, surpassing a long list of existing manual results.
Double Auction on Diffusion Network
Authors: Li Miao , Cao Yuhan , Zhao Dengji
Published: March 2024

Mechanism design on social networks has attracted extensive attention recently. The goal is to design mechanisms to incentivize participants to invite more participants via their social networks, and the challenge is that the participants are competitors. Various mechanisms have been proposed for single-/multiple-unit auctions, but it has been shown that it is challenging to design such mechanisms for more complex settings. We move this forward to investigate a double auction on a network where each trader (a buyer or a seller) can link to other buyers and sellers. Incentiving invitation is more difficult than in multi-unit one-sided auctions, because there are two different roles and a buyer (seller) seems happy to invite a seller (buyer), but again the invited seller (buyer) may invite another buyer (seller) to compete with the original buyer (seller). To combat this, we propose a solution called dynamic trade reduction (DTR), which also guarantees a non-negative revenue for the market owner. Interestingly, our solution is also applicable to the multi-unit one-sided auction when there is only one seller linking to only buyers on the network. We believe that the principle of our solution has the potential to be extended to design the multi-item one-sided auction.
Almost Envy-Free Allocations of Indivisible Goods or Chores with Entitlements
Authors: Springer Max , Hajiaghayi MohammadTaghi , Yami Hadi
Published: March 2024

We here address the problem of fairly allocating indivisible goods or chores to n agents with weights that define their entitlement to the set of indivisible resources. Stemming from well-studied fairness concepts such as envy-freeness up to one good (EF1) and envy-freeness up to any good (EFX) for agents with equal entitlements, we present, in this study, the first set of impossibility results alongside algorithmic guarantees for fairness among agents with unequal entitlements.

Within this paper, we expand the concept of envy-freeness up to any good or chore to the weighted context (WEFX and XWEF respectively), demonstrating that these allocations are not guaranteed to exist for two or three agents. Despite these negative results, we develop a WEFX procedure for two agents with integer weights, and furthermore, we devise an approximate WEFX procedure for two agents with normalized weights. We further present a polynomial-time algorithm that guarantees a weighted envy-free allocation up to one chore (1WEF) for any number of agents with additive cost functions. Our work underscores the heightened complexity of the weighted fair division problem when compared to its unweighted counterpart.
Greedy-Based Online Fair Allocation with Adversarial Input: Enabling Best-of-Many-Worlds Guarantees
Authors: Yang Zongjun , Liao Luofeng , Kroer Christian
Published: March 2024

We study an online allocation problem with sequentially arriving items and adversarially chosen agent values, with the goal of balancing fairness and efficiency. Our goal is to study the performance of algorithms that achieve strong guarantees under other input models such as stochastic inputs, in order to achieve robust guarantees against a variety of inputs. To that end, we study the PACE (Pacing According to Current Estimated utility) algorithm, an existing algorithm designed for stochastic input. We show that in the equal-budgets case, PACE is equivalent to an integral greedy algorithm. We go on to show that with natural restrictions on the adversarial input model, both the greedy allocation and PACE have asymptotically bounded multiplicative envy as well as competitive ratio for Nash welfare, with the multiplicative factors either constant or with optimal order dependence on the number of agents. This completes a "best-of-many-worlds" guarantee for PACE, since past work showed that PACE achieves guarantees for stationary and stochastic-but-non-stationary input models.
A Unified Self-Distillation Framework for Multimodal Sentiment Analysis with Uncertain Missing Modalities
Authors: Li Mingcheng , Yang Dingkang , Lei Yuxuan , Wang Shunli , Wang Shuaibing , Su Liuzhen , Yang Kun , Wang Yuzheng , Sun Mingyang , Zhang Lihua
Published: March 2024

Multimodal Sentiment Analysis (MSA) has attracted widespread research attention recently. Most MSA studies are based on the assumption of modality completeness. However, many inevitable factors in real-world scenarios lead to uncertain missing modalities, which invalidate the fixed multimodal fusion approaches. To this end, we propose a Unified multimodal Missing modality self-Distillation Framework (UMDF) to handle the problem of uncertain missing modalities in MSA. Specifically, a unified self-distillation mechanism in UMDF drives a single network to automatically learn robust inherent representations from the consistent distribution of multimodal data. Moreover, we present a multi-grained crossmodal interaction module to deeply mine the complementary semantics among modalities through coarse- and fine-grained crossmodal attention. Eventually, a dynamic feature integration module is introduced to enhance the beneficial semantics in incomplete modalities while filtering the redundant information therein to obtain a refined and robust multimodal representation. Comprehensive experiments on three datasets demonstrate that our framework significantly improves MSA performance under both uncertain missing-modality and complete-modality testing conditions.
Improving Transferability for Cross-Domain Trajectory Prediction via Neural Stochastic Differential Equation
Authors: Park Daehee , Jeong Jaewoo , Yoon Kuk-Jin
Published: March 2024

Multi-agent trajectory prediction is crucial for various practical applications, spurring the construction of many large-scale trajectory datasets, including vehicles and pedestrians.
However, discrepancies exist among datasets due to external factors and data acquisition strategies. External factors include geographical differences and driving styles, while data acquisition strategies include data acquisition rate, history/prediction length, and detector/tracker error.
Consequently, the proficient performance of models trained on large-scale datasets has limited transferability on other small-size datasets, bounding the utilization of existing large-scale datasets.
To address this limitation, we propose a method based on continuous and stochastic representations of Neural Stochastic Differential Equations (NSDE) for alleviating discrepancies due to data acquisition strategy.
We utilize the benefits of continuous representation for handling arbitrary time steps and the use of stochastic representation for handling detector/tracker errors.
Additionally, we propose a dataset-specific diffusion network and its training framework to handle dataset-specific detection/tracking errors.
The effectiveness of our method is validated against state-of-the-art trajectory prediction models on the popular benchmark datasets: nuScenes, Argoverse, Lyft, INTERACTION, and Waymo Open Motion Dataset (WOMD).
Improvement in performance gain on various source and target dataset configurations shows the generalized competence of our approach in addressing cross-dataset discrepancies.
Exploring Domain Incremental Video Highlights Detection with the LiveFood Benchmark
Authors: Pei Sen , Xu Shixiong , Jin Xiaojie
Published: March 2024

Video highlights detection (VHD) is an active research field in computer vision, aiming to locate the most user-appealing clips given raw video inputs. However, most VHD methods are based on the closed world assumption, i.e., a fixed number of highlight categories is defined in advance and all training data are available beforehand. Consequently, existing methods have poor scalability with respect to increasing highlight domains and training data. To address above issues, we propose a novel video highlights detection method named Global Prototype Encoding (GPE) to learn incrementally for adapting to new domains via parameterized prototypes. To facilitate this new research direction, we collect a finely annotated dataset termed LiveFood, including over 5,100 live gourmet videos that consist of four domains: ingredients, cooking, presentation, and eating. To the best of our knowledge, this is the first work to explore video highlights detection in the incremental learning setting, opening up new land to apply VHD for practical scenarios where both the concerned highlight domains and training data increase over time. We demonstrate the effectiveness of GPE through extensive experiments. Notably, GPE surpasses popular domain incremental learning methods on LiveFood, achieving significant mAP improvements on all domains. Concerning the classic datasets, GPE also yields comparable performance as previous arts. The code is available at: https://github.com/ForeverPs/IncrementalVHD_GPE.
Visual Redundancy Removal for Composite Images: A Benchmark Dataset and a Multi-Visual-Effects Driven Incremental Method
Authors: Wang Miaohui , Zhang Rong , Huang Lirong , Li Yanshan
Published: March 2024

Composite images (CIs) typically combine various elements from different scenes, views, and styles, which are a very important information carrier in the era of mixed media such as virtual reality, mixed reality, metaverse, etc. However, the complexity of CI content presents a significant challenge for subsequent visual perception modeling and compression. In addition, the lack of benchmark CI databases also hinders the use of recent advanced data-driven methods. To address these challenges, we first establish one of the earliest visual redundancy prediction (VRP) databases for CIs. Moreover, we propose a multi-visual effect (MVE)-driven incremental learning method that combines the strengths of hand-crafted and data-driven approaches to achieve more accurate VRP modeling. Specifically, we design special incremental rules to learn the visual knowledge flow of MVE. To effectively capture the associated features of MVE, we further develop a three-stage incremental learning approach for VRP based on an encoder-decoder network. Extensive experimental results validate the superiority of the proposed method in terms of subjective, objective, and compression experiments.
Scalable Motion Style Transfer with Constrained Diffusion Generation
Authors: Yin Wenjie , Yu Yi , Yin Hang , Kragic Danica , Björkman Mårten
Published: March 2024

Current training of motion style transfer systems relies on consistency losses across style domains to preserve contents, hindering its scalable application to a large number of domains and private data. Recent image transfer works show the potential of independent training on each domain by leveraging implicit bridging between diffusion models, with the content preservation, however, limited to simple data patterns. We address this by imposing biased sampling in backward diffusion while maintaining the domain independence in the training stage. We construct the bias from the source domain keyframes and apply them as the gradient of content constraints, yielding a framework with keyframe manifold constraint gradients (KMCGs). Our validation demonstrates the success of training separate models to transfer between as many as ten dance motion styles. Comprehensive experiments find a significant improvement in preserving motion contents in comparison to baseline and ablative diffusion-based style transfer models. In addition, we perform a human study for a subjective assessment of the quality of generated dance motions. The results validate the competitiveness of KMCGs.
Interactive Visual Task Learning for Robots
Authors: Gu Weiwei , Sah Anant , Gopalan Nakul
Published: March 2024

We present a framework for robots to learn novel visual concepts and tasks via in-situ linguistic interactions with human users. Previous approaches have either used large pre-trained visual models to infer novel objects zero-shot, or added novel concepts along with their attributes and representations to a concept hierarchy. We extend the approaches that focus on learning visual concept hierarchies by enabling them to learn novel concepts and solve unseen robotics tasks with them. To enable a visual concept learner to solve robotics tasks one-shot, we developed two distinct techniques. Firstly, we propose a novel approach, Hi-Viscont(HIerarchical VISual CONcept learner for Task), which augments information of a novel concept to its parent nodes within a concept hierarchy. This information propagation allows all concepts in a hierarchy to update as novel concepts are taught in a continual learning setting. Secondly, we represent a visual task as a scene graph with language annotations, allowing us to create novel permutations of a demonstrated task zero-shot in-situ. We present two sets of results. Firstly, we compare Hi-Viscont with the baseline model (FALCON) on visual question answering(VQA) in three domains. While being comparable to the baseline model on leaf level concepts, Hi-Viscont achieves an improvement of over 9% on non-leaf concepts on average. Secondly, we conduct a human-subjects experiment where users teach our robot visual tasks in-situ. We compare our model’s performance against the baseline FALCON model. Our framework achieves 33% improvements in success rate metric, and 19% improvements in the object level accuracy compared to the baseline model. With both of these results we demonstrate the ability of our model to learn tasks and concepts in a continual learning setting on the robot.
Approximation Algorithms for Preference Aggregation Using CP-Nets
Authors: Ali Abu Mohammad Hammad , Yang Boting , Zilles Sandra
Published: March 2024

This paper studies the design and analysis of approximation algorithms for aggregating preferences over combinatorial domains, represented using Conditional Preference Networks (CP-nets). Its focus is on aggregating preferences over so-called swaps, for which optimal solutions in general are already known to be of exponential size. We first analyze a trivial 2-approximation algorithm that simply outputs the best of the given input preferences, and establish a structural condition under which the approximation ratio of this algorithm is improved to 4/3. We then propose a polynomial-time approximation algorithm whose outputs are provably no worse than those of the trivial algorithm, but often substantially better. A family of problem instances is presented for which our improved algorithm produces optimal solutions, while, for any ε, the trivial algorithm cannot attain a (2- ε)-approximation. These results may lead to the first polynomial-time approximation algorithm that solves the CP-net aggregation problem for swaps with an approximation ratio substantially better than 2.
Stable Model Semantics for Description Logic Terminologies
Authors: Di Stefano Federica , Šimkus Mantas
Published: March 2024

This paper studies a stable model semantics for Description Logic (DL) knowledge bases (KBs) and for (possibly cyclic) terminologies, ultimately showing that terminologies under the proposed semantics can be equipped with effective reasoning algorithms. The semantics is derived using Quantified Equilibrium Logic, and---in contrast to the usual semantics of DLs based on classical logic---supports default negation and allows to combine the open-world and the closed-world assumptions in a natural way. Towards understanding the computational properties of this and related formalisms, we show a strong undecidability result that applies not only to KBs under the stable model semantics, but also to the more basic setting of minimal model reasoning. Specifically, we show that concept satisfiability in minimal models of an ALCIO KB is undecidable. We then turn our attention to (possibly cyclic) DL terminologies, where ontological axioms are limited to definitions of concept names in terms of complex concepts. This restriction still yields a very rich setting. We show that standard reasoning problems, like concept satisfiability and subsumption, are ExpTime-complete for terminologies expressed in ALCI under the stable model semantics.
Redefining ABA+ Semantics via Abstract Set-to-Set Attacks
Authors: Dimopoulos Yannis , Dvorak Wolfgang , König Matthias , Rapberger Anna , Ulbricht Markus , Woltran Stefan
Published: March 2024

Assumption-based argumentation (ABA) is a powerful defeasible reasoning formalism which is based on the interplay of assumptions, their contraries, and inference rules. ABA with preferences (ABA+) generalizes the basic model by allowing qualitative comparison between assumptions. The integration of preferences however comes with a cost. In ABA+, the evaluation under two central and well-established semantics---grounded and complete semantics---is not guaranteed to yield an outcome. Moreover, while ABA frameworks without preferences allow for a graph-based representation in Dung-style frameworks, an according instantiation for general ABA+ frameworks has not been established so far. In this work, we tackle both issues: First, we develop a novel abstract argumentation formalism based on set-to-set attacks. We show that our so-called Hyper Argumentation Frameworks (HYPAFs) capture ABA+. Second, we propose relaxed variants of complete and grounded semantics for HYPAFs that yield an extension for all frameworks by design, while still faithfully generalizing the established semantics of Dung-style Argumentation Frameworks. We exploit the newly established correspondence between ABA+ and HYPAFs to obtain variants for grounded and complete ABA+ semantics that are guaranteed to yield an outcome. Finally, we discuss basic properties and provide a complexity analysis. Along the way, we settle the computational complexity of several ABA+ semantics.
Dynamic Tangled Derivative Logic of Metric Spaces
Authors: Fernández-Duque David , Montacute Yoàv
Published: March 2024

Dynamical systems are abstract models of interaction between space and time. They are often used in fields such as physics and engineering to understand complex processes, but due to their general nature, they have found applications for studying computational processes, interaction in multi-agent systems, machine learning algorithms and other computer science related phenomena. In the vast majority of applications, a dynamical system consists of the action of a continuous `transition function' on a metric space. In this work, we consider decidable formal systems for reasoning about such structures. Spatial logics can be traced back to the 1940's, but our work follows a more dynamic turn that these logics have taken due to two recent developments: the study of the topological mu-calculus, and the the integration of linear temporal logic with logics based on the Cantor derivative. In this paper, we combine dynamic topological logics based on the Cantor derivative and the `next point in time' operators with an expressively complete fixed point operator to produce a combination of the topological mu-calculus with linear temporal logic. We show that the resulting logics are decidable and have a natural axiomatisation. Moreover, we prove that these logics are complete for interpretations on the Cantor space, the rational numbers, and subspaces thereof.
Linear-Time Verification of Data-Aware Processes Modulo Theories via Covers and Automata
Authors: Gianola Alessandro , Montali Marco , Winkler Sarah
Published: March 2024

The need to model and analyse dynamic systems operating over complex data is ubiquitous in AI and neighboring areas, in particular business process management. Analysing such data-aware systems is a notoriously difficult problem, as they are intrinsically infinite-state. Existing approaches work for specific datatypes, and/or limit themselves to the verification of safety properties. In this paper, we lift both such limitations, studying for the first time linear-time verification for so-called data-aware processes modulo theories (DMTs), from the foundational and practical point of view. The DMT model is very general, as it supports processes operating over variables that can store arbitrary types of data, ranging over infinite domains and equipped with domain-specific predicates. Specifically, we provide four contributions. First, we devise a semi-decision procedure for linear-time verification of DMTs, which works for a very large class of datatypes obeying to mild model-theoretic assumptions. The procedure relies on a unique combination of automata-theoretic and cover computation techniques to respectively deal with linear-time properties and datatypes. Second, we identify an abstract, semantic property that guarantees the existence of a faithful finite-state abstraction of the original system, and show that our method becomes a decision procedure in this case. Third, we identify concrete, checkable classes of systems that satisfy this property, generalising several results in the literature. Finally, we present an implementation and an experimental evaluation over a benchmark of real-world data-aware business processes.
A Compiler for Weak Decomposable Negation Normal Form
Authors: Illner Petr , Kučera Petr
Published: March 2024

This paper integrates weak decomposable negation normal form (wDNNF) circuits, introduced by Akshay et al. in 2018, into the knowledge compilation map. This circuit type generalises decomposable negation normal form (DNNF) circuits in such a way that they allow a restricted form of sharing variables among the inputs of a conjunction node. We show that wDNNF circuits have the same properties as DNNF circuits regarding the queries and transformations presented in the knowledge compilation map, whilst being strictly more succinct than DNNF circuits (that is, they can represent Boolean functions compactly). We also present and evaluate a knowledge compiler, called Bella, for converting CNF formulae into wDNNF circuits. Our experiments demonstrate that wDNNF circuits are suitable for configuration instances.
Decomposing Constraint Networks for Calculating c-Representations
Authors: Wilhelm Marco , Kern-Isberner Gabriele
Published: March 2024

It is well-known from probability theory that network-based methods like Bayesian networks constitute remarkable frameworks for efficient probabilistic reasoning. In this paper, we focus on qualitative default reasoning based on Spohn’s ranking functions for which network-based methods have not yet been studied satisfactorily. With constraint networks, we develop a framework for iterative calculations of c-representations, a family of ranking models of conditional belief bases which show outstanding properties from a commonsense and formal point of view, that are characterized by assigning possible worlds a degree of implausibility via penalizing the falsification of conditionals. Constraint networks unveil the dependencies among these penalty points (and hence among the conditionals) and make it possible to compute the penalty points locally on so-called safe sub-bases. As an application of our framework, we show that skeptical c-inferences can be drawn locally from safe sub-bases without losing validity.
INVESTIGATION OF ENERGY, EXERGY, AND EMISSION PERFORMANCE OF TURBULATORS INSERTS HEAT EXCHANGER WITH TERNARY HYBRID NANOFLUID COOLANT
Authors: Rai Ranjeet , Kumar Vikash , Sahoo Rashmi Rekha
Published: March 2024

The improvement of technologies necessitates using a meticulous heat dissipation method to enhance the exergy efficiency of small air heat exchangers. To improve thermal performance, plain tube (PT) heat exchangers (HX) use passive inserts, such as dimpled twisted turbulator inserts (DTTI) and twisted turbulator inserts (TTI) filled with a ternary hybrid nanofluid (THNF) based on water. Energy, exergy use, and environmental impacts in the future are the primary foci of the inquiry on THNF replacement inserts. Using water as the operating fluid, copper oxide (CuO), aluminum oxide (Al2O3), and titanium oxide (TiO2) nanoparticles are dissolved. The resultant THNF is injected at 0.06%, 0.09%, and 0.12%. Energy, exergy, and emissions are used to study certain features of control fluid flow. The results of the experiments show that using TTI and DTTI along with THNF makes a small air heat exchanger much more efficient at heating and cooling. When DTTI and TTI turbulator plugs are used in regular tubes with THNF of 0.12% (v/v) concentration, friction factor, heat transfer, reversibility, and exergy change all get much better. When DTTI or TTI turbulator plugs are put into the core of the tube, the carbon dioxide (CO2) release goes up by 2.6 to 2.1. People who use DTTI with THNF of 0.12% (v/v) concentration as the operating fluid and a tube plug will get performance evaluation criterion (PEC) numbers between 1.076 and 1.04. A careful study of friction factor, heat transfer, exergy efficiency increase, and environmental effect shows that DTTI is a superior passive device for enhancing heat transfer efficiency, particularly when combined with THNF of 0.12% (v/v) concentration.
Golygyddol: Ymchwil addysg yng Nghymru, o Gymru, ac i Gymru
Authors: Beauchamp Gary , Thomas Enlli , Crick Tom
Published: March 2024

Golygyddol - Cylchgrawn Addysg Cymru 24.2
Hybu chwarae teg: Mentora Deialogaidd yn rhaglen Addysg Gychwynnol Athrawon CaBan
Authors: Jones Luke , Tones Steven , Foulkes Gethin , Jones Rhys C.
Published: March 2024

Nod y papur hwn yw archwilio effaith rhaglen Addysg Gychwynnol Athrawon (AGA) newydd CaBan, sydd wedi dod i’r amlwg mewn ymateb i ddiwygiadau addysgol ehangach yng Nghymru. Yn fwy penodol, ei nod yw dadansoddi canfyddiadau mentoriaid ac Athrawon Cyswllt (ACau) i ddatblygu dealltwriaeth fwy digonol o’r dull mentora sydd wedi’i fabwysiadu gan CaBan. Defnyddiwyd holiaduron a chyfweliadau grŵp i gynhyrchu data gan 15 o fentoriaid a 48 athro cyswllt - gyda’r astudiaeth yn digwydd yn ystod cam olaf y rhaglen flwyddyn ôl-raddedig. Defnyddiwyd proses o ddadansoddi thematig i nodi a dadansoddi patrymau yn y data. Mae’r dull deialogaidd o fentora sydd wedi’i fabwysiadu gan CaBan wedi ail-lunio’r berthynas rhwng y mentoriaid ac ACau. Anogodd bartneriaeth fwy democrataidd sydd wedi grymuso’r ACau i herio rhai agweddau ar ymarfer a chyflawni gweithredoedd mwy creadigol. Mae adborth a thrafodaethau rheolaidd yn canolbwyntio ar ddysgu ar y cyd wedi helpu i leddfu pryderon ACau am werthuso, ond wedi creu rhywfaint o densiwn hefyd gan fod credoau mentoriaid am addysgeg yn fwy tebygol o gael eu herio. Mae gan y canfyddiadau hyn oblygiadau ar gyfer rhaglen CaBan a darparwyr AGA eraill, oherwydd bod mabwysiadu dull deialogaidd wedi arwain at newidiadau dymunol mewn strategaethau mentora. Gwelwyd gwerth yn y newidiadau hyn, ond roeddent yn dibynnu ar y mentoriaid i neilltuo mwy o amser i’r broses ddeialogaidd ac i fod yn llwyr gefnogol i sgyrsiau agored am ddysgu.
Addysgeg o gymharu â pherfformiad wrth addysgu cerddoriaeth yn y dosbarth cynradd: Gwersi o ‘orffennol defnyddiadwy’ yng Nghymru
Authors: Beauchamp Gary , Breeze Thomas
Published: March 2024

Ers dyddiau cynharaf cerddoriaeth yn ysgolion cynradd (elfennol) Cymru a Lloegr, gwelwyd pwysau i roi’r gallu i blant i berfformio cerddoriaeth. Sicrhaodd dylanwad cynnar yr Eglwys sefydledig, a’r gwerth ynghlwm wrth allu i berfformio’n gerddorol fel rhan o ddyheadau diwylliannol neu dreftadaeth ganfyddedig, fod athrawon yn cael eu barnu ar eu gallu i berfformio cerddoriaeth, ac yn eu tro yn dysgu eu disgyblion i wneud yr un peth. Yn wir, trwy gydol deddfwriaeth olynol a chynnydd y proffesiwn addysgu i statws graddedig, mae athrawon ysgolion cynradd wedi cael eu barnu ar eu gallu canfyddedig i berfformio cerddoriaeth eu hunain yn hytrach nag ar eu gallu addysgegol i’w ddysgu. Arweiniodd hyn at sefyllfa lle’r oedd agweddau eraill ar gerddoroldeb yn cael eu hanwybyddu i bob pwrpas, a chyfleoedd i ddatblygu addysgeg amgen, yn seiliedig ar ystod ehangach o sgiliau a diddordebau cerddorol, wedi cael eu hesgeuluso gan lunwyr polisi olynol. Gyda chysyniadu ehangach o gerddoriaeth yn cael ei ragweld yn y Cwricwlwm i Gymru newydd, y ddadl yw bod modd dysgu gwersi o ‘orffennol defnyddiadwy’ a fyddai’n galluogi athrawon ysgolion cynradd i ddatblygu dulliau addysgegol newydd. Byddai hynny wedyn yn caniatáu iddynt ymateb i blant mewn modd gwybodus, artistig a cherddorol sensitif, yn hytrach na bod angen iddynt feddu ar ‘sgiliau’ cerddorol penodol.
First record of the Nearctic blue mud-dauber wasp Chalybion californicum (de Saussure, 1867) from Hungary (Hymenoptera: Sphecidae)
Authors: Vas Zoltán , Kőszegi Klaudia , Takács Attila
Published: March 2024

The Nearctic blue mud-dauber wasp, Chalybion californicum (de Saussure, 1867) (Hymenoptera: Sphecidae), is native to North America, and has been introduced to Europe. In this paper, the species is reported for the first time from Hungary, representing its third country-level record from Europe, following Croatia and Italy.
Climate finance across sub-Saharan Africa: Decision trees and network flows
Authors: Chukwudum Queensley C.
Published: March 2024

The structure of climate finance flows from donors (multilateral sources) to recipients (sub-Saharan African countries) was studied. This is the first study to provide a comprehensive network structure of the climate finance flows into Africa, based on the global public climate finance governance system. Network theory and decision tree techniques were employed. The results obtained generally fit the multilateral funding units (MFUs) into two categories: central funding units (CFUs), which simultaneously attend to the climate-related projects of many African countries, and the boundary funding units (BFUs), which cater to only a few countries at once. An isolated region with no BFUs was identified. African countries within this group could be more exposed to climate financial risk as they rely on only the CFUs. In general, with the exception of mitigation REDD (reducing emissions from deforestation and forest degradation) climate finance, a disproportionate distribution of climate themes, with particular reference to adaptation finance, was observed across sub-Saharan Africa. This has real implications for equitable resource allocation of climate funds. The need for African-bred region-wide MFUs is recommended.
Social capital and trust: The ‘fuel’ of local integration of rural and small-town enterprises
Authors: Tomay Kyra , Hegedűs Márk
Published: March 2024

In the present article we examine a sub-segment of the locally integrated social group: rural small entrepreneurs.  Entrepreneurs are especially interesting from the integration point of view since in the current academic discourse, entrepreneurship is considered as a contextual process, in which entrepreneurs depend on local information and resources, and base their activities on the needs of the local environment, therefore, rural entrepreneurs are commonly studied through the concepts of local embeddedness, social capital and trust. The aim of our paper is to contribute to the understanding of the those mechanisms: the impact of trust among entrepreneurs and their social networks resulting the local integration of this rural group.  The study based on 25 semi-structured in-depth interviews, focuses on the role of values, attitudes, social capital and trust networks in local economic success in three Hungarian settlements of different size: a small village of 300, a small town of 3,000 and a medium-sized town of 30,000 inhabitants.  Our conclusion is that in case of rural small entrepreneurship, community resources (values, attitudes, social capital and trust) are of chief role in the foundation and existence of a local entrepreneurial ecosystem, however, their efforts have to be accompanied by an institutional framework to make them sustainable in the long term.
Evaluation of the Efficiency and Effectiveness of MRI in the Diagnosis of Chronic Shoulder Pain
Authors:   Assistant Professor, Department of Radiology and Imaging Technology, RIMT University, Mandi Gobindgarh, 147301, Punjab, India, Malla Irfan Ahmad , Manzoor Sehrish , Ganie Zubair ul Islam , Malik Maajid Mohi Ud Din
Published: March 2024

Objective: This study aimed to evaluate MRI efficiency in diagnosing chronic shoulder pain causes. Chronic shoulder pain accounts for 5% of musculoskeletal consultations. Method: Thirty-four chronic shoulder pain patients (18-65 years) underwent shoulder MRI. Sequences included T1-weighted, T2-weighted, proton density, and STIR for comprehensive analysis. All patients were clinically diagnosed with chronic shoulder pain prior to imaging. Finding: One patient (1.96%) had normal MRI findings. Thirty-three patients (98.04%) had abnormalities. Rotator cuff injuries were most prevalent, especially partial supraspinatus tears (18.63%). Other findings included bursitis, joint effusion, AC arthropathy, Hill Sachs deformity, AC joint impingement, rotator cuff fatty atrophy, and biceps tendinopathy. Conclusion: MRI provided excellent visualization of soft tissue pathologies causing chronic shoulder pain, noninvasively without ionizing radiation. Combining MRI sequences accurately diagnosed various shoulder conditions. Fat suppression sequences were key for identifying rotator cuff tears. MRI is the gold standard for diagnosing rotator cuff injuries, the most common chronic shoulder pain cause. Novelty: This study demonstrates MRI's utility for evaluating chronic shoulder pain causes. While no single sequence visualizes all shoulder pathologies, using T1-weighted, T2-weighted, proton density, and STIR sequences together provides a comprehensive analysis to guide appropriate patient treatment. Keywords MRI, Chronic shoulder pain, Rotator cuff, Joint effusion, frozen shoulder
Mammalian cell culture and transfection for stable cell lines generation v1
Authors: Ojha Hina
Published: March 2024

Autosomal recessive mutations in PTEN-induced kinase 1 (PINK1) are linked to early-onset Parkinson's disease (PD) [1]. Upon mitochondrial depolarization, PINK1 activates through autophosphorylation and stabilization on mitochonria [2]. Pink1 phosphorylates ubiquitin and Parkin, triggering mitophagy to remove damaged mitochondria in PD [3]. To delve deeper into the impact of PINK1 mutations, a PINK1 knockout (KO) HeLa cell line was utilized as a model system. Additionally, stable cell lines with mutated PINK1 were established to explore differences in functional activity and the formation of the PINK1-TOM complex between wild-type PINK1 and its mutant variants.
DEVELOPMENT OF LEARNING MEDIA DESIGN OF LESSON PLANS: CURRICULUM 2013 REVISED 2018
Authors: Nazwa Intannisa , Handayani Sri , Maharani Shinta , Cakrawati Dewi
Published: March 2024

One of the competencies required when carrying out Education Unit Introduction Program (PPLSP)is the ability to compile a Learning Implementation Plan (RPP). The dynamic development of the curriculum requires students to always update information about the curriculum.The purpose of this study was to develop an android-based learning media for the preparation of lesson plans using the 2013 revised 2018 curriculum and to determine the feasibility of the learning media. The ADDIE (Analysis, Design, Development, Implementation, Evaluation) method is used in the development of learning media. Media feasibility is assessed by media experts, materials experts, and users. The assessment refers to eight aspects of media selection; (1) the suitability of the media with the learning objectives, (2) the suitability of the characteristics of students, (3) can be a source of learning, (4) the efficiency and effectiveness of the use of media, (5) safety for students, (6) the ability of the media in developing activeness and learner creativity, (7) the ability of the media in developing a pleasant learning atmosphere, and (8) the quality of the media. The validation results show that the learning media developed in this study received a fairly decent score from media experts, very feasible from material experts, and feasible based on user responses. The use of learning media for preparing lesson plans is expected to help students understand and make good lesson plans.
STUDY ANALYSIS OF THE NUMBER OF VISITS OF REGIONAL LIBRARY FACILITIES IN THE CITY OF BANDUNG
Authors: Masyithah Mutiara , Akbardin Juang , Maknun Johar
Published: March 2024

The low interest in reading in the community is caused by inadequate library facilities, incomplete book collections and the difficulty of accessing libraries which are explained as the cause of the low interest in visiting librarians. . With this type of descriptive research approach, when writing this article, the author collects data and tries to describe the meaning and benefits of the results of the renovation activities in the library building itself. By choosing the building objects of the West Java Regional Library, the Bandung City Archives and Library Service and Pitimos, the author tries to describe the factors that decrease or increase the number of visits from the spatial form factor. And after that it can be concluded thatA library that facilitates its use is able to attract more visitors.
THE MEANING OF CULTURAL HERITAGE: CIKENDI SPRING PROTECTION FORTRESS
Authors: Razaq Yasmin Nur , Handayani Sri
Published: March 2024

The Cikendi Spring Protection Building is listed in the list of Cultural Heritage sites. From a historical perspective, this building holds a number of stories and memories for the city of Bandung. However, it seems as if Cikendi's struggle is being ignored, the community is less concerned about the existence of the Cultural Heritage Building. In fact, if examined more deeply, the Cultural Heritage Building has so many meanings to be explored. The purpose of this research is to make us aware that the Cikendi Cultural Heritage Site is not just a Dutch heritage building, but it holds history and real evidence that springs are useful for us today, maybe tomorrow, or in the future. This research is qualitative in nature, where the data is obtained based on existing theoretical studies. The existence of the Cikendi Spring Protection Fort is a silent witness to the struggles of our ancestors as well as a tangible result of the work of civilization in the past. The success of our ancestors in utilizing the surrounding environment so that the benefits of Cikendi Springs can still be felt today
Analysis of Circulation Density of the State Capital Worship Complex (IKN)
Authors: Aribawa Ktut Gede
Published: March 2024

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PUASA SENIN KAMIS DAN KORELASINYA DENGAN KECERDASAN EMOSIONAL-SPIRITUAL SISWA
Authors: Permana Irfan Setia
Published: March 2024

AbstractOne of the acts of worship that Allah commands Muslims through as exemplified by Muhammad SAW is fasting. Fasting is ordered to make humans more pious. Fasting can make the soul stable and able to control it self and not easily hit by mental shocks. The approach used is a qualitative type, namely the elaboration of data in the field in the form of data descriptions through sentence structure and does not use mathematical calculations. The location of the object under study is the state high school 5 Cimahi with research time in the 2019/2020 school year period. The data collection technique used was observation. Based on the discussion and analysis of observational data and interviews obtained regarding the correlation of fasting Monday Thursday in developing the emotional and spiritual intelligence of students in high school 5 Cimahi, It can be concluded that Monday fasting has a very important role in developing students' emotional intelligence and spiritual intelligence, which is to develop high self-awareness, develop honesty, patience, form empathy and can train discipline.Keywords: Fasting, Intelligence, Emotional, Spiritual AbstrakSalah satu ibadah yang diperintahkan Allah kepada umat muslim melalui yang dicontohkan Muhammad SAW adalah puasa. Puasa diperintahkan untuk menjadikan manusia agar lebih bertaqwa. Puasa dapat membuat jiwa stabil dan mampu mengendalikan diri dan tidak mudah diterpa goncangan jiwa. Pendekatan yang digunakan adalah jenis kualitatif yaitu penggabaran data dilapangan dalam bentuk deskripsi data melalui susunan kalimat dan tidak menggunakan hitungan matematis. Adapun lokasi dari objek yang diteliti adalah sekolah menengah atas negeri 5 Cimahi dengan waktu penelitian pada periode tahun ajaran 2019/2020. Teknik pengumpulan data yang digunakan adalah dengan observasi. Berdasarkan pembahasan dan analisis dari data observasi maupun wawancara yang didapat mengenai korelasi puasa senin kamis dalam mengembangkan kecerdasan emosional maupun spiritual siswa di sekolah menengah atas negeri 5 Cimahi, didapatkan simpulan bahwa puasa senin kamis mempunyai peran yang sangat penting dalam mengembangkan kecerdasan emosional dan kecerdasan spiritual siswa yaitu dapat mengembangkan kesadaran diri yang tinggi, mengembangkan sikap jujur, sabar, membentuk sikap empati serta dapat melatih sikap disiplin.Kata Kunci: Puasa, Kecerdaasan, Emosional, Spiritual